librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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DanYuHF/sd-class-butterflies-32	DanYuHF	"2024-05-18T14:37:41Z"	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	"2024-05-18T14:37:07Z"	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('DanYuHF/sd-class-butterflies-32') image = pipeline().images[0] image ```
huggingtweets/bnbuzz	huggingtweets	"2021-05-21T20:48:33Z"	5	0	transformers	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2022-03-02T23:29:05Z"	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1306249141018910726/o3bCj_sP_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Barnes & Noble 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@bnbuzz bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@bnbuzz's tweets](https://twitter.com/bnbuzz). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3156</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>821</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>124</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2211</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tso130j/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bnbuzz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2b6k9q0j) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2b6k9q0j/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/bnbuzz'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF	MaziyarPanahi	"2024-05-21T10:09:39Z"	69	0	transformers	[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "conversational", "base_model:MTSAIR/multi_verse_model", "base_model:yam-peleg/Experiment26-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:automerger/Multi_verse_modelExperiment26-7B", "base_model:quantized:automerger/Multi_verse_modelExperiment26-7B" ]	text-generation	"2024-05-21T09:41:13Z"	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - lazymergekit - automerger - conversational - base_model:MTSAIR/multi_verse_model - base_model:yam-peleg/Experiment26-7B - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: Multi_verse_modelExperiment26-7B-GGUF base_model: automerger/Multi_verse_modelExperiment26-7B inference: false model_creator: automerger pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF](https://huggingface.co/MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF) - Model creator: [automerger](https://huggingface.co/automerger) - Original model: [automerger/Multi_verse_modelExperiment26-7B](https://huggingface.co/automerger/Multi_verse_modelExperiment26-7B) ## Description [MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF](https://huggingface.co/MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF) contains GGUF format model files for [automerger/Multi_verse_modelExperiment26-7B](https://huggingface.co/automerger/Multi_verse_modelExperiment26-7B). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
jab11769/my-thai-bert-sentiment-model	jab11769	"2024-11-21T08:03:57Z"	110	0	transformers	[ "transformers", "safetensors", "camembert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-11-21T08:01:51Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tarpalsus/poca-SoccerTwos	tarpalsus	"2024-04-27T10:35:54Z"	11	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	"2024-04-27T10:34:50Z"	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: tarpalsus/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
timm/vit_base_patch16_siglip_512.v2_webli	timm	"2025-02-21T20:10:46Z"	0	0	timm	[ "timm", "pytorch", "safetensors", "transformers", "image-feature-extraction", "siglip", "siglip2", "dataset:webli", "arxiv:2502.14786", "arxiv:2303.15343", "license:apache-2.0", "region:us" ]	image-feature-extraction	"2025-02-21T20:10:33Z"	--- tags: - timm - transformers - image-feature-extraction - siglip - siglip2 library_name: timm license: apache-2.0 datasets: - webli --- # Model card for vit_base_patch16_siglip_512.v2_webli A SigLIP 2 ViT (image encoder only) for `timm`. Equivalent to image tower from https://huggingface.co/timm/ViT-B-16-SigLIP2-512. ## Model Details - Dataset: webli - Papers: - SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features: https://arxiv.org/abs/2502.14786 - Sigmoid Loss for Language Image Pre-Training: https://arxiv.org/abs/2303.15343 ## Citation ```bibtex @article{tschannen2025siglip, title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features}, author={Tschannen, Michael and Gritsenko, Alexey and Wang, Xiao and Naeem, Muhammad Ferjad and Alabdulmohsin, Ibrahim and Parthasarathy, Nikhil and Evans, Talfan and Beyer, Lucas and Xia, Ye and Mustafa, Basil and H'enaff, Olivier and Harmsen, Jeremiah and Steiner, Andreas and Zhai, Xiaohua}, year={2025}, journal={arXiv preprint arXiv:2502.14786} } ``` ```bibtex @inproceedings{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={11975--11986}, year={2023} } ```
FakeMonk/ppo-LunarLander-v2	FakeMonk	"2025-03-18T13:54:58Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2025-03-18T13:54:44Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.27 +/- 18.01 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
unicamp-dl/InRanker-base	unicamp-dl	"2024-09-25T00:30:00Z"	940	5	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "arxiv:2401.06910", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2023-12-13T18:45:00Z"	# InRanker-base (220M parameters) InRanker is a version of monoT5 distilled from [monoT5-3B](https://huggingface.co/castorini/monot5-3b-msmarco-10k) with increased effectiveness on out-of-domain scenarios. Our key insight were to use language models and rerankers to generate as much as possible synthetic "in-domain" training data, i.e., data that closely resembles the data that will be seen at retrieval time. The pipeline used for training consists of two distillation phases that do not require additional user queries or manual annotations: (1) training on existing supervised soft teacher labels, and (2) training on teacher soft labels for synthetic queries generated using a large language model. The paper with further details can be found [here](https://arxiv.org/abs/2401.06910). The code and library are available at https://github.com/unicamp-dl/InRanker ## Usage The library was tested using python 3.10 and is installed with: ```bash pip install inranker ``` The code for inference is: ```python from inranker import T5Ranker model = T5Ranker(model_name_or_path="unicamp-dl/InRanker-base") docs = [ "The capital of France is Paris", "Learn deep learning with InRanker and transformers" ] scores = model.get_scores( query="What is the best way to learn deep learning?", docs=docs ) # Scores are sorted in descending order (most relevant to least) # scores -> [0, 1] sorted_scores = sorted(zip(scores, docs), key=lambda x: x[0], reverse=True) ``` ## How to Cite ``` @misc{laitz2024inranker, title={InRanker: Distilled Rankers for Zero-shot Information Retrieval}, author={Thiago Laitz and Konstantinos Papakostas and Roberto Lotufo and Rodrigo Nogueira}, year={2024}, eprint={2401.06910}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```
TransferGraph/aXhyra_emotion_trained_31415-finetuned-lora-ag_news	TransferGraph	"2024-02-28T01:06:57Z"	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:ag_news", "base_model:aXhyra/emotion_trained_31415", "base_model:adapter:aXhyra/emotion_trained_31415", "license:apache-2.0", "model-index", "region:us" ]	text-classification	"2024-02-27T23:38:46Z"	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - ag_news metrics: - accuracy base_model: aXhyra/emotion_trained_31415 model-index: - name: aXhyra_emotion_trained_31415-finetuned-lora-ag_news results: - task: type: text-classification name: Text Classification dataset: name: ag_news type: ag_news config: default split: test args: default metrics: - type: accuracy value: 0.9398684210526316 name: accuracy --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # aXhyra_emotion_trained_31415-finetuned-lora-ag_news This model is a fine-tuned version of [aXhyra/emotion_trained_31415](https://huggingface.co/aXhyra/emotion_trained_31415) on the ag_news dataset. It achieves the following results on the evaluation set: - accuracy: 0.9399 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.3424 \| None \| 0 \| \| 0.9224 \| 0.2694 \| 0 \| \| 0.9305 \| 0.2013 \| 1 \| \| 0.9378 \| 0.1776 \| 2 \| \| 0.9399 \| 0.1611 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
theship87/qwen25-14b-fork-Q8_0-GGUF	theship87	"2025-02-04T07:29:36Z"	19	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:theship87/qwen25-14b-fork", "base_model:quantized:theship87/qwen25-14b-fork", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-02-04T07:28:28Z"	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-1M/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: theship87/qwen25-14b-fork tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # theship87/qwen25-14b-fork-Q8_0-GGUF This model was converted to GGUF format from [`theship87/qwen25-14b-fork`](https://huggingface.co/theship87/qwen25-14b-fork) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/theship87/qwen25-14b-fork) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -c 2048 ```
ClarenceDan/e5b11a03-6415-4231-928c-e95fce01a8d0	ClarenceDan	"2025-01-28T14:10:57Z"	6	0	peft	[ "peft", "safetensors", "falcon", "axolotl", "generated_from_trainer", "custom_code", "base_model:fxmarty/really-tiny-falcon-testing", "base_model:adapter:fxmarty/really-tiny-falcon-testing", "license:mit", "region:us" ]	null	"2025-01-28T14:10:32Z"	--- library_name: peft license: mit base_model: fxmarty/really-tiny-falcon-testing tags: - axolotl - generated_from_trainer model-index: - name: e5b11a03-6415-4231-928c-e95fce01a8d0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: fxmarty/really-tiny-falcon-testing bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1fd166178dc611b1_train_data.json ds_type: json format: custom path: /workspace/input_data/1fd166178dc611b1_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/e5b11a03-6415-4231-928c-e95fce01a8d0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/1fd166178dc611b1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d2e73c64-4432-4749-92a6-b96236c9a7bb wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d2e73c64-4432-4749-92a6-b96236c9a7bb warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e5b11a03-6415-4231-928c-e95fce01a8d0 This model is a fine-tuned version of [fxmarty/really-tiny-falcon-testing](https://huggingface.co/fxmarty/really-tiny-falcon-testing) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.0733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 44.318 \| 0.0033 \| 1 \| 11.0767 \| \| 44.2889 \| 0.0098 \| 3 \| 11.0764 \| \| 44.291 \| 0.0196 \| 6 \| 11.0754 \| \| 44.2777 \| 0.0294 \| 9 \| 11.0733 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DarrenChensformer/llava-1.5-7b-hf-vsft	DarrenChensformer	"2024-05-24T07:13:21Z"	3	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:llava-hf/llava-1.5-7b-hf", "base_model:adapter:llava-hf/llava-1.5-7b-hf", "region:us" ]	null	"2024-05-24T07:12:01Z"	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: llava-hf/llava-1.5-7b-hf model-index: - name: llava-1.5-7b-hf-vsft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llava-1.5-7b-hf-vsft This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.4e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.15.1
kunalkumarsahoo/q-FrozenLake-v1-4x4-noSlippery	kunalkumarsahoo	"2025-03-18T02:52:16Z"	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2025-03-17T19:45:13Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id='kunalkumarsahoo/q-FrozenLake-v1-4x4-noSlippery', filename='q-learning.pkl') # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model['env_id']) ```
HenryCai1129/adapter-llama-adaptertoxic2nontoxic-100-filtered-50-0.003	HenryCai1129	"2024-04-27T17:20:46Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-27T03:39:13Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
franhinomut/my_distilbert_model	franhinomut	"2023-11-05T19:26:28Z"	6	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:rotten_tomatoes", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-11-05T17:44:50Z"	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - rotten_tomatoes metrics: - accuracy - f1 - precision - recall model-index: - name: my_distilbert_model results: - task: name: Text Classification type: text-classification dataset: name: rotten_tomatoes type: rotten_tomatoes config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.850844277673546 - name: F1 type: f1 value: 0.8508430963429304 - name: Precision type: precision value: 0.8508553928470853 - name: Recall type: recall value: 0.850844277673546 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_distilbert_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.5332 - Accuracy: 0.8508 - F1: 0.8508 - Precision: 0.8509 - Recall: 0.8508 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.4172 \| 1.0 \| 534 \| 0.3729 \| 0.8386 \| 0.8386 \| 0.8392 \| 0.8386 \| \| 0.2351 \| 2.0 \| 1068 \| 0.4376 \| 0.8443 \| 0.8443 \| 0.8444 \| 0.8443 \| \| 0.1635 \| 3.0 \| 1602 \| 0.5332 \| 0.8508 \| 0.8508 \| 0.8509 \| 0.8508 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1
mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF	mradermacher	"2025-02-14T08:54:58Z"	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200", "base_model:quantized:jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-14T08:22:29Z"	--- base_model: jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2 - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q5_K_S.gguf) \| Q5_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q6_K.gguf) \| Q6_K \| 1.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q8_0.gguf) \| Q8_0 \| 2.0 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.f16.gguf) \| f16 \| 3.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF	MrRobotoAI	"2025-02-11T21:17:06Z"	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k", "base_model:quantized:MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k", "endpoints_compatible", "region:us" ]	null	"2025-02-11T21:16:42Z"	--- base_model: MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k`](https://huggingface.co/MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -c 2048 ```
eoruadl/segformer-b0-finetuned-segments-crop_crack-2	eoruadl	"2023-05-26T02:26:18Z"	80	0	transformers	[ "transformers", "pytorch", "segformer", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	null	"2023-05-25T03:29:41Z"	--- license: other tags: - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-crop_crack-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-crop_crack-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1645 - Mean Iou: 0.4027 - Mean Accuracy: 0.6121 - Overall Accuracy: 0.6048 - Accuracy Unlabeled: nan - Accuracy Crack: 0.5696 - Accuracy Potholes: 0.6546 - Iou Unlabeled: 0.0 - Iou Crack: 0.5571 - Iou Potholes: 0.6510 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Crack \| Accuracy Potholes \| Iou Unlabeled \| Iou Crack \| Iou Potholes \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:--------------:\|:-----------------:\|:-------------:\|:---------:\|:------------:\| \| 0.8913 \| 0.07 \| 20 \| 1.0205 \| 0.1919 \| 0.3500 \| 0.3881 \| nan \| 0.5702 \| 0.1298 \| 0.0 \| 0.4481 \| 0.1275 \| \| 0.66 \| 0.14 \| 40 \| 0.7627 \| 0.0675 \| 0.1190 \| 0.1378 \| nan \| 0.2278 \| 0.0102 \| 0.0 \| 0.1924 \| 0.0102 \| \| 0.5279 \| 0.21 \| 60 \| 0.5508 \| 0.0631 \| 0.1109 \| 0.1292 \| nan \| 0.2167 \| 0.0050 \| 0.0 \| 0.1842 \| 0.0050 \| \| 0.5058 \| 0.28 \| 80 \| 0.4507 \| 0.0334 \| 0.0533 \| 0.0614 \| nan \| 0.0999 \| 0.0068 \| 0.0 \| 0.0935 \| 0.0068 \| \| 0.4833 \| 0.35 \| 100 \| 0.3818 \| 0.0268 \| 0.0427 \| 0.0499 \| nan \| 0.0843 \| 0.0012 \| 0.0 \| 0.0791 \| 0.0012 \| \| 0.3508 \| 0.42 \| 120 \| 0.3764 \| 0.0497 \| 0.0796 \| 0.0892 \| nan \| 0.1348 \| 0.0244 \| 0.0 \| 0.1247 \| 0.0243 \| \| 0.4247 \| 0.49 \| 140 \| 0.3068 \| 0.0239 \| 0.0363 \| 0.0367 \| nan \| 0.0391 \| 0.0334 \| 0.0 \| 0.0383 \| 0.0333 \| \| 0.3403 \| 0.56 \| 160 \| 0.3245 \| 0.0593 \| 0.0939 \| 0.1058 \| nan \| 0.1624 \| 0.0254 \| 0.0 \| 0.1526 \| 0.0254 \| \| 0.3684 \| 0.63 \| 180 \| 0.2564 \| 0.0660 \| 0.1026 \| 0.1084 \| nan \| 0.1360 \| 0.0691 \| 0.0 \| 0.1295 \| 0.0685 \| \| 0.2358 \| 0.7 \| 200 \| 0.2359 \| 0.0607 \| 0.0933 \| 0.0951 \| nan \| 0.1041 \| 0.0825 \| 0.0 \| 0.1007 \| 0.0814 \| \| 0.2429 \| 0.77 \| 220 \| 0.2241 \| 0.0693 \| 0.1061 \| 0.1065 \| nan \| 0.1080 \| 0.1043 \| 0.0 \| 0.1053 \| 0.1026 \| \| 0.2958 \| 0.84 \| 240 \| 0.2430 \| 0.0922 \| 0.1421 \| 0.1451 \| nan \| 0.1597 \| 0.1244 \| 0.0 \| 0.1543 \| 0.1223 \| \| 0.3171 \| 0.91 \| 260 \| 0.1997 \| 0.0682 \| 0.1040 \| 0.1008 \| nan \| 0.0854 \| 0.1227 \| 0.0 \| 0.0834 \| 0.1211 \| \| 0.2211 \| 0.98 \| 280 \| 0.2032 \| 0.1085 \| 0.1669 \| 0.1693 \| nan \| 0.1803 \| 0.1536 \| 0.0 \| 0.1743 \| 0.1513 \| \| 0.2602 \| 1.05 \| 300 \| 0.1891 \| 0.1691 \| 0.2637 \| 0.2604 \| nan \| 0.2448 \| 0.2827 \| 0.0 \| 0.2369 \| 0.2704 \| \| 0.2013 \| 1.12 \| 320 \| 0.1831 \| 0.1771 \| 0.2747 \| 0.2637 \| nan \| 0.2111 \| 0.3383 \| 0.0 \| 0.2050 \| 0.3262 \| \| 0.1402 \| 1.19 \| 340 \| 0.1724 \| 0.1062 \| 0.1629 \| 0.1659 \| nan \| 0.1800 \| 0.1458 \| 0.0 \| 0.1738 \| 0.1447 \| \| 0.2118 \| 1.26 \| 360 \| 0.1691 \| 0.1918 \| 0.2995 \| 0.2871 \| nan \| 0.2277 \| 0.3713 \| 0.0 \| 0.2243 \| 0.3511 \| \| 0.1804 \| 1.33 \| 380 \| 0.1668 \| 0.1770 \| 0.2727 \| 0.2692 \| nan \| 0.2523 \| 0.2932 \| 0.0 \| 0.2433 \| 0.2876 \| \| 0.1626 \| 1.4 \| 400 \| 0.1621 \| 0.1398 \| 0.2137 \| 0.2186 \| nan \| 0.2424 \| 0.1849 \| 0.0 \| 0.2347 \| 0.1848 \| \| 0.1191 \| 1.47 \| 420 \| 0.1544 \| 0.1627 \| 0.2492 \| 0.2488 \| nan \| 0.2469 \| 0.2514 \| 0.0 \| 0.2412 \| 0.2470 \| \| 0.1382 \| 1.54 \| 440 \| 0.1522 \| 0.2476 \| 0.3887 \| 0.3796 \| nan \| 0.3364 \| 0.4409 \| 0.0 \| 0.3284 \| 0.4145 \| \| 0.1409 \| 1.61 \| 460 \| 0.1421 \| 0.2045 \| 0.3175 \| 0.3044 \| nan \| 0.2415 \| 0.3936 \| 0.0 \| 0.2369 \| 0.3765 \| \| 0.1764 \| 1.68 \| 480 \| 0.1492 \| 0.1710 \| 0.2626 \| 0.2661 \| nan \| 0.2827 \| 0.2425 \| 0.0 \| 0.2710 \| 0.2422 \| \| 0.1432 \| 1.75 \| 500 \| 0.1484 \| 0.1917 \| 0.2933 \| 0.2932 \| nan \| 0.2926 \| 0.2940 \| 0.0 \| 0.2815 \| 0.2937 \| \| 0.1174 \| 1.82 \| 520 \| 0.1446 \| 0.2908 \| 0.4461 \| 0.4453 \| nan \| 0.4410 \| 0.4512 \| 0.0 \| 0.4250 \| 0.4473 \| \| 0.1001 \| 1.89 \| 540 \| 0.1444 \| 0.1676 \| 0.2596 \| 0.2785 \| nan \| 0.3689 \| 0.1502 \| 0.0 \| 0.3526 \| 0.1502 \| \| 0.1361 \| 1.96 \| 560 \| 0.1326 \| 0.2458 \| 0.3776 \| 0.3656 \| nan \| 0.3081 \| 0.4471 \| 0.0 \| 0.3028 \| 0.4346 \| \| 0.2597 \| 2.03 \| 580 \| 0.1312 \| 0.2476 \| 0.3799 \| 0.3677 \| nan \| 0.3094 \| 0.4505 \| 0.0 \| 0.3042 \| 0.4386 \| \| 0.1009 \| 2.1 \| 600 \| 0.1398 \| 0.2479 \| 0.3841 \| 0.4041 \| nan \| 0.4997 \| 0.2685 \| 0.0 \| 0.4777 \| 0.2660 \| \| 0.126 \| 2.17 \| 620 \| 0.1334 \| 0.2406 \| 0.3708 \| 0.3806 \| nan \| 0.4276 \| 0.3140 \| 0.0 \| 0.4104 \| 0.3115 \| \| 0.2272 \| 2.24 \| 640 \| 0.1298 \| 0.2568 \| 0.3935 \| 0.3811 \| nan \| 0.3221 \| 0.4648 \| 0.0 \| 0.3151 \| 0.4553 \| \| 0.1084 \| 2.31 \| 660 \| 0.1324 \| 0.2169 \| 0.3304 \| 0.3295 \| nan \| 0.3254 \| 0.3354 \| 0.0 \| 0.3169 \| 0.3338 \| \| 0.0982 \| 2.38 \| 680 \| 0.1307 \| 0.3197 \| 0.4938 \| 0.4761 \| nan \| 0.3918 \| 0.5958 \| 0.0 \| 0.3838 \| 0.5754 \| \| 0.0684 \| 2.45 \| 700 \| 0.1245 \| 0.3120 \| 0.4789 \| 0.4595 \| nan \| 0.3672 \| 0.5905 \| 0.0 \| 0.3624 \| 0.5736 \| \| 0.1153 \| 2.52 \| 720 \| 0.1322 \| 0.3729 \| 0.5735 \| 0.5637 \| nan \| 0.5168 \| 0.6302 \| 0.0 \| 0.5023 \| 0.6164 \| \| 0.1812 \| 2.59 \| 740 \| 0.1263 \| 0.2375 \| 0.3641 \| 0.3683 \| nan \| 0.3884 \| 0.3398 \| 0.0 \| 0.3739 \| 0.3385 \| \| 0.1354 \| 2.66 \| 760 \| 0.1177 \| 0.2881 \| 0.4393 \| 0.4293 \| nan \| 0.3814 \| 0.4972 \| 0.0 \| 0.3741 \| 0.4901 \| \| 0.084 \| 2.73 \| 780 \| 0.1286 \| 0.2604 \| 0.3977 \| 0.4027 \| nan \| 0.4269 \| 0.3685 \| 0.0 \| 0.4172 \| 0.3640 \| \| 0.1123 \| 2.8 \| 800 \| 0.1182 \| 0.2988 \| 0.4564 \| 0.4487 \| nan \| 0.4122 \| 0.5005 \| 0.0 \| 0.4016 \| 0.4947 \| \| 0.2121 \| 2.87 \| 820 \| 0.1207 \| 0.3020 \| 0.4599 \| 0.4544 \| nan \| 0.4277 \| 0.4921 \| 0.0 \| 0.4156 \| 0.4905 \| \| 0.1709 \| 2.94 \| 840 \| 0.1220 \| 0.2718 \| 0.4154 \| 0.4020 \| nan \| 0.3383 \| 0.4925 \| 0.0 \| 0.3289 \| 0.4864 \| \| 0.1322 \| 3.01 \| 860 \| 0.1197 \| 0.3103 \| 0.4748 \| 0.4708 \| nan \| 0.4520 \| 0.4976 \| 0.0 \| 0.4362 \| 0.4948 \| \| 0.1593 \| 3.08 \| 880 \| 0.1213 \| 0.3583 \| 0.5493 \| 0.5458 \| nan \| 0.5295 \| 0.5690 \| 0.0 \| 0.5106 \| 0.5643 \| \| 0.2178 \| 3.15 \| 900 \| 0.1225 \| 0.3412 \| 0.5351 \| 0.5130 \| nan \| 0.4071 \| 0.6632 \| 0.0 \| 0.4025 \| 0.6213 \| \| 0.1226 \| 3.22 \| 920 \| 0.1298 \| 0.2484 \| 0.3831 \| 0.4017 \| nan \| 0.4908 \| 0.2753 \| 0.0 \| 0.4700 \| 0.2751 \| \| 0.2689 \| 3.29 \| 940 \| 0.1141 \| 0.3109 \| 0.4744 \| 0.4680 \| nan \| 0.4374 \| 0.5114 \| 0.0 \| 0.4266 \| 0.5060 \| \| 0.1088 \| 3.36 \| 960 \| 0.1213 \| 0.2748 \| 0.4201 \| 0.4224 \| nan \| 0.4335 \| 0.4066 \| 0.0 \| 0.4185 \| 0.4060 \| \| 0.1556 \| 3.43 \| 980 \| 0.1141 \| 0.3654 \| 0.5652 \| 0.5395 \| nan \| 0.4170 \| 0.7134 \| 0.0 \| 0.4136 \| 0.6826 \| \| 0.1136 \| 3.5 \| 1000 \| 0.1190 \| 0.2653 \| 0.4034 \| 0.3979 \| nan \| 0.3718 \| 0.4351 \| 0.0 \| 0.3628 \| 0.4331 \| \| 0.1178 \| 3.57 \| 1020 \| 0.1277 \| 0.2680 \| 0.4118 \| 0.4191 \| nan \| 0.4540 \| 0.3696 \| 0.0 \| 0.4346 \| 0.3694 \| \| 0.1722 \| 3.64 \| 1040 \| 0.1135 \| 0.3246 \| 0.4996 \| 0.4829 \| nan \| 0.4031 \| 0.5962 \| 0.0 \| 0.3983 \| 0.5754 \| \| 0.0791 \| 3.71 \| 1060 \| 0.1134 \| 0.3288 \| 0.5011 \| 0.4976 \| nan \| 0.4810 \| 0.5212 \| 0.0 \| 0.4671 \| 0.5193 \| \| 0.0966 \| 3.78 \| 1080 \| 0.1140 \| 0.3080 \| 0.4682 \| 0.4551 \| nan \| 0.3924 \| 0.5440 \| 0.0 \| 0.3818 \| 0.5422 \| \| 0.0623 \| 3.85 \| 1100 \| 0.1146 \| 0.2690 \| 0.4087 \| 0.4023 \| nan \| 0.3720 \| 0.4453 \| 0.0 \| 0.3625 \| 0.4444 \| \| 0.092 \| 3.92 \| 1120 \| 0.1109 \| 0.3129 \| 0.4748 \| 0.4623 \| nan \| 0.4025 \| 0.5471 \| 0.0 \| 0.3948 \| 0.5438 \| \| 0.0864 \| 3.99 \| 1140 \| 0.1092 \| 0.3253 \| 0.4967 \| 0.4824 \| nan \| 0.4140 \| 0.5794 \| 0.0 \| 0.4073 \| 0.5685 \| \| 0.0701 \| 4.06 \| 1160 \| 0.1166 \| 0.3383 \| 0.5182 \| 0.5190 \| nan \| 0.5232 \| 0.5131 \| 0.0 \| 0.5049 \| 0.5100 \| \| 0.1157 \| 4.13 \| 1180 \| 0.1074 \| 0.3302 \| 0.5049 \| 0.4942 \| nan \| 0.4428 \| 0.5671 \| 0.0 \| 0.4334 \| 0.5573 \| \| 0.1246 \| 4.2 \| 1200 \| 0.1088 \| 0.3465 \| 0.5308 \| 0.5232 \| nan \| 0.4873 \| 0.5742 \| 0.0 \| 0.4755 \| 0.5640 \| \| 0.0657 \| 4.27 \| 1220 \| 0.1109 \| 0.3973 \| 0.6119 \| 0.5906 \| nan \| 0.4885 \| 0.7353 \| 0.0 \| 0.4797 \| 0.7121 \| \| 0.1068 \| 4.34 \| 1240 \| 0.1131 \| 0.2925 \| 0.4449 \| 0.4406 \| nan \| 0.4204 \| 0.4693 \| 0.0 \| 0.4099 \| 0.4676 \| \| 0.0642 \| 4.41 \| 1260 \| 0.1077 \| 0.3242 \| 0.4951 \| 0.4923 \| nan \| 0.4786 \| 0.5116 \| 0.0 \| 0.4642 \| 0.5084 \| \| 0.1347 \| 4.48 \| 1280 \| 0.1058 \| 0.3796 \| 0.5797 \| 0.5708 \| nan \| 0.5282 \| 0.6312 \| 0.0 \| 0.5152 \| 0.6235 \| \| 0.1713 \| 4.55 \| 1300 \| 0.1052 \| 0.3602 \| 0.5499 \| 0.5455 \| nan \| 0.5244 \| 0.5755 \| 0.0 \| 0.5103 \| 0.5703 \| \| 0.0742 \| 4.62 \| 1320 \| 0.1136 \| 0.3116 \| 0.4753 \| 0.4699 \| nan \| 0.4439 \| 0.5066 \| 0.0 \| 0.4301 \| 0.5047 \| \| 0.0751 \| 4.69 \| 1340 \| 0.1096 \| 0.3225 \| 0.4907 \| 0.4738 \| nan \| 0.3932 \| 0.5883 \| 0.0 \| 0.3831 \| 0.5845 \| \| 0.125 \| 4.76 \| 1360 \| 0.1060 \| 0.3501 \| 0.5329 \| 0.5221 \| nan \| 0.4702 \| 0.5957 \| 0.0 \| 0.4620 \| 0.5883 \| \| 0.0921 \| 4.83 \| 1380 \| 0.1062 \| 0.3506 \| 0.5345 \| 0.5304 \| nan \| 0.5107 \| 0.5584 \| 0.0 \| 0.4980 \| 0.5538 \| \| 0.0743 \| 4.9 \| 1400 \| 0.1098 \| 0.2934 \| 0.4457 \| 0.4322 \| nan \| 0.3677 \| 0.5237 \| 0.0 \| 0.3605 \| 0.5198 \| \| 0.0743 \| 4.97 \| 1420 \| 0.1061 \| 0.3805 \| 0.5810 \| 0.5736 \| nan \| 0.5383 \| 0.6236 \| 0.0 \| 0.5243 \| 0.6172 \| \| 0.1287 \| 5.03 \| 1440 \| 0.1061 \| 0.3313 \| 0.5055 \| 0.4904 \| nan \| 0.4181 \| 0.5929 \| 0.0 \| 0.4107 \| 0.5832 \| \| 0.07 \| 5.1 \| 1460 \| 0.1027 \| 0.3451 \| 0.5279 \| 0.5132 \| nan \| 0.4430 \| 0.6129 \| 0.0 \| 0.4367 \| 0.5988 \| \| 0.0795 \| 5.17 \| 1480 \| 0.1068 \| 0.3185 \| 0.4863 \| 0.4746 \| nan \| 0.4189 \| 0.5537 \| 0.0 \| 0.4122 \| 0.5432 \| \| 0.1453 \| 5.24 \| 1500 \| 0.1093 \| 0.3354 \| 0.5096 \| 0.4897 \| nan \| 0.3945 \| 0.6248 \| 0.0 \| 0.3862 \| 0.6200 \| \| 0.075 \| 5.31 \| 1520 \| 0.1103 \| 0.3707 \| 0.5662 \| 0.5564 \| nan \| 0.5095 \| 0.6230 \| 0.0 \| 0.4931 \| 0.6189 \| \| 0.0841 \| 5.38 \| 1540 \| 0.1083 \| 0.3701 \| 0.5655 \| 0.5629 \| nan \| 0.5503 \| 0.5808 \| 0.0 \| 0.5337 \| 0.5766 \| \| 0.0853 \| 5.45 \| 1560 \| 0.1012 \| 0.3796 \| 0.5796 \| 0.5642 \| nan \| 0.4906 \| 0.6686 \| 0.0 \| 0.4852 \| 0.6536 \| \| 0.1633 \| 5.52 \| 1580 \| 0.1090 \| 0.3083 \| 0.4686 \| 0.4549 \| nan \| 0.3895 \| 0.5477 \| 0.0 \| 0.3814 \| 0.5435 \| \| 0.1007 \| 5.59 \| 1600 \| 0.1045 \| 0.3719 \| 0.5677 \| 0.5628 \| nan \| 0.5394 \| 0.5960 \| 0.0 \| 0.5308 \| 0.5849 \| \| 0.1319 \| 5.66 \| 1620 \| 0.1047 \| 0.3841 \| 0.5867 \| 0.5617 \| nan \| 0.4425 \| 0.7309 \| 0.0 \| 0.4355 \| 0.7170 \| \| 0.0882 \| 5.73 \| 1640 \| 0.1060 \| 0.3343 \| 0.5097 \| 0.4991 \| nan \| 0.4488 \| 0.5705 \| 0.0 \| 0.4380 \| 0.5650 \| \| 0.0814 \| 5.8 \| 1660 \| 0.1044 \| 0.3537 \| 0.5410 \| 0.5283 \| nan \| 0.4674 \| 0.6146 \| 0.0 \| 0.4578 \| 0.6034 \| \| 0.0977 \| 5.87 \| 1680 \| 0.1036 \| 0.3880 \| 0.5929 \| 0.5864 \| nan \| 0.5551 \| 0.6307 \| 0.0 \| 0.5432 \| 0.6208 \| \| 0.0964 \| 5.94 \| 1700 \| 0.1090 \| 0.3796 \| 0.5853 \| 0.5572 \| nan \| 0.4229 \| 0.7476 \| 0.0 \| 0.4173 \| 0.7216 \| \| 0.0517 \| 6.01 \| 1720 \| 0.1113 \| 0.3189 \| 0.4857 \| 0.4741 \| nan \| 0.4183 \| 0.5532 \| 0.0 \| 0.4067 \| 0.5501 \| \| 0.2876 \| 6.08 \| 1740 \| 0.1018 \| 0.3736 \| 0.5702 \| 0.5546 \| nan \| 0.4803 \| 0.6601 \| 0.0 \| 0.4736 \| 0.6473 \| \| 0.0886 \| 6.15 \| 1760 \| 0.1096 \| 0.3204 \| 0.4894 \| 0.4862 \| nan \| 0.4709 \| 0.5079 \| 0.0 \| 0.4541 \| 0.5070 \| \| 0.0575 \| 6.22 \| 1780 \| 0.1022 \| 0.3830 \| 0.5893 \| 0.5687 \| nan \| 0.4699 \| 0.7087 \| 0.0 \| 0.4632 \| 0.6857 \| \| 0.0985 \| 6.29 \| 1800 \| 0.1096 \| 0.3548 \| 0.5413 \| 0.5359 \| nan \| 0.5102 \| 0.5725 \| 0.0 \| 0.4943 \| 0.5702 \| \| 0.082 \| 6.36 \| 1820 \| 0.1029 \| 0.3761 \| 0.5756 \| 0.5593 \| nan \| 0.4814 \| 0.6699 \| 0.0 \| 0.4727 \| 0.6555 \| \| 0.0722 \| 6.43 \| 1840 \| 0.1021 \| 0.3625 \| 0.5532 \| 0.5367 \| nan \| 0.4579 \| 0.6485 \| 0.0 \| 0.4512 \| 0.6364 \| \| 0.0703 \| 6.5 \| 1860 \| 0.1058 \| 0.3347 \| 0.5079 \| 0.4932 \| nan \| 0.4229 \| 0.5929 \| 0.0 \| 0.4137 \| 0.5904 \| \| 0.0994 \| 6.57 \| 1880 \| 0.1073 \| 0.4355 \| 0.6656 \| 0.6537 \| nan \| 0.5967 \| 0.7345 \| 0.0 \| 0.5828 \| 0.7238 \| \| 0.0836 \| 6.64 \| 1900 \| 0.0987 \| 0.3738 \| 0.5688 \| 0.5564 \| nan \| 0.4975 \| 0.6401 \| 0.0 \| 0.4868 \| 0.6347 \| \| 0.1076 \| 6.71 \| 1920 \| 0.0986 \| 0.3571 \| 0.5455 \| 0.5293 \| nan \| 0.4523 \| 0.6387 \| 0.0 \| 0.4465 \| 0.6249 \| \| 0.0807 \| 6.78 \| 1940 \| 0.1066 \| 0.3372 \| 0.5142 \| 0.5116 \| nan \| 0.4994 \| 0.5290 \| 0.0 \| 0.4832 \| 0.5282 \| \| 0.115 \| 6.85 \| 1960 \| 0.1040 \| 0.3420 \| 0.5281 \| 0.5075 \| nan \| 0.4093 \| 0.6469 \| 0.0 \| 0.4061 \| 0.6198 \| \| 0.085 \| 6.92 \| 1980 \| 0.1041 \| 0.3572 \| 0.5433 \| 0.5314 \| nan \| 0.4746 \| 0.6119 \| 0.0 \| 0.4646 \| 0.6071 \| \| 0.1058 \| 6.99 \| 2000 \| 0.1009 \| 0.3849 \| 0.5861 \| 0.5740 \| nan \| 0.5160 \| 0.6562 \| 0.0 \| 0.5070 \| 0.6476 \| \| 0.0772 \| 7.06 \| 2020 \| 0.1010 \| 0.3884 \| 0.5918 \| 0.5849 \| nan \| 0.5522 \| 0.6314 \| 0.0 \| 0.5445 \| 0.6208 \| \| 0.0637 \| 7.13 \| 2040 \| 0.1050 \| 0.3735 \| 0.5676 \| 0.5618 \| nan \| 0.5341 \| 0.6012 \| 0.0 \| 0.5228 \| 0.5978 \| \| 0.0741 \| 7.2 \| 2060 \| 0.1050 \| 0.3440 \| 0.5221 \| 0.5126 \| nan \| 0.4670 \| 0.5772 \| 0.0 \| 0.4587 \| 0.5734 \| \| 0.0788 \| 7.27 \| 2080 \| 0.0989 \| 0.3841 \| 0.5854 \| 0.5809 \| nan \| 0.5593 \| 0.6116 \| 0.0 \| 0.5442 \| 0.6081 \| \| 0.0796 \| 7.34 \| 2100 \| 0.1043 \| 0.3486 \| 0.5292 \| 0.5159 \| nan \| 0.4522 \| 0.6063 \| 0.0 \| 0.4434 \| 0.6025 \| \| 0.0731 \| 7.41 \| 2120 \| 0.1055 \| 0.3579 \| 0.5469 \| 0.5311 \| nan \| 0.4553 \| 0.6386 \| 0.0 \| 0.4531 \| 0.6205 \| \| 0.0776 \| 7.48 \| 2140 \| 0.1064 \| 0.3907 \| 0.5965 \| 0.5932 \| nan \| 0.5773 \| 0.6157 \| 0.0 \| 0.5596 \| 0.6126 \| \| 0.1098 \| 7.55 \| 2160 \| 0.1024 \| 0.3833 \| 0.5843 \| 0.5723 \| nan \| 0.5149 \| 0.6536 \| 0.0 \| 0.5047 \| 0.6452 \| \| 0.0612 \| 7.62 \| 2180 \| 0.1065 \| 0.3192 \| 0.4841 \| 0.4620 \| nan \| 0.3565 \| 0.6117 \| 0.0 \| 0.3520 \| 0.6055 \| \| 0.0744 \| 7.69 \| 2200 \| 0.1019 \| 0.4083 \| 0.6322 \| 0.6027 \| nan \| 0.4616 \| 0.8027 \| 0.0 \| 0.4592 \| 0.7658 \| \| 0.0509 \| 7.76 \| 2220 \| 0.1080 \| 0.3455 \| 0.5260 \| 0.5183 \| nan \| 0.4815 \| 0.5705 \| 0.0 \| 0.4695 \| 0.5668 \| \| 0.066 \| 7.83 \| 2240 \| 0.1011 \| 0.3887 \| 0.5968 \| 0.5743 \| nan \| 0.4668 \| 0.7267 \| 0.0 \| 0.4587 \| 0.7075 \| \| 0.0642 \| 7.9 \| 2260 \| 0.0998 \| 0.4185 \| 0.6434 \| 0.6277 \| nan \| 0.5526 \| 0.7342 \| 0.0 \| 0.5447 \| 0.7107 \| \| 0.0715 \| 7.97 \| 2280 \| 0.1082 \| 0.3556 \| 0.5419 \| 0.5436 \| nan \| 0.5518 \| 0.5319 \| 0.0 \| 0.5357 \| 0.5310 \| \| 0.0647 \| 8.04 \| 2300 \| 0.0984 \| 0.3973 \| 0.6082 \| 0.5916 \| nan \| 0.5124 \| 0.7040 \| 0.0 \| 0.5064 \| 0.6855 \| \| 0.0843 \| 8.11 \| 2320 \| 0.1093 \| 0.3623 \| 0.5511 \| 0.5376 \| nan \| 0.4733 \| 0.6290 \| 0.0 \| 0.4637 \| 0.6231 \| \| 0.0778 \| 8.18 \| 2340 \| 0.1025 \| 0.3724 \| 0.5669 \| 0.5612 \| nan \| 0.5341 \| 0.5996 \| 0.0 \| 0.5258 \| 0.5914 \| \| 0.1393 \| 8.25 \| 2360 \| 0.1042 \| 0.3804 \| 0.5800 \| 0.5758 \| nan \| 0.5559 \| 0.6041 \| 0.0 \| 0.5449 \| 0.5964 \| \| 0.0695 \| 8.32 \| 2380 \| 0.1002 \| 0.3749 \| 0.5706 \| 0.5499 \| nan \| 0.4506 \| 0.6907 \| 0.0 \| 0.4444 \| 0.6803 \| \| 0.0628 \| 8.39 \| 2400 \| 0.0974 \| 0.4024 \| 0.6150 \| 0.5932 \| nan \| 0.4887 \| 0.7413 \| 0.0 \| 0.4828 \| 0.7243 \| \| 0.1012 \| 8.46 \| 2420 \| 0.1000 \| 0.3580 \| 0.5460 \| 0.5331 \| nan \| 0.4717 \| 0.6204 \| 0.0 \| 0.4648 \| 0.6093 \| \| 0.0858 \| 8.53 \| 2440 \| 0.0940 \| 0.3873 \| 0.5899 \| 0.5728 \| nan \| 0.4910 \| 0.6887 \| 0.0 \| 0.4857 \| 0.6762 \| \| 0.042 \| 8.6 \| 2460 \| 0.0991 \| 0.3531 \| 0.5368 \| 0.5232 \| nan \| 0.4582 \| 0.6153 \| 0.0 \| 0.4528 \| 0.6064 \| \| 0.082 \| 8.67 \| 2480 \| 0.0957 \| 0.4199 \| 0.6402 \| 0.6301 \| nan \| 0.5820 \| 0.6984 \| 0.0 \| 0.5728 \| 0.6869 \| \| 0.0559 \| 8.74 \| 2500 \| 0.1000 \| 0.3834 \| 0.5835 \| 0.5694 \| nan \| 0.5019 \| 0.6651 \| 0.0 \| 0.4961 \| 0.6542 \| \| 0.0519 \| 8.81 \| 2520 \| 0.1014 \| 0.3645 \| 0.5568 \| 0.5520 \| nan \| 0.5292 \| 0.5843 \| 0.0 \| 0.5118 \| 0.5816 \| \| 0.0575 \| 8.88 \| 2540 \| 0.1006 \| 0.3780 \| 0.5758 \| 0.5652 \| nan \| 0.5147 \| 0.6369 \| 0.0 \| 0.5010 \| 0.6329 \| \| 0.1153 \| 8.95 \| 2560 \| 0.0985 \| 0.3824 \| 0.5831 \| 0.5708 \| nan \| 0.5125 \| 0.6536 \| 0.0 \| 0.5016 \| 0.6455 \| \| 0.1345 \| 9.02 \| 2580 \| 0.1130 \| 0.3045 \| 0.4620 \| 0.4461 \| nan \| 0.3704 \| 0.5535 \| 0.0 \| 0.3644 \| 0.5490 \| \| 0.0678 \| 9.09 \| 2600 \| 0.1044 \| 0.3596 \| 0.5473 \| 0.5352 \| nan \| 0.4771 \| 0.6176 \| 0.0 \| 0.4645 \| 0.6145 \| \| 0.0532 \| 9.16 \| 2620 \| 0.0980 \| 0.3829 \| 0.5827 \| 0.5744 \| nan \| 0.5350 \| 0.6303 \| 0.0 \| 0.5243 \| 0.6245 \| \| 0.0556 \| 9.23 \| 2640 \| 0.1005 \| 0.3665 \| 0.5566 \| 0.5421 \| nan \| 0.4729 \| 0.6402 \| 0.0 \| 0.4652 \| 0.6344 \| \| 0.0745 \| 9.3 \| 2660 \| 0.0989 \| 0.4017 \| 0.6134 \| 0.6049 \| nan \| 0.5643 \| 0.6626 \| 0.0 \| 0.5552 \| 0.6498 \| \| 0.0788 \| 9.37 \| 2680 \| 0.1000 \| 0.3568 \| 0.5423 \| 0.5330 \| nan \| 0.4883 \| 0.5963 \| 0.0 \| 0.4801 \| 0.5904 \| \| 0.0725 \| 9.44 \| 2700 \| 0.0989 \| 0.3925 \| 0.5978 \| 0.5835 \| nan \| 0.5150 \| 0.6807 \| 0.0 \| 0.5035 \| 0.6740 \| \| 0.0922 \| 9.51 \| 2720 \| 0.0968 \| 0.3560 \| 0.5427 \| 0.5291 \| nan \| 0.4637 \| 0.6218 \| 0.0 \| 0.4587 \| 0.6093 \| \| 0.1217 \| 9.58 \| 2740 \| 0.0951 \| 0.3694 \| 0.5643 \| 0.5454 \| nan \| 0.4551 \| 0.6736 \| 0.0 \| 0.4489 \| 0.6593 \| \| 0.0473 \| 9.65 \| 2760 \| 0.0952 \| 0.4051 \| 0.6193 \| 0.5989 \| nan \| 0.5010 \| 0.7377 \| 0.0 \| 0.4921 \| 0.7233 \| \| 0.0838 \| 9.72 \| 2780 \| 0.0949 \| 0.3779 \| 0.5748 \| 0.5603 \| nan \| 0.4908 \| 0.6589 \| 0.0 \| 0.4830 \| 0.6507 \| \| 0.0802 \| 9.79 \| 2800 \| 0.1027 \| 0.3680 \| 0.5599 \| 0.5489 \| nan \| 0.4966 \| 0.6231 \| 0.0 \| 0.4846 \| 0.6195 \| \| 0.0536 \| 9.86 \| 2820 \| 0.0998 \| 0.3787 \| 0.5779 \| 0.5686 \| nan \| 0.5241 \| 0.6317 \| 0.0 \| 0.5157 \| 0.6205 \| \| 0.0887 \| 9.93 \| 2840 \| 0.1017 \| 0.3687 \| 0.5618 \| 0.5519 \| nan \| 0.5046 \| 0.6190 \| 0.0 \| 0.4930 \| 0.6132 \| \| 0.1005 \| 10.0 \| 2860 \| 0.0981 \| 0.3995 \| 0.6066 \| 0.5950 \| nan \| 0.5391 \| 0.6742 \| 0.0 \| 0.5306 \| 0.6679 \| \| 0.0955 \| 10.07 \| 2880 \| 0.1009 \| 0.4048 \| 0.6180 \| 0.6121 \| nan \| 0.5836 \| 0.6525 \| 0.0 \| 0.5674 \| 0.6470 \| \| 0.0685 \| 10.14 \| 2900 \| 0.0979 \| 0.3750 \| 0.5759 \| 0.5561 \| nan \| 0.4616 \| 0.6901 \| 0.0 \| 0.4564 \| 0.6686 \| \| 0.1119 \| 10.21 \| 2920 \| 0.1039 \| 0.3550 \| 0.5408 \| 0.5183 \| nan \| 0.4109 \| 0.6706 \| 0.0 \| 0.4069 \| 0.6581 \| \| 0.063 \| 10.28 \| 2940 \| 0.1012 \| 0.3828 \| 0.5835 \| 0.5779 \| nan \| 0.5508 \| 0.6163 \| 0.0 \| 0.5394 \| 0.6091 \| \| 0.1124 \| 10.35 \| 2960 \| 0.1001 \| 0.4356 \| 0.6655 \| 0.6579 \| nan \| 0.6212 \| 0.7099 \| 0.0 \| 0.6058 \| 0.7009 \| \| 0.0653 \| 10.42 \| 2980 \| 0.1057 \| 0.3463 \| 0.5254 \| 0.5119 \| nan \| 0.4474 \| 0.6034 \| 0.0 \| 0.4395 \| 0.5994 \| \| 0.1077 \| 10.49 \| 3000 \| 0.0984 \| 0.3910 \| 0.5983 \| 0.5798 \| nan \| 0.4917 \| 0.7048 \| 0.0 \| 0.4844 \| 0.6886 \| \| 0.1254 \| 10.56 \| 3020 \| 0.1064 \| 0.3948 \| 0.6018 \| 0.5854 \| nan \| 0.5071 \| 0.6964 \| 0.0 \| 0.4959 \| 0.6884 \| \| 0.0687 \| 10.63 \| 3040 \| 0.1167 \| 0.3306 \| 0.5029 \| 0.5026 \| nan \| 0.5011 \| 0.5047 \| 0.0 \| 0.4882 \| 0.5037 \| \| 0.0693 \| 10.7 \| 3060 \| 0.0991 \| 0.3962 \| 0.6024 \| 0.5960 \| nan \| 0.5654 \| 0.6394 \| 0.0 \| 0.5576 \| 0.6311 \| \| 0.0875 \| 10.77 \| 3080 \| 0.0943 \| 0.4272 \| 0.6528 \| 0.6338 \| nan \| 0.5432 \| 0.7623 \| 0.0 \| 0.5367 \| 0.7448 \| \| 0.0695 \| 10.84 \| 3100 \| 0.1042 \| 0.3865 \| 0.5879 \| 0.5820 \| nan \| 0.5540 \| 0.6218 \| 0.0 \| 0.5395 \| 0.6200 \| \| 0.051 \| 10.91 \| 3120 \| 0.0968 \| 0.3849 \| 0.5854 \| 0.5682 \| nan \| 0.4858 \| 0.6851 \| 0.0 \| 0.4789 \| 0.6760 \| \| 0.0641 \| 10.98 \| 3140 \| 0.0981 \| 0.4032 \| 0.6161 \| 0.6010 \| nan \| 0.5286 \| 0.7037 \| 0.0 \| 0.5193 \| 0.6902 \| \| 0.0716 \| 11.05 \| 3160 \| 0.1036 \| 0.3798 \| 0.5809 \| 0.5680 \| nan \| 0.5061 \| 0.6558 \| 0.0 \| 0.4964 \| 0.6429 \| \| 0.1087 \| 11.12 \| 3180 \| 0.1015 \| 0.3836 \| 0.5855 \| 0.5738 \| nan \| 0.5179 \| 0.6531 \| 0.0 \| 0.5080 \| 0.6430 \| \| 0.0522 \| 11.19 \| 3200 \| 0.1043 \| 0.3675 \| 0.5603 \| 0.5403 \| nan \| 0.4449 \| 0.6756 \| 0.0 \| 0.4363 \| 0.6662 \| \| 0.0912 \| 11.26 \| 3220 \| 0.1019 \| 0.3668 \| 0.5583 \| 0.5457 \| nan \| 0.4856 \| 0.6309 \| 0.0 \| 0.4780 \| 0.6224 \| \| 0.0911 \| 11.33 \| 3240 \| 0.1003 \| 0.4162 \| 0.6366 \| 0.6229 \| nan \| 0.5571 \| 0.7162 \| 0.0 \| 0.5488 \| 0.6998 \| \| 0.0779 \| 11.4 \| 3260 \| 0.1138 \| 0.3192 \| 0.4855 \| 0.4757 \| nan \| 0.4288 \| 0.5422 \| 0.0 \| 0.4172 \| 0.5403 \| \| 0.0594 \| 11.47 \| 3280 \| 0.1012 \| 0.3781 \| 0.5795 \| 0.5589 \| nan \| 0.4605 \| 0.6984 \| 0.0 \| 0.4549 \| 0.6794 \| \| 0.0775 \| 11.54 \| 3300 \| 0.1063 \| 0.3682 \| 0.5641 \| 0.5606 \| nan \| 0.5441 \| 0.5841 \| 0.0 \| 0.5303 \| 0.5742 \| \| 0.0738 \| 11.61 \| 3320 \| 0.1052 \| 0.4021 \| 0.6189 \| 0.6026 \| nan \| 0.5244 \| 0.7134 \| 0.0 \| 0.5169 \| 0.6894 \| \| 0.1114 \| 11.68 \| 3340 \| 0.1013 \| 0.3984 \| 0.6099 \| 0.5986 \| nan \| 0.5449 \| 0.6749 \| 0.0 \| 0.5362 \| 0.6590 \| \| 0.1441 \| 11.75 \| 3360 \| 0.0991 \| 0.4060 \| 0.6212 \| 0.6055 \| nan \| 0.5302 \| 0.7123 \| 0.0 \| 0.5229 \| 0.6951 \| \| 0.0778 \| 11.82 \| 3380 \| 0.0978 \| 0.3829 \| 0.5812 \| 0.5692 \| nan \| 0.5123 \| 0.6501 \| 0.0 \| 0.5056 \| 0.6430 \| \| 0.0796 \| 11.89 \| 3400 \| 0.0990 \| 0.3648 \| 0.5630 \| 0.5386 \| nan \| 0.4222 \| 0.7039 \| 0.0 \| 0.4210 \| 0.6735 \| \| 0.0625 \| 11.96 \| 3420 \| 0.0953 \| 0.4138 \| 0.6336 \| 0.6163 \| nan \| 0.5337 \| 0.7335 \| 0.0 \| 0.5236 \| 0.7179 \| \| 0.1022 \| 12.03 \| 3440 \| 0.1007 \| 0.3783 \| 0.5756 \| 0.5592 \| nan \| 0.4808 \| 0.6705 \| 0.0 \| 0.4707 \| 0.6641 \| \| 0.0541 \| 12.1 \| 3460 \| 0.0966 \| 0.3732 \| 0.5707 \| 0.5482 \| nan \| 0.4404 \| 0.7011 \| 0.0 \| 0.4363 \| 0.6833 \| \| 0.0861 \| 12.17 \| 3480 \| 0.0937 \| 0.4198 \| 0.6419 \| 0.6271 \| nan \| 0.5564 \| 0.7274 \| 0.0 \| 0.5458 \| 0.7137 \| \| 0.0598 \| 12.24 \| 3500 \| 0.1007 \| 0.3859 \| 0.5902 \| 0.5806 \| nan \| 0.5349 \| 0.6454 \| 0.0 \| 0.5183 \| 0.6394 \| \| 0.0741 \| 12.31 \| 3520 \| 0.0924 \| 0.3955 \| 0.6036 \| 0.5847 \| nan \| 0.4947 \| 0.7124 \| 0.0 \| 0.4876 \| 0.6989 \| \| 0.0746 \| 12.38 \| 3540 \| 0.0940 \| 0.4016 \| 0.6119 \| 0.5964 \| nan \| 0.5224 \| 0.7014 \| 0.0 \| 0.5123 \| 0.6924 \| \| 0.107 \| 12.45 \| 3560 \| 0.0969 \| 0.3998 \| 0.6108 \| 0.5977 \| nan \| 0.5348 \| 0.6868 \| 0.0 \| 0.5240 \| 0.6756 \| \| 0.117 \| 12.52 \| 3580 \| 0.0981 \| 0.3908 \| 0.5950 \| 0.5836 \| nan \| 0.5292 \| 0.6608 \| 0.0 \| 0.5181 \| 0.6542 \| \| 0.1021 \| 12.59 \| 3600 \| 0.0924 \| 0.3829 \| 0.5822 \| 0.5617 \| nan \| 0.4638 \| 0.7007 \| 0.0 \| 0.4566 \| 0.6921 \| \| 0.0572 \| 12.66 \| 3620 \| 0.1013 \| 0.3601 \| 0.5463 \| 0.5259 \| nan \| 0.4286 \| 0.6640 \| 0.0 \| 0.4255 \| 0.6548 \| \| 0.0759 \| 12.73 \| 3640 \| 0.0979 \| 0.3960 \| 0.6059 \| 0.5892 \| nan \| 0.5095 \| 0.7023 \| 0.0 \| 0.5035 \| 0.6845 \| \| 0.0436 \| 12.8 \| 3660 \| 0.1000 \| 0.3826 \| 0.5826 \| 0.5684 \| nan \| 0.5009 \| 0.6642 \| 0.0 \| 0.4930 \| 0.6549 \| \| 0.0726 \| 12.87 \| 3680 \| 0.1099 \| 0.3625 \| 0.5515 \| 0.5490 \| nan \| 0.5374 \| 0.5655 \| 0.0 \| 0.5264 \| 0.5612 \| \| 0.0732 \| 12.94 \| 3700 \| 0.0957 \| 0.4221 \| 0.6484 \| 0.6269 \| nan \| 0.5241 \| 0.7726 \| 0.0 \| 0.5172 \| 0.7489 \| \| 0.0698 \| 13.01 \| 3720 \| 0.0966 \| 0.3963 \| 0.6034 \| 0.5926 \| nan \| 0.5408 \| 0.6660 \| 0.0 \| 0.5307 \| 0.6581 \| \| 0.0412 \| 13.08 \| 3740 \| 0.1060 \| 0.3700 \| 0.5626 \| 0.5468 \| nan \| 0.4717 \| 0.6534 \| 0.0 \| 0.4624 \| 0.6475 \| \| 0.0526 \| 13.15 \| 3760 \| 0.0964 \| 0.4019 \| 0.6140 \| 0.6033 \| nan \| 0.5520 \| 0.6761 \| 0.0 \| 0.5438 \| 0.6618 \| \| 0.0823 \| 13.22 \| 3780 \| 0.1005 \| 0.4183 \| 0.6401 \| 0.6340 \| nan \| 0.6047 \| 0.6756 \| 0.0 \| 0.5908 \| 0.6640 \| \| 0.0512 \| 13.29 \| 3800 \| 0.1025 \| 0.3815 \| 0.5806 \| 0.5646 \| nan \| 0.4883 \| 0.6728 \| 0.0 \| 0.4781 \| 0.6664 \| \| 0.0669 \| 13.36 \| 3820 \| 0.0999 \| 0.4168 \| 0.6386 \| 0.6294 \| nan \| 0.5856 \| 0.6915 \| 0.0 \| 0.5657 \| 0.6847 \| \| 0.0758 \| 13.43 \| 3840 \| 0.1001 \| 0.4030 \| 0.6161 \| 0.6041 \| nan \| 0.5467 \| 0.6856 \| 0.0 \| 0.5354 \| 0.6737 \| \| 0.0683 \| 13.5 \| 3860 \| 0.0969 \| 0.3761 \| 0.5735 \| 0.5568 \| nan \| 0.4775 \| 0.6694 \| 0.0 \| 0.4706 \| 0.6576 \| \| 0.0931 \| 13.57 \| 3880 \| 0.0965 \| 0.3775 \| 0.5753 \| 0.5643 \| nan \| 0.5118 \| 0.6388 \| 0.0 \| 0.5013 \| 0.6312 \| \| 0.059 \| 13.64 \| 3900 \| 0.0986 \| 0.4052 \| 0.6174 \| 0.6013 \| nan \| 0.5245 \| 0.7104 \| 0.0 \| 0.5130 \| 0.7027 \| \| 0.0525 \| 13.71 \| 3920 \| 0.0924 \| 0.4245 \| 0.6472 \| 0.6284 \| nan \| 0.5387 \| 0.7558 \| 0.0 \| 0.5305 \| 0.7430 \| \| 0.0734 \| 13.78 \| 3940 \| 0.0907 \| 0.4063 \| 0.6198 \| 0.6020 \| nan \| 0.5167 \| 0.7230 \| 0.0 \| 0.5094 \| 0.7095 \| \| 0.0456 \| 13.85 \| 3960 \| 0.0908 \| 0.3870 \| 0.5904 \| 0.5646 \| nan \| 0.4414 \| 0.7394 \| 0.0 \| 0.4393 \| 0.7218 \| \| 0.0653 \| 13.92 \| 3980 \| 0.0932 \| 0.4026 \| 0.6147 \| 0.5965 \| nan \| 0.5096 \| 0.7198 \| 0.0 \| 0.5009 \| 0.7068 \| \| 0.0591 \| 13.99 \| 4000 \| 0.0953 \| 0.3852 \| 0.5869 \| 0.5636 \| nan \| 0.4523 \| 0.7214 \| 0.0 \| 0.4500 \| 0.7055 \| \| 0.0771 \| 14.06 \| 4020 \| 0.1007 \| 0.3849 \| 0.5862 \| 0.5743 \| nan \| 0.5173 \| 0.6551 \| 0.0 \| 0.5038 \| 0.6509 \| \| 0.0572 \| 14.13 \| 4040 \| 0.0985 \| 0.3753 \| 0.5702 \| 0.5552 \| nan \| 0.4835 \| 0.6570 \| 0.0 \| 0.4753 \| 0.6505 \| \| 0.0563 \| 14.2 \| 4060 \| 0.0978 \| 0.3712 \| 0.5643 \| 0.5481 \| nan \| 0.4710 \| 0.6576 \| 0.0 \| 0.4597 \| 0.6540 \| \| 0.0621 \| 14.27 \| 4080 \| 0.0949 \| 0.3919 \| 0.5957 \| 0.5788 \| nan \| 0.4979 \| 0.6935 \| 0.0 \| 0.4863 \| 0.6894 \| \| 0.0597 \| 14.34 \| 4100 \| 0.0975 \| 0.4067 \| 0.6217 \| 0.6139 \| nan \| 0.5763 \| 0.6672 \| 0.0 \| 0.5563 \| 0.6636 \| \| 0.1111 \| 14.41 \| 4120 \| 0.0950 \| 0.4474 \| 0.6829 \| 0.6768 \| nan \| 0.6477 \| 0.7182 \| 0.0 \| 0.6360 \| 0.7061 \| \| 0.085 \| 14.48 \| 4140 \| 0.0910 \| 0.4132 \| 0.6283 \| 0.6087 \| nan \| 0.5152 \| 0.7414 \| 0.0 \| 0.5073 \| 0.7323 \| \| 0.0666 \| 14.55 \| 4160 \| 0.1019 \| 0.3930 \| 0.5982 \| 0.5877 \| nan \| 0.5377 \| 0.6586 \| 0.0 \| 0.5245 \| 0.6546 \| \| 0.1503 \| 14.62 \| 4180 \| 0.1062 \| 0.3686 \| 0.5630 \| 0.5551 \| nan \| 0.5175 \| 0.6084 \| 0.0 \| 0.4999 \| 0.6058 \| \| 0.063 \| 14.69 \| 4200 \| 0.1064 \| 0.3895 \| 0.5936 \| 0.5812 \| nan \| 0.5223 \| 0.6648 \| 0.0 \| 0.5094 \| 0.6591 \| \| 0.0682 \| 14.76 \| 4220 \| 0.1044 \| 0.3867 \| 0.5878 \| 0.5787 \| nan \| 0.5356 \| 0.6400 \| 0.0 \| 0.5270 \| 0.6330 \| \| 0.0833 \| 14.83 \| 4240 \| 0.0972 \| 0.3817 \| 0.5799 \| 0.5590 \| nan \| 0.4592 \| 0.7007 \| 0.0 \| 0.4525 \| 0.6926 \| \| 0.0557 \| 14.9 \| 4260 \| 0.0965 \| 0.3803 \| 0.5799 \| 0.5575 \| nan \| 0.4502 \| 0.7097 \| 0.0 \| 0.4444 \| 0.6964 \| \| 0.0737 \| 14.97 \| 4280 \| 0.1025 \| 0.3795 \| 0.5776 \| 0.5674 \| nan \| 0.5182 \| 0.6371 \| 0.0 \| 0.5067 \| 0.6319 \| \| 0.093 \| 15.03 \| 4300 \| 0.1014 \| 0.3918 \| 0.6044 \| 0.5804 \| nan \| 0.4660 \| 0.7427 \| 0.0 \| 0.4618 \| 0.7136 \| \| 0.0825 \| 15.1 \| 4320 \| 0.1048 \| 0.4051 \| 0.6194 \| 0.6190 \| nan \| 0.6175 \| 0.6212 \| 0.0 \| 0.6021 \| 0.6131 \| \| 0.0863 \| 15.17 \| 4340 \| 0.0955 \| 0.3903 \| 0.5952 \| 0.5774 \| nan \| 0.4926 \| 0.6977 \| 0.0 \| 0.4810 \| 0.6900 \| \| 0.0825 \| 15.24 \| 4360 \| 0.1049 \| 0.3582 \| 0.5471 \| 0.5436 \| nan \| 0.5265 \| 0.5678 \| 0.0 \| 0.5100 \| 0.5645 \| \| 0.0717 \| 15.31 \| 4380 \| 0.1047 \| 0.3870 \| 0.5921 \| 0.5870 \| nan \| 0.5629 \| 0.6213 \| 0.0 \| 0.5432 \| 0.6178 \| \| 0.047 \| 15.38 \| 4400 \| 0.0992 \| 0.3720 \| 0.5667 \| 0.5580 \| nan \| 0.5167 \| 0.6167 \| 0.0 \| 0.5094 \| 0.6067 \| \| 0.0532 \| 15.45 \| 4420 \| 0.1012 \| 0.4088 \| 0.6250 \| 0.6110 \| nan \| 0.5442 \| 0.7057 \| 0.0 \| 0.5336 \| 0.6929 \| \| 0.1046 \| 15.52 \| 4440 \| 0.1059 \| 0.3797 \| 0.5782 \| 0.5660 \| nan \| 0.5079 \| 0.6484 \| 0.0 \| 0.4975 \| 0.6415 \| \| 0.0546 \| 15.59 \| 4460 \| 0.1080 \| 0.3915 \| 0.5964 \| 0.5879 \| nan \| 0.5474 \| 0.6455 \| 0.0 \| 0.5340 \| 0.6406 \| \| 0.0841 \| 15.66 \| 4480 \| 0.1140 \| 0.3641 \| 0.5538 \| 0.5455 \| nan \| 0.5059 \| 0.6017 \| 0.0 \| 0.4930 \| 0.5994 \| \| 0.0394 \| 15.73 \| 4500 \| 0.1091 \| 0.3526 \| 0.5362 \| 0.5222 \| nan \| 0.4553 \| 0.6172 \| 0.0 \| 0.4493 \| 0.6086 \| \| 0.0973 \| 15.8 \| 4520 \| 0.1024 \| 0.3926 \| 0.6013 \| 0.5813 \| nan \| 0.4857 \| 0.7168 \| 0.0 \| 0.4798 \| 0.6981 \| \| 0.0938 \| 15.87 \| 4540 \| 0.0978 \| 0.4063 \| 0.6196 \| 0.6063 \| nan \| 0.5431 \| 0.6961 \| 0.0 \| 0.5319 \| 0.6872 \| \| 0.0702 \| 15.94 \| 4560 \| 0.1018 \| 0.3707 \| 0.5631 \| 0.5493 \| nan \| 0.4837 \| 0.6425 \| 0.0 \| 0.4766 \| 0.6356 \| \| 0.1085 \| 16.01 \| 4580 \| 0.1011 \| 0.3503 \| 0.5317 \| 0.5169 \| nan \| 0.4462 \| 0.6171 \| 0.0 \| 0.4402 \| 0.6106 \| \| 0.0878 \| 16.08 \| 4600 \| 0.1007 \| 0.3855 \| 0.5882 \| 0.5806 \| nan \| 0.5445 \| 0.6319 \| 0.0 \| 0.5276 \| 0.6289 \| \| 0.0874 \| 16.15 \| 4620 \| 0.1043 \| 0.4080 \| 0.6223 \| 0.6162 \| nan \| 0.5869 \| 0.6578 \| 0.0 \| 0.5723 \| 0.6518 \| \| 0.075 \| 16.22 \| 4640 \| 0.1019 \| 0.3917 \| 0.5975 \| 0.5870 \| nan \| 0.5369 \| 0.6582 \| 0.0 \| 0.5249 \| 0.6502 \| \| 0.0755 \| 16.29 \| 4660 \| 0.0958 \| 0.3885 \| 0.5909 \| 0.5714 \| nan \| 0.4785 \| 0.7032 \| 0.0 \| 0.4725 \| 0.6930 \| \| 0.0771 \| 16.36 \| 4680 \| 0.0946 \| 0.4212 \| 0.6422 \| 0.6286 \| nan \| 0.5634 \| 0.7211 \| 0.0 \| 0.5512 \| 0.7125 \| \| 0.0579 \| 16.43 \| 4700 \| 0.0952 \| 0.3851 \| 0.5855 \| 0.5689 \| nan \| 0.4895 \| 0.6816 \| 0.0 \| 0.4812 \| 0.6741 \| \| 0.0791 \| 16.5 \| 4720 \| 0.0994 \| 0.4017 \| 0.6118 \| 0.6045 \| nan \| 0.5694 \| 0.6542 \| 0.0 \| 0.5547 \| 0.6504 \| \| 0.0928 \| 16.57 \| 4740 \| 0.0967 \| 0.3869 \| 0.5899 \| 0.5662 \| nan \| 0.4531 \| 0.7266 \| 0.0 \| 0.4474 \| 0.7135 \| \| 0.0609 \| 16.64 \| 4760 \| 0.1021 \| 0.3885 \| 0.5917 \| 0.5831 \| nan \| 0.5420 \| 0.6413 \| 0.0 \| 0.5273 \| 0.6383 \| \| 0.0553 \| 16.71 \| 4780 \| 0.1022 \| 0.4056 \| 0.6183 \| 0.6107 \| nan \| 0.5747 \| 0.6618 \| 0.0 \| 0.5618 \| 0.6549 \| \| 0.0837 \| 16.78 \| 4800 \| 0.1002 \| 0.3934 \| 0.5988 \| 0.5893 \| nan \| 0.5437 \| 0.6539 \| 0.0 \| 0.5298 \| 0.6503 \| \| 0.0636 \| 16.85 \| 4820 \| 0.0963 \| 0.3874 \| 0.5885 \| 0.5722 \| nan \| 0.4942 \| 0.6829 \| 0.0 \| 0.4861 \| 0.6762 \| \| 0.0836 \| 16.92 \| 4840 \| 0.1014 \| 0.4027 \| 0.6121 \| 0.6065 \| nan \| 0.5802 \| 0.6439 \| 0.0 \| 0.5677 \| 0.6402 \| \| 0.0463 \| 16.99 \| 4860 \| 0.1040 \| 0.3669 \| 0.5578 \| 0.5357 \| nan \| 0.4300 \| 0.6856 \| 0.0 \| 0.4252 \| 0.6756 \| \| 0.0552 \| 17.06 \| 4880 \| 0.1026 \| 0.4088 \| 0.6217 \| 0.6110 \| nan \| 0.5602 \| 0.6832 \| 0.0 \| 0.5512 \| 0.6751 \| \| 0.0901 \| 17.13 \| 4900 \| 0.1116 \| 0.3744 \| 0.5694 \| 0.5661 \| nan \| 0.5504 \| 0.5885 \| 0.0 \| 0.5380 \| 0.5852 \| \| 0.0519 \| 17.2 \| 4920 \| 0.0992 \| 0.3929 \| 0.5994 \| 0.5878 \| nan \| 0.5325 \| 0.6662 \| 0.0 \| 0.5262 \| 0.6525 \| \| 0.0495 \| 17.27 \| 4940 \| 0.1049 \| 0.3701 \| 0.5613 \| 0.5436 \| nan \| 0.4586 \| 0.6641 \| 0.0 \| 0.4504 \| 0.6598 \| \| 0.0791 \| 17.34 \| 4960 \| 0.1030 \| 0.3959 \| 0.6027 \| 0.5925 \| nan \| 0.5437 \| 0.6618 \| 0.0 \| 0.5303 \| 0.6575 \| \| 0.072 \| 17.41 \| 4980 \| 0.1069 \| 0.4010 \| 0.6135 \| 0.6103 \| nan \| 0.5950 \| 0.6320 \| 0.0 \| 0.5733 \| 0.6296 \| \| 0.0881 \| 17.48 \| 5000 \| 0.0962 \| 0.4056 \| 0.6166 \| 0.5961 \| nan \| 0.4985 \| 0.7346 \| 0.0 \| 0.4911 \| 0.7258 \| \| 0.0783 \| 17.55 \| 5020 \| 0.0974 \| 0.4107 \| 0.6262 \| 0.6147 \| nan \| 0.5597 \| 0.6928 \| 0.0 \| 0.5503 \| 0.6819 \| \| 0.0639 \| 17.62 \| 5040 \| 0.1122 \| 0.3669 \| 0.5592 \| 0.5528 \| nan \| 0.5222 \| 0.5961 \| 0.0 \| 0.5082 \| 0.5924 \| \| 0.0634 \| 17.69 \| 5060 \| 0.0936 \| 0.4078 \| 0.6249 \| 0.6017 \| nan \| 0.4911 \| 0.7586 \| 0.0 \| 0.4893 \| 0.7342 \| \| 0.0665 \| 17.76 \| 5080 \| 0.1103 \| 0.3627 \| 0.5506 \| 0.5446 \| nan \| 0.5163 \| 0.5849 \| 0.0 \| 0.5058 \| 0.5823 \| \| 0.0535 \| 17.83 \| 5100 \| 0.0981 \| 0.3984 \| 0.6161 \| 0.5936 \| nan \| 0.4860 \| 0.7463 \| 0.0 \| 0.4841 \| 0.7111 \| \| 0.0595 \| 17.9 \| 5120 \| 0.1121 \| 0.3565 \| 0.5415 \| 0.5253 \| nan \| 0.4481 \| 0.6348 \| 0.0 \| 0.4388 \| 0.6308 \| \| 0.0766 \| 17.97 \| 5140 \| 0.1016 \| 0.4211 \| 0.6429 \| 0.6322 \| nan \| 0.5813 \| 0.7045 \| 0.0 \| 0.5721 \| 0.6913 \| \| 0.0752 \| 18.04 \| 5160 \| 0.1117 \| 0.4288 \| 0.6552 \| 0.6538 \| nan \| 0.6470 \| 0.6634 \| 0.0 \| 0.6252 \| 0.6611 \| \| 0.0826 \| 18.11 \| 5180 \| 0.1059 \| 0.3650 \| 0.5539 \| 0.5360 \| nan \| 0.4506 \| 0.6572 \| 0.0 \| 0.4418 \| 0.6530 \| \| 0.0555 \| 18.18 \| 5200 \| 0.0977 \| 0.3816 \| 0.5802 \| 0.5690 \| nan \| 0.5150 \| 0.6455 \| 0.0 \| 0.5079 \| 0.6369 \| \| 0.0844 \| 18.25 \| 5220 \| 0.1058 \| 0.3774 \| 0.5753 \| 0.5649 \| nan \| 0.5154 \| 0.6352 \| 0.0 \| 0.5008 \| 0.6315 \| \| 0.0631 \| 18.32 \| 5240 \| 0.1041 \| 0.3874 \| 0.5906 \| 0.5842 \| nan \| 0.5540 \| 0.6272 \| 0.0 \| 0.5404 \| 0.6218 \| \| 0.0583 \| 18.39 \| 5260 \| 0.1024 \| 0.4053 \| 0.6169 \| 0.6015 \| nan \| 0.5277 \| 0.7061 \| 0.0 \| 0.5174 \| 0.6983 \| \| 0.0647 \| 18.46 \| 5280 \| 0.1029 \| 0.3825 \| 0.5807 \| 0.5692 \| nan \| 0.5147 \| 0.6466 \| 0.0 \| 0.5070 \| 0.6406 \| \| 0.0379 \| 18.53 \| 5300 \| 0.1033 \| 0.3833 \| 0.5829 \| 0.5694 \| nan \| 0.5047 \| 0.6611 \| 0.0 \| 0.4966 \| 0.6533 \| \| 0.0959 \| 18.6 \| 5320 \| 0.1012 \| 0.4060 \| 0.6188 \| 0.6004 \| nan \| 0.5126 \| 0.7250 \| 0.0 \| 0.5044 \| 0.7135 \| \| 0.0785 \| 18.67 \| 5340 \| 0.1038 \| 0.4056 \| 0.6194 \| 0.6113 \| nan \| 0.5729 \| 0.6659 \| 0.0 \| 0.5561 \| 0.6606 \| \| 0.1013 \| 18.74 \| 5360 \| 0.1033 \| 0.3819 \| 0.5815 \| 0.5661 \| nan \| 0.4925 \| 0.6705 \| 0.0 \| 0.4824 \| 0.6633 \| \| 0.0619 \| 18.81 \| 5380 \| 0.1055 \| 0.3839 \| 0.5839 \| 0.5698 \| nan \| 0.5026 \| 0.6651 \| 0.0 \| 0.4930 \| 0.6588 \| \| 0.0915 \| 18.88 \| 5400 \| 0.1080 \| 0.3987 \| 0.6086 \| 0.6066 \| nan \| 0.5971 \| 0.6200 \| 0.0 \| 0.5796 \| 0.6165 \| \| 0.0667 \| 18.95 \| 5420 \| 0.1058 \| 0.3991 \| 0.6078 \| 0.6031 \| nan \| 0.5807 \| 0.6350 \| 0.0 \| 0.5677 \| 0.6296 \| \| 0.0429 \| 19.02 \| 5440 \| 0.1002 \| 0.4102 \| 0.6238 \| 0.6093 \| nan \| 0.5401 \| 0.7074 \| 0.0 \| 0.5312 \| 0.6994 \| \| 0.0876 \| 19.09 \| 5460 \| 0.0976 \| 0.3997 \| 0.6082 \| 0.5909 \| nan \| 0.5081 \| 0.7083 \| 0.0 \| 0.5016 \| 0.6975 \| \| 0.0726 \| 19.16 \| 5480 \| 0.1051 \| 0.3825 \| 0.5820 \| 0.5711 \| nan \| 0.5191 \| 0.6449 \| 0.0 \| 0.5056 \| 0.6418 \| \| 0.0569 \| 19.23 \| 5500 \| 0.1003 \| 0.4022 \| 0.6136 \| 0.6001 \| nan \| 0.5352 \| 0.6921 \| 0.0 \| 0.5272 \| 0.6793 \| \| 0.067 \| 19.3 \| 5520 \| 0.1161 \| 0.3676 \| 0.5613 \| 0.5550 \| nan \| 0.5249 \| 0.5978 \| 0.0 \| 0.5078 \| 0.5950 \| \| 0.0766 \| 19.37 \| 5540 \| 0.1063 \| 0.4164 \| 0.6348 \| 0.6266 \| nan \| 0.5874 \| 0.6821 \| 0.0 \| 0.5729 \| 0.6762 \| \| 0.0685 \| 19.44 \| 5560 \| 0.1023 \| 0.3958 \| 0.6022 \| 0.5937 \| nan \| 0.5531 \| 0.6512 \| 0.0 \| 0.5445 \| 0.6429 \| \| 0.0555 \| 19.51 \| 5580 \| 0.1103 \| 0.3905 \| 0.5950 \| 0.5787 \| nan \| 0.5008 \| 0.6892 \| 0.0 \| 0.4931 \| 0.6783 \| \| 0.167 \| 19.58 \| 5600 \| 0.1035 \| 0.4108 \| 0.6278 \| 0.6181 \| nan \| 0.5719 \| 0.6836 \| 0.0 \| 0.5637 \| 0.6688 \| \| 0.0579 \| 19.65 \| 5620 \| 0.1107 \| 0.3577 \| 0.5439 \| 0.5374 \| nan \| 0.5060 \| 0.5819 \| 0.0 \| 0.4975 \| 0.5757 \| \| 0.0623 \| 19.72 \| 5640 \| 0.1070 \| 0.3780 \| 0.5753 \| 0.5643 \| nan \| 0.5118 \| 0.6389 \| 0.0 \| 0.5030 \| 0.6311 \| \| 0.1008 \| 19.79 \| 5660 \| 0.1047 \| 0.3979 \| 0.6090 \| 0.5997 \| nan \| 0.5552 \| 0.6627 \| 0.0 \| 0.5405 \| 0.6533 \| \| 0.0678 \| 19.86 \| 5680 \| 0.1049 \| 0.3886 \| 0.5958 \| 0.5804 \| nan \| 0.5068 \| 0.6848 \| 0.0 \| 0.4973 \| 0.6686 \| \| 0.0353 \| 19.93 \| 5700 \| 0.1079 \| 0.3932 \| 0.5995 \| 0.5892 \| nan \| 0.5404 \| 0.6586 \| 0.0 \| 0.5242 \| 0.6554 \| \| 0.0664 \| 20.0 \| 5720 \| 0.1053 \| 0.3807 \| 0.5798 \| 0.5705 \| nan \| 0.5263 \| 0.6333 \| 0.0 \| 0.5126 \| 0.6294 \| \| 0.0584 \| 20.07 \| 5740 \| 0.1091 \| 0.3800 \| 0.5791 \| 0.5726 \| nan \| 0.5414 \| 0.6168 \| 0.0 \| 0.5265 \| 0.6137 \| \| 0.055 \| 20.14 \| 5760 \| 0.1089 \| 0.3871 \| 0.5925 \| 0.5853 \| nan \| 0.5509 \| 0.6341 \| 0.0 \| 0.5292 \| 0.6323 \| \| 0.0696 \| 20.21 \| 5780 \| 0.1059 \| 0.3759 \| 0.5725 \| 0.5631 \| nan \| 0.5181 \| 0.6269 \| 0.0 \| 0.5064 \| 0.6212 \| \| 0.0913 \| 20.28 \| 5800 \| 0.0992 \| 0.4003 \| 0.6087 \| 0.5924 \| nan \| 0.5146 \| 0.7027 \| 0.0 \| 0.5035 \| 0.6974 \| \| 0.0819 \| 20.35 \| 5820 \| 0.1042 \| 0.3924 \| 0.5979 \| 0.5918 \| nan \| 0.5625 \| 0.6334 \| 0.0 \| 0.5474 \| 0.6298 \| \| 0.0512 \| 20.42 \| 5840 \| 0.1012 \| 0.4067 \| 0.6203 \| 0.6092 \| nan \| 0.5561 \| 0.6846 \| 0.0 \| 0.5451 \| 0.6750 \| \| 0.0628 \| 20.49 \| 5860 \| 0.0973 \| 0.4046 \| 0.6175 \| 0.6029 \| nan \| 0.5329 \| 0.7022 \| 0.0 \| 0.5232 \| 0.6906 \| \| 0.0559 \| 20.56 \| 5880 \| 0.1116 \| 0.3773 \| 0.5761 \| 0.5727 \| nan \| 0.5563 \| 0.5959 \| 0.0 \| 0.5392 \| 0.5928 \| \| 0.0882 \| 20.63 \| 5900 \| 0.1051 \| 0.3807 \| 0.5795 \| 0.5646 \| nan \| 0.4936 \| 0.6653 \| 0.0 \| 0.4845 \| 0.6577 \| \| 0.0775 \| 20.7 \| 5920 \| 0.1082 \| 0.3772 \| 0.5752 \| 0.5643 \| nan \| 0.5123 \| 0.6381 \| 0.0 \| 0.4955 \| 0.6362 \| \| 0.0467 \| 20.77 \| 5940 \| 0.1074 \| 0.3795 \| 0.5788 \| 0.5681 \| nan \| 0.5173 \| 0.6403 \| 0.0 \| 0.5064 \| 0.6320 \| \| 0.0628 \| 20.84 \| 5960 \| 0.1120 \| 0.3721 \| 0.5676 \| 0.5542 \| nan \| 0.4902 \| 0.6449 \| 0.0 \| 0.4765 \| 0.6398 \| \| 0.0498 \| 20.91 \| 5980 \| 0.1123 \| 0.3742 \| 0.5709 \| 0.5643 \| nan \| 0.5328 \| 0.6090 \| 0.0 \| 0.5185 \| 0.6042 \| \| 0.0759 \| 20.98 \| 6000 \| 0.1103 \| 0.3724 \| 0.5753 \| 0.5459 \| nan \| 0.4054 \| 0.7452 \| 0.0 \| 0.4033 \| 0.7140 \| \| 0.0536 \| 21.05 \| 6020 \| 0.1172 \| 0.3340 \| 0.5067 \| 0.4955 \| nan \| 0.4418 \| 0.5717 \| 0.0 \| 0.4326 \| 0.5694 \| \| 0.0564 \| 21.12 \| 6040 \| 0.1075 \| 0.3964 \| 0.6035 \| 0.5965 \| nan \| 0.5629 \| 0.6440 \| 0.0 \| 0.5505 \| 0.6388 \| \| 0.0609 \| 21.19 \| 6060 \| 0.1077 \| 0.3845 \| 0.5847 \| 0.5715 \| nan \| 0.5085 \| 0.6609 \| 0.0 \| 0.5027 \| 0.6508 \| \| 0.0696 \| 21.26 \| 6080 \| 0.1104 \| 0.4082 \| 0.6222 \| 0.6173 \| nan \| 0.5938 \| 0.6505 \| 0.0 \| 0.5812 \| 0.6433 \| \| 0.0555 \| 21.33 \| 6100 \| 0.1043 \| 0.3853 \| 0.5897 \| 0.5757 \| nan \| 0.5086 \| 0.6709 \| 0.0 \| 0.5021 \| 0.6539 \| \| 0.0505 \| 21.4 \| 6120 \| 0.1034 \| 0.3913 \| 0.5972 \| 0.5778 \| nan \| 0.4850 \| 0.7094 \| 0.0 \| 0.4794 \| 0.6945 \| \| 0.0508 \| 21.47 \| 6140 \| 0.1060 \| 0.3877 \| 0.5898 \| 0.5776 \| nan \| 0.5194 \| 0.6602 \| 0.0 \| 0.5137 \| 0.6493 \| \| 0.0515 \| 21.54 \| 6160 \| 0.1052 \| 0.4081 \| 0.6214 \| 0.6114 \| nan \| 0.5639 \| 0.6789 \| 0.0 \| 0.5508 \| 0.6734 \| \| 0.0769 \| 21.61 \| 6180 \| 0.1032 \| 0.4138 \| 0.6296 \| 0.6228 \| nan \| 0.5901 \| 0.6692 \| 0.0 \| 0.5812 \| 0.6602 \| \| 0.092 \| 21.68 \| 6200 \| 0.0974 \| 0.4061 \| 0.6193 \| 0.6031 \| nan \| 0.5260 \| 0.7125 \| 0.0 \| 0.5203 \| 0.6979 \| \| 0.0601 \| 21.75 \| 6220 \| 0.1035 \| 0.3928 \| 0.5969 \| 0.5861 \| nan \| 0.5344 \| 0.6594 \| 0.0 \| 0.5232 \| 0.6553 \| \| 0.0717 \| 21.82 \| 6240 \| 0.1106 \| 0.3681 \| 0.5595 \| 0.5480 \| nan \| 0.4933 \| 0.6256 \| 0.0 \| 0.4825 \| 0.6219 \| \| 0.0547 \| 21.89 \| 6260 \| 0.1035 \| 0.3779 \| 0.5745 \| 0.5630 \| nan \| 0.5084 \| 0.6406 \| 0.0 \| 0.4988 \| 0.6349 \| \| 0.0503 \| 21.96 \| 6280 \| 0.0977 \| 0.3996 \| 0.6081 \| 0.5946 \| nan \| 0.5299 \| 0.6862 \| 0.0 \| 0.5209 \| 0.6780 \| \| 0.0875 \| 22.03 \| 6300 \| 0.1110 \| 0.3631 \| 0.5536 \| 0.5458 \| nan \| 0.5087 \| 0.5984 \| 0.0 \| 0.4949 \| 0.5943 \| \| 0.0485 \| 22.1 \| 6320 \| 0.1036 \| 0.4038 \| 0.6149 \| 0.6035 \| nan \| 0.5491 \| 0.6806 \| 0.0 \| 0.5375 \| 0.6740 \| \| 0.0694 \| 22.17 \| 6340 \| 0.1074 \| 0.3869 \| 0.5889 \| 0.5807 \| nan \| 0.5412 \| 0.6366 \| 0.0 \| 0.5285 \| 0.6324 \| \| 0.0328 \| 22.24 \| 6360 \| 0.1043 \| 0.3778 \| 0.5742 \| 0.5648 \| nan \| 0.5201 \| 0.6283 \| 0.0 \| 0.5112 \| 0.6220 \| \| 0.0644 \| 22.31 \| 6380 \| 0.0997 \| 0.4024 \| 0.6122 \| 0.5886 \| nan \| 0.4759 \| 0.7485 \| 0.0 \| 0.4712 \| 0.7361 \| \| 0.0591 \| 22.38 \| 6400 \| 0.1037 \| 0.3833 \| 0.5816 \| 0.5681 \| nan \| 0.5038 \| 0.6593 \| 0.0 \| 0.4961 \| 0.6539 \| \| 0.0717 \| 22.45 \| 6420 \| 0.1007 \| 0.4057 \| 0.6171 \| 0.6022 \| nan \| 0.5313 \| 0.7028 \| 0.0 \| 0.5201 \| 0.6969 \| \| 0.0575 \| 22.52 \| 6440 \| 0.1002 \| 0.4074 \| 0.6198 \| 0.6098 \| nan \| 0.5620 \| 0.6777 \| 0.0 \| 0.5518 \| 0.6705 \| \| 0.1045 \| 22.59 \| 6460 \| 0.1041 \| 0.4056 \| 0.6185 \| 0.6153 \| nan \| 0.6000 \| 0.6369 \| 0.0 \| 0.5845 \| 0.6324 \| \| 0.0701 \| 22.66 \| 6480 \| 0.1090 \| 0.3983 \| 0.6062 \| 0.5987 \| nan \| 0.5628 \| 0.6497 \| 0.0 \| 0.5489 \| 0.6459 \| \| 0.0791 \| 22.73 \| 6500 \| 0.1074 \| 0.4078 \| 0.6232 \| 0.6183 \| nan \| 0.5951 \| 0.6513 \| 0.0 \| 0.5757 \| 0.6478 \| \| 0.1456 \| 22.8 \| 6520 \| 0.1056 \| 0.4139 \| 0.6308 \| 0.6196 \| nan \| 0.5662 \| 0.6954 \| 0.0 \| 0.5513 \| 0.6905 \| \| 0.0788 \| 22.87 \| 6540 \| 0.1059 \| 0.4029 \| 0.6140 \| 0.6070 \| nan \| 0.5736 \| 0.6543 \| 0.0 \| 0.5591 \| 0.6495 \| \| 0.0538 \| 22.94 \| 6560 \| 0.1062 \| 0.4059 \| 0.6187 \| 0.6094 \| nan \| 0.5648 \| 0.6726 \| 0.0 \| 0.5519 \| 0.6658 \| \| 0.0545 \| 23.01 \| 6580 \| 0.1071 \| 0.4160 \| 0.6354 \| 0.6271 \| nan \| 0.5873 \| 0.6835 \| 0.0 \| 0.5708 \| 0.6772 \| \| 0.0738 \| 23.08 \| 6600 \| 0.1064 \| 0.3976 \| 0.6068 \| 0.5960 \| nan \| 0.5448 \| 0.6687 \| 0.0 \| 0.5302 \| 0.6625 \| \| 0.0679 \| 23.15 \| 6620 \| 0.1076 \| 0.3948 \| 0.6018 \| 0.5888 \| nan \| 0.5268 \| 0.6768 \| 0.0 \| 0.5142 \| 0.6701 \| \| 0.1012 \| 23.22 \| 6640 \| 0.1099 \| 0.3703 \| 0.5644 \| 0.5528 \| nan \| 0.4977 \| 0.6310 \| 0.0 \| 0.4850 \| 0.6258 \| \| 0.0583 \| 23.29 \| 6660 \| 0.1104 \| 0.3857 \| 0.5879 \| 0.5763 \| nan \| 0.5209 \| 0.6549 \| 0.0 \| 0.5085 \| 0.6486 \| \| 0.0462 \| 23.36 \| 6680 \| 0.1082 \| 0.4024 \| 0.6134 \| 0.5988 \| nan \| 0.5290 \| 0.6978 \| 0.0 \| 0.5161 \| 0.6911 \| \| 0.0696 \| 23.43 \| 6700 \| 0.1156 \| 0.3763 \| 0.5749 \| 0.5718 \| nan \| 0.5570 \| 0.5928 \| 0.0 \| 0.5376 \| 0.5912 \| \| 0.0475 \| 23.5 \| 6720 \| 0.1037 \| 0.3997 \| 0.6083 \| 0.5931 \| nan \| 0.5205 \| 0.6962 \| 0.0 \| 0.5108 \| 0.6884 \| \| 0.0699 \| 23.57 \| 6740 \| 0.1043 \| 0.3914 \| 0.5949 \| 0.5843 \| nan \| 0.5341 \| 0.6556 \| 0.0 \| 0.5231 \| 0.6512 \| \| 0.0417 \| 23.64 \| 6760 \| 0.1163 \| 0.3607 \| 0.5474 \| 0.5366 \| nan \| 0.4848 \| 0.6100 \| 0.0 \| 0.4749 \| 0.6073 \| \| 0.0656 \| 23.71 \| 6780 \| 0.1082 \| 0.4003 \| 0.6111 \| 0.6040 \| nan \| 0.5701 \| 0.6520 \| 0.0 \| 0.5530 \| 0.6479 \| \| 0.0548 \| 23.78 \| 6800 \| 0.1101 \| 0.3748 \| 0.5692 \| 0.5564 \| nan \| 0.4952 \| 0.6433 \| 0.0 \| 0.4864 \| 0.6381 \| \| 0.0842 \| 23.85 \| 6820 \| 0.1125 \| 0.3775 \| 0.5762 \| 0.5683 \| nan \| 0.5307 \| 0.6217 \| 0.0 \| 0.5171 \| 0.6154 \| \| 0.0606 \| 23.92 \| 6840 \| 0.1219 \| 0.3894 \| 0.5949 \| 0.5934 \| nan \| 0.5866 \| 0.6031 \| 0.0 \| 0.5670 \| 0.6013 \| \| 0.0683 \| 23.99 \| 6860 \| 0.1059 \| 0.4190 \| 0.6404 \| 0.6311 \| nan \| 0.5866 \| 0.6943 \| 0.0 \| 0.5708 \| 0.6863 \| \| 0.0599 \| 24.06 \| 6880 \| 0.1132 \| 0.3841 \| 0.5879 \| 0.5801 \| nan \| 0.5430 \| 0.6328 \| 0.0 \| 0.5232 \| 0.6291 \| \| 0.0531 \| 24.13 \| 6900 \| 0.1041 \| 0.4171 \| 0.6364 \| 0.6275 \| nan \| 0.5851 \| 0.6877 \| 0.0 \| 0.5722 \| 0.6791 \| \| 0.0506 \| 24.2 \| 6920 \| 0.1240 \| 0.3742 \| 0.5702 \| 0.5697 \| nan \| 0.5673 \| 0.5731 \| 0.0 \| 0.5512 \| 0.5714 \| \| 0.0749 \| 24.27 \| 6940 \| 0.1059 \| 0.3990 \| 0.6091 \| 0.5980 \| nan \| 0.5449 \| 0.6733 \| 0.0 \| 0.5291 \| 0.6680 \| \| 0.055 \| 24.34 \| 6960 \| 0.1098 \| 0.3805 \| 0.5797 \| 0.5696 \| nan \| 0.5212 \| 0.6382 \| 0.0 \| 0.5078 \| 0.6338 \| \| 0.0508 \| 24.41 \| 6980 \| 0.1068 \| 0.3771 \| 0.5758 \| 0.5622 \| nan \| 0.4975 \| 0.6541 \| 0.0 \| 0.4927 \| 0.6385 \| \| 0.0675 \| 24.48 \| 7000 \| 0.1159 \| 0.3933 \| 0.5984 \| 0.5846 \| nan \| 0.5186 \| 0.6782 \| 0.0 \| 0.5072 \| 0.6727 \| \| 0.0594 \| 24.55 \| 7020 \| 0.1044 \| 0.3917 \| 0.5951 \| 0.5778 \| nan \| 0.4952 \| 0.6949 \| 0.0 \| 0.4859 \| 0.6890 \| \| 0.0629 \| 24.62 \| 7040 \| 0.1095 \| 0.3850 \| 0.5858 \| 0.5761 \| nan \| 0.5297 \| 0.6419 \| 0.0 \| 0.5158 \| 0.6390 \| \| 0.0708 \| 24.69 \| 7060 \| 0.1064 \| 0.4145 \| 0.6313 \| 0.6212 \| nan \| 0.5731 \| 0.6894 \| 0.0 \| 0.5616 \| 0.6820 \| \| 0.0666 \| 24.76 \| 7080 \| 0.1121 \| 0.3841 \| 0.5854 \| 0.5764 \| nan \| 0.5336 \| 0.6372 \| 0.0 \| 0.5197 \| 0.6327 \| \| 0.0617 \| 24.83 \| 7100 \| 0.1102 \| 0.3909 \| 0.5951 \| 0.5857 \| nan \| 0.5411 \| 0.6491 \| 0.0 \| 0.5278 \| 0.6450 \| \| 0.0788 \| 24.9 \| 7120 \| 0.1096 \| 0.3790 \| 0.5769 \| 0.5661 \| nan \| 0.5146 \| 0.6393 \| 0.0 \| 0.5009 \| 0.6361 \| \| 0.0431 \| 24.97 \| 7140 \| 0.1163 \| 0.3701 \| 0.5643 \| 0.5539 \| nan \| 0.5045 \| 0.6241 \| 0.0 \| 0.4896 \| 0.6208 \| \| 0.0694 \| 25.03 \| 7160 \| 0.1113 \| 0.3804 \| 0.5782 \| 0.5636 \| nan \| 0.4939 \| 0.6625 \| 0.0 \| 0.4870 \| 0.6540 \| \| 0.0847 \| 25.1 \| 7180 \| 0.1056 \| 0.3921 \| 0.5954 \| 0.5841 \| nan \| 0.5298 \| 0.6611 \| 0.0 \| 0.5241 \| 0.6521 \| \| 0.0751 \| 25.17 \| 7200 \| 0.1059 \| 0.4104 \| 0.6234 \| 0.6080 \| nan \| 0.5341 \| 0.7128 \| 0.0 \| 0.5258 \| 0.7053 \| \| 0.0536 \| 25.24 \| 7220 \| 0.1109 \| 0.3948 \| 0.6002 \| 0.5890 \| nan \| 0.5357 \| 0.6647 \| 0.0 \| 0.5242 \| 0.6604 \| \| 0.0481 \| 25.31 \| 7240 \| 0.1039 \| 0.4307 \| 0.6552 \| 0.6441 \| nan \| 0.5911 \| 0.7194 \| 0.0 \| 0.5818 \| 0.7102 \| \| 0.0758 \| 25.38 \| 7260 \| 0.1100 \| 0.4104 \| 0.6277 \| 0.6223 \| nan \| 0.5963 \| 0.6591 \| 0.0 \| 0.5787 \| 0.6525 \| \| 0.0696 \| 25.45 \| 7280 \| 0.0968 \| 0.4143 \| 0.6307 \| 0.6154 \| nan \| 0.5424 \| 0.7190 \| 0.0 \| 0.5334 \| 0.7096 \| \| 0.0652 \| 25.52 \| 7300 \| 0.1013 \| 0.4363 \| 0.6657 \| 0.6581 \| nan \| 0.6223 \| 0.7090 \| 0.0 \| 0.6065 \| 0.7025 \| \| 0.0723 \| 25.59 \| 7320 \| 0.0987 \| 0.4145 \| 0.6297 \| 0.6129 \| nan \| 0.5329 \| 0.7264 \| 0.0 \| 0.5244 \| 0.7191 \| \| 0.0583 \| 25.66 \| 7340 \| 0.1021 \| 0.4088 \| 0.6217 \| 0.6118 \| nan \| 0.5646 \| 0.6788 \| 0.0 \| 0.5541 \| 0.6724 \| \| 0.0482 \| 25.73 \| 7360 \| 0.1056 \| 0.4003 \| 0.6074 \| 0.5959 \| nan \| 0.5410 \| 0.6738 \| 0.0 \| 0.5312 \| 0.6697 \| \| 0.0559 \| 25.8 \| 7380 \| 0.0982 \| 0.4233 \| 0.6432 \| 0.6306 \| nan \| 0.5706 \| 0.7159 \| 0.0 \| 0.5617 \| 0.7081 \| \| 0.0805 \| 25.87 \| 7400 \| 0.1083 \| 0.3842 \| 0.5842 \| 0.5740 \| nan \| 0.5255 \| 0.6428 \| 0.0 \| 0.5147 \| 0.6379 \| \| 0.1062 \| 25.94 \| 7420 \| 0.1046 \| 0.4005 \| 0.6088 \| 0.5963 \| nan \| 0.5369 \| 0.6807 \| 0.0 \| 0.5247 \| 0.6768 \| \| 0.0548 \| 26.01 \| 7440 \| 0.1070 \| 0.3973 \| 0.6036 \| 0.5912 \| nan \| 0.5318 \| 0.6755 \| 0.0 \| 0.5220 \| 0.6699 \| \| 0.0477 \| 26.08 \| 7460 \| 0.1069 \| 0.3864 \| 0.5881 \| 0.5769 \| nan \| 0.5233 \| 0.6529 \| 0.0 \| 0.5124 \| 0.6468 \| \| 0.0514 \| 26.15 \| 7480 \| 0.1129 \| 0.3872 \| 0.5908 \| 0.5823 \| nan \| 0.5415 \| 0.6402 \| 0.0 \| 0.5257 \| 0.6358 \| \| 0.076 \| 26.22 \| 7500 \| 0.1038 \| 0.4098 \| 0.6238 \| 0.6110 \| nan \| 0.5504 \| 0.6971 \| 0.0 \| 0.5416 \| 0.6880 \| \| 0.038 \| 26.29 \| 7520 \| 0.1104 \| 0.4091 \| 0.6236 \| 0.6187 \| nan \| 0.5952 \| 0.6521 \| 0.0 \| 0.5818 \| 0.6455 \| \| 0.0465 \| 26.36 \| 7540 \| 0.1140 \| 0.3794 \| 0.5786 \| 0.5698 \| nan \| 0.5278 \| 0.6294 \| 0.0 \| 0.5130 \| 0.6252 \| \| 0.0718 \| 26.43 \| 7560 \| 0.1159 \| 0.3726 \| 0.5682 \| 0.5619 \| nan \| 0.5318 \| 0.6047 \| 0.0 \| 0.5172 \| 0.6005 \| \| 0.0567 \| 26.5 \| 7580 \| 0.1162 \| 0.3708 \| 0.5644 \| 0.5568 \| nan \| 0.5201 \| 0.6088 \| 0.0 \| 0.5082 \| 0.6041 \| \| 0.058 \| 26.57 \| 7600 \| 0.1082 \| 0.3897 \| 0.5927 \| 0.5824 \| nan \| 0.5336 \| 0.6517 \| 0.0 \| 0.5232 \| 0.6460 \| \| 0.0621 \| 26.64 \| 7620 \| 0.1048 \| 0.4165 \| 0.6344 \| 0.6211 \| nan \| 0.5578 \| 0.7110 \| 0.0 \| 0.5451 \| 0.7043 \| \| 0.0574 \| 26.71 \| 7640 \| 0.1091 \| 0.4002 \| 0.6090 \| 0.5964 \| nan \| 0.5364 \| 0.6817 \| 0.0 \| 0.5249 \| 0.6757 \| \| 0.0508 \| 26.78 \| 7660 \| 0.1133 \| 0.3822 \| 0.5827 \| 0.5712 \| nan \| 0.5162 \| 0.6492 \| 0.0 \| 0.5062 \| 0.6403 \| \| 0.0689 \| 26.85 \| 7680 \| 0.1139 \| 0.3822 \| 0.5817 \| 0.5713 \| nan \| 0.5219 \| 0.6414 \| 0.0 \| 0.5110 \| 0.6357 \| \| 0.0607 \| 26.92 \| 7700 \| 0.1134 \| 0.3671 \| 0.5589 \| 0.5465 \| nan \| 0.4872 \| 0.6306 \| 0.0 \| 0.4798 \| 0.6216 \| \| 0.0603 \| 26.99 \| 7720 \| 0.1109 \| 0.4023 \| 0.6137 \| 0.6017 \| nan \| 0.5446 \| 0.6828 \| 0.0 \| 0.5364 \| 0.6705 \| \| 0.0585 \| 27.06 \| 7740 \| 0.1139 \| 0.3758 \| 0.5727 \| 0.5630 \| nan \| 0.5166 \| 0.6288 \| 0.0 \| 0.5092 \| 0.6182 \| \| 0.0567 \| 27.13 \| 7760 \| 0.1123 \| 0.3915 \| 0.5966 \| 0.5859 \| nan \| 0.5345 \| 0.6588 \| 0.0 \| 0.5227 \| 0.6520 \| \| 0.0624 \| 27.2 \| 7780 \| 0.1119 \| 0.3845 \| 0.5848 \| 0.5748 \| nan \| 0.5268 \| 0.6429 \| 0.0 \| 0.5165 \| 0.6370 \| \| 0.064 \| 27.27 \| 7800 \| 0.1139 \| 0.3941 \| 0.5979 \| 0.5880 \| nan \| 0.5410 \| 0.6548 \| 0.0 \| 0.5327 \| 0.6495 \| \| 0.0613 \| 27.34 \| 7820 \| 0.1131 \| 0.3836 \| 0.5842 \| 0.5729 \| nan \| 0.5192 \| 0.6491 \| 0.0 \| 0.5118 \| 0.6390 \| \| 0.0365 \| 27.41 \| 7840 \| 0.1096 \| 0.4107 \| 0.6239 \| 0.6126 \| nan \| 0.5589 \| 0.6888 \| 0.0 \| 0.5503 \| 0.6818 \| \| 0.0684 \| 27.48 \| 7860 \| 0.1080 \| 0.4064 \| 0.6187 \| 0.6106 \| nan \| 0.5720 \| 0.6654 \| 0.0 \| 0.5598 \| 0.6593 \| \| 0.061 \| 27.55 \| 7880 \| 0.1027 \| 0.4219 \| 0.6425 \| 0.6294 \| nan \| 0.5671 \| 0.7179 \| 0.0 \| 0.5572 \| 0.7085 \| \| 0.0635 \| 27.62 \| 7900 \| 0.1123 \| 0.3687 \| 0.5601 \| 0.5467 \| nan \| 0.4828 \| 0.6374 \| 0.0 \| 0.4744 \| 0.6317 \| \| 0.0551 \| 27.69 \| 7920 \| 0.1061 \| 0.3908 \| 0.5932 \| 0.5717 \| nan \| 0.4688 \| 0.7176 \| 0.0 \| 0.4620 \| 0.7104 \| \| 0.0603 \| 27.76 \| 7940 \| 0.1027 \| 0.4160 \| 0.6330 \| 0.6149 \| nan \| 0.5284 \| 0.7376 \| 0.0 \| 0.5227 \| 0.7253 \| \| 0.0471 \| 27.83 \| 7960 \| 0.1092 \| 0.4077 \| 0.6190 \| 0.6099 \| nan \| 0.5666 \| 0.6714 \| 0.0 \| 0.5558 \| 0.6674 \| \| 0.0854 \| 27.9 \| 7980 \| 0.1072 \| 0.4176 \| 0.6367 \| 0.6285 \| nan \| 0.5897 \| 0.6836 \| 0.0 \| 0.5788 \| 0.6739 \| \| 0.0851 \| 27.97 \| 8000 \| 0.1080 \| 0.4052 \| 0.6151 \| 0.6025 \| nan \| 0.5423 \| 0.6880 \| 0.0 \| 0.5336 \| 0.6820 \| \| 0.0857 \| 28.04 \| 8020 \| 0.1099 \| 0.3839 \| 0.5828 \| 0.5687 \| nan \| 0.5009 \| 0.6648 \| 0.0 \| 0.4912 \| 0.6605 \| \| 0.0603 \| 28.11 \| 8040 \| 0.1049 \| 0.4042 \| 0.6144 \| 0.5994 \| nan \| 0.5278 \| 0.7009 \| 0.0 \| 0.5179 \| 0.6946 \| \| 0.0278 \| 28.18 \| 8060 \| 0.1033 \| 0.4285 \| 0.6528 \| 0.6353 \| nan \| 0.5515 \| 0.7541 \| 0.0 \| 0.5439 \| 0.7415 \| \| 0.0671 \| 28.25 \| 8080 \| 0.1065 \| 0.4027 \| 0.6119 \| 0.5998 \| nan \| 0.5422 \| 0.6816 \| 0.0 \| 0.5319 \| 0.6761 \| \| 0.0595 \| 28.32 \| 8100 \| 0.1110 \| 0.3899 \| 0.5918 \| 0.5788 \| nan \| 0.5166 \| 0.6670 \| 0.0 \| 0.5086 \| 0.6612 \| \| 0.0576 \| 28.39 \| 8120 \| 0.1103 \| 0.3906 \| 0.5928 \| 0.5779 \| nan \| 0.5068 \| 0.6788 \| 0.0 \| 0.5011 \| 0.6707 \| \| 0.0707 \| 28.46 \| 8140 \| 0.1052 \| 0.4157 \| 0.6330 \| 0.6148 \| nan \| 0.5275 \| 0.7386 \| 0.0 \| 0.5214 \| 0.7257 \| \| 0.0356 \| 28.53 \| 8160 \| 0.1061 \| 0.4085 \| 0.6210 \| 0.6131 \| nan \| 0.5752 \| 0.6668 \| 0.0 \| 0.5650 \| 0.6606 \| \| 0.0676 \| 28.6 \| 8180 \| 0.1087 \| 0.3997 \| 0.6077 \| 0.5972 \| nan \| 0.5469 \| 0.6685 \| 0.0 \| 0.5346 \| 0.6644 \| \| 0.0487 \| 28.67 \| 8200 \| 0.1139 \| 0.3940 \| 0.5980 \| 0.5874 \| nan \| 0.5366 \| 0.6595 \| 0.0 \| 0.5255 \| 0.6564 \| \| 0.0657 \| 28.74 \| 8220 \| 0.1055 \| 0.4071 \| 0.6216 \| 0.6017 \| nan \| 0.5068 \| 0.7364 \| 0.0 \| 0.5028 \| 0.7184 \| \| 0.0666 \| 28.81 \| 8240 \| 0.1090 \| 0.3899 \| 0.5928 \| 0.5821 \| nan \| 0.5306 \| 0.6551 \| 0.0 \| 0.5190 \| 0.6506 \| \| 0.072 \| 28.88 \| 8260 \| 0.1126 \| 0.3887 \| 0.5916 \| 0.5824 \| nan \| 0.5384 \| 0.6449 \| 0.0 \| 0.5237 \| 0.6425 \| \| 0.0403 \| 28.95 \| 8280 \| 0.1039 \| 0.4177 \| 0.6371 \| 0.6190 \| nan \| 0.5327 \| 0.7415 \| 0.0 \| 0.5240 \| 0.7291 \| \| 0.0667 \| 29.02 \| 8300 \| 0.1123 \| 0.4005 \| 0.6100 \| 0.6021 \| nan \| 0.5644 \| 0.6556 \| 0.0 \| 0.5483 \| 0.6532 \| \| 0.0517 \| 29.09 \| 8320 \| 0.1127 \| 0.4039 \| 0.6154 \| 0.6126 \| nan \| 0.5988 \| 0.6321 \| 0.0 \| 0.5834 \| 0.6283 \| \| 0.0737 \| 29.16 \| 8340 \| 0.1096 \| 0.4032 \| 0.6140 \| 0.6050 \| nan \| 0.5622 \| 0.6658 \| 0.0 \| 0.5464 \| 0.6633 \| \| 0.0777 \| 29.23 \| 8360 \| 0.1092 \| 0.3886 \| 0.5910 \| 0.5792 \| nan \| 0.5225 \| 0.6596 \| 0.0 \| 0.5129 \| 0.6528 \| \| 0.0747 \| 29.3 \| 8380 \| 0.1060 \| 0.4068 \| 0.6179 \| 0.6057 \| nan \| 0.5472 \| 0.6886 \| 0.0 \| 0.5373 \| 0.6832 \| \| 0.0561 \| 29.37 \| 8400 \| 0.1048 \| 0.4209 \| 0.6399 \| 0.6294 \| nan \| 0.5794 \| 0.7003 \| 0.0 \| 0.5673 \| 0.6955 \| \| 0.0913 \| 29.44 \| 8420 \| 0.1078 \| 0.4049 \| 0.6154 \| 0.6051 \| nan \| 0.5555 \| 0.6754 \| 0.0 \| 0.5434 \| 0.6714 \| \| 0.0655 \| 29.51 \| 8440 \| 0.1021 \| 0.4420 \| 0.6742 \| 0.6628 \| nan \| 0.6083 \| 0.7401 \| 0.0 \| 0.5965 \| 0.7294 \| \| 0.0562 \| 29.58 \| 8460 \| 0.1028 \| 0.4275 \| 0.6515 \| 0.6410 \| nan \| 0.5909 \| 0.7121 \| 0.0 \| 0.5781 \| 0.7044 \| \| 0.0371 \| 29.65 \| 8480 \| 0.1092 \| 0.4100 \| 0.6243 \| 0.6188 \| nan \| 0.5923 \| 0.6564 \| 0.0 \| 0.5778 \| 0.6522 \| \| 0.0756 \| 29.72 \| 8500 \| 0.1074 \| 0.4026 \| 0.6127 \| 0.6000 \| nan \| 0.5390 \| 0.6864 \| 0.0 \| 0.5307 \| 0.6772 \| \| 0.0523 \| 29.79 \| 8520 \| 0.1113 \| 0.3981 \| 0.6052 \| 0.5948 \| nan \| 0.5454 \| 0.6650 \| 0.0 \| 0.5357 \| 0.6587 \| \| 0.0611 \| 29.86 \| 8540 \| 0.1122 \| 0.3965 \| 0.6025 \| 0.5903 \| nan \| 0.5324 \| 0.6726 \| 0.0 \| 0.5213 \| 0.6683 \| \| 0.0658 \| 29.93 \| 8560 \| 0.1081 \| 0.4066 \| 0.6173 \| 0.6028 \| nan \| 0.5334 \| 0.7013 \| 0.0 \| 0.5234 \| 0.6964 \| \| 0.0454 \| 30.0 \| 8580 \| 0.1136 \| 0.3825 \| 0.5811 \| 0.5692 \| nan \| 0.5123 \| 0.6500 \| 0.0 \| 0.5010 \| 0.6465 \| \| 0.0712 \| 30.07 \| 8600 \| 0.1102 \| 0.3950 \| 0.6016 \| 0.5869 \| nan \| 0.5168 \| 0.6863 \| 0.0 \| 0.5081 \| 0.6768 \| \| 0.0706 \| 30.14 \| 8620 \| 0.1105 \| 0.4028 \| 0.6133 \| 0.6055 \| nan \| 0.5685 \| 0.6580 \| 0.0 \| 0.5558 \| 0.6527 \| \| 0.0428 \| 30.21 \| 8640 \| 0.1071 \| 0.4201 \| 0.6395 \| 0.6314 \| nan \| 0.5924 \| 0.6867 \| 0.0 \| 0.5772 \| 0.6832 \| \| 0.0585 \| 30.28 \| 8660 \| 0.1068 \| 0.3963 \| 0.6027 \| 0.5879 \| nan \| 0.5175 \| 0.6879 \| 0.0 \| 0.5074 \| 0.6815 \| \| 0.0451 \| 30.35 \| 8680 \| 0.1121 \| 0.3878 \| 0.5892 \| 0.5750 \| nan \| 0.5073 \| 0.6711 \| 0.0 \| 0.4957 \| 0.6676 \| \| 0.0681 \| 30.42 \| 8700 \| 0.1105 \| 0.3939 \| 0.5999 \| 0.5877 \| nan \| 0.5295 \| 0.6702 \| 0.0 \| 0.5147 \| 0.6670 \| \| 0.0569 \| 30.49 \| 8720 \| 0.1199 \| 0.3891 \| 0.5933 \| 0.5868 \| nan \| 0.5558 \| 0.6308 \| 0.0 \| 0.5392 \| 0.6282 \| \| 0.0507 \| 30.56 \| 8740 \| 0.1106 \| 0.3939 \| 0.5999 \| 0.5866 \| nan \| 0.5234 \| 0.6764 \| 0.0 \| 0.5125 \| 0.6691 \| \| 0.0545 \| 30.63 \| 8760 \| 0.1124 \| 0.3977 \| 0.6045 \| 0.5908 \| nan \| 0.5252 \| 0.6839 \| 0.0 \| 0.5137 \| 0.6793 \| \| 0.0561 \| 30.7 \| 8780 \| 0.1094 \| 0.4062 \| 0.6182 \| 0.6034 \| nan \| 0.5328 \| 0.7036 \| 0.0 \| 0.5221 \| 0.6965 \| \| 0.0581 \| 30.77 \| 8800 \| 0.1055 \| 0.4146 \| 0.6303 \| 0.6153 \| nan \| 0.5436 \| 0.7169 \| 0.0 \| 0.5365 \| 0.7074 \| \| 0.0662 \| 30.84 \| 8820 \| 0.1102 \| 0.3981 \| 0.6038 \| 0.5898 \| nan \| 0.5233 \| 0.6842 \| 0.0 \| 0.5183 \| 0.6760 \| \| 0.0985 \| 30.91 \| 8840 \| 0.1095 \| 0.4037 \| 0.6138 \| 0.5981 \| nan \| 0.5230 \| 0.7045 \| 0.0 \| 0.5149 \| 0.6962 \| \| 0.0481 \| 30.98 \| 8860 \| 0.1037 \| 0.4105 \| 0.6261 \| 0.6114 \| nan \| 0.5414 \| 0.7107 \| 0.0 \| 0.5354 \| 0.6960 \| \| 0.0495 \| 31.05 \| 8880 \| 0.1048 \| 0.4012 \| 0.6100 \| 0.5912 \| nan \| 0.5015 \| 0.7186 \| 0.0 \| 0.4946 \| 0.7090 \| \| 0.0445 \| 31.12 \| 8900 \| 0.1117 \| 0.3896 \| 0.5919 \| 0.5799 \| nan \| 0.5222 \| 0.6617 \| 0.0 \| 0.5117 \| 0.6572 \| \| 0.0649 \| 31.19 \| 8920 \| 0.1117 \| 0.3992 \| 0.6078 \| 0.5945 \| nan \| 0.5308 \| 0.6848 \| 0.0 \| 0.5195 \| 0.6780 \| \| 0.0542 \| 31.26 \| 8940 \| 0.1112 \| 0.4042 \| 0.6163 \| 0.6091 \| nan \| 0.5746 \| 0.6581 \| 0.0 \| 0.5601 \| 0.6527 \| \| 0.0388 \| 31.33 \| 8960 \| 0.1093 \| 0.4151 \| 0.6319 \| 0.6238 \| nan \| 0.5852 \| 0.6786 \| 0.0 \| 0.5735 \| 0.6717 \| \| 0.0571 \| 31.4 \| 8980 \| 0.1079 \| 0.4119 \| 0.6266 \| 0.6158 \| nan \| 0.5642 \| 0.6890 \| 0.0 \| 0.5530 \| 0.6826 \| \| 0.0547 \| 31.47 \| 9000 \| 0.1190 \| 0.3846 \| 0.5866 \| 0.5809 \| nan \| 0.5541 \| 0.6191 \| 0.0 \| 0.5375 \| 0.6163 \| \| 0.0497 \| 31.54 \| 9020 \| 0.1115 \| 0.4016 \| 0.6121 \| 0.5997 \| nan \| 0.5405 \| 0.6838 \| 0.0 \| 0.5276 \| 0.6772 \| \| 0.0499 \| 31.61 \| 9040 \| 0.1167 \| 0.3834 \| 0.5823 \| 0.5683 \| nan \| 0.5012 \| 0.6634 \| 0.0 \| 0.4911 \| 0.6589 \| \| 0.05 \| 31.68 \| 9060 \| 0.1162 \| 0.3891 \| 0.5913 \| 0.5764 \| nan \| 0.5056 \| 0.6769 \| 0.0 \| 0.4935 \| 0.6737 \| \| 0.0432 \| 31.75 \| 9080 \| 0.1174 \| 0.3884 \| 0.5913 \| 0.5837 \| nan \| 0.5476 \| 0.6351 \| 0.0 \| 0.5347 \| 0.6306 \| \| 0.064 \| 31.82 \| 9100 \| 0.1095 \| 0.4059 \| 0.6168 \| 0.6028 \| nan \| 0.5363 \| 0.6972 \| 0.0 \| 0.5282 \| 0.6895 \| \| 0.1138 \| 31.89 \| 9120 \| 0.1126 \| 0.3874 \| 0.5887 \| 0.5786 \| nan \| 0.5301 \| 0.6473 \| 0.0 \| 0.5210 \| 0.6413 \| \| 0.0552 \| 31.96 \| 9140 \| 0.1116 \| 0.4048 \| 0.6161 \| 0.6099 \| nan \| 0.5804 \| 0.6517 \| 0.0 \| 0.5680 \| 0.6464 \| \| 0.0758 \| 32.03 \| 9160 \| 0.1203 \| 0.3815 \| 0.5817 \| 0.5796 \| nan \| 0.5696 \| 0.5938 \| 0.0 \| 0.5529 \| 0.5917 \| \| 0.0574 \| 32.1 \| 9180 \| 0.1061 \| 0.4369 \| 0.6653 \| 0.6533 \| nan \| 0.5962 \| 0.7343 \| 0.0 \| 0.5868 \| 0.7239 \| \| 0.0659 \| 32.17 \| 9200 \| 0.1076 \| 0.4212 \| 0.6402 \| 0.6300 \| nan \| 0.5814 \| 0.6990 \| 0.0 \| 0.5699 \| 0.6938 \| \| 0.0398 \| 32.24 \| 9220 \| 0.1049 \| 0.4489 \| 0.6830 \| 0.6686 \| nan \| 0.6001 \| 0.7659 \| 0.0 \| 0.5899 \| 0.7568 \| \| 0.0415 \| 32.31 \| 9240 \| 0.1133 \| 0.3993 \| 0.6068 \| 0.6029 \| nan \| 0.5843 \| 0.6293 \| 0.0 \| 0.5709 \| 0.6270 \| \| 0.0584 \| 32.38 \| 9260 \| 0.1088 \| 0.4236 \| 0.6438 \| 0.6345 \| nan \| 0.5899 \| 0.6978 \| 0.0 \| 0.5772 \| 0.6935 \| \| 0.0549 \| 32.45 \| 9280 \| 0.1144 \| 0.3987 \| 0.6073 \| 0.6013 \| nan \| 0.5730 \| 0.6416 \| 0.0 \| 0.5574 \| 0.6386 \| \| 0.0529 \| 32.52 \| 9300 \| 0.1072 \| 0.4039 \| 0.6137 \| 0.5992 \| nan \| 0.5301 \| 0.6972 \| 0.0 \| 0.5213 \| 0.6902 \| \| 0.0378 \| 32.59 \| 9320 \| 0.1089 \| 0.4051 \| 0.6155 \| 0.6065 \| nan \| 0.5632 \| 0.6679 \| 0.0 \| 0.5529 \| 0.6623 \| \| 0.0556 \| 32.66 \| 9340 \| 0.1079 \| 0.4099 \| 0.6234 \| 0.6112 \| nan \| 0.5532 \| 0.6935 \| 0.0 \| 0.5418 \| 0.6879 \| \| 0.0568 \| 32.73 \| 9360 \| 0.1080 \| 0.4036 \| 0.6139 \| 0.6029 \| nan \| 0.5501 \| 0.6778 \| 0.0 \| 0.5417 \| 0.6691 \| \| 0.0458 \| 32.8 \| 9380 \| 0.1082 \| 0.3948 \| 0.6006 \| 0.5823 \| nan \| 0.4951 \| 0.7061 \| 0.0 \| 0.4890 \| 0.6954 \| \| 0.0472 \| 32.87 \| 9400 \| 0.1129 \| 0.3881 \| 0.5897 \| 0.5777 \| nan \| 0.5208 \| 0.6585 \| 0.0 \| 0.5101 \| 0.6542 \| \| 0.0611 \| 32.94 \| 9420 \| 0.1105 \| 0.4011 \| 0.6099 \| 0.5982 \| nan \| 0.5425 \| 0.6774 \| 0.0 \| 0.5320 \| 0.6713 \| \| 0.0702 \| 33.01 \| 9440 \| 0.1063 \| 0.4287 \| 0.6539 \| 0.6425 \| nan \| 0.5880 \| 0.7198 \| 0.0 \| 0.5767 \| 0.7095 \| \| 0.0702 \| 33.08 \| 9460 \| 0.1115 \| 0.4024 \| 0.6120 \| 0.5960 \| nan \| 0.5196 \| 0.7044 \| 0.0 \| 0.5106 \| 0.6966 \| \| 0.0603 \| 33.15 \| 9480 \| 0.1138 \| 0.4139 \| 0.6295 \| 0.6236 \| nan \| 0.5954 \| 0.6635 \| 0.0 \| 0.5841 \| 0.6576 \| \| 0.0661 \| 33.22 \| 9500 \| 0.1127 \| 0.4018 \| 0.6123 \| 0.6039 \| nan \| 0.5634 \| 0.6612 \| 0.0 \| 0.5546 \| 0.6510 \| \| 0.0755 \| 33.29 \| 9520 \| 0.1180 \| 0.3938 \| 0.5994 \| 0.5928 \| nan \| 0.5613 \| 0.6375 \| 0.0 \| 0.5498 \| 0.6318 \| \| 0.0888 \| 33.36 \| 9540 \| 0.1158 \| 0.3836 \| 0.5831 \| 0.5707 \| nan \| 0.5114 \| 0.6547 \| 0.0 \| 0.5016 \| 0.6491 \| \| 0.0356 \| 33.43 \| 9560 \| 0.1073 \| 0.4202 \| 0.6388 \| 0.6276 \| nan \| 0.5739 \| 0.7037 \| 0.0 \| 0.5638 \| 0.6968 \| \| 0.0656 \| 33.5 \| 9580 \| 0.1074 \| 0.4245 \| 0.6459 \| 0.6319 \| nan \| 0.5652 \| 0.7266 \| 0.0 \| 0.5540 \| 0.7195 \| \| 0.0565 \| 33.57 \| 9600 \| 0.1060 \| 0.4143 \| 0.6305 \| 0.6188 \| nan \| 0.5633 \| 0.6977 \| 0.0 \| 0.5518 \| 0.6912 \| \| 0.0528 \| 33.64 \| 9620 \| 0.1071 \| 0.4197 \| 0.6385 \| 0.6272 \| nan \| 0.5731 \| 0.7038 \| 0.0 \| 0.5618 \| 0.6972 \| \| 0.0711 \| 33.71 \| 9640 \| 0.1103 \| 0.3996 \| 0.6072 \| 0.5939 \| nan \| 0.5303 \| 0.6841 \| 0.0 \| 0.5209 \| 0.6778 \| \| 0.0614 \| 33.78 \| 9660 \| 0.1138 \| 0.3993 \| 0.6070 \| 0.5963 \| nan \| 0.5451 \| 0.6688 \| 0.0 \| 0.5338 \| 0.6642 \| \| 0.065 \| 33.85 \| 9680 \| 0.1057 \| 0.4227 \| 0.6436 \| 0.6319 \| nan \| 0.5761 \| 0.7111 \| 0.0 \| 0.5653 \| 0.7028 \| \| 0.0544 \| 33.92 \| 9700 \| 0.1117 \| 0.4167 \| 0.6350 \| 0.6289 \| nan \| 0.5998 \| 0.6702 \| 0.0 \| 0.5829 \| 0.6671 \| \| 0.0802 \| 33.99 \| 9720 \| 0.1120 \| 0.4201 \| 0.6393 \| 0.6274 \| nan \| 0.5707 \| 0.7079 \| 0.0 \| 0.5570 \| 0.7034 \| \| 0.0339 \| 34.06 \| 9740 \| 0.1107 \| 0.3944 \| 0.6009 \| 0.5895 \| nan \| 0.5351 \| 0.6667 \| 0.0 \| 0.5253 \| 0.6580 \| \| 0.0436 \| 34.13 \| 9760 \| 0.1137 \| 0.3919 \| 0.5963 \| 0.5868 \| nan \| 0.5415 \| 0.6510 \| 0.0 \| 0.5296 \| 0.6460 \| \| 0.0643 \| 34.2 \| 9780 \| 0.1190 \| 0.3739 \| 0.5688 \| 0.5534 \| nan \| 0.4798 \| 0.6579 \| 0.0 \| 0.4696 \| 0.6522 \| \| 0.0929 \| 34.27 \| 9800 \| 0.1212 \| 0.3715 \| 0.5642 \| 0.5525 \| nan \| 0.4970 \| 0.6313 \| 0.0 \| 0.4872 \| 0.6274 \| \| 0.0672 \| 34.34 \| 9820 \| 0.1117 \| 0.4173 \| 0.6350 \| 0.6266 \| nan \| 0.5864 \| 0.6836 \| 0.0 \| 0.5719 \| 0.6800 \| \| 0.0449 \| 34.41 \| 9840 \| 0.1230 \| 0.4036 \| 0.6159 \| 0.6156 \| nan \| 0.6142 \| 0.6176 \| 0.0 \| 0.5966 \| 0.6143 \| \| 0.0705 \| 34.48 \| 9860 \| 0.1216 \| 0.3900 \| 0.5935 \| 0.5888 \| nan \| 0.5663 \| 0.6208 \| 0.0 \| 0.5542 \| 0.6158 \| \| 0.0562 \| 34.55 \| 9880 \| 0.1070 \| 0.4167 \| 0.6335 \| 0.6195 \| nan \| 0.5530 \| 0.7139 \| 0.0 \| 0.5441 \| 0.7061 \| \| 0.0467 \| 34.62 \| 9900 \| 0.1171 \| 0.3916 \| 0.5964 \| 0.5874 \| nan \| 0.5448 \| 0.6479 \| 0.0 \| 0.5373 \| 0.6375 \| \| 0.0446 \| 34.69 \| 9920 \| 0.1073 \| 0.4216 \| 0.6433 \| 0.6304 \| nan \| 0.5687 \| 0.7179 \| 0.0 \| 0.5602 \| 0.7046 \| \| 0.0357 \| 34.76 \| 9940 \| 0.1117 \| 0.4014 \| 0.6101 \| 0.5983 \| nan \| 0.5421 \| 0.6780 \| 0.0 \| 0.5326 \| 0.6716 \| \| 0.0796 \| 34.83 \| 9960 \| 0.1119 \| 0.4133 \| 0.6285 \| 0.6173 \| nan \| 0.5638 \| 0.6932 \| 0.0 \| 0.5526 \| 0.6872 \| \| 0.0422 \| 34.9 \| 9980 \| 0.1066 \| 0.4291 \| 0.6528 \| 0.6371 \| nan \| 0.5622 \| 0.7434 \| 0.0 \| 0.5536 \| 0.7337 \| \| 0.0479 \| 34.97 \| 10000 \| 0.1121 \| 0.4023 \| 0.6115 \| 0.6004 \| nan \| 0.5474 \| 0.6755 \| 0.0 \| 0.5353 \| 0.6716 \| \| 0.0533 \| 35.03 \| 10020 \| 0.1114 \| 0.4237 \| 0.6446 \| 0.6379 \| nan \| 0.6056 \| 0.6837 \| 0.0 \| 0.5938 \| 0.6771 \| \| 0.0513 \| 35.1 \| 10040 \| 0.1068 \| 0.4100 \| 0.6243 \| 0.6062 \| nan \| 0.5200 \| 0.7285 \| 0.0 \| 0.5133 \| 0.7166 \| \| 0.0507 \| 35.17 \| 10060 \| 0.1109 \| 0.4109 \| 0.6245 \| 0.6168 \| nan \| 0.5802 \| 0.6687 \| 0.0 \| 0.5692 \| 0.6635 \| \| 0.0736 \| 35.24 \| 10080 \| 0.1067 \| 0.4224 \| 0.6437 \| 0.6291 \| nan \| 0.5593 \| 0.7281 \| 0.0 \| 0.5517 \| 0.7154 \| \| 0.0623 \| 35.31 \| 10100 \| 0.1096 \| 0.4269 \| 0.6492 \| 0.6406 \| nan \| 0.5997 \| 0.6986 \| 0.0 \| 0.5869 \| 0.6937 \| \| 0.0485 \| 35.38 \| 10120 \| 0.1056 \| 0.4104 \| 0.6249 \| 0.6069 \| nan \| 0.5214 \| 0.7283 \| 0.0 \| 0.5147 \| 0.7166 \| \| 0.0484 \| 35.45 \| 10140 \| 0.1086 \| 0.3962 \| 0.6042 \| 0.5857 \| nan \| 0.4974 \| 0.7109 \| 0.0 \| 0.4910 \| 0.6977 \| \| 0.0861 \| 35.52 \| 10160 \| 0.1110 \| 0.4128 \| 0.6277 \| 0.6159 \| nan \| 0.5594 \| 0.6960 \| 0.0 \| 0.5489 \| 0.6896 \| \| 0.0554 \| 35.59 \| 10180 \| 0.1121 \| 0.4058 \| 0.6174 \| 0.6043 \| nan \| 0.5415 \| 0.6933 \| 0.0 \| 0.5297 \| 0.6876 \| \| 0.0644 \| 35.66 \| 10200 \| 0.1164 \| 0.4025 \| 0.6136 \| 0.6056 \| nan \| 0.5674 \| 0.6597 \| 0.0 \| 0.5515 \| 0.6559 \| \| 0.0423 \| 35.73 \| 10220 \| 0.1160 \| 0.4010 \| 0.6113 \| 0.6057 \| nan \| 0.5791 \| 0.6434 \| 0.0 \| 0.5636 \| 0.6393 \| \| 0.0635 \| 35.8 \| 10240 \| 0.1149 \| 0.3953 \| 0.6015 \| 0.5915 \| nan \| 0.5435 \| 0.6596 \| 0.0 \| 0.5312 \| 0.6547 \| \| 0.0527 \| 35.87 \| 10260 \| 0.1177 \| 0.3884 \| 0.5921 \| 0.5818 \| nan \| 0.5326 \| 0.6516 \| 0.0 \| 0.5174 \| 0.6477 \| \| 0.0597 \| 35.94 \| 10280 \| 0.1154 \| 0.4002 \| 0.6095 \| 0.6014 \| nan \| 0.5626 \| 0.6564 \| 0.0 \| 0.5497 \| 0.6508 \| \| 0.0587 \| 36.01 \| 10300 \| 0.1229 \| 0.3802 \| 0.5794 \| 0.5697 \| nan \| 0.5236 \| 0.6351 \| 0.0 \| 0.5092 \| 0.6314 \| \| 0.0824 \| 36.08 \| 10320 \| 0.1179 \| 0.3843 \| 0.5840 \| 0.5730 \| nan \| 0.5205 \| 0.6476 \| 0.0 \| 0.5101 \| 0.6428 \| \| 0.0613 \| 36.15 \| 10340 \| 0.1139 \| 0.4204 \| 0.6403 \| 0.6316 \| nan \| 0.5904 \| 0.6902 \| 0.0 \| 0.5767 \| 0.6846 \| \| 0.0694 \| 36.22 \| 10360 \| 0.1128 \| 0.4148 \| 0.6304 \| 0.6198 \| nan \| 0.5690 \| 0.6919 \| 0.0 \| 0.5571 \| 0.6872 \| \| 0.0486 \| 36.29 \| 10380 \| 0.1109 \| 0.4050 \| 0.6157 \| 0.6031 \| nan \| 0.5429 \| 0.6886 \| 0.0 \| 0.5324 \| 0.6825 \| \| 0.0532 \| 36.36 \| 10400 \| 0.1103 \| 0.4188 \| 0.6367 \| 0.6263 \| nan \| 0.5766 \| 0.6968 \| 0.0 \| 0.5642 \| 0.6923 \| \| 0.0475 \| 36.43 \| 10420 \| 0.1064 \| 0.4134 \| 0.6276 \| 0.6130 \| nan \| 0.5434 \| 0.7119 \| 0.0 \| 0.5348 \| 0.7053 \| \| 0.0517 \| 36.5 \| 10440 \| 0.1207 \| 0.3857 \| 0.5873 \| 0.5794 \| nan \| 0.5415 \| 0.6332 \| 0.0 \| 0.5270 \| 0.6302 \| \| 0.0735 \| 36.57 \| 10460 \| 0.1099 \| 0.3859 \| 0.5864 \| 0.5697 \| nan \| 0.4903 \| 0.6825 \| 0.0 \| 0.4844 \| 0.6734 \| \| 0.0684 \| 36.64 \| 10480 \| 0.1094 \| 0.4065 \| 0.6178 \| 0.6019 \| nan \| 0.5261 \| 0.7095 \| 0.0 \| 0.5170 \| 0.7025 \| \| 0.064 \| 36.71 \| 10500 \| 0.1138 \| 0.3915 \| 0.5956 \| 0.5805 \| nan \| 0.5084 \| 0.6828 \| 0.0 \| 0.5004 \| 0.6740 \| \| 0.064 \| 36.78 \| 10520 \| 0.1178 \| 0.3826 \| 0.5824 \| 0.5732 \| nan \| 0.5295 \| 0.6352 \| 0.0 \| 0.5183 \| 0.6296 \| \| 0.0701 \| 36.85 \| 10540 \| 0.1147 \| 0.4016 \| 0.6121 \| 0.6014 \| nan \| 0.5499 \| 0.6744 \| 0.0 \| 0.5352 \| 0.6694 \| \| 0.0884 \| 36.92 \| 10560 \| 0.1096 \| 0.4059 \| 0.6185 \| 0.6044 \| nan \| 0.5370 \| 0.6999 \| 0.0 \| 0.5266 \| 0.6911 \| \| 0.0406 \| 36.99 \| 10580 \| 0.1140 \| 0.4039 \| 0.6149 \| 0.6051 \| nan \| 0.5586 \| 0.6711 \| 0.0 \| 0.5462 \| 0.6656 \| \| 0.0589 \| 37.06 \| 10600 \| 0.1115 \| 0.4139 \| 0.6291 \| 0.6160 \| nan \| 0.5535 \| 0.7047 \| 0.0 \| 0.5438 \| 0.6978 \| \| 0.0367 \| 37.13 \| 10620 \| 0.1103 \| 0.4022 \| 0.6109 \| 0.5993 \| nan \| 0.5436 \| 0.6782 \| 0.0 \| 0.5355 \| 0.6712 \| \| 0.0363 \| 37.2 \| 10640 \| 0.1144 \| 0.4107 \| 0.6249 \| 0.6156 \| nan \| 0.5714 \| 0.6783 \| 0.0 \| 0.5610 \| 0.6711 \| \| 0.0537 \| 37.27 \| 10660 \| 0.1125 \| 0.4075 \| 0.6204 \| 0.6098 \| nan \| 0.5592 \| 0.6817 \| 0.0 \| 0.5469 \| 0.6757 \| \| 0.0431 \| 37.34 \| 10680 \| 0.1080 \| 0.4125 \| 0.6306 \| 0.6215 \| nan \| 0.5782 \| 0.6830 \| 0.0 \| 0.5676 \| 0.6698 \| \| 0.053 \| 37.41 \| 10700 \| 0.1130 \| 0.4065 \| 0.6178 \| 0.6057 \| nan \| 0.5479 \| 0.6877 \| 0.0 \| 0.5378 \| 0.6817 \| \| 0.0438 \| 37.48 \| 10720 \| 0.1102 \| 0.4001 \| 0.6094 \| 0.5955 \| nan \| 0.5292 \| 0.6895 \| 0.0 \| 0.5206 \| 0.6795 \| \| 0.0357 \| 37.55 \| 10740 \| 0.1086 \| 0.4125 \| 0.6299 \| 0.6162 \| nan \| 0.5511 \| 0.7086 \| 0.0 \| 0.5440 \| 0.6935 \| \| 0.0541 \| 37.62 \| 10760 \| 0.1099 \| 0.4322 \| 0.6578 \| 0.6492 \| nan \| 0.6081 \| 0.7076 \| 0.0 \| 0.5946 \| 0.7019 \| \| 0.0595 \| 37.69 \| 10780 \| 0.1073 \| 0.4116 \| 0.6255 \| 0.6111 \| nan \| 0.5422 \| 0.7089 \| 0.0 \| 0.5317 \| 0.7031 \| \| 0.047 \| 37.76 \| 10800 \| 0.1060 \| 0.4182 \| 0.6364 \| 0.6203 \| nan \| 0.5430 \| 0.7299 \| 0.0 \| 0.5329 \| 0.7218 \| \| 0.052 \| 37.83 \| 10820 \| 0.1080 \| 0.4136 \| 0.6293 \| 0.6140 \| nan \| 0.5413 \| 0.7172 \| 0.0 \| 0.5313 \| 0.7094 \| \| 0.0454 \| 37.9 \| 10840 \| 0.1097 \| 0.4111 \| 0.6249 \| 0.6138 \| nan \| 0.5608 \| 0.6890 \| 0.0 \| 0.5509 \| 0.6824 \| \| 0.0286 \| 37.97 \| 10860 \| 0.1166 \| 0.3979 \| 0.6052 \| 0.5938 \| nan \| 0.5395 \| 0.6708 \| 0.0 \| 0.5283 \| 0.6653 \| \| 0.0668 \| 38.04 \| 10880 \| 0.1129 \| 0.3900 \| 0.5936 \| 0.5802 \| nan \| 0.5162 \| 0.6709 \| 0.0 \| 0.5035 \| 0.6664 \| \| 0.0514 \| 38.11 \| 10900 \| 0.1100 \| 0.4109 \| 0.6258 \| 0.6143 \| nan \| 0.5594 \| 0.6922 \| 0.0 \| 0.5464 \| 0.6863 \| \| 0.0527 \| 38.18 \| 10920 \| 0.1212 \| 0.3845 \| 0.5861 \| 0.5761 \| nan \| 0.5287 \| 0.6435 \| 0.0 \| 0.5153 \| 0.6381 \| \| 0.0552 \| 38.25 \| 10940 \| 0.1182 \| 0.4089 \| 0.6237 \| 0.6157 \| nan \| 0.5775 \| 0.6699 \| 0.0 \| 0.5617 \| 0.6649 \| \| 0.0407 \| 38.32 \| 10960 \| 0.1182 \| 0.3928 \| 0.5986 \| 0.5898 \| nan \| 0.5478 \| 0.6494 \| 0.0 \| 0.5336 \| 0.6448 \| \| 0.04 \| 38.39 \| 10980 \| 0.1181 \| 0.3935 \| 0.5990 \| 0.5847 \| nan \| 0.5162 \| 0.6819 \| 0.0 \| 0.5046 \| 0.6758 \| \| 0.0804 \| 38.46 \| 11000 \| 0.1143 \| 0.4091 \| 0.6236 \| 0.6146 \| nan \| 0.5715 \| 0.6758 \| 0.0 \| 0.5586 \| 0.6686 \| \| 0.0596 \| 38.53 \| 11020 \| 0.1141 \| 0.4105 \| 0.6249 \| 0.6139 \| nan \| 0.5615 \| 0.6883 \| 0.0 \| 0.5504 \| 0.6810 \| \| 0.0469 \| 38.6 \| 11040 \| 0.1142 \| 0.3961 \| 0.6018 \| 0.5896 \| nan \| 0.5312 \| 0.6723 \| 0.0 \| 0.5216 \| 0.6666 \| \| 0.0541 \| 38.67 \| 11060 \| 0.1122 \| 0.4125 \| 0.6275 \| 0.6151 \| nan \| 0.5559 \| 0.6992 \| 0.0 \| 0.5456 \| 0.6917 \| \| 0.0416 \| 38.74 \| 11080 \| 0.1178 \| 0.3886 \| 0.5910 \| 0.5796 \| nan \| 0.5256 \| 0.6564 \| 0.0 \| 0.5154 \| 0.6503 \| \| 0.0531 \| 38.81 \| 11100 \| 0.1226 \| 0.3728 \| 0.5666 \| 0.5544 \| nan \| 0.4964 \| 0.6368 \| 0.0 \| 0.4839 \| 0.6346 \| \| 0.0496 \| 38.88 \| 11120 \| 0.1189 \| 0.3823 \| 0.5817 \| 0.5684 \| nan \| 0.5045 \| 0.6590 \| 0.0 \| 0.4931 \| 0.6537 \| \| 0.0618 \| 38.95 \| 11140 \| 0.1101 \| 0.4022 \| 0.6122 \| 0.5964 \| nan \| 0.5211 \| 0.7033 \| 0.0 \| 0.5127 \| 0.6939 \| \| 0.048 \| 39.02 \| 11160 \| 0.1109 \| 0.4032 \| 0.6133 \| 0.6032 \| nan \| 0.5547 \| 0.6720 \| 0.0 \| 0.5417 \| 0.6678 \| \| 0.0514 \| 39.09 \| 11180 \| 0.1079 \| 0.4098 \| 0.6222 \| 0.6097 \| nan \| 0.5499 \| 0.6945 \| 0.0 \| 0.5405 \| 0.6887 \| \| 0.0641 \| 39.16 \| 11200 \| 0.1137 \| 0.4002 \| 0.6089 \| 0.5993 \| nan \| 0.5533 \| 0.6645 \| 0.0 \| 0.5395 \| 0.6611 \| \| 0.0599 \| 39.23 \| 11220 \| 0.1120 \| 0.3904 \| 0.5921 \| 0.5778 \| nan \| 0.5096 \| 0.6746 \| 0.0 \| 0.5016 \| 0.6697 \| \| 0.0461 \| 39.3 \| 11240 \| 0.1111 \| 0.4080 \| 0.6213 \| 0.6097 \| nan \| 0.5542 \| 0.6883 \| 0.0 \| 0.5420 \| 0.6821 \| \| 0.0528 \| 39.37 \| 11260 \| 0.1132 \| 0.4002 \| 0.6085 \| 0.6006 \| nan \| 0.5627 \| 0.6543 \| 0.0 \| 0.5492 \| 0.6513 \| \| 0.0639 \| 39.44 \| 11280 \| 0.1210 \| 0.3819 \| 0.5799 \| 0.5691 \| nan \| 0.5178 \| 0.6420 \| 0.0 \| 0.5079 \| 0.6379 \| \| 0.0401 \| 39.51 \| 11300 \| 0.1091 \| 0.4083 \| 0.6199 \| 0.6065 \| nan \| 0.5425 \| 0.6973 \| 0.0 \| 0.5334 \| 0.6913 \| \| 0.0556 \| 39.58 \| 11320 \| 0.1209 \| 0.3832 \| 0.5847 \| 0.5781 \| nan \| 0.5468 \| 0.6226 \| 0.0 \| 0.5314 \| 0.6182 \| \| 0.0368 \| 39.65 \| 11340 \| 0.1180 \| 0.4000 \| 0.6106 \| 0.6050 \| nan \| 0.5785 \| 0.6426 \| 0.0 \| 0.5627 \| 0.6372 \| \| 0.0513 \| 39.72 \| 11360 \| 0.1123 \| 0.4063 \| 0.6165 \| 0.6012 \| nan \| 0.5284 \| 0.7046 \| 0.0 \| 0.5171 \| 0.7019 \| \| 0.0826 \| 39.79 \| 11380 \| 0.1104 \| 0.4131 \| 0.6305 \| 0.6182 \| nan \| 0.5593 \| 0.7017 \| 0.0 \| 0.5515 \| 0.6879 \| \| 0.0766 \| 39.86 \| 11400 \| 0.1149 \| 0.4170 \| 0.6348 \| 0.6244 \| nan \| 0.5743 \| 0.6954 \| 0.0 \| 0.5611 \| 0.6898 \| \| 0.0659 \| 39.93 \| 11420 \| 0.1211 \| 0.3917 \| 0.5972 \| 0.5888 \| nan \| 0.5486 \| 0.6458 \| 0.0 \| 0.5330 \| 0.6422 \| \| 0.06 \| 40.0 \| 11440 \| 0.1126 \| 0.3998 \| 0.6096 \| 0.5937 \| nan \| 0.5176 \| 0.7017 \| 0.0 \| 0.5063 \| 0.6931 \| \| 0.0537 \| 40.07 \| 11460 \| 0.1259 \| 0.3692 \| 0.5615 \| 0.5512 \| nan \| 0.5021 \| 0.6210 \| 0.0 \| 0.4927 \| 0.6149 \| \| 0.0479 \| 40.14 \| 11480 \| 0.1106 \| 0.4105 \| 0.6242 \| 0.6076 \| nan \| 0.5285 \| 0.7199 \| 0.0 \| 0.5194 \| 0.7122 \| \| 0.0321 \| 40.21 \| 11500 \| 0.1123 \| 0.4040 \| 0.6143 \| 0.6014 \| nan \| 0.5395 \| 0.6892 \| 0.0 \| 0.5272 \| 0.6847 \| \| 0.0524 \| 40.28 \| 11520 \| 0.1164 \| 0.4069 \| 0.6192 \| 0.6100 \| nan \| 0.5661 \| 0.6722 \| 0.0 \| 0.5521 \| 0.6686 \| \| 0.0563 \| 40.35 \| 11540 \| 0.1095 \| 0.4202 \| 0.6388 \| 0.6237 \| nan \| 0.5518 \| 0.7257 \| 0.0 \| 0.5430 \| 0.7177 \| \| 0.0514 \| 40.42 \| 11560 \| 0.1114 \| 0.4076 \| 0.6202 \| 0.6099 \| nan \| 0.5607 \| 0.6798 \| 0.0 \| 0.5523 \| 0.6703 \| \| 0.0479 \| 40.49 \| 11580 \| 0.1137 \| 0.4109 \| 0.6250 \| 0.6136 \| nan \| 0.5591 \| 0.6910 \| 0.0 \| 0.5486 \| 0.6841 \| \| 0.0517 \| 40.56 \| 11600 \| 0.1156 \| 0.4003 \| 0.6083 \| 0.5995 \| nan \| 0.5575 \| 0.6590 \| 0.0 \| 0.5481 \| 0.6529 \| \| 0.0563 \| 40.63 \| 11620 \| 0.1163 \| 0.3948 \| 0.6015 \| 0.5878 \| nan \| 0.5219 \| 0.6812 \| 0.0 \| 0.5150 \| 0.6695 \| \| 0.0491 \| 40.7 \| 11640 \| 0.1229 \| 0.3860 \| 0.5879 \| 0.5790 \| nan \| 0.5365 \| 0.6393 \| 0.0 \| 0.5261 \| 0.6320 \| \| 0.051 \| 40.77 \| 11660 \| 0.1138 \| 0.3930 \| 0.5982 \| 0.5859 \| nan \| 0.5273 \| 0.6691 \| 0.0 \| 0.5165 \| 0.6626 \| \| 0.0494 \| 40.84 \| 11680 \| 0.1059 \| 0.4256 \| 0.6476 \| 0.6312 \| nan \| 0.5530 \| 0.7422 \| 0.0 \| 0.5453 \| 0.7315 \| \| 0.0705 \| 40.91 \| 11700 \| 0.1069 \| 0.4230 \| 0.6425 \| 0.6260 \| nan \| 0.5468 \| 0.7382 \| 0.0 \| 0.5390 \| 0.7300 \| \| 0.0465 \| 40.98 \| 11720 \| 0.1070 \| 0.4215 \| 0.6440 \| 0.6266 \| nan \| 0.5433 \| 0.7446 \| 0.0 \| 0.5364 \| 0.7281 \| \| 0.0837 \| 41.05 \| 11740 \| 0.1146 \| 0.3999 \| 0.6078 \| 0.5955 \| nan \| 0.5368 \| 0.6787 \| 0.0 \| 0.5284 \| 0.6715 \| \| 0.0777 \| 41.12 \| 11760 \| 0.1216 \| 0.3942 \| 0.6009 \| 0.5968 \| nan \| 0.5773 \| 0.6245 \| 0.0 \| 0.5629 \| 0.6196 \| \| 0.0417 \| 41.19 \| 11780 \| 0.1254 \| 0.3881 \| 0.5918 \| 0.5885 \| nan \| 0.5727 \| 0.6108 \| 0.0 \| 0.5574 \| 0.6069 \| \| 0.0598 \| 41.26 \| 11800 \| 0.1229 \| 0.3891 \| 0.5932 \| 0.5923 \| nan \| 0.5878 \| 0.5986 \| 0.0 \| 0.5732 \| 0.5940 \| \| 0.0301 \| 41.33 \| 11820 \| 0.1221 \| 0.3850 \| 0.5863 \| 0.5787 \| nan \| 0.5424 \| 0.6302 \| 0.0 \| 0.5297 \| 0.6253 \| \| 0.0453 \| 41.4 \| 11840 \| 0.1182 \| 0.3894 \| 0.5914 \| 0.5777 \| nan \| 0.5120 \| 0.6709 \| 0.0 \| 0.5023 \| 0.6660 \| \| 0.0604 \| 41.47 \| 11860 \| 0.1100 \| 0.4093 \| 0.6222 \| 0.6110 \| nan \| 0.5577 \| 0.6866 \| 0.0 \| 0.5474 \| 0.6806 \| \| 0.0395 \| 41.54 \| 11880 \| 0.1083 \| 0.4225 \| 0.6423 \| 0.6276 \| nan \| 0.5577 \| 0.7269 \| 0.0 \| 0.5478 \| 0.7195 \| \| 0.0398 \| 41.61 \| 11900 \| 0.1160 \| 0.3965 \| 0.6040 \| 0.5954 \| nan \| 0.5544 \| 0.6536 \| 0.0 \| 0.5438 \| 0.6458 \| \| 0.0525 \| 41.68 \| 11920 \| 0.1172 \| 0.4088 \| 0.6222 \| 0.6108 \| nan \| 0.5564 \| 0.6879 \| 0.0 \| 0.5434 \| 0.6831 \| \| 0.0716 \| 41.75 \| 11940 \| 0.1147 \| 0.4078 \| 0.6204 \| 0.6118 \| nan \| 0.5706 \| 0.6702 \| 0.0 \| 0.5609 \| 0.6625 \| \| 0.0372 \| 41.82 \| 11960 \| 0.1132 \| 0.4104 \| 0.6239 \| 0.6105 \| nan \| 0.5467 \| 0.7012 \| 0.0 \| 0.5359 \| 0.6954 \| \| 0.0522 \| 41.89 \| 11980 \| 0.1139 \| 0.3990 \| 0.6076 \| 0.5988 \| nan \| 0.5571 \| 0.6580 \| 0.0 \| 0.5432 \| 0.6540 \| \| 0.059 \| 41.96 \| 12000 \| 0.1186 \| 0.3962 \| 0.6030 \| 0.5933 \| nan \| 0.5474 \| 0.6585 \| 0.0 \| 0.5345 \| 0.6542 \| \| 0.0464 \| 42.03 \| 12020 \| 0.1203 \| 0.3952 \| 0.6022 \| 0.5935 \| nan \| 0.5518 \| 0.6527 \| 0.0 \| 0.5374 \| 0.6481 \| \| 0.0619 \| 42.1 \| 12040 \| 0.1182 \| 0.4100 \| 0.6250 \| 0.6173 \| nan \| 0.5803 \| 0.6697 \| 0.0 \| 0.5673 \| 0.6627 \| \| 0.0589 \| 42.17 \| 12060 \| 0.1105 \| 0.4101 \| 0.6285 \| 0.6105 \| nan \| 0.5247 \| 0.7324 \| 0.0 \| 0.5171 \| 0.7133 \| \| 0.063 \| 42.24 \| 12080 \| 0.1278 \| 0.3852 \| 0.5893 \| 0.5869 \| nan \| 0.5755 \| 0.6031 \| 0.0 \| 0.5545 \| 0.6011 \| \| 0.0675 \| 42.31 \| 12100 \| 0.1134 \| 0.4103 \| 0.6260 \| 0.6170 \| nan \| 0.5743 \| 0.6776 \| 0.0 \| 0.5622 \| 0.6686 \| \| 0.0708 \| 42.38 \| 12120 \| 0.1164 \| 0.4023 \| 0.6131 \| 0.6056 \| nan \| 0.5699 \| 0.6562 \| 0.0 \| 0.5552 \| 0.6517 \| \| 0.0557 \| 42.45 \| 12140 \| 0.1262 \| 0.3816 \| 0.5815 \| 0.5708 \| nan \| 0.5198 \| 0.6432 \| 0.0 \| 0.5053 \| 0.6396 \| \| 0.0526 \| 42.52 \| 12160 \| 0.1141 \| 0.4095 \| 0.6247 \| 0.6122 \| nan \| 0.5528 \| 0.6965 \| 0.0 \| 0.5395 \| 0.6891 \| \| 0.0635 \| 42.59 \| 12180 \| 0.1190 \| 0.4089 \| 0.6236 \| 0.6173 \| nan \| 0.5873 \| 0.6599 \| 0.0 \| 0.5731 \| 0.6536 \| \| 0.0425 \| 42.66 \| 12200 \| 0.1188 \| 0.4183 \| 0.6376 \| 0.6290 \| nan \| 0.5880 \| 0.6872 \| 0.0 \| 0.5734 \| 0.6816 \| \| 0.0476 \| 42.73 \| 12220 \| 0.1221 \| 0.3947 \| 0.6017 \| 0.5930 \| nan \| 0.5516 \| 0.6517 \| 0.0 \| 0.5362 \| 0.6480 \| \| 0.0531 \| 42.8 \| 12240 \| 0.1193 \| 0.3969 \| 0.6048 \| 0.5938 \| nan \| 0.5414 \| 0.6681 \| 0.0 \| 0.5295 \| 0.6612 \| \| 0.0331 \| 42.87 \| 12260 \| 0.1268 \| 0.3839 \| 0.5854 \| 0.5740 \| nan \| 0.5195 \| 0.6514 \| 0.0 \| 0.5040 \| 0.6475 \| \| 0.0495 \| 42.94 \| 12280 \| 0.1174 \| 0.3938 \| 0.5993 \| 0.5880 \| nan \| 0.5337 \| 0.6650 \| 0.0 \| 0.5213 \| 0.6601 \| \| 0.0396 \| 43.01 \| 12300 \| 0.1299 \| 0.3703 \| 0.5629 \| 0.5492 \| nan \| 0.4842 \| 0.6415 \| 0.0 \| 0.4731 \| 0.6379 \| \| 0.0514 \| 43.08 \| 12320 \| 0.1224 \| 0.3799 \| 0.5773 \| 0.5596 \| nan \| 0.4751 \| 0.6794 \| 0.0 \| 0.4668 \| 0.6729 \| \| 0.056 \| 43.15 \| 12340 \| 0.1169 \| 0.3892 \| 0.5912 \| 0.5799 \| nan \| 0.5261 \| 0.6563 \| 0.0 \| 0.5160 \| 0.6515 \| \| 0.042 \| 43.22 \| 12360 \| 0.1229 \| 0.3834 \| 0.5832 \| 0.5753 \| nan \| 0.5376 \| 0.6287 \| 0.0 \| 0.5239 \| 0.6262 \| \| 0.0368 \| 43.29 \| 12380 \| 0.1113 \| 0.4063 \| 0.6184 \| 0.6052 \| nan \| 0.5420 \| 0.6949 \| 0.0 \| 0.5310 \| 0.6878 \| \| 0.0754 \| 43.36 \| 12400 \| 0.1184 \| 0.4094 \| 0.6238 \| 0.6173 \| nan \| 0.5862 \| 0.6613 \| 0.0 \| 0.5710 \| 0.6570 \| \| 0.0679 \| 43.43 \| 12420 \| 0.1228 \| 0.3917 \| 0.5960 \| 0.5863 \| nan \| 0.5401 \| 0.6518 \| 0.0 \| 0.5272 \| 0.6478 \| \| 0.0273 \| 43.5 \| 12440 \| 0.1160 \| 0.4167 \| 0.6353 \| 0.6262 \| nan \| 0.5827 \| 0.6878 \| 0.0 \| 0.5713 \| 0.6789 \| \| 0.0388 \| 43.57 \| 12460 \| 0.1253 \| 0.3746 \| 0.5697 \| 0.5599 \| nan \| 0.5132 \| 0.6262 \| 0.0 \| 0.5050 \| 0.6190 \| \| 0.0656 \| 43.64 \| 12480 \| 0.1175 \| 0.3939 \| 0.5999 \| 0.5937 \| nan \| 0.5639 \| 0.6359 \| 0.0 \| 0.5491 \| 0.6326 \| \| 0.0824 \| 43.71 \| 12500 \| 0.1223 \| 0.3841 \| 0.5832 \| 0.5715 \| nan \| 0.5157 \| 0.6508 \| 0.0 \| 0.5051 \| 0.6472 \| \| 0.0476 \| 43.78 \| 12520 \| 0.1173 \| 0.4021 \| 0.6113 \| 0.5994 \| nan \| 0.5427 \| 0.6799 \| 0.0 \| 0.5324 \| 0.6738 \| \| 0.0546 \| 43.85 \| 12540 \| 0.1118 \| 0.4030 \| 0.6137 \| 0.6014 \| nan \| 0.5427 \| 0.6847 \| 0.0 \| 0.5312 \| 0.6778 \| \| 0.0656 \| 43.92 \| 12560 \| 0.1140 \| 0.3969 \| 0.6039 \| 0.5901 \| nan \| 0.5242 \| 0.6835 \| 0.0 \| 0.5129 \| 0.6778 \| \| 0.0554 \| 43.99 \| 12580 \| 0.1119 \| 0.4157 \| 0.6322 \| 0.6187 \| nan \| 0.5545 \| 0.7099 \| 0.0 \| 0.5442 \| 0.7030 \| \| 0.0441 \| 44.06 \| 12600 \| 0.1164 \| 0.4031 \| 0.6119 \| 0.5994 \| nan \| 0.5396 \| 0.6841 \| 0.0 \| 0.5307 \| 0.6787 \| \| 0.0582 \| 44.13 \| 12620 \| 0.1168 \| 0.3984 \| 0.6050 \| 0.5909 \| nan \| 0.5236 \| 0.6864 \| 0.0 \| 0.5147 \| 0.6805 \| \| 0.0626 \| 44.2 \| 12640 \| 0.1194 \| 0.4031 \| 0.6135 \| 0.6024 \| nan \| 0.5498 \| 0.6771 \| 0.0 \| 0.5362 \| 0.6730 \| \| 0.0551 \| 44.27 \| 12660 \| 0.1198 \| 0.4025 \| 0.6123 \| 0.6028 \| nan \| 0.5575 \| 0.6671 \| 0.0 \| 0.5452 \| 0.6624 \| \| 0.0708 \| 44.34 \| 12680 \| 0.1168 \| 0.4075 \| 0.6199 \| 0.6074 \| nan \| 0.5474 \| 0.6925 \| 0.0 \| 0.5405 \| 0.6821 \| \| 0.0422 \| 44.41 \| 12700 \| 0.1175 \| 0.4118 \| 0.6256 \| 0.6143 \| nan \| 0.5607 \| 0.6904 \| 0.0 \| 0.5511 \| 0.6843 \| \| 0.041 \| 44.48 \| 12720 \| 0.1197 \| 0.3948 \| 0.6002 \| 0.5892 \| nan \| 0.5368 \| 0.6637 \| 0.0 \| 0.5270 \| 0.6574 \| \| 0.0457 \| 44.55 \| 12740 \| 0.1262 \| 0.3800 \| 0.5777 \| 0.5687 \| nan \| 0.5255 \| 0.6299 \| 0.0 \| 0.5143 \| 0.6257 \| \| 0.0505 \| 44.62 \| 12760 \| 0.1203 \| 0.4088 \| 0.6216 \| 0.6111 \| nan \| 0.5605 \| 0.6828 \| 0.0 \| 0.5511 \| 0.6753 \| \| 0.0756 \| 44.69 \| 12780 \| 0.1193 \| 0.4003 \| 0.6084 \| 0.5956 \| nan \| 0.5342 \| 0.6827 \| 0.0 \| 0.5232 \| 0.6776 \| \| 0.0524 \| 44.76 \| 12800 \| 0.1176 \| 0.4112 \| 0.6254 \| 0.6145 \| nan \| 0.5626 \| 0.6883 \| 0.0 \| 0.5517 \| 0.6818 \| \| 0.0453 \| 44.83 \| 12820 \| 0.1186 \| 0.4082 \| 0.6213 \| 0.6089 \| nan \| 0.5497 \| 0.6930 \| 0.0 \| 0.5384 \| 0.6862 \| \| 0.0472 \| 44.9 \| 12840 \| 0.1171 \| 0.4040 \| 0.6143 \| 0.5969 \| nan \| 0.5136 \| 0.7150 \| 0.0 \| 0.5056 \| 0.7064 \| \| 0.0379 \| 44.97 \| 12860 \| 0.1112 \| 0.4157 \| 0.6311 \| 0.6184 \| nan \| 0.5576 \| 0.7047 \| 0.0 \| 0.5485 \| 0.6987 \| \| 0.0531 \| 45.03 \| 12880 \| 0.1145 \| 0.4120 \| 0.6260 \| 0.6160 \| nan \| 0.5682 \| 0.6839 \| 0.0 \| 0.5557 \| 0.6802 \| \| 0.0827 \| 45.1 \| 12900 \| 0.1152 \| 0.4029 \| 0.6115 \| 0.5955 \| nan \| 0.5194 \| 0.7035 \| 0.0 \| 0.5112 \| 0.6975 \| \| 0.059 \| 45.17 \| 12920 \| 0.1147 \| 0.4074 \| 0.6193 \| 0.6055 \| nan \| 0.5395 \| 0.6991 \| 0.0 \| 0.5289 \| 0.6934 \| \| 0.04 \| 45.24 \| 12940 \| 0.1174 \| 0.4030 \| 0.6118 \| 0.5986 \| nan \| 0.5356 \| 0.6881 \| 0.0 \| 0.5272 \| 0.6820 \| \| 0.0507 \| 45.31 \| 12960 \| 0.1189 \| 0.3989 \| 0.6061 \| 0.5976 \| nan \| 0.5571 \| 0.6550 \| 0.0 \| 0.5463 \| 0.6504 \| \| 0.0452 \| 45.38 \| 12980 \| 0.1159 \| 0.4157 \| 0.6314 \| 0.6207 \| nan \| 0.5692 \| 0.6937 \| 0.0 \| 0.5592 \| 0.6877 \| \| 0.0903 \| 45.45 \| 13000 \| 0.1112 \| 0.4208 \| 0.6417 \| 0.6281 \| nan \| 0.5631 \| 0.7203 \| 0.0 \| 0.5561 \| 0.7064 \| \| 0.069 \| 45.52 \| 13020 \| 0.1184 \| 0.4015 \| 0.6092 \| 0.5934 \| nan \| 0.5176 \| 0.7009 \| 0.0 \| 0.5096 \| 0.6950 \| \| 0.0335 \| 45.59 \| 13040 \| 0.1217 \| 0.4103 \| 0.6243 \| 0.6129 \| nan \| 0.5582 \| 0.6905 \| 0.0 \| 0.5492 \| 0.6816 \| \| 0.0633 \| 45.66 \| 13060 \| 0.1198 \| 0.4039 \| 0.6153 \| 0.6027 \| nan \| 0.5427 \| 0.6878 \| 0.0 \| 0.5312 \| 0.6805 \| \| 0.0637 \| 45.73 \| 13080 \| 0.1216 \| 0.3914 \| 0.5954 \| 0.5861 \| nan \| 0.5418 \| 0.6490 \| 0.0 \| 0.5287 \| 0.6455 \| \| 0.0472 \| 45.8 \| 13100 \| 0.1251 \| 0.3894 \| 0.5929 \| 0.5831 \| nan \| 0.5364 \| 0.6493 \| 0.0 \| 0.5218 \| 0.6465 \| \| 0.0381 \| 45.87 \| 13120 \| 0.1189 \| 0.3956 \| 0.6014 \| 0.5908 \| nan \| 0.5399 \| 0.6630 \| 0.0 \| 0.5282 \| 0.6586 \| \| 0.0505 \| 45.94 \| 13140 \| 0.1216 \| 0.4037 \| 0.6136 \| 0.6042 \| nan \| 0.5594 \| 0.6677 \| 0.0 \| 0.5468 \| 0.6644 \| \| 0.047 \| 46.01 \| 13160 \| 0.1162 \| 0.4090 \| 0.6214 \| 0.6106 \| nan \| 0.5593 \| 0.6835 \| 0.0 \| 0.5477 \| 0.6793 \| \| 0.0589 \| 46.08 \| 13180 \| 0.1255 \| 0.3884 \| 0.5921 \| 0.5822 \| nan \| 0.5351 \| 0.6490 \| 0.0 \| 0.5178 \| 0.6476 \| \| 0.0298 \| 46.15 \| 13200 \| 0.1116 \| 0.4160 \| 0.6327 \| 0.6171 \| nan \| 0.5427 \| 0.7228 \| 0.0 \| 0.5318 \| 0.7161 \| \| 0.0654 \| 46.22 \| 13220 \| 0.1166 \| 0.3992 \| 0.6076 \| 0.6006 \| nan \| 0.5673 \| 0.6480 \| 0.0 \| 0.5562 \| 0.6415 \| \| 0.0596 \| 46.29 \| 13240 \| 0.1115 \| 0.4193 \| 0.6383 \| 0.6267 \| nan \| 0.5710 \| 0.7057 \| 0.0 \| 0.5597 \| 0.6982 \| \| 0.0631 \| 46.36 \| 13260 \| 0.1221 \| 0.3924 \| 0.5961 \| 0.5863 \| nan \| 0.5392 \| 0.6531 \| 0.0 \| 0.5271 \| 0.6503 \| \| 0.041 \| 46.43 \| 13280 \| 0.1134 \| 0.4005 \| 0.6092 \| 0.5951 \| nan \| 0.5273 \| 0.6912 \| 0.0 \| 0.5201 \| 0.6813 \| \| 0.0301 \| 46.5 \| 13300 \| 0.1180 \| 0.4089 \| 0.6212 \| 0.6090 \| nan \| 0.5510 \| 0.6913 \| 0.0 \| 0.5405 \| 0.6864 \| \| 0.0595 \| 46.57 \| 13320 \| 0.1196 \| 0.4049 \| 0.6160 \| 0.6065 \| nan \| 0.5609 \| 0.6712 \| 0.0 \| 0.5476 \| 0.6670 \| \| 0.0703 \| 46.64 \| 13340 \| 0.1169 \| 0.4161 \| 0.6333 \| 0.6234 \| nan \| 0.5760 \| 0.6906 \| 0.0 \| 0.5625 \| 0.6859 \| \| 0.0622 \| 46.71 \| 13360 \| 0.1203 \| 0.3973 \| 0.6036 \| 0.5906 \| nan \| 0.5284 \| 0.6788 \| 0.0 \| 0.5172 \| 0.6747 \| \| 0.0249 \| 46.78 \| 13380 \| 0.1190 \| 0.4172 \| 0.6348 \| 0.6237 \| nan \| 0.5704 \| 0.6993 \| 0.0 \| 0.5580 \| 0.6937 \| \| 0.0541 \| 46.85 \| 13400 \| 0.1206 \| 0.3964 \| 0.6036 \| 0.5969 \| nan \| 0.5650 \| 0.6421 \| 0.0 \| 0.5517 \| 0.6374 \| \| 0.0673 \| 46.92 \| 13420 \| 0.1213 \| 0.4007 \| 0.6100 \| 0.6014 \| nan \| 0.5603 \| 0.6597 \| 0.0 \| 0.5461 \| 0.6560 \| \| 0.0667 \| 46.99 \| 13440 \| 0.1177 \| 0.4128 \| 0.6280 \| 0.6143 \| nan \| 0.5490 \| 0.7070 \| 0.0 \| 0.5387 \| 0.6996 \| \| 0.0325 \| 47.06 \| 13460 \| 0.1166 \| 0.3931 \| 0.5977 \| 0.5817 \| nan \| 0.5053 \| 0.6901 \| 0.0 \| 0.4965 \| 0.6827 \| \| 0.0893 \| 47.13 \| 13480 \| 0.1157 \| 0.4041 \| 0.6149 \| 0.6047 \| nan \| 0.5560 \| 0.6738 \| 0.0 \| 0.5421 \| 0.6703 \| \| 0.0593 \| 47.2 \| 13500 \| 0.1273 \| 0.3963 \| 0.6032 \| 0.5948 \| nan \| 0.5548 \| 0.6515 \| 0.0 \| 0.5399 \| 0.6489 \| \| 0.0562 \| 47.27 \| 13520 \| 0.1264 \| 0.3918 \| 0.5970 \| 0.5899 \| nan \| 0.5558 \| 0.6381 \| 0.0 \| 0.5415 \| 0.6339 \| \| 0.0416 \| 47.34 \| 13540 \| 0.1136 \| 0.4165 \| 0.6334 \| 0.6212 \| nan \| 0.5627 \| 0.7041 \| 0.0 \| 0.5528 \| 0.6968 \| \| 0.0633 \| 47.41 \| 13560 \| 0.1207 \| 0.3946 \| 0.6011 \| 0.5935 \| nan \| 0.5574 \| 0.6447 \| 0.0 \| 0.5444 \| 0.6393 \| \| 0.0614 \| 47.48 \| 13580 \| 0.1201 \| 0.3989 \| 0.6067 \| 0.5951 \| nan \| 0.5399 \| 0.6734 \| 0.0 \| 0.5294 \| 0.6672 \| \| 0.0576 \| 47.55 \| 13600 \| 0.1212 \| 0.3955 \| 0.6017 \| 0.5912 \| nan \| 0.5412 \| 0.6621 \| 0.0 \| 0.5318 \| 0.6547 \| \| 0.0458 \| 47.62 \| 13620 \| 0.1210 \| 0.4040 \| 0.6157 \| 0.6028 \| nan \| 0.5415 \| 0.6899 \| 0.0 \| 0.5318 \| 0.6801 \| \| 0.0736 \| 47.69 \| 13640 \| 0.1167 \| 0.4056 \| 0.6186 \| 0.6048 \| nan \| 0.5389 \| 0.6982 \| 0.0 \| 0.5314 \| 0.6854 \| \| 0.0593 \| 47.76 \| 13660 \| 0.1197 \| 0.4103 \| 0.6240 \| 0.6110 \| nan \| 0.5490 \| 0.6990 \| 0.0 \| 0.5382 \| 0.6926 \| \| 0.0367 \| 47.83 \| 13680 \| 0.1237 \| 0.4128 \| 0.6286 \| 0.6204 \| nan \| 0.5815 \| 0.6756 \| 0.0 \| 0.5676 \| 0.6709 \| \| 0.0658 \| 47.9 \| 13700 \| 0.1225 \| 0.4110 \| 0.6256 \| 0.6146 \| nan \| 0.5621 \| 0.6891 \| 0.0 \| 0.5496 \| 0.6833 \| \| 0.0312 \| 47.97 \| 13720 \| 0.1301 \| 0.3874 \| 0.5892 \| 0.5800 \| nan \| 0.5362 \| 0.6422 \| 0.0 \| 0.5225 \| 0.6397 \| \| 0.04 \| 48.04 \| 13740 \| 0.1336 \| 0.3802 \| 0.5782 \| 0.5671 \| nan \| 0.5141 \| 0.6423 \| 0.0 \| 0.5002 \| 0.6405 \| \| 0.0635 \| 48.11 \| 13760 \| 0.1228 \| 0.3945 \| 0.5998 \| 0.5876 \| nan \| 0.5295 \| 0.6700 \| 0.0 \| 0.5201 \| 0.6635 \| \| 0.0541 \| 48.18 \| 13780 \| 0.1237 \| 0.3857 \| 0.5861 \| 0.5691 \| nan \| 0.4880 \| 0.6842 \| 0.0 \| 0.4795 \| 0.6776 \| \| 0.0531 \| 48.25 \| 13800 \| 0.1261 \| 0.3886 \| 0.5925 \| 0.5851 \| nan \| 0.5497 \| 0.6354 \| 0.0 \| 0.5348 \| 0.6310 \| \| 0.0584 \| 48.32 \| 13820 \| 0.1193 \| 0.4071 \| 0.6206 \| 0.6109 \| nan \| 0.5644 \| 0.6769 \| 0.0 \| 0.5511 \| 0.6702 \| \| 0.0627 \| 48.39 \| 13840 \| 0.1207 \| 0.3981 \| 0.6054 \| 0.5907 \| nan \| 0.5203 \| 0.6905 \| 0.0 \| 0.5106 \| 0.6836 \| \| 0.0568 \| 48.46 \| 13860 \| 0.1247 \| 0.3874 \| 0.5897 \| 0.5776 \| nan \| 0.5197 \| 0.6597 \| 0.0 \| 0.5077 \| 0.6544 \| \| 0.0551 \| 48.53 \| 13880 \| 0.1252 \| 0.4000 \| 0.6084 \| 0.5956 \| nan \| 0.5343 \| 0.6825 \| 0.0 \| 0.5250 \| 0.6750 \| \| 0.0351 \| 48.6 \| 13900 \| 0.1226 \| 0.4014 \| 0.6111 \| 0.5991 \| nan \| 0.5415 \| 0.6808 \| 0.0 \| 0.5297 \| 0.6746 \| \| 0.0386 \| 48.67 \| 13920 \| 0.1240 \| 0.4083 \| 0.6214 \| 0.6138 \| nan \| 0.5777 \| 0.6650 \| 0.0 \| 0.5657 \| 0.6593 \| \| 0.0458 \| 48.74 \| 13940 \| 0.1176 \| 0.4101 \| 0.6239 \| 0.6105 \| nan \| 0.5465 \| 0.7013 \| 0.0 \| 0.5381 \| 0.6921 \| \| 0.0501 \| 48.81 \| 13960 \| 0.1255 \| 0.3877 \| 0.5895 \| 0.5811 \| nan \| 0.5410 \| 0.6381 \| 0.0 \| 0.5302 \| 0.6329 \| \| 0.0431 \| 48.88 \| 13980 \| 0.1200 \| 0.3947 \| 0.5995 \| 0.5833 \| nan \| 0.5057 \| 0.6934 \| 0.0 \| 0.4981 \| 0.6860 \| \| 0.0455 \| 48.95 \| 14000 \| 0.1208 \| 0.4034 \| 0.6141 \| 0.6014 \| nan \| 0.5403 \| 0.6879 \| 0.0 \| 0.5290 \| 0.6813 \| \| 0.0837 \| 49.02 \| 14020 \| 0.1222 \| 0.3921 \| 0.5965 \| 0.5840 \| nan \| 0.5242 \| 0.6689 \| 0.0 \| 0.5126 \| 0.6639 \| \| 0.0647 \| 49.09 \| 14040 \| 0.1238 \| 0.3866 \| 0.5873 \| 0.5754 \| nan \| 0.5188 \| 0.6558 \| 0.0 \| 0.5092 \| 0.6505 \| \| 0.0433 \| 49.16 \| 14060 \| 0.1159 \| 0.4174 \| 0.6348 \| 0.6215 \| nan \| 0.5581 \| 0.7115 \| 0.0 \| 0.5502 \| 0.7019 \| \| 0.053 \| 49.23 \| 14080 \| 0.1260 \| 0.3943 \| 0.5992 \| 0.5900 \| nan \| 0.5461 \| 0.6524 \| 0.0 \| 0.5331 \| 0.6497 \| \| 0.0606 \| 49.3 \| 14100 \| 0.1238 \| 0.4070 \| 0.6191 \| 0.6121 \| nan \| 0.5784 \| 0.6599 \| 0.0 \| 0.5649 \| 0.6560 \| \| 0.0694 \| 49.37 \| 14120 \| 0.1211 \| 0.3991 \| 0.6061 \| 0.5919 \| nan \| 0.5239 \| 0.6884 \| 0.0 \| 0.5154 \| 0.6820 \| \| 0.0464 \| 49.44 \| 14140 \| 0.1193 \| 0.4089 \| 0.6221 \| 0.6072 \| nan \| 0.5359 \| 0.7084 \| 0.0 \| 0.5289 \| 0.6978 \| \| 0.0369 \| 49.51 \| 14160 \| 0.1206 \| 0.4100 \| 0.6244 \| 0.6166 \| nan \| 0.5793 \| 0.6696 \| 0.0 \| 0.5701 \| 0.6600 \| \| 0.0474 \| 49.58 \| 14180 \| 0.1108 \| 0.4124 \| 0.6290 \| 0.6146 \| nan \| 0.5455 \| 0.7125 \| 0.0 \| 0.5388 \| 0.6985 \| \| 0.0726 \| 49.65 \| 14200 \| 0.1217 \| 0.4000 \| 0.6092 \| 0.5982 \| nan \| 0.5459 \| 0.6725 \| 0.0 \| 0.5306 \| 0.6696 \| \| 0.0409 \| 49.72 \| 14220 \| 0.1181 \| 0.3998 \| 0.6087 \| 0.5990 \| nan \| 0.5527 \| 0.6647 \| 0.0 \| 0.5408 \| 0.6587 \| \| 0.0454 \| 49.79 \| 14240 \| 0.1254 \| 0.3920 \| 0.5972 \| 0.5878 \| nan \| 0.5431 \| 0.6513 \| 0.0 \| 0.5284 \| 0.6476 \| \| 0.0417 \| 49.86 \| 14260 \| 0.1230 \| 0.3977 \| 0.6052 \| 0.5967 \| nan \| 0.5562 \| 0.6542 \| 0.0 \| 0.5438 \| 0.6493 \| \| 0.0277 \| 49.93 \| 14280 \| 0.1235 \| 0.4016 \| 0.6117 \| 0.6013 \| nan \| 0.5517 \| 0.6716 \| 0.0 \| 0.5391 \| 0.6658 \| \| 0.0382 \| 50.0 \| 14300 \| 0.1176 \| 0.4171 \| 0.6350 \| 0.6262 \| nan \| 0.5840 \| 0.6860 \| 0.0 \| 0.5710 \| 0.6804 \| \| 0.0484 \| 50.07 \| 14320 \| 0.1211 \| 0.4062 \| 0.6180 \| 0.6092 \| nan \| 0.5671 \| 0.6689 \| 0.0 \| 0.5528 \| 0.6656 \| \| 0.0426 \| 50.14 \| 14340 \| 0.1200 \| 0.3995 \| 0.6061 \| 0.5933 \| nan \| 0.5322 \| 0.6800 \| 0.0 \| 0.5233 \| 0.6750 \| \| 0.0702 \| 50.21 \| 14360 \| 0.1214 \| 0.4081 \| 0.6206 \| 0.6106 \| nan \| 0.5625 \| 0.6787 \| 0.0 \| 0.5503 \| 0.6741 \| \| 0.0586 \| 50.28 \| 14380 \| 0.1156 \| 0.4158 \| 0.6306 \| 0.6198 \| nan \| 0.5681 \| 0.6931 \| 0.0 \| 0.5581 \| 0.6892 \| \| 0.0334 \| 50.35 \| 14400 \| 0.1194 \| 0.4092 \| 0.6211 \| 0.6075 \| nan \| 0.5426 \| 0.6996 \| 0.0 \| 0.5322 \| 0.6953 \| \| 0.0356 \| 50.42 \| 14420 \| 0.1204 \| 0.3922 \| 0.5946 \| 0.5777 \| nan \| 0.4973 \| 0.6918 \| 0.0 \| 0.4885 \| 0.6881 \| \| 0.0487 \| 50.49 \| 14440 \| 0.1225 \| 0.3798 \| 0.5764 \| 0.5594 \| nan \| 0.4780 \| 0.6748 \| 0.0 \| 0.4685 \| 0.6709 \| \| 0.0658 \| 50.56 \| 14460 \| 0.1177 \| 0.4083 \| 0.6206 \| 0.6072 \| nan \| 0.5435 \| 0.6976 \| 0.0 \| 0.5318 \| 0.6932 \| \| 0.0488 \| 50.63 \| 14480 \| 0.1181 \| 0.4163 \| 0.6343 \| 0.6269 \| nan \| 0.5912 \| 0.6774 \| 0.0 \| 0.5751 \| 0.6740 \| \| 0.0528 \| 50.7 \| 14500 \| 0.1181 \| 0.4003 \| 0.6079 \| 0.5952 \| nan \| 0.5346 \| 0.6812 \| 0.0 \| 0.5268 \| 0.6741 \| \| 0.0684 \| 50.77 \| 14520 \| 0.1206 \| 0.3911 \| 0.5946 \| 0.5824 \| nan \| 0.5243 \| 0.6649 \| 0.0 \| 0.5136 \| 0.6598 \| \| 0.053 \| 50.84 \| 14540 \| 0.1228 \| 0.3835 \| 0.5830 \| 0.5688 \| nan \| 0.5013 \| 0.6647 \| 0.0 \| 0.4920 \| 0.6585 \| \| 0.0794 \| 50.91 \| 14560 \| 0.1239 \| 0.3882 \| 0.5905 \| 0.5791 \| nan \| 0.5251 \| 0.6558 \| 0.0 \| 0.5134 \| 0.6513 \| \| 0.0488 \| 50.98 \| 14580 \| 0.1189 \| 0.3930 \| 0.5968 \| 0.5831 \| nan \| 0.5176 \| 0.6761 \| 0.0 \| 0.5064 \| 0.6724 \| \| 0.0493 \| 51.05 \| 14600 \| 0.1230 \| 0.3923 \| 0.5958 \| 0.5854 \| nan \| 0.5354 \| 0.6563 \| 0.0 \| 0.5228 \| 0.6540 \| \| 0.0582 \| 51.12 \| 14620 \| 0.1244 \| 0.3940 \| 0.5989 \| 0.5908 \| nan \| 0.5523 \| 0.6455 \| 0.0 \| 0.5394 \| 0.6425 \| \| 0.0475 \| 51.19 \| 14640 \| 0.1236 \| 0.4012 \| 0.6091 \| 0.5986 \| nan \| 0.5483 \| 0.6700 \| 0.0 \| 0.5384 \| 0.6653 \| \| 0.0545 \| 51.26 \| 14660 \| 0.1248 \| 0.3903 \| 0.5935 \| 0.5872 \| nan \| 0.5567 \| 0.6304 \| 0.0 \| 0.5435 \| 0.6274 \| \| 0.0492 \| 51.33 \| 14680 \| 0.1122 \| 0.4187 \| 0.6367 \| 0.6269 \| nan \| 0.5803 \| 0.6931 \| 0.0 \| 0.5694 \| 0.6866 \| \| 0.0309 \| 51.4 \| 14700 \| 0.1187 \| 0.4032 \| 0.6127 \| 0.6034 \| nan \| 0.5589 \| 0.6666 \| 0.0 \| 0.5472 \| 0.6625 \| \| 0.0704 \| 51.47 \| 14720 \| 0.1288 \| 0.3908 \| 0.5946 \| 0.5876 \| nan \| 0.5538 \| 0.6354 \| 0.0 \| 0.5411 \| 0.6312 \| \| 0.0562 \| 51.54 \| 14740 \| 0.1269 \| 0.3872 \| 0.5889 \| 0.5784 \| nan \| 0.5283 \| 0.6494 \| 0.0 \| 0.5153 \| 0.6462 \| \| 0.0286 \| 51.61 \| 14760 \| 0.1188 \| 0.4067 \| 0.6191 \| 0.6117 \| nan \| 0.5762 \| 0.6620 \| 0.0 \| 0.5620 \| 0.6581 \| \| 0.0515 \| 51.68 \| 14780 \| 0.1219 \| 0.4045 \| 0.6158 \| 0.6075 \| nan \| 0.5678 \| 0.6639 \| 0.0 \| 0.5537 \| 0.6599 \| \| 0.0691 \| 51.75 \| 14800 \| 0.1238 \| 0.3924 \| 0.5969 \| 0.5844 \| nan \| 0.5244 \| 0.6695 \| 0.0 \| 0.5122 \| 0.6652 \| \| 0.0502 \| 51.82 \| 14820 \| 0.1216 \| 0.3820 \| 0.5806 \| 0.5642 \| nan \| 0.4859 \| 0.6753 \| 0.0 \| 0.4759 \| 0.6701 \| \| 0.0396 \| 51.89 \| 14840 \| 0.1181 \| 0.4122 \| 0.6271 \| 0.6130 \| nan \| 0.5457 \| 0.7084 \| 0.0 \| 0.5369 \| 0.6999 \| \| 0.0741 \| 51.96 \| 14860 \| 0.1233 \| 0.3905 \| 0.5938 \| 0.5820 \| nan \| 0.5255 \| 0.6620 \| 0.0 \| 0.5133 \| 0.6581 \| \| 0.0551 \| 52.03 \| 14880 \| 0.1255 \| 0.3985 \| 0.6087 \| 0.6026 \| nan \| 0.5733 \| 0.6441 \| 0.0 \| 0.5551 \| 0.6405 \| \| 0.0283 \| 52.1 \| 14900 \| 0.1244 \| 0.3995 \| 0.6105 \| 0.6028 \| nan \| 0.5660 \| 0.6550 \| 0.0 \| 0.5483 \| 0.6503 \| \| 0.0491 \| 52.17 \| 14920 \| 0.1237 \| 0.4060 \| 0.6201 \| 0.6134 \| nan \| 0.5814 \| 0.6588 \| 0.0 \| 0.5636 \| 0.6544 \| \| 0.05 \| 52.24 \| 14940 \| 0.1200 \| 0.4039 \| 0.6140 \| 0.6027 \| nan \| 0.5485 \| 0.6796 \| 0.0 \| 0.5366 \| 0.6751 \| \| 0.0899 \| 52.31 \| 14960 \| 0.1238 \| 0.3956 \| 0.6016 \| 0.5884 \| nan \| 0.5252 \| 0.6781 \| 0.0 \| 0.5112 \| 0.6756 \| \| 0.0671 \| 52.38 \| 14980 \| 0.1216 \| 0.3991 \| 0.6066 \| 0.5938 \| nan \| 0.5327 \| 0.6806 \| 0.0 \| 0.5206 \| 0.6766 \| \| 0.0375 \| 52.45 \| 15000 \| 0.1234 \| 0.3994 \| 0.6089 \| 0.6014 \| nan \| 0.5655 \| 0.6523 \| 0.0 \| 0.5491 \| 0.6490 \| \| 0.0578 \| 52.52 \| 15020 \| 0.1246 \| 0.4046 \| 0.6165 \| 0.6091 \| nan \| 0.5736 \| 0.6594 \| 0.0 \| 0.5586 \| 0.6551 \| \| 0.0374 \| 52.59 \| 15040 \| 0.1284 \| 0.3909 \| 0.5953 \| 0.5882 \| nan \| 0.5545 \| 0.6361 \| 0.0 \| 0.5399 \| 0.6329 \| \| 0.0584 \| 52.66 \| 15060 \| 0.1293 \| 0.3846 \| 0.5853 \| 0.5732 \| nan \| 0.5155 \| 0.6551 \| 0.0 \| 0.5022 \| 0.6515 \| \| 0.0268 \| 52.73 \| 15080 \| 0.1204 \| 0.3987 \| 0.6066 \| 0.5932 \| nan \| 0.5292 \| 0.6839 \| 0.0 \| 0.5205 \| 0.6756 \| \| 0.052 \| 52.8 \| 15100 \| 0.1214 \| 0.4045 \| 0.6154 \| 0.6038 \| nan \| 0.5481 \| 0.6827 \| 0.0 \| 0.5392 \| 0.6745 \| \| 0.0435 \| 52.87 \| 15120 \| 0.1214 \| 0.4085 \| 0.6224 \| 0.6101 \| nan \| 0.5512 \| 0.6936 \| 0.0 \| 0.5380 \| 0.6876 \| \| 0.0292 \| 52.94 \| 15140 \| 0.1293 \| 0.3909 \| 0.5962 \| 0.5895 \| nan \| 0.5577 \| 0.6347 \| 0.0 \| 0.5425 \| 0.6302 \| \| 0.05 \| 53.01 \| 15160 \| 0.1291 \| 0.3860 \| 0.5885 \| 0.5795 \| nan \| 0.5366 \| 0.6403 \| 0.0 \| 0.5221 \| 0.6359 \| \| 0.0295 \| 53.08 \| 15180 \| 0.1244 \| 0.3880 \| 0.5909 \| 0.5792 \| nan \| 0.5233 \| 0.6584 \| 0.0 \| 0.5108 \| 0.6532 \| \| 0.0513 \| 53.15 \| 15200 \| 0.1239 \| 0.4069 \| 0.6201 \| 0.6150 \| nan \| 0.5904 \| 0.6499 \| 0.0 \| 0.5756 \| 0.6453 \| \| 0.0311 \| 53.22 \| 15220 \| 0.1239 \| 0.4078 \| 0.6218 \| 0.6141 \| nan \| 0.5773 \| 0.6663 \| 0.0 \| 0.5625 \| 0.6609 \| \| 0.0451 \| 53.29 \| 15240 \| 0.1133 \| 0.4074 \| 0.6210 \| 0.6034 \| nan \| 0.5196 \| 0.7224 \| 0.0 \| 0.5123 \| 0.7100 \| \| 0.0499 \| 53.36 \| 15260 \| 0.1225 \| 0.3979 \| 0.6060 \| 0.6006 \| nan \| 0.5746 \| 0.6374 \| 0.0 \| 0.5609 \| 0.6327 \| \| 0.0464 \| 53.43 \| 15280 \| 0.1217 \| 0.3967 \| 0.6038 \| 0.5917 \| nan \| 0.5340 \| 0.6736 \| 0.0 \| 0.5224 \| 0.6678 \| \| 0.0563 \| 53.5 \| 15300 \| 0.1262 \| 0.3951 \| 0.6021 \| 0.5932 \| nan \| 0.5510 \| 0.6531 \| 0.0 \| 0.5358 \| 0.6495 \| \| 0.0593 \| 53.57 \| 15320 \| 0.1279 \| 0.3914 \| 0.5961 \| 0.5859 \| nan \| 0.5373 \| 0.6548 \| 0.0 \| 0.5240 \| 0.6501 \| \| 0.0471 \| 53.64 \| 15340 \| 0.1222 \| 0.4000 \| 0.6091 \| 0.5982 \| nan \| 0.5457 \| 0.6726 \| 0.0 \| 0.5346 \| 0.6654 \| \| 0.0541 \| 53.71 \| 15360 \| 0.1205 \| 0.3989 \| 0.6066 \| 0.5951 \| nan \| 0.5401 \| 0.6731 \| 0.0 \| 0.5324 \| 0.6642 \| \| 0.0317 \| 53.78 \| 15380 \| 0.1253 \| 0.3982 \| 0.6060 \| 0.5953 \| nan \| 0.5442 \| 0.6677 \| 0.0 \| 0.5320 \| 0.6628 \| \| 0.0601 \| 53.85 \| 15400 \| 0.1250 \| 0.4022 \| 0.6124 \| 0.6051 \| nan \| 0.5705 \| 0.6542 \| 0.0 \| 0.5556 \| 0.6509 \| \| 0.0378 \| 53.92 \| 15420 \| 0.1226 \| 0.4001 \| 0.6085 \| 0.5946 \| nan \| 0.5283 \| 0.6888 \| 0.0 \| 0.5174 \| 0.6829 \| \| 0.0628 \| 53.99 \| 15440 \| 0.1342 \| 0.3911 \| 0.5961 \| 0.5913 \| nan \| 0.5681 \| 0.6241 \| 0.0 \| 0.5515 \| 0.6217 \| \| 0.0553 \| 54.06 \| 15460 \| 0.1246 \| 0.4043 \| 0.6148 \| 0.6042 \| nan \| 0.5531 \| 0.6766 \| 0.0 \| 0.5416 \| 0.6712 \| \| 0.0324 \| 54.13 \| 15480 \| 0.1285 \| 0.3999 \| 0.6092 \| 0.6014 \| nan \| 0.5644 \| 0.6540 \| 0.0 \| 0.5494 \| 0.6504 \| \| 0.0586 \| 54.2 \| 15500 \| 0.1316 \| 0.3930 \| 0.5981 \| 0.5896 \| nan \| 0.5489 \| 0.6474 \| 0.0 \| 0.5359 \| 0.6432 \| \| 0.0502 \| 54.27 \| 15520 \| 0.1309 \| 0.3949 \| 0.6009 \| 0.5913 \| nan \| 0.5455 \| 0.6563 \| 0.0 \| 0.5333 \| 0.6515 \| \| 0.05 \| 54.34 \| 15540 \| 0.1290 \| 0.3965 \| 0.6029 \| 0.5931 \| nan \| 0.5458 \| 0.6601 \| 0.0 \| 0.5341 \| 0.6553 \| \| 0.0527 \| 54.41 \| 15560 \| 0.1325 \| 0.3893 \| 0.5921 \| 0.5838 \| nan \| 0.5446 \| 0.6396 \| 0.0 \| 0.5316 \| 0.6363 \| \| 0.0679 \| 54.48 \| 15580 \| 0.1296 \| 0.3861 \| 0.5870 \| 0.5763 \| nan \| 0.5253 \| 0.6487 \| 0.0 \| 0.5142 \| 0.6441 \| \| 0.0425 \| 54.55 \| 15600 \| 0.1295 \| 0.3998 \| 0.6086 \| 0.5983 \| nan \| 0.5495 \| 0.6676 \| 0.0 \| 0.5352 \| 0.6643 \| \| 0.052 \| 54.62 \| 15620 \| 0.1360 \| 0.3840 \| 0.5848 \| 0.5792 \| nan \| 0.5525 \| 0.6172 \| 0.0 \| 0.5368 \| 0.6151 \| \| 0.0828 \| 54.69 \| 15640 \| 0.1263 \| 0.3994 \| 0.6075 \| 0.5972 \| nan \| 0.5476 \| 0.6675 \| 0.0 \| 0.5355 \| 0.6625 \| \| 0.0405 \| 54.76 \| 15660 \| 0.1217 \| 0.4202 \| 0.6394 \| 0.6313 \| nan \| 0.5927 \| 0.6861 \| 0.0 \| 0.5821 \| 0.6784 \| \| 0.0625 \| 54.83 \| 15680 \| 0.1303 \| 0.4003 \| 0.6095 \| 0.5998 \| nan \| 0.5537 \| 0.6653 \| 0.0 \| 0.5408 \| 0.6602 \| \| 0.0437 \| 54.9 \| 15700 \| 0.1317 \| 0.3983 \| 0.6065 \| 0.5985 \| nan \| 0.5605 \| 0.6524 \| 0.0 \| 0.5464 \| 0.6486 \| \| 0.0491 \| 54.97 \| 15720 \| 0.1313 \| 0.3993 \| 0.6079 \| 0.5967 \| nan \| 0.5432 \| 0.6726 \| 0.0 \| 0.5301 \| 0.6677 \| \| 0.0486 \| 55.03 \| 15740 \| 0.1330 \| 0.3963 \| 0.6026 \| 0.5940 \| nan \| 0.5527 \| 0.6525 \| 0.0 \| 0.5403 \| 0.6486 \| \| 0.0358 \| 55.1 \| 15760 \| 0.1327 \| 0.3970 \| 0.6040 \| 0.5934 \| nan \| 0.5432 \| 0.6647 \| 0.0 \| 0.5309 \| 0.6602 \| \| 0.0846 \| 55.17 \| 15780 \| 0.1352 \| 0.3895 \| 0.5929 \| 0.5860 \| nan \| 0.5533 \| 0.6324 \| 0.0 \| 0.5383 \| 0.6303 \| \| 0.0498 \| 55.24 \| 15800 \| 0.1211 \| 0.4155 \| 0.6322 \| 0.6221 \| nan \| 0.5737 \| 0.6906 \| 0.0 \| 0.5632 \| 0.6831 \| \| 0.0625 \| 55.31 \| 15820 \| 0.1304 \| 0.4036 \| 0.6137 \| 0.6053 \| nan \| 0.5650 \| 0.6624 \| 0.0 \| 0.5534 \| 0.6573 \| \| 0.0397 \| 55.38 \| 15840 \| 0.1327 \| 0.3898 \| 0.5930 \| 0.5859 \| nan \| 0.5519 \| 0.6341 \| 0.0 \| 0.5394 \| 0.6300 \| \| 0.0489 \| 55.45 \| 15860 \| 0.1304 \| 0.3867 \| 0.5871 \| 0.5757 \| nan \| 0.5213 \| 0.6529 \| 0.0 \| 0.5120 \| 0.6481 \| \| 0.0519 \| 55.52 \| 15880 \| 0.1239 \| 0.4146 \| 0.6313 \| 0.6235 \| nan \| 0.5863 \| 0.6763 \| 0.0 \| 0.5726 \| 0.6712 \| \| 0.0507 \| 55.59 \| 15900 \| 0.1257 \| 0.4189 \| 0.6379 \| 0.6331 \| nan \| 0.6100 \| 0.6658 \| 0.0 \| 0.5961 \| 0.6605 \| \| 0.0253 \| 55.66 \| 15920 \| 0.1291 \| 0.4040 \| 0.6144 \| 0.6067 \| nan \| 0.5697 \| 0.6592 \| 0.0 \| 0.5586 \| 0.6533 \| \| 0.0597 \| 55.73 \| 15940 \| 0.1317 \| 0.4038 \| 0.6148 \| 0.6064 \| nan \| 0.5663 \| 0.6633 \| 0.0 \| 0.5532 \| 0.6581 \| \| 0.0527 \| 55.8 \| 15960 \| 0.1329 \| 0.3985 \| 0.6074 \| 0.6039 \| nan \| 0.5873 \| 0.6275 \| 0.0 \| 0.5728 \| 0.6228 \| \| 0.0635 \| 55.87 \| 15980 \| 0.1332 \| 0.3944 \| 0.6002 \| 0.5944 \| nan \| 0.5671 \| 0.6332 \| 0.0 \| 0.5538 \| 0.6294 \| \| 0.0637 \| 55.94 \| 16000 \| 0.1292 \| 0.4004 \| 0.6089 \| 0.5975 \| nan \| 0.5432 \| 0.6746 \| 0.0 \| 0.5307 \| 0.6705 \| \| 0.041 \| 56.01 \| 16020 \| 0.1308 \| 0.3898 \| 0.5930 \| 0.5835 \| nan \| 0.5383 \| 0.6476 \| 0.0 \| 0.5249 \| 0.6444 \| \| 0.0595 \| 56.08 \| 16040 \| 0.1216 \| 0.3951 \| 0.6009 \| 0.5847 \| nan \| 0.5071 \| 0.6948 \| 0.0 \| 0.4991 \| 0.6860 \| \| 0.0483 \| 56.15 \| 16060 \| 0.1295 \| 0.3952 \| 0.6016 \| 0.5915 \| nan \| 0.5432 \| 0.6600 \| 0.0 \| 0.5305 \| 0.6551 \| \| 0.0588 \| 56.22 \| 16080 \| 0.1227 \| 0.4187 \| 0.6378 \| 0.6299 \| nan \| 0.5921 \| 0.6834 \| 0.0 \| 0.5789 \| 0.6771 \| \| 0.0393 \| 56.29 \| 16100 \| 0.1260 \| 0.3935 \| 0.5994 \| 0.5911 \| nan \| 0.5512 \| 0.6477 \| 0.0 \| 0.5380 \| 0.6425 \| \| 0.034 \| 56.36 \| 16120 \| 0.1317 \| 0.3893 \| 0.5924 \| 0.5772 \| nan \| 0.5049 \| 0.6799 \| 0.0 \| 0.4946 \| 0.6734 \| \| 0.0823 \| 56.43 \| 16140 \| 0.1311 \| 0.3899 \| 0.5948 \| 0.5877 \| nan \| 0.5539 \| 0.6356 \| 0.0 \| 0.5384 \| 0.6313 \| \| 0.0544 \| 56.5 \| 16160 \| 0.1254 \| 0.4000 \| 0.6085 \| 0.5959 \| nan \| 0.5359 \| 0.6810 \| 0.0 \| 0.5244 \| 0.6756 \| \| 0.0461 \| 56.57 \| 16180 \| 0.1246 \| 0.4085 \| 0.6226 \| 0.6132 \| nan \| 0.5685 \| 0.6767 \| 0.0 \| 0.5544 \| 0.6710 \| \| 0.0467 \| 56.64 \| 16200 \| 0.1238 \| 0.4134 \| 0.6290 \| 0.6163 \| nan \| 0.5557 \| 0.7024 \| 0.0 \| 0.5472 \| 0.6928 \| \| 0.0344 \| 56.71 \| 16220 \| 0.1241 \| 0.4035 \| 0.6138 \| 0.6003 \| nan \| 0.5359 \| 0.6917 \| 0.0 \| 0.5265 \| 0.6841 \| \| 0.0341 \| 56.78 \| 16240 \| 0.1231 \| 0.4037 \| 0.6147 \| 0.6020 \| nan \| 0.5411 \| 0.6884 \| 0.0 \| 0.5288 \| 0.6823 \| \| 0.0378 \| 56.85 \| 16260 \| 0.1259 \| 0.3942 \| 0.6001 \| 0.5881 \| nan \| 0.5308 \| 0.6694 \| 0.0 \| 0.5195 \| 0.6632 \| \| 0.0353 \| 56.92 \| 16280 \| 0.1176 \| 0.4199 \| 0.6393 \| 0.6277 \| nan \| 0.5719 \| 0.7068 \| 0.0 \| 0.5602 \| 0.6996 \| \| 0.0257 \| 56.99 \| 16300 \| 0.1345 \| 0.3828 \| 0.5828 \| 0.5747 \| nan \| 0.5362 \| 0.6294 \| 0.0 \| 0.5215 \| 0.6270 \| \| 0.0481 \| 57.06 \| 16320 \| 0.1231 \| 0.4071 \| 0.6193 \| 0.6085 \| nan \| 0.5572 \| 0.6813 \| 0.0 \| 0.5443 \| 0.6770 \| \| 0.0463 \| 57.13 \| 16340 \| 0.1201 \| 0.4106 \| 0.6247 \| 0.6154 \| nan \| 0.5712 \| 0.6781 \| 0.0 \| 0.5578 \| 0.6741 \| \| 0.0721 \| 57.2 \| 16360 \| 0.1240 \| 0.4030 \| 0.6129 \| 0.5992 \| nan \| 0.5336 \| 0.6922 \| 0.0 \| 0.5219 \| 0.6872 \| \| 0.0491 \| 57.27 \| 16380 \| 0.1247 \| 0.4028 \| 0.6124 \| 0.5981 \| nan \| 0.5300 \| 0.6947 \| 0.0 \| 0.5212 \| 0.6871 \| \| 0.0442 \| 57.34 \| 16400 \| 0.1247 \| 0.3936 \| 0.5986 \| 0.5879 \| nan \| 0.5368 \| 0.6604 \| 0.0 \| 0.5248 \| 0.6560 \| \| 0.033 \| 57.41 \| 16420 \| 0.1229 \| 0.3972 \| 0.6045 \| 0.5943 \| nan \| 0.5459 \| 0.6631 \| 0.0 \| 0.5342 \| 0.6574 \| \| 0.0642 \| 57.48 \| 16440 \| 0.1263 \| 0.3895 \| 0.5918 \| 0.5794 \| nan \| 0.5199 \| 0.6638 \| 0.0 \| 0.5104 \| 0.6581 \| \| 0.0723 \| 57.55 \| 16460 \| 0.1288 \| 0.3982 \| 0.6052 \| 0.5945 \| nan \| 0.5436 \| 0.6667 \| 0.0 \| 0.5336 \| 0.6609 \| \| 0.0419 \| 57.62 \| 16480 \| 0.1236 \| 0.4125 \| 0.6282 \| 0.6182 \| nan \| 0.5707 \| 0.6857 \| 0.0 \| 0.5584 \| 0.6792 \| \| 0.0589 \| 57.69 \| 16500 \| 0.1206 \| 0.3981 \| 0.6052 \| 0.5913 \| nan \| 0.5248 \| 0.6857 \| 0.0 \| 0.5170 \| 0.6773 \| \| 0.0326 \| 57.76 \| 16520 \| 0.1240 \| 0.4017 \| 0.6114 \| 0.6017 \| nan \| 0.5555 \| 0.6673 \| 0.0 \| 0.5433 \| 0.6620 \| \| 0.0464 \| 57.83 \| 16540 \| 0.1190 \| 0.4215 \| 0.6411 \| 0.6274 \| nan \| 0.5621 \| 0.7201 \| 0.0 \| 0.5541 \| 0.7105 \| \| 0.1178 \| 57.9 \| 16560 \| 0.1206 \| 0.4110 \| 0.6258 \| 0.6160 \| nan \| 0.5690 \| 0.6826 \| 0.0 \| 0.5570 \| 0.6759 \| \| 0.0288 \| 57.97 \| 16580 \| 0.1133 \| 0.4235 \| 0.6438 \| 0.6321 \| nan \| 0.5762 \| 0.7114 \| 0.0 \| 0.5649 \| 0.7055 \| \| 0.0357 \| 58.04 \| 16600 \| 0.1180 \| 0.4079 \| 0.6215 \| 0.6091 \| nan \| 0.5499 \| 0.6931 \| 0.0 \| 0.5419 \| 0.6818 \| \| 0.0538 \| 58.11 \| 16620 \| 0.1146 \| 0.4108 \| 0.6266 \| 0.6115 \| nan \| 0.5397 \| 0.7134 \| 0.0 \| 0.5327 \| 0.6996 \| \| 0.0723 \| 58.18 \| 16640 \| 0.1273 \| 0.4001 \| 0.6088 \| 0.6027 \| nan \| 0.5737 \| 0.6440 \| 0.0 \| 0.5595 \| 0.6408 \| \| 0.067 \| 58.25 \| 16660 \| 0.1268 \| 0.4025 \| 0.6128 \| 0.6040 \| nan \| 0.5621 \| 0.6636 \| 0.0 \| 0.5472 \| 0.6604 \| \| 0.0266 \| 58.32 \| 16680 \| 0.1264 \| 0.4002 \| 0.6088 \| 0.5982 \| nan \| 0.5480 \| 0.6695 \| 0.0 \| 0.5359 \| 0.6648 \| \| 0.059 \| 58.39 \| 16700 \| 0.1394 \| 0.3774 \| 0.5747 \| 0.5684 \| nan \| 0.5384 \| 0.6111 \| 0.0 \| 0.5231 \| 0.6091 \| \| 0.0593 \| 58.46 \| 16720 \| 0.1235 \| 0.3978 \| 0.6050 \| 0.5954 \| nan \| 0.5497 \| 0.6602 \| 0.0 \| 0.5385 \| 0.6549 \| \| 0.0553 \| 58.53 \| 16740 \| 0.1228 \| 0.4133 \| 0.6291 \| 0.6213 \| nan \| 0.5845 \| 0.6736 \| 0.0 \| 0.5692 \| 0.6707 \| \| 0.0515 \| 58.6 \| 16760 \| 0.1160 \| 0.4095 \| 0.6217 \| 0.6109 \| nan \| 0.5594 \| 0.6840 \| 0.0 \| 0.5489 \| 0.6796 \| \| 0.0645 \| 58.67 \| 16780 \| 0.1267 \| 0.3939 \| 0.5988 \| 0.5922 \| nan \| 0.5605 \| 0.6372 \| 0.0 \| 0.5483 \| 0.6335 \| \| 0.0381 \| 58.74 \| 16800 \| 0.1269 \| 0.3945 \| 0.6006 \| 0.5932 \| nan \| 0.5583 \| 0.6428 \| 0.0 \| 0.5446 \| 0.6389 \| \| 0.0416 \| 58.81 \| 16820 \| 0.1242 \| 0.4127 \| 0.6289 \| 0.6230 \| nan \| 0.5948 \| 0.6629 \| 0.0 \| 0.5790 \| 0.6592 \| \| 0.0551 \| 58.88 \| 16840 \| 0.1181 \| 0.3974 \| 0.6043 \| 0.5913 \| nan \| 0.5294 \| 0.6792 \| 0.0 \| 0.5190 \| 0.6733 \| \| 0.0582 \| 58.95 \| 16860 \| 0.1222 \| 0.4053 \| 0.6176 \| 0.6078 \| nan \| 0.5610 \| 0.6742 \| 0.0 \| 0.5472 \| 0.6687 \| \| 0.0375 \| 59.02 \| 16880 \| 0.1209 \| 0.3982 \| 0.6060 \| 0.5920 \| nan \| 0.5252 \| 0.6869 \| 0.0 \| 0.5140 \| 0.6805 \| \| 0.0424 \| 59.09 \| 16900 \| 0.1289 \| 0.3936 \| 0.5993 \| 0.5904 \| nan \| 0.5479 \| 0.6506 \| 0.0 \| 0.5339 \| 0.6469 \| \| 0.047 \| 59.16 \| 16920 \| 0.1235 \| 0.4029 \| 0.6127 \| 0.6021 \| nan \| 0.5512 \| 0.6743 \| 0.0 \| 0.5394 \| 0.6692 \| \| 0.0456 \| 59.23 \| 16940 \| 0.1251 \| 0.4059 \| 0.6174 \| 0.6060 \| nan \| 0.5516 \| 0.6833 \| 0.0 \| 0.5383 \| 0.6793 \| \| 0.0565 \| 59.3 \| 16960 \| 0.1298 \| 0.3842 \| 0.5843 \| 0.5753 \| nan \| 0.5322 \| 0.6364 \| 0.0 \| 0.5196 \| 0.6331 \| \| 0.0602 \| 59.37 \| 16980 \| 0.1238 \| 0.3932 \| 0.5980 \| 0.5860 \| nan \| 0.5291 \| 0.6668 \| 0.0 \| 0.5180 \| 0.6614 \| \| 0.0679 \| 59.44 \| 17000 \| 0.1222 \| 0.3969 \| 0.6035 \| 0.5917 \| nan \| 0.5351 \| 0.6719 \| 0.0 \| 0.5240 \| 0.6666 \| \| 0.0919 \| 59.51 \| 17020 \| 0.1258 \| 0.3948 \| 0.6013 \| 0.5931 \| nan \| 0.5543 \| 0.6482 \| 0.0 \| 0.5403 \| 0.6440 \| \| 0.0655 \| 59.58 \| 17040 \| 0.1275 \| 0.4066 \| 0.6196 \| 0.6147 \| nan \| 0.5914 \| 0.6477 \| 0.0 \| 0.5761 \| 0.6438 \| \| 0.0299 \| 59.65 \| 17060 \| 0.1251 \| 0.4013 \| 0.6108 \| 0.6008 \| nan \| 0.5530 \| 0.6685 \| 0.0 \| 0.5403 \| 0.6636 \| \| 0.0425 \| 59.72 \| 17080 \| 0.1224 \| 0.4102 \| 0.6239 \| 0.6141 \| nan \| 0.5675 \| 0.6802 \| 0.0 \| 0.5560 \| 0.6747 \| \| 0.0624 \| 59.79 \| 17100 \| 0.1277 \| 0.3981 \| 0.6056 \| 0.5987 \| nan \| 0.5661 \| 0.6450 \| 0.0 \| 0.5527 \| 0.6415 \| \| 0.0394 \| 59.86 \| 17120 \| 0.1295 \| 0.3786 \| 0.5758 \| 0.5650 \| nan \| 0.5138 \| 0.6378 \| 0.0 \| 0.5020 \| 0.6337 \| \| 0.046 \| 59.93 \| 17140 \| 0.1267 \| 0.3951 \| 0.6014 \| 0.5938 \| nan \| 0.5572 \| 0.6456 \| 0.0 \| 0.5435 \| 0.6418 \| \| 0.0339 \| 60.0 \| 17160 \| 0.1208 \| 0.4100 \| 0.6241 \| 0.6140 \| nan \| 0.5659 \| 0.6823 \| 0.0 \| 0.5537 \| 0.6764 \| \| 0.1047 \| 60.07 \| 17180 \| 0.1276 \| 0.4058 \| 0.6178 \| 0.6133 \| nan \| 0.5920 \| 0.6436 \| 0.0 \| 0.5796 \| 0.6379 \| \| 0.0593 \| 60.14 \| 17200 \| 0.1229 \| 0.4115 \| 0.6269 \| 0.6178 \| nan \| 0.5746 \| 0.6792 \| 0.0 \| 0.5618 \| 0.6727 \| \| 0.0405 \| 60.21 \| 17220 \| 0.1226 \| 0.4167 \| 0.6342 \| 0.6260 \| nan \| 0.5868 \| 0.6816 \| 0.0 \| 0.5723 \| 0.6779 \| \| 0.042 \| 60.28 \| 17240 \| 0.1257 \| 0.4050 \| 0.6161 \| 0.6079 \| nan \| 0.5687 \| 0.6634 \| 0.0 \| 0.5542 \| 0.6608 \| \| 0.0654 \| 60.35 \| 17260 \| 0.1216 \| 0.4151 \| 0.6312 \| 0.6227 \| nan \| 0.5821 \| 0.6803 \| 0.0 \| 0.5684 \| 0.6770 \| \| 0.0559 \| 60.42 \| 17280 \| 0.1241 \| 0.4065 \| 0.6184 \| 0.6100 \| nan \| 0.5704 \| 0.6663 \| 0.0 \| 0.5571 \| 0.6625 \| \| 0.0484 \| 60.49 \| 17300 \| 0.1283 \| 0.3892 \| 0.5910 \| 0.5794 \| nan \| 0.5241 \| 0.6578 \| 0.0 \| 0.5127 \| 0.6549 \| \| 0.0523 \| 60.56 \| 17320 \| 0.1207 \| 0.4085 \| 0.6211 \| 0.6118 \| nan \| 0.5677 \| 0.6744 \| 0.0 \| 0.5559 \| 0.6696 \| \| 0.0497 \| 60.63 \| 17340 \| 0.1213 \| 0.4032 \| 0.6131 \| 0.6000 \| nan \| 0.5376 \| 0.6885 \| 0.0 \| 0.5265 \| 0.6832 \| \| 0.0382 \| 60.7 \| 17360 \| 0.1299 \| 0.3912 \| 0.5946 \| 0.5842 \| nan \| 0.5348 \| 0.6543 \| 0.0 \| 0.5231 \| 0.6505 \| \| 0.0324 \| 60.77 \| 17380 \| 0.1315 \| 0.3837 \| 0.5830 \| 0.5724 \| nan \| 0.5216 \| 0.6444 \| 0.0 \| 0.5097 \| 0.6413 \| \| 0.0337 \| 60.84 \| 17400 \| 0.1305 \| 0.3874 \| 0.5888 \| 0.5772 \| nan \| 0.5218 \| 0.6557 \| 0.0 \| 0.5094 \| 0.6527 \| \| 0.0516 \| 60.91 \| 17420 \| 0.1287 \| 0.3849 \| 0.5845 \| 0.5703 \| nan \| 0.5027 \| 0.6664 \| 0.0 \| 0.4920 \| 0.6626 \| \| 0.0677 \| 60.98 \| 17440 \| 0.1273 \| 0.3905 \| 0.5935 \| 0.5816 \| nan \| 0.5246 \| 0.6625 \| 0.0 \| 0.5132 \| 0.6581 \| \| 0.0589 \| 61.05 \| 17460 \| 0.1267 \| 0.4085 \| 0.6220 \| 0.6139 \| nan \| 0.5753 \| 0.6688 \| 0.0 \| 0.5611 \| 0.6645 \| \| 0.0455 \| 61.12 \| 17480 \| 0.1294 \| 0.3895 \| 0.5923 \| 0.5833 \| nan \| 0.5402 \| 0.6445 \| 0.0 \| 0.5281 \| 0.6405 \| \| 0.0516 \| 61.19 \| 17500 \| 0.1234 \| 0.3983 \| 0.6054 \| 0.5924 \| nan \| 0.5302 \| 0.6805 \| 0.0 \| 0.5201 \| 0.6747 \| \| 0.0328 \| 61.26 \| 17520 \| 0.1268 \| 0.4004 \| 0.6089 \| 0.5993 \| nan \| 0.5533 \| 0.6645 \| 0.0 \| 0.5411 \| 0.6602 \| \| 0.0484 \| 61.33 \| 17540 \| 0.1286 \| 0.4047 \| 0.6160 \| 0.6080 \| nan \| 0.5700 \| 0.6620 \| 0.0 \| 0.5562 \| 0.6577 \| \| 0.0405 \| 61.4 \| 17560 \| 0.1216 \| 0.3997 \| 0.6072 \| 0.5920 \| nan \| 0.5195 \| 0.6950 \| 0.0 \| 0.5097 \| 0.6893 \| \| 0.0542 \| 61.47 \| 17580 \| 0.1232 \| 0.4060 \| 0.6175 \| 0.6075 \| nan \| 0.5597 \| 0.6754 \| 0.0 \| 0.5471 \| 0.6709 \| \| 0.0472 \| 61.54 \| 17600 \| 0.1268 \| 0.4030 \| 0.6148 \| 0.6108 \| nan \| 0.5919 \| 0.6376 \| 0.0 \| 0.5747 \| 0.6344 \| \| 0.0415 \| 61.61 \| 17620 \| 0.1235 \| 0.4038 \| 0.6149 \| 0.6057 \| nan \| 0.5614 \| 0.6685 \| 0.0 \| 0.5495 \| 0.6621 \| \| 0.042 \| 61.68 \| 17640 \| 0.1290 \| 0.3923 \| 0.5977 \| 0.5907 \| nan \| 0.5572 \| 0.6381 \| 0.0 \| 0.5423 \| 0.6347 \| \| 0.031 \| 61.75 \| 17660 \| 0.1262 \| 0.4077 \| 0.6213 \| 0.6120 \| nan \| 0.5676 \| 0.6750 \| 0.0 \| 0.5527 \| 0.6704 \| \| 0.0493 \| 61.82 \| 17680 \| 0.1200 \| 0.4099 \| 0.6230 \| 0.6144 \| nan \| 0.5737 \| 0.6722 \| 0.0 \| 0.5626 \| 0.6672 \| \| 0.0564 \| 61.89 \| 17700 \| 0.1184 \| 0.4237 \| 0.6440 \| 0.6321 \| nan \| 0.5750 \| 0.7131 \| 0.0 \| 0.5643 \| 0.7068 \| \| 0.0418 \| 61.96 \| 17720 \| 0.1224 \| 0.4060 \| 0.6166 \| 0.6036 \| nan \| 0.5419 \| 0.6913 \| 0.0 \| 0.5319 \| 0.6860 \| \| 0.0349 \| 62.03 \| 17740 \| 0.1203 \| 0.4182 \| 0.6357 \| 0.6255 \| nan \| 0.5766 \| 0.6949 \| 0.0 \| 0.5652 \| 0.6896 \| \| 0.0387 \| 62.1 \| 17760 \| 0.1227 \| 0.4034 \| 0.6128 \| 0.5994 \| nan \| 0.5355 \| 0.6902 \| 0.0 \| 0.5258 \| 0.6844 \| \| 0.0334 \| 62.17 \| 17780 \| 0.1229 \| 0.4040 \| 0.6141 \| 0.6007 \| nan \| 0.5367 \| 0.6914 \| 0.0 \| 0.5253 \| 0.6866 \| \| 0.0421 \| 62.24 \| 17800 \| 0.1211 \| 0.4114 \| 0.6249 \| 0.6135 \| nan \| 0.5590 \| 0.6908 \| 0.0 \| 0.5481 \| 0.6861 \| \| 0.0383 \| 62.31 \| 17820 \| 0.1168 \| 0.4243 \| 0.6450 \| 0.6336 \| nan \| 0.5793 \| 0.7107 \| 0.0 \| 0.5694 \| 0.7035 \| \| 0.0577 \| 62.38 \| 17840 \| 0.1226 \| 0.4050 \| 0.6157 \| 0.6072 \| nan \| 0.5667 \| 0.6647 \| 0.0 \| 0.5547 \| 0.6604 \| \| 0.0474 \| 62.45 \| 17860 \| 0.1269 \| 0.3946 \| 0.6000 \| 0.5905 \| nan \| 0.5451 \| 0.6550 \| 0.0 \| 0.5333 \| 0.6505 \| \| 0.0459 \| 62.52 \| 17880 \| 0.1271 \| 0.3987 \| 0.6066 \| 0.5947 \| nan \| 0.5377 \| 0.6754 \| 0.0 \| 0.5248 \| 0.6714 \| \| 0.0696 \| 62.59 \| 17900 \| 0.1212 \| 0.4024 \| 0.6118 \| 0.5958 \| nan \| 0.5197 \| 0.7038 \| 0.0 \| 0.5105 \| 0.6968 \| \| 0.0564 \| 62.66 \| 17920 \| 0.1246 \| 0.4043 \| 0.6144 \| 0.6055 \| nan \| 0.5631 \| 0.6658 \| 0.0 \| 0.5512 \| 0.6617 \| \| 0.0413 \| 62.73 \| 17940 \| 0.1368 \| 0.4038 \| 0.6159 \| 0.6125 \| nan \| 0.5965 \| 0.6353 \| 0.0 \| 0.5789 \| 0.6323 \| \| 0.0557 \| 62.8 \| 17960 \| 0.1336 \| 0.3887 \| 0.5913 \| 0.5831 \| nan \| 0.5439 \| 0.6387 \| 0.0 \| 0.5303 \| 0.6359 \| \| 0.0745 \| 62.87 \| 17980 \| 0.1350 \| 0.3845 \| 0.5850 \| 0.5792 \| nan \| 0.5514 \| 0.6186 \| 0.0 \| 0.5374 \| 0.6162 \| \| 0.0373 \| 62.94 \| 18000 \| 0.1362 \| 0.3806 \| 0.5785 \| 0.5687 \| nan \| 0.5222 \| 0.6347 \| 0.0 \| 0.5097 \| 0.6320 \| \| 0.0444 \| 63.01 \| 18020 \| 0.1370 \| 0.3900 \| 0.5937 \| 0.5868 \| nan \| 0.5541 \| 0.6332 \| 0.0 \| 0.5395 \| 0.6305 \| \| 0.0376 \| 63.08 \| 18040 \| 0.1276 \| 0.4144 \| 0.6307 \| 0.6230 \| nan \| 0.5866 \| 0.6747 \| 0.0 \| 0.5725 \| 0.6707 \| \| 0.068 \| 63.15 \| 18060 \| 0.1358 \| 0.3986 \| 0.6070 \| 0.6023 \| nan \| 0.5796 \| 0.6344 \| 0.0 \| 0.5627 \| 0.6330 \| \| 0.0352 \| 63.22 \| 18080 \| 0.1278 \| 0.4128 \| 0.6295 \| 0.6233 \| nan \| 0.5940 \| 0.6650 \| 0.0 \| 0.5750 \| 0.6635 \| \| 0.0455 \| 63.29 \| 18100 \| 0.1282 \| 0.3923 \| 0.5974 \| 0.5870 \| nan \| 0.5376 \| 0.6572 \| 0.0 \| 0.5233 \| 0.6536 \| \| 0.0699 \| 63.36 \| 18120 \| 0.1322 \| 0.3878 \| 0.5909 \| 0.5837 \| nan \| 0.5496 \| 0.6322 \| 0.0 \| 0.5337 \| 0.6298 \| \| 0.0577 \| 63.43 \| 18140 \| 0.1246 \| 0.4096 \| 0.6243 \| 0.6175 \| nan \| 0.5852 \| 0.6633 \| 0.0 \| 0.5698 \| 0.6590 \| \| 0.066 \| 63.5 \| 18160 \| 0.1188 \| 0.4148 \| 0.6319 \| 0.6209 \| nan \| 0.5681 \| 0.6958 \| 0.0 \| 0.5564 \| 0.6880 \| \| 0.0381 \| 63.57 \| 18180 \| 0.1238 \| 0.4058 \| 0.6170 \| 0.6069 \| nan \| 0.5585 \| 0.6756 \| 0.0 \| 0.5475 \| 0.6699 \| \| 0.0732 \| 63.64 \| 18200 \| 0.1311 \| 0.3962 \| 0.6030 \| 0.5948 \| nan \| 0.5554 \| 0.6506 \| 0.0 \| 0.5419 \| 0.6468 \| \| 0.0627 \| 63.71 \| 18220 \| 0.1355 \| 0.3880 \| 0.5907 \| 0.5824 \| nan \| 0.5425 \| 0.6390 \| 0.0 \| 0.5283 \| 0.6357 \| \| 0.0399 \| 63.78 \| 18240 \| 0.1307 \| 0.4002 \| 0.6085 \| 0.5968 \| nan \| 0.5411 \| 0.6758 \| 0.0 \| 0.5278 \| 0.6728 \| \| 0.0506 \| 63.85 \| 18260 \| 0.1281 \| 0.3963 \| 0.6028 \| 0.5911 \| nan \| 0.5350 \| 0.6705 \| 0.0 \| 0.5267 \| 0.6622 \| \| 0.0413 \| 63.92 \| 18280 \| 0.1309 \| 0.4001 \| 0.6094 \| 0.6023 \| nan \| 0.5686 \| 0.6502 \| 0.0 \| 0.5539 \| 0.6465 \| \| 0.0483 \| 63.99 \| 18300 \| 0.1214 \| 0.4190 \| 0.6381 \| 0.6297 \| nan \| 0.5895 \| 0.6866 \| 0.0 \| 0.5771 \| 0.6799 \| \| 0.0598 \| 64.06 \| 18320 \| 0.1249 \| 0.4109 \| 0.6258 \| 0.6185 \| nan \| 0.5835 \| 0.6681 \| 0.0 \| 0.5695 \| 0.6632 \| \| 0.0399 \| 64.13 \| 18340 \| 0.1217 \| 0.4119 \| 0.6268 \| 0.6207 \| nan \| 0.5915 \| 0.6620 \| 0.0 \| 0.5765 \| 0.6591 \| \| 0.0371 \| 64.2 \| 18360 \| 0.1292 \| 0.4058 \| 0.6177 \| 0.6107 \| nan \| 0.5772 \| 0.6581 \| 0.0 \| 0.5611 \| 0.6564 \| \| 0.0652 \| 64.27 \| 18380 \| 0.1275 \| 0.4013 \| 0.6101 \| 0.6035 \| nan \| 0.5717 \| 0.6486 \| 0.0 \| 0.5585 \| 0.6455 \| \| 0.0572 \| 64.34 \| 18400 \| 0.1243 \| 0.4078 \| 0.6194 \| 0.6039 \| nan \| 0.5299 \| 0.7089 \| 0.0 \| 0.5188 \| 0.7047 \| \| 0.0716 \| 64.41 \| 18420 \| 0.1268 \| 0.4040 \| 0.6149 \| 0.6076 \| nan \| 0.5725 \| 0.6573 \| 0.0 \| 0.5584 \| 0.6537 \| \| 0.0573 \| 64.48 \| 18440 \| 0.1212 \| 0.4150 \| 0.6319 \| 0.6215 \| nan \| 0.5716 \| 0.6922 \| 0.0 \| 0.5600 \| 0.6852 \| \| 0.0388 \| 64.55 \| 18460 \| 0.1242 \| 0.4099 \| 0.6236 \| 0.6129 \| nan \| 0.5616 \| 0.6856 \| 0.0 \| 0.5489 \| 0.6809 \| \| 0.0617 \| 64.62 \| 18480 \| 0.1284 \| 0.3819 \| 0.5800 \| 0.5706 \| nan \| 0.5259 \| 0.6341 \| 0.0 \| 0.5151 \| 0.6306 \| \| 0.037 \| 64.69 \| 18500 \| 0.1310 \| 0.3940 \| 0.5983 \| 0.5847 \| nan \| 0.5200 \| 0.6766 \| 0.0 \| 0.5096 \| 0.6723 \| \| 0.0548 \| 64.76 \| 18520 \| 0.1238 \| 0.4171 \| 0.6353 \| 0.6274 \| nan \| 0.5898 \| 0.6808 \| 0.0 \| 0.5743 \| 0.6771 \| \| 0.0642 \| 64.83 \| 18540 \| 0.1183 \| 0.4147 \| 0.6303 \| 0.6164 \| nan \| 0.5497 \| 0.7110 \| 0.0 \| 0.5410 \| 0.7030 \| \| 0.0558 \| 64.9 \| 18560 \| 0.1317 \| 0.3938 \| 0.5993 \| 0.5891 \| nan \| 0.5400 \| 0.6587 \| 0.0 \| 0.5263 \| 0.6552 \| \| 0.0822 \| 64.97 \| 18580 \| 0.1352 \| 0.3894 \| 0.5926 \| 0.5839 \| nan \| 0.5420 \| 0.6433 \| 0.0 \| 0.5283 \| 0.6400 \| \| 0.059 \| 65.03 \| 18600 \| 0.1276 \| 0.4014 \| 0.6098 \| 0.5984 \| nan \| 0.5437 \| 0.6760 \| 0.0 \| 0.5334 \| 0.6707 \| \| 0.0363 \| 65.1 \| 18620 \| 0.1325 \| 0.3981 \| 0.6061 \| 0.5961 \| nan \| 0.5484 \| 0.6638 \| 0.0 \| 0.5345 \| 0.6599 \| \| 0.0669 \| 65.17 \| 18640 \| 0.1320 \| 0.3849 \| 0.5862 \| 0.5728 \| nan \| 0.5090 \| 0.6633 \| 0.0 \| 0.4985 \| 0.6563 \| \| 0.0681 \| 65.24 \| 18660 \| 0.1285 \| 0.4108 \| 0.6258 \| 0.6158 \| nan \| 0.5684 \| 0.6832 \| 0.0 \| 0.5544 \| 0.6782 \| \| 0.0343 \| 65.31 \| 18680 \| 0.1320 \| 0.4098 \| 0.6251 \| 0.6181 \| nan \| 0.5849 \| 0.6653 \| 0.0 \| 0.5677 \| 0.6617 \| \| 0.0558 \| 65.38 \| 18700 \| 0.1281 \| 0.4002 \| 0.6084 \| 0.5984 \| nan \| 0.5507 \| 0.6662 \| 0.0 \| 0.5397 \| 0.6608 \| \| 0.05 \| 65.45 \| 18720 \| 0.1310 \| 0.3947 \| 0.6001 \| 0.5909 \| nan \| 0.5472 \| 0.6530 \| 0.0 \| 0.5355 \| 0.6484 \| \| 0.0404 \| 65.52 \| 18740 \| 0.1316 \| 0.3999 \| 0.6088 \| 0.5994 \| nan \| 0.5547 \| 0.6629 \| 0.0 \| 0.5407 \| 0.6589 \| \| 0.0355 \| 65.59 \| 18760 \| 0.1285 \| 0.3993 \| 0.6068 \| 0.5985 \| nan \| 0.5586 \| 0.6551 \| 0.0 \| 0.5472 \| 0.6508 \| \| 0.0455 \| 65.66 \| 18780 \| 0.1306 \| 0.4073 \| 0.6203 \| 0.6140 \| nan \| 0.5839 \| 0.6568 \| 0.0 \| 0.5681 \| 0.6538 \| \| 0.059 \| 65.73 \| 18800 \| 0.1328 \| 0.3955 \| 0.6013 \| 0.5927 \| nan \| 0.5517 \| 0.6509 \| 0.0 \| 0.5396 \| 0.6467 \| \| 0.0586 \| 65.8 \| 18820 \| 0.1332 \| 0.3901 \| 0.5932 \| 0.5812 \| nan \| 0.5243 \| 0.6620 \| 0.0 \| 0.5122 \| 0.6581 \| \| 0.0623 \| 65.87 \| 18840 \| 0.1326 \| 0.3927 \| 0.5977 \| 0.5905 \| nan \| 0.5560 \| 0.6394 \| 0.0 \| 0.5426 \| 0.6355 \| \| 0.0575 \| 65.94 \| 18860 \| 0.1245 \| 0.4123 \| 0.6266 \| 0.6143 \| nan \| 0.5557 \| 0.6974 \| 0.0 \| 0.5453 \| 0.6917 \| \| 0.0393 \| 66.01 \| 18880 \| 0.1289 \| 0.4026 \| 0.6127 \| 0.6047 \| nan \| 0.5665 \| 0.6589 \| 0.0 \| 0.5530 \| 0.6548 \| \| 0.0639 \| 66.08 \| 18900 \| 0.1443 \| 0.3850 \| 0.5862 \| 0.5813 \| nan \| 0.5577 \| 0.6146 \| 0.0 \| 0.5435 \| 0.6116 \| \| 0.0405 \| 66.15 \| 18920 \| 0.1353 \| 0.3964 \| 0.6044 \| 0.5979 \| nan \| 0.5670 \| 0.6418 \| 0.0 \| 0.5509 \| 0.6384 \| \| 0.0587 \| 66.22 \| 18940 \| 0.1373 \| 0.3952 \| 0.6023 \| 0.5987 \| nan \| 0.5816 \| 0.6230 \| 0.0 \| 0.5658 \| 0.6199 \| \| 0.0508 \| 66.29 \| 18960 \| 0.1358 \| 0.4007 \| 0.6107 \| 0.6070 \| nan \| 0.5892 \| 0.6322 \| 0.0 \| 0.5729 \| 0.6293 \| \| 0.0443 \| 66.36 \| 18980 \| 0.1239 \| 0.4181 \| 0.6367 \| 0.6293 \| nan \| 0.5942 \| 0.6791 \| 0.0 \| 0.5796 \| 0.6749 \| \| 0.0382 \| 66.43 \| 19000 \| 0.1318 \| 0.3970 \| 0.6042 \| 0.5949 \| nan \| 0.5508 \| 0.6576 \| 0.0 \| 0.5367 \| 0.6542 \| \| 0.0562 \| 66.5 \| 19020 \| 0.1377 \| 0.3838 \| 0.5840 \| 0.5747 \| nan \| 0.5303 \| 0.6376 \| 0.0 \| 0.5168 \| 0.6347 \| \| 0.0402 \| 66.57 \| 19040 \| 0.1338 \| 0.4016 \| 0.6112 \| 0.6046 \| nan \| 0.5732 \| 0.6493 \| 0.0 \| 0.5587 \| 0.6460 \| \| 0.0356 \| 66.64 \| 19060 \| 0.1337 \| 0.3910 \| 0.5943 \| 0.5852 \| nan \| 0.5415 \| 0.6471 \| 0.0 \| 0.5294 \| 0.6435 \| \| 0.0493 \| 66.71 \| 19080 \| 0.1313 \| 0.3951 \| 0.6017 \| 0.5954 \| nan \| 0.5657 \| 0.6377 \| 0.0 \| 0.5508 \| 0.6344 \| \| 0.0472 \| 66.78 \| 19100 \| 0.1316 \| 0.3926 \| 0.5972 \| 0.5864 \| nan \| 0.5348 \| 0.6596 \| 0.0 \| 0.5220 \| 0.6559 \| \| 0.0391 \| 66.85 \| 19120 \| 0.1312 \| 0.3978 \| 0.6059 \| 0.5992 \| nan \| 0.5672 \| 0.6445 \| 0.0 \| 0.5524 \| 0.6411 \| \| 0.0429 \| 66.92 \| 19140 \| 0.1260 \| 0.4015 \| 0.6105 \| 0.6002 \| nan \| 0.5507 \| 0.6704 \| 0.0 \| 0.5402 \| 0.6643 \| \| 0.0674 \| 66.99 \| 19160 \| 0.1308 \| 0.3944 \| 0.5997 \| 0.5900 \| nan \| 0.5439 \| 0.6555 \| 0.0 \| 0.5327 \| 0.6506 \| \| 0.038 \| 67.06 \| 19180 \| 0.1310 \| 0.3913 \| 0.5949 \| 0.5835 \| nan \| 0.5292 \| 0.6606 \| 0.0 \| 0.5201 \| 0.6537 \| \| 0.0428 \| 67.13 \| 19200 \| 0.1404 \| 0.3871 \| 0.5890 \| 0.5823 \| nan \| 0.5499 \| 0.6282 \| 0.0 \| 0.5373 \| 0.6239 \| \| 0.0307 \| 67.2 \| 19220 \| 0.1370 \| 0.3985 \| 0.6068 \| 0.6002 \| nan \| 0.5688 \| 0.6447 \| 0.0 \| 0.5542 \| 0.6414 \| \| 0.0586 \| 67.27 \| 19240 \| 0.1421 \| 0.3854 \| 0.5874 \| 0.5832 \| nan \| 0.5633 \| 0.6115 \| 0.0 \| 0.5477 \| 0.6085 \| \| 0.0524 \| 67.34 \| 19260 \| 0.1355 \| 0.4031 \| 0.6141 \| 0.6104 \| nan \| 0.5928 \| 0.6355 \| 0.0 \| 0.5778 \| 0.6315 \| \| 0.0399 \| 67.41 \| 19280 \| 0.1307 \| 0.4049 \| 0.6163 \| 0.6093 \| nan \| 0.5760 \| 0.6566 \| 0.0 \| 0.5622 \| 0.6526 \| \| 0.0532 \| 67.48 \| 19300 \| 0.1304 \| 0.4009 \| 0.6095 \| 0.5992 \| nan \| 0.5496 \| 0.6694 \| 0.0 \| 0.5375 \| 0.6654 \| \| 0.049 \| 67.55 \| 19320 \| 0.1303 \| 0.4013 \| 0.6109 \| 0.6023 \| nan \| 0.5613 \| 0.6605 \| 0.0 \| 0.5469 \| 0.6569 \| \| 0.0606 \| 67.62 \| 19340 \| 0.1310 \| 0.3943 \| 0.5998 \| 0.5910 \| nan \| 0.5490 \| 0.6507 \| 0.0 \| 0.5363 \| 0.6467 \| \| 0.052 \| 67.69 \| 19360 \| 0.1347 \| 0.3890 \| 0.5913 \| 0.5814 \| nan \| 0.5338 \| 0.6488 \| 0.0 \| 0.5222 \| 0.6450 \| \| 0.0269 \| 67.76 \| 19380 \| 0.1327 \| 0.4041 \| 0.6151 \| 0.6057 \| nan \| 0.5607 \| 0.6694 \| 0.0 \| 0.5467 \| 0.6655 \| \| 0.0532 \| 67.83 \| 19400 \| 0.1305 \| 0.4113 \| 0.6260 \| 0.6169 \| nan \| 0.5736 \| 0.6783 \| 0.0 \| 0.5595 \| 0.6744 \| \| 0.0577 \| 67.9 \| 19420 \| 0.1384 \| 0.3826 \| 0.5817 \| 0.5715 \| nan \| 0.5228 \| 0.6406 \| 0.0 \| 0.5101 \| 0.6378 \| \| 0.0584 \| 67.97 \| 19440 \| 0.1350 \| 0.3954 \| 0.6020 \| 0.5930 \| nan \| 0.5498 \| 0.6542 \| 0.0 \| 0.5355 \| 0.6507 \| \| 0.0514 \| 68.04 \| 19460 \| 0.1307 \| 0.4104 \| 0.6252 \| 0.6189 \| nan \| 0.5889 \| 0.6614 \| 0.0 \| 0.5742 \| 0.6570 \| \| 0.0626 \| 68.11 \| 19480 \| 0.1332 \| 0.3916 \| 0.5960 \| 0.5881 \| nan \| 0.5504 \| 0.6417 \| 0.0 \| 0.5372 \| 0.6376 \| \| 0.0406 \| 68.18 \| 19500 \| 0.1278 \| 0.4086 \| 0.6216 \| 0.6118 \| nan \| 0.5645 \| 0.6788 \| 0.0 \| 0.5528 \| 0.6732 \| \| 0.0402 \| 68.25 \| 19520 \| 0.1327 \| 0.4012 \| 0.6108 \| 0.6031 \| nan \| 0.5660 \| 0.6556 \| 0.0 \| 0.5522 \| 0.6514 \| \| 0.037 \| 68.32 \| 19540 \| 0.1390 \| 0.3915 \| 0.5958 \| 0.5871 \| nan \| 0.5455 \| 0.6462 \| 0.0 \| 0.5309 \| 0.6435 \| \| 0.0443 \| 68.39 \| 19560 \| 0.1389 \| 0.3892 \| 0.5924 \| 0.5844 \| nan \| 0.5466 \| 0.6382 \| 0.0 \| 0.5323 \| 0.6353 \| \| 0.0505 \| 68.46 \| 19580 \| 0.1295 \| 0.3994 \| 0.6069 \| 0.5951 \| nan \| 0.5386 \| 0.6753 \| 0.0 \| 0.5275 \| 0.6707 \| \| 0.0406 \| 68.53 \| 19600 \| 0.1301 \| 0.3996 \| 0.6071 \| 0.5958 \| nan \| 0.5420 \| 0.6721 \| 0.0 \| 0.5322 \| 0.6666 \| \| 0.0547 \| 68.6 \| 19620 \| 0.1296 \| 0.4008 \| 0.6091 \| 0.6004 \| nan \| 0.5588 \| 0.6594 \| 0.0 \| 0.5469 \| 0.6555 \| \| 0.0524 \| 68.67 \| 19640 \| 0.1277 \| 0.4142 \| 0.6303 \| 0.6233 \| nan \| 0.5899 \| 0.6707 \| 0.0 \| 0.5755 \| 0.6670 \| \| 0.0577 \| 68.74 \| 19660 \| 0.1322 \| 0.3975 \| 0.6050 \| 0.6001 \| nan \| 0.5768 \| 0.6332 \| 0.0 \| 0.5632 \| 0.6294 \| \| 0.0336 \| 68.81 \| 19680 \| 0.1286 \| 0.4058 \| 0.6168 \| 0.6064 \| nan \| 0.5563 \| 0.6774 \| 0.0 \| 0.5434 \| 0.6740 \| \| 0.0304 \| 68.88 \| 19700 \| 0.1258 \| 0.4094 \| 0.6223 \| 0.6137 \| nan \| 0.5724 \| 0.6723 \| 0.0 \| 0.5604 \| 0.6678 \| \| 0.0383 \| 68.95 \| 19720 \| 0.1304 \| 0.4000 \| 0.6087 \| 0.6014 \| nan \| 0.5664 \| 0.6511 \| 0.0 \| 0.5523 \| 0.6477 \| \| 0.0391 \| 69.02 \| 19740 \| 0.1283 \| 0.4009 \| 0.6101 \| 0.6011 \| nan \| 0.5582 \| 0.6621 \| 0.0 \| 0.5444 \| 0.6583 \| \| 0.0466 \| 69.09 \| 19760 \| 0.1284 \| 0.4000 \| 0.6081 \| 0.5982 \| nan \| 0.5507 \| 0.6656 \| 0.0 \| 0.5387 \| 0.6615 \| \| 0.0631 \| 69.16 \| 19780 \| 0.1464 \| 0.3725 \| 0.5666 \| 0.5612 \| nan \| 0.5353 \| 0.5979 \| 0.0 \| 0.5221 \| 0.5954 \| \| 0.0438 \| 69.23 \| 19800 \| 0.1353 \| 0.3925 \| 0.5967 \| 0.5864 \| nan \| 0.5371 \| 0.6564 \| 0.0 \| 0.5252 \| 0.6524 \| \| 0.0327 \| 69.3 \| 19820 \| 0.1283 \| 0.4058 \| 0.6177 \| 0.6077 \| nan \| 0.5602 \| 0.6752 \| 0.0 \| 0.5480 \| 0.6693 \| \| 0.0555 \| 69.37 \| 19840 \| 0.1258 \| 0.4101 \| 0.6244 \| 0.6165 \| nan \| 0.5787 \| 0.6702 \| 0.0 \| 0.5643 \| 0.6661 \| \| 0.0504 \| 69.44 \| 19860 \| 0.1280 \| 0.4038 \| 0.6148 \| 0.6077 \| nan \| 0.5739 \| 0.6557 \| 0.0 \| 0.5598 \| 0.6515 \| \| 0.037 \| 69.51 \| 19880 \| 0.1316 \| 0.4001 \| 0.6089 \| 0.6006 \| nan \| 0.5610 \| 0.6567 \| 0.0 \| 0.5472 \| 0.6530 \| \| 0.0705 \| 69.58 \| 19900 \| 0.1313 \| 0.4062 \| 0.6179 \| 0.6071 \| nan \| 0.5552 \| 0.6807 \| 0.0 \| 0.5415 \| 0.6772 \| \| 0.0336 \| 69.65 \| 19920 \| 0.1310 \| 0.4024 \| 0.6112 \| 0.6003 \| nan \| 0.5479 \| 0.6746 \| 0.0 \| 0.5369 \| 0.6702 \| \| 0.058 \| 69.72 \| 19940 \| 0.1318 \| 0.4051 \| 0.6159 \| 0.6062 \| nan \| 0.5598 \| 0.6721 \| 0.0 \| 0.5469 \| 0.6685 \| \| 0.0476 \| 69.79 \| 19960 \| 0.1279 \| 0.4017 \| 0.6107 \| 0.6001 \| nan \| 0.5493 \| 0.6720 \| 0.0 \| 0.5371 \| 0.6679 \| \| 0.0533 \| 69.86 \| 19980 \| 0.1345 \| 0.3896 \| 0.5923 \| 0.5803 \| nan \| 0.5227 \| 0.6619 \| 0.0 \| 0.5102 \| 0.6585 \| \| 0.0515 \| 69.93 \| 20000 \| 0.1283 \| 0.3967 \| 0.6035 \| 0.5931 \| nan \| 0.5437 \| 0.6632 \| 0.0 \| 0.5305 \| 0.6596 \| \| 0.0469 \| 70.0 \| 20020 \| 0.1314 \| 0.4017 \| 0.6114 \| 0.6025 \| nan \| 0.5597 \| 0.6631 \| 0.0 \| 0.5455 \| 0.6597 \| \| 0.0507 \| 70.07 \| 20040 \| 0.1325 \| 0.3937 \| 0.5987 \| 0.5901 \| nan \| 0.5487 \| 0.6488 \| 0.0 \| 0.5356 \| 0.6456 \| \| 0.0592 \| 70.14 \| 20060 \| 0.1292 \| 0.4006 \| 0.6082 \| 0.5954 \| nan \| 0.5343 \| 0.6822 \| 0.0 \| 0.5223 \| 0.6794 \| \| 0.0406 \| 70.21 \| 20080 \| 0.1273 \| 0.4125 \| 0.6271 \| 0.6189 \| nan \| 0.5794 \| 0.6748 \| 0.0 \| 0.5663 \| 0.6713 \| \| 0.087 \| 70.28 \| 20100 \| 0.1337 \| 0.3938 \| 0.5985 \| 0.5872 \| nan \| 0.5333 \| 0.6638 \| 0.0 \| 0.5214 \| 0.6601 \| \| 0.0517 \| 70.35 \| 20120 \| 0.1344 \| 0.3940 \| 0.5998 \| 0.5927 \| nan \| 0.5590 \| 0.6405 \| 0.0 \| 0.5444 \| 0.6377 \| \| 0.0367 \| 70.42 \| 20140 \| 0.1299 \| 0.4078 \| 0.6209 \| 0.6131 \| nan \| 0.5757 \| 0.6662 \| 0.0 \| 0.5608 \| 0.6627 \| \| 0.0399 \| 70.49 \| 20160 \| 0.1349 \| 0.4007 \| 0.6090 \| 0.6000 \| nan \| 0.5571 \| 0.6608 \| 0.0 \| 0.5447 \| 0.6573 \| \| 0.0467 \| 70.56 \| 20180 \| 0.1321 \| 0.4002 \| 0.6083 \| 0.5996 \| nan \| 0.5582 \| 0.6584 \| 0.0 \| 0.5451 \| 0.6555 \| \| 0.062 \| 70.63 \| 20200 \| 0.1352 \| 0.3914 \| 0.5957 \| 0.5867 \| nan \| 0.5438 \| 0.6477 \| 0.0 \| 0.5288 \| 0.6453 \| \| 0.0731 \| 70.7 \| 20220 \| 0.1338 \| 0.3886 \| 0.5906 \| 0.5810 \| nan \| 0.5348 \| 0.6464 \| 0.0 \| 0.5229 \| 0.6429 \| \| 0.0468 \| 70.77 \| 20240 \| 0.1322 \| 0.3952 \| 0.6006 \| 0.5862 \| nan \| 0.5174 \| 0.6839 \| 0.0 \| 0.5075 \| 0.6781 \| \| 0.0408 \| 70.84 \| 20260 \| 0.1342 \| 0.3924 \| 0.5970 \| 0.5840 \| nan \| 0.5221 \| 0.6718 \| 0.0 \| 0.5104 \| 0.6669 \| \| 0.0619 \| 70.91 \| 20280 \| 0.1265 \| 0.4080 \| 0.6202 \| 0.6109 \| nan \| 0.5664 \| 0.6740 \| 0.0 \| 0.5543 \| 0.6696 \| \| 0.0495 \| 70.98 \| 20300 \| 0.1316 \| 0.3940 \| 0.5985 \| 0.5875 \| nan \| 0.5353 \| 0.6617 \| 0.0 \| 0.5239 \| 0.6582 \| \| 0.0412 \| 71.05 \| 20320 \| 0.1300 \| 0.4106 \| 0.6241 \| 0.6137 \| nan \| 0.5640 \| 0.6841 \| 0.0 \| 0.5523 \| 0.6795 \| \| 0.0369 \| 71.12 \| 20340 \| 0.1322 \| 0.3999 \| 0.6072 \| 0.5963 \| nan \| 0.5444 \| 0.6699 \| 0.0 \| 0.5344 \| 0.6654 \| \| 0.0461 \| 71.19 \| 20360 \| 0.1339 \| 0.3798 \| 0.5763 \| 0.5636 \| nan \| 0.5030 \| 0.6496 \| 0.0 \| 0.4933 \| 0.6460 \| \| 0.0468 \| 71.26 \| 20380 \| 0.1298 \| 0.3899 \| 0.5924 \| 0.5817 \| nan \| 0.5304 \| 0.6544 \| 0.0 \| 0.5200 \| 0.6498 \| \| 0.038 \| 71.33 \| 20400 \| 0.1381 \| 0.3826 \| 0.5811 \| 0.5718 \| nan \| 0.5272 \| 0.6350 \| 0.0 \| 0.5165 \| 0.6314 \| \| 0.045 \| 71.4 \| 20420 \| 0.1348 \| 0.3822 \| 0.5799 \| 0.5682 \| nan \| 0.5126 \| 0.6472 \| 0.0 \| 0.5047 \| 0.6419 \| \| 0.0574 \| 71.47 \| 20440 \| 0.1331 \| 0.3852 \| 0.5847 \| 0.5735 \| nan \| 0.5203 \| 0.6490 \| 0.0 \| 0.5122 \| 0.6435 \| \| 0.0433 \| 71.54 \| 20460 \| 0.1299 \| 0.3994 \| 0.6066 \| 0.5968 \| nan \| 0.5501 \| 0.6632 \| 0.0 \| 0.5389 \| 0.6594 \| \| 0.0638 \| 71.61 \| 20480 \| 0.1334 \| 0.4066 \| 0.6189 \| 0.6152 \| nan \| 0.5974 \| 0.6404 \| 0.0 \| 0.5825 \| 0.6374 \| \| 0.0322 \| 71.68 \| 20500 \| 0.1359 \| 0.4104 \| 0.6236 \| 0.6161 \| nan \| 0.5802 \| 0.6671 \| 0.0 \| 0.5681 \| 0.6630 \| \| 0.0327 \| 71.75 \| 20520 \| 0.1412 \| 0.3886 \| 0.5915 \| 0.5878 \| nan \| 0.5701 \| 0.6128 \| 0.0 \| 0.5553 \| 0.6107 \| \| 0.0499 \| 71.82 \| 20540 \| 0.1322 \| 0.4036 \| 0.6132 \| 0.6016 \| nan \| 0.5461 \| 0.6803 \| 0.0 \| 0.5350 \| 0.6758 \| \| 0.0697 \| 71.89 \| 20560 \| 0.1274 \| 0.3984 \| 0.6057 \| 0.5919 \| nan \| 0.5258 \| 0.6856 \| 0.0 \| 0.5163 \| 0.6790 \| \| 0.0682 \| 71.96 \| 20580 \| 0.1347 \| 0.3877 \| 0.5891 \| 0.5782 \| nan \| 0.5260 \| 0.6523 \| 0.0 \| 0.5150 \| 0.6482 \| \| 0.0541 \| 72.03 \| 20600 \| 0.1352 \| 0.3900 \| 0.5928 \| 0.5837 \| nan \| 0.5406 \| 0.6450 \| 0.0 \| 0.5273 \| 0.6428 \| \| 0.054 \| 72.1 \| 20620 \| 0.1345 \| 0.3975 \| 0.6047 \| 0.6001 \| nan \| 0.5781 \| 0.6313 \| 0.0 \| 0.5634 \| 0.6291 \| \| 0.0468 \| 72.17 \| 20640 \| 0.1301 \| 0.3954 \| 0.5999 \| 0.5881 \| nan \| 0.5318 \| 0.6681 \| 0.0 \| 0.5218 \| 0.6645 \| \| 0.0502 \| 72.24 \| 20660 \| 0.1278 \| 0.3941 \| 0.5984 \| 0.5897 \| nan \| 0.5481 \| 0.6487 \| 0.0 \| 0.5370 \| 0.6453 \| \| 0.0407 \| 72.31 \| 20680 \| 0.1241 \| 0.4099 \| 0.6230 \| 0.6107 \| nan \| 0.5517 \| 0.6943 \| 0.0 \| 0.5439 \| 0.6859 \| \| 0.0268 \| 72.38 \| 20700 \| 0.1253 \| 0.4153 \| 0.6313 \| 0.6227 \| nan \| 0.5819 \| 0.6807 \| 0.0 \| 0.5728 \| 0.6731 \| \| 0.0513 \| 72.45 \| 20720 \| 0.1253 \| 0.4097 \| 0.6230 \| 0.6144 \| nan \| 0.5730 \| 0.6731 \| 0.0 \| 0.5614 \| 0.6678 \| \| 0.0414 \| 72.52 \| 20740 \| 0.1278 \| 0.4098 \| 0.6230 \| 0.6163 \| nan \| 0.5842 \| 0.6618 \| 0.0 \| 0.5729 \| 0.6565 \| \| 0.0338 \| 72.59 \| 20760 \| 0.1303 \| 0.4045 \| 0.6148 \| 0.6065 \| nan \| 0.5664 \| 0.6633 \| 0.0 \| 0.5542 \| 0.6593 \| \| 0.0341 \| 72.66 \| 20780 \| 0.1319 \| 0.4048 \| 0.6156 \| 0.6073 \| nan \| 0.5678 \| 0.6634 \| 0.0 \| 0.5543 \| 0.6600 \| \| 0.0833 \| 72.73 \| 20800 \| 0.1276 \| 0.4023 \| 0.6111 \| 0.5985 \| nan \| 0.5384 \| 0.6837 \| 0.0 \| 0.5289 \| 0.6782 \| \| 0.0703 \| 72.8 \| 20820 \| 0.1229 \| 0.4153 \| 0.6308 \| 0.6173 \| nan \| 0.5530 \| 0.7087 \| 0.0 \| 0.5436 \| 0.7022 \| \| 0.0637 \| 72.87 \| 20840 \| 0.1359 \| 0.3952 \| 0.6011 \| 0.5966 \| nan \| 0.5747 \| 0.6275 \| 0.0 \| 0.5611 \| 0.6244 \| \| 0.0407 \| 72.94 \| 20860 \| 0.1295 \| 0.4040 \| 0.6140 \| 0.6052 \| nan \| 0.5631 \| 0.6648 \| 0.0 \| 0.5515 \| 0.6605 \| \| 0.0472 \| 73.01 \| 20880 \| 0.1296 \| 0.4063 \| 0.6169 \| 0.6073 \| nan \| 0.5611 \| 0.6727 \| 0.0 \| 0.5507 \| 0.6683 \| \| 0.0446 \| 73.08 \| 20900 \| 0.1275 \| 0.4123 \| 0.6269 \| 0.6161 \| nan \| 0.5644 \| 0.6895 \| 0.0 \| 0.5512 \| 0.6857 \| \| 0.0425 \| 73.15 \| 20920 \| 0.1234 \| 0.4294 \| 0.6535 \| 0.6466 \| nan \| 0.6136 \| 0.6933 \| 0.0 \| 0.6005 \| 0.6878 \| \| 0.062 \| 73.22 \| 20940 \| 0.1307 \| 0.4085 \| 0.6212 \| 0.6162 \| nan \| 0.5923 \| 0.6500 \| 0.0 \| 0.5788 \| 0.6467 \| \| 0.063 \| 73.29 \| 20960 \| 0.1336 \| 0.3908 \| 0.5938 \| 0.5852 \| nan \| 0.5444 \| 0.6432 \| 0.0 \| 0.5324 \| 0.6401 \| \| 0.0607 \| 73.36 \| 20980 \| 0.1282 \| 0.4006 \| 0.6093 \| 0.5981 \| nan \| 0.5447 \| 0.6739 \| 0.0 \| 0.5339 \| 0.6677 \| \| 0.0634 \| 73.43 \| 21000 \| 0.1284 \| 0.4121 \| 0.6270 \| 0.6207 \| nan \| 0.5908 \| 0.6632 \| 0.0 \| 0.5772 \| 0.6590 \| \| 0.0321 \| 73.5 \| 21020 \| 0.1250 \| 0.4242 \| 0.6450 \| 0.6333 \| nan \| 0.5776 \| 0.7123 \| 0.0 \| 0.5653 \| 0.7074 \| \| 0.0469 \| 73.57 \| 21040 \| 0.1280 \| 0.4010 \| 0.6100 \| 0.5963 \| nan \| 0.5309 \| 0.6891 \| 0.0 \| 0.5224 \| 0.6807 \| \| 0.0363 \| 73.64 \| 21060 \| 0.1423 \| 0.3809 \| 0.5795 \| 0.5729 \| nan \| 0.5415 \| 0.6175 \| 0.0 \| 0.5279 \| 0.6146 \| \| 0.0618 \| 73.71 \| 21080 \| 0.1352 \| 0.4023 \| 0.6124 \| 0.6070 \| nan \| 0.5809 \| 0.6439 \| 0.0 \| 0.5661 \| 0.6409 \| \| 0.0451 \| 73.78 \| 21100 \| 0.1339 \| 0.3911 \| 0.5947 \| 0.5847 \| nan \| 0.5369 \| 0.6525 \| 0.0 \| 0.5236 \| 0.6497 \| \| 0.052 \| 73.85 \| 21120 \| 0.1332 \| 0.3994 \| 0.6072 \| 0.5983 \| nan \| 0.5558 \| 0.6587 \| 0.0 \| 0.5430 \| 0.6551 \| \| 0.0609 \| 73.92 \| 21140 \| 0.1358 \| 0.3974 \| 0.6042 \| 0.5963 \| nan \| 0.5584 \| 0.6501 \| 0.0 \| 0.5452 \| 0.6469 \| \| 0.0317 \| 73.99 \| 21160 \| 0.1330 \| 0.4011 \| 0.6102 \| 0.6024 \| nan \| 0.5649 \| 0.6556 \| 0.0 \| 0.5512 \| 0.6520 \| \| 0.045 \| 74.06 \| 21180 \| 0.1327 \| 0.4006 \| 0.6110 \| 0.6044 \| nan \| 0.5730 \| 0.6491 \| 0.0 \| 0.5557 \| 0.6461 \| \| 0.0502 \| 74.13 \| 21200 \| 0.1305 \| 0.4103 \| 0.6253 \| 0.6185 \| nan \| 0.5859 \| 0.6647 \| 0.0 \| 0.5698 \| 0.6610 \| \| 0.0479 \| 74.2 \| 21220 \| 0.1312 \| 0.4024 \| 0.6115 \| 0.6013 \| nan \| 0.5524 \| 0.6706 \| 0.0 \| 0.5422 \| 0.6649 \| \| 0.0429 \| 74.27 \| 21240 \| 0.1316 \| 0.4032 \| 0.6130 \| 0.6013 \| nan \| 0.5457 \| 0.6802 \| 0.0 \| 0.5356 \| 0.6740 \| \| 0.0228 \| 74.34 \| 21260 \| 0.1289 \| 0.4093 \| 0.6223 \| 0.6130 \| nan \| 0.5686 \| 0.6760 \| 0.0 \| 0.5559 \| 0.6721 \| \| 0.046 \| 74.41 \| 21280 \| 0.1306 \| 0.4117 \| 0.6260 \| 0.6165 \| nan \| 0.5710 \| 0.6810 \| 0.0 \| 0.5587 \| 0.6765 \| \| 0.0512 \| 74.48 \| 21300 \| 0.1318 \| 0.4072 \| 0.6188 \| 0.6090 \| nan \| 0.5618 \| 0.6759 \| 0.0 \| 0.5515 \| 0.6702 \| \| 0.0423 \| 74.55 \| 21320 \| 0.1265 \| 0.4172 \| 0.6356 \| 0.6268 \| nan \| 0.5847 \| 0.6865 \| 0.0 \| 0.5732 \| 0.6783 \| \| 0.0475 \| 74.62 \| 21340 \| 0.1390 \| 0.3906 \| 0.5965 \| 0.5917 \| nan \| 0.5690 \| 0.6241 \| 0.0 \| 0.5502 \| 0.6215 \| \| 0.0332 \| 74.69 \| 21360 \| 0.1335 \| 0.3968 \| 0.6045 \| 0.5978 \| nan \| 0.5659 \| 0.6430 \| 0.0 \| 0.5509 \| 0.6394 \| \| 0.0352 \| 74.76 \| 21380 \| 0.1243 \| 0.4161 \| 0.6337 \| 0.6220 \| nan \| 0.5663 \| 0.7011 \| 0.0 \| 0.5549 \| 0.6935 \| \| 0.0451 \| 74.83 \| 21400 \| 0.1339 \| 0.3971 \| 0.6044 \| 0.5985 \| nan \| 0.5703 \| 0.6384 \| 0.0 \| 0.5559 \| 0.6353 \| \| 0.0416 \| 74.9 \| 21420 \| 0.1276 \| 0.4103 \| 0.6241 \| 0.6137 \| nan \| 0.5636 \| 0.6846 \| 0.0 \| 0.5521 \| 0.6788 \| \| 0.0412 \| 74.97 \| 21440 \| 0.1230 \| 0.4082 \| 0.6201 \| 0.6079 \| nan \| 0.5501 \| 0.6900 \| 0.0 \| 0.5398 \| 0.6848 \| \| 0.045 \| 75.03 \| 21460 \| 0.1242 \| 0.4154 \| 0.6321 \| 0.6249 \| nan \| 0.5903 \| 0.6738 \| 0.0 \| 0.5771 \| 0.6692 \| \| 0.0531 \| 75.1 \| 21480 \| 0.1232 \| 0.4053 \| 0.6165 \| 0.6048 \| nan \| 0.5484 \| 0.6847 \| 0.0 \| 0.5373 \| 0.6785 \| \| 0.052 \| 75.17 \| 21500 \| 0.1280 \| 0.4013 \| 0.6102 \| 0.6008 \| nan \| 0.5561 \| 0.6642 \| 0.0 \| 0.5435 \| 0.6603 \| \| 0.0443 \| 75.24 \| 21520 \| 0.1266 \| 0.4048 \| 0.6157 \| 0.6026 \| nan \| 0.5398 \| 0.6916 \| 0.0 \| 0.5289 \| 0.6856 \| \| 0.0419 \| 75.31 \| 21540 \| 0.1272 \| 0.4029 \| 0.6130 \| 0.6015 \| nan \| 0.5464 \| 0.6796 \| 0.0 \| 0.5342 \| 0.6746 \| \| 0.0372 \| 75.38 \| 21560 \| 0.1249 \| 0.4158 \| 0.6328 \| 0.6220 \| nan \| 0.5703 \| 0.6952 \| 0.0 \| 0.5581 \| 0.6894 \| \| 0.0643 \| 75.45 \| 21580 \| 0.1266 \| 0.4141 \| 0.6304 \| 0.6203 \| nan \| 0.5719 \| 0.6890 \| 0.0 \| 0.5573 \| 0.6849 \| \| 0.0559 \| 75.52 \| 21600 \| 0.1250 \| 0.4058 \| 0.6164 \| 0.6033 \| nan \| 0.5407 \| 0.6921 \| 0.0 \| 0.5311 \| 0.6863 \| \| 0.0358 \| 75.59 \| 21620 \| 0.1322 \| 0.4051 \| 0.6163 \| 0.6066 \| nan \| 0.5598 \| 0.6728 \| 0.0 \| 0.5462 \| 0.6691 \| \| 0.0394 \| 75.66 \| 21640 \| 0.1330 \| 0.3984 \| 0.6061 \| 0.5958 \| nan \| 0.5468 \| 0.6653 \| 0.0 \| 0.5338 \| 0.6614 \| \| 0.0598 \| 75.73 \| 21660 \| 0.1284 \| 0.4062 \| 0.6182 \| 0.6094 \| nan \| 0.5673 \| 0.6691 \| 0.0 \| 0.5543 \| 0.6645 \| \| 0.0364 \| 75.8 \| 21680 \| 0.1252 \| 0.4079 \| 0.6203 \| 0.6100 \| nan \| 0.5606 \| 0.6799 \| 0.0 \| 0.5504 \| 0.6734 \| \| 0.0478 \| 75.87 \| 21700 \| 0.1279 \| 0.4115 \| 0.6258 \| 0.6168 \| nan \| 0.5740 \| 0.6775 \| 0.0 \| 0.5619 \| 0.6726 \| \| 0.0306 \| 75.94 \| 21720 \| 0.1306 \| 0.4225 \| 0.6435 \| 0.6407 \| nan \| 0.6276 \| 0.6593 \| 0.0 \| 0.6117 \| 0.6558 \| \| 0.0431 \| 76.01 \| 21740 \| 0.1295 \| 0.4091 \| 0.6218 \| 0.6135 \| nan \| 0.5740 \| 0.6695 \| 0.0 \| 0.5618 \| 0.6654 \| \| 0.0539 \| 76.08 \| 21760 \| 0.1326 \| 0.3949 \| 0.5999 \| 0.5857 \| nan \| 0.5179 \| 0.6818 \| 0.0 \| 0.5089 \| 0.6758 \| \| 0.0637 \| 76.15 \| 21780 \| 0.1379 \| 0.4052 \| 0.6168 \| 0.6083 \| nan \| 0.5676 \| 0.6660 \| 0.0 \| 0.5523 \| 0.6632 \| \| 0.0624 \| 76.22 \| 21800 \| 0.1338 \| 0.3944 \| 0.5994 \| 0.5892 \| nan \| 0.5405 \| 0.6583 \| 0.0 \| 0.5279 \| 0.6551 \| \| 0.0442 \| 76.29 \| 21820 \| 0.1301 \| 0.4035 \| 0.6139 \| 0.6036 \| nan \| 0.5544 \| 0.6733 \| 0.0 \| 0.5430 \| 0.6675 \| \| 0.0366 \| 76.36 \| 21840 \| 0.1292 \| 0.4165 \| 0.6340 \| 0.6257 \| nan \| 0.5858 \| 0.6822 \| 0.0 \| 0.5718 \| 0.6776 \| \| 0.0453 \| 76.43 \| 21860 \| 0.1269 \| 0.4122 \| 0.6276 \| 0.6184 \| nan \| 0.5745 \| 0.6807 \| 0.0 \| 0.5613 \| 0.6754 \| \| 0.0363 \| 76.5 \| 21880 \| 0.1256 \| 0.4151 \| 0.6318 \| 0.6228 \| nan \| 0.5800 \| 0.6835 \| 0.0 \| 0.5673 \| 0.6781 \| \| 0.0313 \| 76.57 \| 21900 \| 0.1279 \| 0.4064 \| 0.6185 \| 0.6092 \| nan \| 0.5652 \| 0.6718 \| 0.0 \| 0.5525 \| 0.6668 \| \| 0.037 \| 76.64 \| 21920 \| 0.1283 \| 0.4153 \| 0.6319 \| 0.6247 \| nan \| 0.5903 \| 0.6736 \| 0.0 \| 0.5760 \| 0.6699 \| \| 0.0594 \| 76.71 \| 21940 \| 0.1316 \| 0.4008 \| 0.6097 \| 0.5987 \| nan \| 0.5465 \| 0.6729 \| 0.0 \| 0.5337 \| 0.6686 \| \| 0.0367 \| 76.78 \| 21960 \| 0.1358 \| 0.4039 \| 0.6149 \| 0.6073 \| nan \| 0.5710 \| 0.6589 \| 0.0 \| 0.5566 \| 0.6550 \| \| 0.0706 \| 76.85 \| 21980 \| 0.1323 \| 0.4012 \| 0.6101 \| 0.5992 \| nan \| 0.5470 \| 0.6733 \| 0.0 \| 0.5357 \| 0.6678 \| \| 0.0365 \| 76.92 \| 22000 \| 0.1348 \| 0.4044 \| 0.6157 \| 0.6093 \| nan \| 0.5786 \| 0.6529 \| 0.0 \| 0.5627 \| 0.6505 \| \| 0.0505 \| 76.99 \| 22020 \| 0.1331 \| 0.3992 \| 0.6075 \| 0.6004 \| nan \| 0.5660 \| 0.6491 \| 0.0 \| 0.5522 \| 0.6455 \| \| 0.0686 \| 77.06 \| 22040 \| 0.1312 \| 0.4068 \| 0.6193 \| 0.6132 \| nan \| 0.5842 \| 0.6543 \| 0.0 \| 0.5688 \| 0.6517 \| \| 0.0622 \| 77.13 \| 22060 \| 0.1229 \| 0.4144 \| 0.6305 \| 0.6200 \| nan \| 0.5698 \| 0.6911 \| 0.0 \| 0.5576 \| 0.6855 \| \| 0.0569 \| 77.2 \| 22080 \| 0.1279 \| 0.4063 \| 0.6179 \| 0.6087 \| nan \| 0.5648 \| 0.6709 \| 0.0 \| 0.5532 \| 0.6657 \| \| 0.0493 \| 77.27 \| 22100 \| 0.1378 \| 0.3819 \| 0.5808 \| 0.5727 \| nan \| 0.5343 \| 0.6272 \| 0.0 \| 0.5224 \| 0.6235 \| \| 0.0668 \| 77.34 \| 22120 \| 0.1307 \| 0.4046 \| 0.6150 \| 0.6066 \| nan \| 0.5666 \| 0.6635 \| 0.0 \| 0.5546 \| 0.6591 \| \| 0.0427 \| 77.41 \| 22140 \| 0.1302 \| 0.4081 \| 0.6208 \| 0.6116 \| nan \| 0.5676 \| 0.6740 \| 0.0 \| 0.5561 \| 0.6682 \| \| 0.0675 \| 77.48 \| 22160 \| 0.1269 \| 0.4046 \| 0.6148 \| 0.6034 \| nan \| 0.5491 \| 0.6805 \| 0.0 \| 0.5389 \| 0.6750 \| \| 0.0414 \| 77.55 \| 22180 \| 0.1250 \| 0.4155 \| 0.6320 \| 0.6237 \| nan \| 0.5840 \| 0.6800 \| 0.0 \| 0.5720 \| 0.6746 \| \| 0.056 \| 77.62 \| 22200 \| 0.1344 \| 0.4026 \| 0.6128 \| 0.6064 \| nan \| 0.5757 \| 0.6499 \| 0.0 \| 0.5612 \| 0.6467 \| \| 0.0407 \| 77.69 \| 22220 \| 0.1326 \| 0.4118 \| 0.6271 \| 0.6210 \| nan \| 0.5917 \| 0.6625 \| 0.0 \| 0.5759 \| 0.6595 \| \| 0.0389 \| 77.76 \| 22240 \| 0.1333 \| 0.4030 \| 0.6136 \| 0.6083 \| nan \| 0.5831 \| 0.6441 \| 0.0 \| 0.5685 \| 0.6405 \| \| 0.0608 \| 77.83 \| 22260 \| 0.1288 \| 0.4213 \| 0.6408 \| 0.6305 \| nan \| 0.5812 \| 0.7004 \| 0.0 \| 0.5685 \| 0.6955 \| \| 0.0261 \| 77.9 \| 22280 \| 0.1290 \| 0.4125 \| 0.6272 \| 0.6188 \| nan \| 0.5788 \| 0.6756 \| 0.0 \| 0.5660 \| 0.6714 \| \| 0.0723 \| 77.97 \| 22300 \| 0.1319 \| 0.4067 \| 0.6185 \| 0.6094 \| nan \| 0.5657 \| 0.6713 \| 0.0 \| 0.5536 \| 0.6667 \| \| 0.0395 \| 78.04 \| 22320 \| 0.1303 \| 0.4193 \| 0.6380 \| 0.6303 \| nan \| 0.5940 \| 0.6819 \| 0.0 \| 0.5809 \| 0.6771 \| \| 0.0643 \| 78.11 \| 22340 \| 0.1276 \| 0.4234 \| 0.6436 \| 0.6335 \| nan \| 0.5854 \| 0.7017 \| 0.0 \| 0.5728 \| 0.6974 \| \| 0.0358 \| 78.18 \| 22360 \| 0.1347 \| 0.4090 \| 0.6218 \| 0.6156 \| nan \| 0.5859 \| 0.6576 \| 0.0 \| 0.5724 \| 0.6546 \| \| 0.031 \| 78.25 \| 22380 \| 0.1302 \| 0.4126 \| 0.6277 \| 0.6197 \| nan \| 0.5813 \| 0.6742 \| 0.0 \| 0.5694 \| 0.6684 \| \| 0.052 \| 78.32 \| 22400 \| 0.1287 \| 0.4118 \| 0.6267 \| 0.6187 \| nan \| 0.5805 \| 0.6730 \| 0.0 \| 0.5686 \| 0.6668 \| \| 0.0456 \| 78.39 \| 22420 \| 0.1299 \| 0.4139 \| 0.6295 \| 0.6216 \| nan \| 0.5838 \| 0.6752 \| 0.0 \| 0.5720 \| 0.6698 \| \| 0.0418 \| 78.46 \| 22440 \| 0.1270 \| 0.4102 \| 0.6235 \| 0.6144 \| nan \| 0.5712 \| 0.6757 \| 0.0 \| 0.5588 \| 0.6717 \| \| 0.0554 \| 78.53 \| 22460 \| 0.1304 \| 0.4086 \| 0.6208 \| 0.6112 \| nan \| 0.5650 \| 0.6767 \| 0.0 \| 0.5533 \| 0.6723 \| \| 0.0708 \| 78.6 \| 22480 \| 0.1304 \| 0.4131 \| 0.6286 \| 0.6208 \| nan \| 0.5836 \| 0.6735 \| 0.0 \| 0.5699 \| 0.6694 \| \| 0.0296 \| 78.67 \| 22500 \| 0.1293 \| 0.4155 \| 0.6320 \| 0.6252 \| nan \| 0.5926 \| 0.6715 \| 0.0 \| 0.5801 \| 0.6663 \| \| 0.038 \| 78.74 \| 22520 \| 0.1298 \| 0.4101 \| 0.6232 \| 0.6148 \| nan \| 0.5748 \| 0.6716 \| 0.0 \| 0.5632 \| 0.6672 \| \| 0.055 \| 78.81 \| 22540 \| 0.1274 \| 0.4137 \| 0.6288 \| 0.6180 \| nan \| 0.5662 \| 0.6914 \| 0.0 \| 0.5554 \| 0.6858 \| \| 0.0344 \| 78.88 \| 22560 \| 0.1281 \| 0.4135 \| 0.6280 \| 0.6179 \| nan \| 0.5697 \| 0.6863 \| 0.0 \| 0.5602 \| 0.6804 \| \| 0.0408 \| 78.95 \| 22580 \| 0.1272 \| 0.4107 \| 0.6235 \| 0.6093 \| nan \| 0.5419 \| 0.7051 \| 0.0 \| 0.5319 \| 0.7003 \| \| 0.0648 \| 79.02 \| 22600 \| 0.1278 \| 0.3979 \| 0.6039 \| 0.5908 \| nan \| 0.5283 \| 0.6794 \| 0.0 \| 0.5190 \| 0.6746 \| \| 0.0444 \| 79.09 \| 22620 \| 0.1340 \| 0.3936 \| 0.5992 \| 0.5901 \| nan \| 0.5470 \| 0.6513 \| 0.0 \| 0.5321 \| 0.6487 \| \| 0.0376 \| 79.16 \| 22640 \| 0.1368 \| 0.3986 \| 0.6079 \| 0.5999 \| nan \| 0.5616 \| 0.6543 \| 0.0 \| 0.5437 \| 0.6521 \| \| 0.0768 \| 79.23 \| 22660 \| 0.1265 \| 0.4021 \| 0.6112 \| 0.5987 \| nan \| 0.5394 \| 0.6829 \| 0.0 \| 0.5273 \| 0.6791 \| \| 0.0432 \| 79.3 \| 22680 \| 0.1280 \| 0.4080 \| 0.6203 \| 0.6098 \| nan \| 0.5596 \| 0.6809 \| 0.0 \| 0.5467 \| 0.6773 \| \| 0.0477 \| 79.37 \| 22700 \| 0.1285 \| 0.4121 \| 0.6265 \| 0.6139 \| nan \| 0.5537 \| 0.6992 \| 0.0 \| 0.5409 \| 0.6954 \| \| 0.0634 \| 79.44 \| 22720 \| 0.1311 \| 0.4041 \| 0.6151 \| 0.6094 \| nan \| 0.5820 \| 0.6482 \| 0.0 \| 0.5676 \| 0.6447 \| \| 0.0322 \| 79.51 \| 22740 \| 0.1287 \| 0.4002 \| 0.6079 \| 0.5975 \| nan \| 0.5475 \| 0.6684 \| 0.0 \| 0.5362 \| 0.6643 \| \| 0.0401 \| 79.58 \| 22760 \| 0.1259 \| 0.4084 \| 0.6203 \| 0.6098 \| nan \| 0.5595 \| 0.6811 \| 0.0 \| 0.5478 \| 0.6775 \| \| 0.0386 \| 79.65 \| 22780 \| 0.1337 \| 0.4023 \| 0.6116 \| 0.6036 \| nan \| 0.5657 \| 0.6575 \| 0.0 \| 0.5526 \| 0.6542 \| \| 0.0336 \| 79.72 \| 22800 \| 0.1303 \| 0.4125 \| 0.6272 \| 0.6181 \| nan \| 0.5743 \| 0.6802 \| 0.0 \| 0.5611 \| 0.6765 \| \| 0.0417 \| 79.79 \| 22820 \| 0.1330 \| 0.4032 \| 0.6134 \| 0.6070 \| nan \| 0.5763 \| 0.6504 \| 0.0 \| 0.5627 \| 0.6469 \| \| 0.0582 \| 79.86 \| 22840 \| 0.1268 \| 0.4139 \| 0.6282 \| 0.6183 \| nan \| 0.5709 \| 0.6854 \| 0.0 \| 0.5613 \| 0.6803 \| \| 0.0399 \| 79.93 \| 22860 \| 0.1373 \| 0.3947 \| 0.5993 \| 0.5887 \| nan \| 0.5380 \| 0.6606 \| 0.0 \| 0.5267 \| 0.6574 \| \| 0.0632 \| 80.0 \| 22880 \| 0.1368 \| 0.4059 \| 0.6177 \| 0.6112 \| nan \| 0.5805 \| 0.6549 \| 0.0 \| 0.5655 \| 0.6523 \| \| 0.0389 \| 80.07 \| 22900 \| 0.1319 \| 0.4126 \| 0.6275 \| 0.6176 \| nan \| 0.5703 \| 0.6848 \| 0.0 \| 0.5569 \| 0.6811 \| \| 0.0442 \| 80.14 \| 22920 \| 0.1323 \| 0.4117 \| 0.6256 \| 0.6183 \| nan \| 0.5835 \| 0.6678 \| 0.0 \| 0.5710 \| 0.6640 \| \| 0.0357 \| 80.21 \| 22940 \| 0.1349 \| 0.4198 \| 0.6383 \| 0.6302 \| nan \| 0.5919 \| 0.6846 \| 0.0 \| 0.5774 \| 0.6821 \| \| 0.0423 \| 80.28 \| 22960 \| 0.1423 \| 0.3925 \| 0.5974 \| 0.5928 \| nan \| 0.5713 \| 0.6234 \| 0.0 \| 0.5562 \| 0.6214 \| \| 0.0498 \| 80.35 \| 22980 \| 0.1398 \| 0.3982 \| 0.6053 \| 0.5965 \| nan \| 0.5544 \| 0.6562 \| 0.0 \| 0.5417 \| 0.6529 \| \| 0.0612 \| 80.42 \| 23000 \| 0.1364 \| 0.3984 \| 0.6053 \| 0.5963 \| nan \| 0.5535 \| 0.6571 \| 0.0 \| 0.5426 \| 0.6527 \| \| 0.0408 \| 80.49 \| 23020 \| 0.1381 \| 0.3994 \| 0.6074 \| 0.5989 \| nan \| 0.5587 \| 0.6560 \| 0.0 \| 0.5456 \| 0.6527 \| \| 0.059 \| 80.56 \| 23040 \| 0.1391 \| 0.3871 \| 0.5885 \| 0.5794 \| nan \| 0.5360 \| 0.6410 \| 0.0 \| 0.5231 \| 0.6382 \| \| 0.0348 \| 80.63 \| 23060 \| 0.1414 \| 0.3866 \| 0.5877 \| 0.5791 \| nan \| 0.5383 \| 0.6370 \| 0.0 \| 0.5248 \| 0.6349 \| \| 0.0644 \| 80.7 \| 23080 \| 0.1388 \| 0.4017 \| 0.6105 \| 0.6000 \| nan \| 0.5499 \| 0.6712 \| 0.0 \| 0.5365 \| 0.6687 \| \| 0.0492 \| 80.77 \| 23100 \| 0.1416 \| 0.3878 \| 0.5897 \| 0.5841 \| nan \| 0.5576 \| 0.6219 \| 0.0 \| 0.5429 \| 0.6204 \| \| 0.0341 \| 80.84 \| 23120 \| 0.1310 \| 0.4121 \| 0.6269 \| 0.6189 \| nan \| 0.5806 \| 0.6732 \| 0.0 \| 0.5665 \| 0.6697 \| \| 0.0532 \| 80.91 \| 23140 \| 0.1325 \| 0.4010 \| 0.6095 \| 0.6019 \| nan \| 0.5656 \| 0.6534 \| 0.0 \| 0.5534 \| 0.6496 \| \| 0.0627 \| 80.98 \| 23160 \| 0.1349 \| 0.4024 \| 0.6117 \| 0.6025 \| nan \| 0.5589 \| 0.6645 \| 0.0 \| 0.5463 \| 0.6609 \| \| 0.0332 \| 81.05 \| 23180 \| 0.1328 \| 0.3964 \| 0.6035 \| 0.5929 \| nan \| 0.5421 \| 0.6650 \| 0.0 \| 0.5331 \| 0.6562 \| \| 0.0428 \| 81.12 \| 23200 \| 0.1223 \| 0.4180 \| 0.6361 \| 0.6220 \| nan \| 0.5545 \| 0.7177 \| 0.0 \| 0.5460 \| 0.7081 \| \| 0.0549 \| 81.19 \| 23220 \| 0.1257 \| 0.4225 \| 0.6427 \| 0.6324 \| nan \| 0.5833 \| 0.7021 \| 0.0 \| 0.5718 \| 0.6957 \| \| 0.0439 \| 81.26 \| 23240 \| 0.1307 \| 0.4168 \| 0.6343 \| 0.6276 \| nan \| 0.5956 \| 0.6730 \| 0.0 \| 0.5813 \| 0.6690 \| \| 0.034 \| 81.33 \| 23260 \| 0.1301 \| 0.3953 \| 0.6006 \| 0.5892 \| nan \| 0.5351 \| 0.6660 \| 0.0 \| 0.5239 \| 0.6619 \| \| 0.0329 \| 81.4 \| 23280 \| 0.1359 \| 0.4038 \| 0.6138 \| 0.6065 \| nan \| 0.5715 \| 0.6562 \| 0.0 \| 0.5589 \| 0.6526 \| \| 0.0452 \| 81.47 \| 23300 \| 0.1382 \| 0.4003 \| 0.6087 \| 0.6019 \| nan \| 0.5694 \| 0.6481 \| 0.0 \| 0.5564 \| 0.6446 \| \| 0.0373 \| 81.54 \| 23320 \| 0.1551 \| 0.3851 \| 0.5872 \| 0.5839 \| nan \| 0.5681 \| 0.6063 \| 0.0 \| 0.5515 \| 0.6037 \| \| 0.065 \| 81.61 \| 23340 \| 0.1439 \| 0.3916 \| 0.5964 \| 0.5873 \| nan \| 0.5438 \| 0.6491 \| 0.0 \| 0.5295 \| 0.6452 \| \| 0.0315 \| 81.68 \| 23360 \| 0.1339 \| 0.4128 \| 0.6285 \| 0.6203 \| nan \| 0.5815 \| 0.6755 \| 0.0 \| 0.5676 \| 0.6707 \| \| 0.049 \| 81.75 \| 23380 \| 0.1423 \| 0.3936 \| 0.5998 \| 0.5946 \| nan \| 0.5696 \| 0.6301 \| 0.0 \| 0.5537 \| 0.6272 \| \| 0.0667 \| 81.82 \| 23400 \| 0.1389 \| 0.4047 \| 0.6173 \| 0.6131 \| nan \| 0.5932 \| 0.6414 \| 0.0 \| 0.5765 \| 0.6377 \| \| 0.0344 \| 81.89 \| 23420 \| 0.1419 \| 0.4025 \| 0.6133 \| 0.6093 \| nan \| 0.5902 \| 0.6363 \| 0.0 \| 0.5743 \| 0.6332 \| \| 0.047 \| 81.96 \| 23440 \| 0.1386 \| 0.4020 \| 0.6119 \| 0.6044 \| nan \| 0.5681 \| 0.6557 \| 0.0 \| 0.5529 \| 0.6531 \| \| 0.0454 \| 82.03 \| 23460 \| 0.1369 \| 0.3993 \| 0.6072 \| 0.5982 \| nan \| 0.5552 \| 0.6592 \| 0.0 \| 0.5422 \| 0.6557 \| \| 0.0384 \| 82.1 \| 23480 \| 0.1395 \| 0.4028 \| 0.6129 \| 0.6046 \| nan \| 0.5651 \| 0.6606 \| 0.0 \| 0.5514 \| 0.6570 \| \| 0.0305 \| 82.17 \| 23500 \| 0.1370 \| 0.3989 \| 0.6070 \| 0.5990 \| nan \| 0.5609 \| 0.6532 \| 0.0 \| 0.5480 \| 0.6488 \| \| 0.0568 \| 82.24 \| 23520 \| 0.1343 \| 0.4053 \| 0.6166 \| 0.6078 \| nan \| 0.5660 \| 0.6672 \| 0.0 \| 0.5532 \| 0.6628 \| \| 0.0334 \| 82.31 \| 23540 \| 0.1357 \| 0.4091 \| 0.6224 \| 0.6137 \| nan \| 0.5720 \| 0.6729 \| 0.0 \| 0.5584 \| 0.6689 \| \| 0.0537 \| 82.38 \| 23560 \| 0.1394 \| 0.4009 \| 0.6111 \| 0.6059 \| nan \| 0.5809 \| 0.6414 \| 0.0 \| 0.5642 \| 0.6386 \| \| 0.0279 \| 82.45 \| 23580 \| 0.1371 \| 0.4030 \| 0.6140 \| 0.6072 \| nan \| 0.5744 \| 0.6536 \| 0.0 \| 0.5584 \| 0.6506 \| \| 0.0451 \| 82.52 \| 23600 \| 0.1349 \| 0.3994 \| 0.6079 \| 0.5996 \| nan \| 0.5600 \| 0.6557 \| 0.0 \| 0.5455 \| 0.6526 \| \| 0.0296 \| 82.59 \| 23620 \| 0.1382 \| 0.3891 \| 0.5918 \| 0.5826 \| nan \| 0.5389 \| 0.6446 \| 0.0 \| 0.5257 \| 0.6416 \| \| 0.046 \| 82.66 \| 23640 \| 0.1418 \| 0.3936 \| 0.5995 \| 0.5941 \| nan \| 0.5680 \| 0.6311 \| 0.0 \| 0.5516 \| 0.6290 \| \| 0.0264 \| 82.73 \| 23660 \| 0.1395 \| 0.3954 \| 0.6013 \| 0.5948 \| nan \| 0.5633 \| 0.6394 \| 0.0 \| 0.5479 \| 0.6383 \| \| 0.03 \| 82.8 \| 23680 \| 0.1385 \| 0.3869 \| 0.5876 \| 0.5791 \| nan \| 0.5387 \| 0.6365 \| 0.0 \| 0.5257 \| 0.6351 \| \| 0.0363 \| 82.87 \| 23700 \| 0.1318 \| 0.4097 \| 0.6237 \| 0.6155 \| nan \| 0.5761 \| 0.6713 \| 0.0 \| 0.5618 \| 0.6674 \| \| 0.0431 \| 82.94 \| 23720 \| 0.1385 \| 0.3955 \| 0.6026 \| 0.6002 \| nan \| 0.5886 \| 0.6165 \| 0.0 \| 0.5738 \| 0.6126 \| \| 0.0482 \| 83.01 \| 23740 \| 0.1362 \| 0.4077 \| 0.6211 \| 0.6167 \| nan \| 0.5953 \| 0.6469 \| 0.0 \| 0.5794 \| 0.6436 \| \| 0.0567 \| 83.08 \| 23760 \| 0.1360 \| 0.4009 \| 0.6102 \| 0.6055 \| nan \| 0.5832 \| 0.6372 \| 0.0 \| 0.5684 \| 0.6342 \| \| 0.0421 \| 83.15 \| 23780 \| 0.1339 \| 0.4012 \| 0.6105 \| 0.6024 \| nan \| 0.5635 \| 0.6576 \| 0.0 \| 0.5491 \| 0.6545 \| \| 0.0276 \| 83.22 \| 23800 \| 0.1245 \| 0.4040 \| 0.6140 \| 0.6017 \| nan \| 0.5427 \| 0.6853 \| 0.0 \| 0.5351 \| 0.6768 \| \| 0.0399 \| 83.29 \| 23820 \| 0.1282 \| 0.4062 \| 0.6180 \| 0.6103 \| nan \| 0.5736 \| 0.6623 \| 0.0 \| 0.5591 \| 0.6595 \| \| 0.0668 \| 83.36 \| 23840 \| 0.1209 \| 0.4128 \| 0.6268 \| 0.6158 \| nan \| 0.5631 \| 0.6905 \| 0.0 \| 0.5512 \| 0.6871 \| \| 0.0724 \| 83.43 \| 23860 \| 0.1171 \| 0.4235 \| 0.6430 \| 0.6311 \| nan \| 0.5744 \| 0.7116 \| 0.0 \| 0.5651 \| 0.7053 \| \| 0.0305 \| 83.5 \| 23880 \| 0.1233 \| 0.3994 \| 0.6056 \| 0.5922 \| nan \| 0.5285 \| 0.6826 \| 0.0 \| 0.5200 \| 0.6780 \| \| 0.0511 \| 83.57 \| 23900 \| 0.1239 \| 0.4040 \| 0.6131 \| 0.5990 \| nan \| 0.5315 \| 0.6947 \| 0.0 \| 0.5235 \| 0.6884 \| \| 0.0642 \| 83.64 \| 23920 \| 0.1187 \| 0.4160 \| 0.6318 \| 0.6182 \| nan \| 0.5529 \| 0.7107 \| 0.0 \| 0.5442 \| 0.7037 \| \| 0.0447 \| 83.71 \| 23940 \| 0.1320 \| 0.4064 \| 0.6182 \| 0.6108 \| nan \| 0.5755 \| 0.6610 \| 0.0 \| 0.5605 \| 0.6588 \| \| 0.0501 \| 83.78 \| 23960 \| 0.1347 \| 0.4039 \| 0.6148 \| 0.6079 \| nan \| 0.5754 \| 0.6541 \| 0.0 \| 0.5599 \| 0.6519 \| \| 0.0487 \| 83.85 \| 23980 \| 0.1337 \| 0.4049 \| 0.6159 \| 0.6073 \| nan \| 0.5659 \| 0.6659 \| 0.0 \| 0.5516 \| 0.6631 \| \| 0.0503 \| 83.92 \| 24000 \| 0.1315 \| 0.4037 \| 0.6141 \| 0.6065 \| nan \| 0.5703 \| 0.6580 \| 0.0 \| 0.5557 \| 0.6555 \| \| 0.0645 \| 83.99 \| 24020 \| 0.1336 \| 0.4075 \| 0.6196 \| 0.6129 \| nan \| 0.5808 \| 0.6584 \| 0.0 \| 0.5670 \| 0.6556 \| \| 0.0594 \| 84.06 \| 24040 \| 0.1313 \| 0.4135 \| 0.6288 \| 0.6211 \| nan \| 0.5843 \| 0.6734 \| 0.0 \| 0.5707 \| 0.6698 \| \| 0.0609 \| 84.13 \| 24060 \| 0.1293 \| 0.3993 \| 0.6070 \| 0.5960 \| nan \| 0.5436 \| 0.6703 \| 0.0 \| 0.5325 \| 0.6653 \| \| 0.0481 \| 84.2 \| 24080 \| 0.1323 \| 0.4014 \| 0.6107 \| 0.6013 \| nan \| 0.5563 \| 0.6651 \| 0.0 \| 0.5434 \| 0.6608 \| \| 0.0588 \| 84.27 \| 24100 \| 0.1349 \| 0.4005 \| 0.6090 \| 0.5998 \| nan \| 0.5557 \| 0.6623 \| 0.0 \| 0.5422 \| 0.6594 \| \| 0.048 \| 84.34 \| 24120 \| 0.1355 \| 0.4008 \| 0.6094 \| 0.6009 \| nan \| 0.5603 \| 0.6585 \| 0.0 \| 0.5467 \| 0.6558 \| \| 0.0632 \| 84.41 \| 24140 \| 0.1372 \| 0.3928 \| 0.5971 \| 0.5875 \| nan \| 0.5420 \| 0.6521 \| 0.0 \| 0.5302 \| 0.6481 \| \| 0.03 \| 84.48 \| 24160 \| 0.1276 \| 0.4117 \| 0.6264 \| 0.6175 \| nan \| 0.5753 \| 0.6775 \| 0.0 \| 0.5624 \| 0.6728 \| \| 0.0259 \| 84.55 \| 24180 \| 0.1246 \| 0.4170 \| 0.6344 \| 0.6231 \| nan \| 0.5693 \| 0.6994 \| 0.0 \| 0.5560 \| 0.6950 \| \| 0.0608 \| 84.62 \| 24200 \| 0.1367 \| 0.3899 \| 0.5926 \| 0.5828 \| nan \| 0.5358 \| 0.6494 \| 0.0 \| 0.5217 \| 0.6479 \| \| 0.0522 \| 84.69 \| 24220 \| 0.1196 \| 0.4153 \| 0.6307 \| 0.6169 \| nan \| 0.5513 \| 0.7101 \| 0.0 \| 0.5414 \| 0.7045 \| \| 0.0388 \| 84.76 \| 24240 \| 0.1241 \| 0.4147 \| 0.6301 \| 0.6182 \| nan \| 0.5616 \| 0.6986 \| 0.0 \| 0.5500 \| 0.6941 \| \| 0.0394 \| 84.83 \| 24260 \| 0.1251 \| 0.4085 \| 0.6202 \| 0.6078 \| nan \| 0.5483 \| 0.6921 \| 0.0 \| 0.5383 \| 0.6871 \| \| 0.0479 \| 84.9 \| 24280 \| 0.1229 \| 0.4106 \| 0.6241 \| 0.6107 \| nan \| 0.5466 \| 0.7017 \| 0.0 \| 0.5370 \| 0.6947 \| \| 0.0274 \| 84.97 \| 24300 \| 0.1269 \| 0.4044 \| 0.6145 \| 0.6062 \| nan \| 0.5669 \| 0.6621 \| 0.0 \| 0.5557 \| 0.6576 \| \| 0.0535 \| 85.03 \| 24320 \| 0.1239 \| 0.4137 \| 0.6285 \| 0.6161 \| nan \| 0.5565 \| 0.7006 \| 0.0 \| 0.5457 \| 0.6955 \| \| 0.0606 \| 85.1 \| 24340 \| 0.1325 \| 0.4055 \| 0.6164 \| 0.6079 \| nan \| 0.5676 \| 0.6652 \| 0.0 \| 0.5548 \| 0.6617 \| \| 0.0442 \| 85.17 \| 24360 \| 0.1277 \| 0.4107 \| 0.6243 \| 0.6133 \| nan \| 0.5611 \| 0.6874 \| 0.0 \| 0.5502 \| 0.6820 \| \| 0.0468 \| 85.24 \| 24380 \| 0.1271 \| 0.4038 \| 0.6135 \| 0.6024 \| nan \| 0.5497 \| 0.6773 \| 0.0 \| 0.5390 \| 0.6725 \| \| 0.0469 \| 85.31 \| 24400 \| 0.1268 \| 0.4125 \| 0.6272 \| 0.6174 \| nan \| 0.5708 \| 0.6837 \| 0.0 \| 0.5584 \| 0.6792 \| \| 0.0608 \| 85.38 \| 24420 \| 0.1288 \| 0.4134 \| 0.6283 \| 0.6178 \| nan \| 0.5679 \| 0.6887 \| 0.0 \| 0.5586 \| 0.6815 \| \| 0.0392 \| 85.45 \| 24440 \| 0.1383 \| 0.4009 \| 0.6101 \| 0.6031 \| nan \| 0.5695 \| 0.6507 \| 0.0 \| 0.5570 \| 0.6459 \| \| 0.0374 \| 85.52 \| 24460 \| 0.1317 \| 0.4031 \| 0.6131 \| 0.6047 \| nan \| 0.5647 \| 0.6616 \| 0.0 \| 0.5531 \| 0.6563 \| \| 0.0402 \| 85.59 \| 24480 \| 0.1369 \| 0.3936 \| 0.5980 \| 0.5897 \| nan \| 0.5500 \| 0.6460 \| 0.0 \| 0.5372 \| 0.6435 \| \| 0.0456 \| 85.66 \| 24500 \| 0.1329 \| 0.4034 \| 0.6128 \| 0.6043 \| nan \| 0.5638 \| 0.6618 \| 0.0 \| 0.5516 \| 0.6585 \| \| 0.0569 \| 85.73 \| 24520 \| 0.1275 \| 0.4094 \| 0.6219 \| 0.6123 \| nan \| 0.5667 \| 0.6771 \| 0.0 \| 0.5561 \| 0.6722 \| \| 0.0462 \| 85.8 \| 24540 \| 0.1270 \| 0.4120 \| 0.6260 \| 0.6155 \| nan \| 0.5651 \| 0.6870 \| 0.0 \| 0.5553 \| 0.6808 \| \| 0.0404 \| 85.87 \| 24560 \| 0.1275 \| 0.4213 \| 0.6405 \| 0.6318 \| nan \| 0.5904 \| 0.6906 \| 0.0 \| 0.5790 \| 0.6849 \| \| 0.0607 \| 85.94 \| 24580 \| 0.1314 \| 0.4166 \| 0.6333 \| 0.6232 \| nan \| 0.5751 \| 0.6914 \| 0.0 \| 0.5616 \| 0.6881 \| \| 0.049 \| 86.01 \| 24600 \| 0.1333 \| 0.4056 \| 0.6162 \| 0.6077 \| nan \| 0.5672 \| 0.6653 \| 0.0 \| 0.5549 \| 0.6618 \| \| 0.0521 \| 86.08 \| 24620 \| 0.1291 \| 0.4117 \| 0.6258 \| 0.6168 \| nan \| 0.5739 \| 0.6777 \| 0.0 \| 0.5614 \| 0.6738 \| \| 0.0465 \| 86.15 \| 24640 \| 0.1360 \| 0.4036 \| 0.6131 \| 0.6044 \| nan \| 0.5630 \| 0.6631 \| 0.0 \| 0.5516 \| 0.6590 \| \| 0.0343 \| 86.22 \| 24660 \| 0.1302 \| 0.4059 \| 0.6168 \| 0.6024 \| nan \| 0.5337 \| 0.7000 \| 0.0 \| 0.5240 \| 0.6936 \| \| 0.0775 \| 86.29 \| 24680 \| 0.1349 \| 0.3989 \| 0.6063 \| 0.5939 \| nan \| 0.5345 \| 0.6782 \| 0.0 \| 0.5243 \| 0.6724 \| \| 0.0545 \| 86.36 \| 24700 \| 0.1388 \| 0.4019 \| 0.6108 \| 0.5993 \| nan \| 0.5445 \| 0.6771 \| 0.0 \| 0.5332 \| 0.6724 \| \| 0.038 \| 86.43 \| 24720 \| 0.1364 \| 0.4060 \| 0.6174 \| 0.6098 \| nan \| 0.5735 \| 0.6613 \| 0.0 \| 0.5600 \| 0.6580 \| \| 0.0364 \| 86.5 \| 24740 \| 0.1362 \| 0.4118 \| 0.6267 \| 0.6198 \| nan \| 0.5868 \| 0.6665 \| 0.0 \| 0.5727 \| 0.6625 \| \| 0.0425 \| 86.57 \| 24760 \| 0.1459 \| 0.3935 \| 0.5988 \| 0.5922 \| nan \| 0.5607 \| 0.6369 \| 0.0 \| 0.5465 \| 0.6341 \| \| 0.0309 \| 86.64 \| 24780 \| 0.1395 \| 0.4010 \| 0.6094 \| 0.6020 \| nan \| 0.5666 \| 0.6523 \| 0.0 \| 0.5551 \| 0.6480 \| \| 0.0433 \| 86.71 \| 24800 \| 0.1349 \| 0.4137 \| 0.6295 \| 0.6186 \| nan \| 0.5668 \| 0.6922 \| 0.0 \| 0.5576 \| 0.6836 \| \| 0.0416 \| 86.78 \| 24820 \| 0.1438 \| 0.3978 \| 0.6057 \| 0.6008 \| nan \| 0.5774 \| 0.6341 \| 0.0 \| 0.5614 \| 0.6321 \| \| 0.0379 \| 86.85 \| 24840 \| 0.1411 \| 0.4047 \| 0.6163 \| 0.6101 \| nan \| 0.5804 \| 0.6522 \| 0.0 \| 0.5635 \| 0.6505 \| \| 0.055 \| 86.92 \| 24860 \| 0.1392 \| 0.4012 \| 0.6110 \| 0.6051 \| nan \| 0.5770 \| 0.6450 \| 0.0 \| 0.5621 \| 0.6415 \| \| 0.0443 \| 86.99 \| 24880 \| 0.1352 \| 0.4083 \| 0.6224 \| 0.6161 \| nan \| 0.5859 \| 0.6589 \| 0.0 \| 0.5700 \| 0.6549 \| \| 0.0477 \| 87.06 \| 24900 \| 0.1369 \| 0.3984 \| 0.6064 \| 0.5983 \| nan \| 0.5595 \| 0.6533 \| 0.0 \| 0.5456 \| 0.6498 \| \| 0.0368 \| 87.13 \| 24920 \| 0.1333 \| 0.3991 \| 0.6066 \| 0.5943 \| nan \| 0.5358 \| 0.6773 \| 0.0 \| 0.5244 \| 0.6730 \| \| 0.0661 \| 87.2 \| 24940 \| 0.1361 \| 0.4045 \| 0.6150 \| 0.6058 \| nan \| 0.5615 \| 0.6686 \| 0.0 \| 0.5482 \| 0.6653 \| \| 0.0496 \| 87.27 \| 24960 \| 0.1370 \| 0.4037 \| 0.6136 \| 0.6030 \| nan \| 0.5521 \| 0.6752 \| 0.0 \| 0.5387 \| 0.6723 \| \| 0.032 \| 87.34 \| 24980 \| 0.1454 \| 0.3995 \| 0.6087 \| 0.6031 \| nan \| 0.5763 \| 0.6411 \| 0.0 \| 0.5592 \| 0.6394 \| \| 0.059 \| 87.41 \| 25000 \| 0.1345 \| 0.4120 \| 0.6263 \| 0.6169 \| nan \| 0.5722 \| 0.6804 \| 0.0 \| 0.5589 \| 0.6771 \| \| 0.0473 \| 87.48 \| 25020 \| 0.1363 \| 0.4124 \| 0.6278 \| 0.6222 \| nan \| 0.5955 \| 0.6602 \| 0.0 \| 0.5795 \| 0.6577 \| \| 0.0307 \| 87.55 \| 25040 \| 0.1346 \| 0.4096 \| 0.6224 \| 0.6108 \| nan \| 0.5554 \| 0.6895 \| 0.0 \| 0.5441 \| 0.6846 \| \| 0.0373 \| 87.62 \| 25060 \| 0.1337 \| 0.4039 \| 0.6139 \| 0.6032 \| nan \| 0.5520 \| 0.6758 \| 0.0 \| 0.5408 \| 0.6709 \| \| 0.0438 \| 87.69 \| 25080 \| 0.1348 \| 0.4078 \| 0.6200 \| 0.6112 \| nan \| 0.5695 \| 0.6704 \| 0.0 \| 0.5569 \| 0.6664 \| \| 0.0671 \| 87.76 \| 25100 \| 0.1471 \| 0.3939 \| 0.5995 \| 0.5952 \| nan \| 0.5748 \| 0.6241 \| 0.0 \| 0.5596 \| 0.6222 \| \| 0.0646 \| 87.83 \| 25120 \| 0.1451 \| 0.3930 \| 0.5977 \| 0.5892 \| nan \| 0.5484 \| 0.6470 \| 0.0 \| 0.5352 \| 0.6438 \| \| 0.061 \| 87.9 \| 25140 \| 0.1409 \| 0.3986 \| 0.6063 \| 0.5996 \| nan \| 0.5674 \| 0.6453 \| 0.0 \| 0.5529 \| 0.6430 \| \| 0.0553 \| 87.97 \| 25160 \| 0.1410 \| 0.3854 \| 0.5856 \| 0.5733 \| nan \| 0.5144 \| 0.6568 \| 0.0 \| 0.5025 \| 0.6537 \| \| 0.0578 \| 88.04 \| 25180 \| 0.1430 \| 0.3821 \| 0.5811 \| 0.5712 \| nan \| 0.5238 \| 0.6385 \| 0.0 \| 0.5108 \| 0.6355 \| \| 0.0398 \| 88.11 \| 25200 \| 0.1438 \| 0.3929 \| 0.5984 \| 0.5929 \| nan \| 0.5670 \| 0.6297 \| 0.0 \| 0.5523 \| 0.6265 \| \| 0.0354 \| 88.18 \| 25220 \| 0.1352 \| 0.4088 \| 0.6219 \| 0.6109 \| nan \| 0.5583 \| 0.6856 \| 0.0 \| 0.5468 \| 0.6797 \| \| 0.0503 \| 88.25 \| 25240 \| 0.1412 \| 0.3996 \| 0.6073 \| 0.5983 \| nan \| 0.5554 \| 0.6592 \| 0.0 \| 0.5429 \| 0.6559 \| \| 0.0609 \| 88.32 \| 25260 \| 0.1397 \| 0.4018 \| 0.6112 \| 0.6038 \| nan \| 0.5686 \| 0.6538 \| 0.0 \| 0.5554 \| 0.6501 \| \| 0.0325 \| 88.39 \| 25280 \| 0.1286 \| 0.4165 \| 0.6332 \| 0.6237 \| nan \| 0.5782 \| 0.6882 \| 0.0 \| 0.5660 \| 0.6835 \| \| 0.0372 \| 88.46 \| 25300 \| 0.1293 \| 0.4101 \| 0.6227 \| 0.6093 \| nan \| 0.5456 \| 0.6998 \| 0.0 \| 0.5356 \| 0.6946 \| \| 0.0307 \| 88.53 \| 25320 \| 0.1423 \| 0.3938 \| 0.5981 \| 0.5875 \| nan \| 0.5368 \| 0.6595 \| 0.0 \| 0.5241 \| 0.6573 \| \| 0.041 \| 88.6 \| 25340 \| 0.1358 \| 0.4061 \| 0.6174 \| 0.6051 \| nan \| 0.5467 \| 0.6880 \| 0.0 \| 0.5343 \| 0.6840 \| \| 0.0521 \| 88.67 \| 25360 \| 0.1341 \| 0.4034 \| 0.6135 \| 0.6024 \| nan \| 0.5493 \| 0.6776 \| 0.0 \| 0.5371 \| 0.6732 \| \| 0.0362 \| 88.74 \| 25380 \| 0.1333 \| 0.3982 \| 0.6055 \| 0.5945 \| nan \| 0.5420 \| 0.6691 \| 0.0 \| 0.5297 \| 0.6648 \| \| 0.0498 \| 88.81 \| 25400 \| 0.1319 \| 0.4137 \| 0.6293 \| 0.6199 \| nan \| 0.5753 \| 0.6833 \| 0.0 \| 0.5626 \| 0.6784 \| \| 0.0546 \| 88.88 \| 25420 \| 0.1360 \| 0.4025 \| 0.6119 \| 0.6034 \| nan \| 0.5627 \| 0.6611 \| 0.0 \| 0.5503 \| 0.6574 \| \| 0.0668 \| 88.95 \| 25440 \| 0.1325 \| 0.4124 \| 0.6272 \| 0.6188 \| nan \| 0.5785 \| 0.6759 \| 0.0 \| 0.5657 \| 0.6716 \| \| 0.0495 \| 89.02 \| 25460 \| 0.1338 \| 0.4056 \| 0.6167 \| 0.6095 \| nan \| 0.5753 \| 0.6581 \| 0.0 \| 0.5623 \| 0.6544 \| \| 0.0448 \| 89.09 \| 25480 \| 0.1409 \| 0.3931 \| 0.5971 \| 0.5887 \| nan \| 0.5488 \| 0.6453 \| 0.0 \| 0.5363 \| 0.6429 \| \| 0.0497 \| 89.16 \| 25500 \| 0.1304 \| 0.4132 \| 0.6273 \| 0.6173 \| nan \| 0.5692 \| 0.6854 \| 0.0 \| 0.5585 \| 0.6810 \| \| 0.0394 \| 89.23 \| 25520 \| 0.1276 \| 0.4162 \| 0.6314 \| 0.6168 \| nan \| 0.5470 \| 0.7158 \| 0.0 \| 0.5384 \| 0.7100 \| \| 0.0512 \| 89.3 \| 25540 \| 0.1367 \| 0.3989 \| 0.6067 \| 0.6011 \| nan \| 0.5747 \| 0.6386 \| 0.0 \| 0.5612 \| 0.6355 \| \| 0.0407 \| 89.37 \| 25560 \| 0.1439 \| 0.3935 \| 0.5989 \| 0.5923 \| nan \| 0.5606 \| 0.6372 \| 0.0 \| 0.5458 \| 0.6347 \| \| 0.0397 \| 89.44 \| 25580 \| 0.1383 \| 0.3961 \| 0.6024 \| 0.5948 \| nan \| 0.5586 \| 0.6463 \| 0.0 \| 0.5447 \| 0.6436 \| \| 0.0427 \| 89.51 \| 25600 \| 0.1327 \| 0.4065 \| 0.6176 \| 0.6091 \| nan \| 0.5687 \| 0.6664 \| 0.0 \| 0.5574 \| 0.6620 \| \| 0.045 \| 89.58 \| 25620 \| 0.1308 \| 0.4024 \| 0.6112 \| 0.6009 \| nan \| 0.5516 \| 0.6708 \| 0.0 \| 0.5400 \| 0.6671 \| \| 0.0782 \| 89.65 \| 25640 \| 0.1340 \| 0.4056 \| 0.6170 \| 0.6084 \| nan \| 0.5671 \| 0.6669 \| 0.0 \| 0.5529 \| 0.6640 \| \| 0.0444 \| 89.72 \| 25660 \| 0.1362 \| 0.4023 \| 0.6120 \| 0.6059 \| nan \| 0.5765 \| 0.6476 \| 0.0 \| 0.5638 \| 0.6433 \| \| 0.0374 \| 89.79 \| 25680 \| 0.1337 \| 0.4075 \| 0.6194 \| 0.6108 \| nan \| 0.5698 \| 0.6691 \| 0.0 \| 0.5586 \| 0.6641 \| \| 0.0454 \| 89.86 \| 25700 \| 0.1408 \| 0.3884 \| 0.5914 \| 0.5852 \| nan \| 0.5557 \| 0.6270 \| 0.0 \| 0.5425 \| 0.6228 \| \| 0.0419 \| 89.93 \| 25720 \| 0.1396 \| 0.4013 \| 0.6108 \| 0.6049 \| nan \| 0.5766 \| 0.6451 \| 0.0 \| 0.5624 \| 0.6416 \| \| 0.0137 \| 90.0 \| 25740 \| 0.1317 \| 0.4053 \| 0.6161 \| 0.6069 \| nan \| 0.5626 \| 0.6697 \| 0.0 \| 0.5507 \| 0.6651 \| \| 0.0311 \| 90.07 \| 25760 \| 0.1361 \| 0.4052 \| 0.6171 \| 0.6100 \| nan \| 0.5766 \| 0.6575 \| 0.0 \| 0.5613 \| 0.6542 \| \| 0.056 \| 90.14 \| 25780 \| 0.1377 \| 0.4023 \| 0.6118 \| 0.6064 \| nan \| 0.5805 \| 0.6432 \| 0.0 \| 0.5671 \| 0.6397 \| \| 0.0491 \| 90.21 \| 25800 \| 0.1399 \| 0.3961 \| 0.6025 \| 0.5945 \| nan \| 0.5565 \| 0.6484 \| 0.0 \| 0.5443 \| 0.6439 \| \| 0.0285 \| 90.28 \| 25820 \| 0.1357 \| 0.4028 \| 0.6123 \| 0.6026 \| nan \| 0.5562 \| 0.6684 \| 0.0 \| 0.5448 \| 0.6637 \| \| 0.0253 \| 90.35 \| 25840 \| 0.1371 \| 0.4051 \| 0.6162 \| 0.6098 \| nan \| 0.5790 \| 0.6534 \| 0.0 \| 0.5668 \| 0.6485 \| \| 0.0409 \| 90.42 \| 25860 \| 0.1414 \| 0.3962 \| 0.6025 \| 0.5945 \| nan \| 0.5561 \| 0.6490 \| 0.0 \| 0.5428 \| 0.6458 \| \| 0.0414 \| 90.49 \| 25880 \| 0.1411 \| 0.3999 \| 0.6082 \| 0.6037 \| nan \| 0.5823 \| 0.6340 \| 0.0 \| 0.5682 \| 0.6314 \| \| 0.0659 \| 90.56 \| 25900 \| 0.1362 \| 0.4085 \| 0.6211 \| 0.6134 \| nan \| 0.5765 \| 0.6657 \| 0.0 \| 0.5647 \| 0.6607 \| \| 0.0347 \| 90.63 \| 25920 \| 0.1415 \| 0.4101 \| 0.6243 \| 0.6219 \| nan \| 0.6107 \| 0.6378 \| 0.0 \| 0.5956 \| 0.6346 \| \| 0.0418 \| 90.7 \| 25940 \| 0.1382 \| 0.4075 \| 0.6202 \| 0.6139 \| nan \| 0.5834 \| 0.6571 \| 0.0 \| 0.5693 \| 0.6533 \| \| 0.037 \| 90.77 \| 25960 \| 0.1406 \| 0.4010 \| 0.6100 \| 0.6016 \| nan \| 0.5611 \| 0.6589 \| 0.0 \| 0.5482 \| 0.6549 \| \| 0.035 \| 90.84 \| 25980 \| 0.1385 \| 0.4007 \| 0.6098 \| 0.5996 \| nan \| 0.5510 \| 0.6685 \| 0.0 \| 0.5379 \| 0.6642 \| \| 0.0251 \| 90.91 \| 26000 \| 0.1377 \| 0.4023 \| 0.6129 \| 0.6064 \| nan \| 0.5756 \| 0.6501 \| 0.0 \| 0.5596 \| 0.6474 \| \| 0.0462 \| 90.98 \| 26020 \| 0.1474 \| 0.3867 \| 0.5884 \| 0.5814 \| nan \| 0.5482 \| 0.6286 \| 0.0 \| 0.5336 \| 0.6264 \| \| 0.0427 \| 91.05 \| 26040 \| 0.1511 \| 0.3862 \| 0.5882 \| 0.5807 \| nan \| 0.5452 \| 0.6311 \| 0.0 \| 0.5305 \| 0.6282 \| \| 0.0458 \| 91.12 \| 26060 \| 0.1428 \| 0.3969 \| 0.6044 \| 0.5985 \| nan \| 0.5704 \| 0.6385 \| 0.0 \| 0.5549 \| 0.6358 \| \| 0.0531 \| 91.19 \| 26080 \| 0.1443 \| 0.3987 \| 0.6076 \| 0.6034 \| nan \| 0.5833 \| 0.6320 \| 0.0 \| 0.5676 \| 0.6284 \| \| 0.0423 \| 91.26 \| 26100 \| 0.1459 \| 0.3914 \| 0.5960 \| 0.5910 \| nan \| 0.5672 \| 0.6248 \| 0.0 \| 0.5516 \| 0.6226 \| \| 0.0371 \| 91.33 \| 26120 \| 0.1455 \| 0.3883 \| 0.5905 \| 0.5828 \| nan \| 0.5459 \| 0.6351 \| 0.0 \| 0.5321 \| 0.6327 \| \| 0.048 \| 91.4 \| 26140 \| 0.1463 \| 0.3924 \| 0.5971 \| 0.5897 \| nan \| 0.5545 \| 0.6397 \| 0.0 \| 0.5406 \| 0.6366 \| \| 0.0556 \| 91.47 \| 26160 \| 0.1397 \| 0.3989 \| 0.6065 \| 0.5978 \| nan \| 0.5561 \| 0.6569 \| 0.0 \| 0.5438 \| 0.6528 \| \| 0.0332 \| 91.54 \| 26180 \| 0.1394 \| 0.3948 \| 0.6001 \| 0.5917 \| nan \| 0.5517 \| 0.6485 \| 0.0 \| 0.5391 \| 0.6453 \| \| 0.0419 \| 91.61 \| 26200 \| 0.1436 \| 0.3829 \| 0.5816 \| 0.5730 \| nan \| 0.5322 \| 0.6310 \| 0.0 \| 0.5205 \| 0.6282 \| \| 0.0299 \| 91.68 \| 26220 \| 0.1404 \| 0.3939 \| 0.5993 \| 0.5912 \| nan \| 0.5526 \| 0.6459 \| 0.0 \| 0.5392 \| 0.6426 \| \| 0.0296 \| 91.75 \| 26240 \| 0.1243 \| 0.4179 \| 0.6349 \| 0.6197 \| nan \| 0.5471 \| 0.7226 \| 0.0 \| 0.5397 \| 0.7140 \| \| 0.0617 \| 91.82 \| 26260 \| 0.1376 \| 0.3930 \| 0.5975 \| 0.5849 \| nan \| 0.5251 \| 0.6698 \| 0.0 \| 0.5163 \| 0.6627 \| \| 0.0632 \| 91.89 \| 26280 \| 0.1400 \| 0.3905 \| 0.5940 \| 0.5822 \| nan \| 0.5258 \| 0.6621 \| 0.0 \| 0.5146 \| 0.6569 \| \| 0.0308 \| 91.96 \| 26300 \| 0.1344 \| 0.3972 \| 0.6049 \| 0.5891 \| nan \| 0.5138 \| 0.6960 \| 0.0 \| 0.5073 \| 0.6843 \| \| 0.0406 \| 92.03 \| 26320 \| 0.1375 \| 0.3958 \| 0.6021 \| 0.5905 \| nan \| 0.5350 \| 0.6693 \| 0.0 \| 0.5261 \| 0.6614 \| \| 0.0434 \| 92.1 \| 26340 \| 0.1336 \| 0.4062 \| 0.6173 \| 0.6067 \| nan \| 0.5560 \| 0.6786 \| 0.0 \| 0.5471 \| 0.6715 \| \| 0.0425 \| 92.17 \| 26360 \| 0.1299 \| 0.4089 \| 0.6216 \| 0.6096 \| nan \| 0.5519 \| 0.6914 \| 0.0 \| 0.5427 \| 0.6842 \| \| 0.0531 \| 92.24 \| 26380 \| 0.1317 \| 0.4099 \| 0.6225 \| 0.6113 \| nan \| 0.5581 \| 0.6869 \| 0.0 \| 0.5492 \| 0.6804 \| \| 0.0498 \| 92.31 \| 26400 \| 0.1392 \| 0.3976 \| 0.6043 \| 0.5955 \| nan \| 0.5536 \| 0.6549 \| 0.0 \| 0.5437 \| 0.6491 \| \| 0.023 \| 92.38 \| 26420 \| 0.1385 \| 0.4018 \| 0.6114 \| 0.6062 \| nan \| 0.5816 \| 0.6412 \| 0.0 \| 0.5704 \| 0.6351 \| \| 0.038 \| 92.45 \| 26440 \| 0.1397 \| 0.3900 \| 0.5919 \| 0.5821 \| nan \| 0.5349 \| 0.6490 \| 0.0 \| 0.5252 \| 0.6447 \| \| 0.0501 \| 92.52 \| 26460 \| 0.1438 \| 0.3923 \| 0.5965 \| 0.5922 \| nan \| 0.5716 \| 0.6214 \| 0.0 \| 0.5592 \| 0.6176 \| \| 0.0316 \| 92.59 \| 26480 \| 0.1429 \| 0.4014 \| 0.6105 \| 0.6061 \| nan \| 0.5852 \| 0.6358 \| 0.0 \| 0.5720 \| 0.6323 \| \| 0.0521 \| 92.66 \| 26500 \| 0.1419 \| 0.3952 \| 0.6006 \| 0.5918 \| nan \| 0.5498 \| 0.6514 \| 0.0 \| 0.5380 \| 0.6476 \| \| 0.0289 \| 92.73 \| 26520 \| 0.1409 \| 0.4051 \| 0.6159 \| 0.6083 \| nan \| 0.5723 \| 0.6595 \| 0.0 \| 0.5608 \| 0.6545 \| \| 0.0563 \| 92.8 \| 26540 \| 0.1381 \| 0.4099 \| 0.6234 \| 0.6162 \| nan \| 0.5819 \| 0.6649 \| 0.0 \| 0.5698 \| 0.6600 \| \| 0.0374 \| 92.87 \| 26560 \| 0.1380 \| 0.4113 \| 0.6251 \| 0.6178 \| nan \| 0.5827 \| 0.6676 \| 0.0 \| 0.5713 \| 0.6628 \| \| 0.0414 \| 92.94 \| 26580 \| 0.1417 \| 0.4073 \| 0.6195 \| 0.6159 \| nan \| 0.5991 \| 0.6398 \| 0.0 \| 0.5858 \| 0.6360 \| \| 0.0567 \| 93.01 \| 26600 \| 0.1399 \| 0.4032 \| 0.6128 \| 0.6068 \| nan \| 0.5783 \| 0.6473 \| 0.0 \| 0.5660 \| 0.6435 \| \| 0.0522 \| 93.08 \| 26620 \| 0.1379 \| 0.4056 \| 0.6160 \| 0.6066 \| nan \| 0.5614 \| 0.6706 \| 0.0 \| 0.5506 \| 0.6661 \| \| 0.0288 \| 93.15 \| 26640 \| 0.1429 \| 0.3865 \| 0.5869 \| 0.5778 \| nan \| 0.5344 \| 0.6394 \| 0.0 \| 0.5229 \| 0.6367 \| \| 0.0358 \| 93.22 \| 26660 \| 0.1399 \| 0.3937 \| 0.5981 \| 0.5883 \| nan \| 0.5414 \| 0.6548 \| 0.0 \| 0.5289 \| 0.6523 \| \| 0.0439 \| 93.29 \| 26680 \| 0.1352 \| 0.4035 \| 0.6134 \| 0.6048 \| nan \| 0.5636 \| 0.6633 \| 0.0 \| 0.5511 \| 0.6596 \| \| 0.046 \| 93.36 \| 26700 \| 0.1332 \| 0.4132 \| 0.6278 \| 0.6175 \| nan \| 0.5680 \| 0.6877 \| 0.0 \| 0.5569 \| 0.6827 \| \| 0.0387 \| 93.43 \| 26720 \| 0.1387 \| 0.4088 \| 0.6214 \| 0.6127 \| nan \| 0.5710 \| 0.6718 \| 0.0 \| 0.5567 \| 0.6697 \| \| 0.04 \| 93.5 \| 26740 \| 0.1375 \| 0.4047 \| 0.6156 \| 0.6073 \| nan \| 0.5675 \| 0.6637 \| 0.0 \| 0.5528 \| 0.6612 \| \| 0.0301 \| 93.57 \| 26760 \| 0.1396 \| 0.3997 \| 0.6079 \| 0.5999 \| nan \| 0.5615 \| 0.6544 \| 0.0 \| 0.5479 \| 0.6512 \| \| 0.0395 \| 93.64 \| 26780 \| 0.1459 \| 0.3913 \| 0.5952 \| 0.5895 \| nan \| 0.5620 \| 0.6285 \| 0.0 \| 0.5489 \| 0.6250 \| \| 0.0595 \| 93.71 \| 26800 \| 0.1459 \| 0.3884 \| 0.5901 \| 0.5826 \| nan \| 0.5468 \| 0.6335 \| 0.0 \| 0.5353 \| 0.6300 \| \| 0.0576 \| 93.78 \| 26820 \| 0.1481 \| 0.3952 \| 0.6012 \| 0.5971 \| nan \| 0.5773 \| 0.6252 \| 0.0 \| 0.5622 \| 0.6233 \| \| 0.0347 \| 93.85 \| 26840 \| 0.1395 \| 0.4003 \| 0.6096 \| 0.6028 \| nan \| 0.5706 \| 0.6485 \| 0.0 \| 0.5551 \| 0.6459 \| \| 0.0334 \| 93.92 \| 26860 \| 0.1340 \| 0.4098 \| 0.6238 \| 0.6149 \| nan \| 0.5719 \| 0.6758 \| 0.0 \| 0.5599 \| 0.6695 \| \| 0.0519 \| 93.99 \| 26880 \| 0.1409 \| 0.3982 \| 0.6059 \| 0.5997 \| nan \| 0.5703 \| 0.6416 \| 0.0 \| 0.5584 \| 0.6363 \| \| 0.0291 \| 94.06 \| 26900 \| 0.1386 \| 0.4097 \| 0.6239 \| 0.6188 \| nan \| 0.5948 \| 0.6530 \| 0.0 \| 0.5797 \| 0.6493 \| \| 0.0693 \| 94.13 \| 26920 \| 0.1459 \| 0.3942 \| 0.5998 \| 0.5942 \| nan \| 0.5677 \| 0.6319 \| 0.0 \| 0.5535 \| 0.6292 \| \| 0.0409 \| 94.2 \| 26940 \| 0.1459 \| 0.3854 \| 0.5864 \| 0.5797 \| nan \| 0.5476 \| 0.6251 \| 0.0 \| 0.5349 \| 0.6214 \| \| 0.0501 \| 94.27 \| 26960 \| 0.1431 \| 0.3930 \| 0.5984 \| 0.5888 \| nan \| 0.5432 \| 0.6536 \| 0.0 \| 0.5299 \| 0.6492 \| \| 0.0692 \| 94.34 \| 26980 \| 0.1418 \| 0.3915 \| 0.5958 \| 0.5879 \| nan \| 0.5503 \| 0.6414 \| 0.0 \| 0.5370 \| 0.6375 \| \| 0.0382 \| 94.41 \| 27000 \| 0.1374 \| 0.4033 \| 0.6142 \| 0.6075 \| nan \| 0.5754 \| 0.6530 \| 0.0 \| 0.5618 \| 0.6482 \| \| 0.0436 \| 94.48 \| 27020 \| 0.1380 \| 0.3945 \| 0.5996 \| 0.5891 \| nan \| 0.5390 \| 0.6603 \| 0.0 \| 0.5282 \| 0.6552 \| \| 0.0345 \| 94.55 \| 27040 \| 0.1319 \| 0.4121 \| 0.6269 \| 0.6186 \| nan \| 0.5788 \| 0.6750 \| 0.0 \| 0.5664 \| 0.6700 \| \| 0.0337 \| 94.62 \| 27060 \| 0.1361 \| 0.3998 \| 0.6071 \| 0.5955 \| nan \| 0.5402 \| 0.6740 \| 0.0 \| 0.5308 \| 0.6685 \| \| 0.0395 \| 94.69 \| 27080 \| 0.1384 \| 0.3971 \| 0.6037 \| 0.5951 \| nan \| 0.5541 \| 0.6532 \| 0.0 \| 0.5420 \| 0.6491 \| \| 0.0359 \| 94.76 \| 27100 \| 0.1364 \| 0.4009 \| 0.6091 \| 0.5998 \| nan \| 0.5553 \| 0.6629 \| 0.0 \| 0.5435 \| 0.6593 \| \| 0.0489 \| 94.83 \| 27120 \| 0.1296 \| 0.4187 \| 0.6362 \| 0.6274 \| nan \| 0.5854 \| 0.6871 \| 0.0 \| 0.5734 \| 0.6827 \| \| 0.0363 \| 94.9 \| 27140 \| 0.1302 \| 0.4124 \| 0.6264 \| 0.6159 \| nan \| 0.5661 \| 0.6867 \| 0.0 \| 0.5553 \| 0.6819 \| \| 0.0555 \| 94.97 \| 27160 \| 0.1306 \| 0.4139 \| 0.6288 \| 0.6181 \| nan \| 0.5669 \| 0.6907 \| 0.0 \| 0.5560 \| 0.6857 \| \| 0.0441 \| 95.03 \| 27180 \| 0.1318 \| 0.4129 \| 0.6274 \| 0.6176 \| nan \| 0.5706 \| 0.6843 \| 0.0 \| 0.5592 \| 0.6796 \| \| 0.0725 \| 95.1 \| 27200 \| 0.1331 \| 0.4148 \| 0.6308 \| 0.6234 \| nan \| 0.5878 \| 0.6738 \| 0.0 \| 0.5746 \| 0.6699 \| \| 0.0404 \| 95.17 \| 27220 \| 0.1430 \| 0.4049 \| 0.6168 \| 0.6137 \| nan \| 0.5992 \| 0.6344 \| 0.0 \| 0.5829 \| 0.6319 \| \| 0.053 \| 95.24 \| 27240 \| 0.1386 \| 0.3961 \| 0.6016 \| 0.5919 \| nan \| 0.5453 \| 0.6580 \| 0.0 \| 0.5348 \| 0.6534 \| \| 0.0397 \| 95.31 \| 27260 \| 0.1318 \| 0.4125 \| 0.6275 \| 0.6191 \| nan \| 0.5788 \| 0.6763 \| 0.0 \| 0.5669 \| 0.6707 \| \| 0.0345 \| 95.38 \| 27280 \| 0.1394 \| 0.3954 \| 0.6011 \| 0.5932 \| nan \| 0.5555 \| 0.6466 \| 0.0 \| 0.5442 \| 0.6420 \| \| 0.051 \| 95.45 \| 27300 \| 0.1428 \| 0.3966 \| 0.6039 \| 0.5993 \| nan \| 0.5772 \| 0.6305 \| 0.0 \| 0.5625 \| 0.6273 \| \| 0.0502 \| 95.52 \| 27320 \| 0.1403 \| 0.3984 \| 0.6061 \| 0.5976 \| nan \| 0.5569 \| 0.6552 \| 0.0 \| 0.5432 \| 0.6521 \| \| 0.0534 \| 95.59 \| 27340 \| 0.1407 \| 0.3952 \| 0.6012 \| 0.5952 \| nan \| 0.5665 \| 0.6358 \| 0.0 \| 0.5523 \| 0.6332 \| \| 0.0511 \| 95.66 \| 27360 \| 0.1413 \| 0.3992 \| 0.6077 \| 0.6022 \| nan \| 0.5758 \| 0.6396 \| 0.0 \| 0.5617 \| 0.6360 \| \| 0.041 \| 95.73 \| 27380 \| 0.1426 \| 0.3920 \| 0.5960 \| 0.5867 \| nan \| 0.5421 \| 0.6499 \| 0.0 \| 0.5293 \| 0.6467 \| \| 0.0541 \| 95.8 \| 27400 \| 0.1391 \| 0.4066 \| 0.6189 \| 0.6132 \| nan \| 0.5860 \| 0.6518 \| 0.0 \| 0.5713 \| 0.6485 \| \| 0.0419 \| 95.87 \| 27420 \| 0.1421 \| 0.3992 \| 0.6079 \| 0.6035 \| nan \| 0.5823 \| 0.6335 \| 0.0 \| 0.5666 \| 0.6309 \| \| 0.0508 \| 95.94 \| 27440 \| 0.1431 \| 0.4007 \| 0.6096 \| 0.6030 \| nan \| 0.5716 \| 0.6475 \| 0.0 \| 0.5571 \| 0.6448 \| \| 0.0451 \| 96.01 \| 27460 \| 0.1401 \| 0.4053 \| 0.6161 \| 0.6081 \| nan \| 0.5699 \| 0.6623 \| 0.0 \| 0.5573 \| 0.6585 \| \| 0.0476 \| 96.08 \| 27480 \| 0.1417 \| 0.4010 \| 0.6093 \| 0.6001 \| nan \| 0.5559 \| 0.6628 \| 0.0 \| 0.5447 \| 0.6582 \| \| 0.0555 \| 96.15 \| 27500 \| 0.1351 \| 0.4107 \| 0.6247 \| 0.6159 \| nan \| 0.5737 \| 0.6757 \| 0.0 \| 0.5602 \| 0.6719 \| \| 0.0488 \| 96.22 \| 27520 \| 0.1341 \| 0.4039 \| 0.6136 \| 0.6043 \| nan \| 0.5601 \| 0.6671 \| 0.0 \| 0.5490 \| 0.6627 \| \| 0.0545 \| 96.29 \| 27540 \| 0.1381 \| 0.4003 \| 0.6079 \| 0.5976 \| nan \| 0.5484 \| 0.6675 \| 0.0 \| 0.5370 \| 0.6638 \| \| 0.042 \| 96.36 \| 27560 \| 0.1403 \| 0.3970 \| 0.6026 \| 0.5900 \| nan \| 0.5301 \| 0.6751 \| 0.0 \| 0.5201 \| 0.6709 \| \| 0.0428 \| 96.43 \| 27580 \| 0.1314 \| 0.4140 \| 0.6295 \| 0.6160 \| nan \| 0.5516 \| 0.7074 \| 0.0 \| 0.5430 \| 0.6991 \| \| 0.04 \| 96.5 \| 27600 \| 0.1385 \| 0.3962 \| 0.6015 \| 0.5906 \| nan \| 0.5384 \| 0.6647 \| 0.0 \| 0.5279 \| 0.6607 \| \| 0.0436 \| 96.57 \| 27620 \| 0.1412 \| 0.3960 \| 0.6013 \| 0.5916 \| nan \| 0.5450 \| 0.6577 \| 0.0 \| 0.5339 \| 0.6540 \| \| 0.0452 \| 96.64 \| 27640 \| 0.1381 \| 0.4071 \| 0.6192 \| 0.6116 \| nan \| 0.5750 \| 0.6634 \| 0.0 \| 0.5614 \| 0.6598 \| \| 0.071 \| 96.71 \| 27660 \| 0.1337 \| 0.4037 \| 0.6130 \| 0.5998 \| nan \| 0.5369 \| 0.6890 \| 0.0 \| 0.5273 \| 0.6837 \| \| 0.0531 \| 96.78 \| 27680 \| 0.1345 \| 0.4067 \| 0.6183 \| 0.6090 \| nan \| 0.5644 \| 0.6723 \| 0.0 \| 0.5519 \| 0.6682 \| \| 0.0387 \| 96.85 \| 27700 \| 0.1330 \| 0.3953 \| 0.6004 \| 0.5891 \| nan \| 0.5355 \| 0.6653 \| 0.0 \| 0.5246 \| 0.6614 \| \| 0.0256 \| 96.92 \| 27720 \| 0.1441 \| 0.3914 \| 0.5962 \| 0.5919 \| nan \| 0.5714 \| 0.6210 \| 0.0 \| 0.5546 \| 0.6195 \| \| 0.0446 \| 96.99 \| 27740 \| 0.1394 \| 0.4004 \| 0.6091 \| 0.6009 \| nan \| 0.5619 \| 0.6562 \| 0.0 \| 0.5482 \| 0.6531 \| \| 0.0332 \| 97.06 \| 27760 \| 0.1365 \| 0.4009 \| 0.6098 \| 0.5984 \| nan \| 0.5444 \| 0.6752 \| 0.0 \| 0.5312 \| 0.6717 \| \| 0.048 \| 97.13 \| 27780 \| 0.1326 \| 0.4006 \| 0.6087 \| 0.5974 \| nan \| 0.5436 \| 0.6737 \| 0.0 \| 0.5314 \| 0.6705 \| \| 0.0597 \| 97.2 \| 27800 \| 0.1361 \| 0.3986 \| 0.6055 \| 0.5931 \| nan \| 0.5339 \| 0.6772 \| 0.0 \| 0.5229 \| 0.6731 \| \| 0.045 \| 97.27 \| 27820 \| 0.1302 \| 0.4122 \| 0.6270 \| 0.6147 \| nan \| 0.5559 \| 0.6982 \| 0.0 \| 0.5463 \| 0.6904 \| \| 0.0439 \| 97.34 \| 27840 \| 0.1272 \| 0.4169 \| 0.6341 \| 0.6226 \| nan \| 0.5675 \| 0.7007 \| 0.0 \| 0.5574 \| 0.6934 \| \| 0.0487 \| 97.41 \| 27860 \| 0.1343 \| 0.4134 \| 0.6295 \| 0.6230 \| nan \| 0.5922 \| 0.6668 \| 0.0 \| 0.5784 \| 0.6619 \| \| 0.0335 \| 97.48 \| 27880 \| 0.1429 \| 0.3926 \| 0.5975 \| 0.5905 \| nan \| 0.5572 \| 0.6378 \| 0.0 \| 0.5433 \| 0.6345 \| \| 0.0414 \| 97.55 \| 27900 \| 0.1402 \| 0.3932 \| 0.5978 \| 0.5879 \| nan \| 0.5406 \| 0.6549 \| 0.0 \| 0.5279 \| 0.6516 \| \| 0.0271 \| 97.62 \| 27920 \| 0.1409 \| 0.4023 \| 0.6125 \| 0.6068 \| nan \| 0.5799 \| 0.6450 \| 0.0 \| 0.5652 \| 0.6417 \| \| 0.0813 \| 97.69 \| 27940 \| 0.1409 \| 0.3995 \| 0.6077 \| 0.5987 \| nan \| 0.5556 \| 0.6597 \| 0.0 \| 0.5426 \| 0.6558 \| \| 0.0414 \| 97.76 \| 27960 \| 0.1411 \| 0.3992 \| 0.6073 \| 0.5994 \| nan \| 0.5619 \| 0.6526 \| 0.0 \| 0.5485 \| 0.6490 \| \| 0.0396 \| 97.83 \| 27980 \| 0.1366 \| 0.4077 \| 0.6204 \| 0.6126 \| nan \| 0.5748 \| 0.6661 \| 0.0 \| 0.5608 \| 0.6622 \| \| 0.0588 \| 97.9 \| 28000 \| 0.1434 \| 0.3959 \| 0.6026 \| 0.5979 \| nan \| 0.5752 \| 0.6301 \| 0.0 \| 0.5607 \| 0.6269 \| \| 0.0402 \| 97.97 \| 28020 \| 0.1398 \| 0.4021 \| 0.6118 \| 0.6036 \| nan \| 0.5643 \| 0.6594 \| 0.0 \| 0.5506 \| 0.6557 \| \| 0.0515 \| 98.04 \| 28040 \| 0.1499 \| 0.3842 \| 0.5852 \| 0.5787 \| nan \| 0.5477 \| 0.6228 \| 0.0 \| 0.5322 \| 0.6205 \| \| 0.0379 \| 98.11 \| 28060 \| 0.1427 \| 0.3942 \| 0.5999 \| 0.5926 \| nan \| 0.5579 \| 0.6420 \| 0.0 \| 0.5433 \| 0.6392 \| \| 0.0602 \| 98.18 \| 28080 \| 0.1352 \| 0.4009 \| 0.6097 \| 0.5998 \| nan \| 0.5524 \| 0.6670 \| 0.0 \| 0.5405 \| 0.6622 \| \| 0.0511 \| 98.25 \| 28100 \| 0.1403 \| 0.3988 \| 0.6063 \| 0.5979 \| nan \| 0.5574 \| 0.6553 \| 0.0 \| 0.5443 \| 0.6522 \| \| 0.0323 \| 98.32 \| 28120 \| 0.1425 \| 0.4006 \| 0.6102 \| 0.6070 \| nan \| 0.5918 \| 0.6285 \| 0.0 \| 0.5762 \| 0.6257 \| \| 0.039 \| 98.39 \| 28140 \| 0.1381 \| 0.4001 \| 0.6080 \| 0.5991 \| nan \| 0.5566 \| 0.6595 \| 0.0 \| 0.5447 \| 0.6555 \| \| 0.0455 \| 98.46 \| 28160 \| 0.1401 \| 0.4017 \| 0.6116 \| 0.6066 \| nan \| 0.5826 \| 0.6406 \| 0.0 \| 0.5676 \| 0.6375 \| \| 0.0367 \| 98.53 \| 28180 \| 0.1367 \| 0.4030 \| 0.6127 \| 0.6039 \| nan \| 0.5620 \| 0.6633 \| 0.0 \| 0.5495 \| 0.6593 \| \| 0.0522 \| 98.6 \| 28200 \| 0.1415 \| 0.3992 \| 0.6069 \| 0.5988 \| nan \| 0.5602 \| 0.6535 \| 0.0 \| 0.5475 \| 0.6502 \| \| 0.0377 \| 98.67 \| 28220 \| 0.1377 \| 0.4126 \| 0.6276 \| 0.6203 \| nan \| 0.5853 \| 0.6699 \| 0.0 \| 0.5718 \| 0.6661 \| \| 0.0496 \| 98.74 \| 28240 \| 0.1432 \| 0.4022 \| 0.6133 \| 0.6075 \| nan \| 0.5796 \| 0.6470 \| 0.0 \| 0.5623 \| 0.6442 \| \| 0.0393 \| 98.81 \| 28260 \| 0.1444 \| 0.4001 \| 0.6091 \| 0.6015 \| nan \| 0.5657 \| 0.6524 \| 0.0 \| 0.5512 \| 0.6491 \| \| 0.0463 \| 98.88 \| 28280 \| 0.1394 \| 0.4045 \| 0.6163 \| 0.6094 \| nan \| 0.5763 \| 0.6563 \| 0.0 \| 0.5615 \| 0.6520 \| \| 0.0439 \| 98.95 \| 28300 \| 0.1450 \| 0.3882 \| 0.5920 \| 0.5866 \| nan \| 0.5609 \| 0.6231 \| 0.0 \| 0.5448 \| 0.6197 \| \| 0.0549 \| 99.02 \| 28320 \| 0.1400 \| 0.3997 \| 0.6088 \| 0.6031 \| nan \| 0.5760 \| 0.6416 \| 0.0 \| 0.5628 \| 0.6362 \| \| 0.0581 \| 99.09 \| 28340 \| 0.1452 \| 0.3913 \| 0.5955 \| 0.5888 \| nan \| 0.5568 \| 0.6342 \| 0.0 \| 0.5433 \| 0.6308 \| \| 0.0305 \| 99.16 \| 28360 \| 0.1348 \| 0.3969 \| 0.6028 \| 0.5896 \| nan \| 0.5267 \| 0.6789 \| 0.0 \| 0.5174 \| 0.6732 \| \| 0.039 \| 99.23 \| 28380 \| 0.1391 \| 0.3979 \| 0.6045 \| 0.5949 \| nan \| 0.5491 \| 0.6599 \| 0.0 \| 0.5376 \| 0.6563 \| \| 0.0454 \| 99.3 \| 28400 \| 0.1439 \| 0.4027 \| 0.6132 \| 0.6071 \| nan \| 0.5784 \| 0.6479 \| 0.0 \| 0.5624 \| 0.6458 \| \| 0.0668 \| 99.37 \| 28420 \| 0.1446 \| 0.4039 \| 0.6162 \| 0.6113 \| nan \| 0.5880 \| 0.6444 \| 0.0 \| 0.5687 \| 0.6430 \| \| 0.0354 \| 99.44 \| 28440 \| 0.1457 \| 0.3944 \| 0.6011 \| 0.5962 \| nan \| 0.5727 \| 0.6296 \| 0.0 \| 0.5555 \| 0.6277 \| \| 0.0557 \| 99.51 \| 28460 \| 0.1402 \| 0.4060 \| 0.6182 \| 0.6110 \| nan \| 0.5765 \| 0.6599 \| 0.0 \| 0.5606 \| 0.6575 \| \| 0.0318 \| 99.58 \| 28480 \| 0.1424 \| 0.3997 \| 0.6086 \| 0.6020 \| nan \| 0.5706 \| 0.6465 \| 0.0 \| 0.5549 \| 0.6442 \| \| 0.0379 \| 99.65 \| 28500 \| 0.1545 \| 0.3816 \| 0.5816 \| 0.5769 \| nan \| 0.5545 \| 0.6086 \| 0.0 \| 0.5372 \| 0.6076 \| \| 0.0471 \| 99.72 \| 28520 \| 0.1443 \| 0.3983 \| 0.6063 \| 0.5994 \| nan \| 0.5666 \| 0.6459 \| 0.0 \| 0.5515 \| 0.6433 \| \| 0.0604 \| 99.79 \| 28540 \| 0.1323 \| 0.4111 \| 0.6245 \| 0.6134 \| nan \| 0.5603 \| 0.6887 \| 0.0 \| 0.5514 \| 0.6819 \| \| 0.0652 \| 99.86 \| 28560 \| 0.1359 \| 0.4074 \| 0.6192 \| 0.6102 \| nan \| 0.5672 \| 0.6712 \| 0.0 \| 0.5542 \| 0.6679 \| \| 0.0558 \| 99.93 \| 28580 \| 0.1393 \| 0.4024 \| 0.6120 \| 0.6056 \| nan \| 0.5747 \| 0.6493 \| 0.0 \| 0.5601 \| 0.6471 \| \| 0.0429 \| 100.0 \| 28600 \| 0.1367 \| 0.4081 \| 0.6207 \| 0.6124 \| nan \| 0.5731 \| 0.6682 \| 0.0 \| 0.5589 \| 0.6654 \| \| 0.0537 \| 100.07 \| 28620 \| 0.1349 \| 0.4046 \| 0.6152 \| 0.6082 \| nan \| 0.5752 \| 0.6552 \| 0.0 \| 0.5611 \| 0.6527 \| \| 0.0531 \| 100.14 \| 28640 \| 0.1383 \| 0.3996 \| 0.6073 \| 0.5994 \| nan \| 0.5618 \| 0.6528 \| 0.0 \| 0.5475 \| 0.6513 \| \| 0.062 \| 100.21 \| 28660 \| 0.1392 \| 0.3925 \| 0.5963 \| 0.5866 \| nan \| 0.5403 \| 0.6523 \| 0.0 \| 0.5268 \| 0.6506 \| \| 0.0537 \| 100.28 \| 28680 \| 0.1357 \| 0.3957 \| 0.6013 \| 0.5910 \| nan \| 0.5418 \| 0.6608 \| 0.0 \| 0.5304 \| 0.6568 \| \| 0.04 \| 100.35 \| 28700 \| 0.1282 \| 0.4035 \| 0.6133 \| 0.6008 \| nan \| 0.5408 \| 0.6859 \| 0.0 \| 0.5309 \| 0.6795 \| \| 0.06 \| 100.42 \| 28720 \| 0.1289 \| 0.4094 \| 0.6228 \| 0.6126 \| nan \| 0.5639 \| 0.6816 \| 0.0 \| 0.5519 \| 0.6762 \| \| 0.0802 \| 100.49 \| 28740 \| 0.1349 \| 0.4065 \| 0.6180 \| 0.6093 \| nan \| 0.5682 \| 0.6678 \| 0.0 \| 0.5547 \| 0.6650 \| \| 0.0503 \| 100.56 \| 28760 \| 0.1431 \| 0.3969 \| 0.6048 \| 0.5994 \| nan \| 0.5736 \| 0.6360 \| 0.0 \| 0.5574 \| 0.6333 \| \| 0.0342 \| 100.63 \| 28780 \| 0.1403 \| 0.3972 \| 0.6049 \| 0.5971 \| nan \| 0.5598 \| 0.6501 \| 0.0 \| 0.5451 \| 0.6464 \| \| 0.048 \| 100.7 \| 28800 \| 0.1414 \| 0.3942 \| 0.6001 \| 0.5926 \| nan \| 0.5568 \| 0.6434 \| 0.0 \| 0.5426 \| 0.6401 \| \| 0.0173 \| 100.77 \| 28820 \| 0.1417 \| 0.3903 \| 0.5938 \| 0.5856 \| nan \| 0.5465 \| 0.6412 \| 0.0 \| 0.5329 \| 0.6381 \| \| 0.0407 \| 100.84 \| 28840 \| 0.1446 \| 0.3984 \| 0.6071 \| 0.6022 \| nan \| 0.5789 \| 0.6352 \| 0.0 \| 0.5623 \| 0.6328 \| \| 0.0357 \| 100.91 \| 28860 \| 0.1456 \| 0.3953 \| 0.6024 \| 0.5985 \| nan \| 0.5801 \| 0.6247 \| 0.0 \| 0.5632 \| 0.6227 \| \| 0.0367 \| 100.98 \| 28880 \| 0.1409 \| 0.3963 \| 0.6032 \| 0.5942 \| nan \| 0.5513 \| 0.6551 \| 0.0 \| 0.5366 \| 0.6524 \| \| 0.0368 \| 101.05 \| 28900 \| 0.1362 \| 0.4043 \| 0.6151 \| 0.6062 \| nan \| 0.5641 \| 0.6660 \| 0.0 \| 0.5498 \| 0.6632 \| \| 0.0273 \| 101.12 \| 28920 \| 0.1392 \| 0.3947 \| 0.6005 \| 0.5916 \| nan \| 0.5495 \| 0.6514 \| 0.0 \| 0.5352 \| 0.6490 \| \| 0.0458 \| 101.19 \| 28940 \| 0.1408 \| 0.3901 \| 0.5933 \| 0.5848 \| nan \| 0.5441 \| 0.6426 \| 0.0 \| 0.5301 \| 0.6401 \| \| 0.0325 \| 101.26 \| 28960 \| 0.1394 \| 0.3946 \| 0.6000 \| 0.5909 \| nan \| 0.5473 \| 0.6528 \| 0.0 \| 0.5340 \| 0.6498 \| \| 0.0801 \| 101.33 \| 28980 \| 0.1462 \| 0.3841 \| 0.5841 \| 0.5751 \| nan \| 0.5320 \| 0.6363 \| 0.0 \| 0.5185 \| 0.6339 \| \| 0.0407 \| 101.4 \| 29000 \| 0.1469 \| 0.3883 \| 0.5908 \| 0.5839 \| nan \| 0.5507 \| 0.6310 \| 0.0 \| 0.5362 \| 0.6286 \| \| 0.0576 \| 101.47 \| 29020 \| 0.1460 \| 0.3894 \| 0.5926 \| 0.5854 \| nan \| 0.5511 \| 0.6342 \| 0.0 \| 0.5368 \| 0.6315 \| \| 0.0462 \| 101.54 \| 29040 \| 0.1414 \| 0.3985 \| 0.6055 \| 0.5971 \| nan \| 0.5571 \| 0.6540 \| 0.0 \| 0.5447 \| 0.6507 \| \| 0.058 \| 101.61 \| 29060 \| 0.1407 \| 0.4095 \| 0.6232 \| 0.6184 \| nan \| 0.5959 \| 0.6504 \| 0.0 \| 0.5809 \| 0.6476 \| \| 0.0439 \| 101.68 \| 29080 \| 0.1394 \| 0.4068 \| 0.6187 \| 0.6103 \| nan \| 0.5702 \| 0.6672 \| 0.0 \| 0.5561 \| 0.6642 \| \| 0.055 \| 101.75 \| 29100 \| 0.1279 \| 0.4234 \| 0.6431 \| 0.6320 \| nan \| 0.5791 \| 0.7071 \| 0.0 \| 0.5684 \| 0.7017 \| \| 0.0292 \| 101.82 \| 29120 \| 0.1327 \| 0.4179 \| 0.6349 \| 0.6278 \| nan \| 0.5942 \| 0.6757 \| 0.0 \| 0.5825 \| 0.6711 \| \| 0.038 \| 101.89 \| 29140 \| 0.1329 \| 0.4090 \| 0.6220 \| 0.6140 \| nan \| 0.5757 \| 0.6684 \| 0.0 \| 0.5630 \| 0.6641 \| \| 0.0326 \| 101.96 \| 29160 \| 0.1346 \| 0.4100 \| 0.6233 \| 0.6164 \| nan \| 0.5836 \| 0.6630 \| 0.0 \| 0.5712 \| 0.6587 \| \| 0.0551 \| 102.03 \| 29180 \| 0.1351 \| 0.4043 \| 0.6138 \| 0.6023 \| nan \| 0.5471 \| 0.6804 \| 0.0 \| 0.5374 \| 0.6755 \| \| 0.0636 \| 102.1 \| 29200 \| 0.1350 \| 0.4041 \| 0.6132 \| 0.6020 \| nan \| 0.5484 \| 0.6779 \| 0.0 \| 0.5386 \| 0.6736 \| \| 0.0591 \| 102.17 \| 29220 \| 0.1343 \| 0.4066 \| 0.6170 \| 0.6048 \| nan \| 0.5463 \| 0.6878 \| 0.0 \| 0.5358 \| 0.6839 \| \| 0.035 \| 102.24 \| 29240 \| 0.1336 \| 0.4145 \| 0.6295 \| 0.6205 \| nan \| 0.5777 \| 0.6812 \| 0.0 \| 0.5668 \| 0.6766 \| \| 0.0554 \| 102.31 \| 29260 \| 0.1406 \| 0.4042 \| 0.6142 \| 0.6065 \| nan \| 0.5698 \| 0.6586 \| 0.0 \| 0.5588 \| 0.6538 \| \| 0.0203 \| 102.38 \| 29280 \| 0.1415 \| 0.4015 \| 0.6102 \| 0.6028 \| nan \| 0.5673 \| 0.6531 \| 0.0 \| 0.5557 \| 0.6489 \| \| 0.0496 \| 102.45 \| 29300 \| 0.1421 \| 0.3988 \| 0.6069 \| 0.6014 \| nan \| 0.5751 \| 0.6387 \| 0.0 \| 0.5600 \| 0.6364 \| \| 0.0575 \| 102.52 \| 29320 \| 0.1365 \| 0.4019 \| 0.6109 \| 0.6024 \| nan \| 0.5619 \| 0.6598 \| 0.0 \| 0.5496 \| 0.6562 \| \| 0.0355 \| 102.59 \| 29340 \| 0.1375 \| 0.4009 \| 0.6097 \| 0.6019 \| nan \| 0.5647 \| 0.6547 \| 0.0 \| 0.5512 \| 0.6515 \| \| 0.0429 \| 102.66 \| 29360 \| 0.1402 \| 0.3941 \| 0.5992 \| 0.5905 \| nan \| 0.5493 \| 0.6490 \| 0.0 \| 0.5366 \| 0.6457 \| \| 0.0533 \| 102.73 \| 29380 \| 0.1410 \| 0.3911 \| 0.5938 \| 0.5832 \| nan \| 0.5322 \| 0.6554 \| 0.0 \| 0.5226 \| 0.6507 \| \| 0.0683 \| 102.8 \| 29400 \| 0.1423 \| 0.3961 \| 0.6019 \| 0.5925 \| nan \| 0.5478 \| 0.6560 \| 0.0 \| 0.5359 \| 0.6523 \| \| 0.0243 \| 102.87 \| 29420 \| 0.1434 \| 0.3908 \| 0.5941 \| 0.5851 \| nan \| 0.5420 \| 0.6462 \| 0.0 \| 0.5297 \| 0.6428 \| \| 0.0301 \| 102.94 \| 29440 \| 0.1408 \| 0.3984 \| 0.6049 \| 0.5922 \| nan \| 0.5314 \| 0.6785 \| 0.0 \| 0.5205 \| 0.6746 \| \| 0.0507 \| 103.01 \| 29460 \| 0.1390 \| 0.3995 \| 0.6069 \| 0.5980 \| nan \| 0.5554 \| 0.6584 \| 0.0 \| 0.5440 \| 0.6545 \| \| 0.0328 \| 103.08 \| 29480 \| 0.1505 \| 0.3834 \| 0.5825 \| 0.5748 \| nan \| 0.5376 \| 0.6275 \| 0.0 \| 0.5242 \| 0.6259 \| \| 0.0327 \| 103.15 \| 29500 \| 0.1428 \| 0.4051 \| 0.6168 \| 0.6117 \| nan \| 0.5869 \| 0.6468 \| 0.0 \| 0.5704 \| 0.6449 \| \| 0.0406 \| 103.22 \| 29520 \| 0.1430 \| 0.4035 \| 0.6134 \| 0.6060 \| nan \| 0.5707 \| 0.6560 \| 0.0 \| 0.5572 \| 0.6533 \| \| 0.054 \| 103.29 \| 29540 \| 0.1451 \| 0.4038 \| 0.6147 \| 0.6094 \| nan \| 0.5840 \| 0.6454 \| 0.0 \| 0.5677 \| 0.6436 \| \| 0.0348 \| 103.36 \| 29560 \| 0.1458 \| 0.4020 \| 0.6120 \| 0.6063 \| nan \| 0.5791 \| 0.6450 \| 0.0 \| 0.5628 \| 0.6432 \| \| 0.0465 \| 103.43 \| 29580 \| 0.1426 \| 0.4065 \| 0.6186 \| 0.6139 \| nan \| 0.5916 \| 0.6456 \| 0.0 \| 0.5760 \| 0.6435 \| \| 0.0405 \| 103.5 \| 29600 \| 0.1457 \| 0.4040 \| 0.6149 \| 0.6096 \| nan \| 0.5840 \| 0.6458 \| 0.0 \| 0.5686 \| 0.6434 \| \| 0.0616 \| 103.57 \| 29620 \| 0.1424 \| 0.4034 \| 0.6135 \| 0.6081 \| nan \| 0.5826 \| 0.6443 \| 0.0 \| 0.5691 \| 0.6410 \| \| 0.0215 \| 103.64 \| 29640 \| 0.1423 \| 0.3978 \| 0.6052 \| 0.5976 \| nan \| 0.5614 \| 0.6490 \| 0.0 \| 0.5471 \| 0.6462 \| \| 0.0513 \| 103.71 \| 29660 \| 0.1399 \| 0.4006 \| 0.6090 \| 0.6007 \| nan \| 0.5611 \| 0.6568 \| 0.0 \| 0.5489 \| 0.6530 \| \| 0.0413 \| 103.78 \| 29680 \| 0.1376 \| 0.3963 \| 0.6017 \| 0.5895 \| nan \| 0.5310 \| 0.6724 \| 0.0 \| 0.5224 \| 0.6664 \| \| 0.037 \| 103.85 \| 29700 \| 0.1341 \| 0.4135 \| 0.6285 \| 0.6198 \| nan \| 0.5783 \| 0.6787 \| 0.0 \| 0.5655 \| 0.6750 \| \| 0.0473 \| 103.92 \| 29720 \| 0.1385 \| 0.4054 \| 0.6157 \| 0.6073 \| nan \| 0.5672 \| 0.6641 \| 0.0 \| 0.5555 \| 0.6606 \| \| 0.0511 \| 103.99 \| 29740 \| 0.1377 \| 0.4109 \| 0.6247 \| 0.6154 \| nan \| 0.5714 \| 0.6779 \| 0.0 \| 0.5588 \| 0.6740 \| \| 0.0509 \| 104.06 \| 29760 \| 0.1364 \| 0.4081 \| 0.6204 \| 0.6113 \| nan \| 0.5679 \| 0.6728 \| 0.0 \| 0.5553 \| 0.6691 \| \| 0.067 \| 104.13 \| 29780 \| 0.1373 \| 0.4058 \| 0.6166 \| 0.6101 \| nan \| 0.5791 \| 0.6540 \| 0.0 \| 0.5675 \| 0.6498 \| \| 0.0322 \| 104.2 \| 29800 \| 0.1404 \| 0.3988 \| 0.6062 \| 0.5987 \| nan \| 0.5629 \| 0.6496 \| 0.0 \| 0.5499 \| 0.6465 \| \| 0.041 \| 104.27 \| 29820 \| 0.1373 \| 0.4025 \| 0.6117 \| 0.6023 \| nan \| 0.5572 \| 0.6662 \| 0.0 \| 0.5456 \| 0.6620 \| \| 0.0457 \| 104.34 \| 29840 \| 0.1380 \| 0.3954 \| 0.6005 \| 0.5900 \| nan \| 0.5400 \| 0.6610 \| 0.0 \| 0.5307 \| 0.6555 \| \| 0.0464 \| 104.41 \| 29860 \| 0.1355 \| 0.3980 \| 0.6045 \| 0.5946 \| nan \| 0.5470 \| 0.6621 \| 0.0 \| 0.5360 \| 0.6579 \| \| 0.0624 \| 104.48 \| 29880 \| 0.1415 \| 0.3976 \| 0.6038 \| 0.5939 \| nan \| 0.5468 \| 0.6608 \| 0.0 \| 0.5358 \| 0.6570 \| \| 0.0669 \| 104.55 \| 29900 \| 0.1404 \| 0.4031 \| 0.6128 \| 0.6064 \| nan \| 0.5760 \| 0.6496 \| 0.0 \| 0.5629 \| 0.6464 \| \| 0.0381 \| 104.62 \| 29920 \| 0.1409 \| 0.4045 \| 0.6146 \| 0.6061 \| nan \| 0.5656 \| 0.6636 \| 0.0 \| 0.5530 \| 0.6604 \| \| 0.0549 \| 104.69 \| 29940 \| 0.1429 \| 0.3938 \| 0.5984 \| 0.5889 \| nan \| 0.5439 \| 0.6528 \| 0.0 \| 0.5316 \| 0.6498 \| \| 0.0484 \| 104.76 \| 29960 \| 0.1436 \| 0.3972 \| 0.6031 \| 0.5944 \| nan \| 0.5529 \| 0.6534 \| 0.0 \| 0.5415 \| 0.6500 \| \| 0.0427 \| 104.83 \| 29980 \| 0.1410 \| 0.4011 \| 0.6098 \| 0.6012 \| nan \| 0.5601 \| 0.6595 \| 0.0 \| 0.5471 \| 0.6561 \| \| 0.0352 \| 104.9 \| 30000 \| 0.1441 \| 0.4030 \| 0.6122 \| 0.6043 \| nan \| 0.5663 \| 0.6581 \| 0.0 \| 0.5541 \| 0.6549 \| \| 0.0494 \| 104.97 \| 30020 \| 0.1468 \| 0.3954 \| 0.6004 \| 0.5900 \| nan \| 0.5403 \| 0.6605 \| 0.0 \| 0.5295 \| 0.6566 \| \| 0.0361 \| 105.03 \| 30040 \| 0.1480 \| 0.3996 \| 0.6073 \| 0.6007 \| nan \| 0.5693 \| 0.6453 \| 0.0 \| 0.5556 \| 0.6431 \| \| 0.0382 \| 105.1 \| 30060 \| 0.1402 \| 0.4086 \| 0.6205 \| 0.6128 \| nan \| 0.5760 \| 0.6650 \| 0.0 \| 0.5640 \| 0.6617 \| \| 0.0376 \| 105.17 \| 30080 \| 0.1398 \| 0.4022 \| 0.6108 \| 0.6020 \| nan \| 0.5603 \| 0.6612 \| 0.0 \| 0.5486 \| 0.6581 \| \| 0.0343 \| 105.24 \| 30100 \| 0.1476 \| 0.3951 \| 0.6002 \| 0.5919 \| nan \| 0.5521 \| 0.6484 \| 0.0 \| 0.5394 \| 0.6459 \| \| 0.0392 \| 105.31 \| 30120 \| 0.1433 \| 0.3952 \| 0.6002 \| 0.5895 \| nan \| 0.5385 \| 0.6619 \| 0.0 \| 0.5267 \| 0.6588 \| \| 0.0626 \| 105.38 \| 30140 \| 0.1419 \| 0.3984 \| 0.6053 \| 0.5946 \| nan \| 0.5433 \| 0.6673 \| 0.0 \| 0.5320 \| 0.6632 \| \| 0.0394 \| 105.45 \| 30160 \| 0.1394 \| 0.4059 \| 0.6165 \| 0.6066 \| nan \| 0.5596 \| 0.6733 \| 0.0 \| 0.5485 \| 0.6691 \| \| 0.0584 \| 105.52 \| 30180 \| 0.1438 \| 0.3974 \| 0.6039 \| 0.5948 \| nan \| 0.5516 \| 0.6561 \| 0.0 \| 0.5392 \| 0.6530 \| \| 0.0305 \| 105.59 \| 30200 \| 0.1424 \| 0.4095 \| 0.6226 \| 0.6149 \| nan \| 0.5780 \| 0.6672 \| 0.0 \| 0.5639 \| 0.6645 \| \| 0.0327 \| 105.66 \| 30220 \| 0.1400 \| 0.4077 \| 0.6195 \| 0.6109 \| nan \| 0.5698 \| 0.6691 \| 0.0 \| 0.5568 \| 0.6663 \| \| 0.0274 \| 105.73 \| 30240 \| 0.1371 \| 0.4106 \| 0.6236 \| 0.6133 \| nan \| 0.5643 \| 0.6828 \| 0.0 \| 0.5535 \| 0.6783 \| \| 0.0268 \| 105.8 \| 30260 \| 0.1393 \| 0.4050 \| 0.6153 \| 0.6061 \| nan \| 0.5624 \| 0.6681 \| 0.0 \| 0.5505 \| 0.6645 \| \| 0.065 \| 105.87 \| 30280 \| 0.1428 \| 0.3987 \| 0.6051 \| 0.5937 \| nan \| 0.5394 \| 0.6708 \| 0.0 \| 0.5291 \| 0.6670 \| \| 0.027 \| 105.94 \| 30300 \| 0.1423 \| 0.3964 \| 0.6017 \| 0.5926 \| nan \| 0.5491 \| 0.6543 \| 0.0 \| 0.5383 \| 0.6509 \| \| 0.0629 \| 106.01 \| 30320 \| 0.1449 \| 0.3912 \| 0.5943 \| 0.5866 \| nan \| 0.5496 \| 0.6391 \| 0.0 \| 0.5374 \| 0.6362 \| \| 0.0332 \| 106.08 \| 30340 \| 0.1425 \| 0.3945 \| 0.5994 \| 0.5905 \| nan \| 0.5478 \| 0.6511 \| 0.0 \| 0.5349 \| 0.6485 \| \| 0.0409 \| 106.15 \| 30360 \| 0.1361 \| 0.4103 \| 0.6242 \| 0.6182 \| nan \| 0.5893 \| 0.6592 \| 0.0 \| 0.5752 \| 0.6558 \| \| 0.0255 \| 106.22 \| 30380 \| 0.1311 \| 0.4151 \| 0.6309 \| 0.6225 \| nan \| 0.5824 \| 0.6794 \| 0.0 \| 0.5695 \| 0.6757 \| \| 0.0532 \| 106.29 \| 30400 \| 0.1347 \| 0.4155 \| 0.6316 \| 0.6248 \| nan \| 0.5923 \| 0.6709 \| 0.0 \| 0.5789 \| 0.6675 \| \| 0.0465 \| 106.36 \| 30420 \| 0.1297 \| 0.4161 \| 0.6322 \| 0.6229 \| nan \| 0.5781 \| 0.6864 \| 0.0 \| 0.5681 \| 0.6801 \| \| 0.043 \| 106.43 \| 30440 \| 0.1338 \| 0.4130 \| 0.6273 \| 0.6190 \| nan \| 0.5792 \| 0.6755 \| 0.0 \| 0.5674 \| 0.6716 \| \| 0.0399 \| 106.5 \| 30460 \| 0.1329 \| 0.4081 \| 0.6196 \| 0.6091 \| nan \| 0.5591 \| 0.6801 \| 0.0 \| 0.5492 \| 0.6750 \| \| 0.0317 \| 106.57 \| 30480 \| 0.1367 \| 0.4048 \| 0.6149 \| 0.6053 \| nan \| 0.5592 \| 0.6707 \| 0.0 \| 0.5473 \| 0.6669 \| \| 0.044 \| 106.64 \| 30500 \| 0.1330 \| 0.4107 \| 0.6240 \| 0.6137 \| nan \| 0.5645 \| 0.6834 \| 0.0 \| 0.5547 \| 0.6775 \| \| 0.032 \| 106.71 \| 30520 \| 0.1401 \| 0.3970 \| 0.6031 \| 0.5927 \| nan \| 0.5428 \| 0.6634 \| 0.0 \| 0.5325 \| 0.6586 \| \| 0.0685 \| 106.78 \| 30540 \| 0.1417 \| 0.3968 \| 0.6033 \| 0.5948 \| nan \| 0.5541 \| 0.6526 \| 0.0 \| 0.5424 \| 0.6481 \| \| 0.0583 \| 106.85 \| 30560 \| 0.1446 \| 0.3953 \| 0.6008 \| 0.5931 \| nan \| 0.5563 \| 0.6453 \| 0.0 \| 0.5439 \| 0.6421 \| \| 0.0505 \| 106.92 \| 30580 \| 0.1420 \| 0.3952 \| 0.6007 \| 0.5926 \| nan \| 0.5537 \| 0.6477 \| 0.0 \| 0.5416 \| 0.6441 \| \| 0.0443 \| 106.99 \| 30600 \| 0.1427 \| 0.3976 \| 0.6045 \| 0.5982 \| nan \| 0.5681 \| 0.6409 \| 0.0 \| 0.5550 \| 0.6377 \| \| 0.0234 \| 107.06 \| 30620 \| 0.1383 \| 0.4030 \| 0.6123 \| 0.6030 \| nan \| 0.5590 \| 0.6655 \| 0.0 \| 0.5470 \| 0.6619 \| \| 0.0602 \| 107.13 \| 30640 \| 0.1444 \| 0.3940 \| 0.5990 \| 0.5902 \| nan \| 0.5479 \| 0.6501 \| 0.0 \| 0.5351 \| 0.6470 \| \| 0.0365 \| 107.2 \| 30660 \| 0.1527 \| 0.3949 \| 0.6016 \| 0.5981 \| nan \| 0.5814 \| 0.6217 \| 0.0 \| 0.5654 \| 0.6192 \| \| 0.0308 \| 107.27 \| 30680 \| 0.1419 \| 0.4012 \| 0.6109 \| 0.6046 \| nan \| 0.5747 \| 0.6471 \| 0.0 \| 0.5601 \| 0.6433 \| \| 0.0443 \| 107.34 \| 30700 \| 0.1461 \| 0.3963 \| 0.6032 \| 0.5966 \| nan \| 0.5648 \| 0.6417 \| 0.0 \| 0.5509 \| 0.6381 \| \| 0.0349 \| 107.41 \| 30720 \| 0.1435 \| 0.4028 \| 0.6131 \| 0.6056 \| nan \| 0.5699 \| 0.6563 \| 0.0 \| 0.5563 \| 0.6520 \| \| 0.0425 \| 107.48 \| 30740 \| 0.1432 \| 0.4098 \| 0.6241 \| 0.6184 \| nan \| 0.5912 \| 0.6571 \| 0.0 \| 0.5763 \| 0.6531 \| \| 0.0521 \| 107.55 \| 30760 \| 0.1499 \| 0.3922 \| 0.5968 \| 0.5901 \| nan \| 0.5582 \| 0.6354 \| 0.0 \| 0.5437 \| 0.6328 \| \| 0.0413 \| 107.62 \| 30780 \| 0.1447 \| 0.3910 \| 0.5945 \| 0.5854 \| nan \| 0.5417 \| 0.6474 \| 0.0 \| 0.5300 \| 0.6432 \| \| 0.0486 \| 107.69 \| 30800 \| 0.1421 \| 0.3947 \| 0.6005 \| 0.5920 \| nan \| 0.5516 \| 0.6494 \| 0.0 \| 0.5385 \| 0.6456 \| \| 0.0647 \| 107.76 \| 30820 \| 0.1455 \| 0.3966 \| 0.6040 \| 0.5983 \| nan \| 0.5713 \| 0.6367 \| 0.0 \| 0.5559 \| 0.6338 \| \| 0.0425 \| 107.83 \| 30840 \| 0.1467 \| 0.3969 \| 0.6040 \| 0.5984 \| nan \| 0.5716 \| 0.6363 \| 0.0 \| 0.5580 \| 0.6327 \| \| 0.0473 \| 107.9 \| 30860 \| 0.1417 \| 0.4017 \| 0.6114 \| 0.6051 \| nan \| 0.5749 \| 0.6480 \| 0.0 \| 0.5606 \| 0.6444 \| \| 0.0565 \| 107.97 \| 30880 \| 0.1413 \| 0.4020 \| 0.6111 \| 0.6042 \| nan \| 0.5716 \| 0.6506 \| 0.0 \| 0.5587 \| 0.6472 \| \| 0.0425 \| 108.04 \| 30900 \| 0.1372 \| 0.4089 \| 0.6218 \| 0.6128 \| nan \| 0.5699 \| 0.6737 \| 0.0 \| 0.5573 \| 0.6693 \| \| 0.0309 \| 108.11 \| 30920 \| 0.1416 \| 0.4015 \| 0.6104 \| 0.6014 \| nan \| 0.5586 \| 0.6621 \| 0.0 \| 0.5461 \| 0.6583 \| \| 0.0375 \| 108.18 \| 30940 \| 0.1403 \| 0.4009 \| 0.6095 \| 0.6007 \| nan \| 0.5589 \| 0.6600 \| 0.0 \| 0.5461 \| 0.6566 \| \| 0.0283 \| 108.25 \| 30960 \| 0.1458 \| 0.3943 \| 0.6000 \| 0.5935 \| nan \| 0.5622 \| 0.6379 \| 0.0 \| 0.5489 \| 0.6341 \| \| 0.0436 \| 108.32 \| 30980 \| 0.1409 \| 0.4041 \| 0.6146 \| 0.6070 \| nan \| 0.5710 \| 0.6581 \| 0.0 \| 0.5586 \| 0.6537 \| \| 0.0593 \| 108.39 \| 31000 \| 0.1380 \| 0.4103 \| 0.6250 \| 0.6159 \| nan \| 0.5727 \| 0.6772 \| 0.0 \| 0.5616 \| 0.6694 \| \| 0.0334 \| 108.46 \| 31020 \| 0.1420 \| 0.4039 \| 0.6155 \| 0.6104 \| nan \| 0.5863 \| 0.6447 \| 0.0 \| 0.5731 \| 0.6385 \| \| 0.0626 \| 108.53 \| 31040 \| 0.1438 \| 0.3976 \| 0.6049 \| 0.5960 \| nan \| 0.5532 \| 0.6567 \| 0.0 \| 0.5404 \| 0.6523 \| \| 0.0484 \| 108.6 \| 31060 \| 0.1431 \| 0.4037 \| 0.6142 \| 0.6086 \| nan \| 0.5823 \| 0.6460 \| 0.0 \| 0.5689 \| 0.6422 \| \| 0.0406 \| 108.67 \| 31080 \| 0.1489 \| 0.3861 \| 0.5868 \| 0.5776 \| nan \| 0.5338 \| 0.6398 \| 0.0 \| 0.5224 \| 0.6359 \| \| 0.0489 \| 108.74 \| 31100 \| 0.1472 \| 0.3924 \| 0.5966 \| 0.5889 \| nan \| 0.5519 \| 0.6414 \| 0.0 \| 0.5393 \| 0.6379 \| \| 0.0429 \| 108.81 \| 31120 \| 0.1426 \| 0.3994 \| 0.6074 \| 0.5981 \| nan \| 0.5541 \| 0.6606 \| 0.0 \| 0.5428 \| 0.6553 \| \| 0.0462 \| 108.88 \| 31140 \| 0.1384 \| 0.4036 \| 0.6142 \| 0.6048 \| nan \| 0.5598 \| 0.6686 \| 0.0 \| 0.5485 \| 0.6623 \| \| 0.0635 \| 108.95 \| 31160 \| 0.1412 \| 0.3991 \| 0.6068 \| 0.5973 \| nan \| 0.5519 \| 0.6617 \| 0.0 \| 0.5405 \| 0.6566 \| \| 0.0302 \| 109.02 \| 31180 \| 0.1424 \| 0.3981 \| 0.6050 \| 0.5950 \| nan \| 0.5469 \| 0.6632 \| 0.0 \| 0.5366 \| 0.6577 \| \| 0.0364 \| 109.09 \| 31200 \| 0.1394 \| 0.4001 \| 0.6085 \| 0.5991 \| nan \| 0.5541 \| 0.6628 \| 0.0 \| 0.5421 \| 0.6581 \| \| 0.029 \| 109.16 \| 31220 \| 0.1378 \| 0.4017 \| 0.6106 \| 0.5996 \| nan \| 0.5470 \| 0.6743 \| 0.0 \| 0.5350 \| 0.6700 \| \| 0.0339 \| 109.23 \| 31240 \| 0.1389 \| 0.4000 \| 0.6086 \| 0.5996 \| nan \| 0.5566 \| 0.6606 \| 0.0 \| 0.5431 \| 0.6568 \| \| 0.0279 \| 109.3 \| 31260 \| 0.1402 \| 0.3991 \| 0.6072 \| 0.5983 \| nan \| 0.5556 \| 0.6589 \| 0.0 \| 0.5429 \| 0.6544 \| \| 0.0624 \| 109.37 \| 31280 \| 0.1404 \| 0.4014 \| 0.6104 \| 0.6014 \| nan \| 0.5585 \| 0.6623 \| 0.0 \| 0.5471 \| 0.6569 \| \| 0.053 \| 109.44 \| 31300 \| 0.1406 \| 0.4067 \| 0.6181 \| 0.6092 \| nan \| 0.5666 \| 0.6695 \| 0.0 \| 0.5547 \| 0.6653 \| \| 0.0456 \| 109.51 \| 31320 \| 0.1376 \| 0.4077 \| 0.6195 \| 0.6110 \| nan \| 0.5703 \| 0.6688 \| 0.0 \| 0.5589 \| 0.6641 \| \| 0.0708 \| 109.58 \| 31340 \| 0.1405 \| 0.4111 \| 0.6249 \| 0.6172 \| nan \| 0.5801 \| 0.6697 \| 0.0 \| 0.5682 \| 0.6650 \| \| 0.0379 \| 109.65 \| 31360 \| 0.1430 \| 0.3966 \| 0.6026 \| 0.5934 \| nan \| 0.5494 \| 0.6557 \| 0.0 \| 0.5388 \| 0.6511 \| \| 0.0352 \| 109.72 \| 31380 \| 0.1384 \| 0.4079 \| 0.6198 \| 0.6081 \| nan \| 0.5522 \| 0.6874 \| 0.0 \| 0.5422 \| 0.6814 \| \| 0.0349 \| 109.79 \| 31400 \| 0.1437 \| 0.4003 \| 0.6088 \| 0.6026 \| nan \| 0.5729 \| 0.6447 \| 0.0 \| 0.5599 \| 0.6410 \| \| 0.0558 \| 109.86 \| 31420 \| 0.1414 \| 0.4068 \| 0.6188 \| 0.6115 \| nan \| 0.5766 \| 0.6610 \| 0.0 \| 0.5635 \| 0.6569 \| \| 0.0442 \| 109.93 \| 31440 \| 0.1451 \| 0.3985 \| 0.6062 \| 0.5979 \| nan \| 0.5579 \| 0.6545 \| 0.0 \| 0.5459 \| 0.6497 \| \| 0.0534 \| 110.0 \| 31460 \| 0.1379 \| 0.4050 \| 0.6162 \| 0.6069 \| nan \| 0.5629 \| 0.6695 \| 0.0 \| 0.5507 \| 0.6642 \| \| 0.0421 \| 110.07 \| 31480 \| 0.1414 \| 0.4014 \| 0.6110 \| 0.6035 \| nan \| 0.5680 \| 0.6539 \| 0.0 \| 0.5550 \| 0.6491 \| \| 0.0564 \| 110.14 \| 31500 \| 0.1416 \| 0.3971 \| 0.6043 \| 0.5960 \| nan \| 0.5565 \| 0.6520 \| 0.0 \| 0.5435 \| 0.6478 \| \| 0.0343 \| 110.21 \| 31520 \| 0.1417 \| 0.3935 \| 0.5984 \| 0.5880 \| nan \| 0.5388 \| 0.6580 \| 0.0 \| 0.5271 \| 0.6535 \| \| 0.0416 \| 110.28 \| 31540 \| 0.1412 \| 0.4071 \| 0.6202 \| 0.6144 \| nan \| 0.5869 \| 0.6536 \| 0.0 \| 0.5717 \| 0.6497 \| \| 0.0324 \| 110.35 \| 31560 \| 0.1429 \| 0.4016 \| 0.6117 \| 0.6049 \| nan \| 0.5723 \| 0.6511 \| 0.0 \| 0.5569 \| 0.6480 \| \| 0.0415 \| 110.42 \| 31580 \| 0.1352 \| 0.4017 \| 0.6112 \| 0.6012 \| nan \| 0.5538 \| 0.6685 \| 0.0 \| 0.5431 \| 0.6619 \| \| 0.0283 \| 110.49 \| 31600 \| 0.1371 \| 0.4013 \| 0.6101 \| 0.6006 \| nan \| 0.5552 \| 0.6650 \| 0.0 \| 0.5434 \| 0.6606 \| \| 0.0585 \| 110.56 \| 31620 \| 0.1397 \| 0.3957 \| 0.6015 \| 0.5915 \| nan \| 0.5437 \| 0.6593 \| 0.0 \| 0.5323 \| 0.6547 \| \| 0.033 \| 110.63 \| 31640 \| 0.1466 \| 0.3939 \| 0.6000 \| 0.5941 \| nan \| 0.5662 \| 0.6337 \| 0.0 \| 0.5513 \| 0.6304 \| \| 0.0484 \| 110.7 \| 31660 \| 0.1462 \| 0.3955 \| 0.6015 \| 0.5926 \| nan \| 0.5497 \| 0.6533 \| 0.0 \| 0.5368 \| 0.6497 \| \| 0.0394 \| 110.77 \| 31680 \| 0.1411 \| 0.4007 \| 0.6095 \| 0.5995 \| nan \| 0.5517 \| 0.6673 \| 0.0 \| 0.5398 \| 0.6623 \| \| 0.0336 \| 110.84 \| 31700 \| 0.1438 \| 0.4000 \| 0.6083 \| 0.6006 \| nan \| 0.5638 \| 0.6528 \| 0.0 \| 0.5517 \| 0.6483 \| \| 0.0494 \| 110.91 \| 31720 \| 0.1421 \| 0.4015 \| 0.6113 \| 0.6047 \| nan \| 0.5736 \| 0.6489 \| 0.0 \| 0.5593 \| 0.6452 \| \| 0.0437 \| 110.98 \| 31740 \| 0.1378 \| 0.4057 \| 0.6173 \| 0.6070 \| nan \| 0.5581 \| 0.6764 \| 0.0 \| 0.5472 \| 0.6698 \| \| 0.0297 \| 111.05 \| 31760 \| 0.1400 \| 0.4040 \| 0.6151 \| 0.6075 \| nan \| 0.5711 \| 0.6591 \| 0.0 \| 0.5577 \| 0.6544 \| \| 0.0461 \| 111.12 \| 31780 \| 0.1390 \| 0.4022 \| 0.6122 \| 0.6005 \| nan \| 0.5444 \| 0.6800 \| 0.0 \| 0.5320 \| 0.6745 \| \| 0.05 \| 111.19 \| 31800 \| 0.1372 \| 0.4108 \| 0.6261 \| 0.6204 \| nan \| 0.5930 \| 0.6592 \| 0.0 \| 0.5775 \| 0.6549 \| \| 0.0462 \| 111.26 \| 31820 \| 0.1406 \| 0.4089 \| 0.6228 \| 0.6165 \| nan \| 0.5863 \| 0.6593 \| 0.0 \| 0.5720 \| 0.6547 \| \| 0.0449 \| 111.33 \| 31840 \| 0.1378 \| 0.4063 \| 0.6185 \| 0.6092 \| nan \| 0.5649 \| 0.6721 \| 0.0 \| 0.5517 \| 0.6672 \| \| 0.0555 \| 111.4 \| 31860 \| 0.1439 \| 0.3987 \| 0.6068 \| 0.6000 \| nan \| 0.5674 \| 0.6463 \| 0.0 \| 0.5545 \| 0.6417 \| \| 0.0436 \| 111.47 \| 31880 \| 0.1423 \| 0.4019 \| 0.6110 \| 0.6005 \| nan \| 0.5501 \| 0.6719 \| 0.0 \| 0.5382 \| 0.6675 \| \| 0.04 \| 111.54 \| 31900 \| 0.1418 \| 0.4043 \| 0.6147 \| 0.6051 \| nan \| 0.5589 \| 0.6706 \| 0.0 \| 0.5461 \| 0.6668 \| \| 0.0296 \| 111.61 \| 31920 \| 0.1345 \| 0.4144 \| 0.6300 \| 0.6195 \| nan \| 0.5693 \| 0.6907 \| 0.0 \| 0.5580 \| 0.6853 \| \| 0.0491 \| 111.68 \| 31940 \| 0.1389 \| 0.4086 \| 0.6220 \| 0.6134 \| nan \| 0.5724 \| 0.6717 \| 0.0 \| 0.5586 \| 0.6673 \| \| 0.0464 \| 111.75 \| 31960 \| 0.1381 \| 0.4099 \| 0.6236 \| 0.6126 \| nan \| 0.5602 \| 0.6869 \| 0.0 \| 0.5483 \| 0.6814 \| \| 0.0358 \| 111.82 \| 31980 \| 0.1402 \| 0.3956 \| 0.6010 \| 0.5893 \| nan \| 0.5334 \| 0.6687 \| 0.0 \| 0.5220 \| 0.6647 \| \| 0.0383 \| 111.89 \| 32000 \| 0.1431 \| 0.3999 \| 0.6082 \| 0.6010 \| nan \| 0.5665 \| 0.6499 \| 0.0 \| 0.5533 \| 0.6463 \| \| 0.0354 \| 111.96 \| 32020 \| 0.1396 \| 0.4058 \| 0.6170 \| 0.6075 \| nan \| 0.5621 \| 0.6719 \| 0.0 \| 0.5494 \| 0.6679 \| \| 0.0343 \| 112.03 \| 32040 \| 0.1427 \| 0.3990 \| 0.6062 \| 0.5965 \| nan \| 0.5501 \| 0.6623 \| 0.0 \| 0.5383 \| 0.6587 \| \| 0.0346 \| 112.1 \| 32060 \| 0.1508 \| 0.3897 \| 0.5922 \| 0.5832 \| nan \| 0.5401 \| 0.6444 \| 0.0 \| 0.5273 \| 0.6418 \| \| 0.0418 \| 112.17 \| 32080 \| 0.1539 \| 0.3873 \| 0.5891 \| 0.5831 \| nan \| 0.5544 \| 0.6239 \| 0.0 \| 0.5408 \| 0.6211 \| \| 0.0481 \| 112.24 \| 32100 \| 0.1502 \| 0.4030 \| 0.6134 \| 0.6096 \| nan \| 0.5915 \| 0.6354 \| 0.0 \| 0.5769 \| 0.6320 \| \| 0.0466 \| 112.31 \| 32120 \| 0.1461 \| 0.4062 \| 0.6187 \| 0.6132 \| nan \| 0.5870 \| 0.6504 \| 0.0 \| 0.5731 \| 0.6456 \| \| 0.0327 \| 112.38 \| 32140 \| 0.1480 \| 0.4039 \| 0.6152 \| 0.6106 \| nan \| 0.5882 \| 0.6422 \| 0.0 \| 0.5730 \| 0.6386 \| \| 0.0546 \| 112.45 \| 32160 \| 0.1359 \| 0.4068 \| 0.6179 \| 0.6060 \| nan \| 0.5491 \| 0.6867 \| 0.0 \| 0.5392 \| 0.6812 \| \| 0.0587 \| 112.52 \| 32180 \| 0.1379 \| 0.4110 \| 0.6247 \| 0.6155 \| nan \| 0.5715 \| 0.6780 \| 0.0 \| 0.5597 \| 0.6734 \| \| 0.0323 \| 112.59 \| 32200 \| 0.1369 \| 0.4087 \| 0.6212 \| 0.6104 \| nan \| 0.5588 \| 0.6837 \| 0.0 \| 0.5489 \| 0.6773 \| \| 0.0352 \| 112.66 \| 32220 \| 0.1385 \| 0.4108 \| 0.6246 \| 0.6159 \| nan \| 0.5743 \| 0.6749 \| 0.0 \| 0.5625 \| 0.6699 \| \| 0.0472 \| 112.73 \| 32240 \| 0.1396 \| 0.4035 \| 0.6133 \| 0.6030 \| nan \| 0.5535 \| 0.6731 \| 0.0 \| 0.5430 \| 0.6674 \| \| 0.042 \| 112.8 \| 32260 \| 0.1448 \| 0.3976 \| 0.6042 \| 0.5956 \| nan \| 0.5548 \| 0.6536 \| 0.0 \| 0.5437 \| 0.6491 \| \| 0.039 \| 112.87 \| 32280 \| 0.1503 \| 0.3957 \| 0.6021 \| 0.5957 \| nan \| 0.5649 \| 0.6393 \| 0.0 \| 0.5501 \| 0.6369 \| \| 0.0444 \| 112.94 \| 32300 \| 0.1502 \| 0.3976 \| 0.6050 \| 0.5994 \| nan \| 0.5728 \| 0.6372 \| 0.0 \| 0.5580 \| 0.6348 \| \| 0.0319 \| 113.01 \| 32320 \| 0.1452 \| 0.4027 \| 0.6123 \| 0.6042 \| nan \| 0.5655 \| 0.6592 \| 0.0 \| 0.5530 \| 0.6550 \| \| 0.0423 \| 113.08 \| 32340 \| 0.1483 \| 0.3946 \| 0.6001 \| 0.5924 \| nan \| 0.5558 \| 0.6445 \| 0.0 \| 0.5425 \| 0.6414 \| \| 0.05 \| 113.15 \| 32360 \| 0.1419 \| 0.4066 \| 0.6184 \| 0.6118 \| nan \| 0.5805 \| 0.6562 \| 0.0 \| 0.5674 \| 0.6523 \| \| 0.0433 \| 113.22 \| 32380 \| 0.1442 \| 0.4053 \| 0.6167 \| 0.6105 \| nan \| 0.5808 \| 0.6526 \| 0.0 \| 0.5667 \| 0.6492 \| \| 0.053 \| 113.29 \| 32400 \| 0.1446 \| 0.4053 \| 0.6167 \| 0.6097 \| nan \| 0.5760 \| 0.6574 \| 0.0 \| 0.5621 \| 0.6537 \| \| 0.0396 \| 113.36 \| 32420 \| 0.1442 \| 0.4007 \| 0.6095 \| 0.6009 \| nan \| 0.5599 \| 0.6590 \| 0.0 \| 0.5477 \| 0.6544 \| \| 0.041 \| 113.43 \| 32440 \| 0.1466 \| 0.3984 \| 0.6058 \| 0.5959 \| nan \| 0.5486 \| 0.6631 \| 0.0 \| 0.5355 \| 0.6597 \| \| 0.0274 \| 113.5 \| 32460 \| 0.1530 \| 0.3884 \| 0.5911 \| 0.5848 \| nan \| 0.5545 \| 0.6278 \| 0.0 \| 0.5394 \| 0.6257 \| \| 0.0428 \| 113.57 \| 32480 \| 0.1453 \| 0.4036 \| 0.6143 \| 0.6065 \| nan \| 0.5691 \| 0.6595 \| 0.0 \| 0.5549 \| 0.6560 \| \| 0.0338 \| 113.64 \| 32500 \| 0.1447 \| 0.4080 \| 0.6209 \| 0.6139 \| nan \| 0.5808 \| 0.6610 \| 0.0 \| 0.5672 \| 0.6568 \| \| 0.028 \| 113.71 \| 32520 \| 0.1471 \| 0.4033 \| 0.6140 \| 0.6072 \| nan \| 0.5746 \| 0.6535 \| 0.0 \| 0.5599 \| 0.6501 \| \| 0.0498 \| 113.78 \| 32540 \| 0.1461 \| 0.3998 \| 0.6083 \| 0.6015 \| nan \| 0.5691 \| 0.6475 \| 0.0 \| 0.5550 \| 0.6443 \| \| 0.0544 \| 113.85 \| 32560 \| 0.1433 \| 0.4072 \| 0.6197 \| 0.6126 \| nan \| 0.5787 \| 0.6607 \| 0.0 \| 0.5661 \| 0.6554 \| \| 0.0623 \| 113.92 \| 32580 \| 0.1428 \| 0.4052 \| 0.6163 \| 0.6075 \| nan \| 0.5653 \| 0.6673 \| 0.0 \| 0.5527 \| 0.6629 \| \| 0.0312 \| 113.99 \| 32600 \| 0.1434 \| 0.4000 \| 0.6083 \| 0.5992 \| nan \| 0.5554 \| 0.6613 \| 0.0 \| 0.5434 \| 0.6567 \| \| 0.0388 \| 114.06 \| 32620 \| 0.1478 \| 0.3976 \| 0.6044 \| 0.5962 \| nan \| 0.5569 \| 0.6520 \| 0.0 \| 0.5452 \| 0.6475 \| \| 0.0468 \| 114.13 \| 32640 \| 0.1442 \| 0.3995 \| 0.6078 \| 0.6000 \| nan \| 0.5630 \| 0.6526 \| 0.0 \| 0.5510 \| 0.6476 \| \| 0.035 \| 114.2 \| 32660 \| 0.1448 \| 0.4012 \| 0.6105 \| 0.6026 \| nan \| 0.5649 \| 0.6560 \| 0.0 \| 0.5521 \| 0.6517 \| \| 0.0511 \| 114.27 \| 32680 \| 0.1362 \| 0.4170 \| 0.6347 \| 0.6275 \| nan \| 0.5929 \| 0.6766 \| 0.0 \| 0.5796 \| 0.6714 \| \| 0.0419 \| 114.34 \| 32700 \| 0.1432 \| 0.4086 \| 0.6217 \| 0.6147 \| nan \| 0.5810 \| 0.6624 \| 0.0 \| 0.5668 \| 0.6590 \| \| 0.0447 \| 114.41 \| 32720 \| 0.1495 \| 0.3947 \| 0.6001 \| 0.5934 \| nan \| 0.5615 \| 0.6387 \| 0.0 \| 0.5486 \| 0.6354 \| \| 0.0331 \| 114.48 \| 32740 \| 0.1483 \| 0.3926 \| 0.5965 \| 0.5877 \| nan \| 0.5460 \| 0.6469 \| 0.0 \| 0.5343 \| 0.6434 \| \| 0.0313 \| 114.55 \| 32760 \| 0.1530 \| 0.3824 \| 0.5810 \| 0.5725 \| nan \| 0.5318 \| 0.6301 \| 0.0 \| 0.5202 \| 0.6270 \| \| 0.0467 \| 114.62 \| 32780 \| 0.1428 \| 0.4008 \| 0.6093 \| 0.6003 \| nan \| 0.5573 \| 0.6614 \| 0.0 \| 0.5447 \| 0.6578 \| \| 0.0465 \| 114.69 \| 32800 \| 0.1453 \| 0.3939 \| 0.5987 \| 0.5894 \| nan \| 0.5449 \| 0.6525 \| 0.0 \| 0.5333 \| 0.6484 \| \| 0.0296 \| 114.76 \| 32820 \| 0.1485 \| 0.3983 \| 0.6061 \| 0.5984 \| nan \| 0.5619 \| 0.6502 \| 0.0 \| 0.5481 \| 0.6469 \| \| 0.0471 \| 114.83 \| 32840 \| 0.1542 \| 0.3854 \| 0.5862 \| 0.5799 \| nan \| 0.5495 \| 0.6229 \| 0.0 \| 0.5360 \| 0.6203 \| \| 0.053 \| 114.9 \| 32860 \| 0.1531 \| 0.3927 \| 0.5976 \| 0.5908 \| nan \| 0.5585 \| 0.6367 \| 0.0 \| 0.5442 \| 0.6340 \| \| 0.045 \| 114.97 \| 32880 \| 0.1509 \| 0.3934 \| 0.5989 \| 0.5936 \| nan \| 0.5682 \| 0.6295 \| 0.0 \| 0.5533 \| 0.6270 \| \| 0.0634 \| 115.03 \| 32900 \| 0.1461 \| 0.4000 \| 0.6089 \| 0.6019 \| nan \| 0.5684 \| 0.6495 \| 0.0 \| 0.5536 \| 0.6465 \| \| 0.0373 \| 115.1 \| 32920 \| 0.1483 \| 0.3984 \| 0.6066 \| 0.6013 \| nan \| 0.5759 \| 0.6373 \| 0.0 \| 0.5605 \| 0.6345 \| \| 0.0397 \| 115.17 \| 32940 \| 0.1473 \| 0.3945 \| 0.6006 \| 0.5921 \| nan \| 0.5518 \| 0.6493 \| 0.0 \| 0.5376 \| 0.6458 \| \| 0.0406 \| 115.24 \| 32960 \| 0.1467 \| 0.3990 \| 0.6076 \| 0.6014 \| nan \| 0.5716 \| 0.6436 \| 0.0 \| 0.5572 \| 0.6399 \| \| 0.0592 \| 115.31 \| 32980 \| 0.1498 \| 0.3967 \| 0.6045 \| 0.5994 \| nan \| 0.5749 \| 0.6340 \| 0.0 \| 0.5594 \| 0.6307 \| \| 0.0336 \| 115.38 \| 33000 \| 0.1446 \| 0.4073 \| 0.6206 \| 0.6153 \| nan \| 0.5898 \| 0.6514 \| 0.0 \| 0.5741 \| 0.6478 \| \| 0.0455 \| 115.45 \| 33020 \| 0.1448 \| 0.4002 \| 0.6090 \| 0.6027 \| nan \| 0.5729 \| 0.6451 \| 0.0 \| 0.5590 \| 0.6415 \| \| 0.0355 \| 115.52 \| 33040 \| 0.1506 \| 0.3882 \| 0.5906 \| 0.5838 \| nan \| 0.5515 \| 0.6296 \| 0.0 \| 0.5388 \| 0.6257 \| \| 0.0355 \| 115.59 \| 33060 \| 0.1511 \| 0.3863 \| 0.5872 \| 0.5783 \| nan \| 0.5356 \| 0.6388 \| 0.0 \| 0.5254 \| 0.6333 \| \| 0.0516 \| 115.66 \| 33080 \| 0.1484 \| 0.3977 \| 0.6052 \| 0.5967 \| nan \| 0.5565 \| 0.6538 \| 0.0 \| 0.5447 \| 0.6485 \| \| 0.0458 \| 115.73 \| 33100 \| 0.1481 \| 0.3984 \| 0.6068 \| 0.6018 \| nan \| 0.5780 \| 0.6357 \| 0.0 \| 0.5635 \| 0.6318 \| \| 0.0501 \| 115.8 \| 33120 \| 0.1461 \| 0.3972 \| 0.6041 \| 0.5960 \| nan \| 0.5570 \| 0.6512 \| 0.0 \| 0.5449 \| 0.6467 \| \| 0.0256 \| 115.87 \| 33140 \| 0.1457 \| 0.4018 \| 0.6111 \| 0.6027 \| nan \| 0.5625 \| 0.6597 \| 0.0 \| 0.5509 \| 0.6544 \| \| 0.0396 \| 115.94 \| 33160 \| 0.1555 \| 0.3859 \| 0.5873 \| 0.5825 \| nan \| 0.5598 \| 0.6148 \| 0.0 \| 0.5464 \| 0.6112 \| \| 0.0451 \| 116.01 \| 33180 \| 0.1509 \| 0.3950 \| 0.6010 \| 0.5934 \| nan \| 0.5572 \| 0.6449 \| 0.0 \| 0.5440 \| 0.6409 \| \| 0.042 \| 116.08 \| 33200 \| 0.1545 \| 0.3941 \| 0.5991 \| 0.5923 \| nan \| 0.5601 \| 0.6381 \| 0.0 \| 0.5485 \| 0.6339 \| \| 0.0271 \| 116.15 \| 33220 \| 0.1467 \| 0.4017 \| 0.6108 \| 0.6047 \| nan \| 0.5751 \| 0.6466 \| 0.0 \| 0.5628 \| 0.6422 \| \| 0.0313 \| 116.22 \| 33240 \| 0.1358 \| 0.4150 \| 0.6307 \| 0.6198 \| nan \| 0.5675 \| 0.6940 \| 0.0 \| 0.5576 \| 0.6874 \| \| 0.0367 \| 116.29 \| 33260 \| 0.1431 \| 0.4047 \| 0.6152 \| 0.6069 \| nan \| 0.5671 \| 0.6632 \| 0.0 \| 0.5554 \| 0.6585 \| \| 0.0519 \| 116.36 \| 33280 \| 0.1426 \| 0.4080 \| 0.6208 \| 0.6128 \| nan \| 0.5745 \| 0.6672 \| 0.0 \| 0.5619 \| 0.6621 \| \| 0.0373 \| 116.43 \| 33300 \| 0.1425 \| 0.4060 \| 0.6178 \| 0.6081 \| nan \| 0.5617 \| 0.6739 \| 0.0 \| 0.5501 \| 0.6680 \| \| 0.0614 \| 116.5 \| 33320 \| 0.1394 \| 0.4093 \| 0.6224 \| 0.6128 \| nan \| 0.5671 \| 0.6776 \| 0.0 \| 0.5555 \| 0.6724 \| \| 0.0415 \| 116.57 \| 33340 \| 0.1493 \| 0.3988 \| 0.6071 \| 0.6023 \| nan \| 0.5792 \| 0.6350 \| 0.0 \| 0.5643 \| 0.6321 \| \| 0.0457 \| 116.64 \| 33360 \| 0.1523 \| 0.3947 \| 0.6008 \| 0.5965 \| nan \| 0.5758 \| 0.6259 \| 0.0 \| 0.5612 \| 0.6230 \| \| 0.0558 \| 116.71 \| 33380 \| 0.1455 \| 0.3974 \| 0.6043 \| 0.5964 \| nan \| 0.5587 \| 0.6499 \| 0.0 \| 0.5467 \| 0.6456 \| \| 0.0446 \| 116.78 \| 33400 \| 0.1471 \| 0.4055 \| 0.6168 \| 0.6097 \| nan \| 0.5755 \| 0.6581 \| 0.0 \| 0.5626 \| 0.6539 \| \| 0.0622 \| 116.85 \| 33420 \| 0.1439 \| 0.4074 \| 0.6199 \| 0.6124 \| nan \| 0.5762 \| 0.6636 \| 0.0 \| 0.5630 \| 0.6593 \| \| 0.0329 \| 116.92 \| 33440 \| 0.1455 \| 0.3923 \| 0.5963 \| 0.5869 \| nan \| 0.5419 \| 0.6508 \| 0.0 \| 0.5297 \| 0.6473 \| \| 0.043 \| 116.99 \| 33460 \| 0.1455 \| 0.3962 \| 0.6025 \| 0.5933 \| nan \| 0.5496 \| 0.6553 \| 0.0 \| 0.5372 \| 0.6516 \| \| 0.0604 \| 117.06 \| 33480 \| 0.1428 \| 0.4045 \| 0.6157 \| 0.6091 \| nan \| 0.5779 \| 0.6535 \| 0.0 \| 0.5641 \| 0.6492 \| \| 0.0357 \| 117.13 \| 33500 \| 0.1426 \| 0.4034 \| 0.6135 \| 0.6053 \| nan \| 0.5660 \| 0.6609 \| 0.0 \| 0.5548 \| 0.6553 \| \| 0.042 \| 117.2 \| 33520 \| 0.1428 \| 0.4057 \| 0.6169 \| 0.6084 \| nan \| 0.5677 \| 0.6661 \| 0.0 \| 0.5566 \| 0.6606 \| \| 0.0581 \| 117.27 \| 33540 \| 0.1446 \| 0.4059 \| 0.6174 \| 0.6091 \| nan \| 0.5695 \| 0.6653 \| 0.0 \| 0.5570 \| 0.6607 \| \| 0.0374 \| 117.34 \| 33560 \| 0.1391 \| 0.4127 \| 0.6284 \| 0.6206 \| nan \| 0.5837 \| 0.6730 \| 0.0 \| 0.5710 \| 0.6673 \| \| 0.035 \| 117.41 \| 33580 \| 0.1397 \| 0.4141 \| 0.6308 \| 0.6224 \| nan \| 0.5824 \| 0.6792 \| 0.0 \| 0.5688 \| 0.6734 \| \| 0.0442 \| 117.48 \| 33600 \| 0.1431 \| 0.4048 \| 0.6161 \| 0.6091 \| nan \| 0.5757 \| 0.6565 \| 0.0 \| 0.5614 \| 0.6529 \| \| 0.055 \| 117.55 \| 33620 \| 0.1419 \| 0.4043 \| 0.6150 \| 0.6055 \| nan \| 0.5602 \| 0.6697 \| 0.0 \| 0.5485 \| 0.6643 \| \| 0.0333 \| 117.62 \| 33640 \| 0.1462 \| 0.4010 \| 0.6101 \| 0.6003 \| nan \| 0.5532 \| 0.6671 \| 0.0 \| 0.5417 \| 0.6613 \| \| 0.0304 \| 117.69 \| 33660 \| 0.1488 \| 0.3981 \| 0.6057 \| 0.5982 \| nan \| 0.5624 \| 0.6491 \| 0.0 \| 0.5496 \| 0.6447 \| \| 0.0341 \| 117.76 \| 33680 \| 0.1490 \| 0.4009 \| 0.6103 \| 0.6032 \| nan \| 0.5695 \| 0.6511 \| 0.0 \| 0.5561 \| 0.6466 \| \| 0.0395 \| 117.83 \| 33700 \| 0.1456 \| 0.4013 \| 0.6108 \| 0.6027 \| nan \| 0.5638 \| 0.6578 \| 0.0 \| 0.5508 \| 0.6531 \| \| 0.0381 \| 117.9 \| 33720 \| 0.1475 \| 0.3948 \| 0.6008 \| 0.5932 \| nan \| 0.5571 \| 0.6445 \| 0.0 \| 0.5447 \| 0.6398 \| \| 0.0426 \| 117.97 \| 33740 \| 0.1519 \| 0.3979 \| 0.6054 \| 0.5986 \| nan \| 0.5658 \| 0.6451 \| 0.0 \| 0.5519 \| 0.6418 \| \| 0.0342 \| 118.04 \| 33760 \| 0.1484 \| 0.3963 \| 0.6032 \| 0.5956 \| nan \| 0.5592 \| 0.6472 \| 0.0 \| 0.5464 \| 0.6425 \| \| 0.0521 \| 118.11 \| 33780 \| 0.1456 \| 0.4068 \| 0.6197 \| 0.6127 \| nan \| 0.5793 \| 0.6601 \| 0.0 \| 0.5645 \| 0.6560 \| \| 0.0322 \| 118.18 \| 33800 \| 0.1493 \| 0.4065 \| 0.6195 \| 0.6127 \| nan \| 0.5803 \| 0.6587 \| 0.0 \| 0.5650 \| 0.6544 \| \| 0.0557 \| 118.25 \| 33820 \| 0.1486 \| 0.4021 \| 0.6117 \| 0.6034 \| nan \| 0.5636 \| 0.6599 \| 0.0 \| 0.5517 \| 0.6546 \| \| 0.037 \| 118.32 \| 33840 \| 0.1490 \| 0.3989 \| 0.6068 \| 0.5983 \| nan \| 0.5573 \| 0.6564 \| 0.0 \| 0.5448 \| 0.6519 \| \| 0.0411 \| 118.39 \| 33860 \| 0.1482 \| 0.4006 \| 0.6098 \| 0.6040 \| nan \| 0.5764 \| 0.6433 \| 0.0 \| 0.5635 \| 0.6384 \| \| 0.0518 \| 118.46 \| 33880 \| 0.1457 \| 0.4024 \| 0.6128 \| 0.6060 \| nan \| 0.5738 \| 0.6518 \| 0.0 \| 0.5608 \| 0.6465 \| \| 0.0355 \| 118.53 \| 33900 \| 0.1527 \| 0.3866 \| 0.5877 \| 0.5794 \| nan \| 0.5394 \| 0.6360 \| 0.0 \| 0.5263 \| 0.6336 \| \| 0.031 \| 118.6 \| 33920 \| 0.1507 \| 0.3941 \| 0.5998 \| 0.5943 \| nan \| 0.5683 \| 0.6313 \| 0.0 \| 0.5539 \| 0.6283 \| \| 0.0343 \| 118.67 \| 33940 \| 0.1495 \| 0.3959 \| 0.6023 \| 0.5971 \| nan \| 0.5722 \| 0.6324 \| 0.0 \| 0.5588 \| 0.6288 \| \| 0.0156 \| 118.74 \| 33960 \| 0.1427 \| 0.4095 \| 0.6238 \| 0.6178 \| nan \| 0.5892 \| 0.6584 \| 0.0 \| 0.5749 \| 0.6535 \| \| 0.0573 \| 118.81 \| 33980 \| 0.1527 \| 0.3916 \| 0.5958 \| 0.5894 \| nan \| 0.5588 \| 0.6329 \| 0.0 \| 0.5446 \| 0.6301 \| \| 0.0417 \| 118.88 \| 34000 \| 0.1475 \| 0.4025 \| 0.6128 \| 0.6074 \| nan \| 0.5817 \| 0.6439 \| 0.0 \| 0.5679 \| 0.6397 \| \| 0.0579 \| 118.95 \| 34020 \| 0.1489 \| 0.3998 \| 0.6083 \| 0.6018 \| nan \| 0.5707 \| 0.6459 \| 0.0 \| 0.5583 \| 0.6411 \| \| 0.0433 \| 119.02 \| 34040 \| 0.1521 \| 0.3923 \| 0.5967 \| 0.5879 \| nan \| 0.5458 \| 0.6476 \| 0.0 \| 0.5331 \| 0.6438 \| \| 0.0453 \| 119.09 \| 34060 \| 0.1493 \| 0.3982 \| 0.6057 \| 0.5983 \| nan \| 0.5627 \| 0.6488 \| 0.0 \| 0.5495 \| 0.6453 \| \| 0.0407 \| 119.16 \| 34080 \| 0.1484 \| 0.3974 \| 0.6046 \| 0.5969 \| nan \| 0.5600 \| 0.6492 \| 0.0 \| 0.5469 \| 0.6454 \| \| 0.0448 \| 119.23 \| 34100 \| 0.1473 \| 0.4011 \| 0.6101 \| 0.6008 \| nan \| 0.5559 \| 0.6644 \| 0.0 \| 0.5434 \| 0.6600 \| \| 0.0426 \| 119.3 \| 34120 \| 0.1448 \| 0.3997 \| 0.6080 \| 0.5986 \| nan \| 0.5541 \| 0.6618 \| 0.0 \| 0.5419 \| 0.6572 \| \| 0.048 \| 119.37 \| 34140 \| 0.1471 \| 0.3988 \| 0.6067 \| 0.5993 \| nan \| 0.5638 \| 0.6496 \| 0.0 \| 0.5506 \| 0.6457 \| \| 0.0532 \| 119.44 \| 34160 \| 0.1497 \| 0.3979 \| 0.6054 \| 0.5980 \| nan \| 0.5629 \| 0.6479 \| 0.0 \| 0.5495 \| 0.6444 \| \| 0.0348 \| 119.51 \| 34180 \| 0.1494 \| 0.3992 \| 0.6076 \| 0.6000 \| nan \| 0.5637 \| 0.6515 \| 0.0 \| 0.5500 \| 0.6477 \| \| 0.0523 \| 119.58 \| 34200 \| 0.1451 \| 0.4004 \| 0.6088 \| 0.5995 \| nan \| 0.5551 \| 0.6625 \| 0.0 \| 0.5421 \| 0.6591 \| \| 0.0579 \| 119.65 \| 34220 \| 0.1465 \| 0.4001 \| 0.6091 \| 0.6031 \| nan \| 0.5746 \| 0.6436 \| 0.0 \| 0.5603 \| 0.6401 \| \| 0.0378 \| 119.72 \| 34240 \| 0.1469 \| 0.3980 \| 0.6060 \| 0.5998 \| nan \| 0.5702 \| 0.6417 \| 0.0 \| 0.5555 \| 0.6385 \| \| 0.0541 \| 119.79 \| 34260 \| 0.1448 \| 0.4015 \| 0.6111 \| 0.6034 \| nan \| 0.5667 \| 0.6555 \| 0.0 \| 0.5530 \| 0.6514 \| \| 0.0384 \| 119.86 \| 34280 \| 0.1462 \| 0.4014 \| 0.6109 \| 0.6046 \| nan \| 0.5744 \| 0.6475 \| 0.0 \| 0.5604 \| 0.6437 \| \| 0.039 \| 119.93 \| 34300 \| 0.1485 \| 0.3983 \| 0.6072 \| 0.6033 \| nan \| 0.5849 \| 0.6294 \| 0.0 \| 0.5680 \| 0.6269 \| \| 0.0294 \| 120.0 \| 34320 \| 0.1521 \| 0.3960 \| 0.6026 \| 0.5967 \| nan \| 0.5683 \| 0.6370 \| 0.0 \| 0.5540 \| 0.6342 \| \| 0.0349 \| 120.07 \| 34340 \| 0.1487 \| 0.3941 \| 0.5994 \| 0.5914 \| nan \| 0.5531 \| 0.6457 \| 0.0 \| 0.5403 \| 0.6421 \| \| 0.0565 \| 120.14 \| 34360 \| 0.1553 \| 0.3824 \| 0.5821 \| 0.5778 \| nan \| 0.5574 \| 0.6068 \| 0.0 \| 0.5438 \| 0.6036 \| \| 0.0509 \| 120.21 \| 34380 \| 0.1516 \| 0.3916 \| 0.5959 \| 0.5892 \| nan \| 0.5576 \| 0.6342 \| 0.0 \| 0.5439 \| 0.6308 \| \| 0.0436 \| 120.28 \| 34400 \| 0.1490 \| 0.3978 \| 0.6053 \| 0.5988 \| nan \| 0.5679 \| 0.6428 \| 0.0 \| 0.5545 \| 0.6389 \| \| 0.0267 \| 120.35 \| 34420 \| 0.1464 \| 0.4001 \| 0.6090 \| 0.6020 \| nan \| 0.5684 \| 0.6497 \| 0.0 \| 0.5545 \| 0.6457 \| \| 0.0671 \| 120.42 \| 34440 \| 0.1477 \| 0.3999 \| 0.6088 \| 0.6000 \| nan \| 0.5583 \| 0.6593 \| 0.0 \| 0.5461 \| 0.6536 \| \| 0.0278 \| 120.49 \| 34460 \| 0.1445 \| 0.4027 \| 0.6134 \| 0.6059 \| nan \| 0.5702 \| 0.6566 \| 0.0 \| 0.5574 \| 0.6507 \| \| 0.0482 \| 120.56 \| 34480 \| 0.1462 \| 0.3966 \| 0.6037 \| 0.5959 \| nan \| 0.5583 \| 0.6492 \| 0.0 \| 0.5443 \| 0.6456 \| \| 0.0429 \| 120.63 \| 34500 \| 0.1421 \| 0.4036 \| 0.6139 \| 0.6052 \| nan \| 0.5639 \| 0.6638 \| 0.0 \| 0.5516 \| 0.6592 \| \| 0.0542 \| 120.7 \| 34520 \| 0.1434 \| 0.4052 \| 0.6164 \| 0.6093 \| nan \| 0.5754 \| 0.6573 \| 0.0 \| 0.5627 \| 0.6528 \| \| 0.0471 \| 120.77 \| 34540 \| 0.1481 \| 0.3965 \| 0.6033 \| 0.5963 \| nan \| 0.5628 \| 0.6439 \| 0.0 \| 0.5487 \| 0.6408 \| \| 0.0414 \| 120.84 \| 34560 \| 0.1448 \| 0.4056 \| 0.6171 \| 0.6094 \| nan \| 0.5730 \| 0.6611 \| 0.0 \| 0.5603 \| 0.6565 \| \| 0.0256 \| 120.91 \| 34580 \| 0.1490 \| 0.3929 \| 0.5976 \| 0.5887 \| nan \| 0.5462 \| 0.6490 \| 0.0 \| 0.5347 \| 0.6439 \| \| 0.0302 \| 120.98 \| 34600 \| 0.1483 \| 0.3950 \| 0.6007 \| 0.5916 \| nan \| 0.5481 \| 0.6533 \| 0.0 \| 0.5365 \| 0.6484 \| \| 0.0324 \| 121.05 \| 34620 \| 0.1489 \| 0.3978 \| 0.6050 \| 0.5984 \| nan \| 0.5667 \| 0.6434 \| 0.0 \| 0.5536 \| 0.6399 \| \| 0.0312 \| 121.12 \| 34640 \| 0.1472 \| 0.4084 \| 0.6216 \| 0.6155 \| nan \| 0.5862 \| 0.6570 \| 0.0 \| 0.5732 \| 0.6520 \| \| 0.0307 \| 121.19 \| 34660 \| 0.1470 \| 0.4079 \| 0.6202 \| 0.6105 \| nan \| 0.5643 \| 0.6761 \| 0.0 \| 0.5531 \| 0.6706 \| \| 0.0315 \| 121.26 \| 34680 \| 0.1415 \| 0.4056 \| 0.6165 \| 0.6055 \| nan \| 0.5526 \| 0.6805 \| 0.0 \| 0.5429 \| 0.6739 \| \| 0.0742 \| 121.33 \| 34700 \| 0.1437 \| 0.4114 \| 0.6261 \| 0.6185 \| nan \| 0.5825 \| 0.6697 \| 0.0 \| 0.5702 \| 0.6640 \| \| 0.0637 \| 121.4 \| 34720 \| 0.1489 \| 0.3998 \| 0.6081 \| 0.6000 \| nan \| 0.5614 \| 0.6547 \| 0.0 \| 0.5490 \| 0.6505 \| \| 0.0347 \| 121.47 \| 34740 \| 0.1466 \| 0.4019 \| 0.6112 \| 0.6025 \| nan \| 0.5607 \| 0.6618 \| 0.0 \| 0.5481 \| 0.6576 \| \| 0.0372 \| 121.54 \| 34760 \| 0.1468 \| 0.4046 \| 0.6156 \| 0.6094 \| nan \| 0.5798 \| 0.6514 \| 0.0 \| 0.5661 \| 0.6477 \| \| 0.0381 \| 121.61 \| 34780 \| 0.1524 \| 0.3970 \| 0.6039 \| 0.5980 \| nan \| 0.5700 \| 0.6378 \| 0.0 \| 0.5567 \| 0.6343 \| \| 0.041 \| 121.68 \| 34800 \| 0.1454 \| 0.4063 \| 0.6180 \| 0.6079 \| nan \| 0.5601 \| 0.6758 \| 0.0 \| 0.5491 \| 0.6700 \| \| 0.0297 \| 121.75 \| 34820 \| 0.1431 \| 0.4073 \| 0.6196 \| 0.6118 \| nan \| 0.5748 \| 0.6644 \| 0.0 \| 0.5633 \| 0.6584 \| \| 0.0492 \| 121.82 \| 34840 \| 0.1466 \| 0.3981 \| 0.6051 \| 0.5932 \| nan \| 0.5364 \| 0.6739 \| 0.0 \| 0.5256 \| 0.6688 \| \| 0.0477 \| 121.89 \| 34860 \| 0.1488 \| 0.3957 \| 0.6021 \| 0.5936 \| nan \| 0.5526 \| 0.6516 \| 0.0 \| 0.5394 \| 0.6477 \| \| 0.0619 \| 121.96 \| 34880 \| 0.1451 \| 0.4035 \| 0.6143 \| 0.6071 \| nan \| 0.5727 \| 0.6560 \| 0.0 \| 0.5592 \| 0.6513 \| \| 0.0383 \| 122.03 \| 34900 \| 0.1453 \| 0.4060 \| 0.6182 \| 0.6119 \| nan \| 0.5821 \| 0.6542 \| 0.0 \| 0.5682 \| 0.6498 \| \| 0.0403 \| 122.1 \| 34920 \| 0.1514 \| 0.3983 \| 0.6058 \| 0.5985 \| nan \| 0.5638 \| 0.6478 \| 0.0 \| 0.5506 \| 0.6442 \| \| 0.0621 \| 122.17 \| 34940 \| 0.1555 \| 0.3965 \| 0.6035 \| 0.5988 \| nan \| 0.5767 \| 0.6303 \| 0.0 \| 0.5622 \| 0.6273 \| \| 0.0478 \| 122.24 \| 34960 \| 0.1467 \| 0.4075 \| 0.6202 \| 0.6111 \| nan \| 0.5674 \| 0.6731 \| 0.0 \| 0.5540 \| 0.6687 \| \| 0.0429 \| 122.31 \| 34980 \| 0.1504 \| 0.3983 \| 0.6058 \| 0.5983 \| nan \| 0.5629 \| 0.6487 \| 0.0 \| 0.5504 \| 0.6444 \| \| 0.042 \| 122.38 \| 35000 \| 0.1482 \| 0.4020 \| 0.6119 \| 0.6028 \| nan \| 0.5590 \| 0.6649 \| 0.0 \| 0.5455 \| 0.6604 \| \| 0.0452 \| 122.45 \| 35020 \| 0.1510 \| 0.3996 \| 0.6083 \| 0.6017 \| nan \| 0.5698 \| 0.6468 \| 0.0 \| 0.5564 \| 0.6424 \| \| 0.0498 \| 122.52 \| 35040 \| 0.1461 \| 0.4067 \| 0.6196 \| 0.6116 \| nan \| 0.5737 \| 0.6654 \| 0.0 \| 0.5602 \| 0.6600 \| \| 0.0304 \| 122.59 \| 35060 \| 0.1484 \| 0.4052 \| 0.6169 \| 0.6094 \| nan \| 0.5735 \| 0.6604 \| 0.0 \| 0.5604 \| 0.6552 \| \| 0.0333 \| 122.66 \| 35080 \| 0.1472 \| 0.3995 \| 0.6078 \| 0.5991 \| nan \| 0.5571 \| 0.6586 \| 0.0 \| 0.5450 \| 0.6534 \| \| 0.0666 \| 122.73 \| 35100 \| 0.1503 \| 0.4007 \| 0.6099 \| 0.6011 \| nan \| 0.5593 \| 0.6605 \| 0.0 \| 0.5466 \| 0.6554 \| \| 0.0443 \| 122.8 \| 35120 \| 0.1483 \| 0.3985 \| 0.6066 \| 0.5979 \| nan \| 0.5566 \| 0.6566 \| 0.0 \| 0.5436 \| 0.6520 \| \| 0.0408 \| 122.87 \| 35140 \| 0.1508 \| 0.4082 \| 0.6220 \| 0.6160 \| nan \| 0.5877 \| 0.6562 \| 0.0 \| 0.5725 \| 0.6520 \| \| 0.0372 \| 122.94 \| 35160 \| 0.1494 \| 0.4043 \| 0.6160 \| 0.6093 \| nan \| 0.5774 \| 0.6545 \| 0.0 \| 0.5627 \| 0.6502 \| \| 0.0389 \| 123.01 \| 35180 \| 0.1499 \| 0.3995 \| 0.6084 \| 0.6006 \| nan \| 0.5632 \| 0.6536 \| 0.0 \| 0.5497 \| 0.6486 \| \| 0.0406 \| 123.08 \| 35200 \| 0.1488 \| 0.4052 \| 0.6171 \| 0.6104 \| nan \| 0.5787 \| 0.6555 \| 0.0 \| 0.5653 \| 0.6502 \| \| 0.0201 \| 123.15 \| 35220 \| 0.1522 \| 0.3984 \| 0.6068 \| 0.6001 \| nan \| 0.5684 \| 0.6452 \| 0.0 \| 0.5539 \| 0.6413 \| \| 0.0364 \| 123.22 \| 35240 \| 0.1483 \| 0.3983 \| 0.6062 \| 0.5974 \| nan \| 0.5555 \| 0.6568 \| 0.0 \| 0.5425 \| 0.6526 \| \| 0.0325 \| 123.29 \| 35260 \| 0.1514 \| 0.3987 \| 0.6075 \| 0.6023 \| nan \| 0.5776 \| 0.6373 \| 0.0 \| 0.5625 \| 0.6336 \| \| 0.0441 \| 123.36 \| 35280 \| 0.1482 \| 0.4024 \| 0.6125 \| 0.6039 \| nan \| 0.5624 \| 0.6626 \| 0.0 \| 0.5504 \| 0.6568 \| \| 0.0436 \| 123.43 \| 35300 \| 0.1490 \| 0.3999 \| 0.6086 \| 0.6010 \| nan \| 0.5652 \| 0.6519 \| 0.0 \| 0.5533 \| 0.6464 \| \| 0.0322 \| 123.5 \| 35320 \| 0.1496 \| 0.4086 \| 0.6221 \| 0.6134 \| nan \| 0.5716 \| 0.6727 \| 0.0 \| 0.5580 \| 0.6678 \| \| 0.0601 \| 123.57 \| 35340 \| 0.1524 \| 0.3954 \| 0.6020 \| 0.5945 \| nan \| 0.5585 \| 0.6456 \| 0.0 \| 0.5435 \| 0.6428 \| \| 0.0398 \| 123.64 \| 35360 \| 0.1438 \| 0.4053 \| 0.6169 \| 0.6067 \| nan \| 0.5582 \| 0.6756 \| 0.0 \| 0.5464 \| 0.6693 \| \| 0.0389 \| 123.71 \| 35380 \| 0.1481 \| 0.4013 \| 0.6103 \| 0.6008 \| nan \| 0.5553 \| 0.6653 \| 0.0 \| 0.5434 \| 0.6605 \| \| 0.0457 \| 123.78 \| 35400 \| 0.1474 \| 0.4103 \| 0.6247 \| 0.6181 \| nan \| 0.5867 \| 0.6627 \| 0.0 \| 0.5728 \| 0.6581 \| \| 0.0522 \| 123.85 \| 35420 \| 0.1487 \| 0.4015 \| 0.6114 \| 0.6044 \| nan \| 0.5712 \| 0.6517 \| 0.0 \| 0.5568 \| 0.6478 \| \| 0.0325 \| 123.92 \| 35440 \| 0.1513 \| 0.4014 \| 0.6107 \| 0.6033 \| nan \| 0.5680 \| 0.6533 \| 0.0 \| 0.5547 \| 0.6494 \| \| 0.0264 \| 123.99 \| 35460 \| 0.1488 \| 0.4036 \| 0.6138 \| 0.6053 \| nan \| 0.5648 \| 0.6627 \| 0.0 \| 0.5520 \| 0.6588 \| \| 0.0499 \| 124.06 \| 35480 \| 0.1457 \| 0.4104 \| 0.6239 \| 0.6143 \| nan \| 0.5687 \| 0.6791 \| 0.0 \| 0.5564 \| 0.6749 \| \| 0.0351 \| 124.13 \| 35500 \| 0.1517 \| 0.3989 \| 0.6065 \| 0.5984 \| nan \| 0.5597 \| 0.6533 \| 0.0 \| 0.5468 \| 0.6500 \| \| 0.032 \| 124.2 \| 35520 \| 0.1519 \| 0.4038 \| 0.6142 \| 0.6065 \| nan \| 0.5699 \| 0.6585 \| 0.0 \| 0.5574 \| 0.6541 \| \| 0.0403 \| 124.27 \| 35540 \| 0.1579 \| 0.3976 \| 0.6051 \| 0.6004 \| nan \| 0.5778 \| 0.6324 \| 0.0 \| 0.5637 \| 0.6291 \| \| 0.0443 \| 124.34 \| 35560 \| 0.1518 \| 0.3974 \| 0.6048 \| 0.5975 \| nan \| 0.5625 \| 0.6472 \| 0.0 \| 0.5487 \| 0.6436 \| \| 0.0917 \| 124.41 \| 35580 \| 0.1504 \| 0.3975 \| 0.6051 \| 0.5985 \| nan \| 0.5668 \| 0.6435 \| 0.0 \| 0.5533 \| 0.6392 \| \| 0.0436 \| 124.48 \| 35600 \| 0.1491 \| 0.3998 \| 0.6085 \| 0.6003 \| nan \| 0.5611 \| 0.6559 \| 0.0 \| 0.5489 \| 0.6506 \| \| 0.067 \| 124.55 \| 35620 \| 0.1492 \| 0.4009 \| 0.6106 \| 0.6046 \| nan \| 0.5760 \| 0.6453 \| 0.0 \| 0.5614 \| 0.6412 \| \| 0.0319 \| 124.62 \| 35640 \| 0.1513 \| 0.3976 \| 0.6050 \| 0.5980 \| nan \| 0.5648 \| 0.6452 \| 0.0 \| 0.5512 \| 0.6414 \| \| 0.0371 \| 124.69 \| 35660 \| 0.1496 \| 0.4007 \| 0.6102 \| 0.6030 \| nan \| 0.5685 \| 0.6518 \| 0.0 \| 0.5553 \| 0.6468 \| \| 0.0439 \| 124.76 \| 35680 \| 0.1558 \| 0.3971 \| 0.6050 \| 0.6012 \| nan \| 0.5827 \| 0.6274 \| 0.0 \| 0.5675 \| 0.6239 \| \| 0.0415 \| 124.83 \| 35700 \| 0.1539 \| 0.4015 \| 0.6113 \| 0.6058 \| nan \| 0.5795 \| 0.6431 \| 0.0 \| 0.5647 \| 0.6397 \| \| 0.0415 \| 124.9 \| 35720 \| 0.1562 \| 0.3911 \| 0.5950 \| 0.5879 \| nan \| 0.5541 \| 0.6358 \| 0.0 \| 0.5414 \| 0.6321 \| \| 0.061 \| 124.97 \| 35740 \| 0.1462 \| 0.4047 \| 0.6153 \| 0.6078 \| nan \| 0.5720 \| 0.6587 \| 0.0 \| 0.5601 \| 0.6539 \| \| 0.0512 \| 125.03 \| 35760 \| 0.1504 \| 0.3974 \| 0.6042 \| 0.5965 \| nan \| 0.5595 \| 0.6490 \| 0.0 \| 0.5458 \| 0.6463 \| \| 0.0342 \| 125.1 \| 35780 \| 0.1517 \| 0.3970 \| 0.6036 \| 0.5954 \| nan \| 0.5558 \| 0.6515 \| 0.0 \| 0.5431 \| 0.6480 \| \| 0.0273 \| 125.17 \| 35800 \| 0.1531 \| 0.4005 \| 0.6095 \| 0.6033 \| nan \| 0.5735 \| 0.6455 \| 0.0 \| 0.5596 \| 0.6420 \| \| 0.0375 \| 125.24 \| 35820 \| 0.1487 \| 0.4040 \| 0.6143 \| 0.6064 \| nan \| 0.5683 \| 0.6603 \| 0.0 \| 0.5560 \| 0.6559 \| \| 0.0314 \| 125.31 \| 35840 \| 0.1483 \| 0.4023 \| 0.6114 \| 0.6024 \| nan \| 0.5594 \| 0.6635 \| 0.0 \| 0.5485 \| 0.6584 \| \| 0.0326 \| 125.38 \| 35860 \| 0.1485 \| 0.4079 \| 0.6202 \| 0.6128 \| nan \| 0.5776 \| 0.6629 \| 0.0 \| 0.5655 \| 0.6582 \| \| 0.0597 \| 125.45 \| 35880 \| 0.1492 \| 0.4051 \| 0.6158 \| 0.6090 \| nan \| 0.5769 \| 0.6546 \| 0.0 \| 0.5648 \| 0.6504 \| \| 0.0245 \| 125.52 \| 35900 \| 0.1520 \| 0.4113 \| 0.6255 \| 0.6197 \| nan \| 0.5924 \| 0.6585 \| 0.0 \| 0.5799 \| 0.6540 \| \| 0.0498 \| 125.59 \| 35920 \| 0.1505 \| 0.4030 \| 0.6130 \| 0.6078 \| nan \| 0.5832 \| 0.6428 \| 0.0 \| 0.5699 \| 0.6390 \| \| 0.0463 \| 125.66 \| 35940 \| 0.1594 \| 0.3866 \| 0.5875 \| 0.5805 \| nan \| 0.5473 \| 0.6276 \| 0.0 \| 0.5349 \| 0.6250 \| \| 0.0334 \| 125.73 \| 35960 \| 0.1548 \| 0.3938 \| 0.5989 \| 0.5932 \| nan \| 0.5663 \| 0.6315 \| 0.0 \| 0.5532 \| 0.6281 \| \| 0.069 \| 125.8 \| 35980 \| 0.1539 \| 0.4000 \| 0.6084 \| 0.6011 \| nan \| 0.5663 \| 0.6506 \| 0.0 \| 0.5533 \| 0.6467 \| \| 0.0496 \| 125.87 \| 36000 \| 0.1514 \| 0.4015 \| 0.6110 \| 0.6066 \| nan \| 0.5855 \| 0.6364 \| 0.0 \| 0.5720 \| 0.6327 \| \| 0.0626 \| 125.94 \| 36020 \| 0.1521 \| 0.3980 \| 0.6056 \| 0.5996 \| nan \| 0.5710 \| 0.6402 \| 0.0 \| 0.5571 \| 0.6370 \| \| 0.0652 \| 126.01 \| 36040 \| 0.1539 \| 0.3953 \| 0.6013 \| 0.5950 \| nan \| 0.5649 \| 0.6377 \| 0.0 \| 0.5517 \| 0.6342 \| \| 0.0588 \| 126.08 \| 36060 \| 0.1532 \| 0.3970 \| 0.6041 \| 0.5978 \| nan \| 0.5675 \| 0.6408 \| 0.0 \| 0.5536 \| 0.6374 \| \| 0.0538 \| 126.15 \| 36080 \| 0.1520 \| 0.3968 \| 0.6035 \| 0.5963 \| nan \| 0.5619 \| 0.6450 \| 0.0 \| 0.5491 \| 0.6415 \| \| 0.0589 \| 126.22 \| 36100 \| 0.1509 \| 0.3902 \| 0.5929 \| 0.5825 \| nan \| 0.5330 \| 0.6528 \| 0.0 \| 0.5220 \| 0.6485 \| \| 0.0563 \| 126.29 \| 36120 \| 0.1543 \| 0.3925 \| 0.5966 \| 0.5885 \| nan \| 0.5499 \| 0.6432 \| 0.0 \| 0.5380 \| 0.6393 \| \| 0.0505 \| 126.36 \| 36140 \| 0.1533 \| 0.3926 \| 0.5970 \| 0.5891 \| nan \| 0.5510 \| 0.6431 \| 0.0 \| 0.5386 \| 0.6393 \| \| 0.0391 \| 126.43 \| 36160 \| 0.1522 \| 0.3994 \| 0.6075 \| 0.6001 \| nan \| 0.5646 \| 0.6504 \| 0.0 \| 0.5517 \| 0.6466 \| \| 0.043 \| 126.5 \| 36180 \| 0.1534 \| 0.3986 \| 0.6066 \| 0.6006 \| nan \| 0.5717 \| 0.6416 \| 0.0 \| 0.5577 \| 0.6382 \| \| 0.0347 \| 126.57 \| 36200 \| 0.1563 \| 0.4006 \| 0.6099 \| 0.6046 \| nan \| 0.5790 \| 0.6409 \| 0.0 \| 0.5647 \| 0.6373 \| \| 0.0391 \| 126.64 \| 36220 \| 0.1442 \| 0.4190 \| 0.6384 \| 0.6312 \| nan \| 0.5971 \| 0.6796 \| 0.0 \| 0.5861 \| 0.6709 \| \| 0.0232 \| 126.71 \| 36240 \| 0.1510 \| 0.3997 \| 0.6087 \| 0.6027 \| nan \| 0.5739 \| 0.6435 \| 0.0 \| 0.5599 \| 0.6393 \| \| 0.0462 \| 126.78 \| 36260 \| 0.1630 \| 0.3812 \| 0.5806 \| 0.5771 \| nan \| 0.5603 \| 0.6010 \| 0.0 \| 0.5447 \| 0.5989 \| \| 0.043 \| 126.85 \| 36280 \| 0.1545 \| 0.3979 \| 0.6054 \| 0.5979 \| nan \| 0.5624 \| 0.6483 \| 0.0 \| 0.5490 \| 0.6446 \| \| 0.0331 \| 126.92 \| 36300 \| 0.1538 \| 0.3974 \| 0.6041 \| 0.5953 \| nan \| 0.5535 \| 0.6546 \| 0.0 \| 0.5410 \| 0.6511 \| \| 0.0365 \| 126.99 \| 36320 \| 0.1527 \| 0.3939 \| 0.5989 \| 0.5917 \| nan \| 0.5570 \| 0.6408 \| 0.0 \| 0.5437 \| 0.6378 \| \| 0.0418 \| 127.06 \| 36340 \| 0.1501 \| 0.3987 \| 0.6060 \| 0.5965 \| nan \| 0.5509 \| 0.6612 \| 0.0 \| 0.5387 \| 0.6574 \| \| 0.0196 \| 127.13 \| 36360 \| 0.1487 \| 0.4037 \| 0.6143 \| 0.6064 \| nan \| 0.5688 \| 0.6599 \| 0.0 \| 0.5556 \| 0.6556 \| \| 0.0242 \| 127.2 \| 36380 \| 0.1473 \| 0.3996 \| 0.6074 \| 0.5972 \| nan \| 0.5483 \| 0.6666 \| 0.0 \| 0.5366 \| 0.6621 \| \| 0.0454 \| 127.27 \| 36400 \| 0.1493 \| 0.4002 \| 0.6087 \| 0.5996 \| nan \| 0.5562 \| 0.6611 \| 0.0 \| 0.5437 \| 0.6570 \| \| 0.0676 \| 127.34 \| 36420 \| 0.1440 \| 0.4070 \| 0.6187 \| 0.6081 \| nan \| 0.5575 \| 0.6800 \| 0.0 \| 0.5469 \| 0.6740 \| \| 0.0584 \| 127.41 \| 36440 \| 0.1431 \| 0.4069 \| 0.6185 \| 0.6086 \| nan \| 0.5609 \| 0.6762 \| 0.0 \| 0.5505 \| 0.6703 \| \| 0.0707 \| 127.48 \| 36460 \| 0.1457 \| 0.4055 \| 0.6165 \| 0.6061 \| nan \| 0.5566 \| 0.6763 \| 0.0 \| 0.5460 \| 0.6704 \| \| 0.042 \| 127.55 \| 36480 \| 0.1439 \| 0.4031 \| 0.6132 \| 0.6026 \| nan \| 0.5522 \| 0.6741 \| 0.0 \| 0.5413 \| 0.6680 \| \| 0.0494 \| 127.62 \| 36500 \| 0.1465 \| 0.4005 \| 0.6088 \| 0.5984 \| nan \| 0.5487 \| 0.6689 \| 0.0 \| 0.5386 \| 0.6628 \| \| 0.0463 \| 127.69 \| 36520 \| 0.1460 \| 0.3998 \| 0.6075 \| 0.5975 \| nan \| 0.5496 \| 0.6654 \| 0.0 \| 0.5391 \| 0.6602 \| \| 0.046 \| 127.76 \| 36540 \| 0.1479 \| 0.4072 \| 0.6203 \| 0.6147 \| nan \| 0.5878 \| 0.6528 \| 0.0 \| 0.5712 \| 0.6505 \| \| 0.0378 \| 127.83 \| 36560 \| 0.1482 \| 0.3987 \| 0.6064 \| 0.5988 \| nan \| 0.5624 \| 0.6504 \| 0.0 \| 0.5483 \| 0.6477 \| \| 0.0307 \| 127.9 \| 36580 \| 0.1467 \| 0.4076 \| 0.6203 \| 0.6131 \| nan \| 0.5784 \| 0.6623 \| 0.0 \| 0.5640 \| 0.6589 \| \| 0.0257 \| 127.97 \| 36600 \| 0.1468 \| 0.4005 \| 0.6095 \| 0.6012 \| nan \| 0.5616 \| 0.6575 \| 0.0 \| 0.5478 \| 0.6538 \| \| 0.0601 \| 128.04 \| 36620 \| 0.1476 \| 0.3991 \| 0.6072 \| 0.5991 \| nan \| 0.5607 \| 0.6537 \| 0.0 \| 0.5471 \| 0.6503 \| \| 0.0378 \| 128.11 \| 36640 \| 0.1491 \| 0.4006 \| 0.6094 \| 0.6025 \| nan \| 0.5695 \| 0.6493 \| 0.0 \| 0.5553 \| 0.6464 \| \| 0.044 \| 128.18 \| 36660 \| 0.1483 \| 0.4020 \| 0.6119 \| 0.6056 \| nan \| 0.5751 \| 0.6488 \| 0.0 \| 0.5601 \| 0.6459 \| \| 0.0437 \| 128.25 \| 36680 \| 0.1506 \| 0.3990 \| 0.6068 \| 0.5987 \| nan \| 0.5597 \| 0.6539 \| 0.0 \| 0.5461 \| 0.6509 \| \| 0.0337 \| 128.32 \| 36700 \| 0.1527 \| 0.3997 \| 0.6087 \| 0.6038 \| nan \| 0.5807 \| 0.6367 \| 0.0 \| 0.5650 \| 0.6340 \| \| 0.0446 \| 128.39 \| 36720 \| 0.1546 \| 0.3956 \| 0.6019 \| 0.5953 \| nan \| 0.5640 \| 0.6398 \| 0.0 \| 0.5499 \| 0.6370 \| \| 0.0252 \| 128.46 \| 36740 \| 0.1519 \| 0.4028 \| 0.6130 \| 0.6064 \| nan \| 0.5751 \| 0.6509 \| 0.0 \| 0.5610 \| 0.6475 \| \| 0.0325 \| 128.53 \| 36760 \| 0.1522 \| 0.3989 \| 0.6068 \| 0.5996 \| nan \| 0.5653 \| 0.6483 \| 0.0 \| 0.5517 \| 0.6451 \| \| 0.0302 \| 128.6 \| 36780 \| 0.1517 \| 0.3955 \| 0.6016 \| 0.5927 \| nan \| 0.5502 \| 0.6530 \| 0.0 \| 0.5369 \| 0.6497 \| \| 0.0718 \| 128.67 \| 36800 \| 0.1473 \| 0.4073 \| 0.6195 \| 0.6116 \| nan \| 0.5740 \| 0.6650 \| 0.0 \| 0.5608 \| 0.6612 \| \| 0.0399 \| 128.74 \| 36820 \| 0.1450 \| 0.4071 \| 0.6195 \| 0.6119 \| nan \| 0.5757 \| 0.6633 \| 0.0 \| 0.5623 \| 0.6591 \| \| 0.0427 \| 128.81 \| 36840 \| 0.1470 \| 0.4045 \| 0.6159 \| 0.6100 \| nan \| 0.5818 \| 0.6500 \| 0.0 \| 0.5673 \| 0.6464 \| \| 0.0431 \| 128.88 \| 36860 \| 0.1462 \| 0.4103 \| 0.6246 \| 0.6185 \| nan \| 0.5897 \| 0.6594 \| 0.0 \| 0.5752 \| 0.6556 \| \| 0.0468 \| 128.95 \| 36880 \| 0.1546 \| 0.3989 \| 0.6073 \| 0.6022 \| nan \| 0.5780 \| 0.6365 \| 0.0 \| 0.5630 \| 0.6335 \| \| 0.0555 \| 129.02 \| 36900 \| 0.1496 \| 0.4045 \| 0.6151 \| 0.6065 \| nan \| 0.5657 \| 0.6645 \| 0.0 \| 0.5520 \| 0.6614 \| \| 0.0424 \| 129.09 \| 36920 \| 0.1499 \| 0.3976 \| 0.6042 \| 0.5959 \| nan \| 0.5561 \| 0.6524 \| 0.0 \| 0.5439 \| 0.6491 \| \| 0.0378 \| 129.16 \| 36940 \| 0.1468 \| 0.4101 \| 0.6240 \| 0.6164 \| nan \| 0.5797 \| 0.6684 \| 0.0 \| 0.5664 \| 0.6641 \| \| 0.0223 \| 129.23 \| 36960 \| 0.1452 \| 0.4101 \| 0.6235 \| 0.6149 \| nan \| 0.5739 \| 0.6731 \| 0.0 \| 0.5629 \| 0.6673 \| \| 0.0419 \| 129.3 \| 36980 \| 0.1462 \| 0.4054 \| 0.6164 \| 0.6094 \| nan \| 0.5757 \| 0.6571 \| 0.0 \| 0.5630 \| 0.6531 \| \| 0.0235 \| 129.37 \| 37000 \| 0.1522 \| 0.3950 \| 0.6006 \| 0.5941 \| nan \| 0.5633 \| 0.6379 \| 0.0 \| 0.5510 \| 0.6340 \| \| 0.0359 \| 129.44 \| 37020 \| 0.1605 \| 0.3807 \| 0.5796 \| 0.5758 \| nan \| 0.5575 \| 0.6018 \| 0.0 \| 0.5435 \| 0.5986 \| \| 0.034 \| 129.51 \| 37040 \| 0.1527 \| 0.3964 \| 0.6029 \| 0.5963 \| nan \| 0.5652 \| 0.6405 \| 0.0 \| 0.5524 \| 0.6367 \| \| 0.0616 \| 129.58 \| 37060 \| 0.1504 \| 0.3987 \| 0.6065 \| 0.5991 \| nan \| 0.5638 \| 0.6492 \| 0.0 \| 0.5510 \| 0.6451 \| \| 0.0536 \| 129.65 \| 37080 \| 0.1535 \| 0.3929 \| 0.5971 \| 0.5875 \| nan \| 0.5418 \| 0.6525 \| 0.0 \| 0.5305 \| 0.6480 \| \| 0.0472 \| 129.72 \| 37100 \| 0.1502 \| 0.3969 \| 0.6035 \| 0.5947 \| nan \| 0.5525 \| 0.6545 \| 0.0 \| 0.5409 \| 0.6499 \| \| 0.0528 \| 129.79 \| 37120 \| 0.1445 \| 0.4027 \| 0.6120 \| 0.6031 \| nan \| 0.5606 \| 0.6634 \| 0.0 \| 0.5494 \| 0.6586 \| \| 0.0544 \| 129.86 \| 37140 \| 0.1512 \| 0.3984 \| 0.6057 \| 0.5973 \| nan \| 0.5569 \| 0.6545 \| 0.0 \| 0.5448 \| 0.6503 \| \| 0.0442 \| 129.93 \| 37160 \| 0.1528 \| 0.3988 \| 0.6063 \| 0.5989 \| nan \| 0.5636 \| 0.6490 \| 0.0 \| 0.5514 \| 0.6450 \| \| 0.0709 \| 130.0 \| 37180 \| 0.1569 \| 0.3990 \| 0.6069 \| 0.6011 \| nan \| 0.5737 \| 0.6400 \| 0.0 \| 0.5605 \| 0.6365 \| \| 0.0451 \| 130.07 \| 37200 \| 0.1532 \| 0.4019 \| 0.6114 \| 0.6046 \| nan \| 0.5723 \| 0.6506 \| 0.0 \| 0.5586 \| 0.6469 \| \| 0.0266 \| 130.14 \| 37220 \| 0.1453 \| 0.4116 \| 0.6258 \| 0.6178 \| nan \| 0.5793 \| 0.6723 \| 0.0 \| 0.5671 \| 0.6677 \| \| 0.0458 \| 130.21 \| 37240 \| 0.1481 \| 0.4059 \| 0.6170 \| 0.6098 \| nan \| 0.5749 \| 0.6591 \| 0.0 \| 0.5628 \| 0.6550 \| \| 0.031 \| 130.28 \| 37260 \| 0.1479 \| 0.4048 \| 0.6149 \| 0.6051 \| nan \| 0.5581 \| 0.6718 \| 0.0 \| 0.5477 \| 0.6665 \| \| 0.0335 \| 130.35 \| 37280 \| 0.1495 \| 0.4028 \| 0.6119 \| 0.6035 \| nan \| 0.5631 \| 0.6608 \| 0.0 \| 0.5525 \| 0.6559 \| \| 0.0461 \| 130.42 \| 37300 \| 0.1491 \| 0.4033 \| 0.6129 \| 0.6038 \| nan \| 0.5604 \| 0.6655 \| 0.0 \| 0.5484 \| 0.6615 \| \| 0.0439 \| 130.49 \| 37320 \| 0.1496 \| 0.3989 \| 0.6063 \| 0.5988 \| nan \| 0.5627 \| 0.6499 \| 0.0 \| 0.5504 \| 0.6462 \| \| 0.0315 \| 130.56 \| 37340 \| 0.1522 \| 0.4044 \| 0.6147 \| 0.6084 \| nan \| 0.5779 \| 0.6516 \| 0.0 \| 0.5654 \| 0.6477 \| \| 0.0365 \| 130.63 \| 37360 \| 0.1515 \| 0.4000 \| 0.6084 \| 0.6014 \| nan \| 0.5681 \| 0.6488 \| 0.0 \| 0.5549 \| 0.6451 \| \| 0.0353 \| 130.7 \| 37380 \| 0.1510 \| 0.4028 \| 0.6126 \| 0.6058 \| nan \| 0.5731 \| 0.6521 \| 0.0 \| 0.5597 \| 0.6486 \| \| 0.0409 \| 130.77 \| 37400 \| 0.1527 \| 0.3927 \| 0.5970 \| 0.5891 \| nan \| 0.5518 \| 0.6422 \| 0.0 \| 0.5392 \| 0.6389 \| \| 0.04 \| 130.84 \| 37420 \| 0.1537 \| 0.3962 \| 0.6025 \| 0.5953 \| nan \| 0.5607 \| 0.6444 \| 0.0 \| 0.5466 \| 0.6419 \| \| 0.0451 \| 130.91 \| 37440 \| 0.1525 \| 0.3971 \| 0.6042 \| 0.5975 \| nan \| 0.5655 \| 0.6429 \| 0.0 \| 0.5512 \| 0.6402 \| \| 0.0447 \| 130.98 \| 37460 \| 0.1510 \| 0.4013 \| 0.6110 \| 0.6057 \| nan \| 0.5801 \| 0.6419 \| 0.0 \| 0.5649 \| 0.6390 \| \| 0.0332 \| 131.05 \| 37480 \| 0.1528 \| 0.3920 \| 0.5960 \| 0.5883 \| nan \| 0.5512 \| 0.6408 \| 0.0 \| 0.5385 \| 0.6376 \| \| 0.0357 \| 131.12 \| 37500 \| 0.1563 \| 0.3908 \| 0.5946 \| 0.5890 \| nan \| 0.5623 \| 0.6269 \| 0.0 \| 0.5480 \| 0.6244 \| \| 0.044 \| 131.19 \| 37520 \| 0.1524 \| 0.4013 \| 0.6103 \| 0.6027 \| nan \| 0.5664 \| 0.6541 \| 0.0 \| 0.5532 \| 0.6507 \| \| 0.0477 \| 131.26 \| 37540 \| 0.1541 \| 0.3992 \| 0.6074 \| 0.6014 \| nan \| 0.5727 \| 0.6420 \| 0.0 \| 0.5583 \| 0.6394 \| \| 0.0491 \| 131.33 \| 37560 \| 0.1568 \| 0.3961 \| 0.6025 \| 0.5959 \| nan \| 0.5645 \| 0.6404 \| 0.0 \| 0.5507 \| 0.6375 \| \| 0.0351 \| 131.4 \| 37580 \| 0.1546 \| 0.3980 \| 0.6060 \| 0.6006 \| nan \| 0.5749 \| 0.6371 \| 0.0 \| 0.5604 \| 0.6337 \| \| 0.0418 \| 131.47 \| 37600 \| 0.1566 \| 0.3972 \| 0.6045 \| 0.5980 \| nan \| 0.5669 \| 0.6420 \| 0.0 \| 0.5531 \| 0.6384 \| \| 0.0398 \| 131.54 \| 37620 \| 0.1554 \| 0.3982 \| 0.6060 \| 0.5989 \| nan \| 0.5650 \| 0.6471 \| 0.0 \| 0.5510 \| 0.6438 \| \| 0.0419 \| 131.61 \| 37640 \| 0.1556 \| 0.3972 \| 0.6041 \| 0.5969 \| nan \| 0.5622 \| 0.6461 \| 0.0 \| 0.5491 \| 0.6424 \| \| 0.0399 \| 131.68 \| 37660 \| 0.1541 \| 0.4005 \| 0.6093 \| 0.6034 \| nan \| 0.5750 \| 0.6436 \| 0.0 \| 0.5619 \| 0.6397 \| \| 0.0434 \| 131.75 \| 37680 \| 0.1436 \| 0.4099 \| 0.6232 \| 0.6146 \| nan \| 0.5733 \| 0.6732 \| 0.0 \| 0.5626 \| 0.6670 \| \| 0.0496 \| 131.82 \| 37700 \| 0.1441 \| 0.4086 \| 0.6212 \| 0.6121 \| nan \| 0.5687 \| 0.6738 \| 0.0 \| 0.5580 \| 0.6678 \| \| 0.047 \| 131.89 \| 37720 \| 0.1436 \| 0.4043 \| 0.6145 \| 0.6040 \| nan \| 0.5537 \| 0.6754 \| 0.0 \| 0.5431 \| 0.6698 \| \| 0.06 \| 131.96 \| 37740 \| 0.1482 \| 0.4009 \| 0.6093 \| 0.5993 \| nan \| 0.5519 \| 0.6667 \| 0.0 \| 0.5408 \| 0.6620 \| \| 0.0701 \| 132.03 \| 37760 \| 0.1498 \| 0.4001 \| 0.6083 \| 0.6002 \| nan \| 0.5616 \| 0.6551 \| 0.0 \| 0.5496 \| 0.6508 \| \| 0.0444 \| 132.1 \| 37780 \| 0.1514 \| 0.3981 \| 0.6052 \| 0.5959 \| nan \| 0.5514 \| 0.6590 \| 0.0 \| 0.5397 \| 0.6546 \| \| 0.0606 \| 132.17 \| 37800 \| 0.1535 \| 0.3989 \| 0.6065 \| 0.5989 \| nan \| 0.5624 \| 0.6507 \| 0.0 \| 0.5504 \| 0.6462 \| \| 0.0293 \| 132.24 \| 37820 \| 0.1494 \| 0.4056 \| 0.6164 \| 0.6064 \| nan \| 0.5582 \| 0.6746 \| 0.0 \| 0.5470 \| 0.6697 \| \| 0.0304 \| 132.31 \| 37840 \| 0.1547 \| 0.3966 \| 0.6038 \| 0.5984 \| nan \| 0.5731 \| 0.6344 \| 0.0 \| 0.5585 \| 0.6314 \| \| 0.0319 \| 132.38 \| 37860 \| 0.1561 \| 0.3934 \| 0.5984 \| 0.5912 \| nan \| 0.5570 \| 0.6398 \| 0.0 \| 0.5440 \| 0.6364 \| \| 0.0734 \| 132.45 \| 37880 \| 0.1559 \| 0.3987 \| 0.6066 \| 0.6003 \| nan \| 0.5704 \| 0.6428 \| 0.0 \| 0.5568 \| 0.6393 \| \| 0.0582 \| 132.52 \| 37900 \| 0.1518 \| 0.4010 \| 0.6095 \| 0.6001 \| nan \| 0.5556 \| 0.6634 \| 0.0 \| 0.5444 \| 0.6587 \| \| 0.0429 \| 132.59 \| 37920 \| 0.1569 \| 0.3952 \| 0.6008 \| 0.5927 \| nan \| 0.5537 \| 0.6480 \| 0.0 \| 0.5400 \| 0.6457 \| \| 0.0513 \| 132.66 \| 37940 \| 0.1588 \| 0.3861 \| 0.5869 \| 0.5805 \| nan \| 0.5502 \| 0.6235 \| 0.0 \| 0.5366 \| 0.6216 \| \| 0.057 \| 132.73 \| 37960 \| 0.1514 \| 0.3984 \| 0.6057 \| 0.5985 \| nan \| 0.5642 \| 0.6471 \| 0.0 \| 0.5516 \| 0.6436 \| \| 0.0506 \| 132.8 \| 37980 \| 0.1480 \| 0.4000 \| 0.6082 \| 0.6013 \| nan \| 0.5682 \| 0.6483 \| 0.0 \| 0.5561 \| 0.6440 \| \| 0.0369 \| 132.87 \| 38000 \| 0.1511 \| 0.3985 \| 0.6062 \| 0.6001 \| nan \| 0.5713 \| 0.6411 \| 0.0 \| 0.5578 \| 0.6376 \| \| 0.0706 \| 132.94 \| 38020 \| 0.1504 \| 0.4011 \| 0.6098 \| 0.6033 \| nan \| 0.5720 \| 0.6476 \| 0.0 \| 0.5590 \| 0.6441 \| \| 0.0479 \| 133.01 \| 38040 \| 0.1517 \| 0.4015 \| 0.6107 \| 0.6051 \| nan \| 0.5784 \| 0.6431 \| 0.0 \| 0.5655 \| 0.6391 \| \| 0.0288 \| 133.08 \| 38060 \| 0.1536 \| 0.3993 \| 0.6077 \| 0.6013 \| nan \| 0.5709 \| 0.6446 \| 0.0 \| 0.5567 \| 0.6411 \| \| 0.0405 \| 133.15 \| 38080 \| 0.1503 \| 0.4080 \| 0.6212 \| 0.6167 \| nan \| 0.5951 \| 0.6473 \| 0.0 \| 0.5800 \| 0.6440 \| \| 0.0503 \| 133.22 \| 38100 \| 0.1550 \| 0.3898 \| 0.5929 \| 0.5859 \| nan \| 0.5526 \| 0.6332 \| 0.0 \| 0.5395 \| 0.6299 \| \| 0.0429 \| 133.29 \| 38120 \| 0.1551 \| 0.3907 \| 0.5942 \| 0.5870 \| nan \| 0.5529 \| 0.6355 \| 0.0 \| 0.5394 \| 0.6326 \| \| 0.0302 \| 133.36 \| 38140 \| 0.1526 \| 0.4010 \| 0.6102 \| 0.6042 \| nan \| 0.5758 \| 0.6446 \| 0.0 \| 0.5617 \| 0.6414 \| \| 0.0298 \| 133.43 \| 38160 \| 0.1541 \| 0.3954 \| 0.6009 \| 0.5926 \| nan \| 0.5531 \| 0.6488 \| 0.0 \| 0.5409 \| 0.6453 \| \| 0.0279 \| 133.5 \| 38180 \| 0.1532 \| 0.3983 \| 0.6057 \| 0.5997 \| nan \| 0.5710 \| 0.6404 \| 0.0 \| 0.5574 \| 0.6375 \| \| 0.0437 \| 133.57 \| 38200 \| 0.1577 \| 0.4037 \| 0.6143 \| 0.6099 \| nan \| 0.5886 \| 0.6401 \| 0.0 \| 0.5730 \| 0.6380 \| \| 0.0671 \| 133.64 \| 38220 \| 0.1539 \| 0.4032 \| 0.6138 \| 0.6077 \| nan \| 0.5784 \| 0.6493 \| 0.0 \| 0.5634 \| 0.6464 \| \| 0.0418 \| 133.71 \| 38240 \| 0.1505 \| 0.3986 \| 0.6062 \| 0.5976 \| nan \| 0.5565 \| 0.6559 \| 0.0 \| 0.5433 \| 0.6525 \| \| 0.0292 \| 133.78 \| 38260 \| 0.1520 \| 0.3969 \| 0.6035 \| 0.5956 \| nan \| 0.5577 \| 0.6493 \| 0.0 \| 0.5447 \| 0.6461 \| \| 0.0575 \| 133.85 \| 38280 \| 0.1528 \| 0.3956 \| 0.6017 \| 0.5942 \| nan \| 0.5583 \| 0.6452 \| 0.0 \| 0.5444 \| 0.6422 \| \| 0.0382 \| 133.92 \| 38300 \| 0.1529 \| 0.4008 \| 0.6096 \| 0.6015 \| nan \| 0.5629 \| 0.6563 \| 0.0 \| 0.5495 \| 0.6530 \| \| 0.0601 \| 133.99 \| 38320 \| 0.1524 \| 0.4019 \| 0.6115 \| 0.6050 \| nan \| 0.5743 \| 0.6486 \| 0.0 \| 0.5606 \| 0.6451 \| \| 0.0487 \| 134.06 \| 38340 \| 0.1542 \| 0.4000 \| 0.6084 \| 0.6006 \| nan \| 0.5637 \| 0.6531 \| 0.0 \| 0.5504 \| 0.6495 \| \| 0.0415 \| 134.13 \| 38360 \| 0.1526 \| 0.3967 \| 0.6031 \| 0.5941 \| nan \| 0.5511 \| 0.6552 \| 0.0 \| 0.5384 \| 0.6517 \| \| 0.0609 \| 134.2 \| 38380 \| 0.1541 \| 0.3978 \| 0.6045 \| 0.5962 \| nan \| 0.5566 \| 0.6524 \| 0.0 \| 0.5447 \| 0.6486 \| \| 0.058 \| 134.27 \| 38400 \| 0.1500 \| 0.4047 \| 0.6154 \| 0.6086 \| nan \| 0.5760 \| 0.6548 \| 0.0 \| 0.5631 \| 0.6509 \| \| 0.0582 \| 134.34 \| 38420 \| 0.1525 \| 0.4032 \| 0.6130 \| 0.6055 \| nan \| 0.5695 \| 0.6564 \| 0.0 \| 0.5573 \| 0.6524 \| \| 0.0446 \| 134.41 \| 38440 \| 0.1532 \| 0.3986 \| 0.6059 \| 0.5977 \| nan \| 0.5588 \| 0.6530 \| 0.0 \| 0.5466 \| 0.6493 \| \| 0.0462 \| 134.48 \| 38460 \| 0.1549 \| 0.3961 \| 0.6022 \| 0.5948 \| nan \| 0.5594 \| 0.6451 \| 0.0 \| 0.5466 \| 0.6419 \| \| 0.0461 \| 134.55 \| 38480 \| 0.1555 \| 0.4033 \| 0.6136 \| 0.6087 \| nan \| 0.5855 \| 0.6417 \| 0.0 \| 0.5713 \| 0.6387 \| \| 0.0581 \| 134.62 \| 38500 \| 0.1539 \| 0.3981 \| 0.6053 \| 0.5973 \| nan \| 0.5587 \| 0.6520 \| 0.0 \| 0.5455 \| 0.6489 \| \| 0.0226 \| 134.69 \| 38520 \| 0.1555 \| 0.3980 \| 0.6052 \| 0.5989 \| nan \| 0.5687 \| 0.6416 \| 0.0 \| 0.5554 \| 0.6386 \| \| 0.0532 \| 134.76 \| 38540 \| 0.1569 \| 0.3912 \| 0.5948 \| 0.5871 \| nan \| 0.5505 \| 0.6391 \| 0.0 \| 0.5377 \| 0.6360 \| \| 0.063 \| 134.83 \| 38560 \| 0.1520 \| 0.4059 \| 0.6173 \| 0.6094 \| nan \| 0.5717 \| 0.6629 \| 0.0 \| 0.5592 \| 0.6584 \| \| 0.0212 \| 134.9 \| 38580 \| 0.1512 \| 0.3986 \| 0.6060 \| 0.5979 \| nan \| 0.5594 \| 0.6525 \| 0.0 \| 0.5471 \| 0.6486 \| \| 0.0271 \| 134.97 \| 38600 \| 0.1508 \| 0.4030 \| 0.6133 \| 0.6077 \| nan \| 0.5812 \| 0.6453 \| 0.0 \| 0.5673 \| 0.6418 \| \| 0.0382 \| 135.03 \| 38620 \| 0.1587 \| 0.3951 \| 0.6011 \| 0.5962 \| nan \| 0.5729 \| 0.6293 \| 0.0 \| 0.5586 \| 0.6267 \| \| 0.0484 \| 135.1 \| 38640 \| 0.1524 \| 0.4055 \| 0.6167 \| 0.6097 \| nan \| 0.5761 \| 0.6573 \| 0.0 \| 0.5627 \| 0.6539 \| \| 0.059 \| 135.17 \| 38660 \| 0.1538 \| 0.4011 \| 0.6098 \| 0.6025 \| nan \| 0.5675 \| 0.6521 \| 0.0 \| 0.5541 \| 0.6493 \| \| 0.0476 \| 135.24 \| 38680 \| 0.1558 \| 0.4015 \| 0.6108 \| 0.6055 \| nan \| 0.5803 \| 0.6414 \| 0.0 \| 0.5660 \| 0.6386 \| \| 0.0486 \| 135.31 \| 38700 \| 0.1552 \| 0.4024 \| 0.6123 \| 0.6065 \| nan \| 0.5786 \| 0.6460 \| 0.0 \| 0.5640 \| 0.6433 \| \| 0.0334 \| 135.38 \| 38720 \| 0.1509 \| 0.4061 \| 0.6173 \| 0.6088 \| nan \| 0.5680 \| 0.6667 \| 0.0 \| 0.5555 \| 0.6630 \| \| 0.0318 \| 135.45 \| 38740 \| 0.1493 \| 0.4051 \| 0.6159 \| 0.6079 \| nan \| 0.5695 \| 0.6623 \| 0.0 \| 0.5564 \| 0.6589 \| \| 0.043 \| 135.52 \| 38760 \| 0.1431 \| 0.4135 \| 0.6280 \| 0.6166 \| nan \| 0.5624 \| 0.6936 \| 0.0 \| 0.5519 \| 0.6886 \| \| 0.0542 \| 135.59 \| 38780 \| 0.1428 \| 0.4146 \| 0.6298 \| 0.6196 \| nan \| 0.5709 \| 0.6887 \| 0.0 \| 0.5598 \| 0.6839 \| \| 0.0299 \| 135.66 \| 38800 \| 0.1449 \| 0.4113 \| 0.6248 \| 0.6155 \| nan \| 0.5711 \| 0.6785 \| 0.0 \| 0.5595 \| 0.6744 \| \| 0.0404 \| 135.73 \| 38820 \| 0.1490 \| 0.4026 \| 0.6117 \| 0.6026 \| nan \| 0.5592 \| 0.6642 \| 0.0 \| 0.5471 \| 0.6608 \| \| 0.0352 \| 135.8 \| 38840 \| 0.1531 \| 0.4032 \| 0.6126 \| 0.6037 \| nan \| 0.5609 \| 0.6643 \| 0.0 \| 0.5492 \| 0.6604 \| \| 0.0424 \| 135.87 \| 38860 \| 0.1481 \| 0.4081 \| 0.6200 \| 0.6098 \| nan \| 0.5614 \| 0.6785 \| 0.0 \| 0.5494 \| 0.6748 \| \| 0.0252 \| 135.94 \| 38880 \| 0.1463 \| 0.4093 \| 0.6219 \| 0.6134 \| nan \| 0.5729 \| 0.6709 \| 0.0 \| 0.5613 \| 0.6667 \| \| 0.0449 \| 136.01 \| 38900 \| 0.1499 \| 0.4064 \| 0.6177 \| 0.6093 \| nan \| 0.5696 \| 0.6657 \| 0.0 \| 0.5571 \| 0.6619 \| \| 0.0575 \| 136.08 \| 38920 \| 0.1500 \| 0.4109 \| 0.6247 \| 0.6164 \| nan \| 0.5771 \| 0.6723 \| 0.0 \| 0.5640 \| 0.6687 \| \| 0.0675 \| 136.15 \| 38940 \| 0.1533 \| 0.3934 \| 0.5985 \| 0.5938 \| nan \| 0.5711 \| 0.6260 \| 0.0 \| 0.5570 \| 0.6233 \| \| 0.056 \| 136.22 \| 38960 \| 0.1516 \| 0.3932 \| 0.5975 \| 0.5891 \| nan \| 0.5486 \| 0.6465 \| 0.0 \| 0.5360 \| 0.6437 \| \| 0.0392 \| 136.29 \| 38980 \| 0.1492 \| 0.4039 \| 0.6144 \| 0.6074 \| nan \| 0.5741 \| 0.6547 \| 0.0 \| 0.5605 \| 0.6513 \| \| 0.0526 \| 136.36 \| 39000 \| 0.1503 \| 0.4006 \| 0.6089 \| 0.6001 \| nan \| 0.5580 \| 0.6598 \| 0.0 \| 0.5450 \| 0.6566 \| \| 0.0475 \| 136.43 \| 39020 \| 0.1529 \| 0.3939 \| 0.5983 \| 0.5889 \| nan \| 0.5438 \| 0.6527 \| 0.0 \| 0.5318 \| 0.6500 \| \| 0.0616 \| 136.5 \| 39040 \| 0.1495 \| 0.4048 \| 0.6151 \| 0.6074 \| nan \| 0.5707 \| 0.6595 \| 0.0 \| 0.5579 \| 0.6564 \| \| 0.0268 \| 136.57 \| 39060 \| 0.1484 \| 0.4002 \| 0.6078 \| 0.5991 \| nan \| 0.5577 \| 0.6578 \| 0.0 \| 0.5461 \| 0.6544 \| \| 0.0397 \| 136.64 \| 39080 \| 0.1477 \| 0.4057 \| 0.6163 \| 0.6086 \| nan \| 0.5720 \| 0.6606 \| 0.0 \| 0.5598 \| 0.6572 \| \| 0.0369 \| 136.71 \| 39100 \| 0.1467 \| 0.4064 \| 0.6173 \| 0.6084 \| nan \| 0.5656 \| 0.6691 \| 0.0 \| 0.5540 \| 0.6651 \| \| 0.0498 \| 136.78 \| 39120 \| 0.1509 \| 0.4031 \| 0.6129 \| 0.6069 \| nan \| 0.5786 \| 0.6471 \| 0.0 \| 0.5648 \| 0.6444 \| \| 0.0314 \| 136.85 \| 39140 \| 0.1509 \| 0.4034 \| 0.6136 \| 0.6081 \| nan \| 0.5819 \| 0.6453 \| 0.0 \| 0.5680 \| 0.6423 \| \| 0.0242 \| 136.92 \| 39160 \| 0.1525 \| 0.4009 \| 0.6092 \| 0.6015 \| nan \| 0.5647 \| 0.6537 \| 0.0 \| 0.5524 \| 0.6504 \| \| 0.0275 \| 136.99 \| 39180 \| 0.1508 \| 0.4037 \| 0.6134 \| 0.6045 \| nan \| 0.5623 \| 0.6644 \| 0.0 \| 0.5507 \| 0.6606 \| \| 0.0601 \| 137.06 \| 39200 \| 0.1513 \| 0.4016 \| 0.6104 \| 0.6030 \| nan \| 0.5672 \| 0.6537 \| 0.0 \| 0.5548 \| 0.6501 \| \| 0.038 \| 137.13 \| 39220 \| 0.1500 \| 0.3981 \| 0.6048 \| 0.5966 \| nan \| 0.5574 \| 0.6521 \| 0.0 \| 0.5458 \| 0.6485 \| \| 0.0553 \| 137.2 \| 39240 \| 0.1452 \| 0.4103 \| 0.6236 \| 0.6157 \| nan \| 0.5776 \| 0.6697 \| 0.0 \| 0.5660 \| 0.6649 \| \| 0.0461 \| 137.27 \| 39260 \| 0.1451 \| 0.4112 \| 0.6251 \| 0.6185 \| nan \| 0.5871 \| 0.6631 \| 0.0 \| 0.5738 \| 0.6598 \| \| 0.0912 \| 137.34 \| 39280 \| 0.1518 \| 0.4021 \| 0.6109 \| 0.6037 \| nan \| 0.5694 \| 0.6524 \| 0.0 \| 0.5568 \| 0.6494 \| \| 0.0429 \| 137.41 \| 39300 \| 0.1595 \| 0.3930 \| 0.5975 \| 0.5919 \| nan \| 0.5651 \| 0.6299 \| 0.0 \| 0.5510 \| 0.6281 \| \| 0.029 \| 137.48 \| 39320 \| 0.1576 \| 0.3970 \| 0.6034 \| 0.5966 \| nan \| 0.5642 \| 0.6427 \| 0.0 \| 0.5502 \| 0.6407 \| \| 0.0493 \| 137.55 \| 39340 \| 0.1569 \| 0.3945 \| 0.5996 \| 0.5931 \| nan \| 0.5621 \| 0.6370 \| 0.0 \| 0.5490 \| 0.6346 \| \| 0.0236 \| 137.62 \| 39360 \| 0.1549 \| 0.3999 \| 0.6080 \| 0.6010 \| nan \| 0.5677 \| 0.6483 \| 0.0 \| 0.5540 \| 0.6457 \| \| 0.0491 \| 137.69 \| 39380 \| 0.1555 \| 0.3986 \| 0.6063 \| 0.6013 \| nan \| 0.5775 \| 0.6351 \| 0.0 \| 0.5632 \| 0.6326 \| \| 0.0346 \| 137.76 \| 39400 \| 0.1545 \| 0.4029 \| 0.6128 \| 0.6075 \| nan \| 0.5825 \| 0.6431 \| 0.0 \| 0.5683 \| 0.6404 \| \| 0.0544 \| 137.83 \| 39420 \| 0.1536 \| 0.3973 \| 0.6035 \| 0.5953 \| nan \| 0.5559 \| 0.6511 \| 0.0 \| 0.5439 \| 0.6480 \| \| 0.0455 \| 137.9 \| 39440 \| 0.1516 \| 0.3992 \| 0.6065 \| 0.5975 \| nan \| 0.5545 \| 0.6584 \| 0.0 \| 0.5432 \| 0.6545 \| \| 0.0309 \| 137.97 \| 39460 \| 0.1523 \| 0.4013 \| 0.6100 \| 0.6028 \| nan \| 0.5684 \| 0.6516 \| 0.0 \| 0.5559 \| 0.6480 \| \| 0.0361 \| 138.04 \| 39480 \| 0.1532 \| 0.4031 \| 0.6131 \| 0.6060 \| nan \| 0.5721 \| 0.6541 \| 0.0 \| 0.5585 \| 0.6508 \| \| 0.0544 \| 138.11 \| 39500 \| 0.1525 \| 0.3985 \| 0.6055 \| 0.5968 \| nan \| 0.5550 \| 0.6559 \| 0.0 \| 0.5434 \| 0.6520 \| \| 0.0375 \| 138.18 \| 39520 \| 0.1490 \| 0.4037 \| 0.6136 \| 0.6042 \| nan \| 0.5596 \| 0.6675 \| 0.0 \| 0.5477 \| 0.6634 \| \| 0.0278 \| 138.25 \| 39540 \| 0.1538 \| 0.3972 \| 0.6036 \| 0.5965 \| nan \| 0.5624 \| 0.6448 \| 0.0 \| 0.5499 \| 0.6416 \| \| 0.0374 \| 138.32 \| 39560 \| 0.1512 \| 0.4049 \| 0.6154 \| 0.6066 \| nan \| 0.5646 \| 0.6662 \| 0.0 \| 0.5534 \| 0.6613 \| \| 0.0416 \| 138.39 \| 39580 \| 0.1583 \| 0.3934 \| 0.5978 \| 0.5895 \| nan \| 0.5500 \| 0.6456 \| 0.0 \| 0.5376 \| 0.6426 \| \| 0.0276 \| 138.46 \| 39600 \| 0.1532 \| 0.4008 \| 0.6092 \| 0.6004 \| nan \| 0.5586 \| 0.6598 \| 0.0 \| 0.5458 \| 0.6565 \| \| 0.0427 \| 138.53 \| 39620 \| 0.1494 \| 0.4101 \| 0.6241 \| 0.6171 \| nan \| 0.5837 \| 0.6646 \| 0.0 \| 0.5704 \| 0.6599 \| \| 0.0536 \| 138.6 \| 39640 \| 0.1505 \| 0.4055 \| 0.6168 \| 0.6089 \| nan \| 0.5715 \| 0.6620 \| 0.0 \| 0.5589 \| 0.6576 \| \| 0.0503 \| 138.67 \| 39660 \| 0.1544 \| 0.3987 \| 0.6063 \| 0.5981 \| nan \| 0.5588 \| 0.6538 \| 0.0 \| 0.5463 \| 0.6498 \| \| 0.0452 \| 138.74 \| 39680 \| 0.1528 \| 0.4037 \| 0.6141 \| 0.6078 \| nan \| 0.5777 \| 0.6506 \| 0.0 \| 0.5648 \| 0.6462 \| \| 0.0399 \| 138.81 \| 39700 \| 0.1564 \| 0.4017 \| 0.6109 \| 0.6033 \| nan \| 0.5671 \| 0.6547 \| 0.0 \| 0.5543 \| 0.6507 \| \| 0.0406 \| 138.88 \| 39720 \| 0.1521 \| 0.3987 \| 0.6060 \| 0.5972 \| nan \| 0.5556 \| 0.6563 \| 0.0 \| 0.5444 \| 0.6516 \| \| 0.0352 \| 138.95 \| 39740 \| 0.1432 \| 0.4106 \| 0.6239 \| 0.6127 \| nan \| 0.5590 \| 0.6888 \| 0.0 \| 0.5499 \| 0.6818 \| \| 0.0447 \| 139.02 \| 39760 \| 0.1430 \| 0.4132 \| 0.6278 \| 0.6173 \| nan \| 0.5671 \| 0.6885 \| 0.0 \| 0.5564 \| 0.6832 \| \| 0.0581 \| 139.09 \| 39780 \| 0.1497 \| 0.4039 \| 0.6137 \| 0.6034 \| nan \| 0.5545 \| 0.6728 \| 0.0 \| 0.5441 \| 0.6677 \| \| 0.0477 \| 139.16 \| 39800 \| 0.1464 \| 0.4074 \| 0.6192 \| 0.6079 \| nan \| 0.5544 \| 0.6840 \| 0.0 \| 0.5436 \| 0.6785 \| \| 0.0235 \| 139.23 \| 39820 \| 0.1505 \| 0.4042 \| 0.6146 \| 0.6068 \| nan \| 0.5696 \| 0.6596 \| 0.0 \| 0.5576 \| 0.6550 \| \| 0.0475 \| 139.3 \| 39840 \| 0.1486 \| 0.4058 \| 0.6174 \| 0.6088 \| nan \| 0.5678 \| 0.6670 \| 0.0 \| 0.5552 \| 0.6622 \| \| 0.0587 \| 139.37 \| 39860 \| 0.1496 \| 0.4004 \| 0.6087 \| 0.5991 \| nan \| 0.5532 \| 0.6641 \| 0.0 \| 0.5416 \| 0.6595 \| \| 0.0586 \| 139.44 \| 39880 \| 0.1534 \| 0.3972 \| 0.6037 \| 0.5931 \| nan \| 0.5422 \| 0.6652 \| 0.0 \| 0.5311 \| 0.6605 \| \| 0.0505 \| 139.51 \| 39900 \| 0.1508 \| 0.3982 \| 0.6049 \| 0.5948 \| nan \| 0.5465 \| 0.6633 \| 0.0 \| 0.5352 \| 0.6593 \| \| 0.053 \| 139.58 \| 39920 \| 0.1520 \| 0.4056 \| 0.6166 \| 0.6093 \| nan \| 0.5741 \| 0.6592 \| 0.0 \| 0.5612 \| 0.6554 \| \| 0.0335 \| 139.65 \| 39940 \| 0.1482 \| 0.4059 \| 0.6171 \| 0.6094 \| nan \| 0.5725 \| 0.6618 \| 0.0 \| 0.5602 \| 0.6575 \| \| 0.0276 \| 139.72 \| 39960 \| 0.1495 \| 0.4001 \| 0.6085 \| 0.6004 \| nan \| 0.5617 \| 0.6552 \| 0.0 \| 0.5487 \| 0.6516 \| \| 0.0341 \| 139.79 \| 39980 \| 0.1484 \| 0.4011 \| 0.6095 \| 0.5996 \| nan \| 0.5520 \| 0.6671 \| 0.0 \| 0.5407 \| 0.6625 \| \| 0.026 \| 139.86 \| 40000 \| 0.1493 \| 0.4072 \| 0.6191 \| 0.6117 \| nan \| 0.5761 \| 0.6621 \| 0.0 \| 0.5634 \| 0.6582 \| \| 0.0444 \| 139.93 \| 40020 \| 0.1592 \| 0.3974 \| 0.6047 \| 0.5993 \| nan \| 0.5734 \| 0.6361 \| 0.0 \| 0.5594 \| 0.6329 \| \| 0.027 \| 140.0 \| 40040 \| 0.1584 \| 0.3954 \| 0.6018 \| 0.5972 \| nan \| 0.5751 \| 0.6286 \| 0.0 \| 0.5608 \| 0.6254 \| \| 0.0495 \| 140.07 \| 40060 \| 0.1632 \| 0.3924 \| 0.5973 \| 0.5918 \| nan \| 0.5653 \| 0.6293 \| 0.0 \| 0.5505 \| 0.6266 \| \| 0.0311 \| 140.14 \| 40080 \| 0.1616 \| 0.3881 \| 0.5903 \| 0.5840 \| nan \| 0.5536 \| 0.6271 \| 0.0 \| 0.5408 \| 0.6234 \| \| 0.0397 \| 140.21 \| 40100 \| 0.1584 \| 0.3914 \| 0.5954 \| 0.5876 \| nan \| 0.5500 \| 0.6408 \| 0.0 \| 0.5375 \| 0.6367 \| \| 0.0438 \| 140.28 \| 40120 \| 0.1594 \| 0.3987 \| 0.6068 \| 0.6007 \| nan \| 0.5714 \| 0.6422 \| 0.0 \| 0.5575 \| 0.6385 \| \| 0.0416 \| 140.35 \| 40140 \| 0.1549 \| 0.3991 \| 0.6072 \| 0.5998 \| nan \| 0.5645 \| 0.6499 \| 0.0 \| 0.5520 \| 0.6454 \| \| 0.0431 \| 140.42 \| 40160 \| 0.1595 \| 0.3929 \| 0.5977 \| 0.5911 \| nan \| 0.5600 \| 0.6354 \| 0.0 \| 0.5472 \| 0.6316 \| \| 0.034 \| 140.49 \| 40180 \| 0.1549 \| 0.3938 \| 0.5984 \| 0.5889 \| nan \| 0.5439 \| 0.6528 \| 0.0 \| 0.5328 \| 0.6487 \| \| 0.0424 \| 140.56 \| 40200 \| 0.1451 \| 0.4079 \| 0.6199 \| 0.6093 \| nan \| 0.5588 \| 0.6809 \| 0.0 \| 0.5490 \| 0.6746 \| \| 0.0433 \| 140.63 \| 40220 \| 0.1434 \| 0.4108 \| 0.6246 \| 0.6151 \| nan \| 0.5698 \| 0.6793 \| 0.0 \| 0.5600 \| 0.6723 \| \| 0.0232 \| 140.7 \| 40240 \| 0.1503 \| 0.4037 \| 0.6139 \| 0.6053 \| nan \| 0.5645 \| 0.6632 \| 0.0 \| 0.5532 \| 0.6579 \| \| 0.0254 \| 140.77 \| 40260 \| 0.1500 \| 0.4055 \| 0.6164 \| 0.6081 \| nan \| 0.5686 \| 0.6641 \| 0.0 \| 0.5574 \| 0.6591 \| \| 0.0236 \| 140.84 \| 40280 \| 0.1564 \| 0.3954 \| 0.6007 \| 0.5915 \| nan \| 0.5475 \| 0.6538 \| 0.0 \| 0.5371 \| 0.6490 \| \| 0.0253 \| 140.91 \| 40300 \| 0.1523 \| 0.4099 \| 0.6233 \| 0.6173 \| nan \| 0.5886 \| 0.6579 \| 0.0 \| 0.5765 \| 0.6532 \| \| 0.0368 \| 140.98 \| 40320 \| 0.1484 \| 0.4070 \| 0.6186 \| 0.6079 \| nan \| 0.5565 \| 0.6807 \| 0.0 \| 0.5479 \| 0.6731 \| \| 0.0309 \| 141.05 \| 40340 \| 0.1485 \| 0.4049 \| 0.6155 \| 0.6053 \| nan \| 0.5569 \| 0.6740 \| 0.0 \| 0.5480 \| 0.6667 \| \| 0.0501 \| 141.12 \| 40360 \| 0.1501 \| 0.4077 \| 0.6196 \| 0.6124 \| nan \| 0.5783 \| 0.6609 \| 0.0 \| 0.5676 \| 0.6553 \| \| 0.0486 \| 141.19 \| 40380 \| 0.1563 \| 0.3967 \| 0.6030 \| 0.5963 \| nan \| 0.5643 \| 0.6416 \| 0.0 \| 0.5521 \| 0.6380 \| \| 0.0445 \| 141.26 \| 40400 \| 0.1529 \| 0.4002 \| 0.6081 \| 0.5992 \| nan \| 0.5563 \| 0.6600 \| 0.0 \| 0.5445 \| 0.6561 \| \| 0.0248 \| 141.33 \| 40420 \| 0.1527 \| 0.3983 \| 0.6055 \| 0.5969 \| nan \| 0.5558 \| 0.6551 \| 0.0 \| 0.5443 \| 0.6504 \| \| 0.0369 \| 141.4 \| 40440 \| 0.1505 \| 0.4024 \| 0.6120 \| 0.6029 \| nan \| 0.5594 \| 0.6647 \| 0.0 \| 0.5476 \| 0.6596 \| \| 0.0344 \| 141.47 \| 40460 \| 0.1514 \| 0.3963 \| 0.6031 \| 0.5964 \| nan \| 0.5649 \| 0.6412 \| 0.0 \| 0.5519 \| 0.6370 \| \| 0.0387 \| 141.54 \| 40480 \| 0.1532 \| 0.3958 \| 0.6024 \| 0.5957 \| nan \| 0.5635 \| 0.6414 \| 0.0 \| 0.5505 \| 0.6369 \| \| 0.0539 \| 141.61 \| 40500 \| 0.1577 \| 0.3937 \| 0.5992 \| 0.5931 \| nan \| 0.5639 \| 0.6344 \| 0.0 \| 0.5496 \| 0.6316 \| \| 0.0318 \| 141.68 \| 40520 \| 0.1595 \| 0.3885 \| 0.5910 \| 0.5832 \| nan \| 0.5457 \| 0.6362 \| 0.0 \| 0.5326 \| 0.6328 \| \| 0.0442 \| 141.75 \| 40540 \| 0.1533 \| 0.4012 \| 0.6113 \| 0.6070 \| nan \| 0.5865 \| 0.6360 \| 0.0 \| 0.5713 \| 0.6322 \| \| 0.0523 \| 141.82 \| 40560 \| 0.1538 \| 0.3975 \| 0.6051 \| 0.5987 \| nan \| 0.5679 \| 0.6423 \| 0.0 \| 0.5543 \| 0.6381 \| \| 0.0584 \| 141.89 \| 40580 \| 0.1520 \| 0.3979 \| 0.6054 \| 0.5978 \| nan \| 0.5616 \| 0.6491 \| 0.0 \| 0.5493 \| 0.6444 \| \| 0.0282 \| 141.96 \| 40600 \| 0.1535 \| 0.3970 \| 0.6039 \| 0.5954 \| nan \| 0.5552 \| 0.6525 \| 0.0 \| 0.5432 \| 0.6478 \| \| 0.0472 \| 142.03 \| 40620 \| 0.1570 \| 0.3935 \| 0.5985 \| 0.5916 \| nan \| 0.5585 \| 0.6385 \| 0.0 \| 0.5458 \| 0.6346 \| \| 0.0548 \| 142.1 \| 40640 \| 0.1528 \| 0.4001 \| 0.6087 \| 0.6006 \| nan \| 0.5618 \| 0.6556 \| 0.0 \| 0.5498 \| 0.6505 \| \| 0.0447 \| 142.17 \| 40660 \| 0.1593 \| 0.3960 \| 0.6030 \| 0.5988 \| nan \| 0.5790 \| 0.6269 \| 0.0 \| 0.5632 \| 0.6248 \| \| 0.084 \| 142.24 \| 40680 \| 0.1610 \| 0.3947 \| 0.6010 \| 0.5956 \| nan \| 0.5698 \| 0.6322 \| 0.0 \| 0.5542 \| 0.6300 \| \| 0.0379 \| 142.31 \| 40700 \| 0.1587 \| 0.3976 \| 0.6051 \| 0.6002 \| nan \| 0.5769 \| 0.6334 \| 0.0 \| 0.5616 \| 0.6311 \| \| 0.0339 \| 142.38 \| 40720 \| 0.1583 \| 0.3962 \| 0.6031 \| 0.5973 \| nan \| 0.5697 \| 0.6365 \| 0.0 \| 0.5544 \| 0.6342 \| \| 0.0367 \| 142.45 \| 40740 \| 0.1572 \| 0.3972 \| 0.6043 \| 0.5986 \| nan \| 0.5714 \| 0.6373 \| 0.0 \| 0.5568 \| 0.6347 \| \| 0.0347 \| 142.52 \| 40760 \| 0.1572 \| 0.3978 \| 0.6053 \| 0.5984 \| nan \| 0.5655 \| 0.6450 \| 0.0 \| 0.5512 \| 0.6421 \| \| 0.0594 \| 142.59 \| 40780 \| 0.1576 \| 0.3944 \| 0.5998 \| 0.5927 \| nan \| 0.5588 \| 0.6409 \| 0.0 \| 0.5455 \| 0.6378 \| \| 0.0574 \| 142.66 \| 40800 \| 0.1578 \| 0.3955 \| 0.6016 \| 0.5945 \| nan \| 0.5603 \| 0.6430 \| 0.0 \| 0.5463 \| 0.6401 \| \| 0.0413 \| 142.73 \| 40820 \| 0.1531 \| 0.4090 \| 0.6224 \| 0.6158 \| nan \| 0.5842 \| 0.6606 \| 0.0 \| 0.5704 \| 0.6567 \| \| 0.0303 \| 142.8 \| 40840 \| 0.1546 \| 0.3935 \| 0.5980 \| 0.5896 \| nan \| 0.5493 \| 0.6468 \| 0.0 \| 0.5370 \| 0.6434 \| \| 0.059 \| 142.87 \| 40860 \| 0.1564 \| 0.3940 \| 0.5990 \| 0.5912 \| nan \| 0.5543 \| 0.6437 \| 0.0 \| 0.5411 \| 0.6409 \| \| 0.0281 \| 142.94 \| 40880 \| 0.1499 \| 0.4013 \| 0.6099 \| 0.6006 \| nan \| 0.5560 \| 0.6639 \| 0.0 \| 0.5437 \| 0.6603 \| \| 0.0236 \| 143.01 \| 40900 \| 0.1530 \| 0.4046 \| 0.6150 \| 0.6045 \| nan \| 0.5545 \| 0.6755 \| 0.0 \| 0.5442 \| 0.6697 \| \| 0.0233 \| 143.08 \| 40920 \| 0.1577 \| 0.3979 \| 0.6048 \| 0.5965 \| nan \| 0.5568 \| 0.6528 \| 0.0 \| 0.5444 \| 0.6493 \| \| 0.0259 \| 143.15 \| 40940 \| 0.1607 \| 0.3934 \| 0.5987 \| 0.5928 \| nan \| 0.5646 \| 0.6327 \| 0.0 \| 0.5495 \| 0.6308 \| \| 0.0479 \| 143.22 \| 40960 \| 0.1600 \| 0.3971 \| 0.6040 \| 0.5973 \| nan \| 0.5650 \| 0.6430 \| 0.0 \| 0.5514 \| 0.6400 \| \| 0.0227 \| 143.29 \| 40980 \| 0.1555 \| 0.3953 \| 0.6012 \| 0.5930 \| nan \| 0.5535 \| 0.6490 \| 0.0 \| 0.5406 \| 0.6454 \| \| 0.042 \| 143.36 \| 41000 \| 0.1562 \| 0.3993 \| 0.6076 \| 0.6016 \| nan \| 0.5733 \| 0.6419 \| 0.0 \| 0.5593 \| 0.6385 \| \| 0.038 \| 143.43 \| 41020 \| 0.1599 \| 0.3970 \| 0.6039 \| 0.5982 \| nan \| 0.5706 \| 0.6373 \| 0.0 \| 0.5567 \| 0.6342 \| \| 0.0294 \| 143.5 \| 41040 \| 0.1543 \| 0.4049 \| 0.6160 \| 0.6093 \| nan \| 0.5774 \| 0.6546 \| 0.0 \| 0.5636 \| 0.6510 \| \| 0.0339 \| 143.57 \| 41060 \| 0.1469 \| 0.4096 \| 0.6229 \| 0.6132 \| nan \| 0.5669 \| 0.6789 \| 0.0 \| 0.5555 \| 0.6732 \| \| 0.0497 \| 143.64 \| 41080 \| 0.1518 \| 0.4030 \| 0.6129 \| 0.6052 \| nan \| 0.5686 \| 0.6571 \| 0.0 \| 0.5551 \| 0.6538 \| \| 0.0534 \| 143.71 \| 41100 \| 0.1525 \| 0.4047 \| 0.6156 \| 0.6077 \| nan \| 0.5704 \| 0.6607 \| 0.0 \| 0.5565 \| 0.6576 \| \| 0.0392 \| 143.78 \| 41120 \| 0.1534 \| 0.4032 \| 0.6134 \| 0.6066 \| nan \| 0.5740 \| 0.6529 \| 0.0 \| 0.5592 \| 0.6505 \| \| 0.036 \| 143.85 \| 41140 \| 0.1553 \| 0.4002 \| 0.6084 \| 0.5999 \| nan \| 0.5596 \| 0.6572 \| 0.0 \| 0.5469 \| 0.6538 \| \| 0.0174 \| 143.92 \| 41160 \| 0.1517 \| 0.4040 \| 0.6145 \| 0.6064 \| nan \| 0.5675 \| 0.6616 \| 0.0 \| 0.5535 \| 0.6586 \| \| 0.0267 \| 143.99 \| 41180 \| 0.1565 \| 0.4000 \| 0.6086 \| 0.6033 \| nan \| 0.5781 \| 0.6390 \| 0.0 \| 0.5631 \| 0.6371 \| \| 0.0364 \| 144.06 \| 41200 \| 0.1525 \| 0.4055 \| 0.6168 \| 0.6093 \| nan \| 0.5735 \| 0.6600 \| 0.0 \| 0.5593 \| 0.6573 \| \| 0.0255 \| 144.13 \| 41220 \| 0.1551 \| 0.3992 \| 0.6069 \| 0.5990 \| nan \| 0.5613 \| 0.6525 \| 0.0 \| 0.5477 \| 0.6499 \| \| 0.03 \| 144.2 \| 41240 \| 0.1559 \| 0.4002 \| 0.6087 \| 0.6015 \| nan \| 0.5670 \| 0.6504 \| 0.0 \| 0.5529 \| 0.6478 \| \| 0.031 \| 144.27 \| 41260 \| 0.1561 \| 0.4013 \| 0.6104 \| 0.6028 \| nan \| 0.5664 \| 0.6545 \| 0.0 \| 0.5524 \| 0.6515 \| \| 0.0492 \| 144.34 \| 41280 \| 0.1582 \| 0.4008 \| 0.6097 \| 0.6043 \| nan \| 0.5784 \| 0.6411 \| 0.0 \| 0.5638 \| 0.6384 \| \| 0.0215 \| 144.41 \| 41300 \| 0.1583 \| 0.3995 \| 0.6075 \| 0.5998 \| nan \| 0.5631 \| 0.6520 \| 0.0 \| 0.5490 \| 0.6494 \| \| 0.029 \| 144.48 \| 41320 \| 0.1604 \| 0.3984 \| 0.6056 \| 0.5990 \| nan \| 0.5672 \| 0.6441 \| 0.0 \| 0.5537 \| 0.6416 \| \| 0.0384 \| 144.55 \| 41340 \| 0.1499 \| 0.4083 \| 0.6210 \| 0.6124 \| nan \| 0.5711 \| 0.6709 \| 0.0 \| 0.5585 \| 0.6664 \| \| 0.0305 \| 144.62 \| 41360 \| 0.1515 \| 0.4070 \| 0.6192 \| 0.6111 \| nan \| 0.5723 \| 0.6660 \| 0.0 \| 0.5593 \| 0.6618 \| \| 0.0531 \| 144.69 \| 41380 \| 0.1557 \| 0.3987 \| 0.6062 \| 0.5982 \| nan \| 0.5601 \| 0.6523 \| 0.0 \| 0.5469 \| 0.6493 \| \| 0.0379 \| 144.76 \| 41400 \| 0.1557 \| 0.4034 \| 0.6135 \| 0.6072 \| nan \| 0.5768 \| 0.6502 \| 0.0 \| 0.5633 \| 0.6469 \| \| 0.0335 \| 144.83 \| 41420 \| 0.1550 \| 0.4004 \| 0.6089 \| 0.6024 \| nan \| 0.5716 \| 0.6461 \| 0.0 \| 0.5587 \| 0.6423 \| \| 0.0373 \| 144.9 \| 41440 \| 0.1558 \| 0.3986 \| 0.6060 \| 0.5995 \| nan \| 0.5687 \| 0.6434 \| 0.0 \| 0.5555 \| 0.6402 \| \| 0.0443 \| 144.97 \| 41460 \| 0.1572 \| 0.3968 \| 0.6032 \| 0.5953 \| nan \| 0.5574 \| 0.6490 \| 0.0 \| 0.5452 \| 0.6453 \| \| 0.0477 \| 145.03 \| 41480 \| 0.1564 \| 0.3984 \| 0.6059 \| 0.5991 \| nan \| 0.5665 \| 0.6453 \| 0.0 \| 0.5535 \| 0.6416 \| \| 0.0492 \| 145.1 \| 41500 \| 0.1542 \| 0.4008 \| 0.6099 \| 0.6037 \| nan \| 0.5742 \| 0.6456 \| 0.0 \| 0.5599 \| 0.6425 \| \| 0.0382 \| 145.17 \| 41520 \| 0.1605 \| 0.3924 \| 0.5968 \| 0.5909 \| nan \| 0.5630 \| 0.6307 \| 0.0 \| 0.5490 \| 0.6282 \| \| 0.0358 \| 145.24 \| 41540 \| 0.1592 \| 0.3941 \| 0.5996 \| 0.5946 \| nan \| 0.5706 \| 0.6285 \| 0.0 \| 0.5563 \| 0.6260 \| \| 0.0268 \| 145.31 \| 41560 \| 0.1532 \| 0.4070 \| 0.6193 \| 0.6135 \| nan \| 0.5856 \| 0.6530 \| 0.0 \| 0.5716 \| 0.6494 \| \| 0.0374 \| 145.38 \| 41580 \| 0.1537 \| 0.3990 \| 0.6068 \| 0.5992 \| nan \| 0.5629 \| 0.6507 \| 0.0 \| 0.5505 \| 0.6464 \| \| 0.0573 \| 145.45 \| 41600 \| 0.1557 \| 0.3996 \| 0.6076 \| 0.6011 \| nan \| 0.5698 \| 0.6455 \| 0.0 \| 0.5568 \| 0.6418 \| \| 0.0314 \| 145.52 \| 41620 \| 0.1561 \| 0.4010 \| 0.6099 \| 0.6036 \| nan \| 0.5734 \| 0.6463 \| 0.0 \| 0.5598 \| 0.6432 \| \| 0.0506 \| 145.59 \| 41640 \| 0.1568 \| 0.3976 \| 0.6047 \| 0.5979 \| nan \| 0.5653 \| 0.6441 \| 0.0 \| 0.5518 \| 0.6411 \| \| 0.0373 \| 145.66 \| 41660 \| 0.1545 \| 0.4053 \| 0.6168 \| 0.6115 \| nan \| 0.5862 \| 0.6474 \| 0.0 \| 0.5712 \| 0.6448 \| \| 0.0481 \| 145.73 \| 41680 \| 0.1531 \| 0.4009 \| 0.6097 \| 0.6027 \| nan \| 0.5694 \| 0.6499 \| 0.0 \| 0.5557 \| 0.6471 \| \| 0.0439 \| 145.8 \| 41700 \| 0.1546 \| 0.4005 \| 0.6090 \| 0.6020 \| nan \| 0.5686 \| 0.6495 \| 0.0 \| 0.5552 \| 0.6464 \| \| 0.031 \| 145.87 \| 41720 \| 0.1506 \| 0.4095 \| 0.6224 \| 0.6133 \| nan \| 0.5696 \| 0.6752 \| 0.0 \| 0.5574 \| 0.6710 \| \| 0.0635 \| 145.94 \| 41740 \| 0.1506 \| 0.4073 \| 0.6194 \| 0.6123 \| nan \| 0.5786 \| 0.6601 \| 0.0 \| 0.5650 \| 0.6568 \| \| 0.027 \| 146.01 \| 41760 \| 0.1543 \| 0.4063 \| 0.6180 \| 0.6124 \| nan \| 0.5860 \| 0.6500 \| 0.0 \| 0.5718 \| 0.6471 \| \| 0.0444 \| 146.08 \| 41780 \| 0.1531 \| 0.3983 \| 0.6055 \| 0.5976 \| nan \| 0.5599 \| 0.6510 \| 0.0 \| 0.5471 \| 0.6479 \| \| 0.0616 \| 146.15 \| 41800 \| 0.1575 \| 0.3939 \| 0.5982 \| 0.5891 \| nan \| 0.5460 \| 0.6504 \| 0.0 \| 0.5341 \| 0.6475 \| \| 0.0395 \| 146.22 \| 41820 \| 0.1557 \| 0.3978 \| 0.6041 \| 0.5943 \| nan \| 0.5476 \| 0.6606 \| 0.0 \| 0.5358 \| 0.6575 \| \| 0.032 \| 146.29 \| 41840 \| 0.1554 \| 0.4036 \| 0.6140 \| 0.6088 \| nan \| 0.5838 \| 0.6443 \| 0.0 \| 0.5687 \| 0.6421 \| \| 0.0256 \| 146.36 \| 41860 \| 0.1514 \| 0.4031 \| 0.6127 \| 0.6035 \| nan \| 0.5598 \| 0.6656 \| 0.0 \| 0.5474 \| 0.6618 \| \| 0.0596 \| 146.43 \| 41880 \| 0.1513 \| 0.4028 \| 0.6124 \| 0.6028 \| nan \| 0.5567 \| 0.6681 \| 0.0 \| 0.5454 \| 0.6629 \| \| 0.0324 \| 146.5 \| 41900 \| 0.1502 \| 0.4034 \| 0.6135 \| 0.6069 \| nan \| 0.5751 \| 0.6519 \| 0.0 \| 0.5635 \| 0.6468 \| \| 0.0448 \| 146.57 \| 41920 \| 0.1506 \| 0.4001 \| 0.6077 \| 0.5971 \| nan \| 0.5468 \| 0.6686 \| 0.0 \| 0.5370 \| 0.6631 \| \| 0.0287 \| 146.64 \| 41940 \| 0.1498 \| 0.4029 \| 0.6121 \| 0.6017 \| nan \| 0.5519 \| 0.6722 \| 0.0 \| 0.5418 \| 0.6669 \| \| 0.031 \| 146.71 \| 41960 \| 0.1551 \| 0.3946 \| 0.6000 \| 0.5928 \| nan \| 0.5587 \| 0.6412 \| 0.0 \| 0.5469 \| 0.6370 \| \| 0.0557 \| 146.78 \| 41980 \| 0.1532 \| 0.3996 \| 0.6078 \| 0.6016 \| nan \| 0.5717 \| 0.6440 \| 0.0 \| 0.5586 \| 0.6400 \| \| 0.0358 \| 146.85 \| 42000 \| 0.1551 \| 0.3965 \| 0.6033 \| 0.5965 \| nan \| 0.5637 \| 0.6429 \| 0.0 \| 0.5503 \| 0.6392 \| \| 0.0291 \| 146.92 \| 42020 \| 0.1555 \| 0.3994 \| 0.6075 \| 0.6006 \| nan \| 0.5676 \| 0.6475 \| 0.0 \| 0.5544 \| 0.6439 \| \| 0.0354 \| 146.99 \| 42040 \| 0.1575 \| 0.3932 \| 0.5977 \| 0.5883 \| nan \| 0.5437 \| 0.6518 \| 0.0 \| 0.5309 \| 0.6487 \| \| 0.0574 \| 147.06 \| 42060 \| 0.1546 \| 0.4018 \| 0.6111 \| 0.6047 \| nan \| 0.5742 \| 0.6480 \| 0.0 \| 0.5606 \| 0.6449 \| \| 0.0559 \| 147.13 \| 42080 \| 0.1543 \| 0.4006 \| 0.6095 \| 0.6031 \| nan \| 0.5725 \| 0.6464 \| 0.0 \| 0.5587 \| 0.6430 \| \| 0.0338 \| 147.2 \| 42100 \| 0.1553 \| 0.3985 \| 0.6062 \| 0.5987 \| nan \| 0.5628 \| 0.6496 \| 0.0 \| 0.5495 \| 0.6461 \| \| 0.0316 \| 147.27 \| 42120 \| 0.1667 \| 0.3871 \| 0.5898 \| 0.5857 \| nan \| 0.5665 \| 0.6130 \| 0.0 \| 0.5502 \| 0.6111 \| \| 0.0291 \| 147.34 \| 42140 \| 0.1649 \| 0.3911 \| 0.5956 \| 0.5918 \| nan \| 0.5732 \| 0.6181 \| 0.0 \| 0.5573 \| 0.6159 \| \| 0.0654 \| 147.41 \| 42160 \| 0.1609 \| 0.3962 \| 0.6035 \| 0.5996 \| nan \| 0.5809 \| 0.6262 \| 0.0 \| 0.5651 \| 0.6236 \| \| 0.036 \| 147.48 \| 42180 \| 0.1605 \| 0.3935 \| 0.5990 \| 0.5939 \| nan \| 0.5693 \| 0.6287 \| 0.0 \| 0.5545 \| 0.6259 \| \| 0.0296 \| 147.55 \| 42200 \| 0.1604 \| 0.3924 \| 0.5974 \| 0.5921 \| nan \| 0.5670 \| 0.6278 \| 0.0 \| 0.5528 \| 0.6245 \| \| 0.0516 \| 147.62 \| 42220 \| 0.1581 \| 0.4018 \| 0.6119 \| 0.6065 \| nan \| 0.5812 \| 0.6425 \| 0.0 \| 0.5668 \| 0.6386 \| \| 0.0197 \| 147.69 \| 42240 \| 0.1578 \| 0.3967 \| 0.6037 \| 0.5973 \| nan \| 0.5667 \| 0.6408 \| 0.0 \| 0.5530 \| 0.6370 \| \| 0.0375 \| 147.76 \| 42260 \| 0.1571 \| 0.4015 \| 0.6109 \| 0.6041 \| nan \| 0.5716 \| 0.6503 \| 0.0 \| 0.5584 \| 0.6461 \| \| 0.0603 \| 147.83 \| 42280 \| 0.1548 \| 0.4072 \| 0.6192 \| 0.6115 \| nan \| 0.5744 \| 0.6641 \| 0.0 \| 0.5616 \| 0.6600 \| \| 0.0418 \| 147.9 \| 42300 \| 0.1553 \| 0.4023 \| 0.6116 \| 0.6034 \| nan \| 0.5643 \| 0.6589 \| 0.0 \| 0.5518 \| 0.6551 \| \| 0.0353 \| 147.97 \| 42320 \| 0.1593 \| 0.3936 \| 0.5986 \| 0.5928 \| nan \| 0.5652 \| 0.6320 \| 0.0 \| 0.5517 \| 0.6291 \| \| 0.0302 \| 148.04 \| 42340 \| 0.1585 \| 0.3934 \| 0.5983 \| 0.5914 \| nan \| 0.5584 \| 0.6381 \| 0.0 \| 0.5455 \| 0.6348 \| \| 0.046 \| 148.11 \| 42360 \| 0.1610 \| 0.3946 \| 0.6000 \| 0.5929 \| nan \| 0.5594 \| 0.6405 \| 0.0 \| 0.5466 \| 0.6373 \| \| 0.0571 \| 148.18 \| 42380 \| 0.1588 \| 0.3972 \| 0.6039 \| 0.5967 \| nan \| 0.5621 \| 0.6458 \| 0.0 \| 0.5489 \| 0.6426 \| \| 0.0391 \| 148.25 \| 42400 \| 0.1569 \| 0.3977 \| 0.6046 \| 0.5973 \| nan \| 0.5621 \| 0.6471 \| 0.0 \| 0.5494 \| 0.6436 \| \| 0.0373 \| 148.32 \| 42420 \| 0.1567 \| 0.3955 \| 0.6012 \| 0.5922 \| nan \| 0.5490 \| 0.6534 \| 0.0 \| 0.5372 \| 0.6494 \| \| 0.0324 \| 148.39 \| 42440 \| 0.1581 \| 0.4057 \| 0.6175 \| 0.6134 \| nan \| 0.5934 \| 0.6417 \| 0.0 \| 0.5787 \| 0.6384 \| \| 0.0379 \| 148.46 \| 42460 \| 0.1572 \| 0.4026 \| 0.6124 \| 0.6056 \| nan \| 0.5732 \| 0.6516 \| 0.0 \| 0.5601 \| 0.6478 \| \| 0.0395 \| 148.53 \| 42480 \| 0.1552 \| 0.3997 \| 0.6078 \| 0.5994 \| nan \| 0.5593 \| 0.6563 \| 0.0 \| 0.5470 \| 0.6521 \| \| 0.0438 \| 148.6 \| 42500 \| 0.1539 \| 0.4018 \| 0.6108 \| 0.6018 \| nan \| 0.5588 \| 0.6628 \| 0.0 \| 0.5473 \| 0.6580 \| \| 0.0231 \| 148.67 \| 42520 \| 0.1576 \| 0.4025 \| 0.6120 \| 0.6048 \| nan \| 0.5705 \| 0.6536 \| 0.0 \| 0.5575 \| 0.6499 \| \| 0.0497 \| 148.74 \| 42540 \| 0.1581 \| 0.4012 \| 0.6103 \| 0.6020 \| nan \| 0.5625 \| 0.6581 \| 0.0 \| 0.5496 \| 0.6541 \| \| 0.0294 \| 148.81 \| 42560 \| 0.1602 \| 0.3977 \| 0.6047 \| 0.5969 \| nan \| 0.5598 \| 0.6496 \| 0.0 \| 0.5470 \| 0.6460 \| \| 0.0426 \| 148.88 \| 42580 \| 0.1535 \| 0.4031 \| 0.6132 \| 0.6048 \| nan \| 0.5648 \| 0.6616 \| 0.0 \| 0.5525 \| 0.6567 \| \| 0.0395 \| 148.95 \| 42600 \| 0.1575 \| 0.3973 \| 0.6043 \| 0.5965 \| nan \| 0.5590 \| 0.6496 \| 0.0 \| 0.5465 \| 0.6453 \| \| 0.0562 \| 149.02 \| 42620 \| 0.1595 \| 0.3994 \| 0.6082 \| 0.6031 \| nan \| 0.5790 \| 0.6373 \| 0.0 \| 0.5644 \| 0.6338 \| \| 0.0736 \| 149.09 \| 42640 \| 0.1629 \| 0.3953 \| 0.6016 \| 0.5959 \| nan \| 0.5687 \| 0.6345 \| 0.0 \| 0.5544 \| 0.6313 \| \| 0.052 \| 149.16 \| 42660 \| 0.1616 \| 0.3940 \| 0.5994 \| 0.5914 \| nan \| 0.5534 \| 0.6453 \| 0.0 \| 0.5408 \| 0.6413 \| \| 0.048 \| 149.23 \| 42680 \| 0.1588 \| 0.3976 \| 0.6050 \| 0.5985 \| nan \| 0.5674 \| 0.6425 \| 0.0 \| 0.5543 \| 0.6386 \| \| 0.0293 \| 149.3 \| 42700 \| 0.1610 \| 0.4012 \| 0.6105 \| 0.6048 \| nan \| 0.5778 \| 0.6432 \| 0.0 \| 0.5638 \| 0.6398 \| \| 0.0414 \| 149.37 \| 42720 \| 0.1605 \| 0.3979 \| 0.6052 \| 0.5994 \| nan \| 0.5715 \| 0.6389 \| 0.0 \| 0.5585 \| 0.6353 \| \| 0.0532 \| 149.44 \| 42740 \| 0.1592 \| 0.3972 \| 0.6037 \| 0.5964 \| nan \| 0.5616 \| 0.6457 \| 0.0 \| 0.5498 \| 0.6418 \| \| 0.0356 \| 149.51 \| 42760 \| 0.1523 \| 0.4045 \| 0.6145 \| 0.6035 \| nan \| 0.5509 \| 0.6781 \| 0.0 \| 0.5417 \| 0.6718 \| \| 0.0472 \| 149.58 \| 42780 \| 0.1524 \| 0.4073 \| 0.6190 \| 0.6103 \| nan \| 0.5684 \| 0.6696 \| 0.0 \| 0.5578 \| 0.6642 \| \| 0.052 \| 149.65 \| 42800 \| 0.1541 \| 0.4035 \| 0.6131 \| 0.6039 \| nan \| 0.5602 \| 0.6660 \| 0.0 \| 0.5500 \| 0.6605 \| \| 0.0344 \| 149.72 \| 42820 \| 0.1555 \| 0.3983 \| 0.6051 \| 0.5963 \| nan \| 0.5547 \| 0.6554 \| 0.0 \| 0.5443 \| 0.6506 \| \| 0.0453 \| 149.79 \| 42840 \| 0.1589 \| 0.3945 \| 0.5993 \| 0.5910 \| nan \| 0.5512 \| 0.6474 \| 0.0 \| 0.5402 \| 0.6434 \| \| 0.0352 \| 149.86 \| 42860 \| 0.1608 \| 0.3969 \| 0.6040 \| 0.5996 \| nan \| 0.5784 \| 0.6296 \| 0.0 \| 0.5629 \| 0.6278 \| \| 0.0548 \| 149.93 \| 42880 \| 0.1603 \| 0.3946 \| 0.6001 \| 0.5956 \| nan \| 0.5738 \| 0.6265 \| 0.0 \| 0.5591 \| 0.6247 \| \| 0.029 \| 150.0 \| 42900 \| 0.1615 \| 0.3914 \| 0.5952 \| 0.5887 \| nan \| 0.5577 \| 0.6328 \| 0.0 \| 0.5439 \| 0.6304 \| \| 0.0502 \| 150.07 \| 42920 \| 0.1654 \| 0.3885 \| 0.5910 \| 0.5861 \| nan \| 0.5627 \| 0.6193 \| 0.0 \| 0.5480 \| 0.6173 \| \| 0.0285 \| 150.14 \| 42940 \| 0.1687 \| 0.3838 \| 0.5838 \| 0.5786 \| nan \| 0.5538 \| 0.6138 \| 0.0 \| 0.5395 \| 0.6119 \| \| 0.0493 \| 150.21 \| 42960 \| 0.1556 \| 0.3979 \| 0.6049 \| 0.5958 \| nan \| 0.5524 \| 0.6575 \| 0.0 \| 0.5406 \| 0.6531 \| \| 0.049 \| 150.28 \| 42980 \| 0.1556 \| 0.4008 \| 0.6096 \| 0.6028 \| nan \| 0.5708 \| 0.6483 \| 0.0 \| 0.5583 \| 0.6442 \| \| 0.0338 \| 150.35 \| 43000 \| 0.1576 \| 0.3974 \| 0.6047 \| 0.5985 \| nan \| 0.5693 \| 0.6400 \| 0.0 \| 0.5556 \| 0.6365 \| \| 0.053 \| 150.42 \| 43020 \| 0.1609 \| 0.3911 \| 0.5948 \| 0.5879 \| nan \| 0.5545 \| 0.6352 \| 0.0 \| 0.5414 \| 0.6320 \| \| 0.0743 \| 150.49 \| 43040 \| 0.1596 \| 0.3957 \| 0.6020 \| 0.5959 \| nan \| 0.5673 \| 0.6367 \| 0.0 \| 0.5536 \| 0.6334 \| \| 0.0405 \| 150.56 \| 43060 \| 0.1555 \| 0.4014 \| 0.6104 \| 0.6037 \| nan \| 0.5717 \| 0.6492 \| 0.0 \| 0.5587 \| 0.6453 \| \| 0.0307 \| 150.63 \| 43080 \| 0.1551 \| 0.4005 \| 0.6089 \| 0.6006 \| nan \| 0.5608 \| 0.6570 \| 0.0 \| 0.5487 \| 0.6529 \| \| 0.0201 \| 150.7 \| 43100 \| 0.1620 \| 0.3922 \| 0.5968 \| 0.5923 \| nan \| 0.5709 \| 0.6226 \| 0.0 \| 0.5564 \| 0.6201 \| \| 0.0537 \| 150.77 \| 43120 \| 0.1620 \| 0.3967 \| 0.6036 \| 0.5988 \| nan \| 0.5758 \| 0.6313 \| 0.0 \| 0.5616 \| 0.6284 \| \| 0.0373 \| 150.84 \| 43140 \| 0.1624 \| 0.3922 \| 0.5963 \| 0.5905 \| nan \| 0.5628 \| 0.6298 \| 0.0 \| 0.5497 \| 0.6268 \| \| 0.048 \| 150.91 \| 43160 \| 0.1598 \| 0.3966 \| 0.6031 \| 0.5960 \| nan \| 0.5623 \| 0.6440 \| 0.0 \| 0.5498 \| 0.6401 \| \| 0.0403 \| 150.98 \| 43180 \| 0.1595 \| 0.3991 \| 0.6071 \| 0.6011 \| nan \| 0.5725 \| 0.6417 \| 0.0 \| 0.5589 \| 0.6383 \| \| 0.0421 \| 151.05 \| 43200 \| 0.1607 \| 0.3987 \| 0.6068 \| 0.6020 \| nan \| 0.5788 \| 0.6348 \| 0.0 \| 0.5642 \| 0.6319 \| \| 0.0268 \| 151.12 \| 43220 \| 0.1595 \| 0.3963 \| 0.6027 \| 0.5955 \| nan \| 0.5611 \| 0.6443 \| 0.0 \| 0.5477 \| 0.6411 \| \| 0.0454 \| 151.19 \| 43240 \| 0.1582 \| 0.3987 \| 0.6064 \| 0.6000 \| nan \| 0.5693 \| 0.6436 \| 0.0 \| 0.5555 \| 0.6407 \| \| 0.0384 \| 151.26 \| 43260 \| 0.1587 \| 0.3963 \| 0.6023 \| 0.5941 \| nan \| 0.5552 \| 0.6494 \| 0.0 \| 0.5441 \| 0.6449 \| \| 0.038 \| 151.33 \| 43280 \| 0.1566 \| 0.4053 \| 0.6162 \| 0.6094 \| nan \| 0.5768 \| 0.6556 \| 0.0 \| 0.5657 \| 0.6501 \| \| 0.0513 \| 151.4 \| 43300 \| 0.1581 \| 0.3988 \| 0.6060 \| 0.5991 \| nan \| 0.5662 \| 0.6458 \| 0.0 \| 0.5543 \| 0.6421 \| \| 0.0534 \| 151.47 \| 43320 \| 0.1594 \| 0.4007 \| 0.6091 \| 0.6025 \| nan \| 0.5711 \| 0.6471 \| 0.0 \| 0.5584 \| 0.6436 \| \| 0.0262 \| 151.54 \| 43340 \| 0.1552 \| 0.4019 \| 0.6109 \| 0.6034 \| nan \| 0.5675 \| 0.6543 \| 0.0 \| 0.5554 \| 0.6505 \| \| 0.0336 \| 151.61 \| 43360 \| 0.1521 \| 0.4086 \| 0.6213 \| 0.6145 \| nan \| 0.5825 \| 0.6601 \| 0.0 \| 0.5706 \| 0.6553 \| \| 0.0275 \| 151.68 \| 43380 \| 0.1525 \| 0.4011 \| 0.6094 \| 0.5995 \| nan \| 0.5520 \| 0.6667 \| 0.0 \| 0.5413 \| 0.6620 \| \| 0.0479 \| 151.75 \| 43400 \| 0.1539 \| 0.4007 \| 0.6088 \| 0.6003 \| nan \| 0.5596 \| 0.6579 \| 0.0 \| 0.5487 \| 0.6535 \| \| 0.0385 \| 151.82 \| 43420 \| 0.1559 \| 0.3995 \| 0.6072 \| 0.5997 \| nan \| 0.5639 \| 0.6506 \| 0.0 \| 0.5509 \| 0.6474 \| \| 0.058 \| 151.89 \| 43440 \| 0.1562 \| 0.3963 \| 0.6023 \| 0.5947 \| nan \| 0.5582 \| 0.6463 \| 0.0 \| 0.5458 \| 0.6431 \| \| 0.0339 \| 151.96 \| 43460 \| 0.1576 \| 0.3935 \| 0.5978 \| 0.5898 \| nan \| 0.5518 \| 0.6437 \| 0.0 \| 0.5397 \| 0.6407 \| \| 0.036 \| 152.03 \| 43480 \| 0.1555 \| 0.4004 \| 0.6089 \| 0.6022 \| nan \| 0.5703 \| 0.6474 \| 0.0 \| 0.5568 \| 0.6446 \| \| 0.0531 \| 152.1 \| 43500 \| 0.1516 \| 0.4098 \| 0.6230 \| 0.6163 \| nan \| 0.5847 \| 0.6613 \| 0.0 \| 0.5713 \| 0.6581 \| \| 0.0335 \| 152.17 \| 43520 \| 0.1523 \| 0.4102 \| 0.6234 \| 0.6159 \| nan \| 0.5803 \| 0.6665 \| 0.0 \| 0.5679 \| 0.6628 \| \| 0.0624 \| 152.24 \| 43540 \| 0.1536 \| 0.4056 \| 0.6159 \| 0.6061 \| nan \| 0.5593 \| 0.6724 \| 0.0 \| 0.5484 \| 0.6684 \| \| 0.0371 \| 152.31 \| 43560 \| 0.1530 \| 0.4067 \| 0.6180 \| 0.6097 \| nan \| 0.5701 \| 0.6658 \| 0.0 \| 0.5580 \| 0.6622 \| \| 0.0465 \| 152.38 \| 43580 \| 0.1524 \| 0.4089 \| 0.6215 \| 0.6132 \| nan \| 0.5735 \| 0.6695 \| 0.0 \| 0.5615 \| 0.6653 \| \| 0.0418 \| 152.45 \| 43600 \| 0.1547 \| 0.4032 \| 0.6125 \| 0.6035 \| nan \| 0.5603 \| 0.6647 \| 0.0 \| 0.5483 \| 0.6613 \| \| 0.0345 \| 152.52 \| 43620 \| 0.1586 \| 0.4001 \| 0.6081 \| 0.6004 \| nan \| 0.5638 \| 0.6524 \| 0.0 \| 0.5508 \| 0.6495 \| \| 0.0273 \| 152.59 \| 43640 \| 0.1571 \| 0.4026 \| 0.6121 \| 0.6048 \| nan \| 0.5702 \| 0.6539 \| 0.0 \| 0.5572 \| 0.6506 \| \| 0.037 \| 152.66 \| 43660 \| 0.1574 \| 0.4023 \| 0.6117 \| 0.6055 \| nan \| 0.5758 \| 0.6477 \| 0.0 \| 0.5624 \| 0.6444 \| \| 0.0434 \| 152.73 \| 43680 \| 0.1594 \| 0.3963 \| 0.6029 \| 0.5969 \| nan \| 0.5680 \| 0.6378 \| 0.0 \| 0.5532 \| 0.6357 \| \| 0.0537 \| 152.8 \| 43700 \| 0.1582 \| 0.3930 \| 0.5976 \| 0.5905 \| nan \| 0.5569 \| 0.6383 \| 0.0 \| 0.5433 \| 0.6356 \| \| 0.0296 \| 152.87 \| 43720 \| 0.1572 \| 0.3980 \| 0.6053 \| 0.5984 \| nan \| 0.5656 \| 0.6450 \| 0.0 \| 0.5517 \| 0.6422 \| \| 0.0347 \| 152.94 \| 43740 \| 0.1557 \| 0.4002 \| 0.6087 \| 0.6025 \| nan \| 0.5731 \| 0.6443 \| 0.0 \| 0.5588 \| 0.6417 \| \| 0.0445 \| 153.01 \| 43760 \| 0.1573 \| 0.3974 \| 0.6047 \| 0.5980 \| nan \| 0.5660 \| 0.6433 \| 0.0 \| 0.5514 \| 0.6410 \| \| 0.0261 \| 153.08 \| 43780 \| 0.1582 \| 0.3998 \| 0.6085 \| 0.6034 \| nan \| 0.5789 \| 0.6380 \| 0.0 \| 0.5637 \| 0.6355 \| \| 0.0459 \| 153.15 \| 43800 \| 0.1565 \| 0.3960 \| 0.6016 \| 0.5910 \| nan \| 0.5404 \| 0.6629 \| 0.0 \| 0.5284 \| 0.6596 \| \| 0.0433 \| 153.22 \| 43820 \| 0.1523 \| 0.4084 \| 0.6211 \| 0.6151 \| nan \| 0.5864 \| 0.6558 \| 0.0 \| 0.5728 \| 0.6525 \| \| 0.0349 \| 153.29 \| 43840 \| 0.1524 \| 0.4102 \| 0.6238 \| 0.6163 \| nan \| 0.5805 \| 0.6670 \| 0.0 \| 0.5676 \| 0.6629 \| \| 0.0428 \| 153.36 \| 43860 \| 0.1548 \| 0.4024 \| 0.6115 \| 0.6032 \| nan \| 0.5633 \| 0.6597 \| 0.0 \| 0.5513 \| 0.6558 \| \| 0.0355 \| 153.43 \| 43880 \| 0.1572 \| 0.3959 \| 0.6016 \| 0.5926 \| nan \| 0.5493 \| 0.6540 \| 0.0 \| 0.5374 \| 0.6504 \| \| 0.0431 \| 153.5 \| 43900 \| 0.1584 \| 0.4006 \| 0.6088 \| 0.6015 \| nan \| 0.5669 \| 0.6507 \| 0.0 \| 0.5552 \| 0.6467 \| \| 0.0461 \| 153.57 \| 43920 \| 0.1606 \| 0.3998 \| 0.6084 \| 0.6031 \| nan \| 0.5778 \| 0.6390 \| 0.0 \| 0.5637 \| 0.6358 \| \| 0.0511 \| 153.64 \| 43940 \| 0.1642 \| 0.3869 \| 0.5886 \| 0.5818 \| nan \| 0.5495 \| 0.6277 \| 0.0 \| 0.5357 \| 0.6251 \| \| 0.0271 \| 153.71 \| 43960 \| 0.1566 \| 0.4038 \| 0.6147 \| 0.6094 \| nan \| 0.5839 \| 0.6456 \| 0.0 \| 0.5694 \| 0.6421 \| \| 0.0602 \| 153.78 \| 43980 \| 0.1515 \| 0.4059 \| 0.6174 \| 0.6103 \| nan \| 0.5767 \| 0.6580 \| 0.0 \| 0.5643 \| 0.6534 \| \| 0.0209 \| 153.85 \| 44000 \| 0.1533 \| 0.3999 \| 0.6080 \| 0.6008 \| nan \| 0.5661 \| 0.6499 \| 0.0 \| 0.5538 \| 0.6460 \| \| 0.0759 \| 153.92 \| 44020 \| 0.1556 \| 0.3985 \| 0.6057 \| 0.5974 \| nan \| 0.5573 \| 0.6541 \| 0.0 \| 0.5451 \| 0.6504 \| \| 0.0247 \| 153.99 \| 44040 \| 0.1591 \| 0.3962 \| 0.6024 \| 0.5939 \| nan \| 0.5535 \| 0.6512 \| 0.0 \| 0.5411 \| 0.6476 \| \| 0.0474 \| 154.06 \| 44060 \| 0.1593 \| 0.3985 \| 0.6063 \| 0.5996 \| nan \| 0.5677 \| 0.6449 \| 0.0 \| 0.5544 \| 0.6411 \| \| 0.0376 \| 154.13 \| 44080 \| 0.1571 \| 0.4042 \| 0.6151 \| 0.6092 \| nan \| 0.5809 \| 0.6493 \| 0.0 \| 0.5666 \| 0.6459 \| \| 0.08 \| 154.2 \| 44100 \| 0.1493 \| 0.4139 \| 0.6290 \| 0.6211 \| nan \| 0.5832 \| 0.6748 \| 0.0 \| 0.5715 \| 0.6701 \| \| 0.0267 \| 154.27 \| 44120 \| 0.1503 \| 0.4077 \| 0.6195 \| 0.6114 \| nan \| 0.5729 \| 0.6661 \| 0.0 \| 0.5615 \| 0.6615 \| \| 0.0581 \| 154.34 \| 44140 \| 0.1577 \| 0.3986 \| 0.6061 \| 0.5982 \| nan \| 0.5608 \| 0.6513 \| 0.0 \| 0.5478 \| 0.6481 \| \| 0.0391 \| 154.41 \| 44160 \| 0.1561 \| 0.4044 \| 0.6147 \| 0.6074 \| nan \| 0.5723 \| 0.6572 \| 0.0 \| 0.5594 \| 0.6537 \| \| 0.0409 \| 154.48 \| 44180 \| 0.1613 \| 0.3942 \| 0.5990 \| 0.5912 \| nan \| 0.5540 \| 0.6440 \| 0.0 \| 0.5414 \| 0.6411 \| \| 0.0392 \| 154.55 \| 44200 \| 0.1581 \| 0.4051 \| 0.6158 \| 0.6092 \| nan \| 0.5774 \| 0.6541 \| 0.0 \| 0.5642 \| 0.6510 \| \| 0.0493 \| 154.62 \| 44220 \| 0.1556 \| 0.4060 \| 0.6169 \| 0.6084 \| nan \| 0.5678 \| 0.6660 \| 0.0 \| 0.5556 \| 0.6623 \| \| 0.0398 \| 154.69 \| 44240 \| 0.1536 \| 0.4051 \| 0.6153 \| 0.6068 \| nan \| 0.5662 \| 0.6645 \| 0.0 \| 0.5545 \| 0.6607 \| \| 0.0668 \| 154.76 \| 44260 \| 0.1561 \| 0.4035 \| 0.6131 \| 0.6044 \| nan \| 0.5629 \| 0.6633 \| 0.0 \| 0.5506 \| 0.6598 \| \| 0.0424 \| 154.83 \| 44280 \| 0.1572 \| 0.4056 \| 0.6165 \| 0.6082 \| nan \| 0.5688 \| 0.6642 \| 0.0 \| 0.5558 \| 0.6610 \| \| 0.0335 \| 154.9 \| 44300 \| 0.1613 \| 0.3955 \| 0.6013 \| 0.5951 \| nan \| 0.5656 \| 0.6370 \| 0.0 \| 0.5520 \| 0.6344 \| \| 0.0389 \| 154.97 \| 44320 \| 0.1542 \| 0.4056 \| 0.6165 \| 0.6093 \| nan \| 0.5751 \| 0.6579 \| 0.0 \| 0.5626 \| 0.6540 \| \| 0.0544 \| 155.03 \| 44340 \| 0.1550 \| 0.4033 \| 0.6127 \| 0.6041 \| nan \| 0.5626 \| 0.6629 \| 0.0 \| 0.5511 \| 0.6587 \| \| 0.0363 \| 155.1 \| 44360 \| 0.1561 \| 0.4014 \| 0.6098 \| 0.6007 \| nan \| 0.5573 \| 0.6624 \| 0.0 \| 0.5460 \| 0.6583 \| \| 0.0339 \| 155.17 \| 44380 \| 0.1559 \| 0.4069 \| 0.6186 \| 0.6119 \| nan \| 0.5801 \| 0.6571 \| 0.0 \| 0.5673 \| 0.6534 \| \| 0.0454 \| 155.24 \| 44400 \| 0.1578 \| 0.4012 \| 0.6098 \| 0.6015 \| nan \| 0.5615 \| 0.6582 \| 0.0 \| 0.5490 \| 0.6547 \| \| 0.0324 \| 155.31 \| 44420 \| 0.1594 \| 0.4023 \| 0.6116 \| 0.6035 \| nan \| 0.5652 \| 0.6579 \| 0.0 \| 0.5525 \| 0.6544 \| \| 0.0245 \| 155.38 \| 44440 \| 0.1549 \| 0.4039 \| 0.6139 \| 0.6047 \| nan \| 0.5609 \| 0.6669 \| 0.0 \| 0.5490 \| 0.6628 \| \| 0.052 \| 155.45 \| 44460 \| 0.1575 \| 0.4052 \| 0.6161 \| 0.6090 \| nan \| 0.5750 \| 0.6573 \| 0.0 \| 0.5619 \| 0.6537 \| \| 0.0567 \| 155.52 \| 44480 \| 0.1606 \| 0.3981 \| 0.6052 \| 0.5974 \| nan \| 0.5604 \| 0.6500 \| 0.0 \| 0.5476 \| 0.6468 \| \| 0.0242 \| 155.59 \| 44500 \| 0.1568 \| 0.4052 \| 0.6159 \| 0.6075 \| nan \| 0.5678 \| 0.6640 \| 0.0 \| 0.5550 \| 0.6607 \| \| 0.0409 \| 155.66 \| 44520 \| 0.1561 \| 0.4050 \| 0.6156 \| 0.6084 \| nan \| 0.5736 \| 0.6577 \| 0.0 \| 0.5604 \| 0.6546 \| \| 0.0433 \| 155.73 \| 44540 \| 0.1580 \| 0.4019 \| 0.6108 \| 0.6030 \| nan \| 0.5657 \| 0.6558 \| 0.0 \| 0.5531 \| 0.6525 \| \| 0.0477 \| 155.8 \| 44560 \| 0.1564 \| 0.4024 \| 0.6116 \| 0.6035 \| nan \| 0.5651 \| 0.6581 \| 0.0 \| 0.5533 \| 0.6540 \| \| 0.0476 \| 155.87 \| 44580 \| 0.1551 \| 0.4018 \| 0.6106 \| 0.6017 \| nan \| 0.5592 \| 0.6621 \| 0.0 \| 0.5473 \| 0.6582 \| \| 0.0267 \| 155.94 \| 44600 \| 0.1574 \| 0.3998 \| 0.6079 \| 0.6008 \| nan \| 0.5670 \| 0.6488 \| 0.0 \| 0.5534 \| 0.6460 \| \| 0.0577 \| 156.01 \| 44620 \| 0.1583 \| 0.4006 \| 0.6091 \| 0.6014 \| nan \| 0.5646 \| 0.6535 \| 0.0 \| 0.5513 \| 0.6506 \| \| 0.0565 \| 156.08 \| 44640 \| 0.1604 \| 0.3990 \| 0.6068 \| 0.5998 \| nan \| 0.5664 \| 0.6472 \| 0.0 \| 0.5522 \| 0.6450 \| \| 0.046 \| 156.15 \| 44660 \| 0.1607 \| 0.3982 \| 0.6054 \| 0.5982 \| nan \| 0.5635 \| 0.6474 \| 0.0 \| 0.5496 \| 0.6450 \| \| 0.0488 \| 156.22 \| 44680 \| 0.1607 \| 0.3995 \| 0.6074 \| 0.6004 \| nan \| 0.5670 \| 0.6478 \| 0.0 \| 0.5531 \| 0.6454 \| \| 0.0552 \| 156.29 \| 44700 \| 0.1602 \| 0.3999 \| 0.6081 \| 0.6003 \| nan \| 0.5632 \| 0.6530 \| 0.0 \| 0.5493 \| 0.6505 \| \| 0.0346 \| 156.36 \| 44720 \| 0.1608 \| 0.3973 \| 0.6040 \| 0.5968 \| nan \| 0.5621 \| 0.6459 \| 0.0 \| 0.5485 \| 0.6434 \| \| 0.0297 \| 156.43 \| 44740 \| 0.1600 \| 0.4007 \| 0.6093 \| 0.6027 \| nan \| 0.5712 \| 0.6475 \| 0.0 \| 0.5570 \| 0.6450 \| \| 0.0598 \| 156.5 \| 44760 \| 0.1597 \| 0.4014 \| 0.6107 \| 0.6045 \| nan \| 0.5745 \| 0.6470 \| 0.0 \| 0.5601 \| 0.6442 \| \| 0.0217 \| 156.57 \| 44780 \| 0.1528 \| 0.4081 \| 0.6206 \| 0.6133 \| nan \| 0.5787 \| 0.6625 \| 0.0 \| 0.5655 \| 0.6588 \| \| 0.0273 \| 156.64 \| 44800 \| 0.1565 \| 0.4030 \| 0.6130 \| 0.6065 \| nan \| 0.5753 \| 0.6506 \| 0.0 \| 0.5612 \| 0.6478 \| \| 0.0314 \| 156.71 \| 44820 \| 0.1505 \| 0.4044 \| 0.6143 \| 0.6047 \| nan \| 0.5593 \| 0.6692 \| 0.0 \| 0.5475 \| 0.6658 \| \| 0.0607 \| 156.78 \| 44840 \| 0.1509 \| 0.4087 \| 0.6214 \| 0.6133 \| nan \| 0.5750 \| 0.6677 \| 0.0 \| 0.5618 \| 0.6644 \| \| 0.0423 \| 156.85 \| 44860 \| 0.1529 \| 0.4047 \| 0.6149 \| 0.6060 \| nan \| 0.5633 \| 0.6665 \| 0.0 \| 0.5511 \| 0.6629 \| \| 0.0407 \| 156.92 \| 44880 \| 0.1563 \| 0.4009 \| 0.6094 \| 0.6015 \| nan \| 0.5638 \| 0.6549 \| 0.0 \| 0.5507 \| 0.6520 \| \| 0.0191 \| 156.99 \| 44900 \| 0.1590 \| 0.3957 \| 0.6015 \| 0.5931 \| nan \| 0.5534 \| 0.6495 \| 0.0 \| 0.5399 \| 0.6471 \| \| 0.0406 \| 157.06 \| 44920 \| 0.1560 \| 0.4047 \| 0.6158 \| 0.6097 \| nan \| 0.5806 \| 0.6509 \| 0.0 \| 0.5658 \| 0.6484 \| \| 0.054 \| 157.13 \| 44940 \| 0.1566 \| 0.4027 \| 0.6125 \| 0.6064 \| nan \| 0.5769 \| 0.6481 \| 0.0 \| 0.5628 \| 0.6454 \| \| 0.0376 \| 157.2 \| 44960 \| 0.1567 \| 0.3995 \| 0.6074 \| 0.5998 \| nan \| 0.5640 \| 0.6507 \| 0.0 \| 0.5508 \| 0.6478 \| \| 0.0719 \| 157.27 \| 44980 \| 0.1580 \| 0.4003 \| 0.6088 \| 0.6025 \| nan \| 0.5720 \| 0.6457 \| 0.0 \| 0.5581 \| 0.6429 \| \| 0.0537 \| 157.34 \| 45000 \| 0.1564 \| 0.4041 \| 0.6145 \| 0.6081 \| nan \| 0.5778 \| 0.6511 \| 0.0 \| 0.5642 \| 0.6481 \| \| 0.0426 \| 157.41 \| 45020 \| 0.1568 \| 0.3976 \| 0.6045 \| 0.5968 \| nan \| 0.5601 \| 0.6489 \| 0.0 \| 0.5468 \| 0.6461 \| \| 0.0359 \| 157.48 \| 45040 \| 0.1552 \| 0.4015 \| 0.6110 \| 0.6051 \| nan \| 0.5770 \| 0.6449 \| 0.0 \| 0.5624 \| 0.6421 \| \| 0.0583 \| 157.55 \| 45060 \| 0.1567 \| 0.3992 \| 0.6069 \| 0.6004 \| nan \| 0.5691 \| 0.6448 \| 0.0 \| 0.5554 \| 0.6421 \| \| 0.0333 \| 157.62 \| 45080 \| 0.1579 \| 0.3976 \| 0.6045 \| 0.5973 \| nan \| 0.5629 \| 0.6460 \| 0.0 \| 0.5495 \| 0.6433 \| \| 0.0316 \| 157.69 \| 45100 \| 0.1577 \| 0.4007 \| 0.6093 \| 0.6025 \| nan \| 0.5695 \| 0.6492 \| 0.0 \| 0.5557 \| 0.6465 \| \| 0.0434 \| 157.76 \| 45120 \| 0.1580 \| 0.4032 \| 0.6134 \| 0.6079 \| nan \| 0.5819 \| 0.6449 \| 0.0 \| 0.5671 \| 0.6423 \| \| 0.0271 \| 157.83 \| 45140 \| 0.1518 \| 0.4019 \| 0.6106 \| 0.6006 \| nan \| 0.5528 \| 0.6685 \| 0.0 \| 0.5405 \| 0.6651 \| \| 0.055 \| 157.9 \| 45160 \| 0.1504 \| 0.4024 \| 0.6113 \| 0.6016 \| nan \| 0.5555 \| 0.6671 \| 0.0 \| 0.5436 \| 0.6635 \| \| 0.0489 \| 157.97 \| 45180 \| 0.1521 \| 0.4054 \| 0.6163 \| 0.6083 \| nan \| 0.5703 \| 0.6623 \| 0.0 \| 0.5570 \| 0.6592 \| \| 0.0329 \| 158.04 \| 45200 \| 0.1561 \| 0.4037 \| 0.6139 \| 0.6075 \| nan \| 0.5768 \| 0.6510 \| 0.0 \| 0.5626 \| 0.6484 \| \| 0.0253 \| 158.11 \| 45220 \| 0.1571 \| 0.4021 \| 0.6113 \| 0.6039 \| nan \| 0.5687 \| 0.6539 \| 0.0 \| 0.5549 \| 0.6513 \| \| 0.0335 \| 158.18 \| 45240 \| 0.1586 \| 0.4026 \| 0.6120 \| 0.6052 \| nan \| 0.5729 \| 0.6511 \| 0.0 \| 0.5592 \| 0.6485 \| \| 0.0423 \| 158.25 \| 45260 \| 0.1580 \| 0.4047 \| 0.6153 \| 0.6087 \| nan \| 0.5774 \| 0.6532 \| 0.0 \| 0.5632 \| 0.6508 \| \| 0.0327 \| 158.32 \| 45280 \| 0.1550 \| 0.4061 \| 0.6177 \| 0.6109 \| nan \| 0.5784 \| 0.6570 \| 0.0 \| 0.5642 \| 0.6542 \| \| 0.0437 \| 158.39 \| 45300 \| 0.1544 \| 0.4023 \| 0.6113 \| 0.6031 \| nan \| 0.5636 \| 0.6590 \| 0.0 \| 0.5517 \| 0.6553 \| \| 0.0308 \| 158.46 \| 45320 \| 0.1534 \| 0.4070 \| 0.6187 \| 0.6113 \| nan \| 0.5755 \| 0.6620 \| 0.0 \| 0.5627 \| 0.6584 \| \| 0.0387 \| 158.53 \| 45340 \| 0.1560 \| 0.4014 \| 0.6101 \| 0.6013 \| nan \| 0.5596 \| 0.6605 \| 0.0 \| 0.5471 \| 0.6572 \| \| 0.0505 \| 158.6 \| 45360 \| 0.1583 \| 0.3960 \| 0.6019 \| 0.5943 \| nan \| 0.5576 \| 0.6463 \| 0.0 \| 0.5445 \| 0.6435 \| \| 0.0599 \| 158.67 \| 45380 \| 0.1555 \| 0.4044 \| 0.6152 \| 0.6086 \| nan \| 0.5772 \| 0.6532 \| 0.0 \| 0.5628 \| 0.6503 \| \| 0.0536 \| 158.74 \| 45400 \| 0.1554 \| 0.4018 \| 0.6106 \| 0.6024 \| nan \| 0.5635 \| 0.6576 \| 0.0 \| 0.5510 \| 0.6543 \| \| 0.0294 \| 158.81 \| 45420 \| 0.1563 \| 0.3995 \| 0.6071 \| 0.5993 \| nan \| 0.5619 \| 0.6524 \| 0.0 \| 0.5490 \| 0.6495 \| \| 0.0377 \| 158.88 \| 45440 \| 0.1575 \| 0.3994 \| 0.6071 \| 0.5985 \| nan \| 0.5575 \| 0.6566 \| 0.0 \| 0.5445 \| 0.6537 \| \| 0.0434 \| 158.95 \| 45460 \| 0.1559 \| 0.4005 \| 0.6089 \| 0.6015 \| nan \| 0.5662 \| 0.6516 \| 0.0 \| 0.5530 \| 0.6486 \| \| 0.0334 \| 159.02 \| 45480 \| 0.1589 \| 0.3987 \| 0.6063 \| 0.6003 \| nan \| 0.5716 \| 0.6409 \| 0.0 \| 0.5580 \| 0.6381 \| \| 0.0397 \| 159.09 \| 45500 \| 0.1553 \| 0.4000 \| 0.6080 \| 0.5998 \| nan \| 0.5604 \| 0.6557 \| 0.0 \| 0.5480 \| 0.6520 \| \| 0.04 \| 159.16 \| 45520 \| 0.1582 \| 0.4017 \| 0.6108 \| 0.6048 \| nan \| 0.5763 \| 0.6452 \| 0.0 \| 0.5629 \| 0.6421 \| \| 0.0534 \| 159.23 \| 45540 \| 0.1530 \| 0.4097 \| 0.6229 \| 0.6149 \| nan \| 0.5765 \| 0.6694 \| 0.0 \| 0.5639 \| 0.6652 \| \| 0.0259 \| 159.3 \| 45560 \| 0.1559 \| 0.4036 \| 0.6138 \| 0.6066 \| nan \| 0.5721 \| 0.6555 \| 0.0 \| 0.5593 \| 0.6517 \| \| 0.054 \| 159.37 \| 45580 \| 0.1590 \| 0.3975 \| 0.6040 \| 0.5945 \| nan \| 0.5496 \| 0.6583 \| 0.0 \| 0.5379 \| 0.6546 \| \| 0.0325 \| 159.44 \| 45600 \| 0.1576 \| 0.3982 \| 0.6050 \| 0.5969 \| nan \| 0.5579 \| 0.6522 \| 0.0 \| 0.5460 \| 0.6487 \| \| 0.042 \| 159.51 \| 45620 \| 0.1573 \| 0.4041 \| 0.6143 \| 0.6084 \| nan \| 0.5804 \| 0.6482 \| 0.0 \| 0.5671 \| 0.6452 \| \| 0.0193 \| 159.58 \| 45640 \| 0.1551 \| 0.4009 \| 0.6089 \| 0.6004 \| nan \| 0.5597 \| 0.6581 \| 0.0 \| 0.5478 \| 0.6550 \| \| 0.0422 \| 159.65 \| 45660 \| 0.1542 \| 0.4050 \| 0.6151 \| 0.6070 \| nan \| 0.5683 \| 0.6618 \| 0.0 \| 0.5567 \| 0.6583 \| \| 0.0445 \| 159.72 \| 45680 \| 0.1521 \| 0.4066 \| 0.6173 \| 0.6079 \| nan \| 0.5628 \| 0.6719 \| 0.0 \| 0.5516 \| 0.6682 \| \| 0.0567 \| 159.79 \| 45700 \| 0.1528 \| 0.4065 \| 0.6172 \| 0.6086 \| nan \| 0.5676 \| 0.6668 \| 0.0 \| 0.5564 \| 0.6630 \| \| 0.0233 \| 159.86 \| 45720 \| 0.1549 \| 0.4039 \| 0.6135 \| 0.6059 \| nan \| 0.5696 \| 0.6575 \| 0.0 \| 0.5573 \| 0.6543 \| \| 0.0398 \| 159.93 \| 45740 \| 0.1511 \| 0.4091 \| 0.6217 \| 0.6145 \| nan \| 0.5804 \| 0.6631 \| 0.0 \| 0.5673 \| 0.6599 \| \| 0.024 \| 160.0 \| 45760 \| 0.1545 \| 0.4023 \| 0.6110 \| 0.6020 \| nan \| 0.5586 \| 0.6635 \| 0.0 \| 0.5469 \| 0.6600 \| \| 0.0352 \| 160.07 \| 45780 \| 0.1543 \| 0.4045 \| 0.6144 \| 0.6060 \| nan \| 0.5659 \| 0.6628 \| 0.0 \| 0.5541 \| 0.6593 \| \| 0.0578 \| 160.14 \| 45800 \| 0.1534 \| 0.4036 \| 0.6130 \| 0.6043 \| nan \| 0.5629 \| 0.6632 \| 0.0 \| 0.5515 \| 0.6593 \| \| 0.0347 \| 160.21 \| 45820 \| 0.1530 \| 0.4069 \| 0.6182 \| 0.6105 \| nan \| 0.5737 \| 0.6628 \| 0.0 \| 0.5619 \| 0.6590 \| \| 0.0323 \| 160.28 \| 45840 \| 0.1535 \| 0.4069 \| 0.6182 \| 0.6093 \| nan \| 0.5670 \| 0.6694 \| 0.0 \| 0.5556 \| 0.6651 \| \| 0.0375 \| 160.35 \| 45860 \| 0.1541 \| 0.4050 \| 0.6152 \| 0.6062 \| nan \| 0.5634 \| 0.6670 \| 0.0 \| 0.5521 \| 0.6627 \| \| 0.0462 \| 160.42 \| 45880 \| 0.1490 \| 0.4082 \| 0.6198 \| 0.6090 \| nan \| 0.5574 \| 0.6823 \| 0.0 \| 0.5479 \| 0.6767 \| \| 0.0627 \| 160.49 \| 45900 \| 0.1498 \| 0.4122 \| 0.6264 \| 0.6183 \| nan \| 0.5795 \| 0.6734 \| 0.0 \| 0.5680 \| 0.6687 \| \| 0.0364 \| 160.56 \| 45920 \| 0.1527 \| 0.4022 \| 0.6106 \| 0.6003 \| nan \| 0.5512 \| 0.6700 \| 0.0 \| 0.5412 \| 0.6653 \| \| 0.0449 \| 160.63 \| 45940 \| 0.1519 \| 0.4071 \| 0.6182 \| 0.6081 \| nan \| 0.5596 \| 0.6768 \| 0.0 \| 0.5493 \| 0.6720 \| \| 0.0395 \| 160.7 \| 45960 \| 0.1534 \| 0.4049 \| 0.6155 \| 0.6076 \| nan \| 0.5702 \| 0.6607 \| 0.0 \| 0.5580 \| 0.6568 \| \| 0.0476 \| 160.77 \| 45980 \| 0.1503 \| 0.4041 \| 0.6138 \| 0.6044 \| nan \| 0.5596 \| 0.6679 \| 0.0 \| 0.5486 \| 0.6638 \| \| 0.0281 \| 160.84 \| 46000 \| 0.1521 \| 0.4053 \| 0.6157 \| 0.6063 \| nan \| 0.5615 \| 0.6700 \| 0.0 \| 0.5497 \| 0.6662 \| \| 0.0444 \| 160.91 \| 46020 \| 0.1528 \| 0.4035 \| 0.6131 \| 0.6046 \| nan \| 0.5643 \| 0.6618 \| 0.0 \| 0.5523 \| 0.6582 \| \| 0.0517 \| 160.98 \| 46040 \| 0.1563 \| 0.4036 \| 0.6134 \| 0.6055 \| nan \| 0.5679 \| 0.6590 \| 0.0 \| 0.5556 \| 0.6553 \| \| 0.0374 \| 161.05 \| 46060 \| 0.1552 \| 0.4035 \| 0.6134 \| 0.6060 \| nan \| 0.5707 \| 0.6562 \| 0.0 \| 0.5578 \| 0.6527 \| \| 0.0353 \| 161.12 \| 46080 \| 0.1553 \| 0.4070 \| 0.6189 \| 0.6115 \| nan \| 0.5761 \| 0.6616 \| 0.0 \| 0.5629 \| 0.6581 \| \| 0.0494 \| 161.19 \| 46100 \| 0.1526 \| 0.4066 \| 0.6182 \| 0.6095 \| nan \| 0.5682 \| 0.6681 \| 0.0 \| 0.5565 \| 0.6634 \| \| 0.0453 \| 161.26 \| 46120 \| 0.1552 \| 0.3992 \| 0.6068 \| 0.5979 \| nan \| 0.5555 \| 0.6582 \| 0.0 \| 0.5444 \| 0.6532 \| \| 0.0335 \| 161.33 \| 46140 \| 0.1573 \| 0.4076 \| 0.6202 \| 0.6142 \| nan \| 0.5853 \| 0.6551 \| 0.0 \| 0.5722 \| 0.6507 \| \| 0.0402 \| 161.4 \| 46160 \| 0.1604 \| 0.3957 \| 0.6016 \| 0.5937 \| nan \| 0.5560 \| 0.6471 \| 0.0 \| 0.5437 \| 0.6435 \| \| 0.0574 \| 161.47 \| 46180 \| 0.1593 \| 0.3985 \| 0.6059 \| 0.6000 \| nan \| 0.5719 \| 0.6398 \| 0.0 \| 0.5587 \| 0.6367 \| \| 0.0447 \| 161.54 \| 46200 \| 0.1573 \| 0.3987 \| 0.6061 \| 0.5983 \| nan \| 0.5610 \| 0.6512 \| 0.0 \| 0.5490 \| 0.6470 \| \| 0.0326 \| 161.61 \| 46220 \| 0.1569 \| 0.4021 \| 0.6115 \| 0.6042 \| nan \| 0.5695 \| 0.6536 \| 0.0 \| 0.5568 \| 0.6494 \| \| 0.0734 \| 161.68 \| 46240 \| 0.1590 \| 0.3999 \| 0.6077 \| 0.6001 \| nan \| 0.5637 \| 0.6517 \| 0.0 \| 0.5514 \| 0.6482 \| \| 0.0387 \| 161.75 \| 46260 \| 0.1560 \| 0.4021 \| 0.6111 \| 0.6032 \| nan \| 0.5654 \| 0.6568 \| 0.0 \| 0.5527 \| 0.6536 \| \| 0.0365 \| 161.82 \| 46280 \| 0.1567 \| 0.4036 \| 0.6135 \| 0.6067 \| nan \| 0.5741 \| 0.6529 \| 0.0 \| 0.5610 \| 0.6497 \| \| 0.0482 \| 161.89 \| 46300 \| 0.1582 \| 0.4021 \| 0.6117 \| 0.6063 \| nan \| 0.5809 \| 0.6424 \| 0.0 \| 0.5669 \| 0.6394 \| \| 0.0539 \| 161.96 \| 46320 \| 0.1593 \| 0.3956 \| 0.6015 \| 0.5941 \| nan \| 0.5588 \| 0.6441 \| 0.0 \| 0.5461 \| 0.6408 \| \| 0.0408 \| 162.03 \| 46340 \| 0.1570 \| 0.3990 \| 0.6062 \| 0.5966 \| nan \| 0.5506 \| 0.6618 \| 0.0 \| 0.5392 \| 0.6579 \| \| 0.0454 \| 162.1 \| 46360 \| 0.1532 \| 0.4070 \| 0.6187 \| 0.6114 \| nan \| 0.5763 \| 0.6612 \| 0.0 \| 0.5635 \| 0.6575 \| \| 0.0307 \| 162.17 \| 46380 \| 0.1544 \| 0.4032 \| 0.6128 \| 0.6044 \| nan \| 0.5646 \| 0.6609 \| 0.0 \| 0.5533 \| 0.6563 \| \| 0.0405 \| 162.24 \| 46400 \| 0.1548 \| 0.4003 \| 0.6085 \| 0.5997 \| nan \| 0.5578 \| 0.6592 \| 0.0 \| 0.5458 \| 0.6552 \| \| 0.0361 \| 162.31 \| 46420 \| 0.1555 \| 0.4026 \| 0.6120 \| 0.6035 \| nan \| 0.5631 \| 0.6608 \| 0.0 \| 0.5508 \| 0.6570 \| \| 0.0503 \| 162.38 \| 46440 \| 0.1564 \| 0.4078 \| 0.6203 \| 0.6142 \| nan \| 0.5848 \| 0.6558 \| 0.0 \| 0.5710 \| 0.6524 \| \| 0.0247 \| 162.45 \| 46460 \| 0.1572 \| 0.3997 \| 0.6074 \| 0.6000 \| nan \| 0.5645 \| 0.6504 \| 0.0 \| 0.5518 \| 0.6472 \| \| 0.0473 \| 162.52 \| 46480 \| 0.1655 \| 0.3915 \| 0.5952 \| 0.5894 \| nan \| 0.5618 \| 0.6287 \| 0.0 \| 0.5482 \| 0.6264 \| \| 0.0372 \| 162.59 \| 46500 \| 0.1646 \| 0.3942 \| 0.5994 \| 0.5940 \| nan \| 0.5681 \| 0.6308 \| 0.0 \| 0.5541 \| 0.6284 \| \| 0.0276 \| 162.66 \| 46520 \| 0.1618 \| 0.3971 \| 0.6035 \| 0.5962 \| nan \| 0.5616 \| 0.6454 \| 0.0 \| 0.5486 \| 0.6425 \| \| 0.037 \| 162.73 \| 46540 \| 0.1604 \| 0.3964 \| 0.6031 \| 0.5966 \| nan \| 0.5657 \| 0.6406 \| 0.0 \| 0.5516 \| 0.6378 \| \| 0.0223 \| 162.8 \| 46560 \| 0.1591 \| 0.4022 \| 0.6120 \| 0.6057 \| nan \| 0.5757 \| 0.6484 \| 0.0 \| 0.5612 \| 0.6454 \| \| 0.0393 \| 162.87 \| 46580 \| 0.1572 \| 0.4013 \| 0.6099 \| 0.6015 \| nan \| 0.5615 \| 0.6583 \| 0.0 \| 0.5494 \| 0.6546 \| \| 0.0633 \| 162.94 \| 46600 \| 0.1575 \| 0.3999 \| 0.6078 \| 0.6002 \| nan \| 0.5639 \| 0.6517 \| 0.0 \| 0.5513 \| 0.6484 \| \| 0.0367 \| 163.01 \| 46620 \| 0.1575 \| 0.4028 \| 0.6123 \| 0.6046 \| nan \| 0.5676 \| 0.6569 \| 0.0 \| 0.5549 \| 0.6535 \| \| 0.036 \| 163.08 \| 46640 \| 0.1565 \| 0.4015 \| 0.6103 \| 0.6017 \| nan \| 0.5606 \| 0.6600 \| 0.0 \| 0.5478 \| 0.6565 \| \| 0.0423 \| 163.15 \| 46660 \| 0.1550 \| 0.4097 \| 0.6233 \| 0.6180 \| nan \| 0.5923 \| 0.6544 \| 0.0 \| 0.5781 \| 0.6509 \| \| 0.0514 \| 163.22 \| 46680 \| 0.1568 \| 0.4015 \| 0.6103 \| 0.6026 \| nan \| 0.5656 \| 0.6551 \| 0.0 \| 0.5538 \| 0.6508 \| \| 0.0368 \| 163.29 \| 46700 \| 0.1605 \| 0.4010 \| 0.6097 \| 0.6033 \| nan \| 0.5726 \| 0.6467 \| 0.0 \| 0.5606 \| 0.6425 \| \| 0.0484 \| 163.36 \| 46720 \| 0.1588 \| 0.3988 \| 0.6060 \| 0.5982 \| nan \| 0.5605 \| 0.6515 \| 0.0 \| 0.5487 \| 0.6476 \| \| 0.0423 \| 163.43 \| 46740 \| 0.1599 \| 0.3977 \| 0.6045 \| 0.5960 \| nan \| 0.5555 \| 0.6536 \| 0.0 \| 0.5434 \| 0.6498 \| \| 0.0361 \| 163.5 \| 46760 \| 0.1577 \| 0.4011 \| 0.6100 \| 0.6032 \| nan \| 0.5707 \| 0.6492 \| 0.0 \| 0.5576 \| 0.6457 \| \| 0.0522 \| 163.57 \| 46780 \| 0.1575 \| 0.4044 \| 0.6152 \| 0.6084 \| nan \| 0.5760 \| 0.6544 \| 0.0 \| 0.5623 \| 0.6509 \| \| 0.029 \| 163.64 \| 46800 \| 0.1570 \| 0.4019 \| 0.6112 \| 0.6033 \| nan \| 0.5654 \| 0.6569 \| 0.0 \| 0.5524 \| 0.6534 \| \| 0.0577 \| 163.71 \| 46820 \| 0.1588 \| 0.3988 \| 0.6064 \| 0.5986 \| nan \| 0.5609 \| 0.6520 \| 0.0 \| 0.5480 \| 0.6485 \| \| 0.0683 \| 163.78 \| 46840 \| 0.1522 \| 0.4061 \| 0.6174 \| 0.6093 \| nan \| 0.5703 \| 0.6645 \| 0.0 \| 0.5576 \| 0.6608 \| \| 0.0334 \| 163.85 \| 46860 \| 0.1555 \| 0.4001 \| 0.6079 \| 0.5989 \| nan \| 0.5561 \| 0.6596 \| 0.0 \| 0.5440 \| 0.6562 \| \| 0.0508 \| 163.92 \| 46880 \| 0.1550 \| 0.4017 \| 0.6105 \| 0.6023 \| nan \| 0.5629 \| 0.6581 \| 0.0 \| 0.5502 \| 0.6550 \| \| 0.0387 \| 163.99 \| 46900 \| 0.1577 \| 0.4043 \| 0.6149 \| 0.6082 \| nan \| 0.5762 \| 0.6536 \| 0.0 \| 0.5624 \| 0.6506 \| \| 0.0225 \| 164.06 \| 46920 \| 0.1571 \| 0.4024 \| 0.6114 \| 0.6025 \| nan \| 0.5600 \| 0.6628 \| 0.0 \| 0.5482 \| 0.6590 \| \| 0.0187 \| 164.13 \| 46940 \| 0.1548 \| 0.4052 \| 0.6157 \| 0.6063 \| nan \| 0.5616 \| 0.6698 \| 0.0 \| 0.5506 \| 0.6651 \| \| 0.0508 \| 164.2 \| 46960 \| 0.1540 \| 0.4002 \| 0.6080 \| 0.5978 \| nan \| 0.5489 \| 0.6671 \| 0.0 \| 0.5380 \| 0.6626 \| \| 0.0309 \| 164.27 \| 46980 \| 0.1558 \| 0.4058 \| 0.6170 \| 0.6089 \| nan \| 0.5704 \| 0.6635 \| 0.0 \| 0.5579 \| 0.6594 \| \| 0.0504 \| 164.34 \| 47000 \| 0.1537 \| 0.4082 \| 0.6204 \| 0.6119 \| nan \| 0.5712 \| 0.6697 \| 0.0 \| 0.5594 \| 0.6652 \| \| 0.0302 \| 164.41 \| 47020 \| 0.1520 \| 0.4010 \| 0.6091 \| 0.5982 \| nan \| 0.5463 \| 0.6719 \| 0.0 \| 0.5355 \| 0.6675 \| \| 0.0522 \| 164.48 \| 47040 \| 0.1521 \| 0.4049 \| 0.6153 \| 0.6068 \| nan \| 0.5658 \| 0.6648 \| 0.0 \| 0.5536 \| 0.6611 \| \| 0.0422 \| 164.55 \| 47060 \| 0.1503 \| 0.4075 \| 0.6193 \| 0.6092 \| nan \| 0.5610 \| 0.6776 \| 0.0 \| 0.5496 \| 0.6731 \| \| 0.0513 \| 164.62 \| 47080 \| 0.1505 \| 0.4060 \| 0.6171 \| 0.6078 \| nan \| 0.5638 \| 0.6703 \| 0.0 \| 0.5526 \| 0.6655 \| \| 0.0213 \| 164.69 \| 47100 \| 0.1537 \| 0.4049 \| 0.6155 \| 0.6071 \| nan \| 0.5667 \| 0.6644 \| 0.0 \| 0.5546 \| 0.6601 \| \| 0.055 \| 164.76 \| 47120 \| 0.1553 \| 0.4060 \| 0.6171 \| 0.6092 \| nan \| 0.5713 \| 0.6630 \| 0.0 \| 0.5590 \| 0.6589 \| \| 0.0471 \| 164.83 \| 47140 \| 0.1543 \| 0.4065 \| 0.6178 \| 0.6094 \| nan \| 0.5693 \| 0.6664 \| 0.0 \| 0.5571 \| 0.6625 \| \| 0.0356 \| 164.9 \| 47160 \| 0.1558 \| 0.4002 \| 0.6081 \| 0.5997 \| nan \| 0.5595 \| 0.6567 \| 0.0 \| 0.5471 \| 0.6533 \| \| 0.0331 \| 164.97 \| 47180 \| 0.1579 \| 0.4015 \| 0.6103 \| 0.6028 \| nan \| 0.5670 \| 0.6536 \| 0.0 \| 0.5542 \| 0.6502 \| \| 0.0318 \| 165.03 \| 47200 \| 0.1541 \| 0.4054 \| 0.6164 \| 0.6078 \| nan \| 0.5671 \| 0.6656 \| 0.0 \| 0.5545 \| 0.6619 \| \| 0.0352 \| 165.1 \| 47220 \| 0.1567 \| 0.4054 \| 0.6166 \| 0.6093 \| nan \| 0.5744 \| 0.6588 \| 0.0 \| 0.5607 \| 0.6556 \| \| 0.0319 \| 165.17 \| 47240 \| 0.1559 \| 0.4064 \| 0.6177 \| 0.6092 \| nan \| 0.5683 \| 0.6671 \| 0.0 \| 0.5563 \| 0.6630 \| \| 0.0567 \| 165.24 \| 47260 \| 0.1607 \| 0.3999 \| 0.6077 \| 0.5998 \| nan \| 0.5621 \| 0.6533 \| 0.0 \| 0.5504 \| 0.6493 \| \| 0.0314 \| 165.31 \| 47280 \| 0.1604 \| 0.4020 \| 0.6112 \| 0.6043 \| nan \| 0.5713 \| 0.6510 \| 0.0 \| 0.5579 \| 0.6480 \| \| 0.0484 \| 165.38 \| 47300 \| 0.1561 \| 0.4037 \| 0.6136 \| 0.6055 \| nan \| 0.5666 \| 0.6605 \| 0.0 \| 0.5540 \| 0.6571 \| \| 0.0373 \| 165.45 \| 47320 \| 0.1606 \| 0.4043 \| 0.6150 \| 0.6094 \| nan \| 0.5828 \| 0.6471 \| 0.0 \| 0.5685 \| 0.6443 \| \| 0.0424 \| 165.52 \| 47340 \| 0.1598 \| 0.4005 \| 0.6091 \| 0.6024 \| nan \| 0.5704 \| 0.6478 \| 0.0 \| 0.5563 \| 0.6453 \| \| 0.0573 \| 165.59 \| 47360 \| 0.1620 \| 0.3956 \| 0.6014 \| 0.5937 \| nan \| 0.5566 \| 0.6462 \| 0.0 \| 0.5436 \| 0.6433 \| \| 0.0423 \| 165.66 \| 47380 \| 0.1615 \| 0.3972 \| 0.6040 \| 0.5968 \| nan \| 0.5622 \| 0.6458 \| 0.0 \| 0.5485 \| 0.6430 \| \| 0.0376 \| 165.73 \| 47400 \| 0.1594 \| 0.3996 \| 0.6079 \| 0.6016 \| nan \| 0.5717 \| 0.6441 \| 0.0 \| 0.5577 \| 0.6412 \| \| 0.0549 \| 165.8 \| 47420 \| 0.1591 \| 0.3993 \| 0.6075 \| 0.6010 \| nan \| 0.5703 \| 0.6446 \| 0.0 \| 0.5561 \| 0.6419 \| \| 0.0606 \| 165.87 \| 47440 \| 0.1613 \| 0.3989 \| 0.6067 \| 0.5999 \| nan \| 0.5675 \| 0.6460 \| 0.0 \| 0.5537 \| 0.6431 \| \| 0.0651 \| 165.94 \| 47460 \| 0.1575 \| 0.4045 \| 0.6146 \| 0.6055 \| nan \| 0.5623 \| 0.6669 \| 0.0 \| 0.5505 \| 0.6629 \| \| 0.0489 \| 166.01 \| 47480 \| 0.1539 \| 0.4062 \| 0.6175 \| 0.6094 \| nan \| 0.5705 \| 0.6646 \| 0.0 \| 0.5585 \| 0.6601 \| \| 0.0394 \| 166.08 \| 47500 \| 0.1597 \| 0.4010 \| 0.6098 \| 0.6025 \| nan \| 0.5677 \| 0.6520 \| 0.0 \| 0.5558 \| 0.6472 \| \| 0.0463 \| 166.15 \| 47520 \| 0.1602 \| 0.4006 \| 0.6091 \| 0.6019 \| nan \| 0.5673 \| 0.6509 \| 0.0 \| 0.5551 \| 0.6466 \| \| 0.031 \| 166.22 \| 47540 \| 0.1581 \| 0.4007 \| 0.6093 \| 0.6026 \| nan \| 0.5706 \| 0.6480 \| 0.0 \| 0.5578 \| 0.6442 \| \| 0.0264 \| 166.29 \| 47560 \| 0.1614 \| 0.3975 \| 0.6042 \| 0.5967 \| nan \| 0.5604 \| 0.6481 \| 0.0 \| 0.5482 \| 0.6442 \| \| 0.0402 \| 166.36 \| 47580 \| 0.1625 \| 0.3968 \| 0.6030 \| 0.5948 \| nan \| 0.5556 \| 0.6505 \| 0.0 \| 0.5435 \| 0.6467 \| \| 0.0564 \| 166.43 \| 47600 \| 0.1604 \| 0.3998 \| 0.6079 \| 0.5998 \| nan \| 0.5608 \| 0.6550 \| 0.0 \| 0.5483 \| 0.6513 \| \| 0.0257 \| 166.5 \| 47620 \| 0.1590 \| 0.3985 \| 0.6060 \| 0.5981 \| nan \| 0.5608 \| 0.6511 \| 0.0 \| 0.5478 \| 0.6476 \| \| 0.03 \| 166.57 \| 47640 \| 0.1620 \| 0.3999 \| 0.6082 \| 0.6024 \| nan \| 0.5748 \| 0.6416 \| 0.0 \| 0.5614 \| 0.6381 \| \| 0.0242 \| 166.64 \| 47660 \| 0.1589 \| 0.3979 \| 0.6048 \| 0.5962 \| nan \| 0.5549 \| 0.6547 \| 0.0 \| 0.5434 \| 0.6502 \| \| 0.0579 \| 166.71 \| 47680 \| 0.1575 \| 0.4017 \| 0.6108 \| 0.6038 \| nan \| 0.5703 \| 0.6514 \| 0.0 \| 0.5583 \| 0.6467 \| \| 0.032 \| 166.78 \| 47700 \| 0.1594 \| 0.4024 \| 0.6123 \| 0.6057 \| nan \| 0.5741 \| 0.6506 \| 0.0 \| 0.5607 \| 0.6466 \| \| 0.0369 \| 166.85 \| 47720 \| 0.1591 \| 0.3980 \| 0.6053 \| 0.5974 \| nan \| 0.5596 \| 0.6511 \| 0.0 \| 0.5464 \| 0.6475 \| \| 0.0404 \| 166.92 \| 47740 \| 0.1600 \| 0.3961 \| 0.6030 \| 0.5963 \| nan \| 0.5646 \| 0.6414 \| 0.0 \| 0.5502 \| 0.6382 \| \| 0.0389 \| 166.99 \| 47760 \| 0.1620 \| 0.3961 \| 0.6030 \| 0.5981 \| nan \| 0.5746 \| 0.6315 \| 0.0 \| 0.5602 \| 0.6280 \| \| 0.0193 \| 167.06 \| 47780 \| 0.1592 \| 0.3963 \| 0.6031 \| 0.5957 \| nan \| 0.5603 \| 0.6459 \| 0.0 \| 0.5468 \| 0.6422 \| \| 0.0352 \| 167.13 \| 47800 \| 0.1607 \| 0.3979 \| 0.6055 \| 0.5986 \| nan \| 0.5655 \| 0.6455 \| 0.0 \| 0.5518 \| 0.6418 \| \| 0.036 \| 167.2 \| 47820 \| 0.1609 \| 0.3980 \| 0.6058 \| 0.5994 \| nan \| 0.5687 \| 0.6429 \| 0.0 \| 0.5547 \| 0.6394 \| \| 0.038 \| 167.27 \| 47840 \| 0.1588 \| 0.3976 \| 0.6048 \| 0.5976 \| nan \| 0.5631 \| 0.6464 \| 0.0 \| 0.5500 \| 0.6427 \| \| 0.0273 \| 167.34 \| 47860 \| 0.1575 \| 0.4013 \| 0.6107 \| 0.6035 \| nan \| 0.5693 \| 0.6521 \| 0.0 \| 0.5556 \| 0.6482 \| \| 0.0337 \| 167.41 \| 47880 \| 0.1580 \| 0.4014 \| 0.6111 \| 0.6047 \| nan \| 0.5742 \| 0.6480 \| 0.0 \| 0.5594 \| 0.6449 \| \| 0.037 \| 167.48 \| 47900 \| 0.1598 \| 0.3976 \| 0.6049 \| 0.5982 \| nan \| 0.5662 \| 0.6437 \| 0.0 \| 0.5525 \| 0.6404 \| \| 0.0265 \| 167.55 \| 47920 \| 0.1614 \| 0.3957 \| 0.6020 \| 0.5942 \| nan \| 0.5567 \| 0.6473 \| 0.0 \| 0.5430 \| 0.6440 \| \| 0.0267 \| 167.62 \| 47940 \| 0.1629 \| 0.3958 \| 0.6029 \| 0.5987 \| nan \| 0.5782 \| 0.6276 \| 0.0 \| 0.5626 \| 0.6248 \| \| 0.0351 \| 167.69 \| 47960 \| 0.1659 \| 0.3923 \| 0.5970 \| 0.5914 \| nan \| 0.5648 \| 0.6293 \| 0.0 \| 0.5507 \| 0.6262 \| \| 0.0314 \| 167.76 \| 47980 \| 0.1612 \| 0.3994 \| 0.6077 \| 0.6014 \| nan \| 0.5713 \| 0.6441 \| 0.0 \| 0.5582 \| 0.6400 \| \| 0.0423 \| 167.83 \| 48000 \| 0.1593 \| 0.4004 \| 0.6090 \| 0.6023 \| nan \| 0.5701 \| 0.6480 \| 0.0 \| 0.5573 \| 0.6440 \| \| 0.0373 \| 167.9 \| 48020 \| 0.1596 \| 0.3969 \| 0.6036 \| 0.5962 \| nan \| 0.5605 \| 0.6468 \| 0.0 \| 0.5475 \| 0.6432 \| \| 0.0383 \| 167.97 \| 48040 \| 0.1591 \| 0.4017 \| 0.6113 \| 0.6048 \| nan \| 0.5741 \| 0.6485 \| 0.0 \| 0.5599 \| 0.6451 \| \| 0.0341 \| 168.04 \| 48060 \| 0.1617 \| 0.3970 \| 0.6039 \| 0.5972 \| nan \| 0.5653 \| 0.6425 \| 0.0 \| 0.5518 \| 0.6393 \| \| 0.0409 \| 168.11 \| 48080 \| 0.1590 \| 0.4032 \| 0.6134 \| 0.6063 \| nan \| 0.5725 \| 0.6544 \| 0.0 \| 0.5590 \| 0.6505 \| \| 0.0353 \| 168.18 \| 48100 \| 0.1590 \| 0.4017 \| 0.6111 \| 0.6051 \| nan \| 0.5763 \| 0.6459 \| 0.0 \| 0.5623 \| 0.6427 \| \| 0.0436 \| 168.25 \| 48120 \| 0.1617 \| 0.3985 \| 0.6067 \| 0.6013 \| nan \| 0.5752 \| 0.6383 \| 0.0 \| 0.5600 \| 0.6355 \| \| 0.0306 \| 168.32 \| 48140 \| 0.1618 \| 0.3957 \| 0.6019 \| 0.5952 \| nan \| 0.5632 \| 0.6406 \| 0.0 \| 0.5497 \| 0.6374 \| \| 0.0351 \| 168.39 \| 48160 \| 0.1614 \| 0.3969 \| 0.6036 \| 0.5965 \| nan \| 0.5621 \| 0.6452 \| 0.0 \| 0.5490 \| 0.6418 \| \| 0.0523 \| 168.46 \| 48180 \| 0.1599 \| 0.3969 \| 0.6037 \| 0.5968 \| nan \| 0.5643 \| 0.6430 \| 0.0 \| 0.5508 \| 0.6399 \| \| 0.0519 \| 168.53 \| 48200 \| 0.1585 \| 0.4017 \| 0.6109 \| 0.6032 \| nan \| 0.5668 \| 0.6550 \| 0.0 \| 0.5533 \| 0.6519 \| \| 0.0465 \| 168.6 \| 48220 \| 0.1611 \| 0.4007 \| 0.6097 \| 0.6035 \| nan \| 0.5740 \| 0.6454 \| 0.0 \| 0.5595 \| 0.6428 \| \| 0.0614 \| 168.67 \| 48240 \| 0.1586 \| 0.4001 \| 0.6089 \| 0.6030 \| nan \| 0.5753 \| 0.6425 \| 0.0 \| 0.5605 \| 0.6397 \| \| 0.0252 \| 168.74 \| 48260 \| 0.1587 \| 0.3985 \| 0.6063 \| 0.5990 \| nan \| 0.5640 \| 0.6486 \| 0.0 \| 0.5503 \| 0.6453 \| \| 0.0531 \| 168.81 \| 48280 \| 0.1608 \| 0.3995 \| 0.6079 \| 0.6021 \| nan \| 0.5744 \| 0.6414 \| 0.0 \| 0.5601 \| 0.6384 \| \| 0.0386 \| 168.88 \| 48300 \| 0.1614 \| 0.3988 \| 0.6069 \| 0.6019 \| nan \| 0.5777 \| 0.6362 \| 0.0 \| 0.5634 \| 0.6332 \| \| 0.035 \| 168.95 \| 48320 \| 0.1618 \| 0.3952 \| 0.6011 \| 0.5956 \| nan \| 0.5691 \| 0.6331 \| 0.0 \| 0.5552 \| 0.6303 \| \| 0.0289 \| 169.02 \| 48340 \| 0.1599 \| 0.3978 \| 0.6053 \| 0.5993 \| nan \| 0.5708 \| 0.6398 \| 0.0 \| 0.5565 \| 0.6370 \| \| 0.0353 \| 169.09 \| 48360 \| 0.1606 \| 0.3976 \| 0.6046 \| 0.5976 \| nan \| 0.5638 \| 0.6455 \| 0.0 \| 0.5504 \| 0.6424 \| \| 0.024 \| 169.16 \| 48380 \| 0.1623 \| 0.3970 \| 0.6036 \| 0.5962 \| nan \| 0.5609 \| 0.6462 \| 0.0 \| 0.5477 \| 0.6432 \| \| 0.0332 \| 169.23 \| 48400 \| 0.1593 \| 0.4006 \| 0.6091 \| 0.6021 \| nan \| 0.5685 \| 0.6498 \| 0.0 \| 0.5552 \| 0.6465 \| \| 0.023 \| 169.3 \| 48420 \| 0.1577 \| 0.4005 \| 0.6089 \| 0.6013 \| nan \| 0.5649 \| 0.6530 \| 0.0 \| 0.5518 \| 0.6496 \| \| 0.0342 \| 169.37 \| 48440 \| 0.1587 \| 0.4004 \| 0.6086 \| 0.6013 \| nan \| 0.5664 \| 0.6508 \| 0.0 \| 0.5541 \| 0.6471 \| \| 0.047 \| 169.44 \| 48460 \| 0.1589 \| 0.4026 \| 0.6120 \| 0.6040 \| nan \| 0.5657 \| 0.6584 \| 0.0 \| 0.5538 \| 0.6540 \| \| 0.0402 \| 169.51 \| 48480 \| 0.1575 \| 0.3979 \| 0.6047 \| 0.5954 \| nan \| 0.5514 \| 0.6579 \| 0.0 \| 0.5399 \| 0.6539 \| \| 0.0516 \| 169.58 \| 48500 \| 0.1585 \| 0.4024 \| 0.6120 \| 0.6047 \| nan \| 0.5698 \| 0.6542 \| 0.0 \| 0.5567 \| 0.6506 \| \| 0.0577 \| 169.65 \| 48520 \| 0.1585 \| 0.4003 \| 0.6088 \| 0.6009 \| nan \| 0.5635 \| 0.6541 \| 0.0 \| 0.5505 \| 0.6505 \| \| 0.0328 \| 169.72 \| 48540 \| 0.1583 \| 0.4048 \| 0.6158 \| 0.6102 \| nan \| 0.5834 \| 0.6482 \| 0.0 \| 0.5692 \| 0.6451 \| \| 0.0377 \| 169.79 \| 48560 \| 0.1614 \| 0.3930 \| 0.5972 \| 0.5891 \| nan \| 0.5507 \| 0.6436 \| 0.0 \| 0.5385 \| 0.6404 \| \| 0.0314 \| 169.86 \| 48580 \| 0.1591 \| 0.4026 \| 0.6122 \| 0.6053 \| nan \| 0.5724 \| 0.6520 \| 0.0 \| 0.5591 \| 0.6486 \| \| 0.0443 \| 169.93 \| 48600 \| 0.1586 \| 0.4021 \| 0.6115 \| 0.6036 \| nan \| 0.5661 \| 0.6568 \| 0.0 \| 0.5532 \| 0.6532 \| \| 0.0502 \| 170.0 \| 48620 \| 0.1580 \| 0.4012 \| 0.6102 \| 0.6030 \| nan \| 0.5685 \| 0.6520 \| 0.0 \| 0.5549 \| 0.6487 \| \| 0.0387 \| 170.07 \| 48640 \| 0.1579 \| 0.4013 \| 0.6103 \| 0.6028 \| nan \| 0.5666 \| 0.6541 \| 0.0 \| 0.5532 \| 0.6507 \| \| 0.0492 \| 170.14 \| 48660 \| 0.1585 \| 0.4043 \| 0.6152 \| 0.6086 \| nan \| 0.5774 \| 0.6530 \| 0.0 \| 0.5633 \| 0.6497 \| \| 0.0544 \| 170.21 \| 48680 \| 0.1595 \| 0.3992 \| 0.6069 \| 0.5997 \| nan \| 0.5651 \| 0.6488 \| 0.0 \| 0.5521 \| 0.6454 \| \| 0.0489 \| 170.28 \| 48700 \| 0.1582 \| 0.3990 \| 0.6066 \| 0.5988 \| nan \| 0.5620 \| 0.6511 \| 0.0 \| 0.5496 \| 0.6475 \| \| 0.0482 \| 170.35 \| 48720 \| 0.1568 \| 0.4027 \| 0.6122 \| 0.6033 \| nan \| 0.5608 \| 0.6635 \| 0.0 \| 0.5503 \| 0.6577 \| \| 0.0322 \| 170.42 \| 48740 \| 0.1596 \| 0.3987 \| 0.6063 \| 0.5982 \| nan \| 0.5593 \| 0.6533 \| 0.0 \| 0.5481 \| 0.6481 \| \| 0.0423 \| 170.49 \| 48760 \| 0.1582 \| 0.4047 \| 0.6156 \| 0.6091 \| nan \| 0.5779 \| 0.6533 \| 0.0 \| 0.5652 \| 0.6488 \| \| 0.0307 \| 170.56 \| 48780 \| 0.1615 \| 0.3967 \| 0.6028 \| 0.5937 \| nan \| 0.5502 \| 0.6554 \| 0.0 \| 0.5393 \| 0.6508 \| \| 0.0453 \| 170.63 \| 48800 \| 0.1604 \| 0.4015 \| 0.6103 \| 0.6031 \| nan \| 0.5685 \| 0.6521 \| 0.0 \| 0.5565 \| 0.6479 \| \| 0.0294 \| 170.7 \| 48820 \| 0.1602 \| 0.4022 \| 0.6116 \| 0.6054 \| nan \| 0.5761 \| 0.6471 \| 0.0 \| 0.5635 \| 0.6430 \| \| 0.0519 \| 170.77 \| 48840 \| 0.1555 \| 0.4037 \| 0.6134 \| 0.6051 \| nan \| 0.5651 \| 0.6618 \| 0.0 \| 0.5538 \| 0.6574 \| \| 0.0466 \| 170.84 \| 48860 \| 0.1555 \| 0.4013 \| 0.6094 \| 0.5999 \| nan \| 0.5542 \| 0.6646 \| 0.0 \| 0.5433 \| 0.6605 \| \| 0.0512 \| 170.91 \| 48880 \| 0.1557 \| 0.4010 \| 0.6094 \| 0.6018 \| nan \| 0.5655 \| 0.6533 \| 0.0 \| 0.5534 \| 0.6496 \| \| 0.0638 \| 170.98 \| 48900 \| 0.1596 \| 0.4014 \| 0.6099 \| 0.6018 \| nan \| 0.5631 \| 0.6568 \| 0.0 \| 0.5513 \| 0.6528 \| \| 0.0374 \| 171.05 \| 48920 \| 0.1579 \| 0.4023 \| 0.6115 \| 0.6032 \| nan \| 0.5639 \| 0.6591 \| 0.0 \| 0.5517 \| 0.6551 \| \| 0.0357 \| 171.12 \| 48940 \| 0.1601 \| 0.4056 \| 0.6171 \| 0.6117 \| nan \| 0.5858 \| 0.6484 \| 0.0 \| 0.5720 \| 0.6449 \| \| 0.0557 \| 171.19 \| 48960 \| 0.1608 \| 0.3996 \| 0.6075 \| 0.6000 \| nan \| 0.5642 \| 0.6508 \| 0.0 \| 0.5516 \| 0.6473 \| \| 0.0451 \| 171.26 \| 48980 \| 0.1598 \| 0.4014 \| 0.6104 \| 0.6039 \| nan \| 0.5729 \| 0.6479 \| 0.0 \| 0.5596 \| 0.6445 \| \| 0.0486 \| 171.33 \| 49000 \| 0.1652 \| 0.3948 \| 0.6003 \| 0.5946 \| nan \| 0.5675 \| 0.6331 \| 0.0 \| 0.5543 \| 0.6301 \| \| 0.0367 \| 171.4 \| 49020 \| 0.1612 \| 0.3989 \| 0.6066 \| 0.6001 \| nan \| 0.5688 \| 0.6444 \| 0.0 \| 0.5555 \| 0.6411 \| \| 0.0243 \| 171.47 \| 49040 \| 0.1601 \| 0.4029 \| 0.6127 \| 0.6056 \| nan \| 0.5712 \| 0.6543 \| 0.0 \| 0.5580 \| 0.6507 \| \| 0.0456 \| 171.54 \| 49060 \| 0.1609 \| 0.4008 \| 0.6096 \| 0.6030 \| nan \| 0.5712 \| 0.6481 \| 0.0 \| 0.5576 \| 0.6447 \| \| 0.0299 \| 171.61 \| 49080 \| 0.1611 \| 0.3999 \| 0.6082 \| 0.6011 \| nan \| 0.5671 \| 0.6493 \| 0.0 \| 0.5539 \| 0.6459 \| \| 0.0376 \| 171.68 \| 49100 \| 0.1630 \| 0.3976 \| 0.6045 \| 0.5974 \| nan \| 0.5631 \| 0.6460 \| 0.0 \| 0.5501 \| 0.6427 \| \| 0.0469 \| 171.75 \| 49120 \| 0.1610 \| 0.3988 \| 0.6065 \| 0.5998 \| nan \| 0.5678 \| 0.6453 \| 0.0 \| 0.5544 \| 0.6420 \| \| 0.0296 \| 171.82 \| 49140 \| 0.1612 \| 0.4024 \| 0.6123 \| 0.6060 \| nan \| 0.5760 \| 0.6485 \| 0.0 \| 0.5623 \| 0.6450 \| \| 0.0282 \| 171.89 \| 49160 \| 0.1614 \| 0.4014 \| 0.6108 \| 0.6048 \| nan \| 0.5765 \| 0.6451 \| 0.0 \| 0.5625 \| 0.6417 \| \| 0.0467 \| 171.96 \| 49180 \| 0.1633 \| 0.3990 \| 0.6067 \| 0.5998 \| nan \| 0.5669 \| 0.6465 \| 0.0 \| 0.5534 \| 0.6435 \| \| 0.0538 \| 172.03 \| 49200 \| 0.1637 \| 0.3965 \| 0.6029 \| 0.5961 \| nan \| 0.5632 \| 0.6427 \| 0.0 \| 0.5497 \| 0.6397 \| \| 0.0423 \| 172.1 \| 49220 \| 0.1617 \| 0.3988 \| 0.6067 \| 0.5999 \| nan \| 0.5677 \| 0.6457 \| 0.0 \| 0.5540 \| 0.6424 \| \| 0.0407 \| 172.17 \| 49240 \| 0.1640 \| 0.3929 \| 0.5974 \| 0.5898 \| nan \| 0.5535 \| 0.6414 \| 0.0 \| 0.5406 \| 0.6381 \| \| 0.0461 \| 172.24 \| 49260 \| 0.1637 \| 0.3995 \| 0.6077 \| 0.6015 \| nan \| 0.5716 \| 0.6439 \| 0.0 \| 0.5575 \| 0.6408 \| \| 0.0485 \| 172.31 \| 49280 \| 0.1633 \| 0.3963 \| 0.6030 \| 0.5971 \| nan \| 0.5692 \| 0.6367 \| 0.0 \| 0.5548 \| 0.6340 \| \| 0.0463 \| 172.38 \| 49300 \| 0.1633 \| 0.3961 \| 0.6025 \| 0.5955 \| nan \| 0.5620 \| 0.6430 \| 0.0 \| 0.5484 \| 0.6399 \| \| 0.0352 \| 172.45 \| 49320 \| 0.1612 \| 0.4005 \| 0.6094 \| 0.6026 \| nan \| 0.5704 \| 0.6484 \| 0.0 \| 0.5564 \| 0.6452 \| \| 0.0319 \| 172.52 \| 49340 \| 0.1627 \| 0.3963 \| 0.6027 \| 0.5953 \| nan \| 0.5598 \| 0.6456 \| 0.0 \| 0.5463 \| 0.6427 \| \| 0.0358 \| 172.59 \| 49360 \| 0.1624 \| 0.3997 \| 0.6083 \| 0.6027 \| nan \| 0.5763 \| 0.6403 \| 0.0 \| 0.5614 \| 0.6376 \| \| 0.0355 \| 172.66 \| 49380 \| 0.1651 \| 0.3955 \| 0.6017 \| 0.5960 \| nan \| 0.5686 \| 0.6348 \| 0.0 \| 0.5543 \| 0.6321 \| \| 0.0628 \| 172.73 \| 49400 \| 0.1657 \| 0.3965 \| 0.6033 \| 0.5974 \| nan \| 0.5689 \| 0.6378 \| 0.0 \| 0.5544 \| 0.6350 \| \| 0.0369 \| 172.8 \| 49420 \| 0.1666 \| 0.3970 \| 0.6042 \| 0.5984 \| nan \| 0.5710 \| 0.6374 \| 0.0 \| 0.5565 \| 0.6344 \| \| 0.0377 \| 172.87 \| 49440 \| 0.1651 \| 0.3935 \| 0.5987 \| 0.5924 \| nan \| 0.5624 \| 0.6349 \| 0.0 \| 0.5484 \| 0.6322 \| \| 0.0602 \| 172.94 \| 49460 \| 0.1617 \| 0.3993 \| 0.6072 \| 0.5990 \| nan \| 0.5598 \| 0.6545 \| 0.0 \| 0.5474 \| 0.6506 \| \| 0.0189 \| 173.01 \| 49480 \| 0.1594 \| 0.4012 \| 0.6101 \| 0.6030 \| nan \| 0.5689 \| 0.6513 \| 0.0 \| 0.5562 \| 0.6475 \| \| 0.0331 \| 173.08 \| 49500 \| 0.1617 \| 0.3993 \| 0.6069 \| 0.5991 \| nan \| 0.5618 \| 0.6519 \| 0.0 \| 0.5494 \| 0.6484 \| \| 0.0355 \| 173.15 \| 49520 \| 0.1606 \| 0.4029 \| 0.6128 \| 0.6071 \| nan \| 0.5797 \| 0.6458 \| 0.0 \| 0.5660 \| 0.6426 \| \| 0.0399 \| 173.22 \| 49540 \| 0.1634 \| 0.3986 \| 0.6062 \| 0.5996 \| nan \| 0.5680 \| 0.6445 \| 0.0 \| 0.5546 \| 0.6413 \| \| 0.0535 \| 173.29 \| 49560 \| 0.1624 \| 0.3953 \| 0.6013 \| 0.5938 \| nan \| 0.5580 \| 0.6445 \| 0.0 \| 0.5447 \| 0.6412 \| \| 0.0507 \| 173.36 \| 49580 \| 0.1622 \| 0.3995 \| 0.6079 \| 0.6011 \| nan \| 0.5685 \| 0.6473 \| 0.0 \| 0.5549 \| 0.6435 \| \| 0.0319 \| 173.43 \| 49600 \| 0.1610 \| 0.4040 \| 0.6149 \| 0.6098 \| nan \| 0.5850 \| 0.6448 \| 0.0 \| 0.5706 \| 0.6413 \| \| 0.0298 \| 173.5 \| 49620 \| 0.1615 \| 0.3952 \| 0.6010 \| 0.5939 \| nan \| 0.5601 \| 0.6420 \| 0.0 \| 0.5471 \| 0.6385 \| \| 0.0262 \| 173.57 \| 49640 \| 0.1600 \| 0.3996 \| 0.6079 \| 0.6013 \| nan \| 0.5699 \| 0.6459 \| 0.0 \| 0.5564 \| 0.6425 \| \| 0.0344 \| 173.64 \| 49660 \| 0.1596 \| 0.3988 \| 0.6063 \| 0.5985 \| nan \| 0.5610 \| 0.6517 \| 0.0 \| 0.5488 \| 0.6477 \| \| 0.0756 \| 173.71 \| 49680 \| 0.1587 \| 0.4005 \| 0.6089 \| 0.6013 \| nan \| 0.5650 \| 0.6527 \| 0.0 \| 0.5526 \| 0.6488 \| \| 0.0351 \| 173.78 \| 49700 \| 0.1579 \| 0.4032 \| 0.6134 \| 0.6064 \| nan \| 0.5730 \| 0.6537 \| 0.0 \| 0.5598 \| 0.6498 \| \| 0.0392 \| 173.85 \| 49720 \| 0.1627 \| 0.3981 \| 0.6056 \| 0.5982 \| nan \| 0.5625 \| 0.6487 \| 0.0 \| 0.5491 \| 0.6453 \| \| 0.0251 \| 173.92 \| 49740 \| 0.1633 \| 0.3994 \| 0.6078 \| 0.6016 \| nan \| 0.5718 \| 0.6439 \| 0.0 \| 0.5574 \| 0.6407 \| \| 0.0425 \| 173.99 \| 49760 \| 0.1596 \| 0.4023 \| 0.6123 \| 0.6062 \| nan \| 0.5769 \| 0.6478 \| 0.0 \| 0.5623 \| 0.6447 \| \| 0.035 \| 174.06 \| 49780 \| 0.1595 \| 0.4014 \| 0.6106 \| 0.6030 \| nan \| 0.5667 \| 0.6544 \| 0.0 \| 0.5530 \| 0.6511 \| \| 0.0434 \| 174.13 \| 49800 \| 0.1600 \| 0.3991 \| 0.6069 \| 0.5987 \| nan \| 0.5600 \| 0.6537 \| 0.0 \| 0.5468 \| 0.6503 \| \| 0.0294 \| 174.2 \| 49820 \| 0.1603 \| 0.4025 \| 0.6121 \| 0.6050 \| nan \| 0.5711 \| 0.6530 \| 0.0 \| 0.5578 \| 0.6496 \| \| 0.0524 \| 174.27 \| 49840 \| 0.1600 \| 0.4039 \| 0.6146 \| 0.6085 \| nan \| 0.5796 \| 0.6495 \| 0.0 \| 0.5655 \| 0.6463 \| \| 0.0348 \| 174.34 \| 49860 \| 0.1614 \| 0.3999 \| 0.6084 \| 0.6017 \| nan \| 0.5695 \| 0.6473 \| 0.0 \| 0.5554 \| 0.6443 \| \| 0.026 \| 174.41 \| 49880 \| 0.1590 \| 0.4025 \| 0.6126 \| 0.6064 \| nan \| 0.5767 \| 0.6485 \| 0.0 \| 0.5623 \| 0.6454 \| \| 0.064 \| 174.48 \| 49900 \| 0.1595 \| 0.3990 \| 0.6067 \| 0.5988 \| nan \| 0.5609 \| 0.6525 \| 0.0 \| 0.5483 \| 0.6487 \| \| 0.0497 \| 174.55 \| 49920 \| 0.1577 \| 0.4033 \| 0.6134 \| 0.6058 \| nan \| 0.5695 \| 0.6572 \| 0.0 \| 0.5567 \| 0.6533 \| \| 0.0587 \| 174.62 \| 49940 \| 0.1607 \| 0.3986 \| 0.6062 \| 0.5981 \| nan \| 0.5598 \| 0.6526 \| 0.0 \| 0.5468 \| 0.6490 \| \| 0.0416 \| 174.69 \| 49960 \| 0.1620 \| 0.3985 \| 0.6061 \| 0.5989 \| nan \| 0.5647 \| 0.6475 \| 0.0 \| 0.5515 \| 0.6441 \| \| 0.0435 \| 174.76 \| 49980 \| 0.1623 \| 0.4011 \| 0.6102 \| 0.6044 \| nan \| 0.5768 \| 0.6436 \| 0.0 \| 0.5629 \| 0.6406 \| \| 0.0342 \| 174.83 \| 50000 \| 0.1619 \| 0.4008 \| 0.6095 \| 0.6026 \| nan \| 0.5698 \| 0.6491 \| 0.0 \| 0.5566 \| 0.6457 \| \| 0.0371 \| 174.9 \| 50020 \| 0.1629 \| 0.3957 \| 0.6016 \| 0.5940 \| nan \| 0.5581 \| 0.6450 \| 0.0 \| 0.5455 \| 0.6415 \| \| 0.0385 \| 174.97 \| 50040 \| 0.1632 \| 0.3991 \| 0.6068 \| 0.5998 \| nan \| 0.5660 \| 0.6477 \| 0.0 \| 0.5532 \| 0.6440 \| \| 0.0436 \| 175.03 \| 50060 \| 0.1618 \| 0.3992 \| 0.6070 \| 0.5996 \| nan \| 0.5642 \| 0.6498 \| 0.0 \| 0.5519 \| 0.6458 \| \| 0.0413 \| 175.1 \| 50080 \| 0.1624 \| 0.4001 \| 0.6085 \| 0.6012 \| nan \| 0.5666 \| 0.6503 \| 0.0 \| 0.5536 \| 0.6467 \| \| 0.0331 \| 175.17 \| 50100 \| 0.1644 \| 0.3974 \| 0.6047 \| 0.5983 \| nan \| 0.5679 \| 0.6415 \| 0.0 \| 0.5540 \| 0.6384 \| \| 0.0468 \| 175.24 \| 50120 \| 0.1648 \| 0.3992 \| 0.6076 \| 0.6015 \| nan \| 0.5728 \| 0.6423 \| 0.0 \| 0.5586 \| 0.6390 \| \| 0.0474 \| 175.31 \| 50140 \| 0.1677 \| 0.3947 \| 0.6007 \| 0.5947 \| nan \| 0.5663 \| 0.6351 \| 0.0 \| 0.5521 \| 0.6319 \| \| 0.0502 \| 175.38 \| 50160 \| 0.1651 \| 0.3949 \| 0.6011 \| 0.5952 \| nan \| 0.5673 \| 0.6349 \| 0.0 \| 0.5529 \| 0.6318 \| \| 0.0261 \| 175.45 \| 50180 \| 0.1635 \| 0.3985 \| 0.6066 \| 0.6017 \| nan \| 0.5780 \| 0.6353 \| 0.0 \| 0.5632 \| 0.6322 \| \| 0.0491 \| 175.52 \| 50200 \| 0.1635 \| 0.3979 \| 0.6056 \| 0.5999 \| nan \| 0.5727 \| 0.6386 \| 0.0 \| 0.5582 \| 0.6355 \| \| 0.0418 \| 175.59 \| 50220 \| 0.1614 \| 0.4002 \| 0.6093 \| 0.6033 \| nan \| 0.5749 \| 0.6437 \| 0.0 \| 0.5599 \| 0.6406 \| \| 0.036 \| 175.66 \| 50240 \| 0.1628 \| 0.3991 \| 0.6072 \| 0.6002 \| nan \| 0.5666 \| 0.6479 \| 0.0 \| 0.5529 \| 0.6445 \| \| 0.0777 \| 175.73 \| 50260 \| 0.1599 \| 0.4040 \| 0.6148 \| 0.6075 \| nan \| 0.5723 \| 0.6573 \| 0.0 \| 0.5585 \| 0.6536 \| \| 0.0435 \| 175.8 \| 50280 \| 0.1611 \| 0.4018 \| 0.6110 \| 0.6029 \| nan \| 0.5640 \| 0.6580 \| 0.0 \| 0.5513 \| 0.6540 \| \| 0.0318 \| 175.87 \| 50300 \| 0.1614 \| 0.4012 \| 0.6100 \| 0.6021 \| nan \| 0.5642 \| 0.6558 \| 0.0 \| 0.5514 \| 0.6523 \| \| 0.0322 \| 175.94 \| 50320 \| 0.1635 \| 0.4010 \| 0.6098 \| 0.6031 \| nan \| 0.5713 \| 0.6483 \| 0.0 \| 0.5574 \| 0.6455 \| \| 0.0314 \| 176.01 \| 50340 \| 0.1647 \| 0.3972 \| 0.6039 \| 0.5970 \| nan \| 0.5637 \| 0.6441 \| 0.0 \| 0.5503 \| 0.6412 \| \| 0.0623 \| 176.08 \| 50360 \| 0.1629 \| 0.3984 \| 0.6058 \| 0.5992 \| nan \| 0.5675 \| 0.6441 \| 0.0 \| 0.5537 \| 0.6413 \| \| 0.0537 \| 176.15 \| 50380 \| 0.1651 \| 0.3992 \| 0.6072 \| 0.6010 \| nan \| 0.5715 \| 0.6430 \| 0.0 \| 0.5576 \| 0.6399 \| \| 0.0581 \| 176.22 \| 50400 \| 0.1641 \| 0.4040 \| 0.6150 \| 0.6099 \| nan \| 0.5857 \| 0.6443 \| 0.0 \| 0.5710 \| 0.6410 \| \| 0.0285 \| 176.29 \| 50420 \| 0.1616 \| 0.3990 \| 0.6071 \| 0.6006 \| nan \| 0.5695 \| 0.6447 \| 0.0 \| 0.5553 \| 0.6416 \| \| 0.027 \| 176.36 \| 50440 \| 0.1616 \| 0.3999 \| 0.6084 \| 0.6019 \| nan \| 0.5704 \| 0.6465 \| 0.0 \| 0.5567 \| 0.6430 \| \| 0.0293 \| 176.43 \| 50460 \| 0.1647 \| 0.3982 \| 0.6058 \| 0.5994 \| nan \| 0.5689 \| 0.6427 \| 0.0 \| 0.5552 \| 0.6395 \| \| 0.0413 \| 176.5 \| 50480 \| 0.1634 \| 0.3961 \| 0.6024 \| 0.5962 \| nan \| 0.5664 \| 0.6384 \| 0.0 \| 0.5528 \| 0.6354 \| \| 0.0301 \| 176.57 \| 50500 \| 0.1634 \| 0.3983 \| 0.6057 \| 0.5987 \| nan \| 0.5649 \| 0.6466 \| 0.0 \| 0.5516 \| 0.6434 \| \| 0.0551 \| 176.64 \| 50520 \| 0.1635 \| 0.3981 \| 0.6054 \| 0.5985 \| nan \| 0.5656 \| 0.6452 \| 0.0 \| 0.5523 \| 0.6420 \| \| 0.0496 \| 176.71 \| 50540 \| 0.1644 \| 0.3982 \| 0.6057 \| 0.5992 \| nan \| 0.5683 \| 0.6431 \| 0.0 \| 0.5550 \| 0.6398 \| \| 0.0339 \| 176.78 \| 50560 \| 0.1609 \| 0.4014 \| 0.6105 \| 0.6037 \| nan \| 0.5712 \| 0.6499 \| 0.0 \| 0.5579 \| 0.6463 \| \| 0.0427 \| 176.85 \| 50580 \| 0.1649 \| 0.3966 \| 0.6030 \| 0.5952 \| nan \| 0.5580 \| 0.6480 \| 0.0 \| 0.5449 \| 0.6447 \| \| 0.0514 \| 176.92 \| 50600 \| 0.1633 \| 0.3996 \| 0.6078 \| 0.6016 \| nan \| 0.5721 \| 0.6436 \| 0.0 \| 0.5584 \| 0.6404 \| \| 0.0513 \| 176.99 \| 50620 \| 0.1626 \| 0.3993 \| 0.6073 \| 0.6000 \| nan \| 0.5653 \| 0.6492 \| 0.0 \| 0.5519 \| 0.6460 \| \| 0.0485 \| 177.06 \| 50640 \| 0.1637 \| 0.4010 \| 0.6098 \| 0.6025 \| nan \| 0.5676 \| 0.6520 \| 0.0 \| 0.5545 \| 0.6485 \| \| 0.0559 \| 177.13 \| 50660 \| 0.1610 \| 0.4012 \| 0.6101 \| 0.6024 \| nan \| 0.5657 \| 0.6546 \| 0.0 \| 0.5529 \| 0.6508 \| \| 0.0633 \| 177.2 \| 50680 \| 0.1609 \| 0.4006 \| 0.6091 \| 0.6010 \| nan \| 0.5621 \| 0.6561 \| 0.0 \| 0.5498 \| 0.6521 \| \| 0.034 \| 177.27 \| 50700 \| 0.1614 \| 0.4003 \| 0.6086 \| 0.6008 \| nan \| 0.5634 \| 0.6538 \| 0.0 \| 0.5510 \| 0.6499 \| \| 0.0388 \| 177.34 \| 50720 \| 0.1605 \| 0.3992 \| 0.6070 \| 0.5996 \| nan \| 0.5644 \| 0.6496 \| 0.0 \| 0.5516 \| 0.6459 \| \| 0.0599 \| 177.41 \| 50740 \| 0.1627 \| 0.4003 \| 0.6088 \| 0.6022 \| nan \| 0.5704 \| 0.6473 \| 0.0 \| 0.5573 \| 0.6437 \| \| 0.0377 \| 177.48 \| 50760 \| 0.1646 \| 0.3997 \| 0.6079 \| 0.6009 \| nan \| 0.5672 \| 0.6487 \| 0.0 \| 0.5537 \| 0.6455 \| \| 0.0519 \| 177.55 \| 50780 \| 0.1634 \| 0.3992 \| 0.6071 \| 0.5993 \| nan \| 0.5623 \| 0.6518 \| 0.0 \| 0.5489 \| 0.6487 \| \| 0.0507 \| 177.62 \| 50800 \| 0.1632 \| 0.3998 \| 0.6083 \| 0.6021 \| nan \| 0.5725 \| 0.6442 \| 0.0 \| 0.5584 \| 0.6411 \| \| 0.0383 \| 177.69 \| 50820 \| 0.1641 \| 0.4050 \| 0.6163 \| 0.6108 \| nan \| 0.5845 \| 0.6481 \| 0.0 \| 0.5701 \| 0.6449 \| \| 0.0316 \| 177.76 \| 50840 \| 0.1622 \| 0.4021 \| 0.6116 \| 0.6046 \| nan \| 0.5710 \| 0.6523 \| 0.0 \| 0.5574 \| 0.6489 \| \| 0.0504 \| 177.83 \| 50860 \| 0.1661 \| 0.3979 \| 0.6051 \| 0.5988 \| nan \| 0.5684 \| 0.6419 \| 0.0 \| 0.5547 \| 0.6390 \| \| 0.0327 \| 177.9 \| 50880 \| 0.1625 \| 0.4013 \| 0.6104 \| 0.6038 \| nan \| 0.5723 \| 0.6486 \| 0.0 \| 0.5583 \| 0.6456 \| \| 0.0279 \| 177.97 \| 50900 \| 0.1615 \| 0.4016 \| 0.6110 \| 0.6048 \| nan \| 0.5750 \| 0.6470 \| 0.0 \| 0.5608 \| 0.6440 \| \| 0.0472 \| 178.04 \| 50920 \| 0.1660 \| 0.3967 \| 0.6033 \| 0.5966 \| nan \| 0.5645 \| 0.6421 \| 0.0 \| 0.5509 \| 0.6391 \| \| 0.0275 \| 178.11 \| 50940 \| 0.1648 \| 0.3980 \| 0.6053 \| 0.5992 \| nan \| 0.5703 \| 0.6403 \| 0.0 \| 0.5563 \| 0.6375 \| \| 0.0668 \| 178.18 \| 50960 \| 0.1609 \| 0.4022 \| 0.6118 \| 0.6052 \| nan \| 0.5738 \| 0.6498 \| 0.0 \| 0.5602 \| 0.6465 \| \| 0.0349 \| 178.25 \| 50980 \| 0.1642 \| 0.3987 \| 0.6062 \| 0.5994 \| nan \| 0.5667 \| 0.6457 \| 0.0 \| 0.5537 \| 0.6423 \| \| 0.0373 \| 178.32 \| 51000 \| 0.1640 \| 0.3984 \| 0.6058 \| 0.5993 \| nan \| 0.5684 \| 0.6431 \| 0.0 \| 0.5554 \| 0.6397 \| \| 0.0329 \| 178.39 \| 51020 \| 0.1622 \| 0.4001 \| 0.6084 \| 0.6018 \| nan \| 0.5706 \| 0.6462 \| 0.0 \| 0.5575 \| 0.6427 \| \| 0.0407 \| 178.46 \| 51040 \| 0.1624 \| 0.3986 \| 0.6061 \| 0.5993 \| nan \| 0.5673 \| 0.6448 \| 0.0 \| 0.5542 \| 0.6415 \| \| 0.0377 \| 178.53 \| 51060 \| 0.1640 \| 0.3976 \| 0.6047 \| 0.5981 \| nan \| 0.5664 \| 0.6430 \| 0.0 \| 0.5531 \| 0.6397 \| \| 0.0322 \| 178.6 \| 51080 \| 0.1634 \| 0.4030 \| 0.6130 \| 0.6064 \| nan \| 0.5751 \| 0.6509 \| 0.0 \| 0.5618 \| 0.6473 \| \| 0.0564 \| 178.67 \| 51100 \| 0.1627 \| 0.4027 \| 0.6125 \| 0.6057 \| nan \| 0.5734 \| 0.6515 \| 0.0 \| 0.5603 \| 0.6478 \| \| 0.0444 \| 178.74 \| 51120 \| 0.1620 \| 0.3988 \| 0.6062 \| 0.5989 \| nan \| 0.5640 \| 0.6485 \| 0.0 \| 0.5517 \| 0.6447 \| \| 0.0631 \| 178.81 \| 51140 \| 0.1634 \| 0.3998 \| 0.6079 \| 0.6013 \| nan \| 0.5701 \| 0.6456 \| 0.0 \| 0.5575 \| 0.6419 \| \| 0.0417 \| 178.88 \| 51160 \| 0.1613 \| 0.4020 \| 0.6112 \| 0.6040 \| nan \| 0.5696 \| 0.6527 \| 0.0 \| 0.5571 \| 0.6488 \| \| 0.0448 \| 178.95 \| 51180 \| 0.1639 \| 0.3972 \| 0.6037 \| 0.5947 \| nan \| 0.5518 \| 0.6556 \| 0.0 \| 0.5400 \| 0.6518 \| \| 0.0298 \| 179.02 \| 51200 \| 0.1593 \| 0.4042 \| 0.6146 \| 0.6069 \| nan \| 0.5700 \| 0.6592 \| 0.0 \| 0.5572 \| 0.6553 \| \| 0.0484 \| 179.09 \| 51220 \| 0.1591 \| 0.4055 \| 0.6166 \| 0.6095 \| nan \| 0.5758 \| 0.6574 \| 0.0 \| 0.5628 \| 0.6537 \| \| 0.0419 \| 179.16 \| 51240 \| 0.1658 \| 0.3988 \| 0.6064 \| 0.5995 \| nan \| 0.5665 \| 0.6464 \| 0.0 \| 0.5530 \| 0.6434 \| \| 0.0416 \| 179.23 \| 51260 \| 0.1617 \| 0.4060 \| 0.6173 \| 0.6101 \| nan \| 0.5756 \| 0.6589 \| 0.0 \| 0.5624 \| 0.6556 \| \| 0.0438 \| 179.3 \| 51280 \| 0.1616 \| 0.4010 \| 0.6092 \| 0.6004 \| nan \| 0.5582 \| 0.6602 \| 0.0 \| 0.5466 \| 0.6563 \| \| 0.0363 \| 179.37 \| 51300 \| 0.1598 \| 0.4088 \| 0.6214 \| 0.6141 \| nan \| 0.5790 \| 0.6639 \| 0.0 \| 0.5667 \| 0.6597 \| \| 0.0367 \| 179.44 \| 51320 \| 0.1611 \| 0.4031 \| 0.6125 \| 0.6035 \| nan \| 0.5605 \| 0.6644 \| 0.0 \| 0.5497 \| 0.6597 \| \| 0.0291 \| 179.51 \| 51340 \| 0.1586 \| 0.4057 \| 0.6165 \| 0.6089 \| nan \| 0.5723 \| 0.6608 \| 0.0 \| 0.5604 \| 0.6567 \| \| 0.0527 \| 179.58 \| 51360 \| 0.1609 \| 0.4018 \| 0.6106 \| 0.6028 \| nan \| 0.5652 \| 0.6560 \| 0.0 \| 0.5534 \| 0.6522 \| \| 0.0405 \| 179.65 \| 51380 \| 0.1633 \| 0.3990 \| 0.6062 \| 0.5985 \| nan \| 0.5616 \| 0.6508 \| 0.0 \| 0.5495 \| 0.6474 \| \| 0.0301 \| 179.72 \| 51400 \| 0.1632 \| 0.4040 \| 0.6143 \| 0.6077 \| nan \| 0.5766 \| 0.6519 \| 0.0 \| 0.5634 \| 0.6485 \| \| 0.037 \| 179.79 \| 51420 \| 0.1627 \| 0.4012 \| 0.6101 \| 0.6034 \| nan \| 0.5713 \| 0.6489 \| 0.0 \| 0.5581 \| 0.6455 \| \| 0.0429 \| 179.86 \| 51440 \| 0.1645 \| 0.3986 \| 0.6062 \| 0.5995 \| nan \| 0.5677 \| 0.6448 \| 0.0 \| 0.5543 \| 0.6416 \| \| 0.0341 \| 179.93 \| 51460 \| 0.1658 \| 0.3949 \| 0.6003 \| 0.5932 \| nan \| 0.5592 \| 0.6415 \| 0.0 \| 0.5461 \| 0.6387 \| \| 0.0147 \| 180.0 \| 51480 \| 0.1638 \| 0.3998 \| 0.6080 \| 0.6020 \| nan \| 0.5731 \| 0.6428 \| 0.0 \| 0.5594 \| 0.6400 \| \| 0.0421 \| 180.07 \| 51500 \| 0.1652 \| 0.3957 \| 0.6016 \| 0.5943 \| nan \| 0.5590 \| 0.6443 \| 0.0 \| 0.5460 \| 0.6412 \| \| 0.0351 \| 180.14 \| 51520 \| 0.1616 \| 0.4022 \| 0.6116 \| 0.6054 \| nan \| 0.5755 \| 0.6478 \| 0.0 \| 0.5621 \| 0.6445 \| \| 0.0594 \| 180.21 \| 51540 \| 0.1617 \| 0.4007 \| 0.6092 \| 0.6023 \| nan \| 0.5694 \| 0.6489 \| 0.0 \| 0.5564 \| 0.6457 \| \| 0.0591 \| 180.28 \| 51560 \| 0.1641 \| 0.3976 \| 0.6045 \| 0.5978 \| nan \| 0.5660 \| 0.6430 \| 0.0 \| 0.5527 \| 0.6401 \| \| 0.0649 \| 180.35 \| 51580 \| 0.1665 \| 0.3970 \| 0.6033 \| 0.5962 \| nan \| 0.5624 \| 0.6442 \| 0.0 \| 0.5497 \| 0.6414 \| \| 0.025 \| 180.42 \| 51600 \| 0.1641 \| 0.3980 \| 0.6050 \| 0.5986 \| nan \| 0.5683 \| 0.6416 \| 0.0 \| 0.5551 \| 0.6389 \| \| 0.0317 \| 180.49 \| 51620 \| 0.1608 \| 0.3993 \| 0.6069 \| 0.5998 \| nan \| 0.5657 \| 0.6482 \| 0.0 \| 0.5527 \| 0.6451 \| \| 0.0449 \| 180.56 \| 51640 \| 0.1625 \| 0.4005 \| 0.6087 \| 0.6011 \| nan \| 0.5646 \| 0.6529 \| 0.0 \| 0.5522 \| 0.6493 \| \| 0.0476 \| 180.63 \| 51660 \| 0.1612 \| 0.4021 \| 0.6113 \| 0.6044 \| nan \| 0.5715 \| 0.6511 \| 0.0 \| 0.5588 \| 0.6476 \| \| 0.0406 \| 180.7 \| 51680 \| 0.1613 \| 0.4023 \| 0.6116 \| 0.6049 \| nan \| 0.5730 \| 0.6501 \| 0.0 \| 0.5602 \| 0.6467 \| \| 0.0477 \| 180.77 \| 51700 \| 0.1661 \| 0.3982 \| 0.6053 \| 0.5994 \| nan \| 0.5711 \| 0.6396 \| 0.0 \| 0.5578 \| 0.6367 \| \| 0.0307 \| 180.84 \| 51720 \| 0.1641 \| 0.3988 \| 0.6065 \| 0.5999 \| nan \| 0.5682 \| 0.6448 \| 0.0 \| 0.5546 \| 0.6418 \| \| 0.0359 \| 180.91 \| 51740 \| 0.1623 \| 0.4004 \| 0.6089 \| 0.6023 \| nan \| 0.5709 \| 0.6468 \| 0.0 \| 0.5575 \| 0.6436 \| \| 0.0368 \| 180.98 \| 51760 \| 0.1622 \| 0.3994 \| 0.6072 \| 0.6009 \| nan \| 0.5707 \| 0.6437 \| 0.0 \| 0.5577 \| 0.6404 \| \| 0.0275 \| 181.05 \| 51780 \| 0.1630 \| 0.3963 \| 0.6024 \| 0.5954 \| nan \| 0.5619 \| 0.6428 \| 0.0 \| 0.5496 \| 0.6394 \| \| 0.0492 \| 181.12 \| 51800 \| 0.1631 \| 0.3978 \| 0.6050 \| 0.5987 \| nan \| 0.5683 \| 0.6417 \| 0.0 \| 0.5548 \| 0.6387 \| \| 0.0153 \| 181.19 \| 51820 \| 0.1636 \| 0.4014 \| 0.6105 \| 0.6040 \| nan \| 0.5730 \| 0.6480 \| 0.0 \| 0.5594 \| 0.6449 \| \| 0.0676 \| 181.26 \| 51840 \| 0.1603 \| 0.4013 \| 0.6103 \| 0.6041 \| nan \| 0.5745 \| 0.6461 \| 0.0 \| 0.5607 \| 0.6432 \| \| 0.0557 \| 181.33 \| 51860 \| 0.1614 \| 0.3986 \| 0.6058 \| 0.5985 \| nan \| 0.5636 \| 0.6480 \| 0.0 \| 0.5508 \| 0.6448 \| \| 0.0369 \| 181.4 \| 51880 \| 0.1628 \| 0.4010 \| 0.6100 \| 0.6042 \| nan \| 0.5766 \| 0.6434 \| 0.0 \| 0.5625 \| 0.6404 \| \| 0.0277 \| 181.47 \| 51900 \| 0.1632 \| 0.3975 \| 0.6045 \| 0.5978 \| nan \| 0.5660 \| 0.6430 \| 0.0 \| 0.5526 \| 0.6400 \| \| 0.0443 \| 181.54 \| 51920 \| 0.1646 \| 0.3991 \| 0.6068 \| 0.6000 \| nan \| 0.5678 \| 0.6457 \| 0.0 \| 0.5547 \| 0.6426 \| \| 0.0273 \| 181.61 \| 51940 \| 0.1630 \| 0.3988 \| 0.6066 \| 0.6003 \| nan \| 0.5704 \| 0.6428 \| 0.0 \| 0.5567 \| 0.6397 \| \| 0.0283 \| 181.68 \| 51960 \| 0.1613 \| 0.4009 \| 0.6098 \| 0.6032 \| nan \| 0.5717 \| 0.6478 \| 0.0 \| 0.5582 \| 0.6445 \| \| 0.0567 \| 181.75 \| 51980 \| 0.1630 \| 0.3976 \| 0.6046 \| 0.5971 \| nan \| 0.5616 \| 0.6475 \| 0.0 \| 0.5487 \| 0.6441 \| \| 0.0417 \| 181.82 \| 52000 \| 0.1617 \| 0.4018 \| 0.6111 \| 0.6048 \| nan \| 0.5750 \| 0.6472 \| 0.0 \| 0.5615 \| 0.6438 \| \| 0.0339 \| 181.89 \| 52020 \| 0.1612 \| 0.4018 \| 0.6111 \| 0.6043 \| nan \| 0.5719 \| 0.6503 \| 0.0 \| 0.5586 \| 0.6468 \| \| 0.0359 \| 181.96 \| 52040 \| 0.1617 \| 0.4018 \| 0.6113 \| 0.6054 \| nan \| 0.5774 \| 0.6452 \| 0.0 \| 0.5635 \| 0.6419 \| \| 0.0483 \| 182.03 \| 52060 \| 0.1638 \| 0.3976 \| 0.6047 \| 0.5975 \| nan \| 0.5631 \| 0.6463 \| 0.0 \| 0.5501 \| 0.6426 \| \| 0.0653 \| 182.1 \| 52080 \| 0.1635 \| 0.4001 \| 0.6084 \| 0.6008 \| nan \| 0.5645 \| 0.6523 \| 0.0 \| 0.5518 \| 0.6484 \| \| 0.0671 \| 182.17 \| 52100 \| 0.1622 \| 0.4007 \| 0.6095 \| 0.6023 \| nan \| 0.5682 \| 0.6508 \| 0.0 \| 0.5548 \| 0.6472 \| \| 0.0425 \| 182.24 \| 52120 \| 0.1616 \| 0.3984 \| 0.6059 \| 0.5987 \| nan \| 0.5644 \| 0.6475 \| 0.0 \| 0.5512 \| 0.6440 \| \| 0.0518 \| 182.31 \| 52140 \| 0.1615 \| 0.4028 \| 0.6128 \| 0.6065 \| nan \| 0.5765 \| 0.6491 \| 0.0 \| 0.5627 \| 0.6457 \| \| 0.0266 \| 182.38 \| 52160 \| 0.1600 \| 0.4014 \| 0.6103 \| 0.6026 \| nan \| 0.5656 \| 0.6550 \| 0.0 \| 0.5529 \| 0.6513 \| \| 0.063 \| 182.45 \| 52180 \| 0.1592 \| 0.4038 \| 0.6142 \| 0.6071 \| nan \| 0.5730 \| 0.6554 \| 0.0 \| 0.5599 \| 0.6516 \| \| 0.045 \| 182.52 \| 52200 \| 0.1598 \| 0.4014 \| 0.6105 \| 0.6032 \| nan \| 0.5681 \| 0.6529 \| 0.0 \| 0.5549 \| 0.6493 \| \| 0.0641 \| 182.59 \| 52220 \| 0.1629 \| 0.4005 \| 0.6092 \| 0.6027 \| nan \| 0.5716 \| 0.6468 \| 0.0 \| 0.5583 \| 0.6434 \| \| 0.0495 \| 182.66 \| 52240 \| 0.1605 \| 0.4028 \| 0.6126 \| 0.6057 \| nan \| 0.5730 \| 0.6521 \| 0.0 \| 0.5600 \| 0.6484 \| \| 0.0391 \| 182.73 \| 52260 \| 0.1613 \| 0.3990 \| 0.6065 \| 0.5980 \| nan \| 0.5571 \| 0.6560 \| 0.0 \| 0.5450 \| 0.6518 \| \| 0.0331 \| 182.8 \| 52280 \| 0.1610 \| 0.4015 \| 0.6107 \| 0.6037 \| nan \| 0.5700 \| 0.6515 \| 0.0 \| 0.5569 \| 0.6476 \| \| 0.0407 \| 182.87 \| 52300 \| 0.1625 \| 0.4001 \| 0.6086 \| 0.6016 \| nan \| 0.5684 \| 0.6488 \| 0.0 \| 0.5554 \| 0.6450 \| \| 0.0248 \| 182.94 \| 52320 \| 0.1653 \| 0.3995 \| 0.6074 \| 0.5997 \| nan \| 0.5632 \| 0.6516 \| 0.0 \| 0.5508 \| 0.6476 \| \| 0.0261 \| 183.01 \| 52340 \| 0.1656 \| 0.4008 \| 0.6095 \| 0.6025 \| nan \| 0.5693 \| 0.6497 \| 0.0 \| 0.5562 \| 0.6461 \| \| 0.0246 \| 183.08 \| 52360 \| 0.1651 \| 0.3955 \| 0.6016 \| 0.5950 \| nan \| 0.5635 \| 0.6398 \| 0.0 \| 0.5499 \| 0.6366 \| \| 0.0643 \| 183.15 \| 52380 \| 0.1644 \| 0.3978 \| 0.6050 \| 0.5984 \| nan \| 0.5666 \| 0.6435 \| 0.0 \| 0.5529 \| 0.6404 \| \| 0.0501 \| 183.22 \| 52400 \| 0.1660 \| 0.3973 \| 0.6043 \| 0.5979 \| nan \| 0.5674 \| 0.6412 \| 0.0 \| 0.5539 \| 0.6381 \| \| 0.0454 \| 183.29 \| 52420 \| 0.1637 \| 0.4008 \| 0.6095 \| 0.6028 \| nan \| 0.5707 \| 0.6483 \| 0.0 \| 0.5575 \| 0.6450 \| \| 0.0524 \| 183.36 \| 52440 \| 0.1624 \| 0.4018 \| 0.6106 \| 0.6024 \| nan \| 0.5636 \| 0.6576 \| 0.0 \| 0.5516 \| 0.6537 \| \| 0.0398 \| 183.43 \| 52460 \| 0.1606 \| 0.4044 \| 0.6150 \| 0.6083 \| nan \| 0.5766 \| 0.6533 \| 0.0 \| 0.5636 \| 0.6497 \| \| 0.0361 \| 183.5 \| 52480 \| 0.1634 \| 0.4001 \| 0.6082 \| 0.6006 \| nan \| 0.5643 \| 0.6521 \| 0.0 \| 0.5519 \| 0.6485 \| \| 0.0587 \| 183.57 \| 52500 \| 0.1630 \| 0.4032 \| 0.6133 \| 0.6071 \| nan \| 0.5773 \| 0.6493 \| 0.0 \| 0.5638 \| 0.6459 \| \| 0.0415 \| 183.64 \| 52520 \| 0.1647 \| 0.4001 \| 0.6083 \| 0.6014 \| nan \| 0.5683 \| 0.6483 \| 0.0 \| 0.5552 \| 0.6450 \| \| 0.0288 \| 183.71 \| 52540 \| 0.1634 \| 0.4037 \| 0.6140 \| 0.6073 \| nan \| 0.5756 \| 0.6523 \| 0.0 \| 0.5624 \| 0.6488 \| \| 0.0336 \| 183.78 \| 52560 \| 0.1609 \| 0.4029 \| 0.6122 \| 0.6039 \| nan \| 0.5643 \| 0.6601 \| 0.0 \| 0.5528 \| 0.6558 \| \| 0.0247 \| 183.85 \| 52580 \| 0.1591 \| 0.4046 \| 0.6151 \| 0.6076 \| nan \| 0.5722 \| 0.6580 \| 0.0 \| 0.5599 \| 0.6539 \| \| 0.0268 \| 183.92 \| 52600 \| 0.1595 \| 0.4032 \| 0.6127 \| 0.6047 \| nan \| 0.5665 \| 0.6590 \| 0.0 \| 0.5551 \| 0.6546 \| \| 0.074 \| 183.99 \| 52620 \| 0.1612 \| 0.4011 \| 0.6093 \| 0.6010 \| nan \| 0.5613 \| 0.6574 \| 0.0 \| 0.5500 \| 0.6532 \| \| 0.0386 \| 184.06 \| 52640 \| 0.1611 \| 0.4027 \| 0.6120 \| 0.6038 \| nan \| 0.5647 \| 0.6594 \| 0.0 \| 0.5530 \| 0.6552 \| \| 0.0276 \| 184.13 \| 52660 \| 0.1626 \| 0.3976 \| 0.6043 \| 0.5970 \| nan \| 0.5618 \| 0.6468 \| 0.0 \| 0.5493 \| 0.6436 \| \| 0.0509 \| 184.2 \| 52680 \| 0.1633 \| 0.3990 \| 0.6064 \| 0.5990 \| nan \| 0.5635 \| 0.6494 \| 0.0 \| 0.5509 \| 0.6460 \| \| 0.0355 \| 184.27 \| 52700 \| 0.1635 \| 0.3984 \| 0.6055 \| 0.5984 \| nan \| 0.5641 \| 0.6470 \| 0.0 \| 0.5515 \| 0.6437 \| \| 0.0396 \| 184.34 \| 52720 \| 0.1662 \| 0.3992 \| 0.6068 \| 0.5998 \| nan \| 0.5668 \| 0.6468 \| 0.0 \| 0.5540 \| 0.6435 \| \| 0.0558 \| 184.41 \| 52740 \| 0.1650 \| 0.4015 \| 0.6104 \| 0.6042 \| nan \| 0.5741 \| 0.6468 \| 0.0 \| 0.5610 \| 0.6436 \| \| 0.0345 \| 184.48 \| 52760 \| 0.1645 \| 0.4035 \| 0.6136 \| 0.6080 \| nan \| 0.5814 \| 0.6457 \| 0.0 \| 0.5680 \| 0.6424 \| \| 0.034 \| 184.55 \| 52780 \| 0.1638 \| 0.3996 \| 0.6074 \| 0.6005 \| nan \| 0.5679 \| 0.6469 \| 0.0 \| 0.5552 \| 0.6436 \| \| 0.0374 \| 184.62 \| 52800 \| 0.1656 \| 0.3957 \| 0.6013 \| 0.5943 \| nan \| 0.5608 \| 0.6419 \| 0.0 \| 0.5482 \| 0.6390 \| \| 0.0321 \| 184.69 \| 52820 \| 0.1639 \| 0.3996 \| 0.6075 \| 0.6015 \| nan \| 0.5725 \| 0.6425 \| 0.0 \| 0.5590 \| 0.6397 \| \| 0.0377 \| 184.76 \| 52840 \| 0.1633 \| 0.3973 \| 0.6039 \| 0.5974 \| nan \| 0.5659 \| 0.6420 \| 0.0 \| 0.5526 \| 0.6391 \| \| 0.0537 \| 184.83 \| 52860 \| 0.1607 \| 0.4022 \| 0.6117 \| 0.6055 \| nan \| 0.5756 \| 0.6478 \| 0.0 \| 0.5621 \| 0.6446 \| \| 0.0501 \| 184.9 \| 52880 \| 0.1628 \| 0.3997 \| 0.6077 \| 0.6006 \| nan \| 0.5664 \| 0.6490 \| 0.0 \| 0.5536 \| 0.6455 \| \| 0.0491 \| 184.97 \| 52900 \| 0.1603 \| 0.4002 \| 0.6084 \| 0.6011 \| nan \| 0.5665 \| 0.6503 \| 0.0 \| 0.5539 \| 0.6467 \| \| 0.0402 \| 185.03 \| 52920 \| 0.1614 \| 0.4019 \| 0.6110 \| 0.6037 \| nan \| 0.5689 \| 0.6532 \| 0.0 \| 0.5562 \| 0.6495 \| \| 0.0423 \| 185.1 \| 52940 \| 0.1610 \| 0.4039 \| 0.6142 \| 0.6074 \| nan \| 0.5751 \| 0.6533 \| 0.0 \| 0.5620 \| 0.6498 \| \| 0.0357 \| 185.17 \| 52960 \| 0.1638 \| 0.3989 \| 0.6065 \| 0.5992 \| nan \| 0.5641 \| 0.6489 \| 0.0 \| 0.5513 \| 0.6455 \| \| 0.0377 \| 185.24 \| 52980 \| 0.1634 \| 0.4018 \| 0.6111 \| 0.6044 \| nan \| 0.5727 \| 0.6494 \| 0.0 \| 0.5594 \| 0.6462 \| \| 0.0543 \| 185.31 \| 53000 \| 0.1621 \| 0.3969 \| 0.6032 \| 0.5956 \| nan \| 0.5591 \| 0.6474 \| 0.0 \| 0.5465 \| 0.6441 \| \| 0.0631 \| 185.38 \| 53020 \| 0.1619 \| 0.4007 \| 0.6092 \| 0.6021 \| nan \| 0.5683 \| 0.6502 \| 0.0 \| 0.5554 \| 0.6466 \| \| 0.0247 \| 185.45 \| 53040 \| 0.1625 \| 0.4022 \| 0.6116 \| 0.6048 \| nan \| 0.5723 \| 0.6510 \| 0.0 \| 0.5591 \| 0.6475 \| \| 0.053 \| 185.52 \| 53060 \| 0.1617 \| 0.4010 \| 0.6096 \| 0.6022 \| nan \| 0.5668 \| 0.6525 \| 0.0 \| 0.5540 \| 0.6489 \| \| 0.034 \| 185.59 \| 53080 \| 0.1620 \| 0.4021 \| 0.6114 \| 0.6044 \| nan \| 0.5713 \| 0.6514 \| 0.0 \| 0.5581 \| 0.6481 \| \| 0.0326 \| 185.66 \| 53100 \| 0.1641 \| 0.4014 \| 0.6102 \| 0.6030 \| nan \| 0.5687 \| 0.6517 \| 0.0 \| 0.5560 \| 0.6481 \| \| 0.0248 \| 185.73 \| 53120 \| 0.1629 \| 0.3997 \| 0.6076 \| 0.5997 \| nan \| 0.5617 \| 0.6536 \| 0.0 \| 0.5493 \| 0.6500 \| \| 0.055 \| 185.8 \| 53140 \| 0.1629 \| 0.4019 \| 0.6110 \| 0.6038 \| nan \| 0.5693 \| 0.6527 \| 0.0 \| 0.5564 \| 0.6493 \| \| 0.0469 \| 185.87 \| 53160 \| 0.1622 \| 0.4023 \| 0.6117 \| 0.6049 \| nan \| 0.5724 \| 0.6511 \| 0.0 \| 0.5589 \| 0.6479 \| \| 0.0753 \| 185.94 \| 53180 \| 0.1643 \| 0.3992 \| 0.6070 \| 0.6000 \| nan \| 0.5665 \| 0.6475 \| 0.0 \| 0.5531 \| 0.6445 \| \| 0.0495 \| 186.01 \| 53200 \| 0.1643 \| 0.4006 \| 0.6094 \| 0.6032 \| nan \| 0.5737 \| 0.6450 \| 0.0 \| 0.5599 \| 0.6420 \| \| 0.0403 \| 186.08 \| 53220 \| 0.1637 \| 0.3981 \| 0.6054 \| 0.5989 \| nan \| 0.5679 \| 0.6430 \| 0.0 \| 0.5545 \| 0.6398 \| \| 0.0418 \| 186.15 \| 53240 \| 0.1644 \| 0.3985 \| 0.6058 \| 0.5988 \| nan \| 0.5654 \| 0.6463 \| 0.0 \| 0.5525 \| 0.6430 \| \| 0.0464 \| 186.22 \| 53260 \| 0.1642 \| 0.4029 \| 0.6127 \| 0.6061 \| nan \| 0.5743 \| 0.6512 \| 0.0 \| 0.5611 \| 0.6477 \| \| 0.0275 \| 186.29 \| 53280 \| 0.1645 \| 0.3998 \| 0.6078 \| 0.6002 \| nan \| 0.5641 \| 0.6514 \| 0.0 \| 0.5512 \| 0.6481 \| \| 0.03 \| 186.36 \| 53300 \| 0.1647 \| 0.4046 \| 0.6155 \| 0.6093 \| nan \| 0.5801 \| 0.6509 \| 0.0 \| 0.5659 \| 0.6478 \| \| 0.0574 \| 186.43 \| 53320 \| 0.1648 \| 0.4008 \| 0.6094 \| 0.6029 \| nan \| 0.5720 \| 0.6467 \| 0.0 \| 0.5586 \| 0.6437 \| \| 0.0351 \| 186.5 \| 53340 \| 0.1629 \| 0.3988 \| 0.6063 \| 0.5996 \| nan \| 0.5674 \| 0.6452 \| 0.0 \| 0.5540 \| 0.6422 \| \| 0.0428 \| 186.57 \| 53360 \| 0.1641 \| 0.4005 \| 0.6090 \| 0.6025 \| nan \| 0.5716 \| 0.6464 \| 0.0 \| 0.5580 \| 0.6434 \| \| 0.029 \| 186.64 \| 53380 \| 0.1642 \| 0.4007 \| 0.6094 \| 0.6029 \| nan \| 0.5721 \| 0.6466 \| 0.0 \| 0.5585 \| 0.6436 \| \| 0.039 \| 186.71 \| 53400 \| 0.1646 \| 0.3992 \| 0.6070 \| 0.6004 \| nan \| 0.5689 \| 0.6451 \| 0.0 \| 0.5555 \| 0.6421 \| \| 0.034 \| 186.78 \| 53420 \| 0.1641 \| 0.4019 \| 0.6113 \| 0.6059 \| nan \| 0.5800 \| 0.6426 \| 0.0 \| 0.5658 \| 0.6398 \| \| 0.0455 \| 186.85 \| 53440 \| 0.1663 \| 0.3957 \| 0.6016 \| 0.5952 \| nan \| 0.5647 \| 0.6386 \| 0.0 \| 0.5513 \| 0.6358 \| \| 0.0256 \| 186.92 \| 53460 \| 0.1655 \| 0.3992 \| 0.6071 \| 0.6012 \| nan \| 0.5731 \| 0.6410 \| 0.0 \| 0.5591 \| 0.6383 \| \| 0.0313 \| 186.99 \| 53480 \| 0.1646 \| 0.3986 \| 0.6061 \| 0.5994 \| nan \| 0.5674 \| 0.6448 \| 0.0 \| 0.5539 \| 0.6420 \| \| 0.0566 \| 187.06 \| 53500 \| 0.1648 \| 0.3980 \| 0.6050 \| 0.5979 \| nan \| 0.5640 \| 0.6459 \| 0.0 \| 0.5510 \| 0.6430 \| \| 0.0317 \| 187.13 \| 53520 \| 0.1600 \| 0.4048 \| 0.6158 \| 0.6103 \| nan \| 0.5838 \| 0.6479 \| 0.0 \| 0.5696 \| 0.6449 \| \| 0.036 \| 187.2 \| 53540 \| 0.1643 \| 0.3951 \| 0.6004 \| 0.5924 \| nan \| 0.5541 \| 0.6466 \| 0.0 \| 0.5418 \| 0.6435 \| \| 0.0255 \| 187.27 \| 53560 \| 0.1635 \| 0.4027 \| 0.6122 \| 0.6057 \| nan \| 0.5746 \| 0.6499 \| 0.0 \| 0.5614 \| 0.6467 \| \| 0.0279 \| 187.34 \| 53580 \| 0.1643 \| 0.4014 \| 0.6102 \| 0.6033 \| nan \| 0.5702 \| 0.6502 \| 0.0 \| 0.5573 \| 0.6470 \| \| 0.0252 \| 187.41 \| 53600 \| 0.1633 \| 0.3990 \| 0.6064 \| 0.5994 \| nan \| 0.5661 \| 0.6466 \| 0.0 \| 0.5533 \| 0.6435 \| \| 0.0377 \| 187.48 \| 53620 \| 0.1651 \| 0.3989 \| 0.6061 \| 0.5987 \| nan \| 0.5633 \| 0.6488 \| 0.0 \| 0.5514 \| 0.6453 \| \| 0.0577 \| 187.55 \| 53640 \| 0.1647 \| 0.3992 \| 0.6067 \| 0.5994 \| nan \| 0.5645 \| 0.6489 \| 0.0 \| 0.5523 \| 0.6454 \| \| 0.0354 \| 187.62 \| 53660 \| 0.1627 \| 0.4030 \| 0.6126 \| 0.6058 \| nan \| 0.5729 \| 0.6523 \| 0.0 \| 0.5600 \| 0.6489 \| \| 0.0411 \| 187.69 \| 53680 \| 0.1619 \| 0.4008 \| 0.6092 \| 0.6019 \| nan \| 0.5670 \| 0.6514 \| 0.0 \| 0.5544 \| 0.6481 \| \| 0.0615 \| 187.76 \| 53700 \| 0.1600 \| 0.4059 \| 0.6169 \| 0.6090 \| nan \| 0.5713 \| 0.6625 \| 0.0 \| 0.5592 \| 0.6586 \| \| 0.0414 \| 187.83 \| 53720 \| 0.1591 \| 0.4049 \| 0.6154 \| 0.6078 \| nan \| 0.5717 \| 0.6592 \| 0.0 \| 0.5593 \| 0.6555 \| \| 0.0269 \| 187.9 \| 53740 \| 0.1622 \| 0.4000 \| 0.6077 \| 0.6001 \| nan \| 0.5639 \| 0.6516 \| 0.0 \| 0.5517 \| 0.6482 \| \| 0.0655 \| 187.97 \| 53760 \| 0.1620 \| 0.4028 \| 0.6121 \| 0.6048 \| nan \| 0.5697 \| 0.6546 \| 0.0 \| 0.5574 \| 0.6511 \| \| 0.0333 \| 188.04 \| 53780 \| 0.1623 \| 0.4043 \| 0.6146 \| 0.6080 \| nan \| 0.5765 \| 0.6526 \| 0.0 \| 0.5636 \| 0.6492 \| \| 0.04 \| 188.11 \| 53800 \| 0.1635 \| 0.4019 \| 0.6109 \| 0.6040 \| nan \| 0.5710 \| 0.6508 \| 0.0 \| 0.5582 \| 0.6474 \| \| 0.0398 \| 188.18 \| 53820 \| 0.1629 \| 0.4003 \| 0.6085 \| 0.6010 \| nan \| 0.5652 \| 0.6518 \| 0.0 \| 0.5526 \| 0.6484 \| \| 0.0267 \| 188.25 \| 53840 \| 0.1633 \| 0.4014 \| 0.6102 \| 0.6031 \| nan \| 0.5692 \| 0.6512 \| 0.0 \| 0.5564 \| 0.6477 \| \| 0.0505 \| 188.32 \| 53860 \| 0.1599 \| 0.4028 \| 0.6122 \| 0.6049 \| nan \| 0.5702 \| 0.6543 \| 0.0 \| 0.5577 \| 0.6506 \| \| 0.0305 \| 188.39 \| 53880 \| 0.1620 \| 0.4022 \| 0.6113 \| 0.6038 \| nan \| 0.5680 \| 0.6547 \| 0.0 \| 0.5557 \| 0.6510 \| \| 0.0601 \| 188.46 \| 53900 \| 0.1617 \| 0.4035 \| 0.6134 \| 0.6062 \| nan \| 0.5721 \| 0.6547 \| 0.0 \| 0.5593 \| 0.6512 \| \| 0.0331 \| 188.53 \| 53920 \| 0.1609 \| 0.4013 \| 0.6099 \| 0.6023 \| nan \| 0.5657 \| 0.6542 \| 0.0 \| 0.5530 \| 0.6508 \| \| 0.0361 \| 188.6 \| 53940 \| 0.1618 \| 0.4018 \| 0.6108 \| 0.6034 \| nan \| 0.5684 \| 0.6532 \| 0.0 \| 0.5556 \| 0.6498 \| \| 0.0467 \| 188.67 \| 53960 \| 0.1609 \| 0.4040 \| 0.6143 \| 0.6075 \| nan \| 0.5750 \| 0.6535 \| 0.0 \| 0.5618 \| 0.6503 \| \| 0.0321 \| 188.74 \| 53980 \| 0.1608 \| 0.4032 \| 0.6129 \| 0.6054 \| nan \| 0.5691 \| 0.6567 \| 0.0 \| 0.5566 \| 0.6532 \| \| 0.0436 \| 188.81 \| 54000 \| 0.1633 \| 0.4012 \| 0.6097 \| 0.6016 \| nan \| 0.5632 \| 0.6562 \| 0.0 \| 0.5511 \| 0.6526 \| \| 0.0534 \| 188.88 \| 54020 \| 0.1608 \| 0.4054 \| 0.6164 \| 0.6098 \| nan \| 0.5782 \| 0.6546 \| 0.0 \| 0.5652 \| 0.6511 \| \| 0.0379 \| 188.95 \| 54040 \| 0.1617 \| 0.4008 \| 0.6091 \| 0.6013 \| nan \| 0.5644 \| 0.6537 \| 0.0 \| 0.5520 \| 0.6502 \| \| 0.0332 \| 189.02 \| 54060 \| 0.1626 \| 0.4001 \| 0.6083 \| 0.6012 \| nan \| 0.5676 \| 0.6490 \| 0.0 \| 0.5545 \| 0.6458 \| \| 0.0466 \| 189.09 \| 54080 \| 0.1632 \| 0.4020 \| 0.6111 \| 0.6040 \| nan \| 0.5703 \| 0.6519 \| 0.0 \| 0.5575 \| 0.6485 \| \| 0.0335 \| 189.16 \| 54100 \| 0.1620 \| 0.4008 \| 0.6094 \| 0.6026 \| nan \| 0.5702 \| 0.6486 \| 0.0 \| 0.5570 \| 0.6455 \| \| 0.062 \| 189.23 \| 54120 \| 0.1631 \| 0.3989 \| 0.6064 \| 0.5995 \| nan \| 0.5666 \| 0.6463 \| 0.0 \| 0.5535 \| 0.6432 \| \| 0.0408 \| 189.3 \| 54140 \| 0.1637 \| 0.4011 \| 0.6096 \| 0.6023 \| nan \| 0.5672 \| 0.6521 \| 0.0 \| 0.5545 \| 0.6487 \| \| 0.0387 \| 189.37 \| 54160 \| 0.1643 \| 0.4039 \| 0.6142 \| 0.6076 \| nan \| 0.5765 \| 0.6518 \| 0.0 \| 0.5633 \| 0.6485 \| \| 0.0454 \| 189.44 \| 54180 \| 0.1621 \| 0.4015 \| 0.6104 \| 0.6032 \| nan \| 0.5688 \| 0.6519 \| 0.0 \| 0.5559 \| 0.6487 \| \| 0.0308 \| 189.51 \| 54200 \| 0.1627 \| 0.4031 \| 0.6126 \| 0.6053 \| nan \| 0.5705 \| 0.6547 \| 0.0 \| 0.5580 \| 0.6512 \| \| 0.0342 \| 189.58 \| 54220 \| 0.1632 \| 0.3992 \| 0.6067 \| 0.5996 \| nan \| 0.5657 \| 0.6477 \| 0.0 \| 0.5531 \| 0.6445 \| \| 0.0484 \| 189.65 \| 54240 \| 0.1624 \| 0.4011 \| 0.6097 \| 0.6029 \| nan \| 0.5702 \| 0.6492 \| 0.0 \| 0.5573 \| 0.6459 \| \| 0.0357 \| 189.72 \| 54260 \| 0.1628 \| 0.4013 \| 0.6100 \| 0.6024 \| nan \| 0.5661 \| 0.6538 \| 0.0 \| 0.5539 \| 0.6501 \| \| 0.0617 \| 189.79 \| 54280 \| 0.1627 \| 0.4001 \| 0.6082 \| 0.6012 \| nan \| 0.5677 \| 0.6487 \| 0.0 \| 0.5551 \| 0.6453 \| \| 0.0466 \| 189.86 \| 54300 \| 0.1621 \| 0.4034 \| 0.6133 \| 0.6064 \| nan \| 0.5734 \| 0.6532 \| 0.0 \| 0.5605 \| 0.6497 \| \| 0.0396 \| 189.93 \| 54320 \| 0.1637 \| 0.4006 \| 0.6090 \| 0.6016 \| nan \| 0.5662 \| 0.6518 \| 0.0 \| 0.5537 \| 0.6483 \| \| 0.0389 \| 190.0 \| 54340 \| 0.1613 \| 0.4019 \| 0.6110 \| 0.6040 \| nan \| 0.5707 \| 0.6514 \| 0.0 \| 0.5578 \| 0.6479 \| \| 0.0438 \| 190.07 \| 54360 \| 0.1631 \| 0.4010 \| 0.6096 \| 0.6022 \| nan \| 0.5668 \| 0.6523 \| 0.0 \| 0.5544 \| 0.6487 \| \| 0.0502 \| 190.14 \| 54380 \| 0.1610 \| 0.4009 \| 0.6095 \| 0.6020 \| nan \| 0.5666 \| 0.6523 \| 0.0 \| 0.5540 \| 0.6488 \| \| 0.0261 \| 190.21 \| 54400 \| 0.1630 \| 0.4038 \| 0.6139 \| 0.6068 \| nan \| 0.5732 \| 0.6545 \| 0.0 \| 0.5605 \| 0.6509 \| \| 0.0491 \| 190.28 \| 54420 \| 0.1634 \| 0.4033 \| 0.6130 \| 0.6056 \| nan \| 0.5705 \| 0.6554 \| 0.0 \| 0.5582 \| 0.6517 \| \| 0.0377 \| 190.35 \| 54440 \| 0.1628 \| 0.4011 \| 0.6096 \| 0.6025 \| nan \| 0.5685 \| 0.6508 \| 0.0 \| 0.5560 \| 0.6472 \| \| 0.0546 \| 190.42 \| 54460 \| 0.1622 \| 0.4012 \| 0.6099 \| 0.6029 \| nan \| 0.5692 \| 0.6507 \| 0.0 \| 0.5566 \| 0.6472 \| \| 0.043 \| 190.49 \| 54480 \| 0.1640 \| 0.4006 \| 0.6089 \| 0.6017 \| nan \| 0.5676 \| 0.6502 \| 0.0 \| 0.5550 \| 0.6467 \| \| 0.0285 \| 190.56 \| 54500 \| 0.1652 \| 0.4013 \| 0.6100 \| 0.6031 \| nan \| 0.5702 \| 0.6498 \| 0.0 \| 0.5575 \| 0.6464 \| \| 0.0486 \| 190.63 \| 54520 \| 0.1637 \| 0.4015 \| 0.6104 \| 0.6039 \| nan \| 0.5727 \| 0.6481 \| 0.0 \| 0.5597 \| 0.6449 \| \| 0.0636 \| 190.7 \| 54540 \| 0.1659 \| 0.3985 \| 0.6054 \| 0.5972 \| nan \| 0.5581 \| 0.6527 \| 0.0 \| 0.5466 \| 0.6490 \| \| 0.0449 \| 190.77 \| 54560 \| 0.1633 \| 0.3998 \| 0.6076 \| 0.6006 \| nan \| 0.5669 \| 0.6483 \| 0.0 \| 0.5544 \| 0.6449 \| \| 0.0223 \| 190.84 \| 54580 \| 0.1622 \| 0.4024 \| 0.6117 \| 0.6049 \| nan \| 0.5724 \| 0.6510 \| 0.0 \| 0.5599 \| 0.6472 \| \| 0.0343 \| 190.91 \| 54600 \| 0.1633 \| 0.3998 \| 0.6076 \| 0.5996 \| nan \| 0.5616 \| 0.6536 \| 0.0 \| 0.5498 \| 0.6497 \| \| 0.0239 \| 190.98 \| 54620 \| 0.1641 \| 0.4033 \| 0.6130 \| 0.6054 \| nan \| 0.5694 \| 0.6565 \| 0.0 \| 0.5573 \| 0.6526 \| \| 0.0474 \| 191.05 \| 54640 \| 0.1620 \| 0.4015 \| 0.6102 \| 0.6023 \| nan \| 0.5647 \| 0.6557 \| 0.0 \| 0.5525 \| 0.6520 \| \| 0.0395 \| 191.12 \| 54660 \| 0.1607 \| 0.4061 \| 0.6173 \| 0.6100 \| nan \| 0.5749 \| 0.6597 \| 0.0 \| 0.5622 \| 0.6561 \| \| 0.0453 \| 191.19 \| 54680 \| 0.1628 \| 0.4033 \| 0.6129 \| 0.6052 \| nan \| 0.5682 \| 0.6576 \| 0.0 \| 0.5559 \| 0.6540 \| \| 0.0376 \| 191.26 \| 54700 \| 0.1613 \| 0.4033 \| 0.6129 \| 0.6050 \| nan \| 0.5673 \| 0.6585 \| 0.0 \| 0.5549 \| 0.6549 \| \| 0.0374 \| 191.33 \| 54720 \| 0.1624 \| 0.4036 \| 0.6134 \| 0.6058 \| nan \| 0.5691 \| 0.6578 \| 0.0 \| 0.5564 \| 0.6543 \| \| 0.0325 \| 191.4 \| 54740 \| 0.1610 \| 0.4028 \| 0.6122 \| 0.6045 \| nan \| 0.5677 \| 0.6566 \| 0.0 \| 0.5554 \| 0.6530 \| \| 0.0426 \| 191.47 \| 54760 \| 0.1616 \| 0.4028 \| 0.6122 \| 0.6046 \| nan \| 0.5684 \| 0.6560 \| 0.0 \| 0.5558 \| 0.6525 \| \| 0.0346 \| 191.54 \| 54780 \| 0.1622 \| 0.4014 \| 0.6101 \| 0.6019 \| nan \| 0.5629 \| 0.6574 \| 0.0 \| 0.5506 \| 0.6537 \| \| 0.0452 \| 191.61 \| 54800 \| 0.1622 \| 0.4036 \| 0.6134 \| 0.6056 \| nan \| 0.5679 \| 0.6590 \| 0.0 \| 0.5554 \| 0.6552 \| \| 0.0408 \| 191.68 \| 54820 \| 0.1605 \| 0.4035 \| 0.6134 \| 0.6056 \| nan \| 0.5683 \| 0.6586 \| 0.0 \| 0.5557 \| 0.6549 \| \| 0.0501 \| 191.75 \| 54840 \| 0.1615 \| 0.4037 \| 0.6136 \| 0.6058 \| nan \| 0.5686 \| 0.6586 \| 0.0 \| 0.5563 \| 0.6547 \| \| 0.0446 \| 191.82 \| 54860 \| 0.1614 \| 0.4023 \| 0.6114 \| 0.6035 \| nan \| 0.5656 \| 0.6572 \| 0.0 \| 0.5536 \| 0.6532 \| \| 0.0511 \| 191.89 \| 54880 \| 0.1619 \| 0.4025 \| 0.6118 \| 0.6043 \| nan \| 0.5683 \| 0.6553 \| 0.0 \| 0.5560 \| 0.6514 \| \| 0.0384 \| 191.96 \| 54900 \| 0.1632 \| 0.4032 \| 0.6129 \| 0.6056 \| nan \| 0.5711 \| 0.6547 \| 0.0 \| 0.5586 \| 0.6509 \| \| 0.0437 \| 192.03 \| 54920 \| 0.1647 \| 0.4011 \| 0.6097 \| 0.6025 \| nan \| 0.5682 \| 0.6513 \| 0.0 \| 0.5555 \| 0.6478 \| \| 0.0264 \| 192.1 \| 54940 \| 0.1626 \| 0.4026 \| 0.6120 \| 0.6045 \| nan \| 0.5689 \| 0.6551 \| 0.0 \| 0.5563 \| 0.6514 \| \| 0.0477 \| 192.17 \| 54960 \| 0.1619 \| 0.4043 \| 0.6148 \| 0.6076 \| nan \| 0.5730 \| 0.6566 \| 0.0 \| 0.5601 \| 0.6530 \| \| 0.0177 \| 192.24 \| 54980 \| 0.1609 \| 0.4028 \| 0.6124 \| 0.6051 \| nan \| 0.5703 \| 0.6545 \| 0.0 \| 0.5574 \| 0.6509 \| \| 0.0508 \| 192.31 \| 55000 \| 0.1607 \| 0.4033 \| 0.6131 \| 0.6053 \| nan \| 0.5679 \| 0.6582 \| 0.0 \| 0.5558 \| 0.6542 \| \| 0.0624 \| 192.38 \| 55020 \| 0.1613 \| 0.4026 \| 0.6120 \| 0.6045 \| nan \| 0.5687 \| 0.6553 \| 0.0 \| 0.5563 \| 0.6516 \| \| 0.0209 \| 192.45 \| 55040 \| 0.1605 \| 0.4020 \| 0.6111 \| 0.6038 \| nan \| 0.5693 \| 0.6529 \| 0.0 \| 0.5565 \| 0.6494 \| \| 0.0388 \| 192.52 \| 55060 \| 0.1616 \| 0.4054 \| 0.6164 \| 0.6091 \| nan \| 0.5740 \| 0.6588 \| 0.0 \| 0.5613 \| 0.6550 \| \| 0.0396 \| 192.59 \| 55080 \| 0.1643 \| 0.4044 \| 0.6148 \| 0.6070 \| nan \| 0.5698 \| 0.6597 \| 0.0 \| 0.5572 \| 0.6559 \| \| 0.0345 \| 192.66 \| 55100 \| 0.1620 \| 0.4046 \| 0.6152 \| 0.6079 \| nan \| 0.5726 \| 0.6578 \| 0.0 \| 0.5598 \| 0.6541 \| \| 0.0537 \| 192.73 \| 55120 \| 0.1638 \| 0.4007 \| 0.6092 \| 0.6013 \| nan \| 0.5638 \| 0.6546 \| 0.0 \| 0.5512 \| 0.6510 \| \| 0.0378 \| 192.8 \| 55140 \| 0.1608 \| 0.4025 \| 0.6120 \| 0.6049 \| nan \| 0.5713 \| 0.6526 \| 0.0 \| 0.5583 \| 0.6491 \| \| 0.0332 \| 192.87 \| 55160 \| 0.1628 \| 0.4022 \| 0.6114 \| 0.6037 \| nan \| 0.5671 \| 0.6557 \| 0.0 \| 0.5546 \| 0.6520 \| \| 0.0426 \| 192.94 \| 55180 \| 0.1614 \| 0.4024 \| 0.6117 \| 0.6042 \| nan \| 0.5685 \| 0.6548 \| 0.0 \| 0.5560 \| 0.6511 \| \| 0.0393 \| 193.01 \| 55200 \| 0.1630 \| 0.4025 \| 0.6119 \| 0.6048 \| nan \| 0.5706 \| 0.6532 \| 0.0 \| 0.5580 \| 0.6495 \| \| 0.0478 \| 193.08 \| 55220 \| 0.1618 \| 0.4019 \| 0.6109 \| 0.6037 \| nan \| 0.5694 \| 0.6525 \| 0.0 \| 0.5568 \| 0.6488 \| \| 0.0322 \| 193.15 \| 55240 \| 0.1623 \| 0.4009 \| 0.6094 \| 0.6021 \| nan \| 0.5672 \| 0.6517 \| 0.0 \| 0.5546 \| 0.6481 \| \| 0.0538 \| 193.22 \| 55260 \| 0.1632 \| 0.4029 \| 0.6125 \| 0.6052 \| nan \| 0.5699 \| 0.6551 \| 0.0 \| 0.5571 \| 0.6516 \| \| 0.0305 \| 193.29 \| 55280 \| 0.1615 \| 0.4022 \| 0.6116 \| 0.6044 \| nan \| 0.5701 \| 0.6531 \| 0.0 \| 0.5571 \| 0.6495 \| \| 0.069 \| 193.36 \| 55300 \| 0.1622 \| 0.4024 \| 0.6118 \| 0.6042 \| nan \| 0.5676 \| 0.6560 \| 0.0 \| 0.5550 \| 0.6522 \| \| 0.0338 \| 193.43 \| 55320 \| 0.1630 \| 0.4018 \| 0.6108 \| 0.6032 \| nan \| 0.5668 \| 0.6548 \| 0.0 \| 0.5541 \| 0.6512 \| \| 0.0292 \| 193.5 \| 55340 \| 0.1613 \| 0.4026 \| 0.6120 \| 0.6046 \| nan \| 0.5688 \| 0.6552 \| 0.0 \| 0.5561 \| 0.6516 \| \| 0.0358 \| 193.57 \| 55360 \| 0.1638 \| 0.4006 \| 0.6090 \| 0.6012 \| nan \| 0.5640 \| 0.6540 \| 0.0 \| 0.5514 \| 0.6505 \| \| 0.0696 \| 193.64 \| 55380 \| 0.1635 \| 0.4012 \| 0.6097 \| 0.6017 \| nan \| 0.5633 \| 0.6562 \| 0.0 \| 0.5510 \| 0.6525 \| \| 0.0393 \| 193.71 \| 55400 \| 0.1620 \| 0.4040 \| 0.6142 \| 0.6070 \| nan \| 0.5728 \| 0.6556 \| 0.0 \| 0.5597 \| 0.6522 \| \| 0.0407 \| 193.78 \| 55420 \| 0.1634 \| 0.4037 \| 0.6138 \| 0.6064 \| nan \| 0.5715 \| 0.6561 \| 0.0 \| 0.5584 \| 0.6526 \| \| 0.0474 \| 193.85 \| 55440 \| 0.1643 \| 0.4013 \| 0.6100 \| 0.6019 \| nan \| 0.5635 \| 0.6564 \| 0.0 \| 0.5511 \| 0.6528 \| \| 0.0386 \| 193.92 \| 55460 \| 0.1628 \| 0.4017 \| 0.6107 \| 0.6030 \| nan \| 0.5665 \| 0.6549 \| 0.0 \| 0.5538 \| 0.6514 \| \| 0.0261 \| 193.99 \| 55480 \| 0.1621 \| 0.4015 \| 0.6106 \| 0.6035 \| nan \| 0.5700 \| 0.6512 \| 0.0 \| 0.5568 \| 0.6479 \| \| 0.0309 \| 194.06 \| 55500 \| 0.1622 \| 0.4001 \| 0.6082 \| 0.6003 \| nan \| 0.5629 \| 0.6535 \| 0.0 \| 0.5503 \| 0.6500 \| \| 0.0536 \| 194.13 \| 55520 \| 0.1593 \| 0.4031 \| 0.6128 \| 0.6054 \| nan \| 0.5699 \| 0.6557 \| 0.0 \| 0.5571 \| 0.6521 \| \| 0.0486 \| 194.2 \| 55540 \| 0.1630 \| 0.4041 \| 0.6143 \| 0.6069 \| nan \| 0.5716 \| 0.6571 \| 0.0 \| 0.5590 \| 0.6534 \| \| 0.0573 \| 194.27 \| 55560 \| 0.1627 \| 0.4030 \| 0.6126 \| 0.6052 \| nan \| 0.5700 \| 0.6552 \| 0.0 \| 0.5573 \| 0.6517 \| \| 0.0245 \| 194.34 \| 55580 \| 0.1621 \| 0.4014 \| 0.6102 \| 0.6024 \| nan \| 0.5649 \| 0.6555 \| 0.0 \| 0.5523 \| 0.6520 \| \| 0.0472 \| 194.41 \| 55600 \| 0.1625 \| 0.4037 \| 0.6138 \| 0.6066 \| nan \| 0.5726 \| 0.6549 \| 0.0 \| 0.5597 \| 0.6514 \| \| 0.0593 \| 194.48 \| 55620 \| 0.1628 \| 0.4025 \| 0.6118 \| 0.6042 \| nan \| 0.5676 \| 0.6561 \| 0.0 \| 0.5549 \| 0.6526 \| \| 0.022 \| 194.55 \| 55640 \| 0.1650 \| 0.4018 \| 0.6108 \| 0.6031 \| nan \| 0.5665 \| 0.6551 \| 0.0 \| 0.5539 \| 0.6516 \| \| 0.0478 \| 194.62 \| 55660 \| 0.1624 \| 0.4027 \| 0.6122 \| 0.6045 \| nan \| 0.5678 \| 0.6565 \| 0.0 \| 0.5552 \| 0.6530 \| \| 0.0375 \| 194.69 \| 55680 \| 0.1619 \| 0.4032 \| 0.6129 \| 0.6052 \| nan \| 0.5682 \| 0.6577 \| 0.0 \| 0.5556 \| 0.6540 \| \| 0.0375 \| 194.76 \| 55700 \| 0.1620 \| 0.4033 \| 0.6130 \| 0.6052 \| nan \| 0.5681 \| 0.6579 \| 0.0 \| 0.5556 \| 0.6542 \| \| 0.0487 \| 194.83 \| 55720 \| 0.1611 \| 0.4035 \| 0.6134 \| 0.6054 \| nan \| 0.5670 \| 0.6598 \| 0.0 \| 0.5546 \| 0.6560 \| \| 0.0363 \| 194.9 \| 55740 \| 0.1623 \| 0.4041 \| 0.6143 \| 0.6068 \| nan \| 0.5708 \| 0.6579 \| 0.0 \| 0.5580 \| 0.6542 \| \| 0.0543 \| 194.97 \| 55760 \| 0.1631 \| 0.4035 \| 0.6134 \| 0.6058 \| nan \| 0.5691 \| 0.6578 \| 0.0 \| 0.5564 \| 0.6542 \| \| 0.0427 \| 195.03 \| 55780 \| 0.1640 \| 0.4029 \| 0.6126 \| 0.6051 \| nan \| 0.5694 \| 0.6559 \| 0.0 \| 0.5565 \| 0.6524 \| \| 0.0257 \| 195.1 \| 55800 \| 0.1627 \| 0.4041 \| 0.6143 \| 0.6071 \| nan \| 0.5727 \| 0.6559 \| 0.0 \| 0.5597 \| 0.6524 \| \| 0.0268 \| 195.17 \| 55820 \| 0.1635 \| 0.4025 \| 0.6118 \| 0.6046 \| nan \| 0.5702 \| 0.6535 \| 0.0 \| 0.5572 \| 0.6502 \| \| 0.0403 \| 195.24 \| 55840 \| 0.1643 \| 0.3999 \| 0.6079 \| 0.6005 \| nan \| 0.5651 \| 0.6507 \| 0.0 \| 0.5522 \| 0.6475 \| \| 0.0375 \| 195.31 \| 55860 \| 0.1632 \| 0.4028 \| 0.6125 \| 0.6053 \| nan \| 0.5709 \| 0.6540 \| 0.0 \| 0.5579 \| 0.6506 \| \| 0.0376 \| 195.38 \| 55880 \| 0.1647 \| 0.4009 \| 0.6094 \| 0.6019 \| nan \| 0.5660 \| 0.6528 \| 0.0 \| 0.5534 \| 0.6493 \| \| 0.0324 \| 195.45 \| 55900 \| 0.1671 \| 0.4016 \| 0.6105 \| 0.6034 \| nan \| 0.5693 \| 0.6516 \| 0.0 \| 0.5564 \| 0.6483 \| \| 0.048 \| 195.52 \| 55920 \| 0.1677 \| 0.4013 \| 0.6101 \| 0.6029 \| nan \| 0.5685 \| 0.6517 \| 0.0 \| 0.5557 \| 0.6483 \| \| 0.0272 \| 195.59 \| 55940 \| 0.1653 \| 0.4014 \| 0.6104 \| 0.6036 \| nan \| 0.5710 \| 0.6498 \| 0.0 \| 0.5578 \| 0.6466 \| \| 0.0483 \| 195.66 \| 55960 \| 0.1639 \| 0.4011 \| 0.6099 \| 0.6026 \| nan \| 0.5680 \| 0.6518 \| 0.0 \| 0.5550 \| 0.6485 \| \| 0.0322 \| 195.73 \| 55980 \| 0.1649 \| 0.4014 \| 0.6102 \| 0.6031 \| nan \| 0.5694 \| 0.6510 \| 0.0 \| 0.5564 \| 0.6477 \| \| 0.0511 \| 195.8 \| 56000 \| 0.1635 \| 0.4014 \| 0.6103 \| 0.6034 \| nan \| 0.5706 \| 0.6500 \| 0.0 \| 0.5575 \| 0.6467 \| \| 0.043 \| 195.87 \| 56020 \| 0.1625 \| 0.4034 \| 0.6133 \| 0.6064 \| nan \| 0.5732 \| 0.6534 \| 0.0 \| 0.5600 \| 0.6500 \| \| 0.036 \| 195.94 \| 56040 \| 0.1633 \| 0.4020 \| 0.6112 \| 0.6040 \| nan \| 0.5697 \| 0.6527 \| 0.0 \| 0.5567 \| 0.6493 \| \| 0.0541 \| 196.01 \| 56060 \| 0.1647 \| 0.4026 \| 0.6122 \| 0.6052 \| nan \| 0.5719 \| 0.6524 \| 0.0 \| 0.5587 \| 0.6490 \| \| 0.0357 \| 196.08 \| 56080 \| 0.1640 \| 0.4016 \| 0.6105 \| 0.6029 \| nan \| 0.5665 \| 0.6545 \| 0.0 \| 0.5540 \| 0.6509 \| \| 0.0456 \| 196.15 \| 56100 \| 0.1658 \| 0.4031 \| 0.6128 \| 0.6057 \| nan \| 0.5720 \| 0.6535 \| 0.0 \| 0.5592 \| 0.6500 \| \| 0.0299 \| 196.22 \| 56120 \| 0.1637 \| 0.4024 \| 0.6117 \| 0.6047 \| nan \| 0.5710 \| 0.6524 \| 0.0 \| 0.5582 \| 0.6490 \| \| 0.053 \| 196.29 \| 56140 \| 0.1650 \| 0.4013 \| 0.6100 \| 0.6028 \| nan \| 0.5681 \| 0.6519 \| 0.0 \| 0.5553 \| 0.6485 \| \| 0.0569 \| 196.36 \| 56160 \| 0.1632 \| 0.4006 \| 0.6090 \| 0.6018 \| nan \| 0.5678 \| 0.6501 \| 0.0 \| 0.5549 \| 0.6468 \| \| 0.0555 \| 196.43 \| 56180 \| 0.1615 \| 0.3999 \| 0.6079 \| 0.6010 \| nan \| 0.5676 \| 0.6483 \| 0.0 \| 0.5544 \| 0.6452 \| \| 0.0226 \| 196.5 \| 56200 \| 0.1645 \| 0.3993 \| 0.6071 \| 0.5997 \| nan \| 0.5645 \| 0.6497 \| 0.0 \| 0.5516 \| 0.6464 \| \| 0.0363 \| 196.57 \| 56220 \| 0.1630 \| 0.3998 \| 0.6078 \| 0.6003 \| nan \| 0.5644 \| 0.6512 \| 0.0 \| 0.5516 \| 0.6478 \| \| 0.0569 \| 196.64 \| 56240 \| 0.1631 \| 0.4024 \| 0.6116 \| 0.6041 \| nan \| 0.5683 \| 0.6550 \| 0.0 \| 0.5556 \| 0.6515 \| \| 0.0272 \| 196.71 \| 56260 \| 0.1638 \| 0.4017 \| 0.6107 \| 0.6037 \| nan \| 0.5700 \| 0.6514 \| 0.0 \| 0.5570 \| 0.6481 \| \| 0.0462 \| 196.78 \| 56280 \| 0.1636 \| 0.4034 \| 0.6134 \| 0.6068 \| nan \| 0.5751 \| 0.6517 \| 0.0 \| 0.5619 \| 0.6484 \| \| 0.052 \| 196.85 \| 56300 \| 0.1639 \| 0.3998 \| 0.6078 \| 0.6005 \| nan \| 0.5660 \| 0.6496 \| 0.0 \| 0.5533 \| 0.6463 \| \| 0.0412 \| 196.92 \| 56320 \| 0.1639 \| 0.4006 \| 0.6089 \| 0.6016 \| nan \| 0.5665 \| 0.6513 \| 0.0 \| 0.5539 \| 0.6480 \| \| 0.0802 \| 196.99 \| 56340 \| 0.1637 \| 0.3998 \| 0.6077 \| 0.6002 \| nan \| 0.5644 \| 0.6510 \| 0.0 \| 0.5518 \| 0.6477 \| \| 0.0398 \| 197.06 \| 56360 \| 0.1636 \| 0.4004 \| 0.6086 \| 0.6013 \| nan \| 0.5666 \| 0.6505 \| 0.0 \| 0.5538 \| 0.6473 \| \| 0.0521 \| 197.13 \| 56380 \| 0.1643 \| 0.4022 \| 0.6114 \| 0.6043 \| nan \| 0.5706 \| 0.6522 \| 0.0 \| 0.5576 \| 0.6489 \| \| 0.0282 \| 197.2 \| 56400 \| 0.1629 \| 0.4013 \| 0.6100 \| 0.6027 \| nan \| 0.5679 \| 0.6521 \| 0.0 \| 0.5551 \| 0.6488 \| \| 0.0327 \| 197.27 \| 56420 \| 0.1629 \| 0.4030 \| 0.6127 \| 0.6060 \| nan \| 0.5740 \| 0.6513 \| 0.0 \| 0.5608 \| 0.6481 \| \| 0.0216 \| 197.34 \| 56440 \| 0.1637 \| 0.4017 \| 0.6106 \| 0.6035 \| nan \| 0.5696 \| 0.6516 \| 0.0 \| 0.5567 \| 0.6483 \| \| 0.054 \| 197.41 \| 56460 \| 0.1649 \| 0.4021 \| 0.6113 \| 0.6043 \| nan \| 0.5709 \| 0.6517 \| 0.0 \| 0.5580 \| 0.6483 \| \| 0.0445 \| 197.48 \| 56480 \| 0.1653 \| 0.4004 \| 0.6087 \| 0.6015 \| nan \| 0.5668 \| 0.6507 \| 0.0 \| 0.5540 \| 0.6473 \| \| 0.0449 \| 197.55 \| 56500 \| 0.1637 \| 0.4006 \| 0.6089 \| 0.6016 \| nan \| 0.5668 \| 0.6509 \| 0.0 \| 0.5542 \| 0.6475 \| \| 0.0416 \| 197.62 \| 56520 \| 0.1650 \| 0.4000 \| 0.6080 \| 0.6007 \| nan \| 0.5659 \| 0.6501 \| 0.0 \| 0.5533 \| 0.6468 \| \| 0.0365 \| 197.69 \| 56540 \| 0.1637 \| 0.4018 \| 0.6108 \| 0.6036 \| nan \| 0.5695 \| 0.6521 \| 0.0 \| 0.5566 \| 0.6488 \| \| 0.0412 \| 197.76 \| 56560 \| 0.1632 \| 0.4009 \| 0.6093 \| 0.6019 \| nan \| 0.5660 \| 0.6527 \| 0.0 \| 0.5534 \| 0.6493 \| \| 0.0386 \| 197.83 \| 56580 \| 0.1651 \| 0.4027 \| 0.6121 \| 0.6050 \| nan \| 0.5708 \| 0.6535 \| 0.0 \| 0.5580 \| 0.6500 \| \| 0.0592 \| 197.9 \| 56600 \| 0.1637 \| 0.3998 \| 0.6077 \| 0.6001 \| nan \| 0.5640 \| 0.6513 \| 0.0 \| 0.5515 \| 0.6480 \| \| 0.0456 \| 197.97 \| 56620 \| 0.1635 \| 0.4017 \| 0.6106 \| 0.6035 \| nan \| 0.5695 \| 0.6517 \| 0.0 \| 0.5566 \| 0.6484 \| \| 0.0449 \| 198.04 \| 56640 \| 0.1630 \| 0.4011 \| 0.6097 \| 0.6021 \| nan \| 0.5658 \| 0.6535 \| 0.0 \| 0.5532 \| 0.6501 \| \| 0.0412 \| 198.11 \| 56660 \| 0.1642 \| 0.4015 \| 0.6103 \| 0.6027 \| nan \| 0.5661 \| 0.6545 \| 0.0 \| 0.5537 \| 0.6509 \| \| 0.051 \| 198.18 \| 56680 \| 0.1641 \| 0.4023 \| 0.6117 \| 0.6045 \| nan \| 0.5701 \| 0.6532 \| 0.0 \| 0.5573 \| 0.6497 \| \| 0.0392 \| 198.25 \| 56700 \| 0.1631 \| 0.4027 \| 0.6122 \| 0.6053 \| nan \| 0.5723 \| 0.6521 \| 0.0 \| 0.5593 \| 0.6487 \| \| 0.0264 \| 198.32 \| 56720 \| 0.1644 \| 0.4004 \| 0.6086 \| 0.6009 \| nan \| 0.5641 \| 0.6531 \| 0.0 \| 0.5515 \| 0.6496 \| \| 0.022 \| 198.39 \| 56740 \| 0.1629 \| 0.4007 \| 0.6093 \| 0.6027 \| nan \| 0.5709 \| 0.6478 \| 0.0 \| 0.5578 \| 0.6445 \| \| 0.0434 \| 198.46 \| 56760 \| 0.1634 \| 0.4008 \| 0.6093 \| 0.6019 \| nan \| 0.5665 \| 0.6521 \| 0.0 \| 0.5539 \| 0.6486 \| \| 0.0362 \| 198.53 \| 56780 \| 0.1633 \| 0.4022 \| 0.6114 \| 0.6041 \| nan \| 0.5692 \| 0.6536 \| 0.0 \| 0.5565 \| 0.6500 \| \| 0.0429 \| 198.6 \| 56800 \| 0.1617 \| 0.4042 \| 0.6146 \| 0.6075 \| nan \| 0.5738 \| 0.6554 \| 0.0 \| 0.5609 \| 0.6517 \| \| 0.0509 \| 198.67 \| 56820 \| 0.1629 \| 0.4029 \| 0.6126 \| 0.6056 \| nan \| 0.5720 \| 0.6532 \| 0.0 \| 0.5590 \| 0.6497 \| \| 0.0231 \| 198.74 \| 56840 \| 0.1634 \| 0.4023 \| 0.6116 \| 0.6046 \| nan \| 0.5713 \| 0.6519 \| 0.0 \| 0.5584 \| 0.6485 \| \| 0.0285 \| 198.81 \| 56860 \| 0.1640 \| 0.4014 \| 0.6103 \| 0.6032 \| nan \| 0.5695 \| 0.6510 \| 0.0 \| 0.5567 \| 0.6476 \| \| 0.0389 \| 198.88 \| 56880 \| 0.1629 \| 0.4011 \| 0.6097 \| 0.6025 \| nan \| 0.5678 \| 0.6516 \| 0.0 \| 0.5552 \| 0.6481 \| \| 0.0367 \| 198.95 \| 56900 \| 0.1652 \| 0.4032 \| 0.6130 \| 0.6060 \| nan \| 0.5723 \| 0.6538 \| 0.0 \| 0.5595 \| 0.6502 \| \| 0.0329 \| 199.02 \| 56920 \| 0.1658 \| 0.4019 \| 0.6109 \| 0.6039 \| nan \| 0.5701 \| 0.6518 \| 0.0 \| 0.5573 \| 0.6483 \| \| 0.0354 \| 199.09 \| 56940 \| 0.1651 \| 0.4016 \| 0.6105 \| 0.6031 \| nan \| 0.5681 \| 0.6529 \| 0.0 \| 0.5554 \| 0.6494 \| \| 0.0381 \| 199.16 \| 56960 \| 0.1618 \| 0.4000 \| 0.6080 \| 0.6007 \| nan \| 0.5657 \| 0.6503 \| 0.0 \| 0.5530 \| 0.6469 \| \| 0.0522 \| 199.23 \| 56980 \| 0.1636 \| 0.4035 \| 0.6134 \| 0.6064 \| nan \| 0.5729 \| 0.6539 \| 0.0 \| 0.5601 \| 0.6504 \| \| 0.0456 \| 199.3 \| 57000 \| 0.1622 \| 0.4012 \| 0.6099 \| 0.6028 \| nan \| 0.5688 \| 0.6510 \| 0.0 \| 0.5558 \| 0.6476 \| \| 0.0521 \| 199.37 \| 57020 \| 0.1648 \| 0.4018 \| 0.6108 \| 0.6034 \| nan \| 0.5681 \| 0.6534 \| 0.0 \| 0.5556 \| 0.6498 \| \| 0.0326 \| 199.44 \| 57040 \| 0.1633 \| 0.4013 \| 0.6102 \| 0.6033 \| nan \| 0.5706 \| 0.6498 \| 0.0 \| 0.5575 \| 0.6465 \| \| 0.0381 \| 199.51 \| 57060 \| 0.1642 \| 0.4023 \| 0.6117 \| 0.6047 \| nan \| 0.5712 \| 0.6521 \| 0.0 \| 0.5584 \| 0.6486 \| \| 0.0582 \| 199.58 \| 57080 \| 0.1632 \| 0.4006 \| 0.6090 \| 0.6014 \| nan \| 0.5651 \| 0.6529 \| 0.0 \| 0.5525 \| 0.6493 \| \| 0.0555 \| 199.65 \| 57100 \| 0.1642 \| 0.4023 \| 0.6116 \| 0.6042 \| nan \| 0.5690 \| 0.6541 \| 0.0 \| 0.5565 \| 0.6505 \| \| 0.0309 \| 199.72 \| 57120 \| 0.1646 \| 0.4016 \| 0.6105 \| 0.6033 \| nan \| 0.5686 \| 0.6524 \| 0.0 \| 0.5561 \| 0.6488 \| \| 0.0437 \| 199.79 \| 57140 \| 0.1641 \| 0.4017 \| 0.6106 \| 0.6033 \| nan \| 0.5686 \| 0.6526 \| 0.0 \| 0.5561 \| 0.6490 \| \| 0.0473 \| 199.86 \| 57160 \| 0.1637 \| 0.4015 \| 0.6103 \| 0.6032 \| nan \| 0.5695 \| 0.6511 \| 0.0 \| 0.5568 \| 0.6476 \| \| 0.0392 \| 199.93 \| 57180 \| 0.1619 \| 0.4018 \| 0.6108 \| 0.6035 \| nan \| 0.5684 \| 0.6532 \| 0.0 \| 0.5557 \| 0.6497 \| \| 0.064 \| 200.0 \| 57200 \| 0.1645 \| 0.4027 \| 0.6121 \| 0.6048 \| nan \| 0.5696 \| 0.6546 \| 0.0 \| 0.5571 \| 0.6510 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
pfunk/CartPole-v1-DQN_lr_0.00005-seed1	pfunk	"2023-03-19T00:16:20Z"	0	0	cleanrl	[ "cleanrl", "tensorboard", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-03-19T00:16:17Z"	--- tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 495.62 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQN Agent Playing CartPole-v1 This is a trained model of a DQN agent playing CartPole-v1. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_lr_0.00005.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_lr_0.00005]" python -m cleanrl_utils.enjoy --exp-name DQN_lr_0.00005 --env-id CartPole-v1 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_lr_0.00005-seed1/raw/main/dqn.py curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_lr_0.00005-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_lr_0.00005-seed1/raw/main/poetry.lock poetry install --all-extras python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_lr_0.00005 --learning-rate 0.00005 --seed 1 ``` # Hyperparameters ```python {'alg_type': 'dqn.py', 'batch_size': 256, 'buffer_size': 300000, 'capture_video': True, 'cuda': True, 'end_e': 0.1, 'env_id': 'CartPole-v1', 'exp_name': 'DQN_lr_0.00005', 'exploration_fraction': 0.2, 'gamma': 1.0, 'hf_entity': 'pfunk', 'learning_rate': 5e-05, 'learning_starts': 1000, 'save_model': True, 'seed': 1, 'start_e': 1.0, 'target_network_frequency': 20, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 500000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
alpindale/Llama-3.2-3B	alpindale	"2024-09-25T19:52:02Z"	207	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-09-25T19:51:31Z"	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-3B, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. Make sure to update your transformers installation via pip install --upgrade transformers. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-3B" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto" ) pipe("The key to life is") ``` ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-3B --include "original/" --local-dir Llama-3.2-3B ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. ## Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Total \| 830k \| 86k \| \| 240 \| 0 \| The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 63.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 40.1 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 19.0 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| avg(prompt/instruction acc loose/strict) \| 59.5 \| 77.4 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 77.7 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 47.3 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 78.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 32.8 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 69.8 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 67.0 \| 70.9 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 34.3 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| 19.8 \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| 63.3 \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| 84.7 \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 58.2 \| 68.9 \| ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro\_avg/acc) \| Portuguese \| 39.82 \| 54.48 \| 62.12 \| \| \| \| Spanish \| 41.5 \| 55.1 \| 62.5 \| \| \| \| Italian \| 39.8 \| 53.8 \| 61.6 \| \| \| \| German \| 39.2 \| 53.3 \| 60.6 \| \| \| \| French \| 40.5 \| 54.6 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 43.3 \| 50.9 \| \| \| \| Thai \| 34.7 \| 44.5 \| 50.3 \| ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
DreamyFrog/ntrdttt	DreamyFrog	"2023-03-07T13:51:54Z"	29	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2023-03-07T13:17:04Z"	--- license: creativeml-openrail-m tags: - text-to-image widget: - text: --- ### ntrdttt Dreambooth model trained by DreamyFrog with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of:
C10X/DSTQWQL	C10X	"2025-03-28T22:56:12Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1", "base_model:adapter:mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1", "region:us" ]	null	"2025-03-28T22:56:00Z"	--- base_model: mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
semindan/xnli_m_bert_only_th	semindan	"2023-01-07T14:29:50Z"	104	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:xnli", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2022-11-29T11:06:54Z"	--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - xnli metrics: - accuracy model-index: - name: xnli_m_bert_only_th results: - task: name: Text Classification type: text-classification dataset: name: xnli type: xnli config: th split: train args: th metrics: - name: Accuracy type: accuracy value: 0.6277108433734939 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xnli_m_bert_only_th This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset. It achieves the following results on the evaluation set: - Loss: 1.4266 - Accuracy: 0.6277 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.7447 \| 1.0 \| 3068 \| 0.8675 \| 0.6205 \| \| 0.6763 \| 2.0 \| 6136 \| 0.8060 \| 0.6602 \| \| 0.6124 \| 3.0 \| 9204 \| 0.8229 \| 0.6586 \| \| 0.5476 \| 4.0 \| 12272 \| 0.8333 \| 0.6542 \| \| 0.4817 \| 5.0 \| 15340 \| 0.8520 \| 0.6618 \| \| 0.4128 \| 6.0 \| 18408 \| 0.9734 \| 0.6426 \| \| 0.3436 \| 7.0 \| 21476 \| 1.0549 \| 0.6365 \| \| 0.2828 \| 8.0 \| 24544 \| 1.1406 \| 0.6321 \| \| 0.2272 \| 9.0 \| 27612 \| 1.3150 \| 0.6301 \| \| 0.1852 \| 10.0 \| 30680 \| 1.4266 \| 0.6277 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1
pe-nlp/R1-Qwen2.5-14B-Instruct-10k	pe-nlp	"2025-01-27T03:09:28Z"	14	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-27T03:04:22Z"	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-14B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: original results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # original This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the Sky-T1 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 12 - total_train_batch_size: 96 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0a0+f70bd71a48.nv24.06 - Datasets 3.1.0 - Tokenizers 0.20.3
featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF	featherless-ai-quants	"2024-10-30T09:29:59Z"	13	0	null	[ "gguf", "text-generation", "base_model:flammenai/Mahou-1.1-llama3-8B", "base_model:quantized:flammenai/Mahou-1.1-llama3-8B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2024-10-30T08:57:01Z"	--- base_model: flammenai/Mahou-1.1-llama3-8B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # flammenai/Mahou-1.1-llama3-8B GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) Optimized GGUF quantization files for enhanced model performance --- ## Available Quantizations 📊 \| Quantization Type \| File \| Size \| \|-------------------\|------\|------\| \| Q8_0 \| [flammenai-Mahou-1.1-llama3-8B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q8_0.gguf) \| 8145.11 MB \| \| Q4_K_S \| [flammenai-Mahou-1.1-llama3-8B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q4_K_S.gguf) \| 4475.28 MB \| \| Q2_K \| [flammenai-Mahou-1.1-llama3-8B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q2_K.gguf) \| 3031.86 MB \| \| Q6_K \| [flammenai-Mahou-1.1-llama3-8B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q6_K.gguf) \| 6290.44 MB \| \| Q3_K_M \| [flammenai-Mahou-1.1-llama3-8B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q3_K_M.gguf) \| 3832.74 MB \| \| Q3_K_S \| [flammenai-Mahou-1.1-llama3-8B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q3_K_S.gguf) \| 3494.74 MB \| \| Q3_K_L \| [flammenai-Mahou-1.1-llama3-8B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q3_K_L.gguf) \| 4121.74 MB \| \| Q4_K_M \| [flammenai-Mahou-1.1-llama3-8B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q4_K_M.gguf) \| 4692.78 MB \| \| Q5_K_S \| [flammenai-Mahou-1.1-llama3-8B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q5_K_S.gguf) \| 5339.90 MB \| \| Q5_K_M \| [flammenai-Mahou-1.1-llama3-8B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q5_K_M.gguf) \| 5467.40 MB \| \| IQ4_XS \| [flammenai-Mahou-1.1-llama3-8B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-IQ4_XS.gguf) \| 4276.62 MB \| --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly - 🛠️ Zero Infrastructure - No server setup or maintenance required - 📚 Vast Compatibility - Support for 2400+ models and counting - 💎 Affordable Pricing - Starting at just $10/month --- Links: [Get Started](https://featherless.ai) \| [Documentation](https://featherless.ai/docs) \| [Models](https://featherless.ai/models)
arjunshajitech/whisper-small-malayalam-v6	arjunshajitech	"2024-05-28T15:52:39Z"	86	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ml", "dataset:thennal/GMaSC", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-05-28T10:59:49Z"	--- language: - ml license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - thennal/GMaSC metrics: - wer model-index: - name: Whisper Small Malayalam - Arjun Shaji results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: thennal/GMaSC type: thennal/GMaSC args: 'config: ml, split: test' metrics: - name: Wer type: wer value: 16.95364238410596 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Malayalam - Arjun Shaji This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the thennal/GMaSC dataset. It achieves the following results on the evaluation set: - Loss: 0.0505 - Wer: 16.9536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0022 \| 10.0 \| 1000 \| 0.0410 \| 18.0132 \| \| 0.0002 \| 20.0 \| 2000 \| 0.0454 \| 17.6159 \| \| 0.0 \| 30.0 \| 3000 \| 0.0486 \| 17.2185 \| \| 0.0 \| 40.0 \| 4000 \| 0.0499 \| 17.1302 \| \| 0.0 \| 50.0 \| 5000 \| 0.0505 \| 16.9536 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
amutha27/adya	amutha27	"2025-03-16T08:21:08Z"	14	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-03-15T11:48:00Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: adya --- # Adya <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `adya` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('amutha27/adya', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
ckw1140/pb	ckw1140	"2025-04-09T14:23:13Z"	0	0	null	[ "region:us" ]	null	"2025-04-09T14:22:59Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
Triangle104/SmolLM2-360M-Q6_K-GGUF	Triangle104	"2024-11-07T05:06:14Z"	5	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:HuggingFaceTB/SmolLM2-360M", "base_model:quantized:HuggingFaceTB/SmolLM2-360M", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-11-07T05:06:11Z"	--- library_name: transformers license: apache-2.0 language: - en tags: - llama-cpp - gguf-my-repo base_model: HuggingFaceTB/SmolLM2-360M --- # Triangle104/SmolLM2-360M-Q6_K-GGUF This model was converted to GGUF format from [`HuggingFaceTB/SmolLM2-360M`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -c 2048 ```
alibidaran/LLAMA3.2-Virtual_doctor_GGUF	alibidaran	"2024-12-22T12:59:55Z"	273	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:alibidaran/LLAMA3.2-Virtual_doctor2", "base_model:quantized:alibidaran/LLAMA3.2-Virtual_doctor2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-12-22T12:59:00Z"	--- base_model: alibidaran/LLAMA3.2-Virtual_doctor2 tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: alibidaran - License: apache-2.0 - Finetuned from model : alibidaran/LLAMA3.2-Virtual_doctor2 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MaziyarPanahi/HELVETE-X-GGUF	MaziyarPanahi	"2024-12-17T01:22:50Z"	68	1	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:HelpingAI/HELVETE-X", "base_model:quantized:HelpingAI/HELVETE-X", "region:us", "conversational" ]	text-generation	"2024-12-17T00:50:38Z"	--- base_model: HelpingAI/HELVETE-X inference: false model_creator: HelpingAI model_name: HELVETE-X-GGUF pipeline_tag: text-generation quantized_by: MaziyarPanahi tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation --- # [MaziyarPanahi/HELVETE-X-GGUF](https://huggingface.co/MaziyarPanahi/HELVETE-X-GGUF) - Model creator: [HelpingAI](https://huggingface.co/HelpingAI) - Original model: [HelpingAI/HELVETE-X](https://huggingface.co/HelpingAI/HELVETE-X) ## Description [MaziyarPanahi/HELVETE-X-GGUF](https://huggingface.co/MaziyarPanahi/HELVETE-X-GGUF) contains GGUF format model files for [HelpingAI/HELVETE-X](https://huggingface.co/HelpingAI/HELVETE-X). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
Jonasbukhave/shawgpt-ft-epoch-13	Jonasbukhave	"2025-02-18T15:21:17Z"	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]	null	"2025-02-18T15:02:04Z"	--- library_name: peft license: apache-2.0 base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ tags: - generated_from_trainer model-index: - name: shawgpt-ft-epoch-13 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # shawgpt-ft-epoch-13 This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 13 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 8.5018 \| 0.5714 \| 1 \| 4.2401 \| \| 8.5334 \| 1.5714 \| 2 \| 4.1568 \| \| 8.0825 \| 2.5714 \| 3 \| 3.9663 \| \| 7.7613 \| 3.5714 \| 4 \| 3.7876 \| \| 7.3597 \| 4.5714 \| 5 \| 3.6281 \| \| 7.0923 \| 5.5714 \| 6 \| 3.4838 \| \| 6.8333 \| 6.5714 \| 7 \| 3.3548 \| \| 6.5917 \| 7.5714 \| 8 \| 3.2436 \| \| 6.4073 \| 8.5714 \| 9 \| 3.1514 \| \| 6.2985 \| 9.5714 \| 10 \| 3.0779 \| \| 6.0792 \| 10.5714 \| 11 \| 3.0223 \| \| 6.0114 \| 11.5714 \| 12 \| 2.9852 \| \| 3.0019 \| 12.5714 \| 13 \| 2.9662 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.1 - Tokenizers 0.21.0
dacorvo/mnist-mlp	dacorvo	"2024-10-24T15:56:49Z"	406	0	transformers	[ "transformers", "pytorch", "safetensors", "mlp", "feature-extraction", "pretrained", "image-classification", "custom_code", "license:apache-2.0", "region:us" ]	image-classification	"2023-10-04T15:05:17Z"	--- license: apache-2.0 pipeline_tag: image-classification tags: - pretrained --- # Model Card for MNIST-MLP This is a simple MLP trained on the MNIST dataset. Its primary use is to be a very simple reference model to test quantization. ## Inputs preprocessing The MNIST images must be normalized and flattened as follows: ``` from torchvision import datasets, transforms transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)), transforms.Lambda(lambda x: torch.flatten(x)), ]) test_set = datasets.MNIST('../data', train=False, download=True, transform=transform) ```
vertings6/47ae0897-9393-430a-901f-c0e31bd9731d	vertings6	"2025-01-15T03:29:55Z"	13	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	"2025-01-15T02:17:15Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 47ae0897-9393-430a-901f-c0e31bd9731d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6df2c62166a98406_train_data.json ds_type: json format: custom path: /workspace/input_data/6df2c62166a98406_train_data.json type: field_instruction: question_title field_output: answer_text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: vertings6/47ae0897-9393-430a-901f-c0e31bd9731d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/6df2c62166a98406_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: f6f83f39-4e70-4bbc-b582-89a1a29e2fb3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: f6f83f39-4e70-4bbc-b582-89a1a29e2fb3 warmup_steps: 10 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 47ae0897-9393-430a-901f-c0e31bd9731d This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0000 \| 1 \| nan \| \| 0.0 \| 0.0001 \| 8 \| nan \| \| 0.0 \| 0.0003 \| 16 \| nan \| \| 0.0 \| 0.0004 \| 24 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
edbeeching/atari_2B_atari_namethisgame_3333	edbeeching	"2024-04-16T14:06:23Z"	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "NameThisGameNoFrameskip-v4", "model-index", "region:us" ]	reinforcement-learning	"2022-10-21T07:18:14Z"	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory - NameThisGameNoFrameskip-v4 model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_namethisgame type: atari_namethisgame metrics: - type: mean_reward value: 23292.50 +/- 2939.43 name: mean_reward verified: false --- A(n) APPO model trained on the atari_namethisgame environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
ZeroXClem/Llama3.1-BestMix-Chem-Einstein-8B	ZeroXClem	"2024-11-09T17:40:56Z"	78	2	null	[ "safetensors", "llama", "merge", "TIES", "Llama3", "BestMix", "Chemistry", "Einstein", "instruction-following", "conversational", "long-form-generation", "scientific", "base_model:bunnycore/Best-Mix-Llama-3.1-8B", "base_model:finetune:bunnycore/Best-Mix-Llama-3.1-8B", "license:apache-2.0", "region:us" ]	null	"2024-10-11T01:03:27Z"	--- license: apache-2.0 tags: - merge - TIES - Llama3 - BestMix - Chemistry - Einstein - instruction-following - conversational - long-form-generation - scientific base_model: - bunnycore/Best-Mix-Llama-3.1-8B --- # ZeroXClem/Llama3.1-BestMix-Chem-Einstein-8B Llama3.1-BestMix-Chem-Einstein-8B is an innovative, meticulously blended model designed to excel in instruction-following, chemistry-focused tasks, and long-form conversational generation. This model fuses the best qualities of multiple Llama3-based architectures, making it highly versatile for both general and specialized tasks. 💻🧠✨ ## 🌟 Family Tree This model is the result of merging the following: - [bunnycore/Best-Mix-Llama-3.1-8B](https://huggingface.co/bunnycore/Best-Mix-Llama-3.1-8B): A balanced blend of top Llama models, optimized for general performance across reasoning, instruction-following, and math. - [USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B](https://huggingface.co/USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B): A model specialized in scientific knowledge and chemistry, excelling in chemistry benchmarks. - [Weyaxi/Einstein-v6.1-Llama3-8B](https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B): Fine-tuned for long-form generation, conversation-heavy tasks, and optimized with cutting-edge techniques for efficient memory usage and fast performance. --- ## 🧬 Model Lineage ### A: bunnycore/Best-Mix-Llama-3.1-8B - A masterful blend of several Llama3 models like Aurora_faustus, TitanFusion, and OpenMath2. - Provides a balanced performance in a variety of tasks such as reasoning, math, and instruction-following. - Key contributor to the overall versatility of the merged model. ### B: USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B - Specializes in chemistry and scientific knowledge, outperforming many larger models in chemistry benchmarks. - Adds scientific rigor and domain-specific expertise to the merged model, making it perfect for scientific and academic tasks. ### C: Weyaxi/Einstein-v6.1-Llama3-8B - Fine-tuned on a wide range of instructive and conversational datasets like WizardLM, Alpaca, and ShareGPT. - Optimized for long-form text generation and enhanced with xformers attention and flash attention techniques for better performance. - Key player in dialogue-based tasks and long conversation generation. --- ## 🛠️ Merge Details This model was merged using the TIES merge method, ensuring a smooth integration of the key strengths from each contributing model. Here's the configuration used: ```yaml yaml Copy code models: - model: bunnycore/Best-Mix-Llama-3.1-8B parameters: density: [1, 0.7, 0.5] weight: 1.0 - model: USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B parameters: density: 0.6 weight: [0.3, 0.7, 1.0] - model: Weyaxi/Einstein-v6.1-Llama3-8B parameters: density: 0.4 weight: - filter: mlp value: 0.5 - filter: self_attn value: 0.7 - value: 0.5 merge_method: ties base_model: bunnycore/Best-Mix-Llama-3.1-8B parameters: normalize: true int8_mask: true dtype: float16 ``` --- ## 🎯 Key Features & Capabilities ### 1. Instruction Following & General Reasoning: With the foundation of Best-Mix, this model excels in general-purpose reasoning, instruction-following, and tasks that require high adaptability. ### 2. Scientific & Chemistry Expertise: Thanks to the contribution from KALE-LM-Chem, this model shines in scientific research, particularly chemistry-focused tasks, making it ideal for academic and research purposes. ### 3. Long-Form & Conversational Mastery: With Einstein-v6.1, the model handles long-form generation effortlessly, excelling in extended conversations and structured dialogue applications. --- ## 🚀 Performance Benchmarks While still in its early stages, Llama3.1-BestMix-Chem-Einstein-8B is expected to perform well across a variety of benchmarks, including: - Chemistry-focused benchmarks (KALE-LM-Chem) - Instruction-following tasks (Best-Mix) - Conversational AI and long-form text generation (Einstein-v6.1) Further testing and evaluation will continue to refine this model's capabilities. --- ## 📜 License This model is open-sourced under the Apache-2.0 License, allowing free use and modification with proper attribution. --- ## 💡 Tags - `merge` - `TIES` - `BestMix` - `Chemistry` - `Einstein` - `instruction-following` - `long-form-generation` - `conversational` ---
aviralkumar28/ppo-LunarLander-v2	aviralkumar28	"2024-01-02T10:23:39Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2024-01-02T10:23:20Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 237.25 +/- 69.85 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Kris8an/v4_sql	Kris8an	"2024-03-22T16:57:45Z"	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-function-calling-v2", "base_model:adapter:Trelis/Llama-2-7b-chat-hf-function-calling-v2", "region:us" ]	null	"2024-03-22T16:42:20Z"	--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-function-calling-v2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
TechxGenus/CursorCore-QW2.5-1.5B-SR-AWQ	TechxGenus	"2024-10-10T06:42:32Z"	76	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "code", "conversational", "arxiv:2410.07002", "base_model:TechxGenus/CursorCore-QW2.5-1.5B-SR", "base_model:quantized:TechxGenus/CursorCore-QW2.5-1.5B-SR", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	"2024-10-08T04:55:01Z"	--- tags: - code base_model: - TechxGenus/CursorCore-QW2.5-1.5B-SR library_name: transformers pipeline_tag: text-generation license: apache-2.0 --- # CursorCore: Assist Programming through Aligning Anything <p align="center"> <a href="http://arxiv.org/abs/2410.07002">[📄arXiv]</a> \| <a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> \| <a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> \| <a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> \| <a href="https://github.com/TechxGenus/CursorWeb">[Web]</a> \| <a href="https://discord.gg/Z5Tev8fV">[Discord]</a> </p> <hr> - [CursorCore: Assist Programming through Aligning Anything](#cursorcore-assist-programming-through-aligning-anything) - [Introduction](#introduction) - [Models](#models) - [Usage](#usage) - [1) Normal chat](#1-normal-chat) - [2) Assistant-Conversation](#2-assistant-conversation) - [3) Web Demo](#3-web-demo) - [Future Work](#future-work) - [Citation](#citation) - [Contribution](#contribution) <hr> ## Introduction CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read [our paper](http://arxiv.org/abs/2410.07002) to learn more. <p align="center"> <img width="100%" alt="conversation" src="https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/conversation.png"> </p> ![CursorWeb](https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/CursorWeb.gif) ## Models Our models have been open-sourced on Hugging Face. You can access our models here: [CursorCore-Series](https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2"). We also provide pre-quantized weights for GPTQ and AWQ here: [CursorCore-Quantization](https://huggingface.co/collections/TechxGenus/cursorcore-quantization-67066431f29f252494ee8cf3) ## Usage Here are some examples of how to use our model: ### 1) Normal chat Script: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-9B", torch_dtype=torch.bfloat16, device_map="auto" ) messages = [ {"role": "user", "content": "Hi!"}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512) print(tokenizer.decode(outputs[0])) ```` Output: ````txt <\|im_start\|>system You are a helpful programming assistant.<\|im_end\|> <\|im_start\|>user Hi!<\|im_end\|> <\|im_start\|>assistant Hello! I'm an AI language model and I can help you with any programming questions you might have. What specific problem or task are you trying to solve?<\|im_end\|> ```` ### 2) Assistant-Conversation In our work, we introduce a new framework of AI-assisted programming task. It is designed for aligning anything during programming process, used for the implementation of features like Tab and Inline Chat. Script 1: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_wf tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-9B", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [ { "type": "code", "lang": "python", "code": """def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" } ], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "" } prompt = tokenizer.apply_chat_template( prepare_input_for_wf(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output 1: ````txt <\|im_start\|>system You are a helpful programming assistant.<\|im_end\|> <\|im_start\|>history ```python def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<\|im_end\|> <\|im_start\|>current ```python def quick_sort(array): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<\|im_end\|> <\|im_start\|>assistant <\|next_start\|>```python def quick_sort(array): if len(array) <= 1: return array pivot = array[len(array) // 2] left = [x for x in array if x < pivot] middle = [x for x in array if x == pivot] right = [x for x in array if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<\|next_end\|> The user has provided a revised code change that corrects the parameter name from `arr` to `array` in the `quick_sort` function. This change ensures consistency in the function definition and avoids potential confusion or errors. To implement this, we will: 1. Update the parameter name in the function definition from `arr` to `array`. 2. Ensure that all references to `arr` within the function are updated to `array`. This will make the function definition and internal references consistent, improving code readability and maintainability.<\|im_end\|> ```` Script 2: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_wf tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-9B", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "Add Docstring." } prompt = tokenizer.apply_chat_template( prepare_input_for_wf(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output 2: ````txt <\|im_start\|>system You are a helpful programming assistant.<\|im_end\|> <\|im_start\|>current ```python def quick_sort(array): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<\|im_end\|> <\|im_start\|>user Add Docstring.<\|im_end\|> <\|im_start\|>assistant <\|next_start\|>```python def quick_sort(array): """ This is an implementation of the quick sort algorithm. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<\|next_end\|><\|im_end\|> ```` For models in Locate-and-Change (LC) and Search-and-Replace (SR) formats, the output examples are as follows: Script for LC: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_lc tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-LC") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-1.5B-LC", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "Add Docstring." } prompt = tokenizer.apply_chat_template( prepare_input_for_lc(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output for LC: ````txt <\|im_start\|>system You are a helpful programming assistant.<\|im_end\|> <\|im_start\|>current ```python 1 def quick_sort(array): 2 if len(arr) <= 1: 3 return arr 4 pivot = arr[len(arr) // 2] 5 left = [x for x in arr if x < pivot] 6 middle = [x for x in arr if x == pivot] 7 right = [x for x in arr if x > pivot] 8 return quick_sort(left) + middle + quick_sort(right) ```<\|im_end\|> <\|im_start\|>user Add Docstring.<\|im_end\|> <\|im_start\|>assistant <\|next_start\|>1,1 ``` '''This function will sort an array using quick sort algorithm''' ```<\|next_end\|> To enhance the readability and maintainability of the code, we should add a docstring to the `quick_sort` function. A docstring provides a clear description of what the function does, which is particularly useful for other developers who might use or modify this code in the future. The docstring will be added immediately after the function definition, explaining that the function uses the quick sort algorithm to sort an array. This will make the code more self-explanatory and easier to understand. Here's the plan: 1. Add a docstring at the beginning of the `quick_sort` function. 2. Ensure the docstring is clear and concise, describing the purpose of the function. This modification will improve the code's documentation without altering its functionality.<\|im_end\|> ```` Script for SR: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_sr tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-SR") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-1.5B-SR", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "Add Docstring." } prompt = tokenizer.apply_chat_template( prepare_input_for_sr(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output for SR: ````txt <\|im_start\|>system You are a helpful programming assistant.<\|im_end\|> <\|im_start\|>current ```python def quick_sort(array): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<\|im_end\|> <\|im_start\|>user Add Docstring.<\|im_end\|> <\|im_start\|>assistant <\|next_start\|>```python def quick_sort(array): <\|search_and_replace\|> def quick_sort(array): """ This function implements quick sort algorithm """ ```<\|next_end\|><\|im_end\|> ```` ### 3) Web Demo We create a web demo for CursorCore. Please visit [CursorWeb](https://github.com/TechxGenus/CursorWeb) for more details. ## Future Work CursorCore is still in a very early stage, and lots of work is needed to achieve a better user experience. For example: - Repository-level editing support - Better and faster editing formats - Better user interface and presentation - ... ## Citation ```bibtex @article{jiang2024cursorcore, title = {CursorCore: Assist Programming through Aligning Anything}, author = {Hao Jiang and Qi Liu and Rui Li and Shengyu Ye and Shijin Wang}, year = {2024}, journal = {arXiv preprint arXiv: 2410.07002} } ``` ## Contribution Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja	atsuki-yamaguchi	"2024-04-22T09:05:45Z"	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "ja", "arxiv:2402.10712", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-02-19T15:02:46Z"	--- license: mit language: ja --- Mistral-7B Japanese [LAPT + Heuristics] === ## How to use ```python from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer model = AutoPeftModelForCausalLM.from_pretrained( "atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja" ) tokenizer = AutoTokenizer.from_pretrained( "atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja" ) # w/ GPU model = AutoPeftModelForCausalLM.from_pretrained( "atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja", device_map="auto", load_in_8bit=True, ) ``` ## Citation ``` @article{yamaguchi2024empirical, title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference}, author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras}, journal={ArXiv}, year={2024}, volume={abs/2402.10712}, url={https://arxiv.org/abs/2402.10712} } ``` ## Link For more details, please visit https://github.com/gucci-j/llm-cva
lesso07/fe6c0a54-7030-4ed5-b7d1-3f431e79c073	lesso07	"2025-01-16T12:36:07Z"	6	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2-9b-it", "base_model:adapter:unsloth/gemma-2-9b-it", "license:gemma", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-01-16T12:19:02Z"	--- library_name: peft license: gemma base_model: unsloth/gemma-2-9b-it tags: - axolotl - generated_from_trainer model-index: - name: fe6c0a54-7030-4ed5-b7d1-3f431e79c073 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2-9b-it bf16: true chat_template: llama3 datasets: - data_files: - 90a049d18dba60fe_train_data.json ds_type: json format: custom path: /workspace/input_data/90a049d18dba60fe_train_data.json type: field_instruction: instruction field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lesso07/fe6c0a54-7030-4ed5-b7d1-3f431e79c073 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/90a049d18dba60fe_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 8b3341b1-8f11-470d-a3fd-6937caa7d938 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8b3341b1-8f11-470d-a3fd-6937caa7d938 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # fe6c0a54-7030-4ed5-b7d1-3f431e79c073 This model is a fine-tuned version of [unsloth/gemma-2-9b-it](https://huggingface.co/unsloth/gemma-2-9b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6951 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.2927 \| 0.0006 \| 1 \| 2.4089 \| \| 2.1984 \| 0.0032 \| 5 \| 2.2714 \| \| 1.8572 \| 0.0065 \| 10 \| 1.9070 \| \| 1.8613 \| 0.0097 \| 15 \| 1.7381 \| \| 1.5576 \| 0.0129 \| 20 \| 1.7004 \| \| 1.6629 \| 0.0162 \| 25 \| 1.6951 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
certainstar/Trained-English-classification-case	certainstar	"2024-07-08T08:06:34Z"	105	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "en", "dataset:Hello-SimpleAI/HC3", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-07-08T07:56:59Z"	--- license: mit datasets: - Hello-SimpleAI/HC3 language: - en metrics: - accuracy --- - 本模型采取 `HC3的英文数据集` 对 `bert-base-cased` 模型进行三轮训练得到结果。 - 其作用是对文本是否为 `GPT` 生成进行分类，所得 `Label` 为0，则不为 `GPT` 生成，反之为1，则是。
sekinko/weights_text	sekinko	"2023-01-02T06:37:09Z"	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2023-01-02T05:11:35Z"	--- license: cc-by-sa-4.0 tags: - generated_from_trainer model-index: - name: weights_text results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # weights_text This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu117 - Tokenizers 0.13.2
AdapterHub/roberta-large-mrpc_houlsby	AdapterHub	"2024-05-05T19:40:44Z"	0	0	adapter-transformers	[ "adapter-transformers", "adapterhub:sts/mrpc", "text-classification", "roberta", "license:apache-2.0", "region:us" ]	text-classification	"2024-05-05T19:40:34Z"	--- tags: - adapterhub:sts/mrpc - text-classification - adapter-transformers - roberta license: "apache-2.0" --- # Adapter `roberta-large-mrpc_houlsby` for roberta-large MRPC adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs). This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-large") adapter_name = model.load_adapter("AdapterHub/roberta-large-mrpc_houlsby") model.set_active_adapters(adapter_name) ``` ## Architecture & Training - Adapter architecture: houlsby - Prediction head: classification - Dataset: [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398) ## Author Information - Author name(s): Andreas Rücklé - Author email: [email protected] - Author links: [Website](http://rueckle.net), [GitHub](https://github.com/arueckle), [Twitter](https://twitter.com/@arueckle) ## Citation ```bibtex @article{pfeiffer2020AdapterHub, title={AdapterHub: A Framework for Adapting Transformers}, author={Jonas Pfeiffer, Andreas R\"uckl\'{e}, Clifton Poth, Aishwarya Kamath, Ivan Vuli\'{c}, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych}, journal={ArXiv}, year={2020} } ``` This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/roberta-large-mrpc_houlsby.yaml.
nouamanetazi/wav2vec2-xls-r-300m-ar	nouamanetazi	"2022-03-23T18:35:04Z"	16	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "ar", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2022-03-02T23:29:05Z"	--- language: - ar license: apache-2.0 tags: - ar - automatic-speech-recognition - common_voice - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - common_voice model-index: - name: XLS-R-300M - Arabic results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: ar metrics: - name: Test WER type: wer value: 1.0 - name: Test CER type: cer value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-ar This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - AR dataset. It achieves the following results on the evaluation set: - eval_loss: 3.0191 - eval_wer: 1.0 - eval_runtime: 252.2389 - eval_samples_per_second: 30.217 - eval_steps_per_second: 0.476 - epoch: 1.0 - step: 340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 #### Evaluation Commands Please use the evaluation script `eval.py` included in the repo. 1. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id nouamanetazi/wav2vec2-xls-r-300m-ar --dataset speech-recognition-community-v2/dev_data --config ar --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ```
jvbjkbjkbfjis/distillbert-base-drug-effectiveness-classification-model	jvbjkbjkbfjis	"2024-06-03T10:40:50Z"	118	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-03T09:28:52Z"	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: distillbert-base-drug-effectiveness-classification-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distillbert-base-drug-effectiveness-classification-model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3800 - F1: 0.4333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 1.4627 \| 0.4117 \| \| 1.6681 \| 2.0 \| 802 \| 1.4132 \| 0.4304 \| \| 1.3968 \| 3.0 \| 1203 \| 1.3890 \| 0.4258 \| \| 1.3289 \| 4.0 \| 1604 \| 1.3800 \| 0.4333 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
nkandpa2/comma-loss-test	nkandpa2	"2025-02-21T19:27:27Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-21T03:53:22Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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cesun/ThinkEdit-deepseek-qwen-14b	cesun	"2025-04-14T19:31:55Z"	46	2	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:2503.22048", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-14T03:28:27Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
espnet/owls_1B_180K_intermediates	espnet	"2025-04-10T14:41:57Z"	0	0	null	[ "region:us" ]	null	"2025-04-10T14:40:09Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
RichardErkhov/mlfoundations-dev_-_llama3-1_8b_baseline_dcft_oh_v3-8bits	RichardErkhov	"2025-04-02T04:03:12Z"	0	0	null	[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-04-02T03:57:24Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama3-1_8b_baseline_dcft_oh_v3 - bnb 8bits - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/llama3-1_8b_baseline_dcft_oh_v3/ Original model description: --- library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: llama3-1_8b_baseline_dcft_oh_v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama3-1_8b_baseline_dcft_oh_v3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the mlfoundations-dev/oh-dcft-v3-sharegpt dataset. It achieves the following results on the evaluation set: - Loss: 0.6423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 512 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 1738 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6519 \| 1.0 \| 423 \| 0.6500 \| \| 0.6088 \| 2.0 \| 846 \| 0.6398 \| \| 0.5839 \| 3.0 \| 1269 \| 0.6423 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1
timm/vit_small_patch32_224.augreg_in21k	timm	"2025-01-21T19:16:44Z"	170	0	timm	[ "timm", "pytorch", "safetensors", "image-classification", "transformers", "dataset:imagenet-21k", "arxiv:2106.10270", "arxiv:2010.11929", "license:apache-2.0", "region:us" ]	image-classification	"2022-12-22T07:55:11Z"	--- tags: - image-classification - timm - transformers library_name: timm license: apache-2.0 datasets: - imagenet-21k --- # Model card for vit_small_patch32_224.augreg_in21k A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 30.9 - GMACs: 1.1 - Activations (M): 2.1 - Image size: 224 x 224 - Papers: - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - Dataset: ImageNet-21k - Original: https://github.com/google-research/vision_transformer ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_small_patch32_224.augreg_in21k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(*data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_small_patch32_224.augreg_in21k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 50, 384) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{steiner2021augreg, title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers}, author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas}, journal={arXiv preprint arXiv:2106.10270}, year={2021} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
RichardErkhov/bunnycore_-_Llama-3.2-3B-Creative-8bits	RichardErkhov	"2025-03-09T10:06:58Z"	0	0	null	[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-03-09T10:03:59Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3.2-3B-Creative - bnb 8bits - Model creator: https://huggingface.co/bunnycore/ - Original model: https://huggingface.co/bunnycore/Llama-3.2-3B-Creative/ Original model description: --- base_model: - bunnycore/Llama-3.2-3B-All-Mix - bunnycore/llama-3.2-3b-story-lora_model library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method using [bunnycore/Llama-3.2-3B-All-Mix](https://huggingface.co/bunnycore/Llama-3.2-3B-All-Mix) + [bunnycore/llama-3.2-3b-story-lora_model](https://huggingface.co/bunnycore/llama-3.2-3b-story-lora_model) as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: bunnycore/Llama-3.2-3B-All-Mix+bunnycore/llama-3.2-3b-story-lora_model dtype: bfloat16 merge_method: passthrough models: - model: bunnycore/Llama-3.2-3B-All-Mix+bunnycore/llama-3.2-3b-story-lora_model ```
Stanford-ILIAD/prism-qwen25-extra-dinosiglip-224px-0_5b	Stanford-ILIAD	"2024-12-12T00:41:53Z"	163	1	transformers	[ "transformers", "robotics", "vlm", "image-text-to-text", "multimodal", "pretraining", "en", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	"2024-12-12T00:19:02Z"	--- library_name: transformers tags: - robotics - vlm - image-text-to-text - multimodal - pretraining license: mit language: - en pipeline_tag: image-text-to-text --- # Prism with Qwen 2.5 0.5B backbone (Prismatic-Compatible Version) This model is trained on the Llava-1.5-Instruct dataset. ## Usage Instructions See the [MiniVLA GitHub README](https://github.com/Stanford-ILIAD/openvla-mini/blob/main/README.md) for instructions on how to use this checkpoint for downstream training and finetuning. ## Citation BibTeX: ```bibtex @article{belkhale24minivla, title={MiniVLA: A Better VLA with a Smaller Footprint}, author={Suneel Belkhale and Dorsa Sadigh}, url={https://github.com/Stanford-ILIAD/openvla-mini} year={2024} } ```
microsoft/Florence-2-large	microsoft	"2024-12-08T22:30:48Z"	667,001	1,386	transformers	[ "transformers", "pytorch", "florence2", "text-generation", "vision", "image-text-to-text", "custom_code", "arxiv:2311.06242", "license:mit", "autotrain_compatible", "region:us" ]	image-text-to-text	"2024-06-15T00:34:55Z"	--- license: mit license_link: https://huggingface.co/microsoft/Florence-2-large/resolve/main/LICENSE pipeline_tag: image-text-to-text tags: - vision --- # Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks ## Model Summary This is a continued pretrained version of Florence-2-large model with 4k context length, only 0.1B samples are used for continue pretraining, thus it might not be trained well. In addition, OCR task has been updated with line separator ('\n'). COCO OD AP 39.8 This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + [Florence-2 technical report](https://arxiv.org/abs/2311.06242). + [Jupyter Notebook for inference and visualization of Florence-2-large](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb) \| Model \| Model size \| Model Description \| \| ------- \| ------------- \| ------------- \| \| Florence-2-base[[HF]](https://huggingface.co/microsoft/Florence-2-base) \| 0.23B \| Pretrained model with FLD-5B \| Florence-2-large[[HF]](https://huggingface.co/microsoft/Florence-2-large) \| 0.77B \| Pretrained model with FLD-5B \| Florence-2-base-ft[[HF]](https://huggingface.co/microsoft/Florence-2-base-ft) \| 0.23B \| Finetuned model on a colletion of downstream tasks \| Florence-2-large-ft[[HF]](https://huggingface.co/microsoft/Florence-2-large-ft) \| 0.77B \| Finetuned model on a colletion of downstream tasks ## How to Get Started with the Model Use the code below to get started with the model. All models are trained with float16. ```python import requests import torch from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device) processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True) prompt = "<OD>" url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=4096, num_beams=3, do_sample=False ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height)) print(parsed_answer) ``` ## Tasks This model is capable of performing different tasks through changing the prompts. First, let's define a function to run a prompt. <details> <summary> Click to expand </summary> ```python import requests import torch from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device) processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True) url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) def run_example(task_prompt, text_input=None): if text_input is None: prompt = task_prompt else: prompt = task_prompt + text_input inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3 ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height)) print(parsed_answer) ``` </details> Here are the tasks `Florence-2` could perform: <details> <summary> Click to expand </summary> ### Caption ```python prompt = "<CAPTION>" run_example(prompt) ``` ### Detailed Caption ```python prompt = "<DETAILED_CAPTION>" run_example(prompt) ``` ### More Detailed Caption ```python prompt = "<MORE_DETAILED_CAPTION>" run_example(prompt) ``` ### Caption to Phrase Grounding caption to phrase grounding task requires additional text input, i.e. caption. Caption to phrase grounding results format: {'\<CAPTION_TO_PHRASE_GROUNDING>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}} ```python task_prompt = "<CAPTION_TO_PHRASE_GROUNDING>" results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.") ``` ### Object Detection OD results format: {'\<OD>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['label1', 'label2', ...]} } ```python prompt = "<OD>" run_example(prompt) ``` ### Dense Region Caption Dense region caption results format: {'\<DENSE_REGION_CAPTION>' : {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['label1', 'label2', ...]} } ```python prompt = "<DENSE_REGION_CAPTION>" run_example(prompt) ``` ### Region proposal Dense region caption results format: {'\<REGION_PROPOSAL>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}} ```python prompt = "<REGION_PROPOSAL>" run_example(prompt) ``` ### OCR ```python prompt = "<OCR>" run_example(prompt) ``` ### OCR with Region OCR with region output format: {'\<OCR_WITH_REGION>': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}} ```python prompt = "<OCR_WITH_REGION>" run_example(prompt) ``` ### Output confidence score with Object Detection ```python def run_example_with_score(task_prompt, text_input=None): if text_input is None: prompt = task_prompt else: prompt = task_prompt + text_input inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3, return_dict_in_generate=True, output_scores=True, ) generated_text = processor.batch_decode(generated_ids.sequences, skip_special_tokens=False)[0] prediction, scores, beam_indices = generated_ids.sequences, generated_ids.scores, generated_ids.beam_indices transition_beam_scores = model.compute_transition_scores( sequences=prediction, scores=scores, beam_indices=beam_indices, ) parsed_answer = processor.post_process_generation(sequence=generated_ids.sequences[0], transition_beam_score=transition_beam_scores[0], task=task_prompt, image_size=(image.width, image.height) ) print(parsed_answer) prompt = "<OD>" run_example_with_score(prompt) ``` for More detailed examples, please refer to [notebook](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb) </details> # Benchmarks ## Florence-2 Zero-shot performance The following table presents the zero-shot performance of generalist vision foundation models on image captioning and object detection evaluation tasks. These models have not been exposed to the training data of the evaluation tasks during their training phase. \| Method \| #params \| COCO Cap. test CIDEr \| NoCaps val CIDEr \| TextCaps val CIDEr \| COCO Det. val2017 mAP \| \|--------\|---------\|----------------------\|------------------\|--------------------\|-----------------------\| \| Flamingo \| 80B \| 84.3 \| - \| - \| - \| \| Florence-2-base\| 0.23B \| 133.0 \| 118.7 \| 70.1 \| 34.7 \| \| Florence-2-large\| 0.77B \| 135.6 \| 120.8 \| 72.8 \| 37.5 \| The following table continues the comparison with performance on other vision-language evaluation tasks. \| Method \| Flickr30k test R@1 \| Refcoco val Accuracy \| Refcoco test-A Accuracy \| Refcoco test-B Accuracy \| Refcoco+ val Accuracy \| Refcoco+ test-A Accuracy \| Refcoco+ test-B Accuracy \| Refcocog val Accuracy \| Refcocog test Accuracy \| Refcoco RES val mIoU \| \|--------\|----------------------\|----------------------\|-------------------------\|-------------------------\|-----------------------\|--------------------------\|--------------------------\|-----------------------\|------------------------\|----------------------\| \| Kosmos-2 \| 78.7 \| 52.3 \| 57.4 \| 47.3 \| 45.5 \| 50.7 \| 42.2 \| 60.6 \| 61.7 \| - \| \| Florence-2-base \| 83.6 \| 53.9 \| 58.4 \| 49.7 \| 51.5 \| 56.4 \| 47.9 \| 66.3 \| 65.1 \| 34.6 \| \| Florence-2-large \| 84.4 \| 56.3 \| 61.6 \| 51.4 \| 53.6 \| 57.9 \| 49.9 \| 68.0 \| 67.0 \| 35.8 \| ## Florence-2 finetuned performance We finetune Florence-2 models with a collection of downstream tasks, resulting two generalist models Florence-2-base-ft and Florence-2-large-ft that can conduct a wide range of downstream tasks. The table below compares the performance of specialist and generalist models on various captioning and Visual Question Answering (VQA) tasks. Specialist models are fine-tuned specifically for each task, whereas generalist models are fine-tuned in a task-agnostic manner across all tasks. The symbol "▲" indicates the usage of external OCR as input. \| Method \| # Params \| COCO Caption Karpathy test CIDEr \| NoCaps val CIDEr \| TextCaps val CIDEr \| VQAv2 test-dev Acc \| TextVQA test-dev Acc \| VizWiz VQA test-dev Acc \| \|----------------\|----------\|-----------------------------------\|------------------\|--------------------\|--------------------\|----------------------\|-------------------------\| \| Specialist Models \| \| \| \| \| \| \| \| \| CoCa \| 2.1B \| 143.6 \| 122.4 \| - \| 82.3 \| - \| - \| \| BLIP-2 \| 7.8B \| 144.5 \| 121.6 \| - \| 82.2 \| - \| - \| \| GIT2 \| 5.1B \| 145.0 \| 126.9 \| 148.6 \| 81.7 \| 67.3 \| 71.0 \| \| Flamingo \| 80B \| 138.1 \| - \| - \| 82.0 \| 54.1 \| 65.7 \| \| PaLI \| 17B \| 149.1 \| 127.0 \| 160.0▲ \| 84.3 \| 58.8 / 73.1▲ \| 71.6 / 74.4▲ \| \| PaLI-X \| 55B \| 149.2 \| 126.3 \| 147.0 / 163.7▲ \| 86.0 \| 71.4 / 80.8▲ \| 70.9 / 74.6▲ \| \| Generalist Models \| \| \| \| \| \| \| \| \| Unified-IO \| 2.9B \| - \| 100.0 \| - \| 77.9 \| - \| 57.4 \| \| Florence-2-base-ft \| 0.23B \| 140.0 \| 116.7 \| 143.9 \| 79.7 \| 63.6 \| 63.6 \| \| Florence-2-large-ft \| 0.77B \| 143.3 \| 124.9 \| 151.1 \| 81.7 \| 73.5 \| 72.6 \| \| Method \| # Params \| COCO Det. val2017 mAP \| Flickr30k test R@1 \| RefCOCO val Accuracy \| RefCOCO test-A Accuracy \| RefCOCO test-B Accuracy \| RefCOCO+ val Accuracy \| RefCOCO+ test-A Accuracy \| RefCOCO+ test-B Accuracy \| RefCOCOg val Accuracy \| RefCOCOg test Accuracy \| RefCOCO RES val mIoU \| \|----------------------\|----------\|-----------------------\|--------------------\|----------------------\|-------------------------\|-------------------------\|------------------------\|---------------------------\|---------------------------\|------------------------\|-----------------------\|------------------------\| \| Specialist Models \| \| \| \| \| \| \| \| \| \| \| \| \| \| SeqTR \| - \| - \| - \| 83.7 \| 86.5 \| 81.2 \| 71.5 \| 76.3 \| 64.9 \| 74.9 \| 74.2 \| - \| \| PolyFormer \| - \| - \| - \| 90.4 \| 92.9 \| 87.2 \| 85.0 \| 89.8 \| 78.0 \| 85.8 \| 85.9 \| 76.9 \| \| UNINEXT \| 0.74B \| 60.6 \| - \| 92.6 \| 94.3 \| 91.5 \| 85.2 \| 89.6 \| 79.8 \| 88.7 \| 89.4 \| - \| \| Ferret \| 13B \| - \| - \| 89.5 \| 92.4 \| 84.4 \| 82.8 \| 88.1 \| 75.2 \| 85.8 \| 86.3 \| - \| \| Generalist Models \| \| \| \| \| \| \| \| \| \| \| \| \| \| UniTAB \| - \| - \| - \| 88.6 \| 91.1 \| 83.8 \| 81.0 \| 85.4 \| 71.6 \| 84.6 \| 84.7 \| - \| \| Florence-2-base-ft \| 0.23B \| 41.4 \| 84.0 \| 92.6 \| 94.8 \| 91.5 \| 86.8 \| 91.7 \| 82.2 \| 89.8 \| 82.2 \| 78.0 \| \| Florence-2-large-ft\| 0.77B \| 43.4 \| 85.2 \| 93.4 \| 95.3 \| 92.0 \| 88.3 \| 92.9 \| 83.6 \| 91.2 \| 91.7 \| 80.5 \| ## BibTex and citation info ``` @article{xiao2023florence, title={Florence-2: Advancing a unified representation for a variety of vision tasks}, author={Xiao, Bin and Wu, Haiping and Xu, Weijian and Dai, Xiyang and Hu, Houdong and Lu, Yumao and Zeng, Michael and Liu, Ce and Yuan, Lu}, journal={arXiv preprint arXiv:2311.06242}, year={2023} } ```
biustnaspust/puszek23	biustnaspust	"2025-01-22T18:32:23Z"	33	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-22T18:27:44Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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akioi/deepseek-coder-6.7b-instruct-finetuned-v5	akioi	"2024-09-22T22:38:46Z"	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-09-22T21:52:21Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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atrost/qwen_3b_rl_math_2_epochs	atrost	"2025-03-21T00:14:05Z"	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-19T18:38:31Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/dolphin-2_2-yi-34b-GGUF	mradermacher	"2024-07-08T00:12:59Z"	246	0	transformers	[ "transformers", "gguf", "en", "dataset:ehartford/dolphin", "dataset:jondurbin/airoboros-2.2.1", "dataset:ehartford/samantha-data", "dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split", "base_model:cognitivecomputations/dolphin-2_2-yi-34b", "base_model:quantized:cognitivecomputations/dolphin-2_2-yi-34b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-07-06T02:06:48Z"	--- base_model: cognitivecomputations/dolphin-2_2-yi-34b datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 - ehartford/samantha-data - ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cognitivecomputations/dolphin-2_2-yi-34b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q2_K.gguf) \| Q2_K \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ3_XS.gguf) \| IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q3_K_S.gguf) \| Q3_K_S \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ3_S.gguf) \| IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ3_M.gguf) \| IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q3_K_M.gguf) \| Q3_K_M \| 16.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q3_K_L.gguf) \| Q3_K_L \| 18.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ4_XS.gguf) \| IQ4_XS \| 18.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q4_K_S.gguf) \| Q4_K_S \| 19.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q4_K_M.gguf) \| Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q5_K_S.gguf) \| Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q5_K_M.gguf) \| Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q6_K.gguf) \| Q6_K \| 28.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q8_0.gguf) \| Q8_0 \| 36.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
c00cjz00/phi-4-R1-medical	c00cjz00	"2025-03-21T00:04:17Z"	110	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "phi", "nlp", "math", "code", "chat", "conversational", "en", "arxiv:2412.08905", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-13T20:46:21Z"	--- license: mit license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - phi - nlp - math - code - chat - conversational inference: parameters: temperature: 0 widget: - messages: - role: user content: How should I explain the Internet? library_name: transformers --- # Phi-4 Model Card [Phi-4 Technical Report](https://arxiv.org/pdf/2412.08905) ## Model Summary \| \| \| \|-------------------------\|-------------------------------------------------------------------------------\| \| Developers \| Microsoft Research \| \| Description \| `phi-4` is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.<br><br>`phi-4` underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures \| \| Architecture \| 14B parameters, dense decoder-only Transformer model \| \| Inputs \| Text, best suited for prompts in the chat format \| \| Context length \| 16K tokens \| \| GPUs \| 1920 H100-80G \| \| Training time \| 21 days \| \| Training data \| 9.8T tokens \| \| Outputs \| Generated text in response to input \| \| Dates \| October 2024 – November 2024 \| \| Status \| Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data \| \| Release date \| December 12, 2024 \| \| License \| MIT \| ## Intended Use \| \| \| \|-------------------------------\|-------------------------------------------------------------------------\| \| Primary Use Cases \| Our model is designed to accelerate research on language models, for use as a building block for generative AI powered features. It provides uses for general purpose AI systems and applications (primarily in English) which require:<br><br>1. Memory/compute constrained environments.<br>2. Latency bound scenarios.<br>3. Reasoning and logic. \| \| Out-of-Scope Use Cases \| Our models is not specifically designed or evaluated for all downstream purposes, thus:<br><br>1. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.<br>2. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case, including the model’s focus on English.<br>3. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. \| ## Data Overview ### Training Datasets Our training data is an extension of the data used for Phi-3 and includes a wide variety of sources from: 1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code. 2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.). 3. Acquired academic books and Q&A datasets. 4. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. Multilingual data constitutes about 8% of our overall data. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. #### Benchmark datasets We evaluated `phi-4` using [OpenAI’s SimpleEval](https://github.com/openai/simple-evals) and our own internal benchmarks to understand the model’s capabilities, more specifically: * MMLU: Popular aggregated dataset for multitask language understanding. * MATH: Challenging competition math problems. * GPQA: Complex, graduate-level science questions. * DROP: Complex comprehension and reasoning. * MGSM: Multi-lingual grade-school math. * HumanEval: Functional code generation. * SimpleQA: Factual responses. ## Safety ### Approach `phi-4` has adopted a robust safety post-training approach. This approach leverages a variety of both open-source and in-house generated synthetic datasets. The overall technique employed to do the safety alignment is a combination of SFT (Supervised Fine-Tuning) and iterative DPO (Direct Preference Optimization), including publicly available datasets focusing on helpfulness and harmlessness as well as various questions and answers targeted to multiple safety categories. ### Safety Evaluation and Red-Teaming Prior to release, `phi-4` followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversation simulation. For qualitative safety evaluation, we collaborated with the independent AI Red Team (AIRT) at Microsoft to assess safety risks posed by `phi-4` in both average and adversarial user scenarios. In the average user scenario, AIRT emulated typical single-turn and multi-turn interactions to identify potentially risky behaviors. The adversarial user scenario tested a wide range of techniques aimed at intentionally subverting the model’s safety training including jailbreaks, encoding-based attacks, multi-turn attacks, and adversarial suffix attacks. Please refer to the technical report for more details on safety alignment. ## Model Quality To understand the capabilities, we compare `phi-4` with a set of models over OpenAI’s SimpleEval benchmark. At the high-level overview of the model quality on representative benchmarks. For the table below, higher numbers indicate better performance: \| Category \| Benchmark \| phi-4 (14B) \| phi-3 (14B) \| Qwen 2.5 (14B instruct) \| GPT-4o-mini \| Llama-3.3 (70B instruct) \| Qwen 2.5 (72B instruct) \| GPT-4o \| \|------------------------------\|---------------\|-----------\|-----------------\|----------------------\|----------------------\|--------------------\|-------------------\|-----------------\| \| Popular Aggregated Benchmark \| MMLU \| 84.8 \| 77.9 \| 79.9 \| 81.8 \| 86.3 \| 85.3 \| 88.1 \| \| Science \| GPQA \| 56.1 \| 31.2 \| 42.9 \| 40.9 \| 49.1 \| 49.0 \| 50.6 \| \| Math \| MGSM<br>MATH \| 80.6<br>80.4 \| 53.5<br>44.6 \| 79.6<br>75.6 \| 86.5<br>73.0 \| 89.1<br>66.3* \| 87.3<br>80.0 \| 90.4<br>74.6 \| \| Code Generation \| HumanEval \| 82.6 \| 67.8 \| 72.1 \| 86.2 \| 78.9* \| 80.4 \| 90.6 \| \| Factual Knowledge \| SimpleQA \| 3.0 \| 7.6 \| 5.4 \| 9.9 \| 20.9 \| 10.2 \| 39.4 \| \| Reasoning \| DROP \| 75.5 \| 68.3 \| 85.5 \| 79.3 \| 90.2 \| 76.7 \| 80.9 \| \* These scores are lower than those reported by Meta, perhaps because simple-evals has a strict formatting requirement that Llama models have particular trouble following. We use the simple-evals framework because it is reproducible, but Meta reports 77 for MATH and 88 for HumanEval on Llama-3.3-70B. ## Usage ### Input Formats Given the nature of the training data, `phi-4` is best suited for prompts using the chat format as follows: ```bash <\|im_start\|>system<\|im_sep\|> You are a medieval knight and must provide explanations to modern people.<\|im_end\|> <\|im_start\|>user<\|im_sep\|> How should I explain the Internet?<\|im_end\|> <\|im_start\|>assistant<\|im_sep\|> ``` ### With `transformers` ```python import transformers pipeline = transformers.pipeline( "text-generation", model="microsoft/phi-4", model_kwargs={"torch_dtype": "auto"}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a medieval knight and must provide explanations to modern people."}, {"role": "user", "content": "How should I explain the Internet?"}, ] outputs = pipeline(messages, max_new_tokens=128) print(outputs[0]["generated_text"][-1]) ``` ## Responsible AI Considerations Like other language models, `phi-4` can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: * Quality of Service: The model is trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. `phi-4` is not intended to support multilingual use. * Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. * Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. * Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. * Limited Scope for Code: Majority of `phi-4` training data is based in Python and uses common packages such as `typing`, `math`, `random`, `collections`, `datetime`, `itertools`. If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Using safety services like [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety) that have advanced guardrails is highly recommended. Important areas for consideration include: * Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. * High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. * Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). * Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. * Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
lesso/54e510f0-0fcf-4093-92f5-d455c3c63086	lesso	"2025-02-09T00:10:32Z"	6	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	"2025-02-07T02:05:16Z"	--- library_name: peft license: mit base_model: microsoft/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 54e510f0-0fcf-4093-92f5-d455c3c63086 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <br> # 54e510f0-0fcf-4093-92f5-d455c3c63086 This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001011 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.7609 \| 0.0000 \| 1 \| 2.2316 \| \| 2.6681 \| 0.0021 \| 50 \| 1.2262 \| \| 1.8951 \| 0.0041 \| 100 \| 1.1818 \| \| 2.0745 \| 0.0062 \| 150 \| 1.1588 \| \| 2.5473 \| 0.0082 \| 200 \| 1.1451 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
TheBloke/deepmoney-34b-200k-base-GGUF	TheBloke	"2024-01-13T00:56:04Z"	571	15	transformers	[ "transformers", "gguf", "yi", "finance", "invest", "en", "zh", "base_model:TriadParty/deepmoney-34b-200k-base", "base_model:quantized:TriadParty/deepmoney-34b-200k-base", "license:apache-2.0", "region:us" ]	null	"2024-01-13T00:36:52Z"	--- base_model: TriadParty/deepmoney-34b-200k-base inference: false language: - en - zh license: apache-2.0 model_creator: triad party model_name: Deepmoney 34B 200K Base model_type: yi prompt_template: '{prompt} ' quantized_by: TheBloke tags: - finance - invest --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Deepmoney 34B 200K Base - GGUF - Model creator: [triad party](https://huggingface.co/TriadParty) - Original model: [Deepmoney 34B 200K Base](https://huggingface.co/TriadParty/deepmoney-34b-200k-base) <!-- description start --> ## Description This repo contains GGUF format model files for [triad party's Deepmoney 34B 200K Base](https://huggingface.co/TriadParty/deepmoney-34b-200k-base). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF) * [triad party's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TriadParty/deepmoney-34b-200k-base) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [deepmoney-34b-200k-base.Q2_K.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q2_K.gguf) \| Q2_K \| 2 \| 12.77 GB\| 15.27 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [deepmoney-34b-200k-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 14.96 GB\| 17.46 GB \| very small, high quality loss \| \| [deepmoney-34b-200k-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 16.65 GB\| 19.15 GB \| very small, high quality loss \| \| [deepmoney-34b-200k-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 18.14 GB\| 20.64 GB \| small, substantial quality loss \| \| [deepmoney-34b-200k-base.Q4_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_0.gguf) \| Q4_0 \| 4 \| 19.47 GB\| 21.97 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [deepmoney-34b-200k-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 19.60 GB\| 22.10 GB \| small, greater quality loss \| \| [deepmoney-34b-200k-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 20.66 GB\| 23.16 GB \| medium, balanced quality - recommended \| \| [deepmoney-34b-200k-base.Q5_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_0.gguf) \| Q5_0 \| 5 \| 23.71 GB\| 26.21 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [deepmoney-34b-200k-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 23.71 GB\| 26.21 GB \| large, low quality loss - recommended \| \| [deepmoney-34b-200k-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 24.32 GB\| 26.82 GB \| large, very low quality loss - recommended \| \| [deepmoney-34b-200k-base.Q6_K.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q6_K.gguf) \| Q6_K \| 6 \| 28.21 GB\| 30.71 GB \| very large, extremely low quality loss \| \| [deepmoney-34b-200k-base.Q8_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q8_0.gguf) \| Q8_0 \| 8 \| 36.54 GB\| 39.04 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/deepmoney-34b-200k-base-GGUF and below it, a specific filename to download, such as: deepmoney-34b-200k-base.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m deepmoney-34b-200k-base.Q4_K_M.gguf --color -c 200000 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 200000` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", # Download the model file first n_ctx=200000, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: triad party's Deepmoney 34B 200K Base # Deepmoney ![767e2d3bba166cd63a83ae54e913d35.jpg](https://cdn-uploads.huggingface.co/production/uploads/630c1adea20a5367812196f6/O0kFm05ZSe6Lw6FhGwx5_.jpeg) Introducing Greed in the Seven Deadly Sins series of models. - Full-para pre-training on Yi-34b - High-quality research reports - High-end cleaning process ### 1. What do I want to do? Most of the current so-called financial models are mostly trained on public knowledge, but in the actual financial field, these public knowledge are often seriously insufficient for the current market interpretability. If you are interested, you can learn about the various propositions of Keynes, Friedman and even current behavioral finance. According to my observation, most financial models cannot make investment judgments. Because they are trained on ordinary textbooks, entry-level analyst exams, and even company public reports. I think this is of very little value for the investment. You can think I'm joking, but the fact is that the logic of many subjective analysts may not be as rigorous as that of large models of 34b and above (excluding those excellent ones, of course). The market is changing every moment, with a lot of news and massive data in real time. For most retail investors, instead of waiting for a crappy analyst to write a report, why not use a large model to make a pipeline? In my plan, this model is the base model of this process. In my plan, models such as information collector, target judge, qualitative analyst, quantitative analyst, and data extractor are all part of this process. . But the model itself is undoubtedly important to master a large number of qualitative and quantitative methods. That's why this model was born. ### 2. About the data As I just said, a lot of public knowledge has some questionable validity - but that doesn't mean it's wrong. The theoretical support behind many research methods in research reports also relies on this knowledge. So in my training, I picked up some college textbooks and some professional books. Not a lot of quantity but good quality. In addition, I selected a large number of research report data from 2019 to December 2023 - these reports are issued by a variety of publishers, including traditional brokers and professional research institutions. Most of them are paid and only available to institutions. But I got them anyway through various means. If you have read research reports, especially high-quality ones, you will find that research reports are all subjective judgment + quantitative analysis, and data support in quantitative analysis is crucial to the entire logical chain. In order to extract this data (most of them are in the form of graphs or tables), I tried a lot of multi-modal models, and the process was very painful. The conclusion is that cog-agent and emu2 are very effective for this kind of tasks. In order to better extract information, I created a process that summarizes the context of research reports as part of the prompt. Finally, I made a blend of the data. General data is not included because it is just for greed. Moreover, the knowledge in industry research reports is comprehensive enough. ### 3. About training Raw text, full parameter training. The base uses long context yi-34b-200k. This is necessary to complete and understand an in-depth report. Of course, I also did a sft. [This](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator) is the analyzer in my process – I haven’t broken down the qualitative and quantitative analysis yet, but I’m already blown away by how well it works. ### More: More technical details and evals coming soon…… ### 1. 我想干什么？当下大多数所谓的金融模型大多在公开知识上进行训练，但在实际的金融领域，这些公开知识对当前的市场可解释性往往严重不足。如果您感兴趣，可以了解一下凯恩斯、弗里德曼乃至当下行为金融学的各类主张。而据我观察，大多数金融模型无法对投资进行判断。因为它们都是在普通的教材、入门的分析师考试，乃至公司的公开报告上训练。我认为这对于投资的价值非常小。你可以当我开玩笑，但事实是很多主观分析师的逻辑性可能还不如34b及以上的大模型来的严谨（当然不包括那些优秀的）。而每时每刻，市场都在变化，大量的新闻，海量的数据都是实时的，对于大多数散户们，与其等待蹩脚的分析师写出报告，为什么不用大模型制作一套pipeline呢？在我的计划中，该模型是这套流程的基座模型，在我的计划中，信息搜集者、标的判断者、定性分析者定性分析者、定量分析者、数据提取者等模型都是该流程的一部分。但模型本身掌握大量的定性和定量方法毫无疑问是重要的。这就是这个模型诞生的理由。 ### 2. 关于数据：正如我刚才所说，很多公开知识的有效性都有些问题——但这并不意味着它们是错误的。在研报中很多研究方法背后的理论支撑也依赖这些知识。所以在我的训练中，我挑选了一些大学教材和一些专业书籍。数量不是很多但质量还不错。另外，我挑选了在2019-2023年12月的大量研究报告数据——这些报告的发布者多种多样，有传统的broke，也有专业研究机构。他们中的大多数是付费的，而且只对机构提供。但无论如何我通过各种各样的手段获取了它们。如果你看过研报，尤其是高质量的那些，你会发现研报都是主观判断+定量分析，而定量分析中的数据支撑对于整个逻辑链条至关重要。为了提取这些数据（他们中的大多数以图形或者表格的形式出现），我尝试了很多多模态模型，过程非常痛苦，结论是cog-agent和emu2对于这类任务很有效。为了更好的提取信息，我制作了一套从研报上下文总结作为prompt一部分的流程。最后，我把这些数据做了一个混合。并没有放入通识数据, 因为它就是为了greed而生的。而且行业研报中的知识足够全。 ### 3：关于训练： raw text，全参数训练。基座采用了长上下文的yi-34b-200k。这对于完成理解一篇深度报告是必须的。当然，我也做了一次sft。这是我的流程中的分析者——目前还没有细分定性和定量分析，但[它的效果](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator)已经让我大吃一惊了。 <!-- original-model-card end -->
huggingtweets/dav_erage-dozendav	huggingtweets	"2022-06-21T01:08:17Z"	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2022-06-21T01:07:07Z"	--- language: en thumbnail: http://www.huggingtweets.com/dav_erage-dozendav/1655773693107/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1517890310642278400/p9HNFjUU_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468744707698307072/TyrOUNkN_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">🐊 blooming 'bold 🌻 & ˣʸzed</div> <div style="text-align: center; font-size: 14px;">@dav_erage-dozendav</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 🐊 blooming 'bold 🌻 & ˣʸzed. \| Data \| 🐊 blooming 'bold 🌻 \| ˣʸzed \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3247 \| 3247 \| \| Retweets \| 279 \| 297 \| \| Short tweets \| 440 \| 427 \| \| Tweets kept \| 2528 \| 2523 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s4htzgm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dav_erage-dozendav's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gqlw7dl) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gqlw7dl/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dav_erage-dozendav') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
PabloSuaLap/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3	PabloSuaLap	"2023-09-11T15:44:00Z"	63	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "base_model:finetune:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	"2023-08-17T18:06:48Z"	--- license: apache-2.0 tags: - generated_from_keras_callback base_model: mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es model-index: - name: P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 This model is a fine-tuned version of [mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es](https://huggingface.co/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7249 - Train End Logits Accuracy: 0.1667 - Train Start Logits Accuracy: 0.1667 - Validation Loss: 3.2576 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.8333 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 4.7073 \| 0.1667 \| 0.1667 \| 3.5715 \| 0.0 \| 0.8333 \| 0 \| \| 3.7249 \| 0.1667 \| 0.1667 \| 3.2576 \| 0.0 \| 0.8333 \| 1 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.2
NikolayKozloff/Llama-3-8B-Swedish-Norwegian-Danish-checkpoint-11525-03_6_2024-Q8_0-GGUF	NikolayKozloff	"2024-06-03T15:54:00Z"	1	1	null	[ "gguf", "pytorch", "llama", "llama-3", "ai-sweden", "llama-cpp", "gguf-my-repo", "text-generation", "sv", "da", "no", "base_model:AI-Sweden-Models/Llama-3-8B", "base_model:quantized:AI-Sweden-Models/Llama-3-8B", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-03T15:53:32Z"	--- language: - sv - da - 'no' tags: - pytorch - llama - llama-3 - ai-sweden - llama-cpp - gguf-my-repo base_model: AI-Sweden-Models/Llama-3-8B pipeline_tag: text-generation inference: parameters: temperature: 0.6 --- # NikolayKozloff/Llama-3-8B-Q8_0-GGUF This model was converted to GGUF format from [`AI-Sweden-Models/Llama-3-8B`](https://huggingface.co/AI-Sweden-Models/Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/AI-Sweden-Models/Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -c 2048 ```
CyberHarem/mikari_izumigamine_mahoushoujosite	CyberHarem	"2023-08-29T20:20:29Z"	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/mikari_izumigamine_mahoushoujosite", "license:mit", "region:us" ]	text-to-image	"2023-08-29T07:38:10Z"	--- license: mit datasets: - CyberHarem/mikari_izumigamine_mahoushoujosite pipeline_tag: text-to-image tags: - art --- # Lora of mikari_izumigamine_mahoushoujosite This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2200, you need to download `2200/mikari_izumigamine_mahoushoujosite.pt` as the embedding and `2200/mikari_izumigamine_mahoushoujosite.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 2200, with the score of 0.993. The trigger words are: 1. `mikari_izumigamine_mahoushoujosite` 2. `hair_ornament, heart, green_eyes, heart_hair_ornament, bow, hair_bow, purple_hair, drill_hair, open_mouth, breasts, smile, long_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 3000 \| 0.974 \| [Download](3000/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-3000](3000/previews/pattern_1.png) \| ![pattern_2-3000](3000/previews/pattern_2.png) \| ![pattern_3-3000](3000/previews/pattern_3.png) \| ![bikini-3000](3000/previews/bikini.png) \| [<NSFW, click to see>](3000/previews/bondage.png) \| ![free-3000](3000/previews/free.png) \| ![maid-3000](3000/previews/maid.png) \| ![miko-3000](3000/previews/miko.png) \| [<NSFW, click to see>](3000/previews/nude.png) \| [<NSFW, click to see>](3000/previews/nude2.png) \| ![suit-3000](3000/previews/suit.png) \| ![yukata-3000](3000/previews/yukata.png) \| \| 2800 \| 0.983 \| [Download](2800/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-2800](2800/previews/pattern_1.png) \| ![pattern_2-2800](2800/previews/pattern_2.png) \| ![pattern_3-2800](2800/previews/pattern_3.png) \| ![bikini-2800](2800/previews/bikini.png) \| [<NSFW, click to see>](2800/previews/bondage.png) \| ![free-2800](2800/previews/free.png) \| ![maid-2800](2800/previews/maid.png) \| ![miko-2800](2800/previews/miko.png) \| [<NSFW, click to see>](2800/previews/nude.png) \| [<NSFW, click to see>](2800/previews/nude2.png) \| ![suit-2800](2800/previews/suit.png) \| ![yukata-2800](2800/previews/yukata.png) \| \| 2600 \| 0.988 \| [Download](2600/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-2600](2600/previews/pattern_1.png) \| ![pattern_2-2600](2600/previews/pattern_2.png) \| ![pattern_3-2600](2600/previews/pattern_3.png) \| ![bikini-2600](2600/previews/bikini.png) \| [<NSFW, click to see>](2600/previews/bondage.png) \| ![free-2600](2600/previews/free.png) \| ![maid-2600](2600/previews/maid.png) \| ![miko-2600](2600/previews/miko.png) \| [<NSFW, click to see>](2600/previews/nude.png) \| [<NSFW, click to see>](2600/previews/nude2.png) \| ![suit-2600](2600/previews/suit.png) \| ![yukata-2600](2600/previews/yukata.png) \| \| 2400 \| 0.984 \| [Download](2400/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-2400](2400/previews/pattern_1.png) \| ![pattern_2-2400](2400/previews/pattern_2.png) \| ![pattern_3-2400](2400/previews/pattern_3.png) \| ![bikini-2400](2400/previews/bikini.png) \| [<NSFW, click to see>](2400/previews/bondage.png) \| ![free-2400](2400/previews/free.png) \| ![maid-2400](2400/previews/maid.png) \| ![miko-2400](2400/previews/miko.png) \| [<NSFW, click to see>](2400/previews/nude.png) \| [<NSFW, click to see>](2400/previews/nude2.png) \| ![suit-2400](2400/previews/suit.png) \| ![yukata-2400](2400/previews/yukata.png) \| \| 2200 \| 0.993 \| [Download](2200/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-2200](2200/previews/pattern_1.png) \| ![pattern_2-2200](2200/previews/pattern_2.png) \| ![pattern_3-2200](2200/previews/pattern_3.png) \| ![bikini-2200](2200/previews/bikini.png) \| [<NSFW, click to see>](2200/previews/bondage.png) \| ![free-2200](2200/previews/free.png) \| ![maid-2200](2200/previews/maid.png) \| ![miko-2200](2200/previews/miko.png) \| [<NSFW, click to see>](2200/previews/nude.png) \| [<NSFW, click to see>](2200/previews/nude2.png) \| ![suit-2200](2200/previews/suit.png) \| ![yukata-2200](2200/previews/yukata.png) \| \| 2000 \| 0.974 \| [Download](2000/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-2000](2000/previews/pattern_1.png) \| ![pattern_2-2000](2000/previews/pattern_2.png) \| ![pattern_3-2000](2000/previews/pattern_3.png) \| ![bikini-2000](2000/previews/bikini.png) \| [<NSFW, click to see>](2000/previews/bondage.png) \| ![free-2000](2000/previews/free.png) \| ![maid-2000](2000/previews/maid.png) \| ![miko-2000](2000/previews/miko.png) \| [<NSFW, click to see>](2000/previews/nude.png) \| [<NSFW, click to see>](2000/previews/nude2.png) \| ![suit-2000](2000/previews/suit.png) \| ![yukata-2000](2000/previews/yukata.png) \| \| 1800 \| 0.984 \| [Download](1800/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-1800](1800/previews/pattern_1.png) \| ![pattern_2-1800](1800/previews/pattern_2.png) \| ![pattern_3-1800](1800/previews/pattern_3.png) \| ![bikini-1800](1800/previews/bikini.png) \| [<NSFW, click to see>](1800/previews/bondage.png) \| ![free-1800](1800/previews/free.png) \| ![maid-1800](1800/previews/maid.png) \| ![miko-1800](1800/previews/miko.png) \| [<NSFW, click to see>](1800/previews/nude.png) \| [<NSFW, click to see>](1800/previews/nude2.png) \| ![suit-1800](1800/previews/suit.png) \| ![yukata-1800](1800/previews/yukata.png) \| \| 1600 \| 0.958 \| [Download](1600/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-1600](1600/previews/pattern_1.png) \| ![pattern_2-1600](1600/previews/pattern_2.png) \| ![pattern_3-1600](1600/previews/pattern_3.png) \| ![bikini-1600](1600/previews/bikini.png) \| [<NSFW, click to see>](1600/previews/bondage.png) \| ![free-1600](1600/previews/free.png) \| ![maid-1600](1600/previews/maid.png) \| ![miko-1600](1600/previews/miko.png) \| [<NSFW, click to see>](1600/previews/nude.png) \| [<NSFW, click to see>](1600/previews/nude2.png) \| ![suit-1600](1600/previews/suit.png) \| ![yukata-1600](1600/previews/yukata.png) \| \| 1400 \| 0.972 \| [Download](1400/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-1400](1400/previews/pattern_1.png) \| ![pattern_2-1400](1400/previews/pattern_2.png) \| ![pattern_3-1400](1400/previews/pattern_3.png) \| ![bikini-1400](1400/previews/bikini.png) \| [<NSFW, click to see>](1400/previews/bondage.png) \| ![free-1400](1400/previews/free.png) \| ![maid-1400](1400/previews/maid.png) \| ![miko-1400](1400/previews/miko.png) \| [<NSFW, click to see>](1400/previews/nude.png) \| [<NSFW, click to see>](1400/previews/nude2.png) \| ![suit-1400](1400/previews/suit.png) \| ![yukata-1400](1400/previews/yukata.png) \| \| 1200 \| 0.872 \| [Download](1200/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-1200](1200/previews/pattern_1.png) \| ![pattern_2-1200](1200/previews/pattern_2.png) \| ![pattern_3-1200](1200/previews/pattern_3.png) \| ![bikini-1200](1200/previews/bikini.png) \| [<NSFW, click to see>](1200/previews/bondage.png) \| ![free-1200](1200/previews/free.png) \| ![maid-1200](1200/previews/maid.png) \| ![miko-1200](1200/previews/miko.png) \| [<NSFW, click to see>](1200/previews/nude.png) \| [<NSFW, click to see>](1200/previews/nude2.png) \| ![suit-1200](1200/previews/suit.png) \| ![yukata-1200](1200/previews/yukata.png) \| \| 1000 \| 0.943 \| [Download](1000/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-1000](1000/previews/pattern_1.png) \| ![pattern_2-1000](1000/previews/pattern_2.png) \| ![pattern_3-1000](1000/previews/pattern_3.png) \| ![bikini-1000](1000/previews/bikini.png) \| [<NSFW, click to see>](1000/previews/bondage.png) \| ![free-1000](1000/previews/free.png) \| ![maid-1000](1000/previews/maid.png) \| ![miko-1000](1000/previews/miko.png) \| [<NSFW, click to see>](1000/previews/nude.png) \| [<NSFW, click to see>](1000/previews/nude2.png) \| ![suit-1000](1000/previews/suit.png) \| ![yukata-1000](1000/previews/yukata.png) \| \| 800 \| 0.852 \| [Download](800/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-800](800/previews/pattern_1.png) \| ![pattern_2-800](800/previews/pattern_2.png) \| ![pattern_3-800](800/previews/pattern_3.png) \| ![bikini-800](800/previews/bikini.png) \| [<NSFW, click to see>](800/previews/bondage.png) \| ![free-800](800/previews/free.png) \| ![maid-800](800/previews/maid.png) \| ![miko-800](800/previews/miko.png) \| [<NSFW, click to see>](800/previews/nude.png) \| [<NSFW, click to see>](800/previews/nude2.png) \| ![suit-800](800/previews/suit.png) \| ![yukata-800](800/previews/yukata.png) \| \| 600 \| 0.924 \| [Download](600/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-600](600/previews/pattern_1.png) \| ![pattern_2-600](600/previews/pattern_2.png) \| ![pattern_3-600](600/previews/pattern_3.png) \| ![bikini-600](600/previews/bikini.png) \| [<NSFW, click to see>](600/previews/bondage.png) \| ![free-600](600/previews/free.png) \| ![maid-600](600/previews/maid.png) \| ![miko-600](600/previews/miko.png) \| [<NSFW, click to see>](600/previews/nude.png) \| [<NSFW, click to see>](600/previews/nude2.png) \| ![suit-600](600/previews/suit.png) \| ![yukata-600](600/previews/yukata.png) \| \| 400 \| 0.840 \| [Download](400/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-400](400/previews/pattern_1.png) \| ![pattern_2-400](400/previews/pattern_2.png) \| ![pattern_3-400](400/previews/pattern_3.png) \| ![bikini-400](400/previews/bikini.png) \| [<NSFW, click to see>](400/previews/bondage.png) \| ![free-400](400/previews/free.png) \| ![maid-400](400/previews/maid.png) \| ![miko-400](400/previews/miko.png) \| [<NSFW, click to see>](400/previews/nude.png) \| [<NSFW, click to see>](400/previews/nude2.png) \| ![suit-400](400/previews/suit.png) \| ![yukata-400](400/previews/yukata.png) \| \| 200 \| 0.174 \| [Download](200/mikari_izumigamine_mahoushoujosite.zip) \| ![pattern_1-200](200/previews/pattern_1.png) \| ![pattern_2-200](200/previews/pattern_2.png) \| ![pattern_3-200](200/previews/pattern_3.png) \| ![bikini-200](200/previews/bikini.png) \| [<NSFW, click to see>](200/previews/bondage.png) \| ![free-200](200/previews/free.png) \| ![maid-200](200/previews/maid.png) \| ![miko-200](200/previews/miko.png) \| [<NSFW, click to see>](200/previews/nude.png) \| [<NSFW, click to see>](200/previews/nude2.png) \| ![suit-200](200/previews/suit.png) \| ![yukata-200](200/previews/yukata.png) \|
minhhien0811/deita_reason_arena_5328	minhhien0811	"2024-08-29T08:07:28Z"	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-08-29T08:04:48Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5	stefan-it	"2023-10-17T21:30:34Z"	2	0	flair	[ "flair", "pytorch", "tensorboard", "token-classification", "sequence-tagger-model", "fr", "base_model:hmteams/teams-base-historic-multilingual-discriminator", "base_model:finetune:hmteams/teams-base-historic-multilingual-discriminator", "license:mit", "region:us" ]	token-classification	"2023-10-17T15:05:52Z"	--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: hmteams/teams-base-historic-multilingual-discriminator widget: - text: Je suis convaincu , a-t43 dit . que nous n"y parviendrions pas , mais nous ne pouvons céder parce que l' état moral de nos troupe* en souffrirait trop . ( Fournier . ) Des avions ennemis lancent dix-sept bombes sur Dunkerque LONDRES . 31 décembre . --- # Fine-tuned Flair Model on French ICDAR-Europeana NER Dataset This Flair model was fine-tuned on the [French ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar) NER Dataset using hmTEAMS as backbone LM. The ICDAR-Europeana NER Dataset is a preprocessed variant of the [Europeana NER Corpora](https://github.com/EuropeanaNewspapers/ner-corpora) for Dutch and French. The following NEs were annotated: `PER`, `LOC` and `ORG`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: \| Configuration \| Run 1 \| Run 2 \| Run 3 \| Run 4 \| Run 5 \| Avg. \| \|-----------------\|--------------\|--------------\|--------------\|--------------\|--------------\|--------------\| \| bs4-e10-lr3e-05 \| [0.7764][1] \| [0.7756][2] \| [0.7723][3] \| [0.7682][4] \| [0.7745][5] \| 77.34 ± 0.29 \| \| bs8-e10-lr3e-05 \| [0.7773][6] \| [0.7676][7] \| [0.7733][8] \| [0.7675][9] \| [0.7726][10] \| 77.17 ± 0.37 \| \| bs8-e10-lr5e-05 \| [0.7778][11] \| [0.783][12] \| [0.7654][13] \| [0.767][14] \| [0.7644][15] \| 77.15 ± 0.75 \| \| bs4-e10-lr5e-05 \| [0.7684][16] \| [0.7582][17] \| [0.7626][18] \| [0.7779][19] \| [0.7693][20] \| 76.73 ± 0.67 \| [1]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
jonelganalon/LexitronLlama-3.2-3B-Instruct-GGUF	jonelganalon	"2025-02-17T23:55:41Z"	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-17T23:04:52Z"	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: jonelganalon - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf	RichardErkhov	"2025-03-25T22:17:58Z"	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-03-25T21:16:52Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) kbtg-kpoint-v2-16bit-safe - GGUF - Model creator: https://huggingface.co/katopz/ - Original model: https://huggingface.co/katopz/kbtg-kpoint-v2-16bit-safe/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [kbtg-kpoint-v2-16bit-safe.Q2_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q2_K.gguf) \| Q2_K \| 1.27GB \| \| [kbtg-kpoint-v2-16bit-safe.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ3_XS.gguf) \| IQ3_XS \| 1.38GB \| \| [kbtg-kpoint-v2-16bit-safe.IQ3_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ3_S.gguf) \| IQ3_S \| 1.44GB \| \| [kbtg-kpoint-v2-16bit-safe.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K_S.gguf) \| Q3_K_S \| 1.44GB \| \| [kbtg-kpoint-v2-16bit-safe.IQ3_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ3_M.gguf) \| IQ3_M \| 1.49GB \| \| [kbtg-kpoint-v2-16bit-safe.Q3_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K.gguf) \| Q3_K \| 1.57GB \| \| [kbtg-kpoint-v2-16bit-safe.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K_M.gguf) \| Q3_K_M \| 1.57GB \| \| [kbtg-kpoint-v2-16bit-safe.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K_L.gguf) \| Q3_K_L \| 1.69GB \| \| [kbtg-kpoint-v2-16bit-safe.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ4_XS.gguf) \| IQ4_XS \| 1.71GB \| \| [kbtg-kpoint-v2-16bit-safe.Q4_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_0.gguf) \| Q4_0 \| 1.79GB \| \| [kbtg-kpoint-v2-16bit-safe.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ4_NL.gguf) \| IQ4_NL \| 1.79GB \| \| [kbtg-kpoint-v2-16bit-safe.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_K_S.gguf) \| Q4_K_S \| 1.8GB \| \| [kbtg-kpoint-v2-16bit-safe.Q4_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_K.gguf) \| Q4_K \| 1.88GB \| \| [kbtg-kpoint-v2-16bit-safe.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_K_M.gguf) \| Q4_K_M \| 1.88GB \| \| [kbtg-kpoint-v2-16bit-safe.Q4_1.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_1.gguf) \| Q4_1 \| 1.95GB \| \| [kbtg-kpoint-v2-16bit-safe.Q5_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_0.gguf) \| Q5_0 \| 2.11GB \| \| [kbtg-kpoint-v2-16bit-safe.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_K_S.gguf) \| Q5_K_S \| 2.11GB \| \| [kbtg-kpoint-v2-16bit-safe.Q5_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_K.gguf) \| Q5_K \| 2.16GB \| \| [kbtg-kpoint-v2-16bit-safe.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_K_M.gguf) \| Q5_K_M \| 2.16GB \| \| [kbtg-kpoint-v2-16bit-safe.Q5_1.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_1.gguf) \| Q5_1 \| 2.28GB \| \| [kbtg-kpoint-v2-16bit-safe.Q6_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q6_K.gguf) \| Q6_K \| 2.46GB \| \| [kbtg-kpoint-v2-16bit-safe.Q8_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q8_0.gguf) \| Q8_0 \| 3.19GB \| Original model description: --- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: katopz - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
TusharAg/my-cute-pembroke-welsh-corgi-puppy	TusharAg	"2024-03-15T11:07:40Z"	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2024-03-15T10:51:23Z"	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My--Cute-Pembroke-Welsh-Corgi-puppy Dreambooth model trained by Tushar Ageawal following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 303102220044 Sample pictures of this concept: ![0](https://huggingface.co/TusharAg/my-cute-pembroke-welsh-corgi-puppy/resolve/main/sample_images/tka_(5).jpeg)
jaeag/practice_model_0222	jaeag	"2025-02-22T08:26:32Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2025-02-22T07:34:49Z"	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf	RichardErkhov	"2025-03-02T03:40:19Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-03-02T03:36:52Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) smolLM-marathi-v1 - GGUF - Model creator: https://huggingface.co/grpathak22/ - Original model: https://huggingface.co/grpathak22/smolLM-marathi-v1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [smolLM-marathi-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q2_K.gguf) \| Q2_K \| 0.08GB \| \| [smolLM-marathi-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ3_XS.gguf) \| IQ3_XS \| 0.08GB \| \| [smolLM-marathi-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ3_S.gguf) \| IQ3_S \| 0.08GB \| \| [smolLM-marathi-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K_S.gguf) \| Q3_K_S \| 0.08GB \| \| [smolLM-marathi-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ3_M.gguf) \| IQ3_M \| 0.08GB \| \| [smolLM-marathi-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K.gguf) \| Q3_K \| 0.09GB \| \| [smolLM-marathi-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K_M.gguf) \| Q3_K_M \| 0.09GB \| \| [smolLM-marathi-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K_L.gguf) \| Q3_K_L \| 0.09GB \| \| [smolLM-marathi-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ4_XS.gguf) \| IQ4_XS \| 0.09GB \| \| [smolLM-marathi-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_0.gguf) \| Q4_0 \| 0.09GB \| \| [smolLM-marathi-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ4_NL.gguf) \| IQ4_NL \| 0.09GB \| \| [smolLM-marathi-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_K_S.gguf) \| Q4_K_S \| 0.1GB \| \| [smolLM-marathi-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_K.gguf) \| Q4_K \| 0.1GB \| \| [smolLM-marathi-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_K_M.gguf) \| Q4_K_M \| 0.1GB \| \| [smolLM-marathi-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_1.gguf) \| Q4_1 \| 0.09GB \| \| [smolLM-marathi-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_0.gguf) \| Q5_0 \| 0.1GB \| \| [smolLM-marathi-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_K_S.gguf) \| Q5_K_S \| 0.1GB \| \| [smolLM-marathi-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_K.gguf) \| Q5_K \| 0.1GB \| \| [smolLM-marathi-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_K_M.gguf) \| Q5_K_M \| 0.1GB \| \| [smolLM-marathi-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_1.gguf) \| Q5_1 \| 0.1GB \| \| [smolLM-marathi-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q6_K.gguf) \| Q6_K \| 0.13GB \| \| [smolLM-marathi-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q8_0.gguf) \| Q8_0 \| 0.13GB \| Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2-mlx	GreenBitAI	"2024-04-14T20:28:00Z"	4	0	mlx	[ "mlx", "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	"2024-04-13T11:44:23Z"	--- license: apache-2.0 tags: - mlx --- # GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2-mlx This quantized low-bit model was converted to MLX format from [`GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2`](). Refer to the [original model card](https://huggingface.co/GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2) for more details on the model. ## Use with mlx ```bash pip install gbx-lm ``` ```python from gbx_lm import load, generate model, tokenizer = load("GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
ArthurZ/mamba-790m	ArthurZ	"2024-02-29T03:47:39Z"	373	0	transformers	[ "transformers", "safetensors", "mamba", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-02-19T12:21:24Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
unknownmixorgacc/p3rs0nl0ra-alisa-batch2	unknownmixorgacc	"2025-04-08T09:49:12Z"	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-04-08T09:49:11Z"	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: p3rs0nl0ra-alisa license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # p3rs0nl0ra-alisa A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `p3rs0nl0ra-alisa` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
VERSIL91/46366d3d-7fa4-42eb-9718-0f62943aabc3	VERSIL91	"2025-01-10T15:28:09Z"	10	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "license:mit", "region:us" ]	null	"2025-01-10T15:14:55Z"	--- library_name: peft license: mit base_model: HuggingFaceH4/zephyr-7b-beta tags: - axolotl - generated_from_trainer model-index: - name: 46366d3d-7fa4-42eb-9718-0f62943aabc3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml accelerate_config: dynamo_backend: inductor mixed_precision: bf16 num_machines: 1 num_processes: auto use_cpu: false adapter: lora base_model: HuggingFaceH4/zephyr-7b-beta bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cf00b55ed8d6929c_train_data.json ds_type: json format: custom path: /workspace/input_data/cf00b55ed8d6929c_train_data.json type: field_instruction: linearized_input field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: VERSIL91/46366d3d-7fa4-42eb-9718-0f62943aabc3 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: cosine max_memory: 0: 70GiB max_steps: 20 micro_batch_size: 2 mlflow_experiment_name: /tmp/cf00b55ed8d6929c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true quantization_config: llm_int8_enable_fp32_cpu_offload: true load_in_8bit: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer torch_compile: true train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 46366d3d-7fa4-42eb-9718-0f62943aabc3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 46366d3d-7fa4-42eb-9718-0f62943aabc3 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 46366d3d-7fa4-42eb-9718-0f62943aabc3 This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0057 \| 1 \| nan \| \| 0.0 \| 0.0284 \| 5 \| nan \| \| 0.0 \| 0.0569 \| 10 \| nan \| \| 0.0 \| 0.0853 \| 15 \| nan \| \| 0.0 \| 0.1138 \| 20 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Brianpuz/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF	Brianpuz	"2025-04-07T00:34:31Z"	0	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-04-07T00:34:26Z"	--- base_model: Qwen/Qwen2.5-0.5B-Instruct language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Brianpuz/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF Absolutely tremendous! This repo features GGUF quantized versions of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) — made possible using the very powerful `llama.cpp`. Believe me, it's fast, it's smart, it's winning. ## Quantized Versions: Only the best quantization. You’ll love it. ## Run with llama.cpp Just plug it in, hit the command line, and boom — you're running world-class AI, folks: ```bash llama-cli --hf-repo Brianpuz/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-0.5b-instruct-q4_k_m.gguf -p "AI First, but also..." ``` This beautiful Hugging Face Space was brought to you by the amazing team at [Antigma Labs](https://antigma.ai). Great people. Big vision. Doing things that matter — and doing them right. Total winners.
lesso06/874e70b1-b059-42dc-bec4-4e4ebe9f0ebe	lesso06	"2025-03-08T13:55:20Z"	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/Yarn-Mistral-7b-64k", "base_model:adapter:NousResearch/Yarn-Mistral-7b-64k", "license:apache-2.0", "region:us" ]	null	"2025-03-08T11:39:18Z"	--- library_name: peft license: apache-2.0 base_model: NousResearch/Yarn-Mistral-7b-64k tags: - axolotl - generated_from_trainer model-index: - name: 874e70b1-b059-42dc-bec4-4e4ebe9f0ebe results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Yarn-Mistral-7b-64k bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3c08cce58a50d6dc_train_data.json ds_type: json format: custom path: /workspace/input_data/3c08cce58a50d6dc_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso06/874e70b1-b059-42dc-bec4-4e4ebe9f0ebe hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000206 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/3c08cce58a50d6dc_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 60 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 02571ba9-f8e4-4154-9e2a-e670f6ca125e wandb_project: 06a wandb_run: your_name wandb_runid: 02571ba9-f8e4-4154-9e2a-e670f6ca125e warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 874e70b1-b059-42dc-bec4-4e4ebe9f0ebe This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000206 - train_batch_size: 4 - eval_batch_size: 4 - seed: 60 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0004 \| 1 \| 1.6355 \| \| 8.3253 \| 0.2073 \| 500 \| 1.0255 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF	mradermacher	"2024-11-29T08:47:25Z"	22	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:zelk12/MT-Gen3-IMM-gemma-2-9B", "base_model:quantized:zelk12/MT-Gen3-IMM-gemma-2-9B", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-11-29T07:48:57Z"	--- base_model: zelk12/MT-Gen3-IMM-gemma-2-9B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/zelk12/MT-Gen3-IMM-gemma-2-9B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q2_K.gguf) \| Q2_K \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q3_K_S.gguf) \| Q3_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q3_K_M.gguf) \| Q3_K_M \| 4.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q3_K_L.gguf) \| Q3_K_L \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.IQ4_XS.gguf) \| IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 5.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q4_K_S.gguf) \| Q4_K_S \| 5.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q4_K_M.gguf) \| Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q5_K_S.gguf) \| Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q5_K_M.gguf) \| Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q6_K.gguf) \| Q6_K \| 7.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q8_0.gguf) \| Q8_0 \| 9.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.f16.gguf) \| f16 \| 18.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
BobMcDear/vit_small_patch16_224_in22k	BobMcDear	"2022-12-23T13:56:12Z"	0	0	null	[ "region:us" ]	null	"2022-12-13T23:11:25Z"	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
jogonba2/barthez-deft-linguistique	jogonba2	"2022-04-14T14:04:46Z"	4	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2022-03-02T23:29:05Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: barthez-deft-linguistique results: - task: name: Summarization type: summarization metrics: - name: Rouge1 type: rouge value: 41.989 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # barthez-deft-linguistique This model is a fine-tuned version of [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) on an unknown dataset. Note: this model is one of the preliminary experiments and it underperforms the models published in the paper (using [MBartHez](https://huggingface.co/moussaKam/mbarthez) and HAL/Wiki pre-training + copy mechanisms) It achieves the following results on the evaluation set: - Loss: 1.7596 - Rouge1: 41.989 - Rouge2: 22.4524 - Rougel: 32.7966 - Rougelsum: 32.7953 - Gen Len: 22.1549 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 3.0569 \| 1.0 \| 108 \| 2.0282 \| 31.6993 \| 14.9483 \| 25.5565 \| 25.4379 \| 18.3803 \| \| 2.2892 \| 2.0 \| 216 \| 1.8553 \| 35.2563 \| 18.019 \| 28.3135 \| 28.2927 \| 18.507 \| \| 1.9062 \| 3.0 \| 324 \| 1.7696 \| 37.4613 \| 18.1488 \| 28.9959 \| 29.0134 \| 19.5352 \| \| 1.716 \| 4.0 \| 432 \| 1.7641 \| 37.6903 \| 18.7496 \| 30.1097 \| 30.1027 \| 18.9577 \| \| 1.5722 \| 5.0 \| 540 \| 1.7781 \| 38.1013 \| 19.8291 \| 29.8142 \| 29.802 \| 19.169 \| \| 1.4655 \| 6.0 \| 648 \| 1.7661 \| 38.3557 \| 20.3309 \| 30.5068 \| 30.4728 \| 19.3662 \| \| 1.3507 \| 7.0 \| 756 \| 1.7596 \| 39.7409 \| 20.2998 \| 31.0849 \| 31.1152 \| 19.3944 \| \| 1.2874 \| 8.0 \| 864 \| 1.7706 \| 37.7846 \| 20.3457 \| 30.6826 \| 30.6321 \| 19.4789 \| \| 1.2641 \| 9.0 \| 972 \| 1.7848 \| 38.7421 \| 19.5701 \| 30.5798 \| 30.6305 \| 19.3944 \| \| 1.1192 \| 10.0 \| 1080 \| 1.8008 \| 40.3313 \| 20.3378 \| 31.8325 \| 31.8648 \| 19.5493 \| \| 1.0724 \| 11.0 \| 1188 \| 1.8450 \| 38.9612 \| 20.5719 \| 31.4496 \| 31.3144 \| 19.8592 \| \| 1.0077 \| 12.0 \| 1296 \| 1.8364 \| 36.5997 \| 18.46 \| 29.1808 \| 29.1705 \| 19.7324 \| \| 0.9362 \| 13.0 \| 1404 \| 1.8677 \| 38.0371 \| 19.2321 \| 30.3893 \| 30.3926 \| 19.6338 \| \| 0.8868 \| 14.0 \| 1512 \| 1.9154 \| 36.4737 \| 18.5314 \| 29.325 \| 29.3634 \| 19.6479 \| \| 0.8335 \| 15.0 \| 1620 \| 1.9344 \| 35.7583 \| 18.0687 \| 27.9666 \| 27.8675 \| 19.8028 \| \| 0.8305 \| 16.0 \| 1728 \| 1.9556 \| 37.2137 \| 18.2199 \| 29.5959 \| 29.5799 \| 19.9577 \| \| 0.8057 \| 17.0 \| 1836 \| 1.9793 \| 36.6834 \| 17.8505 \| 28.6701 \| 28.7145 \| 19.7324 \| \| 0.7869 \| 18.0 \| 1944 \| 1.9994 \| 37.5918 \| 19.1984 \| 28.8569 \| 28.8278 \| 19.7606 \| \| 0.7549 \| 19.0 \| 2052 \| 2.0117 \| 37.3278 \| 18.5169 \| 28.778 \| 28.7737 \| 19.8028 \| \| 0.7497 \| 20.0 \| 2160 \| 2.0189 \| 37.7513 \| 19.1813 \| 29.3675 \| 29.402 \| 19.6901 \| ### Framework versions - Transformers 4.10.2 - Pytorch 1.7.1+cu110 - Datasets 1.11.0 - Tokenizers 0.10.3
charliezjw/ppo-LunarLander-v2	charliezjw	"2023-12-16T16:04:31Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-12-16T16:04:09Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 223.46 +/- 25.25 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
SanXM1/Vel-Magnum-SE	SanXM1	"2025-04-06T05:14:31Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE", "base_model:merge:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE", "base_model:Sao10K/Llama-3.3-70B-Vulpecula-r1", "base_model:merge:Sao10K/Llama-3.3-70B-Vulpecula-r1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-06T04:21:22Z"	--- base_model: - Doctor-Shotgun/L3.3-70B-Magnum-v4-SE - Sao10K/Llama-3.3-70B-Vulpecula-r1 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Doctor-Shotgun/L3.3-70B-Magnum-v4-SE](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v4-SE) * [Sao10K/Llama-3.3-70B-Vulpecula-r1](https://huggingface.co/Sao10K/Llama-3.3-70B-Vulpecula-r1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Sao10K/Llama-3.3-70B-Vulpecula-r1 - model: Doctor-Shotgun/L3.3-70B-Magnum-v4-SE merge_method: slerp base_model: Sao10K/Llama-3.3-70B-Vulpecula-r1 parameters: t: - value: 0.8 dtype: bfloat16 tokenizer_source: base ```
tevf0/eee-486-assignment-2-part-1-best-model-run-2	tevf0	"2025-04-05T23:16:55Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-04-05T23:15:22Z"	--- library_name: transformers tags: [] --- # eee-486-assignment-2-part-1-best-model-run-2 This is a fine-tuned BERT model for the GLUE RTE task. ## Model Details - Base Model: `bert-base-uncased` - Task: Textual Entailment (GLUE RTE) - Language: English - License: Apache 2.0 - Finetuned From: `bert-base-uncased` ## Training Details - Learning Rate: 2e-05 - Epochs: 5 - Max Sequence Length: 512 - Dropout Rate: 0.1 ## Evaluation - Validation Accuracy: 67.15%
Multi-Domain-Expert-Learning/expert-uspto	Multi-Domain-Expert-Learning	"2023-04-30T23:50:10Z"	19	1	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-04-30T21:33:12Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: expert-uspto results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # expert-uspto This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2220 - Accuracy: 0.5362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2735 \| 0.01 \| 200 \| 2.2464 \| 0.5325 \| \| 2.2557 \| 0.01 \| 400 \| 2.2417 \| 0.5331 \| \| 2.2342 \| 0.02 \| 600 \| 2.2342 \| 0.5344 \| \| 2.2241 \| 0.03 \| 800 \| 2.2267 \| 0.5355 \| \| 2.229 \| 0.03 \| 1000 \| 2.2220 \| 0.5362 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
qiufengqijun/mini_qwen_pt	qiufengqijun	"2025-01-19T05:37:43Z"	46	0	null	[ "safetensors", "qwen2", "text-generation", "conversational", "zh", "en", "dataset:BAAI/IndustryCorpus2", "dataset:BAAI/Infinity-Instruct", "dataset:BAAI/Infinity-Preference", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	text-generation	"2025-01-18T08:13:04Z"	--- language: - zh - en base_model: - Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation license: apache-2.0 datasets: - BAAI/IndustryCorpus2 - BAAI/Infinity-Instruct - BAAI/Infinity-Preference --- # mini_qwen ## Introduction mini_qwen是一个从头开始训练的1B参数的大型语言模型(LLM)项目，包括预训练(PT)、微调(SFT)和直接偏好优化(DPO)3个部分。其中预训练和微调仅需要12G显存即可训练，直接偏好优化仅需要14G显存即可训练，这意味着使用T4显卡就可以开始你的训练之旅。 mini_qwen是以Qwen2.5-0.5B-Instruct模型为基础，通过扩充模型隐藏状态层数、隐藏状态维度和注意力头数，增加参数量到1B，并进行参数随机初始化。训练数据使用北京智源人工智能研究院的预训练(16B token)、微调(9M 条)和偏好数据(60K 条)，使用flash_attention_2进行加速，使用deepspeed在6张H800上训练25h(pt 1epoch)、43h(sft 3epoch)、1h(dpo 3epoch)。这是一次非常有趣且有价值的尝试，在整个过程中，本项目探究了尺度定律(scaling law)、复读机现象与微调阶段的知识注入，也解决了很多bug。本项目将尽可能详细地介绍整个训练过程，也欢迎交流讨论。更多内容详见：https://github.com/qiufengqijun/mini_qwen ## Quickstart 使用方法如下： ``` from transformers import AutoModelForCausalLM, AutoTokenizer import logging logging.getLogger("transformers").setLevel(logging.ERROR) # 忽略警告 # 加载分词器与模型 model_path = "/path/to/your/model" model = AutoModelForCausalLM.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) while True: prompt = input("用户：") text = prompt # 预训练模型 text = f"<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" # 微调和直接偏好优化模型 model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate(**model_inputs, max_new_tokens=512) generated_ids = [ output_ids[len(input_ids) :] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print("助手：", response) ```
nlp-esg-scoring/bert-base-finetuned-esg-gri-clean	nlp-esg-scoring	"2022-07-25T07:54:29Z"	5	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2022-07-25T02:32:56Z"	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: nlp-esg-scoring/bert-base-finetuned-esg-gri-clean results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nlp-esg-scoring/bert-base-finetuned-esg-gri-clean This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9511 - Validation Loss: 1.5293 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -797, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.9468 \| 1.5190 \| 0 \| \| 1.9433 \| 1.5186 \| 1 \| \| 1.9569 \| 1.4843 \| 2 \| \| 1.9510 \| 1.5563 \| 3 \| \| 1.9451 \| 1.5308 \| 4 \| \| 1.9576 \| 1.5209 \| 5 \| \| 1.9464 \| 1.5324 \| 6 \| \| 1.9525 \| 1.5168 \| 7 \| \| 1.9488 \| 1.5340 \| 8 \| \| 1.9511 \| 1.5293 \| 9 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1
LaurelApollo/onepiece_style_LoRA	LaurelApollo	"2025-04-07T00:11:20Z"	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2025-04-07T00:11:10Z"	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: art in DISCOELYSIUM style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - LaurelApollo/onepiece_style_LoRA <Gallery /> ## Model description These are LaurelApollo/onepiece_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use art in DISCOELYSIUM style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](LaurelApollo/onepiece_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
aroobarehman3232/Gpt2XL-finetuned-Tax	aroobarehman3232	"2025-04-14T15:25:23Z"	0	0	null	[ "region:us" ]	null	"2025-04-14T15:25:23Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
PrunaAI/vgg19_bn-turbo-tiny-green-smashed	PrunaAI	"2024-08-02T15:26:51Z"	2	0	pruna-engine	[ "pruna-engine", "region:us" ]	null	"2024-03-07T13:53:17Z"	--- library_name: pruna-engine thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed by combining quantization, xformers, jit, cuda graphs, triton. - *How does the model quality change?* The quality of the model output might slightly vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 2. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir vgg19_bn-turbo-tiny-green-smashed huggingface-cli download PrunaAI/vgg19_bn-turbo-tiny-green-smashed --local-dir vgg19_bn-turbo-tiny-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "vgg19_bn-turbo-tiny-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "vgg19_bn-turbo-tiny-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. import torch; image = torch.rand(1, 3, 224, 224).to('cuda') smashed_model(image) ``` ## Configurations The configuration info are in `model/smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model vgg19_bn before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
distillslm/alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it	distillslm	"2025-03-13T19:12:38Z"	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "trl", "gkd", "conversational", "arxiv:2306.13649", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-10T07:57:33Z"	--- base_model: google/gemma-2-2b-it library_name: transformers model_name: alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it tags: - generated_from_trainer - trl - gkd licence: license --- # Model Card for alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="distillslm/alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/rucnyz/huggingface/runs/g1aurazd) This model was trained with GKD, a method introduced in [On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes](https://huggingface.co/papers/2306.13649). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.6.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite GKD as: ```bibtex @inproceedings{agarwal2024on-policy, title = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}}, author = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem}, year = 2024, booktitle = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024}, publisher = {OpenReview.net}, url = {https://openreview.net/forum?id=3zKtaqxLhW}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
toilaluan/smol-lm-2-135m-latent-encoder	toilaluan	"2025-03-13T05:55:55Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-11T15:07:29Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Juderocks22/Puppet	Juderocks22	"2025-04-02T12:34:56Z"	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	"2025-04-02T12:34:43Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
markury/Smokey-v3	markury	"2024-09-05T00:16:52Z"	1,730	1	diffusers	[ "diffusers", "flux", "flux-diffusers", "text-to-image", "simpletuner", "safe-for-work", "lora", "template:sd-lora", "standard", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2024-09-04T07:18:49Z"	--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - safe-for-work - lora - template:sd-lora - standard inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'a photo of Smokey the cat, a white and grey cat sitting in a bathtub with a rubber duck' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # Smokey-v3 This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` a photo of Smokey the cat, a white and grey cat sitting in a bathtub with a rubber duck ``` ## Validation settings - CFG: `3.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder was not trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 90 - Training steps: 2800 - Learning rate: 0.0001 - Effective batch size: 8 - Micro-batch size: 8 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: Pure BF16 - Quantised: Yes: int8-quanto - Xformers: Not used - LoRA Rank: 16 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### Smokey-512-crop - Repeats: 0 - Total number of images: 114 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### Smokey-1024-crop - Repeats: 0 - Total number of images: 124 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'markury/Smokey-v3' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "a photo of Smokey the cat, a white and grey cat sitting in a bathtub with a rubber duck" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.0, ).images[0] image.save("output.png", format="PNG") ```
great0001/cca76d0b-87d1-4dd4-8533-0ee375236526	great0001	"2025-01-21T17:43:25Z"	6	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:adapter:NousResearch/Hermes-2-Pro-Mistral-7B", "license:apache-2.0", "region:us" ]	null	"2025-01-21T17:41:14Z"	--- library_name: peft license: apache-2.0 base_model: NousResearch/Hermes-2-Pro-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: cca76d0b-87d1-4dd4-8533-0ee375236526 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Hermes-2-Pro-Mistral-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ddd8b0dd75e40feb_train_data.json ds_type: json format: custom path: /workspace/input_data/ddd8b0dd75e40feb_train_data.json type: field_input: '' field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: great0001/cca76d0b-87d1-4dd4-8533-0ee375236526 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/ddd8b0dd75e40feb_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: fae321c3-44fb-4850-ae2a-8665061455bc wandb_project: Mine-SN56-20-Gradients-On-Demand wandb_run: your_name wandb_runid: fae321c3-44fb-4850-ae2a-8665061455bc warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # cca76d0b-87d1-4dd4-8533-0ee375236526 This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4833 \| 0.0011 \| 1 \| nan \| \| 1.1015 \| 0.0033 \| 3 \| nan \| \| 2.5063 \| 0.0065 \| 6 \| nan \| \| 1.813 \| 0.0098 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
zinoubm/OrpoLlama-3.2-1B-Instruct	zinoubm	"2025-01-12T21:09:11Z"	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-12T21:07:38Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
trangtrannnnn/23d7bf23-536a-49c2-9966-f1db4f75054e	trangtrannnnn	"2025-01-27T07:56:12Z"	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-3B", "base_model:adapter:Qwen/Qwen2.5-3B", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-01-27T07:30:40Z"	--- library_name: peft license: other base_model: Qwen/Qwen2.5-3B tags: - axolotl - generated_from_trainer model-index: - name: 23d7bf23-536a-49c2-9966-f1db4f75054e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-3B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e3f7343345b9b21f_train_data.json ds_type: json format: custom path: /workspace/input_data/e3f7343345b9b21f_train_data.json type: field_instruction: description field_output: code format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: trangtrannnnn/23d7bf23-536a-49c2-9966-f1db4f75054e hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/e3f7343345b9b21f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: db8f3cf6-8e27-4f7a-a1cc-9f92fa694ab2 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: db8f3cf6-8e27-4f7a-a1cc-9f92fa694ab2 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 23d7bf23-536a-49c2-9966-f1db4f75054e This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5265 \| 0.0214 \| 200 \| 0.5356 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
AntoineGourru/Mistral_qlora_drome_R256A512BS64E2MLR1-2e5x2	AntoineGourru	"2024-02-19T15:34:08Z"	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	"2024-02-19T15:33:13Z"	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0
mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF	mradermacher	"2025-03-18T22:43:01Z"	0	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "en", "base_model:sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2", "base_model:quantized:sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-03-18T22:03:50Z"	--- base_model: sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2 language: - en library_name: transformers quantized_by: mradermacher tags: - unsloth - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
abu1111/bge-synthesisQA-query-encoder	abu1111	"2025-02-07T11:15:37Z"	5	0	null	[ "safetensors", "bert", "en", "arxiv:2309.07597", "license:other", "region:us" ]	null	"2025-02-07T04:48:59Z"	--- language: - en tag: - dragon - retriever - conversation - multi-turn - conversational query license: - other --- ## Model Description We introduce bge-synthesisQA, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the [bge](https://huggingface.co/BAAI/bge-large-en-v1.5) retriever. Please note that bge-synthesisQA is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the query encoder of bge-synthesisQA for getting the query embeddings, and you also need the context encoder to get context embeddings, which can be found [here](https://huggingface.co/abu1111/bge-synthesisQA-context-encoder). Both query encoder and context encoder share the same tokenizer. ## Other Resources ## Benchmark Results <style type="text/css"> .tg {border:none;border-collapse:collapse;border-spacing:0;} .tg td{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden; padding:10px 5px;word-break:normal;} .tg th{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:normal; overflow:hidden;padding:10px 5px;word-break:normal;} .tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:center} .tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:center} </style> <table class="tg"> <thead> <tr> <th class="tg-0pky" rowspan="2"></th> <th class="tg-c3ow" colspan="3">Average</th> <th class="tg-c3ow" colspan="3">Doc2Dial</th> <th class="tg-c3ow" colspan="3">QuAC</th> <th class="tg-c3ow" colspan="3">QReCC</th> </tr> <tr> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> </tr> </thead> <tbody> <tr> <td class="tg-0pky">bge-large-en-v1.5</td> <td class="tg-c3ow">37.75</td> <td class="tg-c3ow">71.55</td> <td class="tg-c3ow">91.54</td> <td class="tg-c3ow">32.55</td> <td class="tg-c3ow">66.13</td> <td class="tg-c3ow">86.98</td> <td class="tg-c3ow">47.93</td> <td class="tg-c3ow">74.26</td> <td class="tg-c3ow">92.02</td> <td class="tg-c3ow">32.76</td> <td class="tg-c3ow">74.27</td> <td class="tg-c3ow">95.63</td> </tr> <tr> <td class="tg-0pky">bge-synthesisQA</td> <td class="tg-c3ow">51.72</td> <td class="tg-c3ow">83.12</td> <td class="tg-c3ow">95.02</td> <td class="tg-c3ow">38.72</td> <td class="tg-c3ow">71.39</td> <td class="tg-c3ow">88.86</td> <td class="tg-c3ow">50.71</td> <td class="tg-c3ow">83.57</td> <td class="tg-c3ow">97.13</td> <td class="tg-c3ow">65.73</td> <td class="tg-c3ow">94.41</td> <td class="tg-c3ow">99.07</td> </tr> </tbody> </table> Retrieval results across five multi-turn QA datasets (Doc2Dial, QuAC, QReCC) with the average top-1 top-5 and top-20 recall scores. ## How to use ```python import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('abu1111/bge-synthesisQA-query-encoder') query_encoder = AutoModel.from_pretrained('abu1111/bge-synthesisQA-query-encoder') context_encoder = AutoModel.from_pretrained('abu1111/bge-synthesisQA-context-encoder') query = [ {"role": "user", "content": "Can you help me understand how to choose a health insurance plan?"}, {"role": "agent", "content": "Sure, are you looking for individual coverage or a family plan?"}, {"role": "user", "content": "I need a family plan."} ] contexts = [ "Health Insurance Guide \| Choosing the Right Plan \nWhen selecting a health insurance plan, it's important to consider your family's specific needs. Factors such as the number of family members, medical history, and budget should all play a role in your decision. Plans are typically divided into categories: Bronze, Silver, Gold, and Platinum, with each offering different levels of coverage and premiums. For families with frequent medical visits, a plan with a higher premium but lower out-of-pocket costs (like a Gold or Platinum plan) might make sense. Conversely, if your family is generally healthy, a Bronze plan with lower premiums might be more cost-effective.", "Health Insurance Basics \| Individual vs. Family Coverage \nIndividual health insurance plans cover only one person, while family plans provide coverage for an entire household. Family plans usually have a higher premium but include shared deductibles and out-of-pocket maximums. When choosing a family plan, consider factors like the total coverage provided, the network of doctors and hospitals, and whether the plan includes benefits such as pediatric care. Understanding these distinctions can help you make an informed decision about the best option for you and your loved ones." ] ## convert query into a format as follows: ## user: {user}\nagent: {agent}\nuser: {user} formatted_query = '\n'.join([turn['role'] + ": " + turn['content'] for turn in query]).strip() ## get query and context embeddings query_input = tokenizer(formatted_query, return_tensors='pt') ctx_input = tokenizer(contexts, padding=True, truncation=True, max_length=512, return_tensors='pt') query_emb = query_encoder(query_input).last_hidden_state[:, 0, :] ctx_emb = context_encoder(ctx_input).last_hidden_state[:, 0, :] ## Compute similarity scores using dot product similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx) ## rank the similarity (from highest to lowest) ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx) ``` ## Evaluations on Multi-Turn QA Retrieval Benchmark (UPDATE!!) We evaluate multi-turn QA retrieval on three datasets: Doc2Dial, QuAC, and QReCC, which can be found in the [ChatRAG Bench](https://huggingface.co/datasets/nvidia/ChatRAG-Bench). The evaluation scripts can be found [here](https://huggingface.co/nvidia/dragon-multiturn-query-encoder/tree/main/evaluation). ## License bge-synthesisQA is built on top of [bge](https://arxiv.org/abs/2309.07597). We refer users to the original license of the BAAI/bge-large-en-v1.5 model. bge-synthesisQA is also subject to the [Terms of Use](https://openai.com/policies/terms-of-use).

DanYuHF/sd-class-butterflies-32

DanYuHF

"2024-05-18T14:37:41Z"

44

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

"2024-05-18T14:37:07Z"

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('DanYuHF/sd-class-butterflies-32') image = pipeline().images[0] image ```

huggingtweets/bnbuzz

huggingtweets

"2021-05-21T20:48:33Z"

5

0

transformers

[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2022-03-02T23:29:05Z"

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1306249141018910726/o3bCj_sP_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Barnes & Noble 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@bnbuzz bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@bnbuzz's tweets](https://twitter.com/bnbuzz). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3156</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>821</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>124</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2211</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tso130j/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bnbuzz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2b6k9q0j) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2b6k9q0j/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/bnbuzz'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF

MaziyarPanahi

"2024-05-21T10:09:39Z"

69

0

transformers

[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "conversational", "base_model:MTSAIR/multi_verse_model", "base_model:yam-peleg/Experiment26-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:automerger/Multi_verse_modelExperiment26-7B", "base_model:quantized:automerger/Multi_verse_modelExperiment26-7B" ]

text-generation

"2024-05-21T09:41:13Z"

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - lazymergekit - automerger - conversational - base_model:MTSAIR/multi_verse_model - base_model:yam-peleg/Experiment26-7B - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: Multi_verse_modelExperiment26-7B-GGUF base_model: automerger/Multi_verse_modelExperiment26-7B inference: false model_creator: automerger pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF](https://huggingface.co/MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF) - Model creator: [automerger](https://huggingface.co/automerger) - Original model: [automerger/Multi_verse_modelExperiment26-7B](https://huggingface.co/automerger/Multi_verse_modelExperiment26-7B) ## Description [MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF](https://huggingface.co/MaziyarPanahi/Multi_verse_modelExperiment26-7B-GGUF) contains GGUF format model files for [automerger/Multi_verse_modelExperiment26-7B](https://huggingface.co/automerger/Multi_verse_modelExperiment26-7B). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

jab11769/my-thai-bert-sentiment-model

jab11769

"2024-11-21T08:03:57Z"

110

0

transformers

[ "transformers", "safetensors", "camembert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-11-21T08:01:51Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

tarpalsus/poca-SoccerTwos

tarpalsus

"2024-04-27T10:35:54Z"

11

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

"2024-04-27T10:34:50Z"

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: tarpalsus/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

timm/vit_base_patch16_siglip_512.v2_webli

timm

"2025-02-21T20:10:46Z"

0

timm

[ "timm", "pytorch", "safetensors", "transformers", "image-feature-extraction", "siglip", "siglip2", "dataset:webli", "arxiv:2502.14786", "arxiv:2303.15343", "license:apache-2.0", "region:us" ]

image-feature-extraction

"2025-02-21T20:10:33Z"

--- tags: - timm - transformers - image-feature-extraction - siglip - siglip2 library_name: timm license: apache-2.0 datasets: - webli --- # Model card for vit_base_patch16_siglip_512.v2_webli A SigLIP 2 ViT (image encoder only) for `timm`. Equivalent to image tower from https://huggingface.co/timm/ViT-B-16-SigLIP2-512. ## Model Details - **Dataset:** webli - **Papers:** - SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features: https://arxiv.org/abs/2502.14786 - Sigmoid Loss for Language Image Pre-Training: https://arxiv.org/abs/2303.15343 ## Citation ```bibtex @article{tschannen2025siglip, title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features}, author={Tschannen, Michael and Gritsenko, Alexey and Wang, Xiao and Naeem, Muhammad Ferjad and Alabdulmohsin, Ibrahim and Parthasarathy, Nikhil and Evans, Talfan and Beyer, Lucas and Xia, Ye and Mustafa, Basil and H'enaff, Olivier and Harmsen, Jeremiah and Steiner, Andreas and Zhai, Xiaohua}, year={2025}, journal={arXiv preprint arXiv:2502.14786} } ``` ```bibtex @inproceedings{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={11975--11986}, year={2023} } ```

FakeMonk/ppo-LunarLander-v2

FakeMonk

"2025-03-18T13:54:58Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2025-03-18T13:54:44Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.27 +/- 18.01 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

unicamp-dl/InRanker-base

unicamp-dl

"2024-09-25T00:30:00Z"

940

5

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "arxiv:2401.06910", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2023-12-13T18:45:00Z"

# InRanker-base (220M parameters) InRanker is a version of monoT5 distilled from [monoT5-3B](https://huggingface.co/castorini/monot5-3b-msmarco-10k) with increased effectiveness on out-of-domain scenarios. Our key insight were to use language models and rerankers to generate as much as possible synthetic "in-domain" training data, i.e., data that closely resembles the data that will be seen at retrieval time. The pipeline used for training consists of two distillation phases that do not require additional user queries or manual annotations: (1) training on existing supervised soft teacher labels, and (2) training on teacher soft labels for synthetic queries generated using a large language model. The paper with further details can be found [here](https://arxiv.org/abs/2401.06910). The code and library are available at https://github.com/unicamp-dl/InRanker ## Usage The library was tested using python 3.10 and is installed with: ```bash pip install inranker ``` The code for inference is: ```python from inranker import T5Ranker model = T5Ranker(model_name_or_path="unicamp-dl/InRanker-base") docs = [ "The capital of France is Paris", "Learn deep learning with InRanker and transformers" ] scores = model.get_scores( query="What is the best way to learn deep learning?", docs=docs ) # Scores are sorted in descending order (most relevant to least) # scores -> [0, 1] sorted_scores = sorted(zip(scores, docs), key=lambda x: x[0], reverse=True) ``` ## How to Cite ``` @misc{laitz2024inranker, title={InRanker: Distilled Rankers for Zero-shot Information Retrieval}, author={Thiago Laitz and Konstantinos Papakostas and Roberto Lotufo and Rodrigo Nogueira}, year={2024}, eprint={2401.06910}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```

TransferGraph/aXhyra_emotion_trained_31415-finetuned-lora-ag_news

TransferGraph

"2024-02-28T01:06:57Z"

0

peft

[ "peft", "safetensors", "parquet", "text-classification", "dataset:ag_news", "base_model:aXhyra/emotion_trained_31415", "base_model:adapter:aXhyra/emotion_trained_31415", "license:apache-2.0", "model-index", "region:us" ]

text-classification

"2024-02-27T23:38:46Z"

--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - ag_news metrics: - accuracy base_model: aXhyra/emotion_trained_31415 model-index: - name: aXhyra_emotion_trained_31415-finetuned-lora-ag_news results: - task: type: text-classification name: Text Classification dataset: name: ag_news type: ag_news config: default split: test args: default metrics: - type: accuracy value: 0.9398684210526316 name: accuracy ---  # aXhyra_emotion_trained_31415-finetuned-lora-ag_news This model is a fine-tuned version of [aXhyra/emotion_trained_31415](https://huggingface.co/aXhyra/emotion_trained_31415) on the ag_news dataset. It achieves the following results on the evaluation set: - accuracy: 0.9399 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.3424 | None | 0 | | 0.9224 | 0.2694 | 0 | | 0.9305 | 0.2013 | 1 | | 0.9378 | 0.1776 | 2 | | 0.9399 | 0.1611 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2

theship87/qwen25-14b-fork-Q8_0-GGUF

theship87

"2025-02-04T07:29:36Z"

19

0

transformers

[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:theship87/qwen25-14b-fork", "base_model:quantized:theship87/qwen25-14b-fork", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-02-04T07:28:28Z"

--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-1M/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: theship87/qwen25-14b-fork tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # theship87/qwen25-14b-fork-Q8_0-GGUF This model was converted to GGUF format from [`theship87/qwen25-14b-fork`](https://huggingface.co/theship87/qwen25-14b-fork) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/theship87/qwen25-14b-fork) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -c 2048 ```

ClarenceDan/e5b11a03-6415-4231-928c-e95fce01a8d0

ClarenceDan

"2025-01-28T14:10:57Z"

6

0

peft

[ "peft", "safetensors", "falcon", "axolotl", "generated_from_trainer", "custom_code", "base_model:fxmarty/really-tiny-falcon-testing", "base_model:adapter:fxmarty/really-tiny-falcon-testing", "license:mit", "region:us" ]

null

"2025-01-28T14:10:32Z"

--- library_name: peft license: mit base_model: fxmarty/really-tiny-falcon-testing tags: - axolotl - generated_from_trainer model-index: - name: e5b11a03-6415-4231-928c-e95fce01a8d0 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: fxmarty/really-tiny-falcon-testing bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1fd166178dc611b1_train_data.json ds_type: json format: custom path: /workspace/input_data/1fd166178dc611b1_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/e5b11a03-6415-4231-928c-e95fce01a8d0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/1fd166178dc611b1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d2e73c64-4432-4749-92a6-b96236c9a7bb wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d2e73c64-4432-4749-92a6-b96236c9a7bb warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e5b11a03-6415-4231-928c-e95fce01a8d0 This model is a fine-tuned version of [fxmarty/really-tiny-falcon-testing](https://huggingface.co/fxmarty/really-tiny-falcon-testing) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.0733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 44.318 | 0.0033 | 1 | 11.0767 | | 44.2889 | 0.0098 | 3 | 11.0764 | | 44.291 | 0.0196 | 6 | 11.0754 | | 44.2777 | 0.0294 | 9 | 11.0733 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

DarrenChensformer/llava-1.5-7b-hf-vsft

DarrenChensformer

"2024-05-24T07:13:21Z"

3

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:llava-hf/llava-1.5-7b-hf", "base_model:adapter:llava-hf/llava-1.5-7b-hf", "region:us" ]

null

"2024-05-24T07:12:01Z"

--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: llava-hf/llava-1.5-7b-hf model-index: - name: llava-1.5-7b-hf-vsft results: [] ---  # llava-1.5-7b-hf-vsft This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.4e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.15.1

kunalkumarsahoo/q-FrozenLake-v1-4x4-noSlippery

kunalkumarsahoo

"2025-03-18T02:52:16Z"

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2025-03-17T19:45:13Z"

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id='kunalkumarsahoo/q-FrozenLake-v1-4x4-noSlippery', filename='q-learning.pkl') # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model['env_id']) ```

HenryCai1129/adapter-llama-adaptertoxic2nontoxic-100-filtered-50-0.003

HenryCai1129

"2024-04-27T17:20:46Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-27T03:39:13Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

franhinomut/my_distilbert_model

franhinomut

"2023-11-05T19:26:28Z"

6

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:rotten_tomatoes", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2023-11-05T17:44:50Z"

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - rotten_tomatoes metrics: - accuracy - f1 - precision - recall model-index: - name: my_distilbert_model results: - task: name: Text Classification type: text-classification dataset: name: rotten_tomatoes type: rotten_tomatoes config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.850844277673546 - name: F1 type: f1 value: 0.8508430963429304 - name: Precision type: precision value: 0.8508553928470853 - name: Recall type: recall value: 0.850844277673546 ---  # my_distilbert_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.5332 - Accuracy: 0.8508 - F1: 0.8508 - Precision: 0.8509 - Recall: 0.8508 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.4172 | 1.0 | 534 | 0.3729 | 0.8386 | 0.8386 | 0.8392 | 0.8386 | | 0.2351 | 2.0 | 1068 | 0.4376 | 0.8443 | 0.8443 | 0.8444 | 0.8443 | | 0.1635 | 3.0 | 1602 | 0.5332 | 0.8508 | 0.8508 | 0.8509 | 0.8508 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1

mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF

mradermacher

"2025-02-14T08:54:58Z"

0

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200", "base_model:quantized:jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-02-14T08:22:29Z"

--- base_model: jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2 - trl --- ## About      static quants of https://huggingface.co/jan-hq/AlphaMaze-v0.1-1.5B-GRPO-cp-200  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q2_K.gguf) | Q2_K | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q3_K_S.gguf) | Q3_K_S | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q3_K_L.gguf) | Q3_K_L | 1.1 | | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.IQ4_XS.gguf) | IQ4_XS | 1.1 | | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q5_K_S.gguf) | Q5_K_S | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q5_K_M.gguf) | Q5_K_M | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q6_K.gguf) | Q6_K | 1.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/AlphaMaze-v0.1-1.5B-GRPO-cp-200-GGUF/resolve/main/AlphaMaze-v0.1-1.5B-GRPO-cp-200.f16.gguf) | f16 | 3.7 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF

MrRobotoAI

"2025-02-11T21:17:06Z"

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k", "base_model:quantized:MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k", "endpoints_compatible", "region:us" ]

null

"2025-02-11T21:16:42Z"

--- base_model: MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k`](https://huggingface.co/MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/DarkIdol-LongWriter-8B-Uncensored-1048k-Q4_K_M-GGUF --hf-file darkidol-longwriter-8b-uncensored-1048k-q4_k_m.gguf -c 2048 ```

eoruadl/segformer-b0-finetuned-segments-crop_crack-2

eoruadl

"2023-05-26T02:26:18Z"

80

0

transformers

[ "transformers", "pytorch", "segformer", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]

null

"2023-05-25T03:29:41Z"

--- license: other tags: - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-crop_crack-2 results: [] ---  # segformer-b0-finetuned-segments-crop_crack-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1645 - Mean Iou: 0.4027 - Mean Accuracy: 0.6121 - Overall Accuracy: 0.6048 - Accuracy Unlabeled: nan - Accuracy Crack: 0.5696 - Accuracy Potholes: 0.6546 - Iou Unlabeled: 0.0 - Iou Crack: 0.5571 - Iou Potholes: 0.6510 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Crack | Accuracy Potholes | Iou Unlabeled | Iou Crack | Iou Potholes | |:-------------:|:------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-----------------:|:-------------:|:---------:|:------------:| | 0.8913 | 0.07 | 20 | 1.0205 | 0.1919 | 0.3500 | 0.3881 | nan | 0.5702 | 0.1298 | 0.0 | 0.4481 | 0.1275 | | 0.66 | 0.14 | 40 | 0.7627 | 0.0675 | 0.1190 | 0.1378 | nan | 0.2278 | 0.0102 | 0.0 | 0.1924 | 0.0102 | | 0.5279 | 0.21 | 60 | 0.5508 | 0.0631 | 0.1109 | 0.1292 | nan | 0.2167 | 0.0050 | 0.0 | 0.1842 | 0.0050 | | 0.5058 | 0.28 | 80 | 0.4507 | 0.0334 | 0.0533 | 0.0614 | nan | 0.0999 | 0.0068 | 0.0 | 0.0935 | 0.0068 | | 0.4833 | 0.35 | 100 | 0.3818 | 0.0268 | 0.0427 | 0.0499 | nan | 0.0843 | 0.0012 | 0.0 | 0.0791 | 0.0012 | | 0.3508 | 0.42 | 120 | 0.3764 | 0.0497 | 0.0796 | 0.0892 | nan | 0.1348 | 0.0244 | 0.0 | 0.1247 | 0.0243 | | 0.4247 | 0.49 | 140 | 0.3068 | 0.0239 | 0.0363 | 0.0367 | nan | 0.0391 | 0.0334 | 0.0 | 0.0383 | 0.0333 | | 0.3403 | 0.56 | 160 | 0.3245 | 0.0593 | 0.0939 | 0.1058 | nan | 0.1624 | 0.0254 | 0.0 | 0.1526 | 0.0254 | | 0.3684 | 0.63 | 180 | 0.2564 | 0.0660 | 0.1026 | 0.1084 | nan | 0.1360 | 0.0691 | 0.0 | 0.1295 | 0.0685 | | 0.2358 | 0.7 | 200 | 0.2359 | 0.0607 | 0.0933 | 0.0951 | nan | 0.1041 | 0.0825 | 0.0 | 0.1007 | 0.0814 | | 0.2429 | 0.77 | 220 | 0.2241 | 0.0693 | 0.1061 | 0.1065 | nan | 0.1080 | 0.1043 | 0.0 | 0.1053 | 0.1026 | | 0.2958 | 0.84 | 240 | 0.2430 | 0.0922 | 0.1421 | 0.1451 | nan | 0.1597 | 0.1244 | 0.0 | 0.1543 | 0.1223 | | 0.3171 | 0.91 | 260 | 0.1997 | 0.0682 | 0.1040 | 0.1008 | nan | 0.0854 | 0.1227 | 0.0 | 0.0834 | 0.1211 | | 0.2211 | 0.98 | 280 | 0.2032 | 0.1085 | 0.1669 | 0.1693 | nan | 0.1803 | 0.1536 | 0.0 | 0.1743 | 0.1513 | | 0.2602 | 1.05 | 300 | 0.1891 | 0.1691 | 0.2637 | 0.2604 | nan | 0.2448 | 0.2827 | 0.0 | 0.2369 | 0.2704 | | 0.2013 | 1.12 | 320 | 0.1831 | 0.1771 | 0.2747 | 0.2637 | nan | 0.2111 | 0.3383 | 0.0 | 0.2050 | 0.3262 | | 0.1402 | 1.19 | 340 | 0.1724 | 0.1062 | 0.1629 | 0.1659 | nan | 0.1800 | 0.1458 | 0.0 | 0.1738 | 0.1447 | | 0.2118 | 1.26 | 360 | 0.1691 | 0.1918 | 0.2995 | 0.2871 | nan | 0.2277 | 0.3713 | 0.0 | 0.2243 | 0.3511 | | 0.1804 | 1.33 | 380 | 0.1668 | 0.1770 | 0.2727 | 0.2692 | nan | 0.2523 | 0.2932 | 0.0 | 0.2433 | 0.2876 | | 0.1626 | 1.4 | 400 | 0.1621 | 0.1398 | 0.2137 | 0.2186 | nan | 0.2424 | 0.1849 | 0.0 | 0.2347 | 0.1848 | | 0.1191 | 1.47 | 420 | 0.1544 | 0.1627 | 0.2492 | 0.2488 | nan | 0.2469 | 0.2514 | 0.0 | 0.2412 | 0.2470 | | 0.1382 | 1.54 | 440 | 0.1522 | 0.2476 | 0.3887 | 0.3796 | nan | 0.3364 | 0.4409 | 0.0 | 0.3284 | 0.4145 | | 0.1409 | 1.61 | 460 | 0.1421 | 0.2045 | 0.3175 | 0.3044 | nan | 0.2415 | 0.3936 | 0.0 | 0.2369 | 0.3765 | | 0.1764 | 1.68 | 480 | 0.1492 | 0.1710 | 0.2626 | 0.2661 | nan | 0.2827 | 0.2425 | 0.0 | 0.2710 | 0.2422 | | 0.1432 | 1.75 | 500 | 0.1484 | 0.1917 | 0.2933 | 0.2932 | nan | 0.2926 | 0.2940 | 0.0 | 0.2815 | 0.2937 | | 0.1174 | 1.82 | 520 | 0.1446 | 0.2908 | 0.4461 | 0.4453 | nan | 0.4410 | 0.4512 | 0.0 | 0.4250 | 0.4473 | | 0.1001 | 1.89 | 540 | 0.1444 | 0.1676 | 0.2596 | 0.2785 | nan | 0.3689 | 0.1502 | 0.0 | 0.3526 | 0.1502 | | 0.1361 | 1.96 | 560 | 0.1326 | 0.2458 | 0.3776 | 0.3656 | nan | 0.3081 | 0.4471 | 0.0 | 0.3028 | 0.4346 | | 0.2597 | 2.03 | 580 | 0.1312 | 0.2476 | 0.3799 | 0.3677 | nan | 0.3094 | 0.4505 | 0.0 | 0.3042 | 0.4386 | | 0.1009 | 2.1 | 600 | 0.1398 | 0.2479 | 0.3841 | 0.4041 | nan | 0.4997 | 0.2685 | 0.0 | 0.4777 | 0.2660 | | 0.126 | 2.17 | 620 | 0.1334 | 0.2406 | 0.3708 | 0.3806 | nan | 0.4276 | 0.3140 | 0.0 | 0.4104 | 0.3115 | | 0.2272 | 2.24 | 640 | 0.1298 | 0.2568 | 0.3935 | 0.3811 | nan | 0.3221 | 0.4648 | 0.0 | 0.3151 | 0.4553 | | 0.1084 | 2.31 | 660 | 0.1324 | 0.2169 | 0.3304 | 0.3295 | nan | 0.3254 | 0.3354 | 0.0 | 0.3169 | 0.3338 | | 0.0982 | 2.38 | 680 | 0.1307 | 0.3197 | 0.4938 | 0.4761 | nan | 0.3918 | 0.5958 | 0.0 | 0.3838 | 0.5754 | | 0.0684 | 2.45 | 700 | 0.1245 | 0.3120 | 0.4789 | 0.4595 | nan | 0.3672 | 0.5905 | 0.0 | 0.3624 | 0.5736 | | 0.1153 | 2.52 | 720 | 0.1322 | 0.3729 | 0.5735 | 0.5637 | nan | 0.5168 | 0.6302 | 0.0 | 0.5023 | 0.6164 | | 0.1812 | 2.59 | 740 | 0.1263 | 0.2375 | 0.3641 | 0.3683 | nan | 0.3884 | 0.3398 | 0.0 | 0.3739 | 0.3385 | | 0.1354 | 2.66 | 760 | 0.1177 | 0.2881 | 0.4393 | 0.4293 | nan | 0.3814 | 0.4972 | 0.0 | 0.3741 | 0.4901 | | 0.084 | 2.73 | 780 | 0.1286 | 0.2604 | 0.3977 | 0.4027 | nan | 0.4269 | 0.3685 | 0.0 | 0.4172 | 0.3640 | | 0.1123 | 2.8 | 800 | 0.1182 | 0.2988 | 0.4564 | 0.4487 | nan | 0.4122 | 0.5005 | 0.0 | 0.4016 | 0.4947 | | 0.2121 | 2.87 | 820 | 0.1207 | 0.3020 | 0.4599 | 0.4544 | nan | 0.4277 | 0.4921 | 0.0 | 0.4156 | 0.4905 | | 0.1709 | 2.94 | 840 | 0.1220 | 0.2718 | 0.4154 | 0.4020 | nan | 0.3383 | 0.4925 | 0.0 | 0.3289 | 0.4864 | | 0.1322 | 3.01 | 860 | 0.1197 | 0.3103 | 0.4748 | 0.4708 | nan | 0.4520 | 0.4976 | 0.0 | 0.4362 | 0.4948 | | 0.1593 | 3.08 | 880 | 0.1213 | 0.3583 | 0.5493 | 0.5458 | nan | 0.5295 | 0.5690 | 0.0 | 0.5106 | 0.5643 | | 0.2178 | 3.15 | 900 | 0.1225 | 0.3412 | 0.5351 | 0.5130 | nan | 0.4071 | 0.6632 | 0.0 | 0.4025 | 0.6213 | | 0.1226 | 3.22 | 920 | 0.1298 | 0.2484 | 0.3831 | 0.4017 | nan | 0.4908 | 0.2753 | 0.0 | 0.4700 | 0.2751 | | 0.2689 | 3.29 | 940 | 0.1141 | 0.3109 | 0.4744 | 0.4680 | nan | 0.4374 | 0.5114 | 0.0 | 0.4266 | 0.5060 | | 0.1088 | 3.36 | 960 | 0.1213 | 0.2748 | 0.4201 | 0.4224 | nan | 0.4335 | 0.4066 | 0.0 | 0.4185 | 0.4060 | | 0.1556 | 3.43 | 980 | 0.1141 | 0.3654 | 0.5652 | 0.5395 | nan | 0.4170 | 0.7134 | 0.0 | 0.4136 | 0.6826 | | 0.1136 | 3.5 | 1000 | 0.1190 | 0.2653 | 0.4034 | 0.3979 | nan | 0.3718 | 0.4351 | 0.0 | 0.3628 | 0.4331 | | 0.1178 | 3.57 | 1020 | 0.1277 | 0.2680 | 0.4118 | 0.4191 | nan | 0.4540 | 0.3696 | 0.0 | 0.4346 | 0.3694 | | 0.1722 | 3.64 | 1040 | 0.1135 | 0.3246 | 0.4996 | 0.4829 | nan | 0.4031 | 0.5962 | 0.0 | 0.3983 | 0.5754 | | 0.0791 | 3.71 | 1060 | 0.1134 | 0.3288 | 0.5011 | 0.4976 | nan | 0.4810 | 0.5212 | 0.0 | 0.4671 | 0.5193 | | 0.0966 | 3.78 | 1080 | 0.1140 | 0.3080 | 0.4682 | 0.4551 | nan | 0.3924 | 0.5440 | 0.0 | 0.3818 | 0.5422 | | 0.0623 | 3.85 | 1100 | 0.1146 | 0.2690 | 0.4087 | 0.4023 | nan | 0.3720 | 0.4453 | 0.0 | 0.3625 | 0.4444 | | 0.092 | 3.92 | 1120 | 0.1109 | 0.3129 | 0.4748 | 0.4623 | nan | 0.4025 | 0.5471 | 0.0 | 0.3948 | 0.5438 | | 0.0864 | 3.99 | 1140 | 0.1092 | 0.3253 | 0.4967 | 0.4824 | nan | 0.4140 | 0.5794 | 0.0 | 0.4073 | 0.5685 | | 0.0701 | 4.06 | 1160 | 0.1166 | 0.3383 | 0.5182 | 0.5190 | nan | 0.5232 | 0.5131 | 0.0 | 0.5049 | 0.5100 | | 0.1157 | 4.13 | 1180 | 0.1074 | 0.3302 | 0.5049 | 0.4942 | nan | 0.4428 | 0.5671 | 0.0 | 0.4334 | 0.5573 | | 0.1246 | 4.2 | 1200 | 0.1088 | 0.3465 | 0.5308 | 0.5232 | nan | 0.4873 | 0.5742 | 0.0 | 0.4755 | 0.5640 | | 0.0657 | 4.27 | 1220 | 0.1109 | 0.3973 | 0.6119 | 0.5906 | nan | 0.4885 | 0.7353 | 0.0 | 0.4797 | 0.7121 | | 0.1068 | 4.34 | 1240 | 0.1131 | 0.2925 | 0.4449 | 0.4406 | nan | 0.4204 | 0.4693 | 0.0 | 0.4099 | 0.4676 | | 0.0642 | 4.41 | 1260 | 0.1077 | 0.3242 | 0.4951 | 0.4923 | nan | 0.4786 | 0.5116 | 0.0 | 0.4642 | 0.5084 | | 0.1347 | 4.48 | 1280 | 0.1058 | 0.3796 | 0.5797 | 0.5708 | nan | 0.5282 | 0.6312 | 0.0 | 0.5152 | 0.6235 | | 0.1713 | 4.55 | 1300 | 0.1052 | 0.3602 | 0.5499 | 0.5455 | nan | 0.5244 | 0.5755 | 0.0 | 0.5103 | 0.5703 | | 0.0742 | 4.62 | 1320 | 0.1136 | 0.3116 | 0.4753 | 0.4699 | nan | 0.4439 | 0.5066 | 0.0 | 0.4301 | 0.5047 | | 0.0751 | 4.69 | 1340 | 0.1096 | 0.3225 | 0.4907 | 0.4738 | nan | 0.3932 | 0.5883 | 0.0 | 0.3831 | 0.5845 | | 0.125 | 4.76 | 1360 | 0.1060 | 0.3501 | 0.5329 | 0.5221 | nan | 0.4702 | 0.5957 | 0.0 | 0.4620 | 0.5883 | | 0.0921 | 4.83 | 1380 | 0.1062 | 0.3506 | 0.5345 | 0.5304 | nan | 0.5107 | 0.5584 | 0.0 | 0.4980 | 0.5538 | | 0.0743 | 4.9 | 1400 | 0.1098 | 0.2934 | 0.4457 | 0.4322 | nan | 0.3677 | 0.5237 | 0.0 | 0.3605 | 0.5198 | | 0.0743 | 4.97 | 1420 | 0.1061 | 0.3805 | 0.5810 | 0.5736 | nan | 0.5383 | 0.6236 | 0.0 | 0.5243 | 0.6172 | | 0.1287 | 5.03 | 1440 | 0.1061 | 0.3313 | 0.5055 | 0.4904 | nan | 0.4181 | 0.5929 | 0.0 | 0.4107 | 0.5832 | | 0.07 | 5.1 | 1460 | 0.1027 | 0.3451 | 0.5279 | 0.5132 | nan | 0.4430 | 0.6129 | 0.0 | 0.4367 | 0.5988 | | 0.0795 | 5.17 | 1480 | 0.1068 | 0.3185 | 0.4863 | 0.4746 | nan | 0.4189 | 0.5537 | 0.0 | 0.4122 | 0.5432 | | 0.1453 | 5.24 | 1500 | 0.1093 | 0.3354 | 0.5096 | 0.4897 | nan | 0.3945 | 0.6248 | 0.0 | 0.3862 | 0.6200 | | 0.075 | 5.31 | 1520 | 0.1103 | 0.3707 | 0.5662 | 0.5564 | nan | 0.5095 | 0.6230 | 0.0 | 0.4931 | 0.6189 | | 0.0841 | 5.38 | 1540 | 0.1083 | 0.3701 | 0.5655 | 0.5629 | nan | 0.5503 | 0.5808 | 0.0 | 0.5337 | 0.5766 | | 0.0853 | 5.45 | 1560 | 0.1012 | 0.3796 | 0.5796 | 0.5642 | nan | 0.4906 | 0.6686 | 0.0 | 0.4852 | 0.6536 | | 0.1633 | 5.52 | 1580 | 0.1090 | 0.3083 | 0.4686 | 0.4549 | nan | 0.3895 | 0.5477 | 0.0 | 0.3814 | 0.5435 | | 0.1007 | 5.59 | 1600 | 0.1045 | 0.3719 | 0.5677 | 0.5628 | nan | 0.5394 | 0.5960 | 0.0 | 0.5308 | 0.5849 | | 0.1319 | 5.66 | 1620 | 0.1047 | 0.3841 | 0.5867 | 0.5617 | nan | 0.4425 | 0.7309 | 0.0 | 0.4355 | 0.7170 | | 0.0882 | 5.73 | 1640 | 0.1060 | 0.3343 | 0.5097 | 0.4991 | nan | 0.4488 | 0.5705 | 0.0 | 0.4380 | 0.5650 | | 0.0814 | 5.8 | 1660 | 0.1044 | 0.3537 | 0.5410 | 0.5283 | nan | 0.4674 | 0.6146 | 0.0 | 0.4578 | 0.6034 | | 0.0977 | 5.87 | 1680 | 0.1036 | 0.3880 | 0.5929 | 0.5864 | nan | 0.5551 | 0.6307 | 0.0 | 0.5432 | 0.6208 | | 0.0964 | 5.94 | 1700 | 0.1090 | 0.3796 | 0.5853 | 0.5572 | nan | 0.4229 | 0.7476 | 0.0 | 0.4173 | 0.7216 | | 0.0517 | 6.01 | 1720 | 0.1113 | 0.3189 | 0.4857 | 0.4741 | nan | 0.4183 | 0.5532 | 0.0 | 0.4067 | 0.5501 | | 0.2876 | 6.08 | 1740 | 0.1018 | 0.3736 | 0.5702 | 0.5546 | nan | 0.4803 | 0.6601 | 0.0 | 0.4736 | 0.6473 | | 0.0886 | 6.15 | 1760 | 0.1096 | 0.3204 | 0.4894 | 0.4862 | nan | 0.4709 | 0.5079 | 0.0 | 0.4541 | 0.5070 | | 0.0575 | 6.22 | 1780 | 0.1022 | 0.3830 | 0.5893 | 0.5687 | nan | 0.4699 | 0.7087 | 0.0 | 0.4632 | 0.6857 | | 0.0985 | 6.29 | 1800 | 0.1096 | 0.3548 | 0.5413 | 0.5359 | nan | 0.5102 | 0.5725 | 0.0 | 0.4943 | 0.5702 | | 0.082 | 6.36 | 1820 | 0.1029 | 0.3761 | 0.5756 | 0.5593 | nan | 0.4814 | 0.6699 | 0.0 | 0.4727 | 0.6555 | | 0.0722 | 6.43 | 1840 | 0.1021 | 0.3625 | 0.5532 | 0.5367 | nan | 0.4579 | 0.6485 | 0.0 | 0.4512 | 0.6364 | | 0.0703 | 6.5 | 1860 | 0.1058 | 0.3347 | 0.5079 | 0.4932 | nan | 0.4229 | 0.5929 | 0.0 | 0.4137 | 0.5904 | | 0.0994 | 6.57 | 1880 | 0.1073 | 0.4355 | 0.6656 | 0.6537 | nan | 0.5967 | 0.7345 | 0.0 | 0.5828 | 0.7238 | | 0.0836 | 6.64 | 1900 | 0.0987 | 0.3738 | 0.5688 | 0.5564 | nan | 0.4975 | 0.6401 | 0.0 | 0.4868 | 0.6347 | | 0.1076 | 6.71 | 1920 | 0.0986 | 0.3571 | 0.5455 | 0.5293 | nan | 0.4523 | 0.6387 | 0.0 | 0.4465 | 0.6249 | | 0.0807 | 6.78 | 1940 | 0.1066 | 0.3372 | 0.5142 | 0.5116 | nan | 0.4994 | 0.5290 | 0.0 | 0.4832 | 0.5282 | | 0.115 | 6.85 | 1960 | 0.1040 | 0.3420 | 0.5281 | 0.5075 | nan | 0.4093 | 0.6469 | 0.0 | 0.4061 | 0.6198 | | 0.085 | 6.92 | 1980 | 0.1041 | 0.3572 | 0.5433 | 0.5314 | nan | 0.4746 | 0.6119 | 0.0 | 0.4646 | 0.6071 | | 0.1058 | 6.99 | 2000 | 0.1009 | 0.3849 | 0.5861 | 0.5740 | nan | 0.5160 | 0.6562 | 0.0 | 0.5070 | 0.6476 | | 0.0772 | 7.06 | 2020 | 0.1010 | 0.3884 | 0.5918 | 0.5849 | nan | 0.5522 | 0.6314 | 0.0 | 0.5445 | 0.6208 | | 0.0637 | 7.13 | 2040 | 0.1050 | 0.3735 | 0.5676 | 0.5618 | nan | 0.5341 | 0.6012 | 0.0 | 0.5228 | 0.5978 | | 0.0741 | 7.2 | 2060 | 0.1050 | 0.3440 | 0.5221 | 0.5126 | nan | 0.4670 | 0.5772 | 0.0 | 0.4587 | 0.5734 | | 0.0788 | 7.27 | 2080 | 0.0989 | 0.3841 | 0.5854 | 0.5809 | nan | 0.5593 | 0.6116 | 0.0 | 0.5442 | 0.6081 | | 0.0796 | 7.34 | 2100 | 0.1043 | 0.3486 | 0.5292 | 0.5159 | nan | 0.4522 | 0.6063 | 0.0 | 0.4434 | 0.6025 | | 0.0731 | 7.41 | 2120 | 0.1055 | 0.3579 | 0.5469 | 0.5311 | nan | 0.4553 | 0.6386 | 0.0 | 0.4531 | 0.6205 | | 0.0776 | 7.48 | 2140 | 0.1064 | 0.3907 | 0.5965 | 0.5932 | nan | 0.5773 | 0.6157 | 0.0 | 0.5596 | 0.6126 | | 0.1098 | 7.55 | 2160 | 0.1024 | 0.3833 | 0.5843 | 0.5723 | nan | 0.5149 | 0.6536 | 0.0 | 0.5047 | 0.6452 | | 0.0612 | 7.62 | 2180 | 0.1065 | 0.3192 | 0.4841 | 0.4620 | nan | 0.3565 | 0.6117 | 0.0 | 0.3520 | 0.6055 | | 0.0744 | 7.69 | 2200 | 0.1019 | 0.4083 | 0.6322 | 0.6027 | nan | 0.4616 | 0.8027 | 0.0 | 0.4592 | 0.7658 | | 0.0509 | 7.76 | 2220 | 0.1080 | 0.3455 | 0.5260 | 0.5183 | nan | 0.4815 | 0.5705 | 0.0 | 0.4695 | 0.5668 | | 0.066 | 7.83 | 2240 | 0.1011 | 0.3887 | 0.5968 | 0.5743 | nan | 0.4668 | 0.7267 | 0.0 | 0.4587 | 0.7075 | | 0.0642 | 7.9 | 2260 | 0.0998 | 0.4185 | 0.6434 | 0.6277 | nan | 0.5526 | 0.7342 | 0.0 | 0.5447 | 0.7107 | | 0.0715 | 7.97 | 2280 | 0.1082 | 0.3556 | 0.5419 | 0.5436 | nan | 0.5518 | 0.5319 | 0.0 | 0.5357 | 0.5310 | | 0.0647 | 8.04 | 2300 | 0.0984 | 0.3973 | 0.6082 | 0.5916 | nan | 0.5124 | 0.7040 | 0.0 | 0.5064 | 0.6855 | | 0.0843 | 8.11 | 2320 | 0.1093 | 0.3623 | 0.5511 | 0.5376 | nan | 0.4733 | 0.6290 | 0.0 | 0.4637 | 0.6231 | | 0.0778 | 8.18 | 2340 | 0.1025 | 0.3724 | 0.5669 | 0.5612 | nan | 0.5341 | 0.5996 | 0.0 | 0.5258 | 0.5914 | | 0.1393 | 8.25 | 2360 | 0.1042 | 0.3804 | 0.5800 | 0.5758 | nan | 0.5559 | 0.6041 | 0.0 | 0.5449 | 0.5964 | | 0.0695 | 8.32 | 2380 | 0.1002 | 0.3749 | 0.5706 | 0.5499 | nan | 0.4506 | 0.6907 | 0.0 | 0.4444 | 0.6803 | | 0.0628 | 8.39 | 2400 | 0.0974 | 0.4024 | 0.6150 | 0.5932 | nan | 0.4887 | 0.7413 | 0.0 | 0.4828 | 0.7243 | | 0.1012 | 8.46 | 2420 | 0.1000 | 0.3580 | 0.5460 | 0.5331 | nan | 0.4717 | 0.6204 | 0.0 | 0.4648 | 0.6093 | | 0.0858 | 8.53 | 2440 | 0.0940 | 0.3873 | 0.5899 | 0.5728 | nan | 0.4910 | 0.6887 | 0.0 | 0.4857 | 0.6762 | | 0.042 | 8.6 | 2460 | 0.0991 | 0.3531 | 0.5368 | 0.5232 | nan | 0.4582 | 0.6153 | 0.0 | 0.4528 | 0.6064 | | 0.082 | 8.67 | 2480 | 0.0957 | 0.4199 | 0.6402 | 0.6301 | nan | 0.5820 | 0.6984 | 0.0 | 0.5728 | 0.6869 | | 0.0559 | 8.74 | 2500 | 0.1000 | 0.3834 | 0.5835 | 0.5694 | nan | 0.5019 | 0.6651 | 0.0 | 0.4961 | 0.6542 | | 0.0519 | 8.81 | 2520 | 0.1014 | 0.3645 | 0.5568 | 0.5520 | nan | 0.5292 | 0.5843 | 0.0 | 0.5118 | 0.5816 | | 0.0575 | 8.88 | 2540 | 0.1006 | 0.3780 | 0.5758 | 0.5652 | nan | 0.5147 | 0.6369 | 0.0 | 0.5010 | 0.6329 | | 0.1153 | 8.95 | 2560 | 0.0985 | 0.3824 | 0.5831 | 0.5708 | nan | 0.5125 | 0.6536 | 0.0 | 0.5016 | 0.6455 | | 0.1345 | 9.02 | 2580 | 0.1130 | 0.3045 | 0.4620 | 0.4461 | nan | 0.3704 | 0.5535 | 0.0 | 0.3644 | 0.5490 | | 0.0678 | 9.09 | 2600 | 0.1044 | 0.3596 | 0.5473 | 0.5352 | nan | 0.4771 | 0.6176 | 0.0 | 0.4645 | 0.6145 | | 0.0532 | 9.16 | 2620 | 0.0980 | 0.3829 | 0.5827 | 0.5744 | nan | 0.5350 | 0.6303 | 0.0 | 0.5243 | 0.6245 | | 0.0556 | 9.23 | 2640 | 0.1005 | 0.3665 | 0.5566 | 0.5421 | nan | 0.4729 | 0.6402 | 0.0 | 0.4652 | 0.6344 | | 0.0745 | 9.3 | 2660 | 0.0989 | 0.4017 | 0.6134 | 0.6049 | nan | 0.5643 | 0.6626 | 0.0 | 0.5552 | 0.6498 | | 0.0788 | 9.37 | 2680 | 0.1000 | 0.3568 | 0.5423 | 0.5330 | nan | 0.4883 | 0.5963 | 0.0 | 0.4801 | 0.5904 | | 0.0725 | 9.44 | 2700 | 0.0989 | 0.3925 | 0.5978 | 0.5835 | nan | 0.5150 | 0.6807 | 0.0 | 0.5035 | 0.6740 | | 0.0922 | 9.51 | 2720 | 0.0968 | 0.3560 | 0.5427 | 0.5291 | nan | 0.4637 | 0.6218 | 0.0 | 0.4587 | 0.6093 | | 0.1217 | 9.58 | 2740 | 0.0951 | 0.3694 | 0.5643 | 0.5454 | nan | 0.4551 | 0.6736 | 0.0 | 0.4489 | 0.6593 | | 0.0473 | 9.65 | 2760 | 0.0952 | 0.4051 | 0.6193 | 0.5989 | nan | 0.5010 | 0.7377 | 0.0 | 0.4921 | 0.7233 | | 0.0838 | 9.72 | 2780 | 0.0949 | 0.3779 | 0.5748 | 0.5603 | nan | 0.4908 | 0.6589 | 0.0 | 0.4830 | 0.6507 | | 0.0802 | 9.79 | 2800 | 0.1027 | 0.3680 | 0.5599 | 0.5489 | nan | 0.4966 | 0.6231 | 0.0 | 0.4846 | 0.6195 | | 0.0536 | 9.86 | 2820 | 0.0998 | 0.3787 | 0.5779 | 0.5686 | nan | 0.5241 | 0.6317 | 0.0 | 0.5157 | 0.6205 | | 0.0887 | 9.93 | 2840 | 0.1017 | 0.3687 | 0.5618 | 0.5519 | nan | 0.5046 | 0.6190 | 0.0 | 0.4930 | 0.6132 | | 0.1005 | 10.0 | 2860 | 0.0981 | 0.3995 | 0.6066 | 0.5950 | nan | 0.5391 | 0.6742 | 0.0 | 0.5306 | 0.6679 | | 0.0955 | 10.07 | 2880 | 0.1009 | 0.4048 | 0.6180 | 0.6121 | nan | 0.5836 | 0.6525 | 0.0 | 0.5674 | 0.6470 | | 0.0685 | 10.14 | 2900 | 0.0979 | 0.3750 | 0.5759 | 0.5561 | nan | 0.4616 | 0.6901 | 0.0 | 0.4564 | 0.6686 | | 0.1119 | 10.21 | 2920 | 0.1039 | 0.3550 | 0.5408 | 0.5183 | nan | 0.4109 | 0.6706 | 0.0 | 0.4069 | 0.6581 | | 0.063 | 10.28 | 2940 | 0.1012 | 0.3828 | 0.5835 | 0.5779 | nan | 0.5508 | 0.6163 | 0.0 | 0.5394 | 0.6091 | | 0.1124 | 10.35 | 2960 | 0.1001 | 0.4356 | 0.6655 | 0.6579 | nan | 0.6212 | 0.7099 | 0.0 | 0.6058 | 0.7009 | | 0.0653 | 10.42 | 2980 | 0.1057 | 0.3463 | 0.5254 | 0.5119 | nan | 0.4474 | 0.6034 | 0.0 | 0.4395 | 0.5994 | | 0.1077 | 10.49 | 3000 | 0.0984 | 0.3910 | 0.5983 | 0.5798 | nan | 0.4917 | 0.7048 | 0.0 | 0.4844 | 0.6886 | | 0.1254 | 10.56 | 3020 | 0.1064 | 0.3948 | 0.6018 | 0.5854 | nan | 0.5071 | 0.6964 | 0.0 | 0.4959 | 0.6884 | | 0.0687 | 10.63 | 3040 | 0.1167 | 0.3306 | 0.5029 | 0.5026 | nan | 0.5011 | 0.5047 | 0.0 | 0.4882 | 0.5037 | | 0.0693 | 10.7 | 3060 | 0.0991 | 0.3962 | 0.6024 | 0.5960 | nan | 0.5654 | 0.6394 | 0.0 | 0.5576 | 0.6311 | | 0.0875 | 10.77 | 3080 | 0.0943 | 0.4272 | 0.6528 | 0.6338 | nan | 0.5432 | 0.7623 | 0.0 | 0.5367 | 0.7448 | | 0.0695 | 10.84 | 3100 | 0.1042 | 0.3865 | 0.5879 | 0.5820 | nan | 0.5540 | 0.6218 | 0.0 | 0.5395 | 0.6200 | | 0.051 | 10.91 | 3120 | 0.0968 | 0.3849 | 0.5854 | 0.5682 | nan | 0.4858 | 0.6851 | 0.0 | 0.4789 | 0.6760 | | 0.0641 | 10.98 | 3140 | 0.0981 | 0.4032 | 0.6161 | 0.6010 | nan | 0.5286 | 0.7037 | 0.0 | 0.5193 | 0.6902 | | 0.0716 | 11.05 | 3160 | 0.1036 | 0.3798 | 0.5809 | 0.5680 | nan | 0.5061 | 0.6558 | 0.0 | 0.4964 | 0.6429 | | 0.1087 | 11.12 | 3180 | 0.1015 | 0.3836 | 0.5855 | 0.5738 | nan | 0.5179 | 0.6531 | 0.0 | 0.5080 | 0.6430 | | 0.0522 | 11.19 | 3200 | 0.1043 | 0.3675 | 0.5603 | 0.5403 | nan | 0.4449 | 0.6756 | 0.0 | 0.4363 | 0.6662 | | 0.0912 | 11.26 | 3220 | 0.1019 | 0.3668 | 0.5583 | 0.5457 | nan | 0.4856 | 0.6309 | 0.0 | 0.4780 | 0.6224 | | 0.0911 | 11.33 | 3240 | 0.1003 | 0.4162 | 0.6366 | 0.6229 | nan | 0.5571 | 0.7162 | 0.0 | 0.5488 | 0.6998 | | 0.0779 | 11.4 | 3260 | 0.1138 | 0.3192 | 0.4855 | 0.4757 | nan | 0.4288 | 0.5422 | 0.0 | 0.4172 | 0.5403 | | 0.0594 | 11.47 | 3280 | 0.1012 | 0.3781 | 0.5795 | 0.5589 | nan | 0.4605 | 0.6984 | 0.0 | 0.4549 | 0.6794 | | 0.0775 | 11.54 | 3300 | 0.1063 | 0.3682 | 0.5641 | 0.5606 | nan | 0.5441 | 0.5841 | 0.0 | 0.5303 | 0.5742 | | 0.0738 | 11.61 | 3320 | 0.1052 | 0.4021 | 0.6189 | 0.6026 | nan | 0.5244 | 0.7134 | 0.0 | 0.5169 | 0.6894 | | 0.1114 | 11.68 | 3340 | 0.1013 | 0.3984 | 0.6099 | 0.5986 | nan | 0.5449 | 0.6749 | 0.0 | 0.5362 | 0.6590 | | 0.1441 | 11.75 | 3360 | 0.0991 | 0.4060 | 0.6212 | 0.6055 | nan | 0.5302 | 0.7123 | 0.0 | 0.5229 | 0.6951 | | 0.0778 | 11.82 | 3380 | 0.0978 | 0.3829 | 0.5812 | 0.5692 | nan | 0.5123 | 0.6501 | 0.0 | 0.5056 | 0.6430 | | 0.0796 | 11.89 | 3400 | 0.0990 | 0.3648 | 0.5630 | 0.5386 | nan | 0.4222 | 0.7039 | 0.0 | 0.4210 | 0.6735 | | 0.0625 | 11.96 | 3420 | 0.0953 | 0.4138 | 0.6336 | 0.6163 | nan | 0.5337 | 0.7335 | 0.0 | 0.5236 | 0.7179 | | 0.1022 | 12.03 | 3440 | 0.1007 | 0.3783 | 0.5756 | 0.5592 | nan | 0.4808 | 0.6705 | 0.0 | 0.4707 | 0.6641 | | 0.0541 | 12.1 | 3460 | 0.0966 | 0.3732 | 0.5707 | 0.5482 | nan | 0.4404 | 0.7011 | 0.0 | 0.4363 | 0.6833 | | 0.0861 | 12.17 | 3480 | 0.0937 | 0.4198 | 0.6419 | 0.6271 | nan | 0.5564 | 0.7274 | 0.0 | 0.5458 | 0.7137 | | 0.0598 | 12.24 | 3500 | 0.1007 | 0.3859 | 0.5902 | 0.5806 | nan | 0.5349 | 0.6454 | 0.0 | 0.5183 | 0.6394 | | 0.0741 | 12.31 | 3520 | 0.0924 | 0.3955 | 0.6036 | 0.5847 | nan | 0.4947 | 0.7124 | 0.0 | 0.4876 | 0.6989 | | 0.0746 | 12.38 | 3540 | 0.0940 | 0.4016 | 0.6119 | 0.5964 | nan | 0.5224 | 0.7014 | 0.0 | 0.5123 | 0.6924 | | 0.107 | 12.45 | 3560 | 0.0969 | 0.3998 | 0.6108 | 0.5977 | nan | 0.5348 | 0.6868 | 0.0 | 0.5240 | 0.6756 | | 0.117 | 12.52 | 3580 | 0.0981 | 0.3908 | 0.5950 | 0.5836 | nan | 0.5292 | 0.6608 | 0.0 | 0.5181 | 0.6542 | | 0.1021 | 12.59 | 3600 | 0.0924 | 0.3829 | 0.5822 | 0.5617 | nan | 0.4638 | 0.7007 | 0.0 | 0.4566 | 0.6921 | | 0.0572 | 12.66 | 3620 | 0.1013 | 0.3601 | 0.5463 | 0.5259 | nan | 0.4286 | 0.6640 | 0.0 | 0.4255 | 0.6548 | | 0.0759 | 12.73 | 3640 | 0.0979 | 0.3960 | 0.6059 | 0.5892 | nan | 0.5095 | 0.7023 | 0.0 | 0.5035 | 0.6845 | | 0.0436 | 12.8 | 3660 | 0.1000 | 0.3826 | 0.5826 | 0.5684 | nan | 0.5009 | 0.6642 | 0.0 | 0.4930 | 0.6549 | | 0.0726 | 12.87 | 3680 | 0.1099 | 0.3625 | 0.5515 | 0.5490 | nan | 0.5374 | 0.5655 | 0.0 | 0.5264 | 0.5612 | | 0.0732 | 12.94 | 3700 | 0.0957 | 0.4221 | 0.6484 | 0.6269 | nan | 0.5241 | 0.7726 | 0.0 | 0.5172 | 0.7489 | | 0.0698 | 13.01 | 3720 | 0.0966 | 0.3963 | 0.6034 | 0.5926 | nan | 0.5408 | 0.6660 | 0.0 | 0.5307 | 0.6581 | | 0.0412 | 13.08 | 3740 | 0.1060 | 0.3700 | 0.5626 | 0.5468 | nan | 0.4717 | 0.6534 | 0.0 | 0.4624 | 0.6475 | | 0.0526 | 13.15 | 3760 | 0.0964 | 0.4019 | 0.6140 | 0.6033 | nan | 0.5520 | 0.6761 | 0.0 | 0.5438 | 0.6618 | | 0.0823 | 13.22 | 3780 | 0.1005 | 0.4183 | 0.6401 | 0.6340 | nan | 0.6047 | 0.6756 | 0.0 | 0.5908 | 0.6640 | | 0.0512 | 13.29 | 3800 | 0.1025 | 0.3815 | 0.5806 | 0.5646 | nan | 0.4883 | 0.6728 | 0.0 | 0.4781 | 0.6664 | | 0.0669 | 13.36 | 3820 | 0.0999 | 0.4168 | 0.6386 | 0.6294 | nan | 0.5856 | 0.6915 | 0.0 | 0.5657 | 0.6847 | | 0.0758 | 13.43 | 3840 | 0.1001 | 0.4030 | 0.6161 | 0.6041 | nan | 0.5467 | 0.6856 | 0.0 | 0.5354 | 0.6737 | | 0.0683 | 13.5 | 3860 | 0.0969 | 0.3761 | 0.5735 | 0.5568 | nan | 0.4775 | 0.6694 | 0.0 | 0.4706 | 0.6576 | | 0.0931 | 13.57 | 3880 | 0.0965 | 0.3775 | 0.5753 | 0.5643 | nan | 0.5118 | 0.6388 | 0.0 | 0.5013 | 0.6312 | | 0.059 | 13.64 | 3900 | 0.0986 | 0.4052 | 0.6174 | 0.6013 | nan | 0.5245 | 0.7104 | 0.0 | 0.5130 | 0.7027 | | 0.0525 | 13.71 | 3920 | 0.0924 | 0.4245 | 0.6472 | 0.6284 | nan | 0.5387 | 0.7558 | 0.0 | 0.5305 | 0.7430 | | 0.0734 | 13.78 | 3940 | 0.0907 | 0.4063 | 0.6198 | 0.6020 | nan | 0.5167 | 0.7230 | 0.0 | 0.5094 | 0.7095 | | 0.0456 | 13.85 | 3960 | 0.0908 | 0.3870 | 0.5904 | 0.5646 | nan | 0.4414 | 0.7394 | 0.0 | 0.4393 | 0.7218 | | 0.0653 | 13.92 | 3980 | 0.0932 | 0.4026 | 0.6147 | 0.5965 | nan | 0.5096 | 0.7198 | 0.0 | 0.5009 | 0.7068 | | 0.0591 | 13.99 | 4000 | 0.0953 | 0.3852 | 0.5869 | 0.5636 | nan | 0.4523 | 0.7214 | 0.0 | 0.4500 | 0.7055 | | 0.0771 | 14.06 | 4020 | 0.1007 | 0.3849 | 0.5862 | 0.5743 | nan | 0.5173 | 0.6551 | 0.0 | 0.5038 | 0.6509 | | 0.0572 | 14.13 | 4040 | 0.0985 | 0.3753 | 0.5702 | 0.5552 | nan | 0.4835 | 0.6570 | 0.0 | 0.4753 | 0.6505 | | 0.0563 | 14.2 | 4060 | 0.0978 | 0.3712 | 0.5643 | 0.5481 | nan | 0.4710 | 0.6576 | 0.0 | 0.4597 | 0.6540 | | 0.0621 | 14.27 | 4080 | 0.0949 | 0.3919 | 0.5957 | 0.5788 | nan | 0.4979 | 0.6935 | 0.0 | 0.4863 | 0.6894 | | 0.0597 | 14.34 | 4100 | 0.0975 | 0.4067 | 0.6217 | 0.6139 | nan | 0.5763 | 0.6672 | 0.0 | 0.5563 | 0.6636 | | 0.1111 | 14.41 | 4120 | 0.0950 | 0.4474 | 0.6829 | 0.6768 | nan | 0.6477 | 0.7182 | 0.0 | 0.6360 | 0.7061 | | 0.085 | 14.48 | 4140 | 0.0910 | 0.4132 | 0.6283 | 0.6087 | nan | 0.5152 | 0.7414 | 0.0 | 0.5073 | 0.7323 | | 0.0666 | 14.55 | 4160 | 0.1019 | 0.3930 | 0.5982 | 0.5877 | nan | 0.5377 | 0.6586 | 0.0 | 0.5245 | 0.6546 | | 0.1503 | 14.62 | 4180 | 0.1062 | 0.3686 | 0.5630 | 0.5551 | nan | 0.5175 | 0.6084 | 0.0 | 0.4999 | 0.6058 | | 0.063 | 14.69 | 4200 | 0.1064 | 0.3895 | 0.5936 | 0.5812 | nan | 0.5223 | 0.6648 | 0.0 | 0.5094 | 0.6591 | | 0.0682 | 14.76 | 4220 | 0.1044 | 0.3867 | 0.5878 | 0.5787 | nan | 0.5356 | 0.6400 | 0.0 | 0.5270 | 0.6330 | | 0.0833 | 14.83 | 4240 | 0.0972 | 0.3817 | 0.5799 | 0.5590 | nan | 0.4592 | 0.7007 | 0.0 | 0.4525 | 0.6926 | | 0.0557 | 14.9 | 4260 | 0.0965 | 0.3803 | 0.5799 | 0.5575 | nan | 0.4502 | 0.7097 | 0.0 | 0.4444 | 0.6964 | | 0.0737 | 14.97 | 4280 | 0.1025 | 0.3795 | 0.5776 | 0.5674 | nan | 0.5182 | 0.6371 | 0.0 | 0.5067 | 0.6319 | | 0.093 | 15.03 | 4300 | 0.1014 | 0.3918 | 0.6044 | 0.5804 | nan | 0.4660 | 0.7427 | 0.0 | 0.4618 | 0.7136 | | 0.0825 | 15.1 | 4320 | 0.1048 | 0.4051 | 0.6194 | 0.6190 | nan | 0.6175 | 0.6212 | 0.0 | 0.6021 | 0.6131 | | 0.0863 | 15.17 | 4340 | 0.0955 | 0.3903 | 0.5952 | 0.5774 | nan | 0.4926 | 0.6977 | 0.0 | 0.4810 | 0.6900 | | 0.0825 | 15.24 | 4360 | 0.1049 | 0.3582 | 0.5471 | 0.5436 | nan | 0.5265 | 0.5678 | 0.0 | 0.5100 | 0.5645 | | 0.0717 | 15.31 | 4380 | 0.1047 | 0.3870 | 0.5921 | 0.5870 | nan | 0.5629 | 0.6213 | 0.0 | 0.5432 | 0.6178 | | 0.047 | 15.38 | 4400 | 0.0992 | 0.3720 | 0.5667 | 0.5580 | nan | 0.5167 | 0.6167 | 0.0 | 0.5094 | 0.6067 | | 0.0532 | 15.45 | 4420 | 0.1012 | 0.4088 | 0.6250 | 0.6110 | nan | 0.5442 | 0.7057 | 0.0 | 0.5336 | 0.6929 | | 0.1046 | 15.52 | 4440 | 0.1059 | 0.3797 | 0.5782 | 0.5660 | nan | 0.5079 | 0.6484 | 0.0 | 0.4975 | 0.6415 | | 0.0546 | 15.59 | 4460 | 0.1080 | 0.3915 | 0.5964 | 0.5879 | nan | 0.5474 | 0.6455 | 0.0 | 0.5340 | 0.6406 | | 0.0841 | 15.66 | 4480 | 0.1140 | 0.3641 | 0.5538 | 0.5455 | nan | 0.5059 | 0.6017 | 0.0 | 0.4930 | 0.5994 | | 0.0394 | 15.73 | 4500 | 0.1091 | 0.3526 | 0.5362 | 0.5222 | nan | 0.4553 | 0.6172 | 0.0 | 0.4493 | 0.6086 | | 0.0973 | 15.8 | 4520 | 0.1024 | 0.3926 | 0.6013 | 0.5813 | nan | 0.4857 | 0.7168 | 0.0 | 0.4798 | 0.6981 | | 0.0938 | 15.87 | 4540 | 0.0978 | 0.4063 | 0.6196 | 0.6063 | nan | 0.5431 | 0.6961 | 0.0 | 0.5319 | 0.6872 | | 0.0702 | 15.94 | 4560 | 0.1018 | 0.3707 | 0.5631 | 0.5493 | nan | 0.4837 | 0.6425 | 0.0 | 0.4766 | 0.6356 | | 0.1085 | 16.01 | 4580 | 0.1011 | 0.3503 | 0.5317 | 0.5169 | nan | 0.4462 | 0.6171 | 0.0 | 0.4402 | 0.6106 | | 0.0878 | 16.08 | 4600 | 0.1007 | 0.3855 | 0.5882 | 0.5806 | nan | 0.5445 | 0.6319 | 0.0 | 0.5276 | 0.6289 | | 0.0874 | 16.15 | 4620 | 0.1043 | 0.4080 | 0.6223 | 0.6162 | nan | 0.5869 | 0.6578 | 0.0 | 0.5723 | 0.6518 | | 0.075 | 16.22 | 4640 | 0.1019 | 0.3917 | 0.5975 | 0.5870 | nan | 0.5369 | 0.6582 | 0.0 | 0.5249 | 0.6502 | | 0.0755 | 16.29 | 4660 | 0.0958 | 0.3885 | 0.5909 | 0.5714 | nan | 0.4785 | 0.7032 | 0.0 | 0.4725 | 0.6930 | | 0.0771 | 16.36 | 4680 | 0.0946 | 0.4212 | 0.6422 | 0.6286 | nan | 0.5634 | 0.7211 | 0.0 | 0.5512 | 0.7125 | | 0.0579 | 16.43 | 4700 | 0.0952 | 0.3851 | 0.5855 | 0.5689 | nan | 0.4895 | 0.6816 | 0.0 | 0.4812 | 0.6741 | | 0.0791 | 16.5 | 4720 | 0.0994 | 0.4017 | 0.6118 | 0.6045 | nan | 0.5694 | 0.6542 | 0.0 | 0.5547 | 0.6504 | | 0.0928 | 16.57 | 4740 | 0.0967 | 0.3869 | 0.5899 | 0.5662 | nan | 0.4531 | 0.7266 | 0.0 | 0.4474 | 0.7135 | | 0.0609 | 16.64 | 4760 | 0.1021 | 0.3885 | 0.5917 | 0.5831 | nan | 0.5420 | 0.6413 | 0.0 | 0.5273 | 0.6383 | | 0.0553 | 16.71 | 4780 | 0.1022 | 0.4056 | 0.6183 | 0.6107 | nan | 0.5747 | 0.6618 | 0.0 | 0.5618 | 0.6549 | | 0.0837 | 16.78 | 4800 | 0.1002 | 0.3934 | 0.5988 | 0.5893 | nan | 0.5437 | 0.6539 | 0.0 | 0.5298 | 0.6503 | | 0.0636 | 16.85 | 4820 | 0.0963 | 0.3874 | 0.5885 | 0.5722 | nan | 0.4942 | 0.6829 | 0.0 | 0.4861 | 0.6762 | | 0.0836 | 16.92 | 4840 | 0.1014 | 0.4027 | 0.6121 | 0.6065 | nan | 0.5802 | 0.6439 | 0.0 | 0.5677 | 0.6402 | | 0.0463 | 16.99 | 4860 | 0.1040 | 0.3669 | 0.5578 | 0.5357 | nan | 0.4300 | 0.6856 | 0.0 | 0.4252 | 0.6756 | | 0.0552 | 17.06 | 4880 | 0.1026 | 0.4088 | 0.6217 | 0.6110 | nan | 0.5602 | 0.6832 | 0.0 | 0.5512 | 0.6751 | | 0.0901 | 17.13 | 4900 | 0.1116 | 0.3744 | 0.5694 | 0.5661 | nan | 0.5504 | 0.5885 | 0.0 | 0.5380 | 0.5852 | | 0.0519 | 17.2 | 4920 | 0.0992 | 0.3929 | 0.5994 | 0.5878 | nan | 0.5325 | 0.6662 | 0.0 | 0.5262 | 0.6525 | | 0.0495 | 17.27 | 4940 | 0.1049 | 0.3701 | 0.5613 | 0.5436 | nan | 0.4586 | 0.6641 | 0.0 | 0.4504 | 0.6598 | | 0.0791 | 17.34 | 4960 | 0.1030 | 0.3959 | 0.6027 | 0.5925 | nan | 0.5437 | 0.6618 | 0.0 | 0.5303 | 0.6575 | | 0.072 | 17.41 | 4980 | 0.1069 | 0.4010 | 0.6135 | 0.6103 | nan | 0.5950 | 0.6320 | 0.0 | 0.5733 | 0.6296 | | 0.0881 | 17.48 | 5000 | 0.0962 | 0.4056 | 0.6166 | 0.5961 | nan | 0.4985 | 0.7346 | 0.0 | 0.4911 | 0.7258 | | 0.0783 | 17.55 | 5020 | 0.0974 | 0.4107 | 0.6262 | 0.6147 | nan | 0.5597 | 0.6928 | 0.0 | 0.5503 | 0.6819 | | 0.0639 | 17.62 | 5040 | 0.1122 | 0.3669 | 0.5592 | 0.5528 | nan | 0.5222 | 0.5961 | 0.0 | 0.5082 | 0.5924 | | 0.0634 | 17.69 | 5060 | 0.0936 | 0.4078 | 0.6249 | 0.6017 | nan | 0.4911 | 0.7586 | 0.0 | 0.4893 | 0.7342 | | 0.0665 | 17.76 | 5080 | 0.1103 | 0.3627 | 0.5506 | 0.5446 | nan | 0.5163 | 0.5849 | 0.0 | 0.5058 | 0.5823 | | 0.0535 | 17.83 | 5100 | 0.0981 | 0.3984 | 0.6161 | 0.5936 | nan | 0.4860 | 0.7463 | 0.0 | 0.4841 | 0.7111 | | 0.0595 | 17.9 | 5120 | 0.1121 | 0.3565 | 0.5415 | 0.5253 | nan | 0.4481 | 0.6348 | 0.0 | 0.4388 | 0.6308 | | 0.0766 | 17.97 | 5140 | 0.1016 | 0.4211 | 0.6429 | 0.6322 | nan | 0.5813 | 0.7045 | 0.0 | 0.5721 | 0.6913 | | 0.0752 | 18.04 | 5160 | 0.1117 | 0.4288 | 0.6552 | 0.6538 | nan | 0.6470 | 0.6634 | 0.0 | 0.6252 | 0.6611 | | 0.0826 | 18.11 | 5180 | 0.1059 | 0.3650 | 0.5539 | 0.5360 | nan | 0.4506 | 0.6572 | 0.0 | 0.4418 | 0.6530 | | 0.0555 | 18.18 | 5200 | 0.0977 | 0.3816 | 0.5802 | 0.5690 | nan | 0.5150 | 0.6455 | 0.0 | 0.5079 | 0.6369 | | 0.0844 | 18.25 | 5220 | 0.1058 | 0.3774 | 0.5753 | 0.5649 | nan | 0.5154 | 0.6352 | 0.0 | 0.5008 | 0.6315 | | 0.0631 | 18.32 | 5240 | 0.1041 | 0.3874 | 0.5906 | 0.5842 | nan | 0.5540 | 0.6272 | 0.0 | 0.5404 | 0.6218 | | 0.0583 | 18.39 | 5260 | 0.1024 | 0.4053 | 0.6169 | 0.6015 | nan | 0.5277 | 0.7061 | 0.0 | 0.5174 | 0.6983 | | 0.0647 | 18.46 | 5280 | 0.1029 | 0.3825 | 0.5807 | 0.5692 | nan | 0.5147 | 0.6466 | 0.0 | 0.5070 | 0.6406 | | 0.0379 | 18.53 | 5300 | 0.1033 | 0.3833 | 0.5829 | 0.5694 | nan | 0.5047 | 0.6611 | 0.0 | 0.4966 | 0.6533 | | 0.0959 | 18.6 | 5320 | 0.1012 | 0.4060 | 0.6188 | 0.6004 | nan | 0.5126 | 0.7250 | 0.0 | 0.5044 | 0.7135 | | 0.0785 | 18.67 | 5340 | 0.1038 | 0.4056 | 0.6194 | 0.6113 | nan | 0.5729 | 0.6659 | 0.0 | 0.5561 | 0.6606 | | 0.1013 | 18.74 | 5360 | 0.1033 | 0.3819 | 0.5815 | 0.5661 | nan | 0.4925 | 0.6705 | 0.0 | 0.4824 | 0.6633 | | 0.0619 | 18.81 | 5380 | 0.1055 | 0.3839 | 0.5839 | 0.5698 | nan | 0.5026 | 0.6651 | 0.0 | 0.4930 | 0.6588 | | 0.0915 | 18.88 | 5400 | 0.1080 | 0.3987 | 0.6086 | 0.6066 | nan | 0.5971 | 0.6200 | 0.0 | 0.5796 | 0.6165 | | 0.0667 | 18.95 | 5420 | 0.1058 | 0.3991 | 0.6078 | 0.6031 | nan | 0.5807 | 0.6350 | 0.0 | 0.5677 | 0.6296 | | 0.0429 | 19.02 | 5440 | 0.1002 | 0.4102 | 0.6238 | 0.6093 | nan | 0.5401 | 0.7074 | 0.0 | 0.5312 | 0.6994 | | 0.0876 | 19.09 | 5460 | 0.0976 | 0.3997 | 0.6082 | 0.5909 | nan | 0.5081 | 0.7083 | 0.0 | 0.5016 | 0.6975 | | 0.0726 | 19.16 | 5480 | 0.1051 | 0.3825 | 0.5820 | 0.5711 | nan | 0.5191 | 0.6449 | 0.0 | 0.5056 | 0.6418 | | 0.0569 | 19.23 | 5500 | 0.1003 | 0.4022 | 0.6136 | 0.6001 | nan | 0.5352 | 0.6921 | 0.0 | 0.5272 | 0.6793 | | 0.067 | 19.3 | 5520 | 0.1161 | 0.3676 | 0.5613 | 0.5550 | nan | 0.5249 | 0.5978 | 0.0 | 0.5078 | 0.5950 | | 0.0766 | 19.37 | 5540 | 0.1063 | 0.4164 | 0.6348 | 0.6266 | nan | 0.5874 | 0.6821 | 0.0 | 0.5729 | 0.6762 | | 0.0685 | 19.44 | 5560 | 0.1023 | 0.3958 | 0.6022 | 0.5937 | nan | 0.5531 | 0.6512 | 0.0 | 0.5445 | 0.6429 | | 0.0555 | 19.51 | 5580 | 0.1103 | 0.3905 | 0.5950 | 0.5787 | nan | 0.5008 | 0.6892 | 0.0 | 0.4931 | 0.6783 | | 0.167 | 19.58 | 5600 | 0.1035 | 0.4108 | 0.6278 | 0.6181 | nan | 0.5719 | 0.6836 | 0.0 | 0.5637 | 0.6688 | | 0.0579 | 19.65 | 5620 | 0.1107 | 0.3577 | 0.5439 | 0.5374 | nan | 0.5060 | 0.5819 | 0.0 | 0.4975 | 0.5757 | | 0.0623 | 19.72 | 5640 | 0.1070 | 0.3780 | 0.5753 | 0.5643 | nan | 0.5118 | 0.6389 | 0.0 | 0.5030 | 0.6311 | | 0.1008 | 19.79 | 5660 | 0.1047 | 0.3979 | 0.6090 | 0.5997 | nan | 0.5552 | 0.6627 | 0.0 | 0.5405 | 0.6533 | | 0.0678 | 19.86 | 5680 | 0.1049 | 0.3886 | 0.5958 | 0.5804 | nan | 0.5068 | 0.6848 | 0.0 | 0.4973 | 0.6686 | | 0.0353 | 19.93 | 5700 | 0.1079 | 0.3932 | 0.5995 | 0.5892 | nan | 0.5404 | 0.6586 | 0.0 | 0.5242 | 0.6554 | | 0.0664 | 20.0 | 5720 | 0.1053 | 0.3807 | 0.5798 | 0.5705 | nan | 0.5263 | 0.6333 | 0.0 | 0.5126 | 0.6294 | | 0.0584 | 20.07 | 5740 | 0.1091 | 0.3800 | 0.5791 | 0.5726 | nan | 0.5414 | 0.6168 | 0.0 | 0.5265 | 0.6137 | | 0.055 | 20.14 | 5760 | 0.1089 | 0.3871 | 0.5925 | 0.5853 | nan | 0.5509 | 0.6341 | 0.0 | 0.5292 | 0.6323 | | 0.0696 | 20.21 | 5780 | 0.1059 | 0.3759 | 0.5725 | 0.5631 | nan | 0.5181 | 0.6269 | 0.0 | 0.5064 | 0.6212 | | 0.0913 | 20.28 | 5800 | 0.0992 | 0.4003 | 0.6087 | 0.5924 | nan | 0.5146 | 0.7027 | 0.0 | 0.5035 | 0.6974 | | 0.0819 | 20.35 | 5820 | 0.1042 | 0.3924 | 0.5979 | 0.5918 | nan | 0.5625 | 0.6334 | 0.0 | 0.5474 | 0.6298 | | 0.0512 | 20.42 | 5840 | 0.1012 | 0.4067 | 0.6203 | 0.6092 | nan | 0.5561 | 0.6846 | 0.0 | 0.5451 | 0.6750 | | 0.0628 | 20.49 | 5860 | 0.0973 | 0.4046 | 0.6175 | 0.6029 | nan | 0.5329 | 0.7022 | 0.0 | 0.5232 | 0.6906 | | 0.0559 | 20.56 | 5880 | 0.1116 | 0.3773 | 0.5761 | 0.5727 | nan | 0.5563 | 0.5959 | 0.0 | 0.5392 | 0.5928 | | 0.0882 | 20.63 | 5900 | 0.1051 | 0.3807 | 0.5795 | 0.5646 | nan | 0.4936 | 0.6653 | 0.0 | 0.4845 | 0.6577 | | 0.0775 | 20.7 | 5920 | 0.1082 | 0.3772 | 0.5752 | 0.5643 | nan | 0.5123 | 0.6381 | 0.0 | 0.4955 | 0.6362 | | 0.0467 | 20.77 | 5940 | 0.1074 | 0.3795 | 0.5788 | 0.5681 | nan | 0.5173 | 0.6403 | 0.0 | 0.5064 | 0.6320 | | 0.0628 | 20.84 | 5960 | 0.1120 | 0.3721 | 0.5676 | 0.5542 | nan | 0.4902 | 0.6449 | 0.0 | 0.4765 | 0.6398 | | 0.0498 | 20.91 | 5980 | 0.1123 | 0.3742 | 0.5709 | 0.5643 | nan | 0.5328 | 0.6090 | 0.0 | 0.5185 | 0.6042 | | 0.0759 | 20.98 | 6000 | 0.1103 | 0.3724 | 0.5753 | 0.5459 | nan | 0.4054 | 0.7452 | 0.0 | 0.4033 | 0.7140 | | 0.0536 | 21.05 | 6020 | 0.1172 | 0.3340 | 0.5067 | 0.4955 | nan | 0.4418 | 0.5717 | 0.0 | 0.4326 | 0.5694 | | 0.0564 | 21.12 | 6040 | 0.1075 | 0.3964 | 0.6035 | 0.5965 | nan | 0.5629 | 0.6440 | 0.0 | 0.5505 | 0.6388 | | 0.0609 | 21.19 | 6060 | 0.1077 | 0.3845 | 0.5847 | 0.5715 | nan | 0.5085 | 0.6609 | 0.0 | 0.5027 | 0.6508 | | 0.0696 | 21.26 | 6080 | 0.1104 | 0.4082 | 0.6222 | 0.6173 | nan | 0.5938 | 0.6505 | 0.0 | 0.5812 | 0.6433 | | 0.0555 | 21.33 | 6100 | 0.1043 | 0.3853 | 0.5897 | 0.5757 | nan | 0.5086 | 0.6709 | 0.0 | 0.5021 | 0.6539 | | 0.0505 | 21.4 | 6120 | 0.1034 | 0.3913 | 0.5972 | 0.5778 | nan | 0.4850 | 0.7094 | 0.0 | 0.4794 | 0.6945 | | 0.0508 | 21.47 | 6140 | 0.1060 | 0.3877 | 0.5898 | 0.5776 | nan | 0.5194 | 0.6602 | 0.0 | 0.5137 | 0.6493 | | 0.0515 | 21.54 | 6160 | 0.1052 | 0.4081 | 0.6214 | 0.6114 | nan | 0.5639 | 0.6789 | 0.0 | 0.5508 | 0.6734 | | 0.0769 | 21.61 | 6180 | 0.1032 | 0.4138 | 0.6296 | 0.6228 | nan | 0.5901 | 0.6692 | 0.0 | 0.5812 | 0.6602 | | 0.092 | 21.68 | 6200 | 0.0974 | 0.4061 | 0.6193 | 0.6031 | nan | 0.5260 | 0.7125 | 0.0 | 0.5203 | 0.6979 | | 0.0601 | 21.75 | 6220 | 0.1035 | 0.3928 | 0.5969 | 0.5861 | nan | 0.5344 | 0.6594 | 0.0 | 0.5232 | 0.6553 | | 0.0717 | 21.82 | 6240 | 0.1106 | 0.3681 | 0.5595 | 0.5480 | nan | 0.4933 | 0.6256 | 0.0 | 0.4825 | 0.6219 | | 0.0547 | 21.89 | 6260 | 0.1035 | 0.3779 | 0.5745 | 0.5630 | nan | 0.5084 | 0.6406 | 0.0 | 0.4988 | 0.6349 | | 0.0503 | 21.96 | 6280 | 0.0977 | 0.3996 | 0.6081 | 0.5946 | nan | 0.5299 | 0.6862 | 0.0 | 0.5209 | 0.6780 | | 0.0875 | 22.03 | 6300 | 0.1110 | 0.3631 | 0.5536 | 0.5458 | nan | 0.5087 | 0.5984 | 0.0 | 0.4949 | 0.5943 | | 0.0485 | 22.1 | 6320 | 0.1036 | 0.4038 | 0.6149 | 0.6035 | nan | 0.5491 | 0.6806 | 0.0 | 0.5375 | 0.6740 | | 0.0694 | 22.17 | 6340 | 0.1074 | 0.3869 | 0.5889 | 0.5807 | nan | 0.5412 | 0.6366 | 0.0 | 0.5285 | 0.6324 | | 0.0328 | 22.24 | 6360 | 0.1043 | 0.3778 | 0.5742 | 0.5648 | nan | 0.5201 | 0.6283 | 0.0 | 0.5112 | 0.6220 | | 0.0644 | 22.31 | 6380 | 0.0997 | 0.4024 | 0.6122 | 0.5886 | nan | 0.4759 | 0.7485 | 0.0 | 0.4712 | 0.7361 | | 0.0591 | 22.38 | 6400 | 0.1037 | 0.3833 | 0.5816 | 0.5681 | nan | 0.5038 | 0.6593 | 0.0 | 0.4961 | 0.6539 | | 0.0717 | 22.45 | 6420 | 0.1007 | 0.4057 | 0.6171 | 0.6022 | nan | 0.5313 | 0.7028 | 0.0 | 0.5201 | 0.6969 | | 0.0575 | 22.52 | 6440 | 0.1002 | 0.4074 | 0.6198 | 0.6098 | nan | 0.5620 | 0.6777 | 0.0 | 0.5518 | 0.6705 | | 0.1045 | 22.59 | 6460 | 0.1041 | 0.4056 | 0.6185 | 0.6153 | nan | 0.6000 | 0.6369 | 0.0 | 0.5845 | 0.6324 | | 0.0701 | 22.66 | 6480 | 0.1090 | 0.3983 | 0.6062 | 0.5987 | nan | 0.5628 | 0.6497 | 0.0 | 0.5489 | 0.6459 | | 0.0791 | 22.73 | 6500 | 0.1074 | 0.4078 | 0.6232 | 0.6183 | nan | 0.5951 | 0.6513 | 0.0 | 0.5757 | 0.6478 | | 0.1456 | 22.8 | 6520 | 0.1056 | 0.4139 | 0.6308 | 0.6196 | nan | 0.5662 | 0.6954 | 0.0 | 0.5513 | 0.6905 | | 0.0788 | 22.87 | 6540 | 0.1059 | 0.4029 | 0.6140 | 0.6070 | nan | 0.5736 | 0.6543 | 0.0 | 0.5591 | 0.6495 | | 0.0538 | 22.94 | 6560 | 0.1062 | 0.4059 | 0.6187 | 0.6094 | nan | 0.5648 | 0.6726 | 0.0 | 0.5519 | 0.6658 | | 0.0545 | 23.01 | 6580 | 0.1071 | 0.4160 | 0.6354 | 0.6271 | nan | 0.5873 | 0.6835 | 0.0 | 0.5708 | 0.6772 | | 0.0738 | 23.08 | 6600 | 0.1064 | 0.3976 | 0.6068 | 0.5960 | nan | 0.5448 | 0.6687 | 0.0 | 0.5302 | 0.6625 | | 0.0679 | 23.15 | 6620 | 0.1076 | 0.3948 | 0.6018 | 0.5888 | nan | 0.5268 | 0.6768 | 0.0 | 0.5142 | 0.6701 | | 0.1012 | 23.22 | 6640 | 0.1099 | 0.3703 | 0.5644 | 0.5528 | nan | 0.4977 | 0.6310 | 0.0 | 0.4850 | 0.6258 | | 0.0583 | 23.29 | 6660 | 0.1104 | 0.3857 | 0.5879 | 0.5763 | nan | 0.5209 | 0.6549 | 0.0 | 0.5085 | 0.6486 | | 0.0462 | 23.36 | 6680 | 0.1082 | 0.4024 | 0.6134 | 0.5988 | nan | 0.5290 | 0.6978 | 0.0 | 0.5161 | 0.6911 | | 0.0696 | 23.43 | 6700 | 0.1156 | 0.3763 | 0.5749 | 0.5718 | nan | 0.5570 | 0.5928 | 0.0 | 0.5376 | 0.5912 | | 0.0475 | 23.5 | 6720 | 0.1037 | 0.3997 | 0.6083 | 0.5931 | nan | 0.5205 | 0.6962 | 0.0 | 0.5108 | 0.6884 | | 0.0699 | 23.57 | 6740 | 0.1043 | 0.3914 | 0.5949 | 0.5843 | nan | 0.5341 | 0.6556 | 0.0 | 0.5231 | 0.6512 | | 0.0417 | 23.64 | 6760 | 0.1163 | 0.3607 | 0.5474 | 0.5366 | nan | 0.4848 | 0.6100 | 0.0 | 0.4749 | 0.6073 | | 0.0656 | 23.71 | 6780 | 0.1082 | 0.4003 | 0.6111 | 0.6040 | nan | 0.5701 | 0.6520 | 0.0 | 0.5530 | 0.6479 | | 0.0548 | 23.78 | 6800 | 0.1101 | 0.3748 | 0.5692 | 0.5564 | nan | 0.4952 | 0.6433 | 0.0 | 0.4864 | 0.6381 | | 0.0842 | 23.85 | 6820 | 0.1125 | 0.3775 | 0.5762 | 0.5683 | nan | 0.5307 | 0.6217 | 0.0 | 0.5171 | 0.6154 | | 0.0606 | 23.92 | 6840 | 0.1219 | 0.3894 | 0.5949 | 0.5934 | nan | 0.5866 | 0.6031 | 0.0 | 0.5670 | 0.6013 | | 0.0683 | 23.99 | 6860 | 0.1059 | 0.4190 | 0.6404 | 0.6311 | nan | 0.5866 | 0.6943 | 0.0 | 0.5708 | 0.6863 | | 0.0599 | 24.06 | 6880 | 0.1132 | 0.3841 | 0.5879 | 0.5801 | nan | 0.5430 | 0.6328 | 0.0 | 0.5232 | 0.6291 | | 0.0531 | 24.13 | 6900 | 0.1041 | 0.4171 | 0.6364 | 0.6275 | nan | 0.5851 | 0.6877 | 0.0 | 0.5722 | 0.6791 | | 0.0506 | 24.2 | 6920 | 0.1240 | 0.3742 | 0.5702 | 0.5697 | nan | 0.5673 | 0.5731 | 0.0 | 0.5512 | 0.5714 | | 0.0749 | 24.27 | 6940 | 0.1059 | 0.3990 | 0.6091 | 0.5980 | nan | 0.5449 | 0.6733 | 0.0 | 0.5291 | 0.6680 | | 0.055 | 24.34 | 6960 | 0.1098 | 0.3805 | 0.5797 | 0.5696 | nan | 0.5212 | 0.6382 | 0.0 | 0.5078 | 0.6338 | | 0.0508 | 24.41 | 6980 | 0.1068 | 0.3771 | 0.5758 | 0.5622 | nan | 0.4975 | 0.6541 | 0.0 | 0.4927 | 0.6385 | | 0.0675 | 24.48 | 7000 | 0.1159 | 0.3933 | 0.5984 | 0.5846 | nan | 0.5186 | 0.6782 | 0.0 | 0.5072 | 0.6727 | | 0.0594 | 24.55 | 7020 | 0.1044 | 0.3917 | 0.5951 | 0.5778 | nan | 0.4952 | 0.6949 | 0.0 | 0.4859 | 0.6890 | | 0.0629 | 24.62 | 7040 | 0.1095 | 0.3850 | 0.5858 | 0.5761 | nan | 0.5297 | 0.6419 | 0.0 | 0.5158 | 0.6390 | | 0.0708 | 24.69 | 7060 | 0.1064 | 0.4145 | 0.6313 | 0.6212 | nan | 0.5731 | 0.6894 | 0.0 | 0.5616 | 0.6820 | | 0.0666 | 24.76 | 7080 | 0.1121 | 0.3841 | 0.5854 | 0.5764 | nan | 0.5336 | 0.6372 | 0.0 | 0.5197 | 0.6327 | | 0.0617 | 24.83 | 7100 | 0.1102 | 0.3909 | 0.5951 | 0.5857 | nan | 0.5411 | 0.6491 | 0.0 | 0.5278 | 0.6450 | | 0.0788 | 24.9 | 7120 | 0.1096 | 0.3790 | 0.5769 | 0.5661 | nan | 0.5146 | 0.6393 | 0.0 | 0.5009 | 0.6361 | | 0.0431 | 24.97 | 7140 | 0.1163 | 0.3701 | 0.5643 | 0.5539 | nan | 0.5045 | 0.6241 | 0.0 | 0.4896 | 0.6208 | | 0.0694 | 25.03 | 7160 | 0.1113 | 0.3804 | 0.5782 | 0.5636 | nan | 0.4939 | 0.6625 | 0.0 | 0.4870 | 0.6540 | | 0.0847 | 25.1 | 7180 | 0.1056 | 0.3921 | 0.5954 | 0.5841 | nan | 0.5298 | 0.6611 | 0.0 | 0.5241 | 0.6521 | | 0.0751 | 25.17 | 7200 | 0.1059 | 0.4104 | 0.6234 | 0.6080 | nan | 0.5341 | 0.7128 | 0.0 | 0.5258 | 0.7053 | | 0.0536 | 25.24 | 7220 | 0.1109 | 0.3948 | 0.6002 | 0.5890 | nan | 0.5357 | 0.6647 | 0.0 | 0.5242 | 0.6604 | | 0.0481 | 25.31 | 7240 | 0.1039 | 0.4307 | 0.6552 | 0.6441 | nan | 0.5911 | 0.7194 | 0.0 | 0.5818 | 0.7102 | | 0.0758 | 25.38 | 7260 | 0.1100 | 0.4104 | 0.6277 | 0.6223 | nan | 0.5963 | 0.6591 | 0.0 | 0.5787 | 0.6525 | | 0.0696 | 25.45 | 7280 | 0.0968 | 0.4143 | 0.6307 | 0.6154 | nan | 0.5424 | 0.7190 | 0.0 | 0.5334 | 0.7096 | | 0.0652 | 25.52 | 7300 | 0.1013 | 0.4363 | 0.6657 | 0.6581 | nan | 0.6223 | 0.7090 | 0.0 | 0.6065 | 0.7025 | | 0.0723 | 25.59 | 7320 | 0.0987 | 0.4145 | 0.6297 | 0.6129 | nan | 0.5329 | 0.7264 | 0.0 | 0.5244 | 0.7191 | | 0.0583 | 25.66 | 7340 | 0.1021 | 0.4088 | 0.6217 | 0.6118 | nan | 0.5646 | 0.6788 | 0.0 | 0.5541 | 0.6724 | | 0.0482 | 25.73 | 7360 | 0.1056 | 0.4003 | 0.6074 | 0.5959 | nan | 0.5410 | 0.6738 | 0.0 | 0.5312 | 0.6697 | | 0.0559 | 25.8 | 7380 | 0.0982 | 0.4233 | 0.6432 | 0.6306 | nan | 0.5706 | 0.7159 | 0.0 | 0.5617 | 0.7081 | | 0.0805 | 25.87 | 7400 | 0.1083 | 0.3842 | 0.5842 | 0.5740 | nan | 0.5255 | 0.6428 | 0.0 | 0.5147 | 0.6379 | | 0.1062 | 25.94 | 7420 | 0.1046 | 0.4005 | 0.6088 | 0.5963 | nan | 0.5369 | 0.6807 | 0.0 | 0.5247 | 0.6768 | | 0.0548 | 26.01 | 7440 | 0.1070 | 0.3973 | 0.6036 | 0.5912 | nan | 0.5318 | 0.6755 | 0.0 | 0.5220 | 0.6699 | | 0.0477 | 26.08 | 7460 | 0.1069 | 0.3864 | 0.5881 | 0.5769 | nan | 0.5233 | 0.6529 | 0.0 | 0.5124 | 0.6468 | | 0.0514 | 26.15 | 7480 | 0.1129 | 0.3872 | 0.5908 | 0.5823 | nan | 0.5415 | 0.6402 | 0.0 | 0.5257 | 0.6358 | | 0.076 | 26.22 | 7500 | 0.1038 | 0.4098 | 0.6238 | 0.6110 | nan | 0.5504 | 0.6971 | 0.0 | 0.5416 | 0.6880 | | 0.038 | 26.29 | 7520 | 0.1104 | 0.4091 | 0.6236 | 0.6187 | nan | 0.5952 | 0.6521 | 0.0 | 0.5818 | 0.6455 | | 0.0465 | 26.36 | 7540 | 0.1140 | 0.3794 | 0.5786 | 0.5698 | nan | 0.5278 | 0.6294 | 0.0 | 0.5130 | 0.6252 | | 0.0718 | 26.43 | 7560 | 0.1159 | 0.3726 | 0.5682 | 0.5619 | nan | 0.5318 | 0.6047 | 0.0 | 0.5172 | 0.6005 | | 0.0567 | 26.5 | 7580 | 0.1162 | 0.3708 | 0.5644 | 0.5568 | nan | 0.5201 | 0.6088 | 0.0 | 0.5082 | 0.6041 | | 0.058 | 26.57 | 7600 | 0.1082 | 0.3897 | 0.5927 | 0.5824 | nan | 0.5336 | 0.6517 | 0.0 | 0.5232 | 0.6460 | | 0.0621 | 26.64 | 7620 | 0.1048 | 0.4165 | 0.6344 | 0.6211 | nan | 0.5578 | 0.7110 | 0.0 | 0.5451 | 0.7043 | | 0.0574 | 26.71 | 7640 | 0.1091 | 0.4002 | 0.6090 | 0.5964 | nan | 0.5364 | 0.6817 | 0.0 | 0.5249 | 0.6757 | | 0.0508 | 26.78 | 7660 | 0.1133 | 0.3822 | 0.5827 | 0.5712 | nan | 0.5162 | 0.6492 | 0.0 | 0.5062 | 0.6403 | | 0.0689 | 26.85 | 7680 | 0.1139 | 0.3822 | 0.5817 | 0.5713 | nan | 0.5219 | 0.6414 | 0.0 | 0.5110 | 0.6357 | | 0.0607 | 26.92 | 7700 | 0.1134 | 0.3671 | 0.5589 | 0.5465 | nan | 0.4872 | 0.6306 | 0.0 | 0.4798 | 0.6216 | | 0.0603 | 26.99 | 7720 | 0.1109 | 0.4023 | 0.6137 | 0.6017 | nan | 0.5446 | 0.6828 | 0.0 | 0.5364 | 0.6705 | | 0.0585 | 27.06 | 7740 | 0.1139 | 0.3758 | 0.5727 | 0.5630 | nan | 0.5166 | 0.6288 | 0.0 | 0.5092 | 0.6182 | | 0.0567 | 27.13 | 7760 | 0.1123 | 0.3915 | 0.5966 | 0.5859 | nan | 0.5345 | 0.6588 | 0.0 | 0.5227 | 0.6520 | | 0.0624 | 27.2 | 7780 | 0.1119 | 0.3845 | 0.5848 | 0.5748 | nan | 0.5268 | 0.6429 | 0.0 | 0.5165 | 0.6370 | | 0.064 | 27.27 | 7800 | 0.1139 | 0.3941 | 0.5979 | 0.5880 | nan | 0.5410 | 0.6548 | 0.0 | 0.5327 | 0.6495 | | 0.0613 | 27.34 | 7820 | 0.1131 | 0.3836 | 0.5842 | 0.5729 | nan | 0.5192 | 0.6491 | 0.0 | 0.5118 | 0.6390 | | 0.0365 | 27.41 | 7840 | 0.1096 | 0.4107 | 0.6239 | 0.6126 | nan | 0.5589 | 0.6888 | 0.0 | 0.5503 | 0.6818 | | 0.0684 | 27.48 | 7860 | 0.1080 | 0.4064 | 0.6187 | 0.6106 | nan | 0.5720 | 0.6654 | 0.0 | 0.5598 | 0.6593 | | 0.061 | 27.55 | 7880 | 0.1027 | 0.4219 | 0.6425 | 0.6294 | nan | 0.5671 | 0.7179 | 0.0 | 0.5572 | 0.7085 | | 0.0635 | 27.62 | 7900 | 0.1123 | 0.3687 | 0.5601 | 0.5467 | nan | 0.4828 | 0.6374 | 0.0 | 0.4744 | 0.6317 | | 0.0551 | 27.69 | 7920 | 0.1061 | 0.3908 | 0.5932 | 0.5717 | nan | 0.4688 | 0.7176 | 0.0 | 0.4620 | 0.7104 | | 0.0603 | 27.76 | 7940 | 0.1027 | 0.4160 | 0.6330 | 0.6149 | nan | 0.5284 | 0.7376 | 0.0 | 0.5227 | 0.7253 | | 0.0471 | 27.83 | 7960 | 0.1092 | 0.4077 | 0.6190 | 0.6099 | nan | 0.5666 | 0.6714 | 0.0 | 0.5558 | 0.6674 | | 0.0854 | 27.9 | 7980 | 0.1072 | 0.4176 | 0.6367 | 0.6285 | nan | 0.5897 | 0.6836 | 0.0 | 0.5788 | 0.6739 | | 0.0851 | 27.97 | 8000 | 0.1080 | 0.4052 | 0.6151 | 0.6025 | nan | 0.5423 | 0.6880 | 0.0 | 0.5336 | 0.6820 | | 0.0857 | 28.04 | 8020 | 0.1099 | 0.3839 | 0.5828 | 0.5687 | nan | 0.5009 | 0.6648 | 0.0 | 0.4912 | 0.6605 | | 0.0603 | 28.11 | 8040 | 0.1049 | 0.4042 | 0.6144 | 0.5994 | nan | 0.5278 | 0.7009 | 0.0 | 0.5179 | 0.6946 | | 0.0278 | 28.18 | 8060 | 0.1033 | 0.4285 | 0.6528 | 0.6353 | nan | 0.5515 | 0.7541 | 0.0 | 0.5439 | 0.7415 | | 0.0671 | 28.25 | 8080 | 0.1065 | 0.4027 | 0.6119 | 0.5998 | nan | 0.5422 | 0.6816 | 0.0 | 0.5319 | 0.6761 | | 0.0595 | 28.32 | 8100 | 0.1110 | 0.3899 | 0.5918 | 0.5788 | nan | 0.5166 | 0.6670 | 0.0 | 0.5086 | 0.6612 | | 0.0576 | 28.39 | 8120 | 0.1103 | 0.3906 | 0.5928 | 0.5779 | nan | 0.5068 | 0.6788 | 0.0 | 0.5011 | 0.6707 | | 0.0707 | 28.46 | 8140 | 0.1052 | 0.4157 | 0.6330 | 0.6148 | nan | 0.5275 | 0.7386 | 0.0 | 0.5214 | 0.7257 | | 0.0356 | 28.53 | 8160 | 0.1061 | 0.4085 | 0.6210 | 0.6131 | nan | 0.5752 | 0.6668 | 0.0 | 0.5650 | 0.6606 | | 0.0676 | 28.6 | 8180 | 0.1087 | 0.3997 | 0.6077 | 0.5972 | nan | 0.5469 | 0.6685 | 0.0 | 0.5346 | 0.6644 | | 0.0487 | 28.67 | 8200 | 0.1139 | 0.3940 | 0.5980 | 0.5874 | nan | 0.5366 | 0.6595 | 0.0 | 0.5255 | 0.6564 | | 0.0657 | 28.74 | 8220 | 0.1055 | 0.4071 | 0.6216 | 0.6017 | nan | 0.5068 | 0.7364 | 0.0 | 0.5028 | 0.7184 | | 0.0666 | 28.81 | 8240 | 0.1090 | 0.3899 | 0.5928 | 0.5821 | nan | 0.5306 | 0.6551 | 0.0 | 0.5190 | 0.6506 | | 0.072 | 28.88 | 8260 | 0.1126 | 0.3887 | 0.5916 | 0.5824 | nan | 0.5384 | 0.6449 | 0.0 | 0.5237 | 0.6425 | | 0.0403 | 28.95 | 8280 | 0.1039 | 0.4177 | 0.6371 | 0.6190 | nan | 0.5327 | 0.7415 | 0.0 | 0.5240 | 0.7291 | | 0.0667 | 29.02 | 8300 | 0.1123 | 0.4005 | 0.6100 | 0.6021 | nan | 0.5644 | 0.6556 | 0.0 | 0.5483 | 0.6532 | | 0.0517 | 29.09 | 8320 | 0.1127 | 0.4039 | 0.6154 | 0.6126 | nan | 0.5988 | 0.6321 | 0.0 | 0.5834 | 0.6283 | | 0.0737 | 29.16 | 8340 | 0.1096 | 0.4032 | 0.6140 | 0.6050 | nan | 0.5622 | 0.6658 | 0.0 | 0.5464 | 0.6633 | | 0.0777 | 29.23 | 8360 | 0.1092 | 0.3886 | 0.5910 | 0.5792 | nan | 0.5225 | 0.6596 | 0.0 | 0.5129 | 0.6528 | | 0.0747 | 29.3 | 8380 | 0.1060 | 0.4068 | 0.6179 | 0.6057 | nan | 0.5472 | 0.6886 | 0.0 | 0.5373 | 0.6832 | | 0.0561 | 29.37 | 8400 | 0.1048 | 0.4209 | 0.6399 | 0.6294 | nan | 0.5794 | 0.7003 | 0.0 | 0.5673 | 0.6955 | | 0.0913 | 29.44 | 8420 | 0.1078 | 0.4049 | 0.6154 | 0.6051 | nan | 0.5555 | 0.6754 | 0.0 | 0.5434 | 0.6714 | | 0.0655 | 29.51 | 8440 | 0.1021 | 0.4420 | 0.6742 | 0.6628 | nan | 0.6083 | 0.7401 | 0.0 | 0.5965 | 0.7294 | | 0.0562 | 29.58 | 8460 | 0.1028 | 0.4275 | 0.6515 | 0.6410 | nan | 0.5909 | 0.7121 | 0.0 | 0.5781 | 0.7044 | | 0.0371 | 29.65 | 8480 | 0.1092 | 0.4100 | 0.6243 | 0.6188 | nan | 0.5923 | 0.6564 | 0.0 | 0.5778 | 0.6522 | | 0.0756 | 29.72 | 8500 | 0.1074 | 0.4026 | 0.6127 | 0.6000 | nan | 0.5390 | 0.6864 | 0.0 | 0.5307 | 0.6772 | | 0.0523 | 29.79 | 8520 | 0.1113 | 0.3981 | 0.6052 | 0.5948 | nan | 0.5454 | 0.6650 | 0.0 | 0.5357 | 0.6587 | | 0.0611 | 29.86 | 8540 | 0.1122 | 0.3965 | 0.6025 | 0.5903 | nan | 0.5324 | 0.6726 | 0.0 | 0.5213 | 0.6683 | | 0.0658 | 29.93 | 8560 | 0.1081 | 0.4066 | 0.6173 | 0.6028 | nan | 0.5334 | 0.7013 | 0.0 | 0.5234 | 0.6964 | | 0.0454 | 30.0 | 8580 | 0.1136 | 0.3825 | 0.5811 | 0.5692 | nan | 0.5123 | 0.6500 | 0.0 | 0.5010 | 0.6465 | | 0.0712 | 30.07 | 8600 | 0.1102 | 0.3950 | 0.6016 | 0.5869 | nan | 0.5168 | 0.6863 | 0.0 | 0.5081 | 0.6768 | | 0.0706 | 30.14 | 8620 | 0.1105 | 0.4028 | 0.6133 | 0.6055 | nan | 0.5685 | 0.6580 | 0.0 | 0.5558 | 0.6527 | | 0.0428 | 30.21 | 8640 | 0.1071 | 0.4201 | 0.6395 | 0.6314 | nan | 0.5924 | 0.6867 | 0.0 | 0.5772 | 0.6832 | | 0.0585 | 30.28 | 8660 | 0.1068 | 0.3963 | 0.6027 | 0.5879 | nan | 0.5175 | 0.6879 | 0.0 | 0.5074 | 0.6815 | | 0.0451 | 30.35 | 8680 | 0.1121 | 0.3878 | 0.5892 | 0.5750 | nan | 0.5073 | 0.6711 | 0.0 | 0.4957 | 0.6676 | | 0.0681 | 30.42 | 8700 | 0.1105 | 0.3939 | 0.5999 | 0.5877 | nan | 0.5295 | 0.6702 | 0.0 | 0.5147 | 0.6670 | | 0.0569 | 30.49 | 8720 | 0.1199 | 0.3891 | 0.5933 | 0.5868 | nan | 0.5558 | 0.6308 | 0.0 | 0.5392 | 0.6282 | | 0.0507 | 30.56 | 8740 | 0.1106 | 0.3939 | 0.5999 | 0.5866 | nan | 0.5234 | 0.6764 | 0.0 | 0.5125 | 0.6691 | | 0.0545 | 30.63 | 8760 | 0.1124 | 0.3977 | 0.6045 | 0.5908 | nan | 0.5252 | 0.6839 | 0.0 | 0.5137 | 0.6793 | | 0.0561 | 30.7 | 8780 | 0.1094 | 0.4062 | 0.6182 | 0.6034 | nan | 0.5328 | 0.7036 | 0.0 | 0.5221 | 0.6965 | | 0.0581 | 30.77 | 8800 | 0.1055 | 0.4146 | 0.6303 | 0.6153 | nan | 0.5436 | 0.7169 | 0.0 | 0.5365 | 0.7074 | | 0.0662 | 30.84 | 8820 | 0.1102 | 0.3981 | 0.6038 | 0.5898 | nan | 0.5233 | 0.6842 | 0.0 | 0.5183 | 0.6760 | | 0.0985 | 30.91 | 8840 | 0.1095 | 0.4037 | 0.6138 | 0.5981 | nan | 0.5230 | 0.7045 | 0.0 | 0.5149 | 0.6962 | | 0.0481 | 30.98 | 8860 | 0.1037 | 0.4105 | 0.6261 | 0.6114 | nan | 0.5414 | 0.7107 | 0.0 | 0.5354 | 0.6960 | | 0.0495 | 31.05 | 8880 | 0.1048 | 0.4012 | 0.6100 | 0.5912 | nan | 0.5015 | 0.7186 | 0.0 | 0.4946 | 0.7090 | | 0.0445 | 31.12 | 8900 | 0.1117 | 0.3896 | 0.5919 | 0.5799 | nan | 0.5222 | 0.6617 | 0.0 | 0.5117 | 0.6572 | | 0.0649 | 31.19 | 8920 | 0.1117 | 0.3992 | 0.6078 | 0.5945 | nan | 0.5308 | 0.6848 | 0.0 | 0.5195 | 0.6780 | | 0.0542 | 31.26 | 8940 | 0.1112 | 0.4042 | 0.6163 | 0.6091 | nan | 0.5746 | 0.6581 | 0.0 | 0.5601 | 0.6527 | | 0.0388 | 31.33 | 8960 | 0.1093 | 0.4151 | 0.6319 | 0.6238 | nan | 0.5852 | 0.6786 | 0.0 | 0.5735 | 0.6717 | | 0.0571 | 31.4 | 8980 | 0.1079 | 0.4119 | 0.6266 | 0.6158 | nan | 0.5642 | 0.6890 | 0.0 | 0.5530 | 0.6826 | | 0.0547 | 31.47 | 9000 | 0.1190 | 0.3846 | 0.5866 | 0.5809 | nan | 0.5541 | 0.6191 | 0.0 | 0.5375 | 0.6163 | | 0.0497 | 31.54 | 9020 | 0.1115 | 0.4016 | 0.6121 | 0.5997 | nan | 0.5405 | 0.6838 | 0.0 | 0.5276 | 0.6772 | | 0.0499 | 31.61 | 9040 | 0.1167 | 0.3834 | 0.5823 | 0.5683 | nan | 0.5012 | 0.6634 | 0.0 | 0.4911 | 0.6589 | | 0.05 | 31.68 | 9060 | 0.1162 | 0.3891 | 0.5913 | 0.5764 | nan | 0.5056 | 0.6769 | 0.0 | 0.4935 | 0.6737 | | 0.0432 | 31.75 | 9080 | 0.1174 | 0.3884 | 0.5913 | 0.5837 | nan | 0.5476 | 0.6351 | 0.0 | 0.5347 | 0.6306 | | 0.064 | 31.82 | 9100 | 0.1095 | 0.4059 | 0.6168 | 0.6028 | nan | 0.5363 | 0.6972 | 0.0 | 0.5282 | 0.6895 | | 0.1138 | 31.89 | 9120 | 0.1126 | 0.3874 | 0.5887 | 0.5786 | nan | 0.5301 | 0.6473 | 0.0 | 0.5210 | 0.6413 | | 0.0552 | 31.96 | 9140 | 0.1116 | 0.4048 | 0.6161 | 0.6099 | nan | 0.5804 | 0.6517 | 0.0 | 0.5680 | 0.6464 | | 0.0758 | 32.03 | 9160 | 0.1203 | 0.3815 | 0.5817 | 0.5796 | nan | 0.5696 | 0.5938 | 0.0 | 0.5529 | 0.5917 | | 0.0574 | 32.1 | 9180 | 0.1061 | 0.4369 | 0.6653 | 0.6533 | nan | 0.5962 | 0.7343 | 0.0 | 0.5868 | 0.7239 | | 0.0659 | 32.17 | 9200 | 0.1076 | 0.4212 | 0.6402 | 0.6300 | nan | 0.5814 | 0.6990 | 0.0 | 0.5699 | 0.6938 | | 0.0398 | 32.24 | 9220 | 0.1049 | 0.4489 | 0.6830 | 0.6686 | nan | 0.6001 | 0.7659 | 0.0 | 0.5899 | 0.7568 | | 0.0415 | 32.31 | 9240 | 0.1133 | 0.3993 | 0.6068 | 0.6029 | nan | 0.5843 | 0.6293 | 0.0 | 0.5709 | 0.6270 | | 0.0584 | 32.38 | 9260 | 0.1088 | 0.4236 | 0.6438 | 0.6345 | nan | 0.5899 | 0.6978 | 0.0 | 0.5772 | 0.6935 | | 0.0549 | 32.45 | 9280 | 0.1144 | 0.3987 | 0.6073 | 0.6013 | nan | 0.5730 | 0.6416 | 0.0 | 0.5574 | 0.6386 | | 0.0529 | 32.52 | 9300 | 0.1072 | 0.4039 | 0.6137 | 0.5992 | nan | 0.5301 | 0.6972 | 0.0 | 0.5213 | 0.6902 | | 0.0378 | 32.59 | 9320 | 0.1089 | 0.4051 | 0.6155 | 0.6065 | nan | 0.5632 | 0.6679 | 0.0 | 0.5529 | 0.6623 | | 0.0556 | 32.66 | 9340 | 0.1079 | 0.4099 | 0.6234 | 0.6112 | nan | 0.5532 | 0.6935 | 0.0 | 0.5418 | 0.6879 | | 0.0568 | 32.73 | 9360 | 0.1080 | 0.4036 | 0.6139 | 0.6029 | nan | 0.5501 | 0.6778 | 0.0 | 0.5417 | 0.6691 | | 0.0458 | 32.8 | 9380 | 0.1082 | 0.3948 | 0.6006 | 0.5823 | nan | 0.4951 | 0.7061 | 0.0 | 0.4890 | 0.6954 | | 0.0472 | 32.87 | 9400 | 0.1129 | 0.3881 | 0.5897 | 0.5777 | nan | 0.5208 | 0.6585 | 0.0 | 0.5101 | 0.6542 | | 0.0611 | 32.94 | 9420 | 0.1105 | 0.4011 | 0.6099 | 0.5982 | nan | 0.5425 | 0.6774 | 0.0 | 0.5320 | 0.6713 | | 0.0702 | 33.01 | 9440 | 0.1063 | 0.4287 | 0.6539 | 0.6425 | nan | 0.5880 | 0.7198 | 0.0 | 0.5767 | 0.7095 | | 0.0702 | 33.08 | 9460 | 0.1115 | 0.4024 | 0.6120 | 0.5960 | nan | 0.5196 | 0.7044 | 0.0 | 0.5106 | 0.6966 | | 0.0603 | 33.15 | 9480 | 0.1138 | 0.4139 | 0.6295 | 0.6236 | nan | 0.5954 | 0.6635 | 0.0 | 0.5841 | 0.6576 | | 0.0661 | 33.22 | 9500 | 0.1127 | 0.4018 | 0.6123 | 0.6039 | nan | 0.5634 | 0.6612 | 0.0 | 0.5546 | 0.6510 | | 0.0755 | 33.29 | 9520 | 0.1180 | 0.3938 | 0.5994 | 0.5928 | nan | 0.5613 | 0.6375 | 0.0 | 0.5498 | 0.6318 | | 0.0888 | 33.36 | 9540 | 0.1158 | 0.3836 | 0.5831 | 0.5707 | nan | 0.5114 | 0.6547 | 0.0 | 0.5016 | 0.6491 | | 0.0356 | 33.43 | 9560 | 0.1073 | 0.4202 | 0.6388 | 0.6276 | nan | 0.5739 | 0.7037 | 0.0 | 0.5638 | 0.6968 | | 0.0656 | 33.5 | 9580 | 0.1074 | 0.4245 | 0.6459 | 0.6319 | nan | 0.5652 | 0.7266 | 0.0 | 0.5540 | 0.7195 | | 0.0565 | 33.57 | 9600 | 0.1060 | 0.4143 | 0.6305 | 0.6188 | nan | 0.5633 | 0.6977 | 0.0 | 0.5518 | 0.6912 | | 0.0528 | 33.64 | 9620 | 0.1071 | 0.4197 | 0.6385 | 0.6272 | nan | 0.5731 | 0.7038 | 0.0 | 0.5618 | 0.6972 | | 0.0711 | 33.71 | 9640 | 0.1103 | 0.3996 | 0.6072 | 0.5939 | nan | 0.5303 | 0.6841 | 0.0 | 0.5209 | 0.6778 | | 0.0614 | 33.78 | 9660 | 0.1138 | 0.3993 | 0.6070 | 0.5963 | nan | 0.5451 | 0.6688 | 0.0 | 0.5338 | 0.6642 | | 0.065 | 33.85 | 9680 | 0.1057 | 0.4227 | 0.6436 | 0.6319 | nan | 0.5761 | 0.7111 | 0.0 | 0.5653 | 0.7028 | | 0.0544 | 33.92 | 9700 | 0.1117 | 0.4167 | 0.6350 | 0.6289 | nan | 0.5998 | 0.6702 | 0.0 | 0.5829 | 0.6671 | | 0.0802 | 33.99 | 9720 | 0.1120 | 0.4201 | 0.6393 | 0.6274 | nan | 0.5707 | 0.7079 | 0.0 | 0.5570 | 0.7034 | | 0.0339 | 34.06 | 9740 | 0.1107 | 0.3944 | 0.6009 | 0.5895 | nan | 0.5351 | 0.6667 | 0.0 | 0.5253 | 0.6580 | | 0.0436 | 34.13 | 9760 | 0.1137 | 0.3919 | 0.5963 | 0.5868 | nan | 0.5415 | 0.6510 | 0.0 | 0.5296 | 0.6460 | | 0.0643 | 34.2 | 9780 | 0.1190 | 0.3739 | 0.5688 | 0.5534 | nan | 0.4798 | 0.6579 | 0.0 | 0.4696 | 0.6522 | | 0.0929 | 34.27 | 9800 | 0.1212 | 0.3715 | 0.5642 | 0.5525 | nan | 0.4970 | 0.6313 | 0.0 | 0.4872 | 0.6274 | | 0.0672 | 34.34 | 9820 | 0.1117 | 0.4173 | 0.6350 | 0.6266 | nan | 0.5864 | 0.6836 | 0.0 | 0.5719 | 0.6800 | | 0.0449 | 34.41 | 9840 | 0.1230 | 0.4036 | 0.6159 | 0.6156 | nan | 0.6142 | 0.6176 | 0.0 | 0.5966 | 0.6143 | | 0.0705 | 34.48 | 9860 | 0.1216 | 0.3900 | 0.5935 | 0.5888 | nan | 0.5663 | 0.6208 | 0.0 | 0.5542 | 0.6158 | | 0.0562 | 34.55 | 9880 | 0.1070 | 0.4167 | 0.6335 | 0.6195 | nan | 0.5530 | 0.7139 | 0.0 | 0.5441 | 0.7061 | | 0.0467 | 34.62 | 9900 | 0.1171 | 0.3916 | 0.5964 | 0.5874 | nan | 0.5448 | 0.6479 | 0.0 | 0.5373 | 0.6375 | | 0.0446 | 34.69 | 9920 | 0.1073 | 0.4216 | 0.6433 | 0.6304 | nan | 0.5687 | 0.7179 | 0.0 | 0.5602 | 0.7046 | | 0.0357 | 34.76 | 9940 | 0.1117 | 0.4014 | 0.6101 | 0.5983 | nan | 0.5421 | 0.6780 | 0.0 | 0.5326 | 0.6716 | | 0.0796 | 34.83 | 9960 | 0.1119 | 0.4133 | 0.6285 | 0.6173 | nan | 0.5638 | 0.6932 | 0.0 | 0.5526 | 0.6872 | | 0.0422 | 34.9 | 9980 | 0.1066 | 0.4291 | 0.6528 | 0.6371 | nan | 0.5622 | 0.7434 | 0.0 | 0.5536 | 0.7337 | | 0.0479 | 34.97 | 10000 | 0.1121 | 0.4023 | 0.6115 | 0.6004 | nan | 0.5474 | 0.6755 | 0.0 | 0.5353 | 0.6716 | | 0.0533 | 35.03 | 10020 | 0.1114 | 0.4237 | 0.6446 | 0.6379 | nan | 0.6056 | 0.6837 | 0.0 | 0.5938 | 0.6771 | | 0.0513 | 35.1 | 10040 | 0.1068 | 0.4100 | 0.6243 | 0.6062 | nan | 0.5200 | 0.7285 | 0.0 | 0.5133 | 0.7166 | | 0.0507 | 35.17 | 10060 | 0.1109 | 0.4109 | 0.6245 | 0.6168 | nan | 0.5802 | 0.6687 | 0.0 | 0.5692 | 0.6635 | | 0.0736 | 35.24 | 10080 | 0.1067 | 0.4224 | 0.6437 | 0.6291 | nan | 0.5593 | 0.7281 | 0.0 | 0.5517 | 0.7154 | | 0.0623 | 35.31 | 10100 | 0.1096 | 0.4269 | 0.6492 | 0.6406 | nan | 0.5997 | 0.6986 | 0.0 | 0.5869 | 0.6937 | | 0.0485 | 35.38 | 10120 | 0.1056 | 0.4104 | 0.6249 | 0.6069 | nan | 0.5214 | 0.7283 | 0.0 | 0.5147 | 0.7166 | | 0.0484 | 35.45 | 10140 | 0.1086 | 0.3962 | 0.6042 | 0.5857 | nan | 0.4974 | 0.7109 | 0.0 | 0.4910 | 0.6977 | | 0.0861 | 35.52 | 10160 | 0.1110 | 0.4128 | 0.6277 | 0.6159 | nan | 0.5594 | 0.6960 | 0.0 | 0.5489 | 0.6896 | | 0.0554 | 35.59 | 10180 | 0.1121 | 0.4058 | 0.6174 | 0.6043 | nan | 0.5415 | 0.6933 | 0.0 | 0.5297 | 0.6876 | | 0.0644 | 35.66 | 10200 | 0.1164 | 0.4025 | 0.6136 | 0.6056 | nan | 0.5674 | 0.6597 | 0.0 | 0.5515 | 0.6559 | | 0.0423 | 35.73 | 10220 | 0.1160 | 0.4010 | 0.6113 | 0.6057 | nan | 0.5791 | 0.6434 | 0.0 | 0.5636 | 0.6393 | | 0.0635 | 35.8 | 10240 | 0.1149 | 0.3953 | 0.6015 | 0.5915 | nan | 0.5435 | 0.6596 | 0.0 | 0.5312 | 0.6547 | | 0.0527 | 35.87 | 10260 | 0.1177 | 0.3884 | 0.5921 | 0.5818 | nan | 0.5326 | 0.6516 | 0.0 | 0.5174 | 0.6477 | | 0.0597 | 35.94 | 10280 | 0.1154 | 0.4002 | 0.6095 | 0.6014 | nan | 0.5626 | 0.6564 | 0.0 | 0.5497 | 0.6508 | | 0.0587 | 36.01 | 10300 | 0.1229 | 0.3802 | 0.5794 | 0.5697 | nan | 0.5236 | 0.6351 | 0.0 | 0.5092 | 0.6314 | | 0.0824 | 36.08 | 10320 | 0.1179 | 0.3843 | 0.5840 | 0.5730 | nan | 0.5205 | 0.6476 | 0.0 | 0.5101 | 0.6428 | | 0.0613 | 36.15 | 10340 | 0.1139 | 0.4204 | 0.6403 | 0.6316 | nan | 0.5904 | 0.6902 | 0.0 | 0.5767 | 0.6846 | | 0.0694 | 36.22 | 10360 | 0.1128 | 0.4148 | 0.6304 | 0.6198 | nan | 0.5690 | 0.6919 | 0.0 | 0.5571 | 0.6872 | | 0.0486 | 36.29 | 10380 | 0.1109 | 0.4050 | 0.6157 | 0.6031 | nan | 0.5429 | 0.6886 | 0.0 | 0.5324 | 0.6825 | | 0.0532 | 36.36 | 10400 | 0.1103 | 0.4188 | 0.6367 | 0.6263 | nan | 0.5766 | 0.6968 | 0.0 | 0.5642 | 0.6923 | | 0.0475 | 36.43 | 10420 | 0.1064 | 0.4134 | 0.6276 | 0.6130 | nan | 0.5434 | 0.7119 | 0.0 | 0.5348 | 0.7053 | | 0.0517 | 36.5 | 10440 | 0.1207 | 0.3857 | 0.5873 | 0.5794 | nan | 0.5415 | 0.6332 | 0.0 | 0.5270 | 0.6302 | | 0.0735 | 36.57 | 10460 | 0.1099 | 0.3859 | 0.5864 | 0.5697 | nan | 0.4903 | 0.6825 | 0.0 | 0.4844 | 0.6734 | | 0.0684 | 36.64 | 10480 | 0.1094 | 0.4065 | 0.6178 | 0.6019 | nan | 0.5261 | 0.7095 | 0.0 | 0.5170 | 0.7025 | | 0.064 | 36.71 | 10500 | 0.1138 | 0.3915 | 0.5956 | 0.5805 | nan | 0.5084 | 0.6828 | 0.0 | 0.5004 | 0.6740 | | 0.064 | 36.78 | 10520 | 0.1178 | 0.3826 | 0.5824 | 0.5732 | nan | 0.5295 | 0.6352 | 0.0 | 0.5183 | 0.6296 | | 0.0701 | 36.85 | 10540 | 0.1147 | 0.4016 | 0.6121 | 0.6014 | nan | 0.5499 | 0.6744 | 0.0 | 0.5352 | 0.6694 | | 0.0884 | 36.92 | 10560 | 0.1096 | 0.4059 | 0.6185 | 0.6044 | nan | 0.5370 | 0.6999 | 0.0 | 0.5266 | 0.6911 | | 0.0406 | 36.99 | 10580 | 0.1140 | 0.4039 | 0.6149 | 0.6051 | nan | 0.5586 | 0.6711 | 0.0 | 0.5462 | 0.6656 | | 0.0589 | 37.06 | 10600 | 0.1115 | 0.4139 | 0.6291 | 0.6160 | nan | 0.5535 | 0.7047 | 0.0 | 0.5438 | 0.6978 | | 0.0367 | 37.13 | 10620 | 0.1103 | 0.4022 | 0.6109 | 0.5993 | nan | 0.5436 | 0.6782 | 0.0 | 0.5355 | 0.6712 | | 0.0363 | 37.2 | 10640 | 0.1144 | 0.4107 | 0.6249 | 0.6156 | nan | 0.5714 | 0.6783 | 0.0 | 0.5610 | 0.6711 | | 0.0537 | 37.27 | 10660 | 0.1125 | 0.4075 | 0.6204 | 0.6098 | nan | 0.5592 | 0.6817 | 0.0 | 0.5469 | 0.6757 | | 0.0431 | 37.34 | 10680 | 0.1080 | 0.4125 | 0.6306 | 0.6215 | nan | 0.5782 | 0.6830 | 0.0 | 0.5676 | 0.6698 | | 0.053 | 37.41 | 10700 | 0.1130 | 0.4065 | 0.6178 | 0.6057 | nan | 0.5479 | 0.6877 | 0.0 | 0.5378 | 0.6817 | | 0.0438 | 37.48 | 10720 | 0.1102 | 0.4001 | 0.6094 | 0.5955 | nan | 0.5292 | 0.6895 | 0.0 | 0.5206 | 0.6795 | | 0.0357 | 37.55 | 10740 | 0.1086 | 0.4125 | 0.6299 | 0.6162 | nan | 0.5511 | 0.7086 | 0.0 | 0.5440 | 0.6935 | | 0.0541 | 37.62 | 10760 | 0.1099 | 0.4322 | 0.6578 | 0.6492 | nan | 0.6081 | 0.7076 | 0.0 | 0.5946 | 0.7019 | | 0.0595 | 37.69 | 10780 | 0.1073 | 0.4116 | 0.6255 | 0.6111 | nan | 0.5422 | 0.7089 | 0.0 | 0.5317 | 0.7031 | | 0.047 | 37.76 | 10800 | 0.1060 | 0.4182 | 0.6364 | 0.6203 | nan | 0.5430 | 0.7299 | 0.0 | 0.5329 | 0.7218 | | 0.052 | 37.83 | 10820 | 0.1080 | 0.4136 | 0.6293 | 0.6140 | nan | 0.5413 | 0.7172 | 0.0 | 0.5313 | 0.7094 | | 0.0454 | 37.9 | 10840 | 0.1097 | 0.4111 | 0.6249 | 0.6138 | nan | 0.5608 | 0.6890 | 0.0 | 0.5509 | 0.6824 | | 0.0286 | 37.97 | 10860 | 0.1166 | 0.3979 | 0.6052 | 0.5938 | nan | 0.5395 | 0.6708 | 0.0 | 0.5283 | 0.6653 | | 0.0668 | 38.04 | 10880 | 0.1129 | 0.3900 | 0.5936 | 0.5802 | nan | 0.5162 | 0.6709 | 0.0 | 0.5035 | 0.6664 | | 0.0514 | 38.11 | 10900 | 0.1100 | 0.4109 | 0.6258 | 0.6143 | nan | 0.5594 | 0.6922 | 0.0 | 0.5464 | 0.6863 | | 0.0527 | 38.18 | 10920 | 0.1212 | 0.3845 | 0.5861 | 0.5761 | nan | 0.5287 | 0.6435 | 0.0 | 0.5153 | 0.6381 | | 0.0552 | 38.25 | 10940 | 0.1182 | 0.4089 | 0.6237 | 0.6157 | nan | 0.5775 | 0.6699 | 0.0 | 0.5617 | 0.6649 | | 0.0407 | 38.32 | 10960 | 0.1182 | 0.3928 | 0.5986 | 0.5898 | nan | 0.5478 | 0.6494 | 0.0 | 0.5336 | 0.6448 | | 0.04 | 38.39 | 10980 | 0.1181 | 0.3935 | 0.5990 | 0.5847 | nan | 0.5162 | 0.6819 | 0.0 | 0.5046 | 0.6758 | | 0.0804 | 38.46 | 11000 | 0.1143 | 0.4091 | 0.6236 | 0.6146 | nan | 0.5715 | 0.6758 | 0.0 | 0.5586 | 0.6686 | | 0.0596 | 38.53 | 11020 | 0.1141 | 0.4105 | 0.6249 | 0.6139 | nan | 0.5615 | 0.6883 | 0.0 | 0.5504 | 0.6810 | | 0.0469 | 38.6 | 11040 | 0.1142 | 0.3961 | 0.6018 | 0.5896 | nan | 0.5312 | 0.6723 | 0.0 | 0.5216 | 0.6666 | | 0.0541 | 38.67 | 11060 | 0.1122 | 0.4125 | 0.6275 | 0.6151 | nan | 0.5559 | 0.6992 | 0.0 | 0.5456 | 0.6917 | | 0.0416 | 38.74 | 11080 | 0.1178 | 0.3886 | 0.5910 | 0.5796 | nan | 0.5256 | 0.6564 | 0.0 | 0.5154 | 0.6503 | | 0.0531 | 38.81 | 11100 | 0.1226 | 0.3728 | 0.5666 | 0.5544 | nan | 0.4964 | 0.6368 | 0.0 | 0.4839 | 0.6346 | | 0.0496 | 38.88 | 11120 | 0.1189 | 0.3823 | 0.5817 | 0.5684 | nan | 0.5045 | 0.6590 | 0.0 | 0.4931 | 0.6537 | | 0.0618 | 38.95 | 11140 | 0.1101 | 0.4022 | 0.6122 | 0.5964 | nan | 0.5211 | 0.7033 | 0.0 | 0.5127 | 0.6939 | | 0.048 | 39.02 | 11160 | 0.1109 | 0.4032 | 0.6133 | 0.6032 | nan | 0.5547 | 0.6720 | 0.0 | 0.5417 | 0.6678 | | 0.0514 | 39.09 | 11180 | 0.1079 | 0.4098 | 0.6222 | 0.6097 | nan | 0.5499 | 0.6945 | 0.0 | 0.5405 | 0.6887 | | 0.0641 | 39.16 | 11200 | 0.1137 | 0.4002 | 0.6089 | 0.5993 | nan | 0.5533 | 0.6645 | 0.0 | 0.5395 | 0.6611 | | 0.0599 | 39.23 | 11220 | 0.1120 | 0.3904 | 0.5921 | 0.5778 | nan | 0.5096 | 0.6746 | 0.0 | 0.5016 | 0.6697 | | 0.0461 | 39.3 | 11240 | 0.1111 | 0.4080 | 0.6213 | 0.6097 | nan | 0.5542 | 0.6883 | 0.0 | 0.5420 | 0.6821 | | 0.0528 | 39.37 | 11260 | 0.1132 | 0.4002 | 0.6085 | 0.6006 | nan | 0.5627 | 0.6543 | 0.0 | 0.5492 | 0.6513 | | 0.0639 | 39.44 | 11280 | 0.1210 | 0.3819 | 0.5799 | 0.5691 | nan | 0.5178 | 0.6420 | 0.0 | 0.5079 | 0.6379 | | 0.0401 | 39.51 | 11300 | 0.1091 | 0.4083 | 0.6199 | 0.6065 | nan | 0.5425 | 0.6973 | 0.0 | 0.5334 | 0.6913 | | 0.0556 | 39.58 | 11320 | 0.1209 | 0.3832 | 0.5847 | 0.5781 | nan | 0.5468 | 0.6226 | 0.0 | 0.5314 | 0.6182 | | 0.0368 | 39.65 | 11340 | 0.1180 | 0.4000 | 0.6106 | 0.6050 | nan | 0.5785 | 0.6426 | 0.0 | 0.5627 | 0.6372 | | 0.0513 | 39.72 | 11360 | 0.1123 | 0.4063 | 0.6165 | 0.6012 | nan | 0.5284 | 0.7046 | 0.0 | 0.5171 | 0.7019 | | 0.0826 | 39.79 | 11380 | 0.1104 | 0.4131 | 0.6305 | 0.6182 | nan | 0.5593 | 0.7017 | 0.0 | 0.5515 | 0.6879 | | 0.0766 | 39.86 | 11400 | 0.1149 | 0.4170 | 0.6348 | 0.6244 | nan | 0.5743 | 0.6954 | 0.0 | 0.5611 | 0.6898 | | 0.0659 | 39.93 | 11420 | 0.1211 | 0.3917 | 0.5972 | 0.5888 | nan | 0.5486 | 0.6458 | 0.0 | 0.5330 | 0.6422 | | 0.06 | 40.0 | 11440 | 0.1126 | 0.3998 | 0.6096 | 0.5937 | nan | 0.5176 | 0.7017 | 0.0 | 0.5063 | 0.6931 | | 0.0537 | 40.07 | 11460 | 0.1259 | 0.3692 | 0.5615 | 0.5512 | nan | 0.5021 | 0.6210 | 0.0 | 0.4927 | 0.6149 | | 0.0479 | 40.14 | 11480 | 0.1106 | 0.4105 | 0.6242 | 0.6076 | nan | 0.5285 | 0.7199 | 0.0 | 0.5194 | 0.7122 | | 0.0321 | 40.21 | 11500 | 0.1123 | 0.4040 | 0.6143 | 0.6014 | nan | 0.5395 | 0.6892 | 0.0 | 0.5272 | 0.6847 | | 0.0524 | 40.28 | 11520 | 0.1164 | 0.4069 | 0.6192 | 0.6100 | nan | 0.5661 | 0.6722 | 0.0 | 0.5521 | 0.6686 | | 0.0563 | 40.35 | 11540 | 0.1095 | 0.4202 | 0.6388 | 0.6237 | nan | 0.5518 | 0.7257 | 0.0 | 0.5430 | 0.7177 | | 0.0514 | 40.42 | 11560 | 0.1114 | 0.4076 | 0.6202 | 0.6099 | nan | 0.5607 | 0.6798 | 0.0 | 0.5523 | 0.6703 | | 0.0479 | 40.49 | 11580 | 0.1137 | 0.4109 | 0.6250 | 0.6136 | nan | 0.5591 | 0.6910 | 0.0 | 0.5486 | 0.6841 | | 0.0517 | 40.56 | 11600 | 0.1156 | 0.4003 | 0.6083 | 0.5995 | nan | 0.5575 | 0.6590 | 0.0 | 0.5481 | 0.6529 | | 0.0563 | 40.63 | 11620 | 0.1163 | 0.3948 | 0.6015 | 0.5878 | nan | 0.5219 | 0.6812 | 0.0 | 0.5150 | 0.6695 | | 0.0491 | 40.7 | 11640 | 0.1229 | 0.3860 | 0.5879 | 0.5790 | nan | 0.5365 | 0.6393 | 0.0 | 0.5261 | 0.6320 | | 0.051 | 40.77 | 11660 | 0.1138 | 0.3930 | 0.5982 | 0.5859 | nan | 0.5273 | 0.6691 | 0.0 | 0.5165 | 0.6626 | | 0.0494 | 40.84 | 11680 | 0.1059 | 0.4256 | 0.6476 | 0.6312 | nan | 0.5530 | 0.7422 | 0.0 | 0.5453 | 0.7315 | | 0.0705 | 40.91 | 11700 | 0.1069 | 0.4230 | 0.6425 | 0.6260 | nan | 0.5468 | 0.7382 | 0.0 | 0.5390 | 0.7300 | | 0.0465 | 40.98 | 11720 | 0.1070 | 0.4215 | 0.6440 | 0.6266 | nan | 0.5433 | 0.7446 | 0.0 | 0.5364 | 0.7281 | | 0.0837 | 41.05 | 11740 | 0.1146 | 0.3999 | 0.6078 | 0.5955 | nan | 0.5368 | 0.6787 | 0.0 | 0.5284 | 0.6715 | | 0.0777 | 41.12 | 11760 | 0.1216 | 0.3942 | 0.6009 | 0.5968 | nan | 0.5773 | 0.6245 | 0.0 | 0.5629 | 0.6196 | | 0.0417 | 41.19 | 11780 | 0.1254 | 0.3881 | 0.5918 | 0.5885 | nan | 0.5727 | 0.6108 | 0.0 | 0.5574 | 0.6069 | | 0.0598 | 41.26 | 11800 | 0.1229 | 0.3891 | 0.5932 | 0.5923 | nan | 0.5878 | 0.5986 | 0.0 | 0.5732 | 0.5940 | | 0.0301 | 41.33 | 11820 | 0.1221 | 0.3850 | 0.5863 | 0.5787 | nan | 0.5424 | 0.6302 | 0.0 | 0.5297 | 0.6253 | | 0.0453 | 41.4 | 11840 | 0.1182 | 0.3894 | 0.5914 | 0.5777 | nan | 0.5120 | 0.6709 | 0.0 | 0.5023 | 0.6660 | | 0.0604 | 41.47 | 11860 | 0.1100 | 0.4093 | 0.6222 | 0.6110 | nan | 0.5577 | 0.6866 | 0.0 | 0.5474 | 0.6806 | | 0.0395 | 41.54 | 11880 | 0.1083 | 0.4225 | 0.6423 | 0.6276 | nan | 0.5577 | 0.7269 | 0.0 | 0.5478 | 0.7195 | | 0.0398 | 41.61 | 11900 | 0.1160 | 0.3965 | 0.6040 | 0.5954 | nan | 0.5544 | 0.6536 | 0.0 | 0.5438 | 0.6458 | | 0.0525 | 41.68 | 11920 | 0.1172 | 0.4088 | 0.6222 | 0.6108 | nan | 0.5564 | 0.6879 | 0.0 | 0.5434 | 0.6831 | | 0.0716 | 41.75 | 11940 | 0.1147 | 0.4078 | 0.6204 | 0.6118 | nan | 0.5706 | 0.6702 | 0.0 | 0.5609 | 0.6625 | | 0.0372 | 41.82 | 11960 | 0.1132 | 0.4104 | 0.6239 | 0.6105 | nan | 0.5467 | 0.7012 | 0.0 | 0.5359 | 0.6954 | | 0.0522 | 41.89 | 11980 | 0.1139 | 0.3990 | 0.6076 | 0.5988 | nan | 0.5571 | 0.6580 | 0.0 | 0.5432 | 0.6540 | | 0.059 | 41.96 | 12000 | 0.1186 | 0.3962 | 0.6030 | 0.5933 | nan | 0.5474 | 0.6585 | 0.0 | 0.5345 | 0.6542 | | 0.0464 | 42.03 | 12020 | 0.1203 | 0.3952 | 0.6022 | 0.5935 | nan | 0.5518 | 0.6527 | 0.0 | 0.5374 | 0.6481 | | 0.0619 | 42.1 | 12040 | 0.1182 | 0.4100 | 0.6250 | 0.6173 | nan | 0.5803 | 0.6697 | 0.0 | 0.5673 | 0.6627 | | 0.0589 | 42.17 | 12060 | 0.1105 | 0.4101 | 0.6285 | 0.6105 | nan | 0.5247 | 0.7324 | 0.0 | 0.5171 | 0.7133 | | 0.063 | 42.24 | 12080 | 0.1278 | 0.3852 | 0.5893 | 0.5869 | nan | 0.5755 | 0.6031 | 0.0 | 0.5545 | 0.6011 | | 0.0675 | 42.31 | 12100 | 0.1134 | 0.4103 | 0.6260 | 0.6170 | nan | 0.5743 | 0.6776 | 0.0 | 0.5622 | 0.6686 | | 0.0708 | 42.38 | 12120 | 0.1164 | 0.4023 | 0.6131 | 0.6056 | nan | 0.5699 | 0.6562 | 0.0 | 0.5552 | 0.6517 | | 0.0557 | 42.45 | 12140 | 0.1262 | 0.3816 | 0.5815 | 0.5708 | nan | 0.5198 | 0.6432 | 0.0 | 0.5053 | 0.6396 | | 0.0526 | 42.52 | 12160 | 0.1141 | 0.4095 | 0.6247 | 0.6122 | nan | 0.5528 | 0.6965 | 0.0 | 0.5395 | 0.6891 | | 0.0635 | 42.59 | 12180 | 0.1190 | 0.4089 | 0.6236 | 0.6173 | nan | 0.5873 | 0.6599 | 0.0 | 0.5731 | 0.6536 | | 0.0425 | 42.66 | 12200 | 0.1188 | 0.4183 | 0.6376 | 0.6290 | nan | 0.5880 | 0.6872 | 0.0 | 0.5734 | 0.6816 | | 0.0476 | 42.73 | 12220 | 0.1221 | 0.3947 | 0.6017 | 0.5930 | nan | 0.5516 | 0.6517 | 0.0 | 0.5362 | 0.6480 | | 0.0531 | 42.8 | 12240 | 0.1193 | 0.3969 | 0.6048 | 0.5938 | nan | 0.5414 | 0.6681 | 0.0 | 0.5295 | 0.6612 | | 0.0331 | 42.87 | 12260 | 0.1268 | 0.3839 | 0.5854 | 0.5740 | nan | 0.5195 | 0.6514 | 0.0 | 0.5040 | 0.6475 | | 0.0495 | 42.94 | 12280 | 0.1174 | 0.3938 | 0.5993 | 0.5880 | nan | 0.5337 | 0.6650 | 0.0 | 0.5213 | 0.6601 | | 0.0396 | 43.01 | 12300 | 0.1299 | 0.3703 | 0.5629 | 0.5492 | nan | 0.4842 | 0.6415 | 0.0 | 0.4731 | 0.6379 | | 0.0514 | 43.08 | 12320 | 0.1224 | 0.3799 | 0.5773 | 0.5596 | nan | 0.4751 | 0.6794 | 0.0 | 0.4668 | 0.6729 | | 0.056 | 43.15 | 12340 | 0.1169 | 0.3892 | 0.5912 | 0.5799 | nan | 0.5261 | 0.6563 | 0.0 | 0.5160 | 0.6515 | | 0.042 | 43.22 | 12360 | 0.1229 | 0.3834 | 0.5832 | 0.5753 | nan | 0.5376 | 0.6287 | 0.0 | 0.5239 | 0.6262 | | 0.0368 | 43.29 | 12380 | 0.1113 | 0.4063 | 0.6184 | 0.6052 | nan | 0.5420 | 0.6949 | 0.0 | 0.5310 | 0.6878 | | 0.0754 | 43.36 | 12400 | 0.1184 | 0.4094 | 0.6238 | 0.6173 | nan | 0.5862 | 0.6613 | 0.0 | 0.5710 | 0.6570 | | 0.0679 | 43.43 | 12420 | 0.1228 | 0.3917 | 0.5960 | 0.5863 | nan | 0.5401 | 0.6518 | 0.0 | 0.5272 | 0.6478 | | 0.0273 | 43.5 | 12440 | 0.1160 | 0.4167 | 0.6353 | 0.6262 | nan | 0.5827 | 0.6878 | 0.0 | 0.5713 | 0.6789 | | 0.0388 | 43.57 | 12460 | 0.1253 | 0.3746 | 0.5697 | 0.5599 | nan | 0.5132 | 0.6262 | 0.0 | 0.5050 | 0.6190 | | 0.0656 | 43.64 | 12480 | 0.1175 | 0.3939 | 0.5999 | 0.5937 | nan | 0.5639 | 0.6359 | 0.0 | 0.5491 | 0.6326 | | 0.0824 | 43.71 | 12500 | 0.1223 | 0.3841 | 0.5832 | 0.5715 | nan | 0.5157 | 0.6508 | 0.0 | 0.5051 | 0.6472 | | 0.0476 | 43.78 | 12520 | 0.1173 | 0.4021 | 0.6113 | 0.5994 | nan | 0.5427 | 0.6799 | 0.0 | 0.5324 | 0.6738 | | 0.0546 | 43.85 | 12540 | 0.1118 | 0.4030 | 0.6137 | 0.6014 | nan | 0.5427 | 0.6847 | 0.0 | 0.5312 | 0.6778 | | 0.0656 | 43.92 | 12560 | 0.1140 | 0.3969 | 0.6039 | 0.5901 | nan | 0.5242 | 0.6835 | 0.0 | 0.5129 | 0.6778 | | 0.0554 | 43.99 | 12580 | 0.1119 | 0.4157 | 0.6322 | 0.6187 | nan | 0.5545 | 0.7099 | 0.0 | 0.5442 | 0.7030 | | 0.0441 | 44.06 | 12600 | 0.1164 | 0.4031 | 0.6119 | 0.5994 | nan | 0.5396 | 0.6841 | 0.0 | 0.5307 | 0.6787 | | 0.0582 | 44.13 | 12620 | 0.1168 | 0.3984 | 0.6050 | 0.5909 | nan | 0.5236 | 0.6864 | 0.0 | 0.5147 | 0.6805 | | 0.0626 | 44.2 | 12640 | 0.1194 | 0.4031 | 0.6135 | 0.6024 | nan | 0.5498 | 0.6771 | 0.0 | 0.5362 | 0.6730 | | 0.0551 | 44.27 | 12660 | 0.1198 | 0.4025 | 0.6123 | 0.6028 | nan | 0.5575 | 0.6671 | 0.0 | 0.5452 | 0.6624 | | 0.0708 | 44.34 | 12680 | 0.1168 | 0.4075 | 0.6199 | 0.6074 | nan | 0.5474 | 0.6925 | 0.0 | 0.5405 | 0.6821 | | 0.0422 | 44.41 | 12700 | 0.1175 | 0.4118 | 0.6256 | 0.6143 | nan | 0.5607 | 0.6904 | 0.0 | 0.5511 | 0.6843 | | 0.041 | 44.48 | 12720 | 0.1197 | 0.3948 | 0.6002 | 0.5892 | nan | 0.5368 | 0.6637 | 0.0 | 0.5270 | 0.6574 | | 0.0457 | 44.55 | 12740 | 0.1262 | 0.3800 | 0.5777 | 0.5687 | nan | 0.5255 | 0.6299 | 0.0 | 0.5143 | 0.6257 | | 0.0505 | 44.62 | 12760 | 0.1203 | 0.4088 | 0.6216 | 0.6111 | nan | 0.5605 | 0.6828 | 0.0 | 0.5511 | 0.6753 | | 0.0756 | 44.69 | 12780 | 0.1193 | 0.4003 | 0.6084 | 0.5956 | nan | 0.5342 | 0.6827 | 0.0 | 0.5232 | 0.6776 | | 0.0524 | 44.76 | 12800 | 0.1176 | 0.4112 | 0.6254 | 0.6145 | nan | 0.5626 | 0.6883 | 0.0 | 0.5517 | 0.6818 | | 0.0453 | 44.83 | 12820 | 0.1186 | 0.4082 | 0.6213 | 0.6089 | nan | 0.5497 | 0.6930 | 0.0 | 0.5384 | 0.6862 | | 0.0472 | 44.9 | 12840 | 0.1171 | 0.4040 | 0.6143 | 0.5969 | nan | 0.5136 | 0.7150 | 0.0 | 0.5056 | 0.7064 | | 0.0379 | 44.97 | 12860 | 0.1112 | 0.4157 | 0.6311 | 0.6184 | nan | 0.5576 | 0.7047 | 0.0 | 0.5485 | 0.6987 | | 0.0531 | 45.03 | 12880 | 0.1145 | 0.4120 | 0.6260 | 0.6160 | nan | 0.5682 | 0.6839 | 0.0 | 0.5557 | 0.6802 | | 0.0827 | 45.1 | 12900 | 0.1152 | 0.4029 | 0.6115 | 0.5955 | nan | 0.5194 | 0.7035 | 0.0 | 0.5112 | 0.6975 | | 0.059 | 45.17 | 12920 | 0.1147 | 0.4074 | 0.6193 | 0.6055 | nan | 0.5395 | 0.6991 | 0.0 | 0.5289 | 0.6934 | | 0.04 | 45.24 | 12940 | 0.1174 | 0.4030 | 0.6118 | 0.5986 | nan | 0.5356 | 0.6881 | 0.0 | 0.5272 | 0.6820 | | 0.0507 | 45.31 | 12960 | 0.1189 | 0.3989 | 0.6061 | 0.5976 | nan | 0.5571 | 0.6550 | 0.0 | 0.5463 | 0.6504 | | 0.0452 | 45.38 | 12980 | 0.1159 | 0.4157 | 0.6314 | 0.6207 | nan | 0.5692 | 0.6937 | 0.0 | 0.5592 | 0.6877 | | 0.0903 | 45.45 | 13000 | 0.1112 | 0.4208 | 0.6417 | 0.6281 | nan | 0.5631 | 0.7203 | 0.0 | 0.5561 | 0.7064 | | 0.069 | 45.52 | 13020 | 0.1184 | 0.4015 | 0.6092 | 0.5934 | nan | 0.5176 | 0.7009 | 0.0 | 0.5096 | 0.6950 | | 0.0335 | 45.59 | 13040 | 0.1217 | 0.4103 | 0.6243 | 0.6129 | nan | 0.5582 | 0.6905 | 0.0 | 0.5492 | 0.6816 | | 0.0633 | 45.66 | 13060 | 0.1198 | 0.4039 | 0.6153 | 0.6027 | nan | 0.5427 | 0.6878 | 0.0 | 0.5312 | 0.6805 | | 0.0637 | 45.73 | 13080 | 0.1216 | 0.3914 | 0.5954 | 0.5861 | nan | 0.5418 | 0.6490 | 0.0 | 0.5287 | 0.6455 | | 0.0472 | 45.8 | 13100 | 0.1251 | 0.3894 | 0.5929 | 0.5831 | nan | 0.5364 | 0.6493 | 0.0 | 0.5218 | 0.6465 | | 0.0381 | 45.87 | 13120 | 0.1189 | 0.3956 | 0.6014 | 0.5908 | nan | 0.5399 | 0.6630 | 0.0 | 0.5282 | 0.6586 | | 0.0505 | 45.94 | 13140 | 0.1216 | 0.4037 | 0.6136 | 0.6042 | nan | 0.5594 | 0.6677 | 0.0 | 0.5468 | 0.6644 | | 0.047 | 46.01 | 13160 | 0.1162 | 0.4090 | 0.6214 | 0.6106 | nan | 0.5593 | 0.6835 | 0.0 | 0.5477 | 0.6793 | | 0.0589 | 46.08 | 13180 | 0.1255 | 0.3884 | 0.5921 | 0.5822 | nan | 0.5351 | 0.6490 | 0.0 | 0.5178 | 0.6476 | | 0.0298 | 46.15 | 13200 | 0.1116 | 0.4160 | 0.6327 | 0.6171 | nan | 0.5427 | 0.7228 | 0.0 | 0.5318 | 0.7161 | | 0.0654 | 46.22 | 13220 | 0.1166 | 0.3992 | 0.6076 | 0.6006 | nan | 0.5673 | 0.6480 | 0.0 | 0.5562 | 0.6415 | | 0.0596 | 46.29 | 13240 | 0.1115 | 0.4193 | 0.6383 | 0.6267 | nan | 0.5710 | 0.7057 | 0.0 | 0.5597 | 0.6982 | | 0.0631 | 46.36 | 13260 | 0.1221 | 0.3924 | 0.5961 | 0.5863 | nan | 0.5392 | 0.6531 | 0.0 | 0.5271 | 0.6503 | | 0.041 | 46.43 | 13280 | 0.1134 | 0.4005 | 0.6092 | 0.5951 | nan | 0.5273 | 0.6912 | 0.0 | 0.5201 | 0.6813 | | 0.0301 | 46.5 | 13300 | 0.1180 | 0.4089 | 0.6212 | 0.6090 | nan | 0.5510 | 0.6913 | 0.0 | 0.5405 | 0.6864 | | 0.0595 | 46.57 | 13320 | 0.1196 | 0.4049 | 0.6160 | 0.6065 | nan | 0.5609 | 0.6712 | 0.0 | 0.5476 | 0.6670 | | 0.0703 | 46.64 | 13340 | 0.1169 | 0.4161 | 0.6333 | 0.6234 | nan | 0.5760 | 0.6906 | 0.0 | 0.5625 | 0.6859 | | 0.0622 | 46.71 | 13360 | 0.1203 | 0.3973 | 0.6036 | 0.5906 | nan | 0.5284 | 0.6788 | 0.0 | 0.5172 | 0.6747 | | 0.0249 | 46.78 | 13380 | 0.1190 | 0.4172 | 0.6348 | 0.6237 | nan | 0.5704 | 0.6993 | 0.0 | 0.5580 | 0.6937 | | 0.0541 | 46.85 | 13400 | 0.1206 | 0.3964 | 0.6036 | 0.5969 | nan | 0.5650 | 0.6421 | 0.0 | 0.5517 | 0.6374 | | 0.0673 | 46.92 | 13420 | 0.1213 | 0.4007 | 0.6100 | 0.6014 | nan | 0.5603 | 0.6597 | 0.0 | 0.5461 | 0.6560 | | 0.0667 | 46.99 | 13440 | 0.1177 | 0.4128 | 0.6280 | 0.6143 | nan | 0.5490 | 0.7070 | 0.0 | 0.5387 | 0.6996 | | 0.0325 | 47.06 | 13460 | 0.1166 | 0.3931 | 0.5977 | 0.5817 | nan | 0.5053 | 0.6901 | 0.0 | 0.4965 | 0.6827 | | 0.0893 | 47.13 | 13480 | 0.1157 | 0.4041 | 0.6149 | 0.6047 | nan | 0.5560 | 0.6738 | 0.0 | 0.5421 | 0.6703 | | 0.0593 | 47.2 | 13500 | 0.1273 | 0.3963 | 0.6032 | 0.5948 | nan | 0.5548 | 0.6515 | 0.0 | 0.5399 | 0.6489 | | 0.0562 | 47.27 | 13520 | 0.1264 | 0.3918 | 0.5970 | 0.5899 | nan | 0.5558 | 0.6381 | 0.0 | 0.5415 | 0.6339 | | 0.0416 | 47.34 | 13540 | 0.1136 | 0.4165 | 0.6334 | 0.6212 | nan | 0.5627 | 0.7041 | 0.0 | 0.5528 | 0.6968 | | 0.0633 | 47.41 | 13560 | 0.1207 | 0.3946 | 0.6011 | 0.5935 | nan | 0.5574 | 0.6447 | 0.0 | 0.5444 | 0.6393 | | 0.0614 | 47.48 | 13580 | 0.1201 | 0.3989 | 0.6067 | 0.5951 | nan | 0.5399 | 0.6734 | 0.0 | 0.5294 | 0.6672 | | 0.0576 | 47.55 | 13600 | 0.1212 | 0.3955 | 0.6017 | 0.5912 | nan | 0.5412 | 0.6621 | 0.0 | 0.5318 | 0.6547 | | 0.0458 | 47.62 | 13620 | 0.1210 | 0.4040 | 0.6157 | 0.6028 | nan | 0.5415 | 0.6899 | 0.0 | 0.5318 | 0.6801 | | 0.0736 | 47.69 | 13640 | 0.1167 | 0.4056 | 0.6186 | 0.6048 | nan | 0.5389 | 0.6982 | 0.0 | 0.5314 | 0.6854 | | 0.0593 | 47.76 | 13660 | 0.1197 | 0.4103 | 0.6240 | 0.6110 | nan | 0.5490 | 0.6990 | 0.0 | 0.5382 | 0.6926 | | 0.0367 | 47.83 | 13680 | 0.1237 | 0.4128 | 0.6286 | 0.6204 | nan | 0.5815 | 0.6756 | 0.0 | 0.5676 | 0.6709 | | 0.0658 | 47.9 | 13700 | 0.1225 | 0.4110 | 0.6256 | 0.6146 | nan | 0.5621 | 0.6891 | 0.0 | 0.5496 | 0.6833 | | 0.0312 | 47.97 | 13720 | 0.1301 | 0.3874 | 0.5892 | 0.5800 | nan | 0.5362 | 0.6422 | 0.0 | 0.5225 | 0.6397 | | 0.04 | 48.04 | 13740 | 0.1336 | 0.3802 | 0.5782 | 0.5671 | nan | 0.5141 | 0.6423 | 0.0 | 0.5002 | 0.6405 | | 0.0635 | 48.11 | 13760 | 0.1228 | 0.3945 | 0.5998 | 0.5876 | nan | 0.5295 | 0.6700 | 0.0 | 0.5201 | 0.6635 | | 0.0541 | 48.18 | 13780 | 0.1237 | 0.3857 | 0.5861 | 0.5691 | nan | 0.4880 | 0.6842 | 0.0 | 0.4795 | 0.6776 | | 0.0531 | 48.25 | 13800 | 0.1261 | 0.3886 | 0.5925 | 0.5851 | nan | 0.5497 | 0.6354 | 0.0 | 0.5348 | 0.6310 | | 0.0584 | 48.32 | 13820 | 0.1193 | 0.4071 | 0.6206 | 0.6109 | nan | 0.5644 | 0.6769 | 0.0 | 0.5511 | 0.6702 | | 0.0627 | 48.39 | 13840 | 0.1207 | 0.3981 | 0.6054 | 0.5907 | nan | 0.5203 | 0.6905 | 0.0 | 0.5106 | 0.6836 | | 0.0568 | 48.46 | 13860 | 0.1247 | 0.3874 | 0.5897 | 0.5776 | nan | 0.5197 | 0.6597 | 0.0 | 0.5077 | 0.6544 | | 0.0551 | 48.53 | 13880 | 0.1252 | 0.4000 | 0.6084 | 0.5956 | nan | 0.5343 | 0.6825 | 0.0 | 0.5250 | 0.6750 | | 0.0351 | 48.6 | 13900 | 0.1226 | 0.4014 | 0.6111 | 0.5991 | nan | 0.5415 | 0.6808 | 0.0 | 0.5297 | 0.6746 | | 0.0386 | 48.67 | 13920 | 0.1240 | 0.4083 | 0.6214 | 0.6138 | nan | 0.5777 | 0.6650 | 0.0 | 0.5657 | 0.6593 | | 0.0458 | 48.74 | 13940 | 0.1176 | 0.4101 | 0.6239 | 0.6105 | nan | 0.5465 | 0.7013 | 0.0 | 0.5381 | 0.6921 | | 0.0501 | 48.81 | 13960 | 0.1255 | 0.3877 | 0.5895 | 0.5811 | nan | 0.5410 | 0.6381 | 0.0 | 0.5302 | 0.6329 | | 0.0431 | 48.88 | 13980 | 0.1200 | 0.3947 | 0.5995 | 0.5833 | nan | 0.5057 | 0.6934 | 0.0 | 0.4981 | 0.6860 | | 0.0455 | 48.95 | 14000 | 0.1208 | 0.4034 | 0.6141 | 0.6014 | nan | 0.5403 | 0.6879 | 0.0 | 0.5290 | 0.6813 | | 0.0837 | 49.02 | 14020 | 0.1222 | 0.3921 | 0.5965 | 0.5840 | nan | 0.5242 | 0.6689 | 0.0 | 0.5126 | 0.6639 | | 0.0647 | 49.09 | 14040 | 0.1238 | 0.3866 | 0.5873 | 0.5754 | nan | 0.5188 | 0.6558 | 0.0 | 0.5092 | 0.6505 | | 0.0433 | 49.16 | 14060 | 0.1159 | 0.4174 | 0.6348 | 0.6215 | nan | 0.5581 | 0.7115 | 0.0 | 0.5502 | 0.7019 | | 0.053 | 49.23 | 14080 | 0.1260 | 0.3943 | 0.5992 | 0.5900 | nan | 0.5461 | 0.6524 | 0.0 | 0.5331 | 0.6497 | | 0.0606 | 49.3 | 14100 | 0.1238 | 0.4070 | 0.6191 | 0.6121 | nan | 0.5784 | 0.6599 | 0.0 | 0.5649 | 0.6560 | | 0.0694 | 49.37 | 14120 | 0.1211 | 0.3991 | 0.6061 | 0.5919 | nan | 0.5239 | 0.6884 | 0.0 | 0.5154 | 0.6820 | | 0.0464 | 49.44 | 14140 | 0.1193 | 0.4089 | 0.6221 | 0.6072 | nan | 0.5359 | 0.7084 | 0.0 | 0.5289 | 0.6978 | | 0.0369 | 49.51 | 14160 | 0.1206 | 0.4100 | 0.6244 | 0.6166 | nan | 0.5793 | 0.6696 | 0.0 | 0.5701 | 0.6600 | | 0.0474 | 49.58 | 14180 | 0.1108 | 0.4124 | 0.6290 | 0.6146 | nan | 0.5455 | 0.7125 | 0.0 | 0.5388 | 0.6985 | | 0.0726 | 49.65 | 14200 | 0.1217 | 0.4000 | 0.6092 | 0.5982 | nan | 0.5459 | 0.6725 | 0.0 | 0.5306 | 0.6696 | | 0.0409 | 49.72 | 14220 | 0.1181 | 0.3998 | 0.6087 | 0.5990 | nan | 0.5527 | 0.6647 | 0.0 | 0.5408 | 0.6587 | | 0.0454 | 49.79 | 14240 | 0.1254 | 0.3920 | 0.5972 | 0.5878 | nan | 0.5431 | 0.6513 | 0.0 | 0.5284 | 0.6476 | | 0.0417 | 49.86 | 14260 | 0.1230 | 0.3977 | 0.6052 | 0.5967 | nan | 0.5562 | 0.6542 | 0.0 | 0.5438 | 0.6493 | | 0.0277 | 49.93 | 14280 | 0.1235 | 0.4016 | 0.6117 | 0.6013 | nan | 0.5517 | 0.6716 | 0.0 | 0.5391 | 0.6658 | | 0.0382 | 50.0 | 14300 | 0.1176 | 0.4171 | 0.6350 | 0.6262 | nan | 0.5840 | 0.6860 | 0.0 | 0.5710 | 0.6804 | | 0.0484 | 50.07 | 14320 | 0.1211 | 0.4062 | 0.6180 | 0.6092 | nan | 0.5671 | 0.6689 | 0.0 | 0.5528 | 0.6656 | | 0.0426 | 50.14 | 14340 | 0.1200 | 0.3995 | 0.6061 | 0.5933 | nan | 0.5322 | 0.6800 | 0.0 | 0.5233 | 0.6750 | | 0.0702 | 50.21 | 14360 | 0.1214 | 0.4081 | 0.6206 | 0.6106 | nan | 0.5625 | 0.6787 | 0.0 | 0.5503 | 0.6741 | | 0.0586 | 50.28 | 14380 | 0.1156 | 0.4158 | 0.6306 | 0.6198 | nan | 0.5681 | 0.6931 | 0.0 | 0.5581 | 0.6892 | | 0.0334 | 50.35 | 14400 | 0.1194 | 0.4092 | 0.6211 | 0.6075 | nan | 0.5426 | 0.6996 | 0.0 | 0.5322 | 0.6953 | | 0.0356 | 50.42 | 14420 | 0.1204 | 0.3922 | 0.5946 | 0.5777 | nan | 0.4973 | 0.6918 | 0.0 | 0.4885 | 0.6881 | | 0.0487 | 50.49 | 14440 | 0.1225 | 0.3798 | 0.5764 | 0.5594 | nan | 0.4780 | 0.6748 | 0.0 | 0.4685 | 0.6709 | | 0.0658 | 50.56 | 14460 | 0.1177 | 0.4083 | 0.6206 | 0.6072 | nan | 0.5435 | 0.6976 | 0.0 | 0.5318 | 0.6932 | | 0.0488 | 50.63 | 14480 | 0.1181 | 0.4163 | 0.6343 | 0.6269 | nan | 0.5912 | 0.6774 | 0.0 | 0.5751 | 0.6740 | | 0.0528 | 50.7 | 14500 | 0.1181 | 0.4003 | 0.6079 | 0.5952 | nan | 0.5346 | 0.6812 | 0.0 | 0.5268 | 0.6741 | | 0.0684 | 50.77 | 14520 | 0.1206 | 0.3911 | 0.5946 | 0.5824 | nan | 0.5243 | 0.6649 | 0.0 | 0.5136 | 0.6598 | | 0.053 | 50.84 | 14540 | 0.1228 | 0.3835 | 0.5830 | 0.5688 | nan | 0.5013 | 0.6647 | 0.0 | 0.4920 | 0.6585 | | 0.0794 | 50.91 | 14560 | 0.1239 | 0.3882 | 0.5905 | 0.5791 | nan | 0.5251 | 0.6558 | 0.0 | 0.5134 | 0.6513 | | 0.0488 | 50.98 | 14580 | 0.1189 | 0.3930 | 0.5968 | 0.5831 | nan | 0.5176 | 0.6761 | 0.0 | 0.5064 | 0.6724 | | 0.0493 | 51.05 | 14600 | 0.1230 | 0.3923 | 0.5958 | 0.5854 | nan | 0.5354 | 0.6563 | 0.0 | 0.5228 | 0.6540 | | 0.0582 | 51.12 | 14620 | 0.1244 | 0.3940 | 0.5989 | 0.5908 | nan | 0.5523 | 0.6455 | 0.0 | 0.5394 | 0.6425 | | 0.0475 | 51.19 | 14640 | 0.1236 | 0.4012 | 0.6091 | 0.5986 | nan | 0.5483 | 0.6700 | 0.0 | 0.5384 | 0.6653 | | 0.0545 | 51.26 | 14660 | 0.1248 | 0.3903 | 0.5935 | 0.5872 | nan | 0.5567 | 0.6304 | 0.0 | 0.5435 | 0.6274 | | 0.0492 | 51.33 | 14680 | 0.1122 | 0.4187 | 0.6367 | 0.6269 | nan | 0.5803 | 0.6931 | 0.0 | 0.5694 | 0.6866 | | 0.0309 | 51.4 | 14700 | 0.1187 | 0.4032 | 0.6127 | 0.6034 | nan | 0.5589 | 0.6666 | 0.0 | 0.5472 | 0.6625 | | 0.0704 | 51.47 | 14720 | 0.1288 | 0.3908 | 0.5946 | 0.5876 | nan | 0.5538 | 0.6354 | 0.0 | 0.5411 | 0.6312 | | 0.0562 | 51.54 | 14740 | 0.1269 | 0.3872 | 0.5889 | 0.5784 | nan | 0.5283 | 0.6494 | 0.0 | 0.5153 | 0.6462 | | 0.0286 | 51.61 | 14760 | 0.1188 | 0.4067 | 0.6191 | 0.6117 | nan | 0.5762 | 0.6620 | 0.0 | 0.5620 | 0.6581 | | 0.0515 | 51.68 | 14780 | 0.1219 | 0.4045 | 0.6158 | 0.6075 | nan | 0.5678 | 0.6639 | 0.0 | 0.5537 | 0.6599 | | 0.0691 | 51.75 | 14800 | 0.1238 | 0.3924 | 0.5969 | 0.5844 | nan | 0.5244 | 0.6695 | 0.0 | 0.5122 | 0.6652 | | 0.0502 | 51.82 | 14820 | 0.1216 | 0.3820 | 0.5806 | 0.5642 | nan | 0.4859 | 0.6753 | 0.0 | 0.4759 | 0.6701 | | 0.0396 | 51.89 | 14840 | 0.1181 | 0.4122 | 0.6271 | 0.6130 | nan | 0.5457 | 0.7084 | 0.0 | 0.5369 | 0.6999 | | 0.0741 | 51.96 | 14860 | 0.1233 | 0.3905 | 0.5938 | 0.5820 | nan | 0.5255 | 0.6620 | 0.0 | 0.5133 | 0.6581 | | 0.0551 | 52.03 | 14880 | 0.1255 | 0.3985 | 0.6087 | 0.6026 | nan | 0.5733 | 0.6441 | 0.0 | 0.5551 | 0.6405 | | 0.0283 | 52.1 | 14900 | 0.1244 | 0.3995 | 0.6105 | 0.6028 | nan | 0.5660 | 0.6550 | 0.0 | 0.5483 | 0.6503 | | 0.0491 | 52.17 | 14920 | 0.1237 | 0.4060 | 0.6201 | 0.6134 | nan | 0.5814 | 0.6588 | 0.0 | 0.5636 | 0.6544 | | 0.05 | 52.24 | 14940 | 0.1200 | 0.4039 | 0.6140 | 0.6027 | nan | 0.5485 | 0.6796 | 0.0 | 0.5366 | 0.6751 | | 0.0899 | 52.31 | 14960 | 0.1238 | 0.3956 | 0.6016 | 0.5884 | nan | 0.5252 | 0.6781 | 0.0 | 0.5112 | 0.6756 | | 0.0671 | 52.38 | 14980 | 0.1216 | 0.3991 | 0.6066 | 0.5938 | nan | 0.5327 | 0.6806 | 0.0 | 0.5206 | 0.6766 | | 0.0375 | 52.45 | 15000 | 0.1234 | 0.3994 | 0.6089 | 0.6014 | nan | 0.5655 | 0.6523 | 0.0 | 0.5491 | 0.6490 | | 0.0578 | 52.52 | 15020 | 0.1246 | 0.4046 | 0.6165 | 0.6091 | nan | 0.5736 | 0.6594 | 0.0 | 0.5586 | 0.6551 | | 0.0374 | 52.59 | 15040 | 0.1284 | 0.3909 | 0.5953 | 0.5882 | nan | 0.5545 | 0.6361 | 0.0 | 0.5399 | 0.6329 | | 0.0584 | 52.66 | 15060 | 0.1293 | 0.3846 | 0.5853 | 0.5732 | nan | 0.5155 | 0.6551 | 0.0 | 0.5022 | 0.6515 | | 0.0268 | 52.73 | 15080 | 0.1204 | 0.3987 | 0.6066 | 0.5932 | nan | 0.5292 | 0.6839 | 0.0 | 0.5205 | 0.6756 | | 0.052 | 52.8 | 15100 | 0.1214 | 0.4045 | 0.6154 | 0.6038 | nan | 0.5481 | 0.6827 | 0.0 | 0.5392 | 0.6745 | | 0.0435 | 52.87 | 15120 | 0.1214 | 0.4085 | 0.6224 | 0.6101 | nan | 0.5512 | 0.6936 | 0.0 | 0.5380 | 0.6876 | | 0.0292 | 52.94 | 15140 | 0.1293 | 0.3909 | 0.5962 | 0.5895 | nan | 0.5577 | 0.6347 | 0.0 | 0.5425 | 0.6302 | | 0.05 | 53.01 | 15160 | 0.1291 | 0.3860 | 0.5885 | 0.5795 | nan | 0.5366 | 0.6403 | 0.0 | 0.5221 | 0.6359 | | 0.0295 | 53.08 | 15180 | 0.1244 | 0.3880 | 0.5909 | 0.5792 | nan | 0.5233 | 0.6584 | 0.0 | 0.5108 | 0.6532 | | 0.0513 | 53.15 | 15200 | 0.1239 | 0.4069 | 0.6201 | 0.6150 | nan | 0.5904 | 0.6499 | 0.0 | 0.5756 | 0.6453 | | 0.0311 | 53.22 | 15220 | 0.1239 | 0.4078 | 0.6218 | 0.6141 | nan | 0.5773 | 0.6663 | 0.0 | 0.5625 | 0.6609 | | 0.0451 | 53.29 | 15240 | 0.1133 | 0.4074 | 0.6210 | 0.6034 | nan | 0.5196 | 0.7224 | 0.0 | 0.5123 | 0.7100 | | 0.0499 | 53.36 | 15260 | 0.1225 | 0.3979 | 0.6060 | 0.6006 | nan | 0.5746 | 0.6374 | 0.0 | 0.5609 | 0.6327 | | 0.0464 | 53.43 | 15280 | 0.1217 | 0.3967 | 0.6038 | 0.5917 | nan | 0.5340 | 0.6736 | 0.0 | 0.5224 | 0.6678 | | 0.0563 | 53.5 | 15300 | 0.1262 | 0.3951 | 0.6021 | 0.5932 | nan | 0.5510 | 0.6531 | 0.0 | 0.5358 | 0.6495 | | 0.0593 | 53.57 | 15320 | 0.1279 | 0.3914 | 0.5961 | 0.5859 | nan | 0.5373 | 0.6548 | 0.0 | 0.5240 | 0.6501 | | 0.0471 | 53.64 | 15340 | 0.1222 | 0.4000 | 0.6091 | 0.5982 | nan | 0.5457 | 0.6726 | 0.0 | 0.5346 | 0.6654 | | 0.0541 | 53.71 | 15360 | 0.1205 | 0.3989 | 0.6066 | 0.5951 | nan | 0.5401 | 0.6731 | 0.0 | 0.5324 | 0.6642 | | 0.0317 | 53.78 | 15380 | 0.1253 | 0.3982 | 0.6060 | 0.5953 | nan | 0.5442 | 0.6677 | 0.0 | 0.5320 | 0.6628 | | 0.0601 | 53.85 | 15400 | 0.1250 | 0.4022 | 0.6124 | 0.6051 | nan | 0.5705 | 0.6542 | 0.0 | 0.5556 | 0.6509 | | 0.0378 | 53.92 | 15420 | 0.1226 | 0.4001 | 0.6085 | 0.5946 | nan | 0.5283 | 0.6888 | 0.0 | 0.5174 | 0.6829 | | 0.0628 | 53.99 | 15440 | 0.1342 | 0.3911 | 0.5961 | 0.5913 | nan | 0.5681 | 0.6241 | 0.0 | 0.5515 | 0.6217 | | 0.0553 | 54.06 | 15460 | 0.1246 | 0.4043 | 0.6148 | 0.6042 | nan | 0.5531 | 0.6766 | 0.0 | 0.5416 | 0.6712 | | 0.0324 | 54.13 | 15480 | 0.1285 | 0.3999 | 0.6092 | 0.6014 | nan | 0.5644 | 0.6540 | 0.0 | 0.5494 | 0.6504 | | 0.0586 | 54.2 | 15500 | 0.1316 | 0.3930 | 0.5981 | 0.5896 | nan | 0.5489 | 0.6474 | 0.0 | 0.5359 | 0.6432 | | 0.0502 | 54.27 | 15520 | 0.1309 | 0.3949 | 0.6009 | 0.5913 | nan | 0.5455 | 0.6563 | 0.0 | 0.5333 | 0.6515 | | 0.05 | 54.34 | 15540 | 0.1290 | 0.3965 | 0.6029 | 0.5931 | nan | 0.5458 | 0.6601 | 0.0 | 0.5341 | 0.6553 | | 0.0527 | 54.41 | 15560 | 0.1325 | 0.3893 | 0.5921 | 0.5838 | nan | 0.5446 | 0.6396 | 0.0 | 0.5316 | 0.6363 | | 0.0679 | 54.48 | 15580 | 0.1296 | 0.3861 | 0.5870 | 0.5763 | nan | 0.5253 | 0.6487 | 0.0 | 0.5142 | 0.6441 | | 0.0425 | 54.55 | 15600 | 0.1295 | 0.3998 | 0.6086 | 0.5983 | nan | 0.5495 | 0.6676 | 0.0 | 0.5352 | 0.6643 | | 0.052 | 54.62 | 15620 | 0.1360 | 0.3840 | 0.5848 | 0.5792 | nan | 0.5525 | 0.6172 | 0.0 | 0.5368 | 0.6151 | | 0.0828 | 54.69 | 15640 | 0.1263 | 0.3994 | 0.6075 | 0.5972 | nan | 0.5476 | 0.6675 | 0.0 | 0.5355 | 0.6625 | | 0.0405 | 54.76 | 15660 | 0.1217 | 0.4202 | 0.6394 | 0.6313 | nan | 0.5927 | 0.6861 | 0.0 | 0.5821 | 0.6784 | | 0.0625 | 54.83 | 15680 | 0.1303 | 0.4003 | 0.6095 | 0.5998 | nan | 0.5537 | 0.6653 | 0.0 | 0.5408 | 0.6602 | | 0.0437 | 54.9 | 15700 | 0.1317 | 0.3983 | 0.6065 | 0.5985 | nan | 0.5605 | 0.6524 | 0.0 | 0.5464 | 0.6486 | | 0.0491 | 54.97 | 15720 | 0.1313 | 0.3993 | 0.6079 | 0.5967 | nan | 0.5432 | 0.6726 | 0.0 | 0.5301 | 0.6677 | | 0.0486 | 55.03 | 15740 | 0.1330 | 0.3963 | 0.6026 | 0.5940 | nan | 0.5527 | 0.6525 | 0.0 | 0.5403 | 0.6486 | | 0.0358 | 55.1 | 15760 | 0.1327 | 0.3970 | 0.6040 | 0.5934 | nan | 0.5432 | 0.6647 | 0.0 | 0.5309 | 0.6602 | | 0.0846 | 55.17 | 15780 | 0.1352 | 0.3895 | 0.5929 | 0.5860 | nan | 0.5533 | 0.6324 | 0.0 | 0.5383 | 0.6303 | | 0.0498 | 55.24 | 15800 | 0.1211 | 0.4155 | 0.6322 | 0.6221 | nan | 0.5737 | 0.6906 | 0.0 | 0.5632 | 0.6831 | | 0.0625 | 55.31 | 15820 | 0.1304 | 0.4036 | 0.6137 | 0.6053 | nan | 0.5650 | 0.6624 | 0.0 | 0.5534 | 0.6573 | | 0.0397 | 55.38 | 15840 | 0.1327 | 0.3898 | 0.5930 | 0.5859 | nan | 0.5519 | 0.6341 | 0.0 | 0.5394 | 0.6300 | | 0.0489 | 55.45 | 15860 | 0.1304 | 0.3867 | 0.5871 | 0.5757 | nan | 0.5213 | 0.6529 | 0.0 | 0.5120 | 0.6481 | | 0.0519 | 55.52 | 15880 | 0.1239 | 0.4146 | 0.6313 | 0.6235 | nan | 0.5863 | 0.6763 | 0.0 | 0.5726 | 0.6712 | | 0.0507 | 55.59 | 15900 | 0.1257 | 0.4189 | 0.6379 | 0.6331 | nan | 0.6100 | 0.6658 | 0.0 | 0.5961 | 0.6605 | | 0.0253 | 55.66 | 15920 | 0.1291 | 0.4040 | 0.6144 | 0.6067 | nan | 0.5697 | 0.6592 | 0.0 | 0.5586 | 0.6533 | | 0.0597 | 55.73 | 15940 | 0.1317 | 0.4038 | 0.6148 | 0.6064 | nan | 0.5663 | 0.6633 | 0.0 | 0.5532 | 0.6581 | | 0.0527 | 55.8 | 15960 | 0.1329 | 0.3985 | 0.6074 | 0.6039 | nan | 0.5873 | 0.6275 | 0.0 | 0.5728 | 0.6228 | | 0.0635 | 55.87 | 15980 | 0.1332 | 0.3944 | 0.6002 | 0.5944 | nan | 0.5671 | 0.6332 | 0.0 | 0.5538 | 0.6294 | | 0.0637 | 55.94 | 16000 | 0.1292 | 0.4004 | 0.6089 | 0.5975 | nan | 0.5432 | 0.6746 | 0.0 | 0.5307 | 0.6705 | | 0.041 | 56.01 | 16020 | 0.1308 | 0.3898 | 0.5930 | 0.5835 | nan | 0.5383 | 0.6476 | 0.0 | 0.5249 | 0.6444 | | 0.0595 | 56.08 | 16040 | 0.1216 | 0.3951 | 0.6009 | 0.5847 | nan | 0.5071 | 0.6948 | 0.0 | 0.4991 | 0.6860 | | 0.0483 | 56.15 | 16060 | 0.1295 | 0.3952 | 0.6016 | 0.5915 | nan | 0.5432 | 0.6600 | 0.0 | 0.5305 | 0.6551 | | 0.0588 | 56.22 | 16080 | 0.1227 | 0.4187 | 0.6378 | 0.6299 | nan | 0.5921 | 0.6834 | 0.0 | 0.5789 | 0.6771 | | 0.0393 | 56.29 | 16100 | 0.1260 | 0.3935 | 0.5994 | 0.5911 | nan | 0.5512 | 0.6477 | 0.0 | 0.5380 | 0.6425 | | 0.034 | 56.36 | 16120 | 0.1317 | 0.3893 | 0.5924 | 0.5772 | nan | 0.5049 | 0.6799 | 0.0 | 0.4946 | 0.6734 | | 0.0823 | 56.43 | 16140 | 0.1311 | 0.3899 | 0.5948 | 0.5877 | nan | 0.5539 | 0.6356 | 0.0 | 0.5384 | 0.6313 | | 0.0544 | 56.5 | 16160 | 0.1254 | 0.4000 | 0.6085 | 0.5959 | nan | 0.5359 | 0.6810 | 0.0 | 0.5244 | 0.6756 | | 0.0461 | 56.57 | 16180 | 0.1246 | 0.4085 | 0.6226 | 0.6132 | nan | 0.5685 | 0.6767 | 0.0 | 0.5544 | 0.6710 | | 0.0467 | 56.64 | 16200 | 0.1238 | 0.4134 | 0.6290 | 0.6163 | nan | 0.5557 | 0.7024 | 0.0 | 0.5472 | 0.6928 | | 0.0344 | 56.71 | 16220 | 0.1241 | 0.4035 | 0.6138 | 0.6003 | nan | 0.5359 | 0.6917 | 0.0 | 0.5265 | 0.6841 | | 0.0341 | 56.78 | 16240 | 0.1231 | 0.4037 | 0.6147 | 0.6020 | nan | 0.5411 | 0.6884 | 0.0 | 0.5288 | 0.6823 | | 0.0378 | 56.85 | 16260 | 0.1259 | 0.3942 | 0.6001 | 0.5881 | nan | 0.5308 | 0.6694 | 0.0 | 0.5195 | 0.6632 | | 0.0353 | 56.92 | 16280 | 0.1176 | 0.4199 | 0.6393 | 0.6277 | nan | 0.5719 | 0.7068 | 0.0 | 0.5602 | 0.6996 | | 0.0257 | 56.99 | 16300 | 0.1345 | 0.3828 | 0.5828 | 0.5747 | nan | 0.5362 | 0.6294 | 0.0 | 0.5215 | 0.6270 | | 0.0481 | 57.06 | 16320 | 0.1231 | 0.4071 | 0.6193 | 0.6085 | nan | 0.5572 | 0.6813 | 0.0 | 0.5443 | 0.6770 | | 0.0463 | 57.13 | 16340 | 0.1201 | 0.4106 | 0.6247 | 0.6154 | nan | 0.5712 | 0.6781 | 0.0 | 0.5578 | 0.6741 | | 0.0721 | 57.2 | 16360 | 0.1240 | 0.4030 | 0.6129 | 0.5992 | nan | 0.5336 | 0.6922 | 0.0 | 0.5219 | 0.6872 | | 0.0491 | 57.27 | 16380 | 0.1247 | 0.4028 | 0.6124 | 0.5981 | nan | 0.5300 | 0.6947 | 0.0 | 0.5212 | 0.6871 | | 0.0442 | 57.34 | 16400 | 0.1247 | 0.3936 | 0.5986 | 0.5879 | nan | 0.5368 | 0.6604 | 0.0 | 0.5248 | 0.6560 | | 0.033 | 57.41 | 16420 | 0.1229 | 0.3972 | 0.6045 | 0.5943 | nan | 0.5459 | 0.6631 | 0.0 | 0.5342 | 0.6574 | | 0.0642 | 57.48 | 16440 | 0.1263 | 0.3895 | 0.5918 | 0.5794 | nan | 0.5199 | 0.6638 | 0.0 | 0.5104 | 0.6581 | | 0.0723 | 57.55 | 16460 | 0.1288 | 0.3982 | 0.6052 | 0.5945 | nan | 0.5436 | 0.6667 | 0.0 | 0.5336 | 0.6609 | | 0.0419 | 57.62 | 16480 | 0.1236 | 0.4125 | 0.6282 | 0.6182 | nan | 0.5707 | 0.6857 | 0.0 | 0.5584 | 0.6792 | | 0.0589 | 57.69 | 16500 | 0.1206 | 0.3981 | 0.6052 | 0.5913 | nan | 0.5248 | 0.6857 | 0.0 | 0.5170 | 0.6773 | | 0.0326 | 57.76 | 16520 | 0.1240 | 0.4017 | 0.6114 | 0.6017 | nan | 0.5555 | 0.6673 | 0.0 | 0.5433 | 0.6620 | | 0.0464 | 57.83 | 16540 | 0.1190 | 0.4215 | 0.6411 | 0.6274 | nan | 0.5621 | 0.7201 | 0.0 | 0.5541 | 0.7105 | | 0.1178 | 57.9 | 16560 | 0.1206 | 0.4110 | 0.6258 | 0.6160 | nan | 0.5690 | 0.6826 | 0.0 | 0.5570 | 0.6759 | | 0.0288 | 57.97 | 16580 | 0.1133 | 0.4235 | 0.6438 | 0.6321 | nan | 0.5762 | 0.7114 | 0.0 | 0.5649 | 0.7055 | | 0.0357 | 58.04 | 16600 | 0.1180 | 0.4079 | 0.6215 | 0.6091 | nan | 0.5499 | 0.6931 | 0.0 | 0.5419 | 0.6818 | | 0.0538 | 58.11 | 16620 | 0.1146 | 0.4108 | 0.6266 | 0.6115 | nan | 0.5397 | 0.7134 | 0.0 | 0.5327 | 0.6996 | | 0.0723 | 58.18 | 16640 | 0.1273 | 0.4001 | 0.6088 | 0.6027 | nan | 0.5737 | 0.6440 | 0.0 | 0.5595 | 0.6408 | | 0.067 | 58.25 | 16660 | 0.1268 | 0.4025 | 0.6128 | 0.6040 | nan | 0.5621 | 0.6636 | 0.0 | 0.5472 | 0.6604 | | 0.0266 | 58.32 | 16680 | 0.1264 | 0.4002 | 0.6088 | 0.5982 | nan | 0.5480 | 0.6695 | 0.0 | 0.5359 | 0.6648 | | 0.059 | 58.39 | 16700 | 0.1394 | 0.3774 | 0.5747 | 0.5684 | nan | 0.5384 | 0.6111 | 0.0 | 0.5231 | 0.6091 | | 0.0593 | 58.46 | 16720 | 0.1235 | 0.3978 | 0.6050 | 0.5954 | nan | 0.5497 | 0.6602 | 0.0 | 0.5385 | 0.6549 | | 0.0553 | 58.53 | 16740 | 0.1228 | 0.4133 | 0.6291 | 0.6213 | nan | 0.5845 | 0.6736 | 0.0 | 0.5692 | 0.6707 | | 0.0515 | 58.6 | 16760 | 0.1160 | 0.4095 | 0.6217 | 0.6109 | nan | 0.5594 | 0.6840 | 0.0 | 0.5489 | 0.6796 | | 0.0645 | 58.67 | 16780 | 0.1267 | 0.3939 | 0.5988 | 0.5922 | nan | 0.5605 | 0.6372 | 0.0 | 0.5483 | 0.6335 | | 0.0381 | 58.74 | 16800 | 0.1269 | 0.3945 | 0.6006 | 0.5932 | nan | 0.5583 | 0.6428 | 0.0 | 0.5446 | 0.6389 | | 0.0416 | 58.81 | 16820 | 0.1242 | 0.4127 | 0.6289 | 0.6230 | nan | 0.5948 | 0.6629 | 0.0 | 0.5790 | 0.6592 | | 0.0551 | 58.88 | 16840 | 0.1181 | 0.3974 | 0.6043 | 0.5913 | nan | 0.5294 | 0.6792 | 0.0 | 0.5190 | 0.6733 | | 0.0582 | 58.95 | 16860 | 0.1222 | 0.4053 | 0.6176 | 0.6078 | nan | 0.5610 | 0.6742 | 0.0 | 0.5472 | 0.6687 | | 0.0375 | 59.02 | 16880 | 0.1209 | 0.3982 | 0.6060 | 0.5920 | nan | 0.5252 | 0.6869 | 0.0 | 0.5140 | 0.6805 | | 0.0424 | 59.09 | 16900 | 0.1289 | 0.3936 | 0.5993 | 0.5904 | nan | 0.5479 | 0.6506 | 0.0 | 0.5339 | 0.6469 | | 0.047 | 59.16 | 16920 | 0.1235 | 0.4029 | 0.6127 | 0.6021 | nan | 0.5512 | 0.6743 | 0.0 | 0.5394 | 0.6692 | | 0.0456 | 59.23 | 16940 | 0.1251 | 0.4059 | 0.6174 | 0.6060 | nan | 0.5516 | 0.6833 | 0.0 | 0.5383 | 0.6793 | | 0.0565 | 59.3 | 16960 | 0.1298 | 0.3842 | 0.5843 | 0.5753 | nan | 0.5322 | 0.6364 | 0.0 | 0.5196 | 0.6331 | | 0.0602 | 59.37 | 16980 | 0.1238 | 0.3932 | 0.5980 | 0.5860 | nan | 0.5291 | 0.6668 | 0.0 | 0.5180 | 0.6614 | | 0.0679 | 59.44 | 17000 | 0.1222 | 0.3969 | 0.6035 | 0.5917 | nan | 0.5351 | 0.6719 | 0.0 | 0.5240 | 0.6666 | | 0.0919 | 59.51 | 17020 | 0.1258 | 0.3948 | 0.6013 | 0.5931 | nan | 0.5543 | 0.6482 | 0.0 | 0.5403 | 0.6440 | | 0.0655 | 59.58 | 17040 | 0.1275 | 0.4066 | 0.6196 | 0.6147 | nan | 0.5914 | 0.6477 | 0.0 | 0.5761 | 0.6438 | | 0.0299 | 59.65 | 17060 | 0.1251 | 0.4013 | 0.6108 | 0.6008 | nan | 0.5530 | 0.6685 | 0.0 | 0.5403 | 0.6636 | | 0.0425 | 59.72 | 17080 | 0.1224 | 0.4102 | 0.6239 | 0.6141 | nan | 0.5675 | 0.6802 | 0.0 | 0.5560 | 0.6747 | | 0.0624 | 59.79 | 17100 | 0.1277 | 0.3981 | 0.6056 | 0.5987 | nan | 0.5661 | 0.6450 | 0.0 | 0.5527 | 0.6415 | | 0.0394 | 59.86 | 17120 | 0.1295 | 0.3786 | 0.5758 | 0.5650 | nan | 0.5138 | 0.6378 | 0.0 | 0.5020 | 0.6337 | | 0.046 | 59.93 | 17140 | 0.1267 | 0.3951 | 0.6014 | 0.5938 | nan | 0.5572 | 0.6456 | 0.0 | 0.5435 | 0.6418 | | 0.0339 | 60.0 | 17160 | 0.1208 | 0.4100 | 0.6241 | 0.6140 | nan | 0.5659 | 0.6823 | 0.0 | 0.5537 | 0.6764 | | 0.1047 | 60.07 | 17180 | 0.1276 | 0.4058 | 0.6178 | 0.6133 | nan | 0.5920 | 0.6436 | 0.0 | 0.5796 | 0.6379 | | 0.0593 | 60.14 | 17200 | 0.1229 | 0.4115 | 0.6269 | 0.6178 | nan | 0.5746 | 0.6792 | 0.0 | 0.5618 | 0.6727 | | 0.0405 | 60.21 | 17220 | 0.1226 | 0.4167 | 0.6342 | 0.6260 | nan | 0.5868 | 0.6816 | 0.0 | 0.5723 | 0.6779 | | 0.042 | 60.28 | 17240 | 0.1257 | 0.4050 | 0.6161 | 0.6079 | nan | 0.5687 | 0.6634 | 0.0 | 0.5542 | 0.6608 | | 0.0654 | 60.35 | 17260 | 0.1216 | 0.4151 | 0.6312 | 0.6227 | nan | 0.5821 | 0.6803 | 0.0 | 0.5684 | 0.6770 | | 0.0559 | 60.42 | 17280 | 0.1241 | 0.4065 | 0.6184 | 0.6100 | nan | 0.5704 | 0.6663 | 0.0 | 0.5571 | 0.6625 | | 0.0484 | 60.49 | 17300 | 0.1283 | 0.3892 | 0.5910 | 0.5794 | nan | 0.5241 | 0.6578 | 0.0 | 0.5127 | 0.6549 | | 0.0523 | 60.56 | 17320 | 0.1207 | 0.4085 | 0.6211 | 0.6118 | nan | 0.5677 | 0.6744 | 0.0 | 0.5559 | 0.6696 | | 0.0497 | 60.63 | 17340 | 0.1213 | 0.4032 | 0.6131 | 0.6000 | nan | 0.5376 | 0.6885 | 0.0 | 0.5265 | 0.6832 | | 0.0382 | 60.7 | 17360 | 0.1299 | 0.3912 | 0.5946 | 0.5842 | nan | 0.5348 | 0.6543 | 0.0 | 0.5231 | 0.6505 | | 0.0324 | 60.77 | 17380 | 0.1315 | 0.3837 | 0.5830 | 0.5724 | nan | 0.5216 | 0.6444 | 0.0 | 0.5097 | 0.6413 | | 0.0337 | 60.84 | 17400 | 0.1305 | 0.3874 | 0.5888 | 0.5772 | nan | 0.5218 | 0.6557 | 0.0 | 0.5094 | 0.6527 | | 0.0516 | 60.91 | 17420 | 0.1287 | 0.3849 | 0.5845 | 0.5703 | nan | 0.5027 | 0.6664 | 0.0 | 0.4920 | 0.6626 | | 0.0677 | 60.98 | 17440 | 0.1273 | 0.3905 | 0.5935 | 0.5816 | nan | 0.5246 | 0.6625 | 0.0 | 0.5132 | 0.6581 | | 0.0589 | 61.05 | 17460 | 0.1267 | 0.4085 | 0.6220 | 0.6139 | nan | 0.5753 | 0.6688 | 0.0 | 0.5611 | 0.6645 | | 0.0455 | 61.12 | 17480 | 0.1294 | 0.3895 | 0.5923 | 0.5833 | nan | 0.5402 | 0.6445 | 0.0 | 0.5281 | 0.6405 | | 0.0516 | 61.19 | 17500 | 0.1234 | 0.3983 | 0.6054 | 0.5924 | nan | 0.5302 | 0.6805 | 0.0 | 0.5201 | 0.6747 | | 0.0328 | 61.26 | 17520 | 0.1268 | 0.4004 | 0.6089 | 0.5993 | nan | 0.5533 | 0.6645 | 0.0 | 0.5411 | 0.6602 | | 0.0484 | 61.33 | 17540 | 0.1286 | 0.4047 | 0.6160 | 0.6080 | nan | 0.5700 | 0.6620 | 0.0 | 0.5562 | 0.6577 | | 0.0405 | 61.4 | 17560 | 0.1216 | 0.3997 | 0.6072 | 0.5920 | nan | 0.5195 | 0.6950 | 0.0 | 0.5097 | 0.6893 | | 0.0542 | 61.47 | 17580 | 0.1232 | 0.4060 | 0.6175 | 0.6075 | nan | 0.5597 | 0.6754 | 0.0 | 0.5471 | 0.6709 | | 0.0472 | 61.54 | 17600 | 0.1268 | 0.4030 | 0.6148 | 0.6108 | nan | 0.5919 | 0.6376 | 0.0 | 0.5747 | 0.6344 | | 0.0415 | 61.61 | 17620 | 0.1235 | 0.4038 | 0.6149 | 0.6057 | nan | 0.5614 | 0.6685 | 0.0 | 0.5495 | 0.6621 | | 0.042 | 61.68 | 17640 | 0.1290 | 0.3923 | 0.5977 | 0.5907 | nan | 0.5572 | 0.6381 | 0.0 | 0.5423 | 0.6347 | | 0.031 | 61.75 | 17660 | 0.1262 | 0.4077 | 0.6213 | 0.6120 | nan | 0.5676 | 0.6750 | 0.0 | 0.5527 | 0.6704 | | 0.0493 | 61.82 | 17680 | 0.1200 | 0.4099 | 0.6230 | 0.6144 | nan | 0.5737 | 0.6722 | 0.0 | 0.5626 | 0.6672 | | 0.0564 | 61.89 | 17700 | 0.1184 | 0.4237 | 0.6440 | 0.6321 | nan | 0.5750 | 0.7131 | 0.0 | 0.5643 | 0.7068 | | 0.0418 | 61.96 | 17720 | 0.1224 | 0.4060 | 0.6166 | 0.6036 | nan | 0.5419 | 0.6913 | 0.0 | 0.5319 | 0.6860 | | 0.0349 | 62.03 | 17740 | 0.1203 | 0.4182 | 0.6357 | 0.6255 | nan | 0.5766 | 0.6949 | 0.0 | 0.5652 | 0.6896 | | 0.0387 | 62.1 | 17760 | 0.1227 | 0.4034 | 0.6128 | 0.5994 | nan | 0.5355 | 0.6902 | 0.0 | 0.5258 | 0.6844 | | 0.0334 | 62.17 | 17780 | 0.1229 | 0.4040 | 0.6141 | 0.6007 | nan | 0.5367 | 0.6914 | 0.0 | 0.5253 | 0.6866 | | 0.0421 | 62.24 | 17800 | 0.1211 | 0.4114 | 0.6249 | 0.6135 | nan | 0.5590 | 0.6908 | 0.0 | 0.5481 | 0.6861 | | 0.0383 | 62.31 | 17820 | 0.1168 | 0.4243 | 0.6450 | 0.6336 | nan | 0.5793 | 0.7107 | 0.0 | 0.5694 | 0.7035 | | 0.0577 | 62.38 | 17840 | 0.1226 | 0.4050 | 0.6157 | 0.6072 | nan | 0.5667 | 0.6647 | 0.0 | 0.5547 | 0.6604 | | 0.0474 | 62.45 | 17860 | 0.1269 | 0.3946 | 0.6000 | 0.5905 | nan | 0.5451 | 0.6550 | 0.0 | 0.5333 | 0.6505 | | 0.0459 | 62.52 | 17880 | 0.1271 | 0.3987 | 0.6066 | 0.5947 | nan | 0.5377 | 0.6754 | 0.0 | 0.5248 | 0.6714 | | 0.0696 | 62.59 | 17900 | 0.1212 | 0.4024 | 0.6118 | 0.5958 | nan | 0.5197 | 0.7038 | 0.0 | 0.5105 | 0.6968 | | 0.0564 | 62.66 | 17920 | 0.1246 | 0.4043 | 0.6144 | 0.6055 | nan | 0.5631 | 0.6658 | 0.0 | 0.5512 | 0.6617 | | 0.0413 | 62.73 | 17940 | 0.1368 | 0.4038 | 0.6159 | 0.6125 | nan | 0.5965 | 0.6353 | 0.0 | 0.5789 | 0.6323 | | 0.0557 | 62.8 | 17960 | 0.1336 | 0.3887 | 0.5913 | 0.5831 | nan | 0.5439 | 0.6387 | 0.0 | 0.5303 | 0.6359 | | 0.0745 | 62.87 | 17980 | 0.1350 | 0.3845 | 0.5850 | 0.5792 | nan | 0.5514 | 0.6186 | 0.0 | 0.5374 | 0.6162 | | 0.0373 | 62.94 | 18000 | 0.1362 | 0.3806 | 0.5785 | 0.5687 | nan | 0.5222 | 0.6347 | 0.0 | 0.5097 | 0.6320 | | 0.0444 | 63.01 | 18020 | 0.1370 | 0.3900 | 0.5937 | 0.5868 | nan | 0.5541 | 0.6332 | 0.0 | 0.5395 | 0.6305 | | 0.0376 | 63.08 | 18040 | 0.1276 | 0.4144 | 0.6307 | 0.6230 | nan | 0.5866 | 0.6747 | 0.0 | 0.5725 | 0.6707 | | 0.068 | 63.15 | 18060 | 0.1358 | 0.3986 | 0.6070 | 0.6023 | nan | 0.5796 | 0.6344 | 0.0 | 0.5627 | 0.6330 | | 0.0352 | 63.22 | 18080 | 0.1278 | 0.4128 | 0.6295 | 0.6233 | nan | 0.5940 | 0.6650 | 0.0 | 0.5750 | 0.6635 | | 0.0455 | 63.29 | 18100 | 0.1282 | 0.3923 | 0.5974 | 0.5870 | nan | 0.5376 | 0.6572 | 0.0 | 0.5233 | 0.6536 | | 0.0699 | 63.36 | 18120 | 0.1322 | 0.3878 | 0.5909 | 0.5837 | nan | 0.5496 | 0.6322 | 0.0 | 0.5337 | 0.6298 | | 0.0577 | 63.43 | 18140 | 0.1246 | 0.4096 | 0.6243 | 0.6175 | nan | 0.5852 | 0.6633 | 0.0 | 0.5698 | 0.6590 | | 0.066 | 63.5 | 18160 | 0.1188 | 0.4148 | 0.6319 | 0.6209 | nan | 0.5681 | 0.6958 | 0.0 | 0.5564 | 0.6880 | | 0.0381 | 63.57 | 18180 | 0.1238 | 0.4058 | 0.6170 | 0.6069 | nan | 0.5585 | 0.6756 | 0.0 | 0.5475 | 0.6699 | | 0.0732 | 63.64 | 18200 | 0.1311 | 0.3962 | 0.6030 | 0.5948 | nan | 0.5554 | 0.6506 | 0.0 | 0.5419 | 0.6468 | | 0.0627 | 63.71 | 18220 | 0.1355 | 0.3880 | 0.5907 | 0.5824 | nan | 0.5425 | 0.6390 | 0.0 | 0.5283 | 0.6357 | | 0.0399 | 63.78 | 18240 | 0.1307 | 0.4002 | 0.6085 | 0.5968 | nan | 0.5411 | 0.6758 | 0.0 | 0.5278 | 0.6728 | | 0.0506 | 63.85 | 18260 | 0.1281 | 0.3963 | 0.6028 | 0.5911 | nan | 0.5350 | 0.6705 | 0.0 | 0.5267 | 0.6622 | | 0.0413 | 63.92 | 18280 | 0.1309 | 0.4001 | 0.6094 | 0.6023 | nan | 0.5686 | 0.6502 | 0.0 | 0.5539 | 0.6465 | | 0.0483 | 63.99 | 18300 | 0.1214 | 0.4190 | 0.6381 | 0.6297 | nan | 0.5895 | 0.6866 | 0.0 | 0.5771 | 0.6799 | | 0.0598 | 64.06 | 18320 | 0.1249 | 0.4109 | 0.6258 | 0.6185 | nan | 0.5835 | 0.6681 | 0.0 | 0.5695 | 0.6632 | | 0.0399 | 64.13 | 18340 | 0.1217 | 0.4119 | 0.6268 | 0.6207 | nan | 0.5915 | 0.6620 | 0.0 | 0.5765 | 0.6591 | | 0.0371 | 64.2 | 18360 | 0.1292 | 0.4058 | 0.6177 | 0.6107 | nan | 0.5772 | 0.6581 | 0.0 | 0.5611 | 0.6564 | | 0.0652 | 64.27 | 18380 | 0.1275 | 0.4013 | 0.6101 | 0.6035 | nan | 0.5717 | 0.6486 | 0.0 | 0.5585 | 0.6455 | | 0.0572 | 64.34 | 18400 | 0.1243 | 0.4078 | 0.6194 | 0.6039 | nan | 0.5299 | 0.7089 | 0.0 | 0.5188 | 0.7047 | | 0.0716 | 64.41 | 18420 | 0.1268 | 0.4040 | 0.6149 | 0.6076 | nan | 0.5725 | 0.6573 | 0.0 | 0.5584 | 0.6537 | | 0.0573 | 64.48 | 18440 | 0.1212 | 0.4150 | 0.6319 | 0.6215 | nan | 0.5716 | 0.6922 | 0.0 | 0.5600 | 0.6852 | | 0.0388 | 64.55 | 18460 | 0.1242 | 0.4099 | 0.6236 | 0.6129 | nan | 0.5616 | 0.6856 | 0.0 | 0.5489 | 0.6809 | | 0.0617 | 64.62 | 18480 | 0.1284 | 0.3819 | 0.5800 | 0.5706 | nan | 0.5259 | 0.6341 | 0.0 | 0.5151 | 0.6306 | | 0.037 | 64.69 | 18500 | 0.1310 | 0.3940 | 0.5983 | 0.5847 | nan | 0.5200 | 0.6766 | 0.0 | 0.5096 | 0.6723 | | 0.0548 | 64.76 | 18520 | 0.1238 | 0.4171 | 0.6353 | 0.6274 | nan | 0.5898 | 0.6808 | 0.0 | 0.5743 | 0.6771 | | 0.0642 | 64.83 | 18540 | 0.1183 | 0.4147 | 0.6303 | 0.6164 | nan | 0.5497 | 0.7110 | 0.0 | 0.5410 | 0.7030 | | 0.0558 | 64.9 | 18560 | 0.1317 | 0.3938 | 0.5993 | 0.5891 | nan | 0.5400 | 0.6587 | 0.0 | 0.5263 | 0.6552 | | 0.0822 | 64.97 | 18580 | 0.1352 | 0.3894 | 0.5926 | 0.5839 | nan | 0.5420 | 0.6433 | 0.0 | 0.5283 | 0.6400 | | 0.059 | 65.03 | 18600 | 0.1276 | 0.4014 | 0.6098 | 0.5984 | nan | 0.5437 | 0.6760 | 0.0 | 0.5334 | 0.6707 | | 0.0363 | 65.1 | 18620 | 0.1325 | 0.3981 | 0.6061 | 0.5961 | nan | 0.5484 | 0.6638 | 0.0 | 0.5345 | 0.6599 | | 0.0669 | 65.17 | 18640 | 0.1320 | 0.3849 | 0.5862 | 0.5728 | nan | 0.5090 | 0.6633 | 0.0 | 0.4985 | 0.6563 | | 0.0681 | 65.24 | 18660 | 0.1285 | 0.4108 | 0.6258 | 0.6158 | nan | 0.5684 | 0.6832 | 0.0 | 0.5544 | 0.6782 | | 0.0343 | 65.31 | 18680 | 0.1320 | 0.4098 | 0.6251 | 0.6181 | nan | 0.5849 | 0.6653 | 0.0 | 0.5677 | 0.6617 | | 0.0558 | 65.38 | 18700 | 0.1281 | 0.4002 | 0.6084 | 0.5984 | nan | 0.5507 | 0.6662 | 0.0 | 0.5397 | 0.6608 | | 0.05 | 65.45 | 18720 | 0.1310 | 0.3947 | 0.6001 | 0.5909 | nan | 0.5472 | 0.6530 | 0.0 | 0.5355 | 0.6484 | | 0.0404 | 65.52 | 18740 | 0.1316 | 0.3999 | 0.6088 | 0.5994 | nan | 0.5547 | 0.6629 | 0.0 | 0.5407 | 0.6589 | | 0.0355 | 65.59 | 18760 | 0.1285 | 0.3993 | 0.6068 | 0.5985 | nan | 0.5586 | 0.6551 | 0.0 | 0.5472 | 0.6508 | | 0.0455 | 65.66 | 18780 | 0.1306 | 0.4073 | 0.6203 | 0.6140 | nan | 0.5839 | 0.6568 | 0.0 | 0.5681 | 0.6538 | | 0.059 | 65.73 | 18800 | 0.1328 | 0.3955 | 0.6013 | 0.5927 | nan | 0.5517 | 0.6509 | 0.0 | 0.5396 | 0.6467 | | 0.0586 | 65.8 | 18820 | 0.1332 | 0.3901 | 0.5932 | 0.5812 | nan | 0.5243 | 0.6620 | 0.0 | 0.5122 | 0.6581 | | 0.0623 | 65.87 | 18840 | 0.1326 | 0.3927 | 0.5977 | 0.5905 | nan | 0.5560 | 0.6394 | 0.0 | 0.5426 | 0.6355 | | 0.0575 | 65.94 | 18860 | 0.1245 | 0.4123 | 0.6266 | 0.6143 | nan | 0.5557 | 0.6974 | 0.0 | 0.5453 | 0.6917 | | 0.0393 | 66.01 | 18880 | 0.1289 | 0.4026 | 0.6127 | 0.6047 | nan | 0.5665 | 0.6589 | 0.0 | 0.5530 | 0.6548 | | 0.0639 | 66.08 | 18900 | 0.1443 | 0.3850 | 0.5862 | 0.5813 | nan | 0.5577 | 0.6146 | 0.0 | 0.5435 | 0.6116 | | 0.0405 | 66.15 | 18920 | 0.1353 | 0.3964 | 0.6044 | 0.5979 | nan | 0.5670 | 0.6418 | 0.0 | 0.5509 | 0.6384 | | 0.0587 | 66.22 | 18940 | 0.1373 | 0.3952 | 0.6023 | 0.5987 | nan | 0.5816 | 0.6230 | 0.0 | 0.5658 | 0.6199 | | 0.0508 | 66.29 | 18960 | 0.1358 | 0.4007 | 0.6107 | 0.6070 | nan | 0.5892 | 0.6322 | 0.0 | 0.5729 | 0.6293 | | 0.0443 | 66.36 | 18980 | 0.1239 | 0.4181 | 0.6367 | 0.6293 | nan | 0.5942 | 0.6791 | 0.0 | 0.5796 | 0.6749 | | 0.0382 | 66.43 | 19000 | 0.1318 | 0.3970 | 0.6042 | 0.5949 | nan | 0.5508 | 0.6576 | 0.0 | 0.5367 | 0.6542 | | 0.0562 | 66.5 | 19020 | 0.1377 | 0.3838 | 0.5840 | 0.5747 | nan | 0.5303 | 0.6376 | 0.0 | 0.5168 | 0.6347 | | 0.0402 | 66.57 | 19040 | 0.1338 | 0.4016 | 0.6112 | 0.6046 | nan | 0.5732 | 0.6493 | 0.0 | 0.5587 | 0.6460 | | 0.0356 | 66.64 | 19060 | 0.1337 | 0.3910 | 0.5943 | 0.5852 | nan | 0.5415 | 0.6471 | 0.0 | 0.5294 | 0.6435 | | 0.0493 | 66.71 | 19080 | 0.1313 | 0.3951 | 0.6017 | 0.5954 | nan | 0.5657 | 0.6377 | 0.0 | 0.5508 | 0.6344 | | 0.0472 | 66.78 | 19100 | 0.1316 | 0.3926 | 0.5972 | 0.5864 | nan | 0.5348 | 0.6596 | 0.0 | 0.5220 | 0.6559 | | 0.0391 | 66.85 | 19120 | 0.1312 | 0.3978 | 0.6059 | 0.5992 | nan | 0.5672 | 0.6445 | 0.0 | 0.5524 | 0.6411 | | 0.0429 | 66.92 | 19140 | 0.1260 | 0.4015 | 0.6105 | 0.6002 | nan | 0.5507 | 0.6704 | 0.0 | 0.5402 | 0.6643 | | 0.0674 | 66.99 | 19160 | 0.1308 | 0.3944 | 0.5997 | 0.5900 | nan | 0.5439 | 0.6555 | 0.0 | 0.5327 | 0.6506 | | 0.038 | 67.06 | 19180 | 0.1310 | 0.3913 | 0.5949 | 0.5835 | nan | 0.5292 | 0.6606 | 0.0 | 0.5201 | 0.6537 | | 0.0428 | 67.13 | 19200 | 0.1404 | 0.3871 | 0.5890 | 0.5823 | nan | 0.5499 | 0.6282 | 0.0 | 0.5373 | 0.6239 | | 0.0307 | 67.2 | 19220 | 0.1370 | 0.3985 | 0.6068 | 0.6002 | nan | 0.5688 | 0.6447 | 0.0 | 0.5542 | 0.6414 | | 0.0586 | 67.27 | 19240 | 0.1421 | 0.3854 | 0.5874 | 0.5832 | nan | 0.5633 | 0.6115 | 0.0 | 0.5477 | 0.6085 | | 0.0524 | 67.34 | 19260 | 0.1355 | 0.4031 | 0.6141 | 0.6104 | nan | 0.5928 | 0.6355 | 0.0 | 0.5778 | 0.6315 | | 0.0399 | 67.41 | 19280 | 0.1307 | 0.4049 | 0.6163 | 0.6093 | nan | 0.5760 | 0.6566 | 0.0 | 0.5622 | 0.6526 | | 0.0532 | 67.48 | 19300 | 0.1304 | 0.4009 | 0.6095 | 0.5992 | nan | 0.5496 | 0.6694 | 0.0 | 0.5375 | 0.6654 | | 0.049 | 67.55 | 19320 | 0.1303 | 0.4013 | 0.6109 | 0.6023 | nan | 0.5613 | 0.6605 | 0.0 | 0.5469 | 0.6569 | | 0.0606 | 67.62 | 19340 | 0.1310 | 0.3943 | 0.5998 | 0.5910 | nan | 0.5490 | 0.6507 | 0.0 | 0.5363 | 0.6467 | | 0.052 | 67.69 | 19360 | 0.1347 | 0.3890 | 0.5913 | 0.5814 | nan | 0.5338 | 0.6488 | 0.0 | 0.5222 | 0.6450 | | 0.0269 | 67.76 | 19380 | 0.1327 | 0.4041 | 0.6151 | 0.6057 | nan | 0.5607 | 0.6694 | 0.0 | 0.5467 | 0.6655 | | 0.0532 | 67.83 | 19400 | 0.1305 | 0.4113 | 0.6260 | 0.6169 | nan | 0.5736 | 0.6783 | 0.0 | 0.5595 | 0.6744 | | 0.0577 | 67.9 | 19420 | 0.1384 | 0.3826 | 0.5817 | 0.5715 | nan | 0.5228 | 0.6406 | 0.0 | 0.5101 | 0.6378 | | 0.0584 | 67.97 | 19440 | 0.1350 | 0.3954 | 0.6020 | 0.5930 | nan | 0.5498 | 0.6542 | 0.0 | 0.5355 | 0.6507 | | 0.0514 | 68.04 | 19460 | 0.1307 | 0.4104 | 0.6252 | 0.6189 | nan | 0.5889 | 0.6614 | 0.0 | 0.5742 | 0.6570 | | 0.0626 | 68.11 | 19480 | 0.1332 | 0.3916 | 0.5960 | 0.5881 | nan | 0.5504 | 0.6417 | 0.0 | 0.5372 | 0.6376 | | 0.0406 | 68.18 | 19500 | 0.1278 | 0.4086 | 0.6216 | 0.6118 | nan | 0.5645 | 0.6788 | 0.0 | 0.5528 | 0.6732 | | 0.0402 | 68.25 | 19520 | 0.1327 | 0.4012 | 0.6108 | 0.6031 | nan | 0.5660 | 0.6556 | 0.0 | 0.5522 | 0.6514 | | 0.037 | 68.32 | 19540 | 0.1390 | 0.3915 | 0.5958 | 0.5871 | nan | 0.5455 | 0.6462 | 0.0 | 0.5309 | 0.6435 | | 0.0443 | 68.39 | 19560 | 0.1389 | 0.3892 | 0.5924 | 0.5844 | nan | 0.5466 | 0.6382 | 0.0 | 0.5323 | 0.6353 | | 0.0505 | 68.46 | 19580 | 0.1295 | 0.3994 | 0.6069 | 0.5951 | nan | 0.5386 | 0.6753 | 0.0 | 0.5275 | 0.6707 | | 0.0406 | 68.53 | 19600 | 0.1301 | 0.3996 | 0.6071 | 0.5958 | nan | 0.5420 | 0.6721 | 0.0 | 0.5322 | 0.6666 | | 0.0547 | 68.6 | 19620 | 0.1296 | 0.4008 | 0.6091 | 0.6004 | nan | 0.5588 | 0.6594 | 0.0 | 0.5469 | 0.6555 | | 0.0524 | 68.67 | 19640 | 0.1277 | 0.4142 | 0.6303 | 0.6233 | nan | 0.5899 | 0.6707 | 0.0 | 0.5755 | 0.6670 | | 0.0577 | 68.74 | 19660 | 0.1322 | 0.3975 | 0.6050 | 0.6001 | nan | 0.5768 | 0.6332 | 0.0 | 0.5632 | 0.6294 | | 0.0336 | 68.81 | 19680 | 0.1286 | 0.4058 | 0.6168 | 0.6064 | nan | 0.5563 | 0.6774 | 0.0 | 0.5434 | 0.6740 | | 0.0304 | 68.88 | 19700 | 0.1258 | 0.4094 | 0.6223 | 0.6137 | nan | 0.5724 | 0.6723 | 0.0 | 0.5604 | 0.6678 | | 0.0383 | 68.95 | 19720 | 0.1304 | 0.4000 | 0.6087 | 0.6014 | nan | 0.5664 | 0.6511 | 0.0 | 0.5523 | 0.6477 | | 0.0391 | 69.02 | 19740 | 0.1283 | 0.4009 | 0.6101 | 0.6011 | nan | 0.5582 | 0.6621 | 0.0 | 0.5444 | 0.6583 | | 0.0466 | 69.09 | 19760 | 0.1284 | 0.4000 | 0.6081 | 0.5982 | nan | 0.5507 | 0.6656 | 0.0 | 0.5387 | 0.6615 | | 0.0631 | 69.16 | 19780 | 0.1464 | 0.3725 | 0.5666 | 0.5612 | nan | 0.5353 | 0.5979 | 0.0 | 0.5221 | 0.5954 | | 0.0438 | 69.23 | 19800 | 0.1353 | 0.3925 | 0.5967 | 0.5864 | nan | 0.5371 | 0.6564 | 0.0 | 0.5252 | 0.6524 | | 0.0327 | 69.3 | 19820 | 0.1283 | 0.4058 | 0.6177 | 0.6077 | nan | 0.5602 | 0.6752 | 0.0 | 0.5480 | 0.6693 | | 0.0555 | 69.37 | 19840 | 0.1258 | 0.4101 | 0.6244 | 0.6165 | nan | 0.5787 | 0.6702 | 0.0 | 0.5643 | 0.6661 | | 0.0504 | 69.44 | 19860 | 0.1280 | 0.4038 | 0.6148 | 0.6077 | nan | 0.5739 | 0.6557 | 0.0 | 0.5598 | 0.6515 | | 0.037 | 69.51 | 19880 | 0.1316 | 0.4001 | 0.6089 | 0.6006 | nan | 0.5610 | 0.6567 | 0.0 | 0.5472 | 0.6530 | | 0.0705 | 69.58 | 19900 | 0.1313 | 0.4062 | 0.6179 | 0.6071 | nan | 0.5552 | 0.6807 | 0.0 | 0.5415 | 0.6772 | | 0.0336 | 69.65 | 19920 | 0.1310 | 0.4024 | 0.6112 | 0.6003 | nan | 0.5479 | 0.6746 | 0.0 | 0.5369 | 0.6702 | | 0.058 | 69.72 | 19940 | 0.1318 | 0.4051 | 0.6159 | 0.6062 | nan | 0.5598 | 0.6721 | 0.0 | 0.5469 | 0.6685 | | 0.0476 | 69.79 | 19960 | 0.1279 | 0.4017 | 0.6107 | 0.6001 | nan | 0.5493 | 0.6720 | 0.0 | 0.5371 | 0.6679 | | 0.0533 | 69.86 | 19980 | 0.1345 | 0.3896 | 0.5923 | 0.5803 | nan | 0.5227 | 0.6619 | 0.0 | 0.5102 | 0.6585 | | 0.0515 | 69.93 | 20000 | 0.1283 | 0.3967 | 0.6035 | 0.5931 | nan | 0.5437 | 0.6632 | 0.0 | 0.5305 | 0.6596 | | 0.0469 | 70.0 | 20020 | 0.1314 | 0.4017 | 0.6114 | 0.6025 | nan | 0.5597 | 0.6631 | 0.0 | 0.5455 | 0.6597 | | 0.0507 | 70.07 | 20040 | 0.1325 | 0.3937 | 0.5987 | 0.5901 | nan | 0.5487 | 0.6488 | 0.0 | 0.5356 | 0.6456 | | 0.0592 | 70.14 | 20060 | 0.1292 | 0.4006 | 0.6082 | 0.5954 | nan | 0.5343 | 0.6822 | 0.0 | 0.5223 | 0.6794 | | 0.0406 | 70.21 | 20080 | 0.1273 | 0.4125 | 0.6271 | 0.6189 | nan | 0.5794 | 0.6748 | 0.0 | 0.5663 | 0.6713 | | 0.087 | 70.28 | 20100 | 0.1337 | 0.3938 | 0.5985 | 0.5872 | nan | 0.5333 | 0.6638 | 0.0 | 0.5214 | 0.6601 | | 0.0517 | 70.35 | 20120 | 0.1344 | 0.3940 | 0.5998 | 0.5927 | nan | 0.5590 | 0.6405 | 0.0 | 0.5444 | 0.6377 | | 0.0367 | 70.42 | 20140 | 0.1299 | 0.4078 | 0.6209 | 0.6131 | nan | 0.5757 | 0.6662 | 0.0 | 0.5608 | 0.6627 | | 0.0399 | 70.49 | 20160 | 0.1349 | 0.4007 | 0.6090 | 0.6000 | nan | 0.5571 | 0.6608 | 0.0 | 0.5447 | 0.6573 | | 0.0467 | 70.56 | 20180 | 0.1321 | 0.4002 | 0.6083 | 0.5996 | nan | 0.5582 | 0.6584 | 0.0 | 0.5451 | 0.6555 | | 0.062 | 70.63 | 20200 | 0.1352 | 0.3914 | 0.5957 | 0.5867 | nan | 0.5438 | 0.6477 | 0.0 | 0.5288 | 0.6453 | | 0.0731 | 70.7 | 20220 | 0.1338 | 0.3886 | 0.5906 | 0.5810 | nan | 0.5348 | 0.6464 | 0.0 | 0.5229 | 0.6429 | | 0.0468 | 70.77 | 20240 | 0.1322 | 0.3952 | 0.6006 | 0.5862 | nan | 0.5174 | 0.6839 | 0.0 | 0.5075 | 0.6781 | | 0.0408 | 70.84 | 20260 | 0.1342 | 0.3924 | 0.5970 | 0.5840 | nan | 0.5221 | 0.6718 | 0.0 | 0.5104 | 0.6669 | | 0.0619 | 70.91 | 20280 | 0.1265 | 0.4080 | 0.6202 | 0.6109 | nan | 0.5664 | 0.6740 | 0.0 | 0.5543 | 0.6696 | | 0.0495 | 70.98 | 20300 | 0.1316 | 0.3940 | 0.5985 | 0.5875 | nan | 0.5353 | 0.6617 | 0.0 | 0.5239 | 0.6582 | | 0.0412 | 71.05 | 20320 | 0.1300 | 0.4106 | 0.6241 | 0.6137 | nan | 0.5640 | 0.6841 | 0.0 | 0.5523 | 0.6795 | | 0.0369 | 71.12 | 20340 | 0.1322 | 0.3999 | 0.6072 | 0.5963 | nan | 0.5444 | 0.6699 | 0.0 | 0.5344 | 0.6654 | | 0.0461 | 71.19 | 20360 | 0.1339 | 0.3798 | 0.5763 | 0.5636 | nan | 0.5030 | 0.6496 | 0.0 | 0.4933 | 0.6460 | | 0.0468 | 71.26 | 20380 | 0.1298 | 0.3899 | 0.5924 | 0.5817 | nan | 0.5304 | 0.6544 | 0.0 | 0.5200 | 0.6498 | | 0.038 | 71.33 | 20400 | 0.1381 | 0.3826 | 0.5811 | 0.5718 | nan | 0.5272 | 0.6350 | 0.0 | 0.5165 | 0.6314 | | 0.045 | 71.4 | 20420 | 0.1348 | 0.3822 | 0.5799 | 0.5682 | nan | 0.5126 | 0.6472 | 0.0 | 0.5047 | 0.6419 | | 0.0574 | 71.47 | 20440 | 0.1331 | 0.3852 | 0.5847 | 0.5735 | nan | 0.5203 | 0.6490 | 0.0 | 0.5122 | 0.6435 | | 0.0433 | 71.54 | 20460 | 0.1299 | 0.3994 | 0.6066 | 0.5968 | nan | 0.5501 | 0.6632 | 0.0 | 0.5389 | 0.6594 | | 0.0638 | 71.61 | 20480 | 0.1334 | 0.4066 | 0.6189 | 0.6152 | nan | 0.5974 | 0.6404 | 0.0 | 0.5825 | 0.6374 | | 0.0322 | 71.68 | 20500 | 0.1359 | 0.4104 | 0.6236 | 0.6161 | nan | 0.5802 | 0.6671 | 0.0 | 0.5681 | 0.6630 | | 0.0327 | 71.75 | 20520 | 0.1412 | 0.3886 | 0.5915 | 0.5878 | nan | 0.5701 | 0.6128 | 0.0 | 0.5553 | 0.6107 | | 0.0499 | 71.82 | 20540 | 0.1322 | 0.4036 | 0.6132 | 0.6016 | nan | 0.5461 | 0.6803 | 0.0 | 0.5350 | 0.6758 | | 0.0697 | 71.89 | 20560 | 0.1274 | 0.3984 | 0.6057 | 0.5919 | nan | 0.5258 | 0.6856 | 0.0 | 0.5163 | 0.6790 | | 0.0682 | 71.96 | 20580 | 0.1347 | 0.3877 | 0.5891 | 0.5782 | nan | 0.5260 | 0.6523 | 0.0 | 0.5150 | 0.6482 | | 0.0541 | 72.03 | 20600 | 0.1352 | 0.3900 | 0.5928 | 0.5837 | nan | 0.5406 | 0.6450 | 0.0 | 0.5273 | 0.6428 | | 0.054 | 72.1 | 20620 | 0.1345 | 0.3975 | 0.6047 | 0.6001 | nan | 0.5781 | 0.6313 | 0.0 | 0.5634 | 0.6291 | | 0.0468 | 72.17 | 20640 | 0.1301 | 0.3954 | 0.5999 | 0.5881 | nan | 0.5318 | 0.6681 | 0.0 | 0.5218 | 0.6645 | | 0.0502 | 72.24 | 20660 | 0.1278 | 0.3941 | 0.5984 | 0.5897 | nan | 0.5481 | 0.6487 | 0.0 | 0.5370 | 0.6453 | | 0.0407 | 72.31 | 20680 | 0.1241 | 0.4099 | 0.6230 | 0.6107 | nan | 0.5517 | 0.6943 | 0.0 | 0.5439 | 0.6859 | | 0.0268 | 72.38 | 20700 | 0.1253 | 0.4153 | 0.6313 | 0.6227 | nan | 0.5819 | 0.6807 | 0.0 | 0.5728 | 0.6731 | | 0.0513 | 72.45 | 20720 | 0.1253 | 0.4097 | 0.6230 | 0.6144 | nan | 0.5730 | 0.6731 | 0.0 | 0.5614 | 0.6678 | | 0.0414 | 72.52 | 20740 | 0.1278 | 0.4098 | 0.6230 | 0.6163 | nan | 0.5842 | 0.6618 | 0.0 | 0.5729 | 0.6565 | | 0.0338 | 72.59 | 20760 | 0.1303 | 0.4045 | 0.6148 | 0.6065 | nan | 0.5664 | 0.6633 | 0.0 | 0.5542 | 0.6593 | | 0.0341 | 72.66 | 20780 | 0.1319 | 0.4048 | 0.6156 | 0.6073 | nan | 0.5678 | 0.6634 | 0.0 | 0.5543 | 0.6600 | | 0.0833 | 72.73 | 20800 | 0.1276 | 0.4023 | 0.6111 | 0.5985 | nan | 0.5384 | 0.6837 | 0.0 | 0.5289 | 0.6782 | | 0.0703 | 72.8 | 20820 | 0.1229 | 0.4153 | 0.6308 | 0.6173 | nan | 0.5530 | 0.7087 | 0.0 | 0.5436 | 0.7022 | | 0.0637 | 72.87 | 20840 | 0.1359 | 0.3952 | 0.6011 | 0.5966 | nan | 0.5747 | 0.6275 | 0.0 | 0.5611 | 0.6244 | | 0.0407 | 72.94 | 20860 | 0.1295 | 0.4040 | 0.6140 | 0.6052 | nan | 0.5631 | 0.6648 | 0.0 | 0.5515 | 0.6605 | | 0.0472 | 73.01 | 20880 | 0.1296 | 0.4063 | 0.6169 | 0.6073 | nan | 0.5611 | 0.6727 | 0.0 | 0.5507 | 0.6683 | | 0.0446 | 73.08 | 20900 | 0.1275 | 0.4123 | 0.6269 | 0.6161 | nan | 0.5644 | 0.6895 | 0.0 | 0.5512 | 0.6857 | | 0.0425 | 73.15 | 20920 | 0.1234 | 0.4294 | 0.6535 | 0.6466 | nan | 0.6136 | 0.6933 | 0.0 | 0.6005 | 0.6878 | | 0.062 | 73.22 | 20940 | 0.1307 | 0.4085 | 0.6212 | 0.6162 | nan | 0.5923 | 0.6500 | 0.0 | 0.5788 | 0.6467 | | 0.063 | 73.29 | 20960 | 0.1336 | 0.3908 | 0.5938 | 0.5852 | nan | 0.5444 | 0.6432 | 0.0 | 0.5324 | 0.6401 | | 0.0607 | 73.36 | 20980 | 0.1282 | 0.4006 | 0.6093 | 0.5981 | nan | 0.5447 | 0.6739 | 0.0 | 0.5339 | 0.6677 | | 0.0634 | 73.43 | 21000 | 0.1284 | 0.4121 | 0.6270 | 0.6207 | nan | 0.5908 | 0.6632 | 0.0 | 0.5772 | 0.6590 | | 0.0321 | 73.5 | 21020 | 0.1250 | 0.4242 | 0.6450 | 0.6333 | nan | 0.5776 | 0.7123 | 0.0 | 0.5653 | 0.7074 | | 0.0469 | 73.57 | 21040 | 0.1280 | 0.4010 | 0.6100 | 0.5963 | nan | 0.5309 | 0.6891 | 0.0 | 0.5224 | 0.6807 | | 0.0363 | 73.64 | 21060 | 0.1423 | 0.3809 | 0.5795 | 0.5729 | nan | 0.5415 | 0.6175 | 0.0 | 0.5279 | 0.6146 | | 0.0618 | 73.71 | 21080 | 0.1352 | 0.4023 | 0.6124 | 0.6070 | nan | 0.5809 | 0.6439 | 0.0 | 0.5661 | 0.6409 | | 0.0451 | 73.78 | 21100 | 0.1339 | 0.3911 | 0.5947 | 0.5847 | nan | 0.5369 | 0.6525 | 0.0 | 0.5236 | 0.6497 | | 0.052 | 73.85 | 21120 | 0.1332 | 0.3994 | 0.6072 | 0.5983 | nan | 0.5558 | 0.6587 | 0.0 | 0.5430 | 0.6551 | | 0.0609 | 73.92 | 21140 | 0.1358 | 0.3974 | 0.6042 | 0.5963 | nan | 0.5584 | 0.6501 | 0.0 | 0.5452 | 0.6469 | | 0.0317 | 73.99 | 21160 | 0.1330 | 0.4011 | 0.6102 | 0.6024 | nan | 0.5649 | 0.6556 | 0.0 | 0.5512 | 0.6520 | | 0.045 | 74.06 | 21180 | 0.1327 | 0.4006 | 0.6110 | 0.6044 | nan | 0.5730 | 0.6491 | 0.0 | 0.5557 | 0.6461 | | 0.0502 | 74.13 | 21200 | 0.1305 | 0.4103 | 0.6253 | 0.6185 | nan | 0.5859 | 0.6647 | 0.0 | 0.5698 | 0.6610 | | 0.0479 | 74.2 | 21220 | 0.1312 | 0.4024 | 0.6115 | 0.6013 | nan | 0.5524 | 0.6706 | 0.0 | 0.5422 | 0.6649 | | 0.0429 | 74.27 | 21240 | 0.1316 | 0.4032 | 0.6130 | 0.6013 | nan | 0.5457 | 0.6802 | 0.0 | 0.5356 | 0.6740 | | 0.0228 | 74.34 | 21260 | 0.1289 | 0.4093 | 0.6223 | 0.6130 | nan | 0.5686 | 0.6760 | 0.0 | 0.5559 | 0.6721 | | 0.046 | 74.41 | 21280 | 0.1306 | 0.4117 | 0.6260 | 0.6165 | nan | 0.5710 | 0.6810 | 0.0 | 0.5587 | 0.6765 | | 0.0512 | 74.48 | 21300 | 0.1318 | 0.4072 | 0.6188 | 0.6090 | nan | 0.5618 | 0.6759 | 0.0 | 0.5515 | 0.6702 | | 0.0423 | 74.55 | 21320 | 0.1265 | 0.4172 | 0.6356 | 0.6268 | nan | 0.5847 | 0.6865 | 0.0 | 0.5732 | 0.6783 | | 0.0475 | 74.62 | 21340 | 0.1390 | 0.3906 | 0.5965 | 0.5917 | nan | 0.5690 | 0.6241 | 0.0 | 0.5502 | 0.6215 | | 0.0332 | 74.69 | 21360 | 0.1335 | 0.3968 | 0.6045 | 0.5978 | nan | 0.5659 | 0.6430 | 0.0 | 0.5509 | 0.6394 | | 0.0352 | 74.76 | 21380 | 0.1243 | 0.4161 | 0.6337 | 0.6220 | nan | 0.5663 | 0.7011 | 0.0 | 0.5549 | 0.6935 | | 0.0451 | 74.83 | 21400 | 0.1339 | 0.3971 | 0.6044 | 0.5985 | nan | 0.5703 | 0.6384 | 0.0 | 0.5559 | 0.6353 | | 0.0416 | 74.9 | 21420 | 0.1276 | 0.4103 | 0.6241 | 0.6137 | nan | 0.5636 | 0.6846 | 0.0 | 0.5521 | 0.6788 | | 0.0412 | 74.97 | 21440 | 0.1230 | 0.4082 | 0.6201 | 0.6079 | nan | 0.5501 | 0.6900 | 0.0 | 0.5398 | 0.6848 | | 0.045 | 75.03 | 21460 | 0.1242 | 0.4154 | 0.6321 | 0.6249 | nan | 0.5903 | 0.6738 | 0.0 | 0.5771 | 0.6692 | | 0.0531 | 75.1 | 21480 | 0.1232 | 0.4053 | 0.6165 | 0.6048 | nan | 0.5484 | 0.6847 | 0.0 | 0.5373 | 0.6785 | | 0.052 | 75.17 | 21500 | 0.1280 | 0.4013 | 0.6102 | 0.6008 | nan | 0.5561 | 0.6642 | 0.0 | 0.5435 | 0.6603 | | 0.0443 | 75.24 | 21520 | 0.1266 | 0.4048 | 0.6157 | 0.6026 | nan | 0.5398 | 0.6916 | 0.0 | 0.5289 | 0.6856 | | 0.0419 | 75.31 | 21540 | 0.1272 | 0.4029 | 0.6130 | 0.6015 | nan | 0.5464 | 0.6796 | 0.0 | 0.5342 | 0.6746 | | 0.0372 | 75.38 | 21560 | 0.1249 | 0.4158 | 0.6328 | 0.6220 | nan | 0.5703 | 0.6952 | 0.0 | 0.5581 | 0.6894 | | 0.0643 | 75.45 | 21580 | 0.1266 | 0.4141 | 0.6304 | 0.6203 | nan | 0.5719 | 0.6890 | 0.0 | 0.5573 | 0.6849 | | 0.0559 | 75.52 | 21600 | 0.1250 | 0.4058 | 0.6164 | 0.6033 | nan | 0.5407 | 0.6921 | 0.0 | 0.5311 | 0.6863 | | 0.0358 | 75.59 | 21620 | 0.1322 | 0.4051 | 0.6163 | 0.6066 | nan | 0.5598 | 0.6728 | 0.0 | 0.5462 | 0.6691 | | 0.0394 | 75.66 | 21640 | 0.1330 | 0.3984 | 0.6061 | 0.5958 | nan | 0.5468 | 0.6653 | 0.0 | 0.5338 | 0.6614 | | 0.0598 | 75.73 | 21660 | 0.1284 | 0.4062 | 0.6182 | 0.6094 | nan | 0.5673 | 0.6691 | 0.0 | 0.5543 | 0.6645 | | 0.0364 | 75.8 | 21680 | 0.1252 | 0.4079 | 0.6203 | 0.6100 | nan | 0.5606 | 0.6799 | 0.0 | 0.5504 | 0.6734 | | 0.0478 | 75.87 | 21700 | 0.1279 | 0.4115 | 0.6258 | 0.6168 | nan | 0.5740 | 0.6775 | 0.0 | 0.5619 | 0.6726 | | 0.0306 | 75.94 | 21720 | 0.1306 | 0.4225 | 0.6435 | 0.6407 | nan | 0.6276 | 0.6593 | 0.0 | 0.6117 | 0.6558 | | 0.0431 | 76.01 | 21740 | 0.1295 | 0.4091 | 0.6218 | 0.6135 | nan | 0.5740 | 0.6695 | 0.0 | 0.5618 | 0.6654 | | 0.0539 | 76.08 | 21760 | 0.1326 | 0.3949 | 0.5999 | 0.5857 | nan | 0.5179 | 0.6818 | 0.0 | 0.5089 | 0.6758 | | 0.0637 | 76.15 | 21780 | 0.1379 | 0.4052 | 0.6168 | 0.6083 | nan | 0.5676 | 0.6660 | 0.0 | 0.5523 | 0.6632 | | 0.0624 | 76.22 | 21800 | 0.1338 | 0.3944 | 0.5994 | 0.5892 | nan | 0.5405 | 0.6583 | 0.0 | 0.5279 | 0.6551 | | 0.0442 | 76.29 | 21820 | 0.1301 | 0.4035 | 0.6139 | 0.6036 | nan | 0.5544 | 0.6733 | 0.0 | 0.5430 | 0.6675 | | 0.0366 | 76.36 | 21840 | 0.1292 | 0.4165 | 0.6340 | 0.6257 | nan | 0.5858 | 0.6822 | 0.0 | 0.5718 | 0.6776 | | 0.0453 | 76.43 | 21860 | 0.1269 | 0.4122 | 0.6276 | 0.6184 | nan | 0.5745 | 0.6807 | 0.0 | 0.5613 | 0.6754 | | 0.0363 | 76.5 | 21880 | 0.1256 | 0.4151 | 0.6318 | 0.6228 | nan | 0.5800 | 0.6835 | 0.0 | 0.5673 | 0.6781 | | 0.0313 | 76.57 | 21900 | 0.1279 | 0.4064 | 0.6185 | 0.6092 | nan | 0.5652 | 0.6718 | 0.0 | 0.5525 | 0.6668 | | 0.037 | 76.64 | 21920 | 0.1283 | 0.4153 | 0.6319 | 0.6247 | nan | 0.5903 | 0.6736 | 0.0 | 0.5760 | 0.6699 | | 0.0594 | 76.71 | 21940 | 0.1316 | 0.4008 | 0.6097 | 0.5987 | nan | 0.5465 | 0.6729 | 0.0 | 0.5337 | 0.6686 | | 0.0367 | 76.78 | 21960 | 0.1358 | 0.4039 | 0.6149 | 0.6073 | nan | 0.5710 | 0.6589 | 0.0 | 0.5566 | 0.6550 | | 0.0706 | 76.85 | 21980 | 0.1323 | 0.4012 | 0.6101 | 0.5992 | nan | 0.5470 | 0.6733 | 0.0 | 0.5357 | 0.6678 | | 0.0365 | 76.92 | 22000 | 0.1348 | 0.4044 | 0.6157 | 0.6093 | nan | 0.5786 | 0.6529 | 0.0 | 0.5627 | 0.6505 | | 0.0505 | 76.99 | 22020 | 0.1331 | 0.3992 | 0.6075 | 0.6004 | nan | 0.5660 | 0.6491 | 0.0 | 0.5522 | 0.6455 | | 0.0686 | 77.06 | 22040 | 0.1312 | 0.4068 | 0.6193 | 0.6132 | nan | 0.5842 | 0.6543 | 0.0 | 0.5688 | 0.6517 | | 0.0622 | 77.13 | 22060 | 0.1229 | 0.4144 | 0.6305 | 0.6200 | nan | 0.5698 | 0.6911 | 0.0 | 0.5576 | 0.6855 | | 0.0569 | 77.2 | 22080 | 0.1279 | 0.4063 | 0.6179 | 0.6087 | nan | 0.5648 | 0.6709 | 0.0 | 0.5532 | 0.6657 | | 0.0493 | 77.27 | 22100 | 0.1378 | 0.3819 | 0.5808 | 0.5727 | nan | 0.5343 | 0.6272 | 0.0 | 0.5224 | 0.6235 | | 0.0668 | 77.34 | 22120 | 0.1307 | 0.4046 | 0.6150 | 0.6066 | nan | 0.5666 | 0.6635 | 0.0 | 0.5546 | 0.6591 | | 0.0427 | 77.41 | 22140 | 0.1302 | 0.4081 | 0.6208 | 0.6116 | nan | 0.5676 | 0.6740 | 0.0 | 0.5561 | 0.6682 | | 0.0675 | 77.48 | 22160 | 0.1269 | 0.4046 | 0.6148 | 0.6034 | nan | 0.5491 | 0.6805 | 0.0 | 0.5389 | 0.6750 | | 0.0414 | 77.55 | 22180 | 0.1250 | 0.4155 | 0.6320 | 0.6237 | nan | 0.5840 | 0.6800 | 0.0 | 0.5720 | 0.6746 | | 0.056 | 77.62 | 22200 | 0.1344 | 0.4026 | 0.6128 | 0.6064 | nan | 0.5757 | 0.6499 | 0.0 | 0.5612 | 0.6467 | | 0.0407 | 77.69 | 22220 | 0.1326 | 0.4118 | 0.6271 | 0.6210 | nan | 0.5917 | 0.6625 | 0.0 | 0.5759 | 0.6595 | | 0.0389 | 77.76 | 22240 | 0.1333 | 0.4030 | 0.6136 | 0.6083 | nan | 0.5831 | 0.6441 | 0.0 | 0.5685 | 0.6405 | | 0.0608 | 77.83 | 22260 | 0.1288 | 0.4213 | 0.6408 | 0.6305 | nan | 0.5812 | 0.7004 | 0.0 | 0.5685 | 0.6955 | | 0.0261 | 77.9 | 22280 | 0.1290 | 0.4125 | 0.6272 | 0.6188 | nan | 0.5788 | 0.6756 | 0.0 | 0.5660 | 0.6714 | | 0.0723 | 77.97 | 22300 | 0.1319 | 0.4067 | 0.6185 | 0.6094 | nan | 0.5657 | 0.6713 | 0.0 | 0.5536 | 0.6667 | | 0.0395 | 78.04 | 22320 | 0.1303 | 0.4193 | 0.6380 | 0.6303 | nan | 0.5940 | 0.6819 | 0.0 | 0.5809 | 0.6771 | | 0.0643 | 78.11 | 22340 | 0.1276 | 0.4234 | 0.6436 | 0.6335 | nan | 0.5854 | 0.7017 | 0.0 | 0.5728 | 0.6974 | | 0.0358 | 78.18 | 22360 | 0.1347 | 0.4090 | 0.6218 | 0.6156 | nan | 0.5859 | 0.6576 | 0.0 | 0.5724 | 0.6546 | | 0.031 | 78.25 | 22380 | 0.1302 | 0.4126 | 0.6277 | 0.6197 | nan | 0.5813 | 0.6742 | 0.0 | 0.5694 | 0.6684 | | 0.052 | 78.32 | 22400 | 0.1287 | 0.4118 | 0.6267 | 0.6187 | nan | 0.5805 | 0.6730 | 0.0 | 0.5686 | 0.6668 | | 0.0456 | 78.39 | 22420 | 0.1299 | 0.4139 | 0.6295 | 0.6216 | nan | 0.5838 | 0.6752 | 0.0 | 0.5720 | 0.6698 | | 0.0418 | 78.46 | 22440 | 0.1270 | 0.4102 | 0.6235 | 0.6144 | nan | 0.5712 | 0.6757 | 0.0 | 0.5588 | 0.6717 | | 0.0554 | 78.53 | 22460 | 0.1304 | 0.4086 | 0.6208 | 0.6112 | nan | 0.5650 | 0.6767 | 0.0 | 0.5533 | 0.6723 | | 0.0708 | 78.6 | 22480 | 0.1304 | 0.4131 | 0.6286 | 0.6208 | nan | 0.5836 | 0.6735 | 0.0 | 0.5699 | 0.6694 | | 0.0296 | 78.67 | 22500 | 0.1293 | 0.4155 | 0.6320 | 0.6252 | nan | 0.5926 | 0.6715 | 0.0 | 0.5801 | 0.6663 | | 0.038 | 78.74 | 22520 | 0.1298 | 0.4101 | 0.6232 | 0.6148 | nan | 0.5748 | 0.6716 | 0.0 | 0.5632 | 0.6672 | | 0.055 | 78.81 | 22540 | 0.1274 | 0.4137 | 0.6288 | 0.6180 | nan | 0.5662 | 0.6914 | 0.0 | 0.5554 | 0.6858 | | 0.0344 | 78.88 | 22560 | 0.1281 | 0.4135 | 0.6280 | 0.6179 | nan | 0.5697 | 0.6863 | 0.0 | 0.5602 | 0.6804 | | 0.0408 | 78.95 | 22580 | 0.1272 | 0.4107 | 0.6235 | 0.6093 | nan | 0.5419 | 0.7051 | 0.0 | 0.5319 | 0.7003 | | 0.0648 | 79.02 | 22600 | 0.1278 | 0.3979 | 0.6039 | 0.5908 | nan | 0.5283 | 0.6794 | 0.0 | 0.5190 | 0.6746 | | 0.0444 | 79.09 | 22620 | 0.1340 | 0.3936 | 0.5992 | 0.5901 | nan | 0.5470 | 0.6513 | 0.0 | 0.5321 | 0.6487 | | 0.0376 | 79.16 | 22640 | 0.1368 | 0.3986 | 0.6079 | 0.5999 | nan | 0.5616 | 0.6543 | 0.0 | 0.5437 | 0.6521 | | 0.0768 | 79.23 | 22660 | 0.1265 | 0.4021 | 0.6112 | 0.5987 | nan | 0.5394 | 0.6829 | 0.0 | 0.5273 | 0.6791 | | 0.0432 | 79.3 | 22680 | 0.1280 | 0.4080 | 0.6203 | 0.6098 | nan | 0.5596 | 0.6809 | 0.0 | 0.5467 | 0.6773 | | 0.0477 | 79.37 | 22700 | 0.1285 | 0.4121 | 0.6265 | 0.6139 | nan | 0.5537 | 0.6992 | 0.0 | 0.5409 | 0.6954 | | 0.0634 | 79.44 | 22720 | 0.1311 | 0.4041 | 0.6151 | 0.6094 | nan | 0.5820 | 0.6482 | 0.0 | 0.5676 | 0.6447 | | 0.0322 | 79.51 | 22740 | 0.1287 | 0.4002 | 0.6079 | 0.5975 | nan | 0.5475 | 0.6684 | 0.0 | 0.5362 | 0.6643 | | 0.0401 | 79.58 | 22760 | 0.1259 | 0.4084 | 0.6203 | 0.6098 | nan | 0.5595 | 0.6811 | 0.0 | 0.5478 | 0.6775 | | 0.0386 | 79.65 | 22780 | 0.1337 | 0.4023 | 0.6116 | 0.6036 | nan | 0.5657 | 0.6575 | 0.0 | 0.5526 | 0.6542 | | 0.0336 | 79.72 | 22800 | 0.1303 | 0.4125 | 0.6272 | 0.6181 | nan | 0.5743 | 0.6802 | 0.0 | 0.5611 | 0.6765 | | 0.0417 | 79.79 | 22820 | 0.1330 | 0.4032 | 0.6134 | 0.6070 | nan | 0.5763 | 0.6504 | 0.0 | 0.5627 | 0.6469 | | 0.0582 | 79.86 | 22840 | 0.1268 | 0.4139 | 0.6282 | 0.6183 | nan | 0.5709 | 0.6854 | 0.0 | 0.5613 | 0.6803 | | 0.0399 | 79.93 | 22860 | 0.1373 | 0.3947 | 0.5993 | 0.5887 | nan | 0.5380 | 0.6606 | 0.0 | 0.5267 | 0.6574 | | 0.0632 | 80.0 | 22880 | 0.1368 | 0.4059 | 0.6177 | 0.6112 | nan | 0.5805 | 0.6549 | 0.0 | 0.5655 | 0.6523 | | 0.0389 | 80.07 | 22900 | 0.1319 | 0.4126 | 0.6275 | 0.6176 | nan | 0.5703 | 0.6848 | 0.0 | 0.5569 | 0.6811 | | 0.0442 | 80.14 | 22920 | 0.1323 | 0.4117 | 0.6256 | 0.6183 | nan | 0.5835 | 0.6678 | 0.0 | 0.5710 | 0.6640 | | 0.0357 | 80.21 | 22940 | 0.1349 | 0.4198 | 0.6383 | 0.6302 | nan | 0.5919 | 0.6846 | 0.0 | 0.5774 | 0.6821 | | 0.0423 | 80.28 | 22960 | 0.1423 | 0.3925 | 0.5974 | 0.5928 | nan | 0.5713 | 0.6234 | 0.0 | 0.5562 | 0.6214 | | 0.0498 | 80.35 | 22980 | 0.1398 | 0.3982 | 0.6053 | 0.5965 | nan | 0.5544 | 0.6562 | 0.0 | 0.5417 | 0.6529 | | 0.0612 | 80.42 | 23000 | 0.1364 | 0.3984 | 0.6053 | 0.5963 | nan | 0.5535 | 0.6571 | 0.0 | 0.5426 | 0.6527 | | 0.0408 | 80.49 | 23020 | 0.1381 | 0.3994 | 0.6074 | 0.5989 | nan | 0.5587 | 0.6560 | 0.0 | 0.5456 | 0.6527 | | 0.059 | 80.56 | 23040 | 0.1391 | 0.3871 | 0.5885 | 0.5794 | nan | 0.5360 | 0.6410 | 0.0 | 0.5231 | 0.6382 | | 0.0348 | 80.63 | 23060 | 0.1414 | 0.3866 | 0.5877 | 0.5791 | nan | 0.5383 | 0.6370 | 0.0 | 0.5248 | 0.6349 | | 0.0644 | 80.7 | 23080 | 0.1388 | 0.4017 | 0.6105 | 0.6000 | nan | 0.5499 | 0.6712 | 0.0 | 0.5365 | 0.6687 | | 0.0492 | 80.77 | 23100 | 0.1416 | 0.3878 | 0.5897 | 0.5841 | nan | 0.5576 | 0.6219 | 0.0 | 0.5429 | 0.6204 | | 0.0341 | 80.84 | 23120 | 0.1310 | 0.4121 | 0.6269 | 0.6189 | nan | 0.5806 | 0.6732 | 0.0 | 0.5665 | 0.6697 | | 0.0532 | 80.91 | 23140 | 0.1325 | 0.4010 | 0.6095 | 0.6019 | nan | 0.5656 | 0.6534 | 0.0 | 0.5534 | 0.6496 | | 0.0627 | 80.98 | 23160 | 0.1349 | 0.4024 | 0.6117 | 0.6025 | nan | 0.5589 | 0.6645 | 0.0 | 0.5463 | 0.6609 | | 0.0332 | 81.05 | 23180 | 0.1328 | 0.3964 | 0.6035 | 0.5929 | nan | 0.5421 | 0.6650 | 0.0 | 0.5331 | 0.6562 | | 0.0428 | 81.12 | 23200 | 0.1223 | 0.4180 | 0.6361 | 0.6220 | nan | 0.5545 | 0.7177 | 0.0 | 0.5460 | 0.7081 | | 0.0549 | 81.19 | 23220 | 0.1257 | 0.4225 | 0.6427 | 0.6324 | nan | 0.5833 | 0.7021 | 0.0 | 0.5718 | 0.6957 | | 0.0439 | 81.26 | 23240 | 0.1307 | 0.4168 | 0.6343 | 0.6276 | nan | 0.5956 | 0.6730 | 0.0 | 0.5813 | 0.6690 | | 0.034 | 81.33 | 23260 | 0.1301 | 0.3953 | 0.6006 | 0.5892 | nan | 0.5351 | 0.6660 | 0.0 | 0.5239 | 0.6619 | | 0.0329 | 81.4 | 23280 | 0.1359 | 0.4038 | 0.6138 | 0.6065 | nan | 0.5715 | 0.6562 | 0.0 | 0.5589 | 0.6526 | | 0.0452 | 81.47 | 23300 | 0.1382 | 0.4003 | 0.6087 | 0.6019 | nan | 0.5694 | 0.6481 | 0.0 | 0.5564 | 0.6446 | | 0.0373 | 81.54 | 23320 | 0.1551 | 0.3851 | 0.5872 | 0.5839 | nan | 0.5681 | 0.6063 | 0.0 | 0.5515 | 0.6037 | | 0.065 | 81.61 | 23340 | 0.1439 | 0.3916 | 0.5964 | 0.5873 | nan | 0.5438 | 0.6491 | 0.0 | 0.5295 | 0.6452 | | 0.0315 | 81.68 | 23360 | 0.1339 | 0.4128 | 0.6285 | 0.6203 | nan | 0.5815 | 0.6755 | 0.0 | 0.5676 | 0.6707 | | 0.049 | 81.75 | 23380 | 0.1423 | 0.3936 | 0.5998 | 0.5946 | nan | 0.5696 | 0.6301 | 0.0 | 0.5537 | 0.6272 | | 0.0667 | 81.82 | 23400 | 0.1389 | 0.4047 | 0.6173 | 0.6131 | nan | 0.5932 | 0.6414 | 0.0 | 0.5765 | 0.6377 | | 0.0344 | 81.89 | 23420 | 0.1419 | 0.4025 | 0.6133 | 0.6093 | nan | 0.5902 | 0.6363 | 0.0 | 0.5743 | 0.6332 | | 0.047 | 81.96 | 23440 | 0.1386 | 0.4020 | 0.6119 | 0.6044 | nan | 0.5681 | 0.6557 | 0.0 | 0.5529 | 0.6531 | | 0.0454 | 82.03 | 23460 | 0.1369 | 0.3993 | 0.6072 | 0.5982 | nan | 0.5552 | 0.6592 | 0.0 | 0.5422 | 0.6557 | | 0.0384 | 82.1 | 23480 | 0.1395 | 0.4028 | 0.6129 | 0.6046 | nan | 0.5651 | 0.6606 | 0.0 | 0.5514 | 0.6570 | | 0.0305 | 82.17 | 23500 | 0.1370 | 0.3989 | 0.6070 | 0.5990 | nan | 0.5609 | 0.6532 | 0.0 | 0.5480 | 0.6488 | | 0.0568 | 82.24 | 23520 | 0.1343 | 0.4053 | 0.6166 | 0.6078 | nan | 0.5660 | 0.6672 | 0.0 | 0.5532 | 0.6628 | | 0.0334 | 82.31 | 23540 | 0.1357 | 0.4091 | 0.6224 | 0.6137 | nan | 0.5720 | 0.6729 | 0.0 | 0.5584 | 0.6689 | | 0.0537 | 82.38 | 23560 | 0.1394 | 0.4009 | 0.6111 | 0.6059 | nan | 0.5809 | 0.6414 | 0.0 | 0.5642 | 0.6386 | | 0.0279 | 82.45 | 23580 | 0.1371 | 0.4030 | 0.6140 | 0.6072 | nan | 0.5744 | 0.6536 | 0.0 | 0.5584 | 0.6506 | | 0.0451 | 82.52 | 23600 | 0.1349 | 0.3994 | 0.6079 | 0.5996 | nan | 0.5600 | 0.6557 | 0.0 | 0.5455 | 0.6526 | | 0.0296 | 82.59 | 23620 | 0.1382 | 0.3891 | 0.5918 | 0.5826 | nan | 0.5389 | 0.6446 | 0.0 | 0.5257 | 0.6416 | | 0.046 | 82.66 | 23640 | 0.1418 | 0.3936 | 0.5995 | 0.5941 | nan | 0.5680 | 0.6311 | 0.0 | 0.5516 | 0.6290 | | 0.0264 | 82.73 | 23660 | 0.1395 | 0.3954 | 0.6013 | 0.5948 | nan | 0.5633 | 0.6394 | 0.0 | 0.5479 | 0.6383 | | 0.03 | 82.8 | 23680 | 0.1385 | 0.3869 | 0.5876 | 0.5791 | nan | 0.5387 | 0.6365 | 0.0 | 0.5257 | 0.6351 | | 0.0363 | 82.87 | 23700 | 0.1318 | 0.4097 | 0.6237 | 0.6155 | nan | 0.5761 | 0.6713 | 0.0 | 0.5618 | 0.6674 | | 0.0431 | 82.94 | 23720 | 0.1385 | 0.3955 | 0.6026 | 0.6002 | nan | 0.5886 | 0.6165 | 0.0 | 0.5738 | 0.6126 | | 0.0482 | 83.01 | 23740 | 0.1362 | 0.4077 | 0.6211 | 0.6167 | nan | 0.5953 | 0.6469 | 0.0 | 0.5794 | 0.6436 | | 0.0567 | 83.08 | 23760 | 0.1360 | 0.4009 | 0.6102 | 0.6055 | nan | 0.5832 | 0.6372 | 0.0 | 0.5684 | 0.6342 | | 0.0421 | 83.15 | 23780 | 0.1339 | 0.4012 | 0.6105 | 0.6024 | nan | 0.5635 | 0.6576 | 0.0 | 0.5491 | 0.6545 | | 0.0276 | 83.22 | 23800 | 0.1245 | 0.4040 | 0.6140 | 0.6017 | nan | 0.5427 | 0.6853 | 0.0 | 0.5351 | 0.6768 | | 0.0399 | 83.29 | 23820 | 0.1282 | 0.4062 | 0.6180 | 0.6103 | nan | 0.5736 | 0.6623 | 0.0 | 0.5591 | 0.6595 | | 0.0668 | 83.36 | 23840 | 0.1209 | 0.4128 | 0.6268 | 0.6158 | nan | 0.5631 | 0.6905 | 0.0 | 0.5512 | 0.6871 | | 0.0724 | 83.43 | 23860 | 0.1171 | 0.4235 | 0.6430 | 0.6311 | nan | 0.5744 | 0.7116 | 0.0 | 0.5651 | 0.7053 | | 0.0305 | 83.5 | 23880 | 0.1233 | 0.3994 | 0.6056 | 0.5922 | nan | 0.5285 | 0.6826 | 0.0 | 0.5200 | 0.6780 | | 0.0511 | 83.57 | 23900 | 0.1239 | 0.4040 | 0.6131 | 0.5990 | nan | 0.5315 | 0.6947 | 0.0 | 0.5235 | 0.6884 | | 0.0642 | 83.64 | 23920 | 0.1187 | 0.4160 | 0.6318 | 0.6182 | nan | 0.5529 | 0.7107 | 0.0 | 0.5442 | 0.7037 | | 0.0447 | 83.71 | 23940 | 0.1320 | 0.4064 | 0.6182 | 0.6108 | nan | 0.5755 | 0.6610 | 0.0 | 0.5605 | 0.6588 | | 0.0501 | 83.78 | 23960 | 0.1347 | 0.4039 | 0.6148 | 0.6079 | nan | 0.5754 | 0.6541 | 0.0 | 0.5599 | 0.6519 | | 0.0487 | 83.85 | 23980 | 0.1337 | 0.4049 | 0.6159 | 0.6073 | nan | 0.5659 | 0.6659 | 0.0 | 0.5516 | 0.6631 | | 0.0503 | 83.92 | 24000 | 0.1315 | 0.4037 | 0.6141 | 0.6065 | nan | 0.5703 | 0.6580 | 0.0 | 0.5557 | 0.6555 | | 0.0645 | 83.99 | 24020 | 0.1336 | 0.4075 | 0.6196 | 0.6129 | nan | 0.5808 | 0.6584 | 0.0 | 0.5670 | 0.6556 | | 0.0594 | 84.06 | 24040 | 0.1313 | 0.4135 | 0.6288 | 0.6211 | nan | 0.5843 | 0.6734 | 0.0 | 0.5707 | 0.6698 | | 0.0609 | 84.13 | 24060 | 0.1293 | 0.3993 | 0.6070 | 0.5960 | nan | 0.5436 | 0.6703 | 0.0 | 0.5325 | 0.6653 | | 0.0481 | 84.2 | 24080 | 0.1323 | 0.4014 | 0.6107 | 0.6013 | nan | 0.5563 | 0.6651 | 0.0 | 0.5434 | 0.6608 | | 0.0588 | 84.27 | 24100 | 0.1349 | 0.4005 | 0.6090 | 0.5998 | nan | 0.5557 | 0.6623 | 0.0 | 0.5422 | 0.6594 | | 0.048 | 84.34 | 24120 | 0.1355 | 0.4008 | 0.6094 | 0.6009 | nan | 0.5603 | 0.6585 | 0.0 | 0.5467 | 0.6558 | | 0.0632 | 84.41 | 24140 | 0.1372 | 0.3928 | 0.5971 | 0.5875 | nan | 0.5420 | 0.6521 | 0.0 | 0.5302 | 0.6481 | | 0.03 | 84.48 | 24160 | 0.1276 | 0.4117 | 0.6264 | 0.6175 | nan | 0.5753 | 0.6775 | 0.0 | 0.5624 | 0.6728 | | 0.0259 | 84.55 | 24180 | 0.1246 | 0.4170 | 0.6344 | 0.6231 | nan | 0.5693 | 0.6994 | 0.0 | 0.5560 | 0.6950 | | 0.0608 | 84.62 | 24200 | 0.1367 | 0.3899 | 0.5926 | 0.5828 | nan | 0.5358 | 0.6494 | 0.0 | 0.5217 | 0.6479 | | 0.0522 | 84.69 | 24220 | 0.1196 | 0.4153 | 0.6307 | 0.6169 | nan | 0.5513 | 0.7101 | 0.0 | 0.5414 | 0.7045 | | 0.0388 | 84.76 | 24240 | 0.1241 | 0.4147 | 0.6301 | 0.6182 | nan | 0.5616 | 0.6986 | 0.0 | 0.5500 | 0.6941 | | 0.0394 | 84.83 | 24260 | 0.1251 | 0.4085 | 0.6202 | 0.6078 | nan | 0.5483 | 0.6921 | 0.0 | 0.5383 | 0.6871 | | 0.0479 | 84.9 | 24280 | 0.1229 | 0.4106 | 0.6241 | 0.6107 | nan | 0.5466 | 0.7017 | 0.0 | 0.5370 | 0.6947 | | 0.0274 | 84.97 | 24300 | 0.1269 | 0.4044 | 0.6145 | 0.6062 | nan | 0.5669 | 0.6621 | 0.0 | 0.5557 | 0.6576 | | 0.0535 | 85.03 | 24320 | 0.1239 | 0.4137 | 0.6285 | 0.6161 | nan | 0.5565 | 0.7006 | 0.0 | 0.5457 | 0.6955 | | 0.0606 | 85.1 | 24340 | 0.1325 | 0.4055 | 0.6164 | 0.6079 | nan | 0.5676 | 0.6652 | 0.0 | 0.5548 | 0.6617 | | 0.0442 | 85.17 | 24360 | 0.1277 | 0.4107 | 0.6243 | 0.6133 | nan | 0.5611 | 0.6874 | 0.0 | 0.5502 | 0.6820 | | 0.0468 | 85.24 | 24380 | 0.1271 | 0.4038 | 0.6135 | 0.6024 | nan | 0.5497 | 0.6773 | 0.0 | 0.5390 | 0.6725 | | 0.0469 | 85.31 | 24400 | 0.1268 | 0.4125 | 0.6272 | 0.6174 | nan | 0.5708 | 0.6837 | 0.0 | 0.5584 | 0.6792 | | 0.0608 | 85.38 | 24420 | 0.1288 | 0.4134 | 0.6283 | 0.6178 | nan | 0.5679 | 0.6887 | 0.0 | 0.5586 | 0.6815 | | 0.0392 | 85.45 | 24440 | 0.1383 | 0.4009 | 0.6101 | 0.6031 | nan | 0.5695 | 0.6507 | 0.0 | 0.5570 | 0.6459 | | 0.0374 | 85.52 | 24460 | 0.1317 | 0.4031 | 0.6131 | 0.6047 | nan | 0.5647 | 0.6616 | 0.0 | 0.5531 | 0.6563 | | 0.0402 | 85.59 | 24480 | 0.1369 | 0.3936 | 0.5980 | 0.5897 | nan | 0.5500 | 0.6460 | 0.0 | 0.5372 | 0.6435 | | 0.0456 | 85.66 | 24500 | 0.1329 | 0.4034 | 0.6128 | 0.6043 | nan | 0.5638 | 0.6618 | 0.0 | 0.5516 | 0.6585 | | 0.0569 | 85.73 | 24520 | 0.1275 | 0.4094 | 0.6219 | 0.6123 | nan | 0.5667 | 0.6771 | 0.0 | 0.5561 | 0.6722 | | 0.0462 | 85.8 | 24540 | 0.1270 | 0.4120 | 0.6260 | 0.6155 | nan | 0.5651 | 0.6870 | 0.0 | 0.5553 | 0.6808 | | 0.0404 | 85.87 | 24560 | 0.1275 | 0.4213 | 0.6405 | 0.6318 | nan | 0.5904 | 0.6906 | 0.0 | 0.5790 | 0.6849 | | 0.0607 | 85.94 | 24580 | 0.1314 | 0.4166 | 0.6333 | 0.6232 | nan | 0.5751 | 0.6914 | 0.0 | 0.5616 | 0.6881 | | 0.049 | 86.01 | 24600 | 0.1333 | 0.4056 | 0.6162 | 0.6077 | nan | 0.5672 | 0.6653 | 0.0 | 0.5549 | 0.6618 | | 0.0521 | 86.08 | 24620 | 0.1291 | 0.4117 | 0.6258 | 0.6168 | nan | 0.5739 | 0.6777 | 0.0 | 0.5614 | 0.6738 | | 0.0465 | 86.15 | 24640 | 0.1360 | 0.4036 | 0.6131 | 0.6044 | nan | 0.5630 | 0.6631 | 0.0 | 0.5516 | 0.6590 | | 0.0343 | 86.22 | 24660 | 0.1302 | 0.4059 | 0.6168 | 0.6024 | nan | 0.5337 | 0.7000 | 0.0 | 0.5240 | 0.6936 | | 0.0775 | 86.29 | 24680 | 0.1349 | 0.3989 | 0.6063 | 0.5939 | nan | 0.5345 | 0.6782 | 0.0 | 0.5243 | 0.6724 | | 0.0545 | 86.36 | 24700 | 0.1388 | 0.4019 | 0.6108 | 0.5993 | nan | 0.5445 | 0.6771 | 0.0 | 0.5332 | 0.6724 | | 0.038 | 86.43 | 24720 | 0.1364 | 0.4060 | 0.6174 | 0.6098 | nan | 0.5735 | 0.6613 | 0.0 | 0.5600 | 0.6580 | | 0.0364 | 86.5 | 24740 | 0.1362 | 0.4118 | 0.6267 | 0.6198 | nan | 0.5868 | 0.6665 | 0.0 | 0.5727 | 0.6625 | | 0.0425 | 86.57 | 24760 | 0.1459 | 0.3935 | 0.5988 | 0.5922 | nan | 0.5607 | 0.6369 | 0.0 | 0.5465 | 0.6341 | | 0.0309 | 86.64 | 24780 | 0.1395 | 0.4010 | 0.6094 | 0.6020 | nan | 0.5666 | 0.6523 | 0.0 | 0.5551 | 0.6480 | | 0.0433 | 86.71 | 24800 | 0.1349 | 0.4137 | 0.6295 | 0.6186 | nan | 0.5668 | 0.6922 | 0.0 | 0.5576 | 0.6836 | | 0.0416 | 86.78 | 24820 | 0.1438 | 0.3978 | 0.6057 | 0.6008 | nan | 0.5774 | 0.6341 | 0.0 | 0.5614 | 0.6321 | | 0.0379 | 86.85 | 24840 | 0.1411 | 0.4047 | 0.6163 | 0.6101 | nan | 0.5804 | 0.6522 | 0.0 | 0.5635 | 0.6505 | | 0.055 | 86.92 | 24860 | 0.1392 | 0.4012 | 0.6110 | 0.6051 | nan | 0.5770 | 0.6450 | 0.0 | 0.5621 | 0.6415 | | 0.0443 | 86.99 | 24880 | 0.1352 | 0.4083 | 0.6224 | 0.6161 | nan | 0.5859 | 0.6589 | 0.0 | 0.5700 | 0.6549 | | 0.0477 | 87.06 | 24900 | 0.1369 | 0.3984 | 0.6064 | 0.5983 | nan | 0.5595 | 0.6533 | 0.0 | 0.5456 | 0.6498 | | 0.0368 | 87.13 | 24920 | 0.1333 | 0.3991 | 0.6066 | 0.5943 | nan | 0.5358 | 0.6773 | 0.0 | 0.5244 | 0.6730 | | 0.0661 | 87.2 | 24940 | 0.1361 | 0.4045 | 0.6150 | 0.6058 | nan | 0.5615 | 0.6686 | 0.0 | 0.5482 | 0.6653 | | 0.0496 | 87.27 | 24960 | 0.1370 | 0.4037 | 0.6136 | 0.6030 | nan | 0.5521 | 0.6752 | 0.0 | 0.5387 | 0.6723 | | 0.032 | 87.34 | 24980 | 0.1454 | 0.3995 | 0.6087 | 0.6031 | nan | 0.5763 | 0.6411 | 0.0 | 0.5592 | 0.6394 | | 0.059 | 87.41 | 25000 | 0.1345 | 0.4120 | 0.6263 | 0.6169 | nan | 0.5722 | 0.6804 | 0.0 | 0.5589 | 0.6771 | | 0.0473 | 87.48 | 25020 | 0.1363 | 0.4124 | 0.6278 | 0.6222 | nan | 0.5955 | 0.6602 | 0.0 | 0.5795 | 0.6577 | | 0.0307 | 87.55 | 25040 | 0.1346 | 0.4096 | 0.6224 | 0.6108 | nan | 0.5554 | 0.6895 | 0.0 | 0.5441 | 0.6846 | | 0.0373 | 87.62 | 25060 | 0.1337 | 0.4039 | 0.6139 | 0.6032 | nan | 0.5520 | 0.6758 | 0.0 | 0.5408 | 0.6709 | | 0.0438 | 87.69 | 25080 | 0.1348 | 0.4078 | 0.6200 | 0.6112 | nan | 0.5695 | 0.6704 | 0.0 | 0.5569 | 0.6664 | | 0.0671 | 87.76 | 25100 | 0.1471 | 0.3939 | 0.5995 | 0.5952 | nan | 0.5748 | 0.6241 | 0.0 | 0.5596 | 0.6222 | | 0.0646 | 87.83 | 25120 | 0.1451 | 0.3930 | 0.5977 | 0.5892 | nan | 0.5484 | 0.6470 | 0.0 | 0.5352 | 0.6438 | | 0.061 | 87.9 | 25140 | 0.1409 | 0.3986 | 0.6063 | 0.5996 | nan | 0.5674 | 0.6453 | 0.0 | 0.5529 | 0.6430 | | 0.0553 | 87.97 | 25160 | 0.1410 | 0.3854 | 0.5856 | 0.5733 | nan | 0.5144 | 0.6568 | 0.0 | 0.5025 | 0.6537 | | 0.0578 | 88.04 | 25180 | 0.1430 | 0.3821 | 0.5811 | 0.5712 | nan | 0.5238 | 0.6385 | 0.0 | 0.5108 | 0.6355 | | 0.0398 | 88.11 | 25200 | 0.1438 | 0.3929 | 0.5984 | 0.5929 | nan | 0.5670 | 0.6297 | 0.0 | 0.5523 | 0.6265 | | 0.0354 | 88.18 | 25220 | 0.1352 | 0.4088 | 0.6219 | 0.6109 | nan | 0.5583 | 0.6856 | 0.0 | 0.5468 | 0.6797 | | 0.0503 | 88.25 | 25240 | 0.1412 | 0.3996 | 0.6073 | 0.5983 | nan | 0.5554 | 0.6592 | 0.0 | 0.5429 | 0.6559 | | 0.0609 | 88.32 | 25260 | 0.1397 | 0.4018 | 0.6112 | 0.6038 | nan | 0.5686 | 0.6538 | 0.0 | 0.5554 | 0.6501 | | 0.0325 | 88.39 | 25280 | 0.1286 | 0.4165 | 0.6332 | 0.6237 | nan | 0.5782 | 0.6882 | 0.0 | 0.5660 | 0.6835 | | 0.0372 | 88.46 | 25300 | 0.1293 | 0.4101 | 0.6227 | 0.6093 | nan | 0.5456 | 0.6998 | 0.0 | 0.5356 | 0.6946 | | 0.0307 | 88.53 | 25320 | 0.1423 | 0.3938 | 0.5981 | 0.5875 | nan | 0.5368 | 0.6595 | 0.0 | 0.5241 | 0.6573 | | 0.041 | 88.6 | 25340 | 0.1358 | 0.4061 | 0.6174 | 0.6051 | nan | 0.5467 | 0.6880 | 0.0 | 0.5343 | 0.6840 | | 0.0521 | 88.67 | 25360 | 0.1341 | 0.4034 | 0.6135 | 0.6024 | nan | 0.5493 | 0.6776 | 0.0 | 0.5371 | 0.6732 | | 0.0362 | 88.74 | 25380 | 0.1333 | 0.3982 | 0.6055 | 0.5945 | nan | 0.5420 | 0.6691 | 0.0 | 0.5297 | 0.6648 | | 0.0498 | 88.81 | 25400 | 0.1319 | 0.4137 | 0.6293 | 0.6199 | nan | 0.5753 | 0.6833 | 0.0 | 0.5626 | 0.6784 | | 0.0546 | 88.88 | 25420 | 0.1360 | 0.4025 | 0.6119 | 0.6034 | nan | 0.5627 | 0.6611 | 0.0 | 0.5503 | 0.6574 | | 0.0668 | 88.95 | 25440 | 0.1325 | 0.4124 | 0.6272 | 0.6188 | nan | 0.5785 | 0.6759 | 0.0 | 0.5657 | 0.6716 | | 0.0495 | 89.02 | 25460 | 0.1338 | 0.4056 | 0.6167 | 0.6095 | nan | 0.5753 | 0.6581 | 0.0 | 0.5623 | 0.6544 | | 0.0448 | 89.09 | 25480 | 0.1409 | 0.3931 | 0.5971 | 0.5887 | nan | 0.5488 | 0.6453 | 0.0 | 0.5363 | 0.6429 | | 0.0497 | 89.16 | 25500 | 0.1304 | 0.4132 | 0.6273 | 0.6173 | nan | 0.5692 | 0.6854 | 0.0 | 0.5585 | 0.6810 | | 0.0394 | 89.23 | 25520 | 0.1276 | 0.4162 | 0.6314 | 0.6168 | nan | 0.5470 | 0.7158 | 0.0 | 0.5384 | 0.7100 | | 0.0512 | 89.3 | 25540 | 0.1367 | 0.3989 | 0.6067 | 0.6011 | nan | 0.5747 | 0.6386 | 0.0 | 0.5612 | 0.6355 | | 0.0407 | 89.37 | 25560 | 0.1439 | 0.3935 | 0.5989 | 0.5923 | nan | 0.5606 | 0.6372 | 0.0 | 0.5458 | 0.6347 | | 0.0397 | 89.44 | 25580 | 0.1383 | 0.3961 | 0.6024 | 0.5948 | nan | 0.5586 | 0.6463 | 0.0 | 0.5447 | 0.6436 | | 0.0427 | 89.51 | 25600 | 0.1327 | 0.4065 | 0.6176 | 0.6091 | nan | 0.5687 | 0.6664 | 0.0 | 0.5574 | 0.6620 | | 0.045 | 89.58 | 25620 | 0.1308 | 0.4024 | 0.6112 | 0.6009 | nan | 0.5516 | 0.6708 | 0.0 | 0.5400 | 0.6671 | | 0.0782 | 89.65 | 25640 | 0.1340 | 0.4056 | 0.6170 | 0.6084 | nan | 0.5671 | 0.6669 | 0.0 | 0.5529 | 0.6640 | | 0.0444 | 89.72 | 25660 | 0.1362 | 0.4023 | 0.6120 | 0.6059 | nan | 0.5765 | 0.6476 | 0.0 | 0.5638 | 0.6433 | | 0.0374 | 89.79 | 25680 | 0.1337 | 0.4075 | 0.6194 | 0.6108 | nan | 0.5698 | 0.6691 | 0.0 | 0.5586 | 0.6641 | | 0.0454 | 89.86 | 25700 | 0.1408 | 0.3884 | 0.5914 | 0.5852 | nan | 0.5557 | 0.6270 | 0.0 | 0.5425 | 0.6228 | | 0.0419 | 89.93 | 25720 | 0.1396 | 0.4013 | 0.6108 | 0.6049 | nan | 0.5766 | 0.6451 | 0.0 | 0.5624 | 0.6416 | | 0.0137 | 90.0 | 25740 | 0.1317 | 0.4053 | 0.6161 | 0.6069 | nan | 0.5626 | 0.6697 | 0.0 | 0.5507 | 0.6651 | | 0.0311 | 90.07 | 25760 | 0.1361 | 0.4052 | 0.6171 | 0.6100 | nan | 0.5766 | 0.6575 | 0.0 | 0.5613 | 0.6542 | | 0.056 | 90.14 | 25780 | 0.1377 | 0.4023 | 0.6118 | 0.6064 | nan | 0.5805 | 0.6432 | 0.0 | 0.5671 | 0.6397 | | 0.0491 | 90.21 | 25800 | 0.1399 | 0.3961 | 0.6025 | 0.5945 | nan | 0.5565 | 0.6484 | 0.0 | 0.5443 | 0.6439 | | 0.0285 | 90.28 | 25820 | 0.1357 | 0.4028 | 0.6123 | 0.6026 | nan | 0.5562 | 0.6684 | 0.0 | 0.5448 | 0.6637 | | 0.0253 | 90.35 | 25840 | 0.1371 | 0.4051 | 0.6162 | 0.6098 | nan | 0.5790 | 0.6534 | 0.0 | 0.5668 | 0.6485 | | 0.0409 | 90.42 | 25860 | 0.1414 | 0.3962 | 0.6025 | 0.5945 | nan | 0.5561 | 0.6490 | 0.0 | 0.5428 | 0.6458 | | 0.0414 | 90.49 | 25880 | 0.1411 | 0.3999 | 0.6082 | 0.6037 | nan | 0.5823 | 0.6340 | 0.0 | 0.5682 | 0.6314 | | 0.0659 | 90.56 | 25900 | 0.1362 | 0.4085 | 0.6211 | 0.6134 | nan | 0.5765 | 0.6657 | 0.0 | 0.5647 | 0.6607 | | 0.0347 | 90.63 | 25920 | 0.1415 | 0.4101 | 0.6243 | 0.6219 | nan | 0.6107 | 0.6378 | 0.0 | 0.5956 | 0.6346 | | 0.0418 | 90.7 | 25940 | 0.1382 | 0.4075 | 0.6202 | 0.6139 | nan | 0.5834 | 0.6571 | 0.0 | 0.5693 | 0.6533 | | 0.037 | 90.77 | 25960 | 0.1406 | 0.4010 | 0.6100 | 0.6016 | nan | 0.5611 | 0.6589 | 0.0 | 0.5482 | 0.6549 | | 0.035 | 90.84 | 25980 | 0.1385 | 0.4007 | 0.6098 | 0.5996 | nan | 0.5510 | 0.6685 | 0.0 | 0.5379 | 0.6642 | | 0.0251 | 90.91 | 26000 | 0.1377 | 0.4023 | 0.6129 | 0.6064 | nan | 0.5756 | 0.6501 | 0.0 | 0.5596 | 0.6474 | | 0.0462 | 90.98 | 26020 | 0.1474 | 0.3867 | 0.5884 | 0.5814 | nan | 0.5482 | 0.6286 | 0.0 | 0.5336 | 0.6264 | | 0.0427 | 91.05 | 26040 | 0.1511 | 0.3862 | 0.5882 | 0.5807 | nan | 0.5452 | 0.6311 | 0.0 | 0.5305 | 0.6282 | | 0.0458 | 91.12 | 26060 | 0.1428 | 0.3969 | 0.6044 | 0.5985 | nan | 0.5704 | 0.6385 | 0.0 | 0.5549 | 0.6358 | | 0.0531 | 91.19 | 26080 | 0.1443 | 0.3987 | 0.6076 | 0.6034 | nan | 0.5833 | 0.6320 | 0.0 | 0.5676 | 0.6284 | | 0.0423 | 91.26 | 26100 | 0.1459 | 0.3914 | 0.5960 | 0.5910 | nan | 0.5672 | 0.6248 | 0.0 | 0.5516 | 0.6226 | | 0.0371 | 91.33 | 26120 | 0.1455 | 0.3883 | 0.5905 | 0.5828 | nan | 0.5459 | 0.6351 | 0.0 | 0.5321 | 0.6327 | | 0.048 | 91.4 | 26140 | 0.1463 | 0.3924 | 0.5971 | 0.5897 | nan | 0.5545 | 0.6397 | 0.0 | 0.5406 | 0.6366 | | 0.0556 | 91.47 | 26160 | 0.1397 | 0.3989 | 0.6065 | 0.5978 | nan | 0.5561 | 0.6569 | 0.0 | 0.5438 | 0.6528 | | 0.0332 | 91.54 | 26180 | 0.1394 | 0.3948 | 0.6001 | 0.5917 | nan | 0.5517 | 0.6485 | 0.0 | 0.5391 | 0.6453 | | 0.0419 | 91.61 | 26200 | 0.1436 | 0.3829 | 0.5816 | 0.5730 | nan | 0.5322 | 0.6310 | 0.0 | 0.5205 | 0.6282 | | 0.0299 | 91.68 | 26220 | 0.1404 | 0.3939 | 0.5993 | 0.5912 | nan | 0.5526 | 0.6459 | 0.0 | 0.5392 | 0.6426 | | 0.0296 | 91.75 | 26240 | 0.1243 | 0.4179 | 0.6349 | 0.6197 | nan | 0.5471 | 0.7226 | 0.0 | 0.5397 | 0.7140 | | 0.0617 | 91.82 | 26260 | 0.1376 | 0.3930 | 0.5975 | 0.5849 | nan | 0.5251 | 0.6698 | 0.0 | 0.5163 | 0.6627 | | 0.0632 | 91.89 | 26280 | 0.1400 | 0.3905 | 0.5940 | 0.5822 | nan | 0.5258 | 0.6621 | 0.0 | 0.5146 | 0.6569 | | 0.0308 | 91.96 | 26300 | 0.1344 | 0.3972 | 0.6049 | 0.5891 | nan | 0.5138 | 0.6960 | 0.0 | 0.5073 | 0.6843 | | 0.0406 | 92.03 | 26320 | 0.1375 | 0.3958 | 0.6021 | 0.5905 | nan | 0.5350 | 0.6693 | 0.0 | 0.5261 | 0.6614 | | 0.0434 | 92.1 | 26340 | 0.1336 | 0.4062 | 0.6173 | 0.6067 | nan | 0.5560 | 0.6786 | 0.0 | 0.5471 | 0.6715 | | 0.0425 | 92.17 | 26360 | 0.1299 | 0.4089 | 0.6216 | 0.6096 | nan | 0.5519 | 0.6914 | 0.0 | 0.5427 | 0.6842 | | 0.0531 | 92.24 | 26380 | 0.1317 | 0.4099 | 0.6225 | 0.6113 | nan | 0.5581 | 0.6869 | 0.0 | 0.5492 | 0.6804 | | 0.0498 | 92.31 | 26400 | 0.1392 | 0.3976 | 0.6043 | 0.5955 | nan | 0.5536 | 0.6549 | 0.0 | 0.5437 | 0.6491 | | 0.023 | 92.38 | 26420 | 0.1385 | 0.4018 | 0.6114 | 0.6062 | nan | 0.5816 | 0.6412 | 0.0 | 0.5704 | 0.6351 | | 0.038 | 92.45 | 26440 | 0.1397 | 0.3900 | 0.5919 | 0.5821 | nan | 0.5349 | 0.6490 | 0.0 | 0.5252 | 0.6447 | | 0.0501 | 92.52 | 26460 | 0.1438 | 0.3923 | 0.5965 | 0.5922 | nan | 0.5716 | 0.6214 | 0.0 | 0.5592 | 0.6176 | | 0.0316 | 92.59 | 26480 | 0.1429 | 0.4014 | 0.6105 | 0.6061 | nan | 0.5852 | 0.6358 | 0.0 | 0.5720 | 0.6323 | | 0.0521 | 92.66 | 26500 | 0.1419 | 0.3952 | 0.6006 | 0.5918 | nan | 0.5498 | 0.6514 | 0.0 | 0.5380 | 0.6476 | | 0.0289 | 92.73 | 26520 | 0.1409 | 0.4051 | 0.6159 | 0.6083 | nan | 0.5723 | 0.6595 | 0.0 | 0.5608 | 0.6545 | | 0.0563 | 92.8 | 26540 | 0.1381 | 0.4099 | 0.6234 | 0.6162 | nan | 0.5819 | 0.6649 | 0.0 | 0.5698 | 0.6600 | | 0.0374 | 92.87 | 26560 | 0.1380 | 0.4113 | 0.6251 | 0.6178 | nan | 0.5827 | 0.6676 | 0.0 | 0.5713 | 0.6628 | | 0.0414 | 92.94 | 26580 | 0.1417 | 0.4073 | 0.6195 | 0.6159 | nan | 0.5991 | 0.6398 | 0.0 | 0.5858 | 0.6360 | | 0.0567 | 93.01 | 26600 | 0.1399 | 0.4032 | 0.6128 | 0.6068 | nan | 0.5783 | 0.6473 | 0.0 | 0.5660 | 0.6435 | | 0.0522 | 93.08 | 26620 | 0.1379 | 0.4056 | 0.6160 | 0.6066 | nan | 0.5614 | 0.6706 | 0.0 | 0.5506 | 0.6661 | | 0.0288 | 93.15 | 26640 | 0.1429 | 0.3865 | 0.5869 | 0.5778 | nan | 0.5344 | 0.6394 | 0.0 | 0.5229 | 0.6367 | | 0.0358 | 93.22 | 26660 | 0.1399 | 0.3937 | 0.5981 | 0.5883 | nan | 0.5414 | 0.6548 | 0.0 | 0.5289 | 0.6523 | | 0.0439 | 93.29 | 26680 | 0.1352 | 0.4035 | 0.6134 | 0.6048 | nan | 0.5636 | 0.6633 | 0.0 | 0.5511 | 0.6596 | | 0.046 | 93.36 | 26700 | 0.1332 | 0.4132 | 0.6278 | 0.6175 | nan | 0.5680 | 0.6877 | 0.0 | 0.5569 | 0.6827 | | 0.0387 | 93.43 | 26720 | 0.1387 | 0.4088 | 0.6214 | 0.6127 | nan | 0.5710 | 0.6718 | 0.0 | 0.5567 | 0.6697 | | 0.04 | 93.5 | 26740 | 0.1375 | 0.4047 | 0.6156 | 0.6073 | nan | 0.5675 | 0.6637 | 0.0 | 0.5528 | 0.6612 | | 0.0301 | 93.57 | 26760 | 0.1396 | 0.3997 | 0.6079 | 0.5999 | nan | 0.5615 | 0.6544 | 0.0 | 0.5479 | 0.6512 | | 0.0395 | 93.64 | 26780 | 0.1459 | 0.3913 | 0.5952 | 0.5895 | nan | 0.5620 | 0.6285 | 0.0 | 0.5489 | 0.6250 | | 0.0595 | 93.71 | 26800 | 0.1459 | 0.3884 | 0.5901 | 0.5826 | nan | 0.5468 | 0.6335 | 0.0 | 0.5353 | 0.6300 | | 0.0576 | 93.78 | 26820 | 0.1481 | 0.3952 | 0.6012 | 0.5971 | nan | 0.5773 | 0.6252 | 0.0 | 0.5622 | 0.6233 | | 0.0347 | 93.85 | 26840 | 0.1395 | 0.4003 | 0.6096 | 0.6028 | nan | 0.5706 | 0.6485 | 0.0 | 0.5551 | 0.6459 | | 0.0334 | 93.92 | 26860 | 0.1340 | 0.4098 | 0.6238 | 0.6149 | nan | 0.5719 | 0.6758 | 0.0 | 0.5599 | 0.6695 | | 0.0519 | 93.99 | 26880 | 0.1409 | 0.3982 | 0.6059 | 0.5997 | nan | 0.5703 | 0.6416 | 0.0 | 0.5584 | 0.6363 | | 0.0291 | 94.06 | 26900 | 0.1386 | 0.4097 | 0.6239 | 0.6188 | nan | 0.5948 | 0.6530 | 0.0 | 0.5797 | 0.6493 | | 0.0693 | 94.13 | 26920 | 0.1459 | 0.3942 | 0.5998 | 0.5942 | nan | 0.5677 | 0.6319 | 0.0 | 0.5535 | 0.6292 | | 0.0409 | 94.2 | 26940 | 0.1459 | 0.3854 | 0.5864 | 0.5797 | nan | 0.5476 | 0.6251 | 0.0 | 0.5349 | 0.6214 | | 0.0501 | 94.27 | 26960 | 0.1431 | 0.3930 | 0.5984 | 0.5888 | nan | 0.5432 | 0.6536 | 0.0 | 0.5299 | 0.6492 | | 0.0692 | 94.34 | 26980 | 0.1418 | 0.3915 | 0.5958 | 0.5879 | nan | 0.5503 | 0.6414 | 0.0 | 0.5370 | 0.6375 | | 0.0382 | 94.41 | 27000 | 0.1374 | 0.4033 | 0.6142 | 0.6075 | nan | 0.5754 | 0.6530 | 0.0 | 0.5618 | 0.6482 | | 0.0436 | 94.48 | 27020 | 0.1380 | 0.3945 | 0.5996 | 0.5891 | nan | 0.5390 | 0.6603 | 0.0 | 0.5282 | 0.6552 | | 0.0345 | 94.55 | 27040 | 0.1319 | 0.4121 | 0.6269 | 0.6186 | nan | 0.5788 | 0.6750 | 0.0 | 0.5664 | 0.6700 | | 0.0337 | 94.62 | 27060 | 0.1361 | 0.3998 | 0.6071 | 0.5955 | nan | 0.5402 | 0.6740 | 0.0 | 0.5308 | 0.6685 | | 0.0395 | 94.69 | 27080 | 0.1384 | 0.3971 | 0.6037 | 0.5951 | nan | 0.5541 | 0.6532 | 0.0 | 0.5420 | 0.6491 | | 0.0359 | 94.76 | 27100 | 0.1364 | 0.4009 | 0.6091 | 0.5998 | nan | 0.5553 | 0.6629 | 0.0 | 0.5435 | 0.6593 | | 0.0489 | 94.83 | 27120 | 0.1296 | 0.4187 | 0.6362 | 0.6274 | nan | 0.5854 | 0.6871 | 0.0 | 0.5734 | 0.6827 | | 0.0363 | 94.9 | 27140 | 0.1302 | 0.4124 | 0.6264 | 0.6159 | nan | 0.5661 | 0.6867 | 0.0 | 0.5553 | 0.6819 | | 0.0555 | 94.97 | 27160 | 0.1306 | 0.4139 | 0.6288 | 0.6181 | nan | 0.5669 | 0.6907 | 0.0 | 0.5560 | 0.6857 | | 0.0441 | 95.03 | 27180 | 0.1318 | 0.4129 | 0.6274 | 0.6176 | nan | 0.5706 | 0.6843 | 0.0 | 0.5592 | 0.6796 | | 0.0725 | 95.1 | 27200 | 0.1331 | 0.4148 | 0.6308 | 0.6234 | nan | 0.5878 | 0.6738 | 0.0 | 0.5746 | 0.6699 | | 0.0404 | 95.17 | 27220 | 0.1430 | 0.4049 | 0.6168 | 0.6137 | nan | 0.5992 | 0.6344 | 0.0 | 0.5829 | 0.6319 | | 0.053 | 95.24 | 27240 | 0.1386 | 0.3961 | 0.6016 | 0.5919 | nan | 0.5453 | 0.6580 | 0.0 | 0.5348 | 0.6534 | | 0.0397 | 95.31 | 27260 | 0.1318 | 0.4125 | 0.6275 | 0.6191 | nan | 0.5788 | 0.6763 | 0.0 | 0.5669 | 0.6707 | | 0.0345 | 95.38 | 27280 | 0.1394 | 0.3954 | 0.6011 | 0.5932 | nan | 0.5555 | 0.6466 | 0.0 | 0.5442 | 0.6420 | | 0.051 | 95.45 | 27300 | 0.1428 | 0.3966 | 0.6039 | 0.5993 | nan | 0.5772 | 0.6305 | 0.0 | 0.5625 | 0.6273 | | 0.0502 | 95.52 | 27320 | 0.1403 | 0.3984 | 0.6061 | 0.5976 | nan | 0.5569 | 0.6552 | 0.0 | 0.5432 | 0.6521 | | 0.0534 | 95.59 | 27340 | 0.1407 | 0.3952 | 0.6012 | 0.5952 | nan | 0.5665 | 0.6358 | 0.0 | 0.5523 | 0.6332 | | 0.0511 | 95.66 | 27360 | 0.1413 | 0.3992 | 0.6077 | 0.6022 | nan | 0.5758 | 0.6396 | 0.0 | 0.5617 | 0.6360 | | 0.041 | 95.73 | 27380 | 0.1426 | 0.3920 | 0.5960 | 0.5867 | nan | 0.5421 | 0.6499 | 0.0 | 0.5293 | 0.6467 | | 0.0541 | 95.8 | 27400 | 0.1391 | 0.4066 | 0.6189 | 0.6132 | nan | 0.5860 | 0.6518 | 0.0 | 0.5713 | 0.6485 | | 0.0419 | 95.87 | 27420 | 0.1421 | 0.3992 | 0.6079 | 0.6035 | nan | 0.5823 | 0.6335 | 0.0 | 0.5666 | 0.6309 | | 0.0508 | 95.94 | 27440 | 0.1431 | 0.4007 | 0.6096 | 0.6030 | nan | 0.5716 | 0.6475 | 0.0 | 0.5571 | 0.6448 | | 0.0451 | 96.01 | 27460 | 0.1401 | 0.4053 | 0.6161 | 0.6081 | nan | 0.5699 | 0.6623 | 0.0 | 0.5573 | 0.6585 | | 0.0476 | 96.08 | 27480 | 0.1417 | 0.4010 | 0.6093 | 0.6001 | nan | 0.5559 | 0.6628 | 0.0 | 0.5447 | 0.6582 | | 0.0555 | 96.15 | 27500 | 0.1351 | 0.4107 | 0.6247 | 0.6159 | nan | 0.5737 | 0.6757 | 0.0 | 0.5602 | 0.6719 | | 0.0488 | 96.22 | 27520 | 0.1341 | 0.4039 | 0.6136 | 0.6043 | nan | 0.5601 | 0.6671 | 0.0 | 0.5490 | 0.6627 | | 0.0545 | 96.29 | 27540 | 0.1381 | 0.4003 | 0.6079 | 0.5976 | nan | 0.5484 | 0.6675 | 0.0 | 0.5370 | 0.6638 | | 0.042 | 96.36 | 27560 | 0.1403 | 0.3970 | 0.6026 | 0.5900 | nan | 0.5301 | 0.6751 | 0.0 | 0.5201 | 0.6709 | | 0.0428 | 96.43 | 27580 | 0.1314 | 0.4140 | 0.6295 | 0.6160 | nan | 0.5516 | 0.7074 | 0.0 | 0.5430 | 0.6991 | | 0.04 | 96.5 | 27600 | 0.1385 | 0.3962 | 0.6015 | 0.5906 | nan | 0.5384 | 0.6647 | 0.0 | 0.5279 | 0.6607 | | 0.0436 | 96.57 | 27620 | 0.1412 | 0.3960 | 0.6013 | 0.5916 | nan | 0.5450 | 0.6577 | 0.0 | 0.5339 | 0.6540 | | 0.0452 | 96.64 | 27640 | 0.1381 | 0.4071 | 0.6192 | 0.6116 | nan | 0.5750 | 0.6634 | 0.0 | 0.5614 | 0.6598 | | 0.071 | 96.71 | 27660 | 0.1337 | 0.4037 | 0.6130 | 0.5998 | nan | 0.5369 | 0.6890 | 0.0 | 0.5273 | 0.6837 | | 0.0531 | 96.78 | 27680 | 0.1345 | 0.4067 | 0.6183 | 0.6090 | nan | 0.5644 | 0.6723 | 0.0 | 0.5519 | 0.6682 | | 0.0387 | 96.85 | 27700 | 0.1330 | 0.3953 | 0.6004 | 0.5891 | nan | 0.5355 | 0.6653 | 0.0 | 0.5246 | 0.6614 | | 0.0256 | 96.92 | 27720 | 0.1441 | 0.3914 | 0.5962 | 0.5919 | nan | 0.5714 | 0.6210 | 0.0 | 0.5546 | 0.6195 | | 0.0446 | 96.99 | 27740 | 0.1394 | 0.4004 | 0.6091 | 0.6009 | nan | 0.5619 | 0.6562 | 0.0 | 0.5482 | 0.6531 | | 0.0332 | 97.06 | 27760 | 0.1365 | 0.4009 | 0.6098 | 0.5984 | nan | 0.5444 | 0.6752 | 0.0 | 0.5312 | 0.6717 | | 0.048 | 97.13 | 27780 | 0.1326 | 0.4006 | 0.6087 | 0.5974 | nan | 0.5436 | 0.6737 | 0.0 | 0.5314 | 0.6705 | | 0.0597 | 97.2 | 27800 | 0.1361 | 0.3986 | 0.6055 | 0.5931 | nan | 0.5339 | 0.6772 | 0.0 | 0.5229 | 0.6731 | | 0.045 | 97.27 | 27820 | 0.1302 | 0.4122 | 0.6270 | 0.6147 | nan | 0.5559 | 0.6982 | 0.0 | 0.5463 | 0.6904 | | 0.0439 | 97.34 | 27840 | 0.1272 | 0.4169 | 0.6341 | 0.6226 | nan | 0.5675 | 0.7007 | 0.0 | 0.5574 | 0.6934 | | 0.0487 | 97.41 | 27860 | 0.1343 | 0.4134 | 0.6295 | 0.6230 | nan | 0.5922 | 0.6668 | 0.0 | 0.5784 | 0.6619 | | 0.0335 | 97.48 | 27880 | 0.1429 | 0.3926 | 0.5975 | 0.5905 | nan | 0.5572 | 0.6378 | 0.0 | 0.5433 | 0.6345 | | 0.0414 | 97.55 | 27900 | 0.1402 | 0.3932 | 0.5978 | 0.5879 | nan | 0.5406 | 0.6549 | 0.0 | 0.5279 | 0.6516 | | 0.0271 | 97.62 | 27920 | 0.1409 | 0.4023 | 0.6125 | 0.6068 | nan | 0.5799 | 0.6450 | 0.0 | 0.5652 | 0.6417 | | 0.0813 | 97.69 | 27940 | 0.1409 | 0.3995 | 0.6077 | 0.5987 | nan | 0.5556 | 0.6597 | 0.0 | 0.5426 | 0.6558 | | 0.0414 | 97.76 | 27960 | 0.1411 | 0.3992 | 0.6073 | 0.5994 | nan | 0.5619 | 0.6526 | 0.0 | 0.5485 | 0.6490 | | 0.0396 | 97.83 | 27980 | 0.1366 | 0.4077 | 0.6204 | 0.6126 | nan | 0.5748 | 0.6661 | 0.0 | 0.5608 | 0.6622 | | 0.0588 | 97.9 | 28000 | 0.1434 | 0.3959 | 0.6026 | 0.5979 | nan | 0.5752 | 0.6301 | 0.0 | 0.5607 | 0.6269 | | 0.0402 | 97.97 | 28020 | 0.1398 | 0.4021 | 0.6118 | 0.6036 | nan | 0.5643 | 0.6594 | 0.0 | 0.5506 | 0.6557 | | 0.0515 | 98.04 | 28040 | 0.1499 | 0.3842 | 0.5852 | 0.5787 | nan | 0.5477 | 0.6228 | 0.0 | 0.5322 | 0.6205 | | 0.0379 | 98.11 | 28060 | 0.1427 | 0.3942 | 0.5999 | 0.5926 | nan | 0.5579 | 0.6420 | 0.0 | 0.5433 | 0.6392 | | 0.0602 | 98.18 | 28080 | 0.1352 | 0.4009 | 0.6097 | 0.5998 | nan | 0.5524 | 0.6670 | 0.0 | 0.5405 | 0.6622 | | 0.0511 | 98.25 | 28100 | 0.1403 | 0.3988 | 0.6063 | 0.5979 | nan | 0.5574 | 0.6553 | 0.0 | 0.5443 | 0.6522 | | 0.0323 | 98.32 | 28120 | 0.1425 | 0.4006 | 0.6102 | 0.6070 | nan | 0.5918 | 0.6285 | 0.0 | 0.5762 | 0.6257 | | 0.039 | 98.39 | 28140 | 0.1381 | 0.4001 | 0.6080 | 0.5991 | nan | 0.5566 | 0.6595 | 0.0 | 0.5447 | 0.6555 | | 0.0455 | 98.46 | 28160 | 0.1401 | 0.4017 | 0.6116 | 0.6066 | nan | 0.5826 | 0.6406 | 0.0 | 0.5676 | 0.6375 | | 0.0367 | 98.53 | 28180 | 0.1367 | 0.4030 | 0.6127 | 0.6039 | nan | 0.5620 | 0.6633 | 0.0 | 0.5495 | 0.6593 | | 0.0522 | 98.6 | 28200 | 0.1415 | 0.3992 | 0.6069 | 0.5988 | nan | 0.5602 | 0.6535 | 0.0 | 0.5475 | 0.6502 | | 0.0377 | 98.67 | 28220 | 0.1377 | 0.4126 | 0.6276 | 0.6203 | nan | 0.5853 | 0.6699 | 0.0 | 0.5718 | 0.6661 | | 0.0496 | 98.74 | 28240 | 0.1432 | 0.4022 | 0.6133 | 0.6075 | nan | 0.5796 | 0.6470 | 0.0 | 0.5623 | 0.6442 | | 0.0393 | 98.81 | 28260 | 0.1444 | 0.4001 | 0.6091 | 0.6015 | nan | 0.5657 | 0.6524 | 0.0 | 0.5512 | 0.6491 | | 0.0463 | 98.88 | 28280 | 0.1394 | 0.4045 | 0.6163 | 0.6094 | nan | 0.5763 | 0.6563 | 0.0 | 0.5615 | 0.6520 | | 0.0439 | 98.95 | 28300 | 0.1450 | 0.3882 | 0.5920 | 0.5866 | nan | 0.5609 | 0.6231 | 0.0 | 0.5448 | 0.6197 | | 0.0549 | 99.02 | 28320 | 0.1400 | 0.3997 | 0.6088 | 0.6031 | nan | 0.5760 | 0.6416 | 0.0 | 0.5628 | 0.6362 | | 0.0581 | 99.09 | 28340 | 0.1452 | 0.3913 | 0.5955 | 0.5888 | nan | 0.5568 | 0.6342 | 0.0 | 0.5433 | 0.6308 | | 0.0305 | 99.16 | 28360 | 0.1348 | 0.3969 | 0.6028 | 0.5896 | nan | 0.5267 | 0.6789 | 0.0 | 0.5174 | 0.6732 | | 0.039 | 99.23 | 28380 | 0.1391 | 0.3979 | 0.6045 | 0.5949 | nan | 0.5491 | 0.6599 | 0.0 | 0.5376 | 0.6563 | | 0.0454 | 99.3 | 28400 | 0.1439 | 0.4027 | 0.6132 | 0.6071 | nan | 0.5784 | 0.6479 | 0.0 | 0.5624 | 0.6458 | | 0.0668 | 99.37 | 28420 | 0.1446 | 0.4039 | 0.6162 | 0.6113 | nan | 0.5880 | 0.6444 | 0.0 | 0.5687 | 0.6430 | | 0.0354 | 99.44 | 28440 | 0.1457 | 0.3944 | 0.6011 | 0.5962 | nan | 0.5727 | 0.6296 | 0.0 | 0.5555 | 0.6277 | | 0.0557 | 99.51 | 28460 | 0.1402 | 0.4060 | 0.6182 | 0.6110 | nan | 0.5765 | 0.6599 | 0.0 | 0.5606 | 0.6575 | | 0.0318 | 99.58 | 28480 | 0.1424 | 0.3997 | 0.6086 | 0.6020 | nan | 0.5706 | 0.6465 | 0.0 | 0.5549 | 0.6442 | | 0.0379 | 99.65 | 28500 | 0.1545 | 0.3816 | 0.5816 | 0.5769 | nan | 0.5545 | 0.6086 | 0.0 | 0.5372 | 0.6076 | | 0.0471 | 99.72 | 28520 | 0.1443 | 0.3983 | 0.6063 | 0.5994 | nan | 0.5666 | 0.6459 | 0.0 | 0.5515 | 0.6433 | | 0.0604 | 99.79 | 28540 | 0.1323 | 0.4111 | 0.6245 | 0.6134 | nan | 0.5603 | 0.6887 | 0.0 | 0.5514 | 0.6819 | | 0.0652 | 99.86 | 28560 | 0.1359 | 0.4074 | 0.6192 | 0.6102 | nan | 0.5672 | 0.6712 | 0.0 | 0.5542 | 0.6679 | | 0.0558 | 99.93 | 28580 | 0.1393 | 0.4024 | 0.6120 | 0.6056 | nan | 0.5747 | 0.6493 | 0.0 | 0.5601 | 0.6471 | | 0.0429 | 100.0 | 28600 | 0.1367 | 0.4081 | 0.6207 | 0.6124 | nan | 0.5731 | 0.6682 | 0.0 | 0.5589 | 0.6654 | | 0.0537 | 100.07 | 28620 | 0.1349 | 0.4046 | 0.6152 | 0.6082 | nan | 0.5752 | 0.6552 | 0.0 | 0.5611 | 0.6527 | | 0.0531 | 100.14 | 28640 | 0.1383 | 0.3996 | 0.6073 | 0.5994 | nan | 0.5618 | 0.6528 | 0.0 | 0.5475 | 0.6513 | | 0.062 | 100.21 | 28660 | 0.1392 | 0.3925 | 0.5963 | 0.5866 | nan | 0.5403 | 0.6523 | 0.0 | 0.5268 | 0.6506 | | 0.0537 | 100.28 | 28680 | 0.1357 | 0.3957 | 0.6013 | 0.5910 | nan | 0.5418 | 0.6608 | 0.0 | 0.5304 | 0.6568 | | 0.04 | 100.35 | 28700 | 0.1282 | 0.4035 | 0.6133 | 0.6008 | nan | 0.5408 | 0.6859 | 0.0 | 0.5309 | 0.6795 | | 0.06 | 100.42 | 28720 | 0.1289 | 0.4094 | 0.6228 | 0.6126 | nan | 0.5639 | 0.6816 | 0.0 | 0.5519 | 0.6762 | | 0.0802 | 100.49 | 28740 | 0.1349 | 0.4065 | 0.6180 | 0.6093 | nan | 0.5682 | 0.6678 | 0.0 | 0.5547 | 0.6650 | | 0.0503 | 100.56 | 28760 | 0.1431 | 0.3969 | 0.6048 | 0.5994 | nan | 0.5736 | 0.6360 | 0.0 | 0.5574 | 0.6333 | | 0.0342 | 100.63 | 28780 | 0.1403 | 0.3972 | 0.6049 | 0.5971 | nan | 0.5598 | 0.6501 | 0.0 | 0.5451 | 0.6464 | | 0.048 | 100.7 | 28800 | 0.1414 | 0.3942 | 0.6001 | 0.5926 | nan | 0.5568 | 0.6434 | 0.0 | 0.5426 | 0.6401 | | 0.0173 | 100.77 | 28820 | 0.1417 | 0.3903 | 0.5938 | 0.5856 | nan | 0.5465 | 0.6412 | 0.0 | 0.5329 | 0.6381 | | 0.0407 | 100.84 | 28840 | 0.1446 | 0.3984 | 0.6071 | 0.6022 | nan | 0.5789 | 0.6352 | 0.0 | 0.5623 | 0.6328 | | 0.0357 | 100.91 | 28860 | 0.1456 | 0.3953 | 0.6024 | 0.5985 | nan | 0.5801 | 0.6247 | 0.0 | 0.5632 | 0.6227 | | 0.0367 | 100.98 | 28880 | 0.1409 | 0.3963 | 0.6032 | 0.5942 | nan | 0.5513 | 0.6551 | 0.0 | 0.5366 | 0.6524 | | 0.0368 | 101.05 | 28900 | 0.1362 | 0.4043 | 0.6151 | 0.6062 | nan | 0.5641 | 0.6660 | 0.0 | 0.5498 | 0.6632 | | 0.0273 | 101.12 | 28920 | 0.1392 | 0.3947 | 0.6005 | 0.5916 | nan | 0.5495 | 0.6514 | 0.0 | 0.5352 | 0.6490 | | 0.0458 | 101.19 | 28940 | 0.1408 | 0.3901 | 0.5933 | 0.5848 | nan | 0.5441 | 0.6426 | 0.0 | 0.5301 | 0.6401 | | 0.0325 | 101.26 | 28960 | 0.1394 | 0.3946 | 0.6000 | 0.5909 | nan | 0.5473 | 0.6528 | 0.0 | 0.5340 | 0.6498 | | 0.0801 | 101.33 | 28980 | 0.1462 | 0.3841 | 0.5841 | 0.5751 | nan | 0.5320 | 0.6363 | 0.0 | 0.5185 | 0.6339 | | 0.0407 | 101.4 | 29000 | 0.1469 | 0.3883 | 0.5908 | 0.5839 | nan | 0.5507 | 0.6310 | 0.0 | 0.5362 | 0.6286 | | 0.0576 | 101.47 | 29020 | 0.1460 | 0.3894 | 0.5926 | 0.5854 | nan | 0.5511 | 0.6342 | 0.0 | 0.5368 | 0.6315 | | 0.0462 | 101.54 | 29040 | 0.1414 | 0.3985 | 0.6055 | 0.5971 | nan | 0.5571 | 0.6540 | 0.0 | 0.5447 | 0.6507 | | 0.058 | 101.61 | 29060 | 0.1407 | 0.4095 | 0.6232 | 0.6184 | nan | 0.5959 | 0.6504 | 0.0 | 0.5809 | 0.6476 | | 0.0439 | 101.68 | 29080 | 0.1394 | 0.4068 | 0.6187 | 0.6103 | nan | 0.5702 | 0.6672 | 0.0 | 0.5561 | 0.6642 | | 0.055 | 101.75 | 29100 | 0.1279 | 0.4234 | 0.6431 | 0.6320 | nan | 0.5791 | 0.7071 | 0.0 | 0.5684 | 0.7017 | | 0.0292 | 101.82 | 29120 | 0.1327 | 0.4179 | 0.6349 | 0.6278 | nan | 0.5942 | 0.6757 | 0.0 | 0.5825 | 0.6711 | | 0.038 | 101.89 | 29140 | 0.1329 | 0.4090 | 0.6220 | 0.6140 | nan | 0.5757 | 0.6684 | 0.0 | 0.5630 | 0.6641 | | 0.0326 | 101.96 | 29160 | 0.1346 | 0.4100 | 0.6233 | 0.6164 | nan | 0.5836 | 0.6630 | 0.0 | 0.5712 | 0.6587 | | 0.0551 | 102.03 | 29180 | 0.1351 | 0.4043 | 0.6138 | 0.6023 | nan | 0.5471 | 0.6804 | 0.0 | 0.5374 | 0.6755 | | 0.0636 | 102.1 | 29200 | 0.1350 | 0.4041 | 0.6132 | 0.6020 | nan | 0.5484 | 0.6779 | 0.0 | 0.5386 | 0.6736 | | 0.0591 | 102.17 | 29220 | 0.1343 | 0.4066 | 0.6170 | 0.6048 | nan | 0.5463 | 0.6878 | 0.0 | 0.5358 | 0.6839 | | 0.035 | 102.24 | 29240 | 0.1336 | 0.4145 | 0.6295 | 0.6205 | nan | 0.5777 | 0.6812 | 0.0 | 0.5668 | 0.6766 | | 0.0554 | 102.31 | 29260 | 0.1406 | 0.4042 | 0.6142 | 0.6065 | nan | 0.5698 | 0.6586 | 0.0 | 0.5588 | 0.6538 | | 0.0203 | 102.38 | 29280 | 0.1415 | 0.4015 | 0.6102 | 0.6028 | nan | 0.5673 | 0.6531 | 0.0 | 0.5557 | 0.6489 | | 0.0496 | 102.45 | 29300 | 0.1421 | 0.3988 | 0.6069 | 0.6014 | nan | 0.5751 | 0.6387 | 0.0 | 0.5600 | 0.6364 | | 0.0575 | 102.52 | 29320 | 0.1365 | 0.4019 | 0.6109 | 0.6024 | nan | 0.5619 | 0.6598 | 0.0 | 0.5496 | 0.6562 | | 0.0355 | 102.59 | 29340 | 0.1375 | 0.4009 | 0.6097 | 0.6019 | nan | 0.5647 | 0.6547 | 0.0 | 0.5512 | 0.6515 | | 0.0429 | 102.66 | 29360 | 0.1402 | 0.3941 | 0.5992 | 0.5905 | nan | 0.5493 | 0.6490 | 0.0 | 0.5366 | 0.6457 | | 0.0533 | 102.73 | 29380 | 0.1410 | 0.3911 | 0.5938 | 0.5832 | nan | 0.5322 | 0.6554 | 0.0 | 0.5226 | 0.6507 | | 0.0683 | 102.8 | 29400 | 0.1423 | 0.3961 | 0.6019 | 0.5925 | nan | 0.5478 | 0.6560 | 0.0 | 0.5359 | 0.6523 | | 0.0243 | 102.87 | 29420 | 0.1434 | 0.3908 | 0.5941 | 0.5851 | nan | 0.5420 | 0.6462 | 0.0 | 0.5297 | 0.6428 | | 0.0301 | 102.94 | 29440 | 0.1408 | 0.3984 | 0.6049 | 0.5922 | nan | 0.5314 | 0.6785 | 0.0 | 0.5205 | 0.6746 | | 0.0507 | 103.01 | 29460 | 0.1390 | 0.3995 | 0.6069 | 0.5980 | nan | 0.5554 | 0.6584 | 0.0 | 0.5440 | 0.6545 | | 0.0328 | 103.08 | 29480 | 0.1505 | 0.3834 | 0.5825 | 0.5748 | nan | 0.5376 | 0.6275 | 0.0 | 0.5242 | 0.6259 | | 0.0327 | 103.15 | 29500 | 0.1428 | 0.4051 | 0.6168 | 0.6117 | nan | 0.5869 | 0.6468 | 0.0 | 0.5704 | 0.6449 | | 0.0406 | 103.22 | 29520 | 0.1430 | 0.4035 | 0.6134 | 0.6060 | nan | 0.5707 | 0.6560 | 0.0 | 0.5572 | 0.6533 | | 0.054 | 103.29 | 29540 | 0.1451 | 0.4038 | 0.6147 | 0.6094 | nan | 0.5840 | 0.6454 | 0.0 | 0.5677 | 0.6436 | | 0.0348 | 103.36 | 29560 | 0.1458 | 0.4020 | 0.6120 | 0.6063 | nan | 0.5791 | 0.6450 | 0.0 | 0.5628 | 0.6432 | | 0.0465 | 103.43 | 29580 | 0.1426 | 0.4065 | 0.6186 | 0.6139 | nan | 0.5916 | 0.6456 | 0.0 | 0.5760 | 0.6435 | | 0.0405 | 103.5 | 29600 | 0.1457 | 0.4040 | 0.6149 | 0.6096 | nan | 0.5840 | 0.6458 | 0.0 | 0.5686 | 0.6434 | | 0.0616 | 103.57 | 29620 | 0.1424 | 0.4034 | 0.6135 | 0.6081 | nan | 0.5826 | 0.6443 | 0.0 | 0.5691 | 0.6410 | | 0.0215 | 103.64 | 29640 | 0.1423 | 0.3978 | 0.6052 | 0.5976 | nan | 0.5614 | 0.6490 | 0.0 | 0.5471 | 0.6462 | | 0.0513 | 103.71 | 29660 | 0.1399 | 0.4006 | 0.6090 | 0.6007 | nan | 0.5611 | 0.6568 | 0.0 | 0.5489 | 0.6530 | | 0.0413 | 103.78 | 29680 | 0.1376 | 0.3963 | 0.6017 | 0.5895 | nan | 0.5310 | 0.6724 | 0.0 | 0.5224 | 0.6664 | | 0.037 | 103.85 | 29700 | 0.1341 | 0.4135 | 0.6285 | 0.6198 | nan | 0.5783 | 0.6787 | 0.0 | 0.5655 | 0.6750 | | 0.0473 | 103.92 | 29720 | 0.1385 | 0.4054 | 0.6157 | 0.6073 | nan | 0.5672 | 0.6641 | 0.0 | 0.5555 | 0.6606 | | 0.0511 | 103.99 | 29740 | 0.1377 | 0.4109 | 0.6247 | 0.6154 | nan | 0.5714 | 0.6779 | 0.0 | 0.5588 | 0.6740 | | 0.0509 | 104.06 | 29760 | 0.1364 | 0.4081 | 0.6204 | 0.6113 | nan | 0.5679 | 0.6728 | 0.0 | 0.5553 | 0.6691 | | 0.067 | 104.13 | 29780 | 0.1373 | 0.4058 | 0.6166 | 0.6101 | nan | 0.5791 | 0.6540 | 0.0 | 0.5675 | 0.6498 | | 0.0322 | 104.2 | 29800 | 0.1404 | 0.3988 | 0.6062 | 0.5987 | nan | 0.5629 | 0.6496 | 0.0 | 0.5499 | 0.6465 | | 0.041 | 104.27 | 29820 | 0.1373 | 0.4025 | 0.6117 | 0.6023 | nan | 0.5572 | 0.6662 | 0.0 | 0.5456 | 0.6620 | | 0.0457 | 104.34 | 29840 | 0.1380 | 0.3954 | 0.6005 | 0.5900 | nan | 0.5400 | 0.6610 | 0.0 | 0.5307 | 0.6555 | | 0.0464 | 104.41 | 29860 | 0.1355 | 0.3980 | 0.6045 | 0.5946 | nan | 0.5470 | 0.6621 | 0.0 | 0.5360 | 0.6579 | | 0.0624 | 104.48 | 29880 | 0.1415 | 0.3976 | 0.6038 | 0.5939 | nan | 0.5468 | 0.6608 | 0.0 | 0.5358 | 0.6570 | | 0.0669 | 104.55 | 29900 | 0.1404 | 0.4031 | 0.6128 | 0.6064 | nan | 0.5760 | 0.6496 | 0.0 | 0.5629 | 0.6464 | | 0.0381 | 104.62 | 29920 | 0.1409 | 0.4045 | 0.6146 | 0.6061 | nan | 0.5656 | 0.6636 | 0.0 | 0.5530 | 0.6604 | | 0.0549 | 104.69 | 29940 | 0.1429 | 0.3938 | 0.5984 | 0.5889 | nan | 0.5439 | 0.6528 | 0.0 | 0.5316 | 0.6498 | | 0.0484 | 104.76 | 29960 | 0.1436 | 0.3972 | 0.6031 | 0.5944 | nan | 0.5529 | 0.6534 | 0.0 | 0.5415 | 0.6500 | | 0.0427 | 104.83 | 29980 | 0.1410 | 0.4011 | 0.6098 | 0.6012 | nan | 0.5601 | 0.6595 | 0.0 | 0.5471 | 0.6561 | | 0.0352 | 104.9 | 30000 | 0.1441 | 0.4030 | 0.6122 | 0.6043 | nan | 0.5663 | 0.6581 | 0.0 | 0.5541 | 0.6549 | | 0.0494 | 104.97 | 30020 | 0.1468 | 0.3954 | 0.6004 | 0.5900 | nan | 0.5403 | 0.6605 | 0.0 | 0.5295 | 0.6566 | | 0.0361 | 105.03 | 30040 | 0.1480 | 0.3996 | 0.6073 | 0.6007 | nan | 0.5693 | 0.6453 | 0.0 | 0.5556 | 0.6431 | | 0.0382 | 105.1 | 30060 | 0.1402 | 0.4086 | 0.6205 | 0.6128 | nan | 0.5760 | 0.6650 | 0.0 | 0.5640 | 0.6617 | | 0.0376 | 105.17 | 30080 | 0.1398 | 0.4022 | 0.6108 | 0.6020 | nan | 0.5603 | 0.6612 | 0.0 | 0.5486 | 0.6581 | | 0.0343 | 105.24 | 30100 | 0.1476 | 0.3951 | 0.6002 | 0.5919 | nan | 0.5521 | 0.6484 | 0.0 | 0.5394 | 0.6459 | | 0.0392 | 105.31 | 30120 | 0.1433 | 0.3952 | 0.6002 | 0.5895 | nan | 0.5385 | 0.6619 | 0.0 | 0.5267 | 0.6588 | | 0.0626 | 105.38 | 30140 | 0.1419 | 0.3984 | 0.6053 | 0.5946 | nan | 0.5433 | 0.6673 | 0.0 | 0.5320 | 0.6632 | | 0.0394 | 105.45 | 30160 | 0.1394 | 0.4059 | 0.6165 | 0.6066 | nan | 0.5596 | 0.6733 | 0.0 | 0.5485 | 0.6691 | | 0.0584 | 105.52 | 30180 | 0.1438 | 0.3974 | 0.6039 | 0.5948 | nan | 0.5516 | 0.6561 | 0.0 | 0.5392 | 0.6530 | | 0.0305 | 105.59 | 30200 | 0.1424 | 0.4095 | 0.6226 | 0.6149 | nan | 0.5780 | 0.6672 | 0.0 | 0.5639 | 0.6645 | | 0.0327 | 105.66 | 30220 | 0.1400 | 0.4077 | 0.6195 | 0.6109 | nan | 0.5698 | 0.6691 | 0.0 | 0.5568 | 0.6663 | | 0.0274 | 105.73 | 30240 | 0.1371 | 0.4106 | 0.6236 | 0.6133 | nan | 0.5643 | 0.6828 | 0.0 | 0.5535 | 0.6783 | | 0.0268 | 105.8 | 30260 | 0.1393 | 0.4050 | 0.6153 | 0.6061 | nan | 0.5624 | 0.6681 | 0.0 | 0.5505 | 0.6645 | | 0.065 | 105.87 | 30280 | 0.1428 | 0.3987 | 0.6051 | 0.5937 | nan | 0.5394 | 0.6708 | 0.0 | 0.5291 | 0.6670 | | 0.027 | 105.94 | 30300 | 0.1423 | 0.3964 | 0.6017 | 0.5926 | nan | 0.5491 | 0.6543 | 0.0 | 0.5383 | 0.6509 | | 0.0629 | 106.01 | 30320 | 0.1449 | 0.3912 | 0.5943 | 0.5866 | nan | 0.5496 | 0.6391 | 0.0 | 0.5374 | 0.6362 | | 0.0332 | 106.08 | 30340 | 0.1425 | 0.3945 | 0.5994 | 0.5905 | nan | 0.5478 | 0.6511 | 0.0 | 0.5349 | 0.6485 | | 0.0409 | 106.15 | 30360 | 0.1361 | 0.4103 | 0.6242 | 0.6182 | nan | 0.5893 | 0.6592 | 0.0 | 0.5752 | 0.6558 | | 0.0255 | 106.22 | 30380 | 0.1311 | 0.4151 | 0.6309 | 0.6225 | nan | 0.5824 | 0.6794 | 0.0 | 0.5695 | 0.6757 | | 0.0532 | 106.29 | 30400 | 0.1347 | 0.4155 | 0.6316 | 0.6248 | nan | 0.5923 | 0.6709 | 0.0 | 0.5789 | 0.6675 | | 0.0465 | 106.36 | 30420 | 0.1297 | 0.4161 | 0.6322 | 0.6229 | nan | 0.5781 | 0.6864 | 0.0 | 0.5681 | 0.6801 | | 0.043 | 106.43 | 30440 | 0.1338 | 0.4130 | 0.6273 | 0.6190 | nan | 0.5792 | 0.6755 | 0.0 | 0.5674 | 0.6716 | | 0.0399 | 106.5 | 30460 | 0.1329 | 0.4081 | 0.6196 | 0.6091 | nan | 0.5591 | 0.6801 | 0.0 | 0.5492 | 0.6750 | | 0.0317 | 106.57 | 30480 | 0.1367 | 0.4048 | 0.6149 | 0.6053 | nan | 0.5592 | 0.6707 | 0.0 | 0.5473 | 0.6669 | | 0.044 | 106.64 | 30500 | 0.1330 | 0.4107 | 0.6240 | 0.6137 | nan | 0.5645 | 0.6834 | 0.0 | 0.5547 | 0.6775 | | 0.032 | 106.71 | 30520 | 0.1401 | 0.3970 | 0.6031 | 0.5927 | nan | 0.5428 | 0.6634 | 0.0 | 0.5325 | 0.6586 | | 0.0685 | 106.78 | 30540 | 0.1417 | 0.3968 | 0.6033 | 0.5948 | nan | 0.5541 | 0.6526 | 0.0 | 0.5424 | 0.6481 | | 0.0583 | 106.85 | 30560 | 0.1446 | 0.3953 | 0.6008 | 0.5931 | nan | 0.5563 | 0.6453 | 0.0 | 0.5439 | 0.6421 | | 0.0505 | 106.92 | 30580 | 0.1420 | 0.3952 | 0.6007 | 0.5926 | nan | 0.5537 | 0.6477 | 0.0 | 0.5416 | 0.6441 | | 0.0443 | 106.99 | 30600 | 0.1427 | 0.3976 | 0.6045 | 0.5982 | nan | 0.5681 | 0.6409 | 0.0 | 0.5550 | 0.6377 | | 0.0234 | 107.06 | 30620 | 0.1383 | 0.4030 | 0.6123 | 0.6030 | nan | 0.5590 | 0.6655 | 0.0 | 0.5470 | 0.6619 | | 0.0602 | 107.13 | 30640 | 0.1444 | 0.3940 | 0.5990 | 0.5902 | nan | 0.5479 | 0.6501 | 0.0 | 0.5351 | 0.6470 | | 0.0365 | 107.2 | 30660 | 0.1527 | 0.3949 | 0.6016 | 0.5981 | nan | 0.5814 | 0.6217 | 0.0 | 0.5654 | 0.6192 | | 0.0308 | 107.27 | 30680 | 0.1419 | 0.4012 | 0.6109 | 0.6046 | nan | 0.5747 | 0.6471 | 0.0 | 0.5601 | 0.6433 | | 0.0443 | 107.34 | 30700 | 0.1461 | 0.3963 | 0.6032 | 0.5966 | nan | 0.5648 | 0.6417 | 0.0 | 0.5509 | 0.6381 | | 0.0349 | 107.41 | 30720 | 0.1435 | 0.4028 | 0.6131 | 0.6056 | nan | 0.5699 | 0.6563 | 0.0 | 0.5563 | 0.6520 | | 0.0425 | 107.48 | 30740 | 0.1432 | 0.4098 | 0.6241 | 0.6184 | nan | 0.5912 | 0.6571 | 0.0 | 0.5763 | 0.6531 | | 0.0521 | 107.55 | 30760 | 0.1499 | 0.3922 | 0.5968 | 0.5901 | nan | 0.5582 | 0.6354 | 0.0 | 0.5437 | 0.6328 | | 0.0413 | 107.62 | 30780 | 0.1447 | 0.3910 | 0.5945 | 0.5854 | nan | 0.5417 | 0.6474 | 0.0 | 0.5300 | 0.6432 | | 0.0486 | 107.69 | 30800 | 0.1421 | 0.3947 | 0.6005 | 0.5920 | nan | 0.5516 | 0.6494 | 0.0 | 0.5385 | 0.6456 | | 0.0647 | 107.76 | 30820 | 0.1455 | 0.3966 | 0.6040 | 0.5983 | nan | 0.5713 | 0.6367 | 0.0 | 0.5559 | 0.6338 | | 0.0425 | 107.83 | 30840 | 0.1467 | 0.3969 | 0.6040 | 0.5984 | nan | 0.5716 | 0.6363 | 0.0 | 0.5580 | 0.6327 | | 0.0473 | 107.9 | 30860 | 0.1417 | 0.4017 | 0.6114 | 0.6051 | nan | 0.5749 | 0.6480 | 0.0 | 0.5606 | 0.6444 | | 0.0565 | 107.97 | 30880 | 0.1413 | 0.4020 | 0.6111 | 0.6042 | nan | 0.5716 | 0.6506 | 0.0 | 0.5587 | 0.6472 | | 0.0425 | 108.04 | 30900 | 0.1372 | 0.4089 | 0.6218 | 0.6128 | nan | 0.5699 | 0.6737 | 0.0 | 0.5573 | 0.6693 | | 0.0309 | 108.11 | 30920 | 0.1416 | 0.4015 | 0.6104 | 0.6014 | nan | 0.5586 | 0.6621 | 0.0 | 0.5461 | 0.6583 | | 0.0375 | 108.18 | 30940 | 0.1403 | 0.4009 | 0.6095 | 0.6007 | nan | 0.5589 | 0.6600 | 0.0 | 0.5461 | 0.6566 | | 0.0283 | 108.25 | 30960 | 0.1458 | 0.3943 | 0.6000 | 0.5935 | nan | 0.5622 | 0.6379 | 0.0 | 0.5489 | 0.6341 | | 0.0436 | 108.32 | 30980 | 0.1409 | 0.4041 | 0.6146 | 0.6070 | nan | 0.5710 | 0.6581 | 0.0 | 0.5586 | 0.6537 | | 0.0593 | 108.39 | 31000 | 0.1380 | 0.4103 | 0.6250 | 0.6159 | nan | 0.5727 | 0.6772 | 0.0 | 0.5616 | 0.6694 | | 0.0334 | 108.46 | 31020 | 0.1420 | 0.4039 | 0.6155 | 0.6104 | nan | 0.5863 | 0.6447 | 0.0 | 0.5731 | 0.6385 | | 0.0626 | 108.53 | 31040 | 0.1438 | 0.3976 | 0.6049 | 0.5960 | nan | 0.5532 | 0.6567 | 0.0 | 0.5404 | 0.6523 | | 0.0484 | 108.6 | 31060 | 0.1431 | 0.4037 | 0.6142 | 0.6086 | nan | 0.5823 | 0.6460 | 0.0 | 0.5689 | 0.6422 | | 0.0406 | 108.67 | 31080 | 0.1489 | 0.3861 | 0.5868 | 0.5776 | nan | 0.5338 | 0.6398 | 0.0 | 0.5224 | 0.6359 | | 0.0489 | 108.74 | 31100 | 0.1472 | 0.3924 | 0.5966 | 0.5889 | nan | 0.5519 | 0.6414 | 0.0 | 0.5393 | 0.6379 | | 0.0429 | 108.81 | 31120 | 0.1426 | 0.3994 | 0.6074 | 0.5981 | nan | 0.5541 | 0.6606 | 0.0 | 0.5428 | 0.6553 | | 0.0462 | 108.88 | 31140 | 0.1384 | 0.4036 | 0.6142 | 0.6048 | nan | 0.5598 | 0.6686 | 0.0 | 0.5485 | 0.6623 | | 0.0635 | 108.95 | 31160 | 0.1412 | 0.3991 | 0.6068 | 0.5973 | nan | 0.5519 | 0.6617 | 0.0 | 0.5405 | 0.6566 | | 0.0302 | 109.02 | 31180 | 0.1424 | 0.3981 | 0.6050 | 0.5950 | nan | 0.5469 | 0.6632 | 0.0 | 0.5366 | 0.6577 | | 0.0364 | 109.09 | 31200 | 0.1394 | 0.4001 | 0.6085 | 0.5991 | nan | 0.5541 | 0.6628 | 0.0 | 0.5421 | 0.6581 | | 0.029 | 109.16 | 31220 | 0.1378 | 0.4017 | 0.6106 | 0.5996 | nan | 0.5470 | 0.6743 | 0.0 | 0.5350 | 0.6700 | | 0.0339 | 109.23 | 31240 | 0.1389 | 0.4000 | 0.6086 | 0.5996 | nan | 0.5566 | 0.6606 | 0.0 | 0.5431 | 0.6568 | | 0.0279 | 109.3 | 31260 | 0.1402 | 0.3991 | 0.6072 | 0.5983 | nan | 0.5556 | 0.6589 | 0.0 | 0.5429 | 0.6544 | | 0.0624 | 109.37 | 31280 | 0.1404 | 0.4014 | 0.6104 | 0.6014 | nan | 0.5585 | 0.6623 | 0.0 | 0.5471 | 0.6569 | | 0.053 | 109.44 | 31300 | 0.1406 | 0.4067 | 0.6181 | 0.6092 | nan | 0.5666 | 0.6695 | 0.0 | 0.5547 | 0.6653 | | 0.0456 | 109.51 | 31320 | 0.1376 | 0.4077 | 0.6195 | 0.6110 | nan | 0.5703 | 0.6688 | 0.0 | 0.5589 | 0.6641 | | 0.0708 | 109.58 | 31340 | 0.1405 | 0.4111 | 0.6249 | 0.6172 | nan | 0.5801 | 0.6697 | 0.0 | 0.5682 | 0.6650 | | 0.0379 | 109.65 | 31360 | 0.1430 | 0.3966 | 0.6026 | 0.5934 | nan | 0.5494 | 0.6557 | 0.0 | 0.5388 | 0.6511 | | 0.0352 | 109.72 | 31380 | 0.1384 | 0.4079 | 0.6198 | 0.6081 | nan | 0.5522 | 0.6874 | 0.0 | 0.5422 | 0.6814 | | 0.0349 | 109.79 | 31400 | 0.1437 | 0.4003 | 0.6088 | 0.6026 | nan | 0.5729 | 0.6447 | 0.0 | 0.5599 | 0.6410 | | 0.0558 | 109.86 | 31420 | 0.1414 | 0.4068 | 0.6188 | 0.6115 | nan | 0.5766 | 0.6610 | 0.0 | 0.5635 | 0.6569 | | 0.0442 | 109.93 | 31440 | 0.1451 | 0.3985 | 0.6062 | 0.5979 | nan | 0.5579 | 0.6545 | 0.0 | 0.5459 | 0.6497 | | 0.0534 | 110.0 | 31460 | 0.1379 | 0.4050 | 0.6162 | 0.6069 | nan | 0.5629 | 0.6695 | 0.0 | 0.5507 | 0.6642 | | 0.0421 | 110.07 | 31480 | 0.1414 | 0.4014 | 0.6110 | 0.6035 | nan | 0.5680 | 0.6539 | 0.0 | 0.5550 | 0.6491 | | 0.0564 | 110.14 | 31500 | 0.1416 | 0.3971 | 0.6043 | 0.5960 | nan | 0.5565 | 0.6520 | 0.0 | 0.5435 | 0.6478 | | 0.0343 | 110.21 | 31520 | 0.1417 | 0.3935 | 0.5984 | 0.5880 | nan | 0.5388 | 0.6580 | 0.0 | 0.5271 | 0.6535 | | 0.0416 | 110.28 | 31540 | 0.1412 | 0.4071 | 0.6202 | 0.6144 | nan | 0.5869 | 0.6536 | 0.0 | 0.5717 | 0.6497 | | 0.0324 | 110.35 | 31560 | 0.1429 | 0.4016 | 0.6117 | 0.6049 | nan | 0.5723 | 0.6511 | 0.0 | 0.5569 | 0.6480 | | 0.0415 | 110.42 | 31580 | 0.1352 | 0.4017 | 0.6112 | 0.6012 | nan | 0.5538 | 0.6685 | 0.0 | 0.5431 | 0.6619 | | 0.0283 | 110.49 | 31600 | 0.1371 | 0.4013 | 0.6101 | 0.6006 | nan | 0.5552 | 0.6650 | 0.0 | 0.5434 | 0.6606 | | 0.0585 | 110.56 | 31620 | 0.1397 | 0.3957 | 0.6015 | 0.5915 | nan | 0.5437 | 0.6593 | 0.0 | 0.5323 | 0.6547 | | 0.033 | 110.63 | 31640 | 0.1466 | 0.3939 | 0.6000 | 0.5941 | nan | 0.5662 | 0.6337 | 0.0 | 0.5513 | 0.6304 | | 0.0484 | 110.7 | 31660 | 0.1462 | 0.3955 | 0.6015 | 0.5926 | nan | 0.5497 | 0.6533 | 0.0 | 0.5368 | 0.6497 | | 0.0394 | 110.77 | 31680 | 0.1411 | 0.4007 | 0.6095 | 0.5995 | nan | 0.5517 | 0.6673 | 0.0 | 0.5398 | 0.6623 | | 0.0336 | 110.84 | 31700 | 0.1438 | 0.4000 | 0.6083 | 0.6006 | nan | 0.5638 | 0.6528 | 0.0 | 0.5517 | 0.6483 | | 0.0494 | 110.91 | 31720 | 0.1421 | 0.4015 | 0.6113 | 0.6047 | nan | 0.5736 | 0.6489 | 0.0 | 0.5593 | 0.6452 | | 0.0437 | 110.98 | 31740 | 0.1378 | 0.4057 | 0.6173 | 0.6070 | nan | 0.5581 | 0.6764 | 0.0 | 0.5472 | 0.6698 | | 0.0297 | 111.05 | 31760 | 0.1400 | 0.4040 | 0.6151 | 0.6075 | nan | 0.5711 | 0.6591 | 0.0 | 0.5577 | 0.6544 | | 0.0461 | 111.12 | 31780 | 0.1390 | 0.4022 | 0.6122 | 0.6005 | nan | 0.5444 | 0.6800 | 0.0 | 0.5320 | 0.6745 | | 0.05 | 111.19 | 31800 | 0.1372 | 0.4108 | 0.6261 | 0.6204 | nan | 0.5930 | 0.6592 | 0.0 | 0.5775 | 0.6549 | | 0.0462 | 111.26 | 31820 | 0.1406 | 0.4089 | 0.6228 | 0.6165 | nan | 0.5863 | 0.6593 | 0.0 | 0.5720 | 0.6547 | | 0.0449 | 111.33 | 31840 | 0.1378 | 0.4063 | 0.6185 | 0.6092 | nan | 0.5649 | 0.6721 | 0.0 | 0.5517 | 0.6672 | | 0.0555 | 111.4 | 31860 | 0.1439 | 0.3987 | 0.6068 | 0.6000 | nan | 0.5674 | 0.6463 | 0.0 | 0.5545 | 0.6417 | | 0.0436 | 111.47 | 31880 | 0.1423 | 0.4019 | 0.6110 | 0.6005 | nan | 0.5501 | 0.6719 | 0.0 | 0.5382 | 0.6675 | | 0.04 | 111.54 | 31900 | 0.1418 | 0.4043 | 0.6147 | 0.6051 | nan | 0.5589 | 0.6706 | 0.0 | 0.5461 | 0.6668 | | 0.0296 | 111.61 | 31920 | 0.1345 | 0.4144 | 0.6300 | 0.6195 | nan | 0.5693 | 0.6907 | 0.0 | 0.5580 | 0.6853 | | 0.0491 | 111.68 | 31940 | 0.1389 | 0.4086 | 0.6220 | 0.6134 | nan | 0.5724 | 0.6717 | 0.0 | 0.5586 | 0.6673 | | 0.0464 | 111.75 | 31960 | 0.1381 | 0.4099 | 0.6236 | 0.6126 | nan | 0.5602 | 0.6869 | 0.0 | 0.5483 | 0.6814 | | 0.0358 | 111.82 | 31980 | 0.1402 | 0.3956 | 0.6010 | 0.5893 | nan | 0.5334 | 0.6687 | 0.0 | 0.5220 | 0.6647 | | 0.0383 | 111.89 | 32000 | 0.1431 | 0.3999 | 0.6082 | 0.6010 | nan | 0.5665 | 0.6499 | 0.0 | 0.5533 | 0.6463 | | 0.0354 | 111.96 | 32020 | 0.1396 | 0.4058 | 0.6170 | 0.6075 | nan | 0.5621 | 0.6719 | 0.0 | 0.5494 | 0.6679 | | 0.0343 | 112.03 | 32040 | 0.1427 | 0.3990 | 0.6062 | 0.5965 | nan | 0.5501 | 0.6623 | 0.0 | 0.5383 | 0.6587 | | 0.0346 | 112.1 | 32060 | 0.1508 | 0.3897 | 0.5922 | 0.5832 | nan | 0.5401 | 0.6444 | 0.0 | 0.5273 | 0.6418 | | 0.0418 | 112.17 | 32080 | 0.1539 | 0.3873 | 0.5891 | 0.5831 | nan | 0.5544 | 0.6239 | 0.0 | 0.5408 | 0.6211 | | 0.0481 | 112.24 | 32100 | 0.1502 | 0.4030 | 0.6134 | 0.6096 | nan | 0.5915 | 0.6354 | 0.0 | 0.5769 | 0.6320 | | 0.0466 | 112.31 | 32120 | 0.1461 | 0.4062 | 0.6187 | 0.6132 | nan | 0.5870 | 0.6504 | 0.0 | 0.5731 | 0.6456 | | 0.0327 | 112.38 | 32140 | 0.1480 | 0.4039 | 0.6152 | 0.6106 | nan | 0.5882 | 0.6422 | 0.0 | 0.5730 | 0.6386 | | 0.0546 | 112.45 | 32160 | 0.1359 | 0.4068 | 0.6179 | 0.6060 | nan | 0.5491 | 0.6867 | 0.0 | 0.5392 | 0.6812 | | 0.0587 | 112.52 | 32180 | 0.1379 | 0.4110 | 0.6247 | 0.6155 | nan | 0.5715 | 0.6780 | 0.0 | 0.5597 | 0.6734 | | 0.0323 | 112.59 | 32200 | 0.1369 | 0.4087 | 0.6212 | 0.6104 | nan | 0.5588 | 0.6837 | 0.0 | 0.5489 | 0.6773 | | 0.0352 | 112.66 | 32220 | 0.1385 | 0.4108 | 0.6246 | 0.6159 | nan | 0.5743 | 0.6749 | 0.0 | 0.5625 | 0.6699 | | 0.0472 | 112.73 | 32240 | 0.1396 | 0.4035 | 0.6133 | 0.6030 | nan | 0.5535 | 0.6731 | 0.0 | 0.5430 | 0.6674 | | 0.042 | 112.8 | 32260 | 0.1448 | 0.3976 | 0.6042 | 0.5956 | nan | 0.5548 | 0.6536 | 0.0 | 0.5437 | 0.6491 | | 0.039 | 112.87 | 32280 | 0.1503 | 0.3957 | 0.6021 | 0.5957 | nan | 0.5649 | 0.6393 | 0.0 | 0.5501 | 0.6369 | | 0.0444 | 112.94 | 32300 | 0.1502 | 0.3976 | 0.6050 | 0.5994 | nan | 0.5728 | 0.6372 | 0.0 | 0.5580 | 0.6348 | | 0.0319 | 113.01 | 32320 | 0.1452 | 0.4027 | 0.6123 | 0.6042 | nan | 0.5655 | 0.6592 | 0.0 | 0.5530 | 0.6550 | | 0.0423 | 113.08 | 32340 | 0.1483 | 0.3946 | 0.6001 | 0.5924 | nan | 0.5558 | 0.6445 | 0.0 | 0.5425 | 0.6414 | | 0.05 | 113.15 | 32360 | 0.1419 | 0.4066 | 0.6184 | 0.6118 | nan | 0.5805 | 0.6562 | 0.0 | 0.5674 | 0.6523 | | 0.0433 | 113.22 | 32380 | 0.1442 | 0.4053 | 0.6167 | 0.6105 | nan | 0.5808 | 0.6526 | 0.0 | 0.5667 | 0.6492 | | 0.053 | 113.29 | 32400 | 0.1446 | 0.4053 | 0.6167 | 0.6097 | nan | 0.5760 | 0.6574 | 0.0 | 0.5621 | 0.6537 | | 0.0396 | 113.36 | 32420 | 0.1442 | 0.4007 | 0.6095 | 0.6009 | nan | 0.5599 | 0.6590 | 0.0 | 0.5477 | 0.6544 | | 0.041 | 113.43 | 32440 | 0.1466 | 0.3984 | 0.6058 | 0.5959 | nan | 0.5486 | 0.6631 | 0.0 | 0.5355 | 0.6597 | | 0.0274 | 113.5 | 32460 | 0.1530 | 0.3884 | 0.5911 | 0.5848 | nan | 0.5545 | 0.6278 | 0.0 | 0.5394 | 0.6257 | | 0.0428 | 113.57 | 32480 | 0.1453 | 0.4036 | 0.6143 | 0.6065 | nan | 0.5691 | 0.6595 | 0.0 | 0.5549 | 0.6560 | | 0.0338 | 113.64 | 32500 | 0.1447 | 0.4080 | 0.6209 | 0.6139 | nan | 0.5808 | 0.6610 | 0.0 | 0.5672 | 0.6568 | | 0.028 | 113.71 | 32520 | 0.1471 | 0.4033 | 0.6140 | 0.6072 | nan | 0.5746 | 0.6535 | 0.0 | 0.5599 | 0.6501 | | 0.0498 | 113.78 | 32540 | 0.1461 | 0.3998 | 0.6083 | 0.6015 | nan | 0.5691 | 0.6475 | 0.0 | 0.5550 | 0.6443 | | 0.0544 | 113.85 | 32560 | 0.1433 | 0.4072 | 0.6197 | 0.6126 | nan | 0.5787 | 0.6607 | 0.0 | 0.5661 | 0.6554 | | 0.0623 | 113.92 | 32580 | 0.1428 | 0.4052 | 0.6163 | 0.6075 | nan | 0.5653 | 0.6673 | 0.0 | 0.5527 | 0.6629 | | 0.0312 | 113.99 | 32600 | 0.1434 | 0.4000 | 0.6083 | 0.5992 | nan | 0.5554 | 0.6613 | 0.0 | 0.5434 | 0.6567 | | 0.0388 | 114.06 | 32620 | 0.1478 | 0.3976 | 0.6044 | 0.5962 | nan | 0.5569 | 0.6520 | 0.0 | 0.5452 | 0.6475 | | 0.0468 | 114.13 | 32640 | 0.1442 | 0.3995 | 0.6078 | 0.6000 | nan | 0.5630 | 0.6526 | 0.0 | 0.5510 | 0.6476 | | 0.035 | 114.2 | 32660 | 0.1448 | 0.4012 | 0.6105 | 0.6026 | nan | 0.5649 | 0.6560 | 0.0 | 0.5521 | 0.6517 | | 0.0511 | 114.27 | 32680 | 0.1362 | 0.4170 | 0.6347 | 0.6275 | nan | 0.5929 | 0.6766 | 0.0 | 0.5796 | 0.6714 | | 0.0419 | 114.34 | 32700 | 0.1432 | 0.4086 | 0.6217 | 0.6147 | nan | 0.5810 | 0.6624 | 0.0 | 0.5668 | 0.6590 | | 0.0447 | 114.41 | 32720 | 0.1495 | 0.3947 | 0.6001 | 0.5934 | nan | 0.5615 | 0.6387 | 0.0 | 0.5486 | 0.6354 | | 0.0331 | 114.48 | 32740 | 0.1483 | 0.3926 | 0.5965 | 0.5877 | nan | 0.5460 | 0.6469 | 0.0 | 0.5343 | 0.6434 | | 0.0313 | 114.55 | 32760 | 0.1530 | 0.3824 | 0.5810 | 0.5725 | nan | 0.5318 | 0.6301 | 0.0 | 0.5202 | 0.6270 | | 0.0467 | 114.62 | 32780 | 0.1428 | 0.4008 | 0.6093 | 0.6003 | nan | 0.5573 | 0.6614 | 0.0 | 0.5447 | 0.6578 | | 0.0465 | 114.69 | 32800 | 0.1453 | 0.3939 | 0.5987 | 0.5894 | nan | 0.5449 | 0.6525 | 0.0 | 0.5333 | 0.6484 | | 0.0296 | 114.76 | 32820 | 0.1485 | 0.3983 | 0.6061 | 0.5984 | nan | 0.5619 | 0.6502 | 0.0 | 0.5481 | 0.6469 | | 0.0471 | 114.83 | 32840 | 0.1542 | 0.3854 | 0.5862 | 0.5799 | nan | 0.5495 | 0.6229 | 0.0 | 0.5360 | 0.6203 | | 0.053 | 114.9 | 32860 | 0.1531 | 0.3927 | 0.5976 | 0.5908 | nan | 0.5585 | 0.6367 | 0.0 | 0.5442 | 0.6340 | | 0.045 | 114.97 | 32880 | 0.1509 | 0.3934 | 0.5989 | 0.5936 | nan | 0.5682 | 0.6295 | 0.0 | 0.5533 | 0.6270 | | 0.0634 | 115.03 | 32900 | 0.1461 | 0.4000 | 0.6089 | 0.6019 | nan | 0.5684 | 0.6495 | 0.0 | 0.5536 | 0.6465 | | 0.0373 | 115.1 | 32920 | 0.1483 | 0.3984 | 0.6066 | 0.6013 | nan | 0.5759 | 0.6373 | 0.0 | 0.5605 | 0.6345 | | 0.0397 | 115.17 | 32940 | 0.1473 | 0.3945 | 0.6006 | 0.5921 | nan | 0.5518 | 0.6493 | 0.0 | 0.5376 | 0.6458 | | 0.0406 | 115.24 | 32960 | 0.1467 | 0.3990 | 0.6076 | 0.6014 | nan | 0.5716 | 0.6436 | 0.0 | 0.5572 | 0.6399 | | 0.0592 | 115.31 | 32980 | 0.1498 | 0.3967 | 0.6045 | 0.5994 | nan | 0.5749 | 0.6340 | 0.0 | 0.5594 | 0.6307 | | 0.0336 | 115.38 | 33000 | 0.1446 | 0.4073 | 0.6206 | 0.6153 | nan | 0.5898 | 0.6514 | 0.0 | 0.5741 | 0.6478 | | 0.0455 | 115.45 | 33020 | 0.1448 | 0.4002 | 0.6090 | 0.6027 | nan | 0.5729 | 0.6451 | 0.0 | 0.5590 | 0.6415 | | 0.0355 | 115.52 | 33040 | 0.1506 | 0.3882 | 0.5906 | 0.5838 | nan | 0.5515 | 0.6296 | 0.0 | 0.5388 | 0.6257 | | 0.0355 | 115.59 | 33060 | 0.1511 | 0.3863 | 0.5872 | 0.5783 | nan | 0.5356 | 0.6388 | 0.0 | 0.5254 | 0.6333 | | 0.0516 | 115.66 | 33080 | 0.1484 | 0.3977 | 0.6052 | 0.5967 | nan | 0.5565 | 0.6538 | 0.0 | 0.5447 | 0.6485 | | 0.0458 | 115.73 | 33100 | 0.1481 | 0.3984 | 0.6068 | 0.6018 | nan | 0.5780 | 0.6357 | 0.0 | 0.5635 | 0.6318 | | 0.0501 | 115.8 | 33120 | 0.1461 | 0.3972 | 0.6041 | 0.5960 | nan | 0.5570 | 0.6512 | 0.0 | 0.5449 | 0.6467 | | 0.0256 | 115.87 | 33140 | 0.1457 | 0.4018 | 0.6111 | 0.6027 | nan | 0.5625 | 0.6597 | 0.0 | 0.5509 | 0.6544 | | 0.0396 | 115.94 | 33160 | 0.1555 | 0.3859 | 0.5873 | 0.5825 | nan | 0.5598 | 0.6148 | 0.0 | 0.5464 | 0.6112 | | 0.0451 | 116.01 | 33180 | 0.1509 | 0.3950 | 0.6010 | 0.5934 | nan | 0.5572 | 0.6449 | 0.0 | 0.5440 | 0.6409 | | 0.042 | 116.08 | 33200 | 0.1545 | 0.3941 | 0.5991 | 0.5923 | nan | 0.5601 | 0.6381 | 0.0 | 0.5485 | 0.6339 | | 0.0271 | 116.15 | 33220 | 0.1467 | 0.4017 | 0.6108 | 0.6047 | nan | 0.5751 | 0.6466 | 0.0 | 0.5628 | 0.6422 | | 0.0313 | 116.22 | 33240 | 0.1358 | 0.4150 | 0.6307 | 0.6198 | nan | 0.5675 | 0.6940 | 0.0 | 0.5576 | 0.6874 | | 0.0367 | 116.29 | 33260 | 0.1431 | 0.4047 | 0.6152 | 0.6069 | nan | 0.5671 | 0.6632 | 0.0 | 0.5554 | 0.6585 | | 0.0519 | 116.36 | 33280 | 0.1426 | 0.4080 | 0.6208 | 0.6128 | nan | 0.5745 | 0.6672 | 0.0 | 0.5619 | 0.6621 | | 0.0373 | 116.43 | 33300 | 0.1425 | 0.4060 | 0.6178 | 0.6081 | nan | 0.5617 | 0.6739 | 0.0 | 0.5501 | 0.6680 | | 0.0614 | 116.5 | 33320 | 0.1394 | 0.4093 | 0.6224 | 0.6128 | nan | 0.5671 | 0.6776 | 0.0 | 0.5555 | 0.6724 | | 0.0415 | 116.57 | 33340 | 0.1493 | 0.3988 | 0.6071 | 0.6023 | nan | 0.5792 | 0.6350 | 0.0 | 0.5643 | 0.6321 | | 0.0457 | 116.64 | 33360 | 0.1523 | 0.3947 | 0.6008 | 0.5965 | nan | 0.5758 | 0.6259 | 0.0 | 0.5612 | 0.6230 | | 0.0558 | 116.71 | 33380 | 0.1455 | 0.3974 | 0.6043 | 0.5964 | nan | 0.5587 | 0.6499 | 0.0 | 0.5467 | 0.6456 | | 0.0446 | 116.78 | 33400 | 0.1471 | 0.4055 | 0.6168 | 0.6097 | nan | 0.5755 | 0.6581 | 0.0 | 0.5626 | 0.6539 | | 0.0622 | 116.85 | 33420 | 0.1439 | 0.4074 | 0.6199 | 0.6124 | nan | 0.5762 | 0.6636 | 0.0 | 0.5630 | 0.6593 | | 0.0329 | 116.92 | 33440 | 0.1455 | 0.3923 | 0.5963 | 0.5869 | nan | 0.5419 | 0.6508 | 0.0 | 0.5297 | 0.6473 | | 0.043 | 116.99 | 33460 | 0.1455 | 0.3962 | 0.6025 | 0.5933 | nan | 0.5496 | 0.6553 | 0.0 | 0.5372 | 0.6516 | | 0.0604 | 117.06 | 33480 | 0.1428 | 0.4045 | 0.6157 | 0.6091 | nan | 0.5779 | 0.6535 | 0.0 | 0.5641 | 0.6492 | | 0.0357 | 117.13 | 33500 | 0.1426 | 0.4034 | 0.6135 | 0.6053 | nan | 0.5660 | 0.6609 | 0.0 | 0.5548 | 0.6553 | | 0.042 | 117.2 | 33520 | 0.1428 | 0.4057 | 0.6169 | 0.6084 | nan | 0.5677 | 0.6661 | 0.0 | 0.5566 | 0.6606 | | 0.0581 | 117.27 | 33540 | 0.1446 | 0.4059 | 0.6174 | 0.6091 | nan | 0.5695 | 0.6653 | 0.0 | 0.5570 | 0.6607 | | 0.0374 | 117.34 | 33560 | 0.1391 | 0.4127 | 0.6284 | 0.6206 | nan | 0.5837 | 0.6730 | 0.0 | 0.5710 | 0.6673 | | 0.035 | 117.41 | 33580 | 0.1397 | 0.4141 | 0.6308 | 0.6224 | nan | 0.5824 | 0.6792 | 0.0 | 0.5688 | 0.6734 | | 0.0442 | 117.48 | 33600 | 0.1431 | 0.4048 | 0.6161 | 0.6091 | nan | 0.5757 | 0.6565 | 0.0 | 0.5614 | 0.6529 | | 0.055 | 117.55 | 33620 | 0.1419 | 0.4043 | 0.6150 | 0.6055 | nan | 0.5602 | 0.6697 | 0.0 | 0.5485 | 0.6643 | | 0.0333 | 117.62 | 33640 | 0.1462 | 0.4010 | 0.6101 | 0.6003 | nan | 0.5532 | 0.6671 | 0.0 | 0.5417 | 0.6613 | | 0.0304 | 117.69 | 33660 | 0.1488 | 0.3981 | 0.6057 | 0.5982 | nan | 0.5624 | 0.6491 | 0.0 | 0.5496 | 0.6447 | | 0.0341 | 117.76 | 33680 | 0.1490 | 0.4009 | 0.6103 | 0.6032 | nan | 0.5695 | 0.6511 | 0.0 | 0.5561 | 0.6466 | | 0.0395 | 117.83 | 33700 | 0.1456 | 0.4013 | 0.6108 | 0.6027 | nan | 0.5638 | 0.6578 | 0.0 | 0.5508 | 0.6531 | | 0.0381 | 117.9 | 33720 | 0.1475 | 0.3948 | 0.6008 | 0.5932 | nan | 0.5571 | 0.6445 | 0.0 | 0.5447 | 0.6398 | | 0.0426 | 117.97 | 33740 | 0.1519 | 0.3979 | 0.6054 | 0.5986 | nan | 0.5658 | 0.6451 | 0.0 | 0.5519 | 0.6418 | | 0.0342 | 118.04 | 33760 | 0.1484 | 0.3963 | 0.6032 | 0.5956 | nan | 0.5592 | 0.6472 | 0.0 | 0.5464 | 0.6425 | | 0.0521 | 118.11 | 33780 | 0.1456 | 0.4068 | 0.6197 | 0.6127 | nan | 0.5793 | 0.6601 | 0.0 | 0.5645 | 0.6560 | | 0.0322 | 118.18 | 33800 | 0.1493 | 0.4065 | 0.6195 | 0.6127 | nan | 0.5803 | 0.6587 | 0.0 | 0.5650 | 0.6544 | | 0.0557 | 118.25 | 33820 | 0.1486 | 0.4021 | 0.6117 | 0.6034 | nan | 0.5636 | 0.6599 | 0.0 | 0.5517 | 0.6546 | | 0.037 | 118.32 | 33840 | 0.1490 | 0.3989 | 0.6068 | 0.5983 | nan | 0.5573 | 0.6564 | 0.0 | 0.5448 | 0.6519 | | 0.0411 | 118.39 | 33860 | 0.1482 | 0.4006 | 0.6098 | 0.6040 | nan | 0.5764 | 0.6433 | 0.0 | 0.5635 | 0.6384 | | 0.0518 | 118.46 | 33880 | 0.1457 | 0.4024 | 0.6128 | 0.6060 | nan | 0.5738 | 0.6518 | 0.0 | 0.5608 | 0.6465 | | 0.0355 | 118.53 | 33900 | 0.1527 | 0.3866 | 0.5877 | 0.5794 | nan | 0.5394 | 0.6360 | 0.0 | 0.5263 | 0.6336 | | 0.031 | 118.6 | 33920 | 0.1507 | 0.3941 | 0.5998 | 0.5943 | nan | 0.5683 | 0.6313 | 0.0 | 0.5539 | 0.6283 | | 0.0343 | 118.67 | 33940 | 0.1495 | 0.3959 | 0.6023 | 0.5971 | nan | 0.5722 | 0.6324 | 0.0 | 0.5588 | 0.6288 | | 0.0156 | 118.74 | 33960 | 0.1427 | 0.4095 | 0.6238 | 0.6178 | nan | 0.5892 | 0.6584 | 0.0 | 0.5749 | 0.6535 | | 0.0573 | 118.81 | 33980 | 0.1527 | 0.3916 | 0.5958 | 0.5894 | nan | 0.5588 | 0.6329 | 0.0 | 0.5446 | 0.6301 | | 0.0417 | 118.88 | 34000 | 0.1475 | 0.4025 | 0.6128 | 0.6074 | nan | 0.5817 | 0.6439 | 0.0 | 0.5679 | 0.6397 | | 0.0579 | 118.95 | 34020 | 0.1489 | 0.3998 | 0.6083 | 0.6018 | nan | 0.5707 | 0.6459 | 0.0 | 0.5583 | 0.6411 | | 0.0433 | 119.02 | 34040 | 0.1521 | 0.3923 | 0.5967 | 0.5879 | nan | 0.5458 | 0.6476 | 0.0 | 0.5331 | 0.6438 | | 0.0453 | 119.09 | 34060 | 0.1493 | 0.3982 | 0.6057 | 0.5983 | nan | 0.5627 | 0.6488 | 0.0 | 0.5495 | 0.6453 | | 0.0407 | 119.16 | 34080 | 0.1484 | 0.3974 | 0.6046 | 0.5969 | nan | 0.5600 | 0.6492 | 0.0 | 0.5469 | 0.6454 | | 0.0448 | 119.23 | 34100 | 0.1473 | 0.4011 | 0.6101 | 0.6008 | nan | 0.5559 | 0.6644 | 0.0 | 0.5434 | 0.6600 | | 0.0426 | 119.3 | 34120 | 0.1448 | 0.3997 | 0.6080 | 0.5986 | nan | 0.5541 | 0.6618 | 0.0 | 0.5419 | 0.6572 | | 0.048 | 119.37 | 34140 | 0.1471 | 0.3988 | 0.6067 | 0.5993 | nan | 0.5638 | 0.6496 | 0.0 | 0.5506 | 0.6457 | | 0.0532 | 119.44 | 34160 | 0.1497 | 0.3979 | 0.6054 | 0.5980 | nan | 0.5629 | 0.6479 | 0.0 | 0.5495 | 0.6444 | | 0.0348 | 119.51 | 34180 | 0.1494 | 0.3992 | 0.6076 | 0.6000 | nan | 0.5637 | 0.6515 | 0.0 | 0.5500 | 0.6477 | | 0.0523 | 119.58 | 34200 | 0.1451 | 0.4004 | 0.6088 | 0.5995 | nan | 0.5551 | 0.6625 | 0.0 | 0.5421 | 0.6591 | | 0.0579 | 119.65 | 34220 | 0.1465 | 0.4001 | 0.6091 | 0.6031 | nan | 0.5746 | 0.6436 | 0.0 | 0.5603 | 0.6401 | | 0.0378 | 119.72 | 34240 | 0.1469 | 0.3980 | 0.6060 | 0.5998 | nan | 0.5702 | 0.6417 | 0.0 | 0.5555 | 0.6385 | | 0.0541 | 119.79 | 34260 | 0.1448 | 0.4015 | 0.6111 | 0.6034 | nan | 0.5667 | 0.6555 | 0.0 | 0.5530 | 0.6514 | | 0.0384 | 119.86 | 34280 | 0.1462 | 0.4014 | 0.6109 | 0.6046 | nan | 0.5744 | 0.6475 | 0.0 | 0.5604 | 0.6437 | | 0.039 | 119.93 | 34300 | 0.1485 | 0.3983 | 0.6072 | 0.6033 | nan | 0.5849 | 0.6294 | 0.0 | 0.5680 | 0.6269 | | 0.0294 | 120.0 | 34320 | 0.1521 | 0.3960 | 0.6026 | 0.5967 | nan | 0.5683 | 0.6370 | 0.0 | 0.5540 | 0.6342 | | 0.0349 | 120.07 | 34340 | 0.1487 | 0.3941 | 0.5994 | 0.5914 | nan | 0.5531 | 0.6457 | 0.0 | 0.5403 | 0.6421 | | 0.0565 | 120.14 | 34360 | 0.1553 | 0.3824 | 0.5821 | 0.5778 | nan | 0.5574 | 0.6068 | 0.0 | 0.5438 | 0.6036 | | 0.0509 | 120.21 | 34380 | 0.1516 | 0.3916 | 0.5959 | 0.5892 | nan | 0.5576 | 0.6342 | 0.0 | 0.5439 | 0.6308 | | 0.0436 | 120.28 | 34400 | 0.1490 | 0.3978 | 0.6053 | 0.5988 | nan | 0.5679 | 0.6428 | 0.0 | 0.5545 | 0.6389 | | 0.0267 | 120.35 | 34420 | 0.1464 | 0.4001 | 0.6090 | 0.6020 | nan | 0.5684 | 0.6497 | 0.0 | 0.5545 | 0.6457 | | 0.0671 | 120.42 | 34440 | 0.1477 | 0.3999 | 0.6088 | 0.6000 | nan | 0.5583 | 0.6593 | 0.0 | 0.5461 | 0.6536 | | 0.0278 | 120.49 | 34460 | 0.1445 | 0.4027 | 0.6134 | 0.6059 | nan | 0.5702 | 0.6566 | 0.0 | 0.5574 | 0.6507 | | 0.0482 | 120.56 | 34480 | 0.1462 | 0.3966 | 0.6037 | 0.5959 | nan | 0.5583 | 0.6492 | 0.0 | 0.5443 | 0.6456 | | 0.0429 | 120.63 | 34500 | 0.1421 | 0.4036 | 0.6139 | 0.6052 | nan | 0.5639 | 0.6638 | 0.0 | 0.5516 | 0.6592 | | 0.0542 | 120.7 | 34520 | 0.1434 | 0.4052 | 0.6164 | 0.6093 | nan | 0.5754 | 0.6573 | 0.0 | 0.5627 | 0.6528 | | 0.0471 | 120.77 | 34540 | 0.1481 | 0.3965 | 0.6033 | 0.5963 | nan | 0.5628 | 0.6439 | 0.0 | 0.5487 | 0.6408 | | 0.0414 | 120.84 | 34560 | 0.1448 | 0.4056 | 0.6171 | 0.6094 | nan | 0.5730 | 0.6611 | 0.0 | 0.5603 | 0.6565 | | 0.0256 | 120.91 | 34580 | 0.1490 | 0.3929 | 0.5976 | 0.5887 | nan | 0.5462 | 0.6490 | 0.0 | 0.5347 | 0.6439 | | 0.0302 | 120.98 | 34600 | 0.1483 | 0.3950 | 0.6007 | 0.5916 | nan | 0.5481 | 0.6533 | 0.0 | 0.5365 | 0.6484 | | 0.0324 | 121.05 | 34620 | 0.1489 | 0.3978 | 0.6050 | 0.5984 | nan | 0.5667 | 0.6434 | 0.0 | 0.5536 | 0.6399 | | 0.0312 | 121.12 | 34640 | 0.1472 | 0.4084 | 0.6216 | 0.6155 | nan | 0.5862 | 0.6570 | 0.0 | 0.5732 | 0.6520 | | 0.0307 | 121.19 | 34660 | 0.1470 | 0.4079 | 0.6202 | 0.6105 | nan | 0.5643 | 0.6761 | 0.0 | 0.5531 | 0.6706 | | 0.0315 | 121.26 | 34680 | 0.1415 | 0.4056 | 0.6165 | 0.6055 | nan | 0.5526 | 0.6805 | 0.0 | 0.5429 | 0.6739 | | 0.0742 | 121.33 | 34700 | 0.1437 | 0.4114 | 0.6261 | 0.6185 | nan | 0.5825 | 0.6697 | 0.0 | 0.5702 | 0.6640 | | 0.0637 | 121.4 | 34720 | 0.1489 | 0.3998 | 0.6081 | 0.6000 | nan | 0.5614 | 0.6547 | 0.0 | 0.5490 | 0.6505 | | 0.0347 | 121.47 | 34740 | 0.1466 | 0.4019 | 0.6112 | 0.6025 | nan | 0.5607 | 0.6618 | 0.0 | 0.5481 | 0.6576 | | 0.0372 | 121.54 | 34760 | 0.1468 | 0.4046 | 0.6156 | 0.6094 | nan | 0.5798 | 0.6514 | 0.0 | 0.5661 | 0.6477 | | 0.0381 | 121.61 | 34780 | 0.1524 | 0.3970 | 0.6039 | 0.5980 | nan | 0.5700 | 0.6378 | 0.0 | 0.5567 | 0.6343 | | 0.041 | 121.68 | 34800 | 0.1454 | 0.4063 | 0.6180 | 0.6079 | nan | 0.5601 | 0.6758 | 0.0 | 0.5491 | 0.6700 | | 0.0297 | 121.75 | 34820 | 0.1431 | 0.4073 | 0.6196 | 0.6118 | nan | 0.5748 | 0.6644 | 0.0 | 0.5633 | 0.6584 | | 0.0492 | 121.82 | 34840 | 0.1466 | 0.3981 | 0.6051 | 0.5932 | nan | 0.5364 | 0.6739 | 0.0 | 0.5256 | 0.6688 | | 0.0477 | 121.89 | 34860 | 0.1488 | 0.3957 | 0.6021 | 0.5936 | nan | 0.5526 | 0.6516 | 0.0 | 0.5394 | 0.6477 | | 0.0619 | 121.96 | 34880 | 0.1451 | 0.4035 | 0.6143 | 0.6071 | nan | 0.5727 | 0.6560 | 0.0 | 0.5592 | 0.6513 | | 0.0383 | 122.03 | 34900 | 0.1453 | 0.4060 | 0.6182 | 0.6119 | nan | 0.5821 | 0.6542 | 0.0 | 0.5682 | 0.6498 | | 0.0403 | 122.1 | 34920 | 0.1514 | 0.3983 | 0.6058 | 0.5985 | nan | 0.5638 | 0.6478 | 0.0 | 0.5506 | 0.6442 | | 0.0621 | 122.17 | 34940 | 0.1555 | 0.3965 | 0.6035 | 0.5988 | nan | 0.5767 | 0.6303 | 0.0 | 0.5622 | 0.6273 | | 0.0478 | 122.24 | 34960 | 0.1467 | 0.4075 | 0.6202 | 0.6111 | nan | 0.5674 | 0.6731 | 0.0 | 0.5540 | 0.6687 | | 0.0429 | 122.31 | 34980 | 0.1504 | 0.3983 | 0.6058 | 0.5983 | nan | 0.5629 | 0.6487 | 0.0 | 0.5504 | 0.6444 | | 0.042 | 122.38 | 35000 | 0.1482 | 0.4020 | 0.6119 | 0.6028 | nan | 0.5590 | 0.6649 | 0.0 | 0.5455 | 0.6604 | | 0.0452 | 122.45 | 35020 | 0.1510 | 0.3996 | 0.6083 | 0.6017 | nan | 0.5698 | 0.6468 | 0.0 | 0.5564 | 0.6424 | | 0.0498 | 122.52 | 35040 | 0.1461 | 0.4067 | 0.6196 | 0.6116 | nan | 0.5737 | 0.6654 | 0.0 | 0.5602 | 0.6600 | | 0.0304 | 122.59 | 35060 | 0.1484 | 0.4052 | 0.6169 | 0.6094 | nan | 0.5735 | 0.6604 | 0.0 | 0.5604 | 0.6552 | | 0.0333 | 122.66 | 35080 | 0.1472 | 0.3995 | 0.6078 | 0.5991 | nan | 0.5571 | 0.6586 | 0.0 | 0.5450 | 0.6534 | | 0.0666 | 122.73 | 35100 | 0.1503 | 0.4007 | 0.6099 | 0.6011 | nan | 0.5593 | 0.6605 | 0.0 | 0.5466 | 0.6554 | | 0.0443 | 122.8 | 35120 | 0.1483 | 0.3985 | 0.6066 | 0.5979 | nan | 0.5566 | 0.6566 | 0.0 | 0.5436 | 0.6520 | | 0.0408 | 122.87 | 35140 | 0.1508 | 0.4082 | 0.6220 | 0.6160 | nan | 0.5877 | 0.6562 | 0.0 | 0.5725 | 0.6520 | | 0.0372 | 122.94 | 35160 | 0.1494 | 0.4043 | 0.6160 | 0.6093 | nan | 0.5774 | 0.6545 | 0.0 | 0.5627 | 0.6502 | | 0.0389 | 123.01 | 35180 | 0.1499 | 0.3995 | 0.6084 | 0.6006 | nan | 0.5632 | 0.6536 | 0.0 | 0.5497 | 0.6486 | | 0.0406 | 123.08 | 35200 | 0.1488 | 0.4052 | 0.6171 | 0.6104 | nan | 0.5787 | 0.6555 | 0.0 | 0.5653 | 0.6502 | | 0.0201 | 123.15 | 35220 | 0.1522 | 0.3984 | 0.6068 | 0.6001 | nan | 0.5684 | 0.6452 | 0.0 | 0.5539 | 0.6413 | | 0.0364 | 123.22 | 35240 | 0.1483 | 0.3983 | 0.6062 | 0.5974 | nan | 0.5555 | 0.6568 | 0.0 | 0.5425 | 0.6526 | | 0.0325 | 123.29 | 35260 | 0.1514 | 0.3987 | 0.6075 | 0.6023 | nan | 0.5776 | 0.6373 | 0.0 | 0.5625 | 0.6336 | | 0.0441 | 123.36 | 35280 | 0.1482 | 0.4024 | 0.6125 | 0.6039 | nan | 0.5624 | 0.6626 | 0.0 | 0.5504 | 0.6568 | | 0.0436 | 123.43 | 35300 | 0.1490 | 0.3999 | 0.6086 | 0.6010 | nan | 0.5652 | 0.6519 | 0.0 | 0.5533 | 0.6464 | | 0.0322 | 123.5 | 35320 | 0.1496 | 0.4086 | 0.6221 | 0.6134 | nan | 0.5716 | 0.6727 | 0.0 | 0.5580 | 0.6678 | | 0.0601 | 123.57 | 35340 | 0.1524 | 0.3954 | 0.6020 | 0.5945 | nan | 0.5585 | 0.6456 | 0.0 | 0.5435 | 0.6428 | | 0.0398 | 123.64 | 35360 | 0.1438 | 0.4053 | 0.6169 | 0.6067 | nan | 0.5582 | 0.6756 | 0.0 | 0.5464 | 0.6693 | | 0.0389 | 123.71 | 35380 | 0.1481 | 0.4013 | 0.6103 | 0.6008 | nan | 0.5553 | 0.6653 | 0.0 | 0.5434 | 0.6605 | | 0.0457 | 123.78 | 35400 | 0.1474 | 0.4103 | 0.6247 | 0.6181 | nan | 0.5867 | 0.6627 | 0.0 | 0.5728 | 0.6581 | | 0.0522 | 123.85 | 35420 | 0.1487 | 0.4015 | 0.6114 | 0.6044 | nan | 0.5712 | 0.6517 | 0.0 | 0.5568 | 0.6478 | | 0.0325 | 123.92 | 35440 | 0.1513 | 0.4014 | 0.6107 | 0.6033 | nan | 0.5680 | 0.6533 | 0.0 | 0.5547 | 0.6494 | | 0.0264 | 123.99 | 35460 | 0.1488 | 0.4036 | 0.6138 | 0.6053 | nan | 0.5648 | 0.6627 | 0.0 | 0.5520 | 0.6588 | | 0.0499 | 124.06 | 35480 | 0.1457 | 0.4104 | 0.6239 | 0.6143 | nan | 0.5687 | 0.6791 | 0.0 | 0.5564 | 0.6749 | | 0.0351 | 124.13 | 35500 | 0.1517 | 0.3989 | 0.6065 | 0.5984 | nan | 0.5597 | 0.6533 | 0.0 | 0.5468 | 0.6500 | | 0.032 | 124.2 | 35520 | 0.1519 | 0.4038 | 0.6142 | 0.6065 | nan | 0.5699 | 0.6585 | 0.0 | 0.5574 | 0.6541 | | 0.0403 | 124.27 | 35540 | 0.1579 | 0.3976 | 0.6051 | 0.6004 | nan | 0.5778 | 0.6324 | 0.0 | 0.5637 | 0.6291 | | 0.0443 | 124.34 | 35560 | 0.1518 | 0.3974 | 0.6048 | 0.5975 | nan | 0.5625 | 0.6472 | 0.0 | 0.5487 | 0.6436 | | 0.0917 | 124.41 | 35580 | 0.1504 | 0.3975 | 0.6051 | 0.5985 | nan | 0.5668 | 0.6435 | 0.0 | 0.5533 | 0.6392 | | 0.0436 | 124.48 | 35600 | 0.1491 | 0.3998 | 0.6085 | 0.6003 | nan | 0.5611 | 0.6559 | 0.0 | 0.5489 | 0.6506 | | 0.067 | 124.55 | 35620 | 0.1492 | 0.4009 | 0.6106 | 0.6046 | nan | 0.5760 | 0.6453 | 0.0 | 0.5614 | 0.6412 | | 0.0319 | 124.62 | 35640 | 0.1513 | 0.3976 | 0.6050 | 0.5980 | nan | 0.5648 | 0.6452 | 0.0 | 0.5512 | 0.6414 | | 0.0371 | 124.69 | 35660 | 0.1496 | 0.4007 | 0.6102 | 0.6030 | nan | 0.5685 | 0.6518 | 0.0 | 0.5553 | 0.6468 | | 0.0439 | 124.76 | 35680 | 0.1558 | 0.3971 | 0.6050 | 0.6012 | nan | 0.5827 | 0.6274 | 0.0 | 0.5675 | 0.6239 | | 0.0415 | 124.83 | 35700 | 0.1539 | 0.4015 | 0.6113 | 0.6058 | nan | 0.5795 | 0.6431 | 0.0 | 0.5647 | 0.6397 | | 0.0415 | 124.9 | 35720 | 0.1562 | 0.3911 | 0.5950 | 0.5879 | nan | 0.5541 | 0.6358 | 0.0 | 0.5414 | 0.6321 | | 0.061 | 124.97 | 35740 | 0.1462 | 0.4047 | 0.6153 | 0.6078 | nan | 0.5720 | 0.6587 | 0.0 | 0.5601 | 0.6539 | | 0.0512 | 125.03 | 35760 | 0.1504 | 0.3974 | 0.6042 | 0.5965 | nan | 0.5595 | 0.6490 | 0.0 | 0.5458 | 0.6463 | | 0.0342 | 125.1 | 35780 | 0.1517 | 0.3970 | 0.6036 | 0.5954 | nan | 0.5558 | 0.6515 | 0.0 | 0.5431 | 0.6480 | | 0.0273 | 125.17 | 35800 | 0.1531 | 0.4005 | 0.6095 | 0.6033 | nan | 0.5735 | 0.6455 | 0.0 | 0.5596 | 0.6420 | | 0.0375 | 125.24 | 35820 | 0.1487 | 0.4040 | 0.6143 | 0.6064 | nan | 0.5683 | 0.6603 | 0.0 | 0.5560 | 0.6559 | | 0.0314 | 125.31 | 35840 | 0.1483 | 0.4023 | 0.6114 | 0.6024 | nan | 0.5594 | 0.6635 | 0.0 | 0.5485 | 0.6584 | | 0.0326 | 125.38 | 35860 | 0.1485 | 0.4079 | 0.6202 | 0.6128 | nan | 0.5776 | 0.6629 | 0.0 | 0.5655 | 0.6582 | | 0.0597 | 125.45 | 35880 | 0.1492 | 0.4051 | 0.6158 | 0.6090 | nan | 0.5769 | 0.6546 | 0.0 | 0.5648 | 0.6504 | | 0.0245 | 125.52 | 35900 | 0.1520 | 0.4113 | 0.6255 | 0.6197 | nan | 0.5924 | 0.6585 | 0.0 | 0.5799 | 0.6540 | | 0.0498 | 125.59 | 35920 | 0.1505 | 0.4030 | 0.6130 | 0.6078 | nan | 0.5832 | 0.6428 | 0.0 | 0.5699 | 0.6390 | | 0.0463 | 125.66 | 35940 | 0.1594 | 0.3866 | 0.5875 | 0.5805 | nan | 0.5473 | 0.6276 | 0.0 | 0.5349 | 0.6250 | | 0.0334 | 125.73 | 35960 | 0.1548 | 0.3938 | 0.5989 | 0.5932 | nan | 0.5663 | 0.6315 | 0.0 | 0.5532 | 0.6281 | | 0.069 | 125.8 | 35980 | 0.1539 | 0.4000 | 0.6084 | 0.6011 | nan | 0.5663 | 0.6506 | 0.0 | 0.5533 | 0.6467 | | 0.0496 | 125.87 | 36000 | 0.1514 | 0.4015 | 0.6110 | 0.6066 | nan | 0.5855 | 0.6364 | 0.0 | 0.5720 | 0.6327 | | 0.0626 | 125.94 | 36020 | 0.1521 | 0.3980 | 0.6056 | 0.5996 | nan | 0.5710 | 0.6402 | 0.0 | 0.5571 | 0.6370 | | 0.0652 | 126.01 | 36040 | 0.1539 | 0.3953 | 0.6013 | 0.5950 | nan | 0.5649 | 0.6377 | 0.0 | 0.5517 | 0.6342 | | 0.0588 | 126.08 | 36060 | 0.1532 | 0.3970 | 0.6041 | 0.5978 | nan | 0.5675 | 0.6408 | 0.0 | 0.5536 | 0.6374 | | 0.0538 | 126.15 | 36080 | 0.1520 | 0.3968 | 0.6035 | 0.5963 | nan | 0.5619 | 0.6450 | 0.0 | 0.5491 | 0.6415 | | 0.0589 | 126.22 | 36100 | 0.1509 | 0.3902 | 0.5929 | 0.5825 | nan | 0.5330 | 0.6528 | 0.0 | 0.5220 | 0.6485 | | 0.0563 | 126.29 | 36120 | 0.1543 | 0.3925 | 0.5966 | 0.5885 | nan | 0.5499 | 0.6432 | 0.0 | 0.5380 | 0.6393 | | 0.0505 | 126.36 | 36140 | 0.1533 | 0.3926 | 0.5970 | 0.5891 | nan | 0.5510 | 0.6431 | 0.0 | 0.5386 | 0.6393 | | 0.0391 | 126.43 | 36160 | 0.1522 | 0.3994 | 0.6075 | 0.6001 | nan | 0.5646 | 0.6504 | 0.0 | 0.5517 | 0.6466 | | 0.043 | 126.5 | 36180 | 0.1534 | 0.3986 | 0.6066 | 0.6006 | nan | 0.5717 | 0.6416 | 0.0 | 0.5577 | 0.6382 | | 0.0347 | 126.57 | 36200 | 0.1563 | 0.4006 | 0.6099 | 0.6046 | nan | 0.5790 | 0.6409 | 0.0 | 0.5647 | 0.6373 | | 0.0391 | 126.64 | 36220 | 0.1442 | 0.4190 | 0.6384 | 0.6312 | nan | 0.5971 | 0.6796 | 0.0 | 0.5861 | 0.6709 | | 0.0232 | 126.71 | 36240 | 0.1510 | 0.3997 | 0.6087 | 0.6027 | nan | 0.5739 | 0.6435 | 0.0 | 0.5599 | 0.6393 | | 0.0462 | 126.78 | 36260 | 0.1630 | 0.3812 | 0.5806 | 0.5771 | nan | 0.5603 | 0.6010 | 0.0 | 0.5447 | 0.5989 | | 0.043 | 126.85 | 36280 | 0.1545 | 0.3979 | 0.6054 | 0.5979 | nan | 0.5624 | 0.6483 | 0.0 | 0.5490 | 0.6446 | | 0.0331 | 126.92 | 36300 | 0.1538 | 0.3974 | 0.6041 | 0.5953 | nan | 0.5535 | 0.6546 | 0.0 | 0.5410 | 0.6511 | | 0.0365 | 126.99 | 36320 | 0.1527 | 0.3939 | 0.5989 | 0.5917 | nan | 0.5570 | 0.6408 | 0.0 | 0.5437 | 0.6378 | | 0.0418 | 127.06 | 36340 | 0.1501 | 0.3987 | 0.6060 | 0.5965 | nan | 0.5509 | 0.6612 | 0.0 | 0.5387 | 0.6574 | | 0.0196 | 127.13 | 36360 | 0.1487 | 0.4037 | 0.6143 | 0.6064 | nan | 0.5688 | 0.6599 | 0.0 | 0.5556 | 0.6556 | | 0.0242 | 127.2 | 36380 | 0.1473 | 0.3996 | 0.6074 | 0.5972 | nan | 0.5483 | 0.6666 | 0.0 | 0.5366 | 0.6621 | | 0.0454 | 127.27 | 36400 | 0.1493 | 0.4002 | 0.6087 | 0.5996 | nan | 0.5562 | 0.6611 | 0.0 | 0.5437 | 0.6570 | | 0.0676 | 127.34 | 36420 | 0.1440 | 0.4070 | 0.6187 | 0.6081 | nan | 0.5575 | 0.6800 | 0.0 | 0.5469 | 0.6740 | | 0.0584 | 127.41 | 36440 | 0.1431 | 0.4069 | 0.6185 | 0.6086 | nan | 0.5609 | 0.6762 | 0.0 | 0.5505 | 0.6703 | | 0.0707 | 127.48 | 36460 | 0.1457 | 0.4055 | 0.6165 | 0.6061 | nan | 0.5566 | 0.6763 | 0.0 | 0.5460 | 0.6704 | | 0.042 | 127.55 | 36480 | 0.1439 | 0.4031 | 0.6132 | 0.6026 | nan | 0.5522 | 0.6741 | 0.0 | 0.5413 | 0.6680 | | 0.0494 | 127.62 | 36500 | 0.1465 | 0.4005 | 0.6088 | 0.5984 | nan | 0.5487 | 0.6689 | 0.0 | 0.5386 | 0.6628 | | 0.0463 | 127.69 | 36520 | 0.1460 | 0.3998 | 0.6075 | 0.5975 | nan | 0.5496 | 0.6654 | 0.0 | 0.5391 | 0.6602 | | 0.046 | 127.76 | 36540 | 0.1479 | 0.4072 | 0.6203 | 0.6147 | nan | 0.5878 | 0.6528 | 0.0 | 0.5712 | 0.6505 | | 0.0378 | 127.83 | 36560 | 0.1482 | 0.3987 | 0.6064 | 0.5988 | nan | 0.5624 | 0.6504 | 0.0 | 0.5483 | 0.6477 | | 0.0307 | 127.9 | 36580 | 0.1467 | 0.4076 | 0.6203 | 0.6131 | nan | 0.5784 | 0.6623 | 0.0 | 0.5640 | 0.6589 | | 0.0257 | 127.97 | 36600 | 0.1468 | 0.4005 | 0.6095 | 0.6012 | nan | 0.5616 | 0.6575 | 0.0 | 0.5478 | 0.6538 | | 0.0601 | 128.04 | 36620 | 0.1476 | 0.3991 | 0.6072 | 0.5991 | nan | 0.5607 | 0.6537 | 0.0 | 0.5471 | 0.6503 | | 0.0378 | 128.11 | 36640 | 0.1491 | 0.4006 | 0.6094 | 0.6025 | nan | 0.5695 | 0.6493 | 0.0 | 0.5553 | 0.6464 | | 0.044 | 128.18 | 36660 | 0.1483 | 0.4020 | 0.6119 | 0.6056 | nan | 0.5751 | 0.6488 | 0.0 | 0.5601 | 0.6459 | | 0.0437 | 128.25 | 36680 | 0.1506 | 0.3990 | 0.6068 | 0.5987 | nan | 0.5597 | 0.6539 | 0.0 | 0.5461 | 0.6509 | | 0.0337 | 128.32 | 36700 | 0.1527 | 0.3997 | 0.6087 | 0.6038 | nan | 0.5807 | 0.6367 | 0.0 | 0.5650 | 0.6340 | | 0.0446 | 128.39 | 36720 | 0.1546 | 0.3956 | 0.6019 | 0.5953 | nan | 0.5640 | 0.6398 | 0.0 | 0.5499 | 0.6370 | | 0.0252 | 128.46 | 36740 | 0.1519 | 0.4028 | 0.6130 | 0.6064 | nan | 0.5751 | 0.6509 | 0.0 | 0.5610 | 0.6475 | | 0.0325 | 128.53 | 36760 | 0.1522 | 0.3989 | 0.6068 | 0.5996 | nan | 0.5653 | 0.6483 | 0.0 | 0.5517 | 0.6451 | | 0.0302 | 128.6 | 36780 | 0.1517 | 0.3955 | 0.6016 | 0.5927 | nan | 0.5502 | 0.6530 | 0.0 | 0.5369 | 0.6497 | | 0.0718 | 128.67 | 36800 | 0.1473 | 0.4073 | 0.6195 | 0.6116 | nan | 0.5740 | 0.6650 | 0.0 | 0.5608 | 0.6612 | | 0.0399 | 128.74 | 36820 | 0.1450 | 0.4071 | 0.6195 | 0.6119 | nan | 0.5757 | 0.6633 | 0.0 | 0.5623 | 0.6591 | | 0.0427 | 128.81 | 36840 | 0.1470 | 0.4045 | 0.6159 | 0.6100 | nan | 0.5818 | 0.6500 | 0.0 | 0.5673 | 0.6464 | | 0.0431 | 128.88 | 36860 | 0.1462 | 0.4103 | 0.6246 | 0.6185 | nan | 0.5897 | 0.6594 | 0.0 | 0.5752 | 0.6556 | | 0.0468 | 128.95 | 36880 | 0.1546 | 0.3989 | 0.6073 | 0.6022 | nan | 0.5780 | 0.6365 | 0.0 | 0.5630 | 0.6335 | | 0.0555 | 129.02 | 36900 | 0.1496 | 0.4045 | 0.6151 | 0.6065 | nan | 0.5657 | 0.6645 | 0.0 | 0.5520 | 0.6614 | | 0.0424 | 129.09 | 36920 | 0.1499 | 0.3976 | 0.6042 | 0.5959 | nan | 0.5561 | 0.6524 | 0.0 | 0.5439 | 0.6491 | | 0.0378 | 129.16 | 36940 | 0.1468 | 0.4101 | 0.6240 | 0.6164 | nan | 0.5797 | 0.6684 | 0.0 | 0.5664 | 0.6641 | | 0.0223 | 129.23 | 36960 | 0.1452 | 0.4101 | 0.6235 | 0.6149 | nan | 0.5739 | 0.6731 | 0.0 | 0.5629 | 0.6673 | | 0.0419 | 129.3 | 36980 | 0.1462 | 0.4054 | 0.6164 | 0.6094 | nan | 0.5757 | 0.6571 | 0.0 | 0.5630 | 0.6531 | | 0.0235 | 129.37 | 37000 | 0.1522 | 0.3950 | 0.6006 | 0.5941 | nan | 0.5633 | 0.6379 | 0.0 | 0.5510 | 0.6340 | | 0.0359 | 129.44 | 37020 | 0.1605 | 0.3807 | 0.5796 | 0.5758 | nan | 0.5575 | 0.6018 | 0.0 | 0.5435 | 0.5986 | | 0.034 | 129.51 | 37040 | 0.1527 | 0.3964 | 0.6029 | 0.5963 | nan | 0.5652 | 0.6405 | 0.0 | 0.5524 | 0.6367 | | 0.0616 | 129.58 | 37060 | 0.1504 | 0.3987 | 0.6065 | 0.5991 | nan | 0.5638 | 0.6492 | 0.0 | 0.5510 | 0.6451 | | 0.0536 | 129.65 | 37080 | 0.1535 | 0.3929 | 0.5971 | 0.5875 | nan | 0.5418 | 0.6525 | 0.0 | 0.5305 | 0.6480 | | 0.0472 | 129.72 | 37100 | 0.1502 | 0.3969 | 0.6035 | 0.5947 | nan | 0.5525 | 0.6545 | 0.0 | 0.5409 | 0.6499 | | 0.0528 | 129.79 | 37120 | 0.1445 | 0.4027 | 0.6120 | 0.6031 | nan | 0.5606 | 0.6634 | 0.0 | 0.5494 | 0.6586 | | 0.0544 | 129.86 | 37140 | 0.1512 | 0.3984 | 0.6057 | 0.5973 | nan | 0.5569 | 0.6545 | 0.0 | 0.5448 | 0.6503 | | 0.0442 | 129.93 | 37160 | 0.1528 | 0.3988 | 0.6063 | 0.5989 | nan | 0.5636 | 0.6490 | 0.0 | 0.5514 | 0.6450 | | 0.0709 | 130.0 | 37180 | 0.1569 | 0.3990 | 0.6069 | 0.6011 | nan | 0.5737 | 0.6400 | 0.0 | 0.5605 | 0.6365 | | 0.0451 | 130.07 | 37200 | 0.1532 | 0.4019 | 0.6114 | 0.6046 | nan | 0.5723 | 0.6506 | 0.0 | 0.5586 | 0.6469 | | 0.0266 | 130.14 | 37220 | 0.1453 | 0.4116 | 0.6258 | 0.6178 | nan | 0.5793 | 0.6723 | 0.0 | 0.5671 | 0.6677 | | 0.0458 | 130.21 | 37240 | 0.1481 | 0.4059 | 0.6170 | 0.6098 | nan | 0.5749 | 0.6591 | 0.0 | 0.5628 | 0.6550 | | 0.031 | 130.28 | 37260 | 0.1479 | 0.4048 | 0.6149 | 0.6051 | nan | 0.5581 | 0.6718 | 0.0 | 0.5477 | 0.6665 | | 0.0335 | 130.35 | 37280 | 0.1495 | 0.4028 | 0.6119 | 0.6035 | nan | 0.5631 | 0.6608 | 0.0 | 0.5525 | 0.6559 | | 0.0461 | 130.42 | 37300 | 0.1491 | 0.4033 | 0.6129 | 0.6038 | nan | 0.5604 | 0.6655 | 0.0 | 0.5484 | 0.6615 | | 0.0439 | 130.49 | 37320 | 0.1496 | 0.3989 | 0.6063 | 0.5988 | nan | 0.5627 | 0.6499 | 0.0 | 0.5504 | 0.6462 | | 0.0315 | 130.56 | 37340 | 0.1522 | 0.4044 | 0.6147 | 0.6084 | nan | 0.5779 | 0.6516 | 0.0 | 0.5654 | 0.6477 | | 0.0365 | 130.63 | 37360 | 0.1515 | 0.4000 | 0.6084 | 0.6014 | nan | 0.5681 | 0.6488 | 0.0 | 0.5549 | 0.6451 | | 0.0353 | 130.7 | 37380 | 0.1510 | 0.4028 | 0.6126 | 0.6058 | nan | 0.5731 | 0.6521 | 0.0 | 0.5597 | 0.6486 | | 0.0409 | 130.77 | 37400 | 0.1527 | 0.3927 | 0.5970 | 0.5891 | nan | 0.5518 | 0.6422 | 0.0 | 0.5392 | 0.6389 | | 0.04 | 130.84 | 37420 | 0.1537 | 0.3962 | 0.6025 | 0.5953 | nan | 0.5607 | 0.6444 | 0.0 | 0.5466 | 0.6419 | | 0.0451 | 130.91 | 37440 | 0.1525 | 0.3971 | 0.6042 | 0.5975 | nan | 0.5655 | 0.6429 | 0.0 | 0.5512 | 0.6402 | | 0.0447 | 130.98 | 37460 | 0.1510 | 0.4013 | 0.6110 | 0.6057 | nan | 0.5801 | 0.6419 | 0.0 | 0.5649 | 0.6390 | | 0.0332 | 131.05 | 37480 | 0.1528 | 0.3920 | 0.5960 | 0.5883 | nan | 0.5512 | 0.6408 | 0.0 | 0.5385 | 0.6376 | | 0.0357 | 131.12 | 37500 | 0.1563 | 0.3908 | 0.5946 | 0.5890 | nan | 0.5623 | 0.6269 | 0.0 | 0.5480 | 0.6244 | | 0.044 | 131.19 | 37520 | 0.1524 | 0.4013 | 0.6103 | 0.6027 | nan | 0.5664 | 0.6541 | 0.0 | 0.5532 | 0.6507 | | 0.0477 | 131.26 | 37540 | 0.1541 | 0.3992 | 0.6074 | 0.6014 | nan | 0.5727 | 0.6420 | 0.0 | 0.5583 | 0.6394 | | 0.0491 | 131.33 | 37560 | 0.1568 | 0.3961 | 0.6025 | 0.5959 | nan | 0.5645 | 0.6404 | 0.0 | 0.5507 | 0.6375 | | 0.0351 | 131.4 | 37580 | 0.1546 | 0.3980 | 0.6060 | 0.6006 | nan | 0.5749 | 0.6371 | 0.0 | 0.5604 | 0.6337 | | 0.0418 | 131.47 | 37600 | 0.1566 | 0.3972 | 0.6045 | 0.5980 | nan | 0.5669 | 0.6420 | 0.0 | 0.5531 | 0.6384 | | 0.0398 | 131.54 | 37620 | 0.1554 | 0.3982 | 0.6060 | 0.5989 | nan | 0.5650 | 0.6471 | 0.0 | 0.5510 | 0.6438 | | 0.0419 | 131.61 | 37640 | 0.1556 | 0.3972 | 0.6041 | 0.5969 | nan | 0.5622 | 0.6461 | 0.0 | 0.5491 | 0.6424 | | 0.0399 | 131.68 | 37660 | 0.1541 | 0.4005 | 0.6093 | 0.6034 | nan | 0.5750 | 0.6436 | 0.0 | 0.5619 | 0.6397 | | 0.0434 | 131.75 | 37680 | 0.1436 | 0.4099 | 0.6232 | 0.6146 | nan | 0.5733 | 0.6732 | 0.0 | 0.5626 | 0.6670 | | 0.0496 | 131.82 | 37700 | 0.1441 | 0.4086 | 0.6212 | 0.6121 | nan | 0.5687 | 0.6738 | 0.0 | 0.5580 | 0.6678 | | 0.047 | 131.89 | 37720 | 0.1436 | 0.4043 | 0.6145 | 0.6040 | nan | 0.5537 | 0.6754 | 0.0 | 0.5431 | 0.6698 | | 0.06 | 131.96 | 37740 | 0.1482 | 0.4009 | 0.6093 | 0.5993 | nan | 0.5519 | 0.6667 | 0.0 | 0.5408 | 0.6620 | | 0.0701 | 132.03 | 37760 | 0.1498 | 0.4001 | 0.6083 | 0.6002 | nan | 0.5616 | 0.6551 | 0.0 | 0.5496 | 0.6508 | | 0.0444 | 132.1 | 37780 | 0.1514 | 0.3981 | 0.6052 | 0.5959 | nan | 0.5514 | 0.6590 | 0.0 | 0.5397 | 0.6546 | | 0.0606 | 132.17 | 37800 | 0.1535 | 0.3989 | 0.6065 | 0.5989 | nan | 0.5624 | 0.6507 | 0.0 | 0.5504 | 0.6462 | | 0.0293 | 132.24 | 37820 | 0.1494 | 0.4056 | 0.6164 | 0.6064 | nan | 0.5582 | 0.6746 | 0.0 | 0.5470 | 0.6697 | | 0.0304 | 132.31 | 37840 | 0.1547 | 0.3966 | 0.6038 | 0.5984 | nan | 0.5731 | 0.6344 | 0.0 | 0.5585 | 0.6314 | | 0.0319 | 132.38 | 37860 | 0.1561 | 0.3934 | 0.5984 | 0.5912 | nan | 0.5570 | 0.6398 | 0.0 | 0.5440 | 0.6364 | | 0.0734 | 132.45 | 37880 | 0.1559 | 0.3987 | 0.6066 | 0.6003 | nan | 0.5704 | 0.6428 | 0.0 | 0.5568 | 0.6393 | | 0.0582 | 132.52 | 37900 | 0.1518 | 0.4010 | 0.6095 | 0.6001 | nan | 0.5556 | 0.6634 | 0.0 | 0.5444 | 0.6587 | | 0.0429 | 132.59 | 37920 | 0.1569 | 0.3952 | 0.6008 | 0.5927 | nan | 0.5537 | 0.6480 | 0.0 | 0.5400 | 0.6457 | | 0.0513 | 132.66 | 37940 | 0.1588 | 0.3861 | 0.5869 | 0.5805 | nan | 0.5502 | 0.6235 | 0.0 | 0.5366 | 0.6216 | | 0.057 | 132.73 | 37960 | 0.1514 | 0.3984 | 0.6057 | 0.5985 | nan | 0.5642 | 0.6471 | 0.0 | 0.5516 | 0.6436 | | 0.0506 | 132.8 | 37980 | 0.1480 | 0.4000 | 0.6082 | 0.6013 | nan | 0.5682 | 0.6483 | 0.0 | 0.5561 | 0.6440 | | 0.0369 | 132.87 | 38000 | 0.1511 | 0.3985 | 0.6062 | 0.6001 | nan | 0.5713 | 0.6411 | 0.0 | 0.5578 | 0.6376 | | 0.0706 | 132.94 | 38020 | 0.1504 | 0.4011 | 0.6098 | 0.6033 | nan | 0.5720 | 0.6476 | 0.0 | 0.5590 | 0.6441 | | 0.0479 | 133.01 | 38040 | 0.1517 | 0.4015 | 0.6107 | 0.6051 | nan | 0.5784 | 0.6431 | 0.0 | 0.5655 | 0.6391 | | 0.0288 | 133.08 | 38060 | 0.1536 | 0.3993 | 0.6077 | 0.6013 | nan | 0.5709 | 0.6446 | 0.0 | 0.5567 | 0.6411 | | 0.0405 | 133.15 | 38080 | 0.1503 | 0.4080 | 0.6212 | 0.6167 | nan | 0.5951 | 0.6473 | 0.0 | 0.5800 | 0.6440 | | 0.0503 | 133.22 | 38100 | 0.1550 | 0.3898 | 0.5929 | 0.5859 | nan | 0.5526 | 0.6332 | 0.0 | 0.5395 | 0.6299 | | 0.0429 | 133.29 | 38120 | 0.1551 | 0.3907 | 0.5942 | 0.5870 | nan | 0.5529 | 0.6355 | 0.0 | 0.5394 | 0.6326 | | 0.0302 | 133.36 | 38140 | 0.1526 | 0.4010 | 0.6102 | 0.6042 | nan | 0.5758 | 0.6446 | 0.0 | 0.5617 | 0.6414 | | 0.0298 | 133.43 | 38160 | 0.1541 | 0.3954 | 0.6009 | 0.5926 | nan | 0.5531 | 0.6488 | 0.0 | 0.5409 | 0.6453 | | 0.0279 | 133.5 | 38180 | 0.1532 | 0.3983 | 0.6057 | 0.5997 | nan | 0.5710 | 0.6404 | 0.0 | 0.5574 | 0.6375 | | 0.0437 | 133.57 | 38200 | 0.1577 | 0.4037 | 0.6143 | 0.6099 | nan | 0.5886 | 0.6401 | 0.0 | 0.5730 | 0.6380 | | 0.0671 | 133.64 | 38220 | 0.1539 | 0.4032 | 0.6138 | 0.6077 | nan | 0.5784 | 0.6493 | 0.0 | 0.5634 | 0.6464 | | 0.0418 | 133.71 | 38240 | 0.1505 | 0.3986 | 0.6062 | 0.5976 | nan | 0.5565 | 0.6559 | 0.0 | 0.5433 | 0.6525 | | 0.0292 | 133.78 | 38260 | 0.1520 | 0.3969 | 0.6035 | 0.5956 | nan | 0.5577 | 0.6493 | 0.0 | 0.5447 | 0.6461 | | 0.0575 | 133.85 | 38280 | 0.1528 | 0.3956 | 0.6017 | 0.5942 | nan | 0.5583 | 0.6452 | 0.0 | 0.5444 | 0.6422 | | 0.0382 | 133.92 | 38300 | 0.1529 | 0.4008 | 0.6096 | 0.6015 | nan | 0.5629 | 0.6563 | 0.0 | 0.5495 | 0.6530 | | 0.0601 | 133.99 | 38320 | 0.1524 | 0.4019 | 0.6115 | 0.6050 | nan | 0.5743 | 0.6486 | 0.0 | 0.5606 | 0.6451 | | 0.0487 | 134.06 | 38340 | 0.1542 | 0.4000 | 0.6084 | 0.6006 | nan | 0.5637 | 0.6531 | 0.0 | 0.5504 | 0.6495 | | 0.0415 | 134.13 | 38360 | 0.1526 | 0.3967 | 0.6031 | 0.5941 | nan | 0.5511 | 0.6552 | 0.0 | 0.5384 | 0.6517 | | 0.0609 | 134.2 | 38380 | 0.1541 | 0.3978 | 0.6045 | 0.5962 | nan | 0.5566 | 0.6524 | 0.0 | 0.5447 | 0.6486 | | 0.058 | 134.27 | 38400 | 0.1500 | 0.4047 | 0.6154 | 0.6086 | nan | 0.5760 | 0.6548 | 0.0 | 0.5631 | 0.6509 | | 0.0582 | 134.34 | 38420 | 0.1525 | 0.4032 | 0.6130 | 0.6055 | nan | 0.5695 | 0.6564 | 0.0 | 0.5573 | 0.6524 | | 0.0446 | 134.41 | 38440 | 0.1532 | 0.3986 | 0.6059 | 0.5977 | nan | 0.5588 | 0.6530 | 0.0 | 0.5466 | 0.6493 | | 0.0462 | 134.48 | 38460 | 0.1549 | 0.3961 | 0.6022 | 0.5948 | nan | 0.5594 | 0.6451 | 0.0 | 0.5466 | 0.6419 | | 0.0461 | 134.55 | 38480 | 0.1555 | 0.4033 | 0.6136 | 0.6087 | nan | 0.5855 | 0.6417 | 0.0 | 0.5713 | 0.6387 | | 0.0581 | 134.62 | 38500 | 0.1539 | 0.3981 | 0.6053 | 0.5973 | nan | 0.5587 | 0.6520 | 0.0 | 0.5455 | 0.6489 | | 0.0226 | 134.69 | 38520 | 0.1555 | 0.3980 | 0.6052 | 0.5989 | nan | 0.5687 | 0.6416 | 0.0 | 0.5554 | 0.6386 | | 0.0532 | 134.76 | 38540 | 0.1569 | 0.3912 | 0.5948 | 0.5871 | nan | 0.5505 | 0.6391 | 0.0 | 0.5377 | 0.6360 | | 0.063 | 134.83 | 38560 | 0.1520 | 0.4059 | 0.6173 | 0.6094 | nan | 0.5717 | 0.6629 | 0.0 | 0.5592 | 0.6584 | | 0.0212 | 134.9 | 38580 | 0.1512 | 0.3986 | 0.6060 | 0.5979 | nan | 0.5594 | 0.6525 | 0.0 | 0.5471 | 0.6486 | | 0.0271 | 134.97 | 38600 | 0.1508 | 0.4030 | 0.6133 | 0.6077 | nan | 0.5812 | 0.6453 | 0.0 | 0.5673 | 0.6418 | | 0.0382 | 135.03 | 38620 | 0.1587 | 0.3951 | 0.6011 | 0.5962 | nan | 0.5729 | 0.6293 | 0.0 | 0.5586 | 0.6267 | | 0.0484 | 135.1 | 38640 | 0.1524 | 0.4055 | 0.6167 | 0.6097 | nan | 0.5761 | 0.6573 | 0.0 | 0.5627 | 0.6539 | | 0.059 | 135.17 | 38660 | 0.1538 | 0.4011 | 0.6098 | 0.6025 | nan | 0.5675 | 0.6521 | 0.0 | 0.5541 | 0.6493 | | 0.0476 | 135.24 | 38680 | 0.1558 | 0.4015 | 0.6108 | 0.6055 | nan | 0.5803 | 0.6414 | 0.0 | 0.5660 | 0.6386 | | 0.0486 | 135.31 | 38700 | 0.1552 | 0.4024 | 0.6123 | 0.6065 | nan | 0.5786 | 0.6460 | 0.0 | 0.5640 | 0.6433 | | 0.0334 | 135.38 | 38720 | 0.1509 | 0.4061 | 0.6173 | 0.6088 | nan | 0.5680 | 0.6667 | 0.0 | 0.5555 | 0.6630 | | 0.0318 | 135.45 | 38740 | 0.1493 | 0.4051 | 0.6159 | 0.6079 | nan | 0.5695 | 0.6623 | 0.0 | 0.5564 | 0.6589 | | 0.043 | 135.52 | 38760 | 0.1431 | 0.4135 | 0.6280 | 0.6166 | nan | 0.5624 | 0.6936 | 0.0 | 0.5519 | 0.6886 | | 0.0542 | 135.59 | 38780 | 0.1428 | 0.4146 | 0.6298 | 0.6196 | nan | 0.5709 | 0.6887 | 0.0 | 0.5598 | 0.6839 | | 0.0299 | 135.66 | 38800 | 0.1449 | 0.4113 | 0.6248 | 0.6155 | nan | 0.5711 | 0.6785 | 0.0 | 0.5595 | 0.6744 | | 0.0404 | 135.73 | 38820 | 0.1490 | 0.4026 | 0.6117 | 0.6026 | nan | 0.5592 | 0.6642 | 0.0 | 0.5471 | 0.6608 | | 0.0352 | 135.8 | 38840 | 0.1531 | 0.4032 | 0.6126 | 0.6037 | nan | 0.5609 | 0.6643 | 0.0 | 0.5492 | 0.6604 | | 0.0424 | 135.87 | 38860 | 0.1481 | 0.4081 | 0.6200 | 0.6098 | nan | 0.5614 | 0.6785 | 0.0 | 0.5494 | 0.6748 | | 0.0252 | 135.94 | 38880 | 0.1463 | 0.4093 | 0.6219 | 0.6134 | nan | 0.5729 | 0.6709 | 0.0 | 0.5613 | 0.6667 | | 0.0449 | 136.01 | 38900 | 0.1499 | 0.4064 | 0.6177 | 0.6093 | nan | 0.5696 | 0.6657 | 0.0 | 0.5571 | 0.6619 | | 0.0575 | 136.08 | 38920 | 0.1500 | 0.4109 | 0.6247 | 0.6164 | nan | 0.5771 | 0.6723 | 0.0 | 0.5640 | 0.6687 | | 0.0675 | 136.15 | 38940 | 0.1533 | 0.3934 | 0.5985 | 0.5938 | nan | 0.5711 | 0.6260 | 0.0 | 0.5570 | 0.6233 | | 0.056 | 136.22 | 38960 | 0.1516 | 0.3932 | 0.5975 | 0.5891 | nan | 0.5486 | 0.6465 | 0.0 | 0.5360 | 0.6437 | | 0.0392 | 136.29 | 38980 | 0.1492 | 0.4039 | 0.6144 | 0.6074 | nan | 0.5741 | 0.6547 | 0.0 | 0.5605 | 0.6513 | | 0.0526 | 136.36 | 39000 | 0.1503 | 0.4006 | 0.6089 | 0.6001 | nan | 0.5580 | 0.6598 | 0.0 | 0.5450 | 0.6566 | | 0.0475 | 136.43 | 39020 | 0.1529 | 0.3939 | 0.5983 | 0.5889 | nan | 0.5438 | 0.6527 | 0.0 | 0.5318 | 0.6500 | | 0.0616 | 136.5 | 39040 | 0.1495 | 0.4048 | 0.6151 | 0.6074 | nan | 0.5707 | 0.6595 | 0.0 | 0.5579 | 0.6564 | | 0.0268 | 136.57 | 39060 | 0.1484 | 0.4002 | 0.6078 | 0.5991 | nan | 0.5577 | 0.6578 | 0.0 | 0.5461 | 0.6544 | | 0.0397 | 136.64 | 39080 | 0.1477 | 0.4057 | 0.6163 | 0.6086 | nan | 0.5720 | 0.6606 | 0.0 | 0.5598 | 0.6572 | | 0.0369 | 136.71 | 39100 | 0.1467 | 0.4064 | 0.6173 | 0.6084 | nan | 0.5656 | 0.6691 | 0.0 | 0.5540 | 0.6651 | | 0.0498 | 136.78 | 39120 | 0.1509 | 0.4031 | 0.6129 | 0.6069 | nan | 0.5786 | 0.6471 | 0.0 | 0.5648 | 0.6444 | | 0.0314 | 136.85 | 39140 | 0.1509 | 0.4034 | 0.6136 | 0.6081 | nan | 0.5819 | 0.6453 | 0.0 | 0.5680 | 0.6423 | | 0.0242 | 136.92 | 39160 | 0.1525 | 0.4009 | 0.6092 | 0.6015 | nan | 0.5647 | 0.6537 | 0.0 | 0.5524 | 0.6504 | | 0.0275 | 136.99 | 39180 | 0.1508 | 0.4037 | 0.6134 | 0.6045 | nan | 0.5623 | 0.6644 | 0.0 | 0.5507 | 0.6606 | | 0.0601 | 137.06 | 39200 | 0.1513 | 0.4016 | 0.6104 | 0.6030 | nan | 0.5672 | 0.6537 | 0.0 | 0.5548 | 0.6501 | | 0.038 | 137.13 | 39220 | 0.1500 | 0.3981 | 0.6048 | 0.5966 | nan | 0.5574 | 0.6521 | 0.0 | 0.5458 | 0.6485 | | 0.0553 | 137.2 | 39240 | 0.1452 | 0.4103 | 0.6236 | 0.6157 | nan | 0.5776 | 0.6697 | 0.0 | 0.5660 | 0.6649 | | 0.0461 | 137.27 | 39260 | 0.1451 | 0.4112 | 0.6251 | 0.6185 | nan | 0.5871 | 0.6631 | 0.0 | 0.5738 | 0.6598 | | 0.0912 | 137.34 | 39280 | 0.1518 | 0.4021 | 0.6109 | 0.6037 | nan | 0.5694 | 0.6524 | 0.0 | 0.5568 | 0.6494 | | 0.0429 | 137.41 | 39300 | 0.1595 | 0.3930 | 0.5975 | 0.5919 | nan | 0.5651 | 0.6299 | 0.0 | 0.5510 | 0.6281 | | 0.029 | 137.48 | 39320 | 0.1576 | 0.3970 | 0.6034 | 0.5966 | nan | 0.5642 | 0.6427 | 0.0 | 0.5502 | 0.6407 | | 0.0493 | 137.55 | 39340 | 0.1569 | 0.3945 | 0.5996 | 0.5931 | nan | 0.5621 | 0.6370 | 0.0 | 0.5490 | 0.6346 | | 0.0236 | 137.62 | 39360 | 0.1549 | 0.3999 | 0.6080 | 0.6010 | nan | 0.5677 | 0.6483 | 0.0 | 0.5540 | 0.6457 | | 0.0491 | 137.69 | 39380 | 0.1555 | 0.3986 | 0.6063 | 0.6013 | nan | 0.5775 | 0.6351 | 0.0 | 0.5632 | 0.6326 | | 0.0346 | 137.76 | 39400 | 0.1545 | 0.4029 | 0.6128 | 0.6075 | nan | 0.5825 | 0.6431 | 0.0 | 0.5683 | 0.6404 | | 0.0544 | 137.83 | 39420 | 0.1536 | 0.3973 | 0.6035 | 0.5953 | nan | 0.5559 | 0.6511 | 0.0 | 0.5439 | 0.6480 | | 0.0455 | 137.9 | 39440 | 0.1516 | 0.3992 | 0.6065 | 0.5975 | nan | 0.5545 | 0.6584 | 0.0 | 0.5432 | 0.6545 | | 0.0309 | 137.97 | 39460 | 0.1523 | 0.4013 | 0.6100 | 0.6028 | nan | 0.5684 | 0.6516 | 0.0 | 0.5559 | 0.6480 | | 0.0361 | 138.04 | 39480 | 0.1532 | 0.4031 | 0.6131 | 0.6060 | nan | 0.5721 | 0.6541 | 0.0 | 0.5585 | 0.6508 | | 0.0544 | 138.11 | 39500 | 0.1525 | 0.3985 | 0.6055 | 0.5968 | nan | 0.5550 | 0.6559 | 0.0 | 0.5434 | 0.6520 | | 0.0375 | 138.18 | 39520 | 0.1490 | 0.4037 | 0.6136 | 0.6042 | nan | 0.5596 | 0.6675 | 0.0 | 0.5477 | 0.6634 | | 0.0278 | 138.25 | 39540 | 0.1538 | 0.3972 | 0.6036 | 0.5965 | nan | 0.5624 | 0.6448 | 0.0 | 0.5499 | 0.6416 | | 0.0374 | 138.32 | 39560 | 0.1512 | 0.4049 | 0.6154 | 0.6066 | nan | 0.5646 | 0.6662 | 0.0 | 0.5534 | 0.6613 | | 0.0416 | 138.39 | 39580 | 0.1583 | 0.3934 | 0.5978 | 0.5895 | nan | 0.5500 | 0.6456 | 0.0 | 0.5376 | 0.6426 | | 0.0276 | 138.46 | 39600 | 0.1532 | 0.4008 | 0.6092 | 0.6004 | nan | 0.5586 | 0.6598 | 0.0 | 0.5458 | 0.6565 | | 0.0427 | 138.53 | 39620 | 0.1494 | 0.4101 | 0.6241 | 0.6171 | nan | 0.5837 | 0.6646 | 0.0 | 0.5704 | 0.6599 | | 0.0536 | 138.6 | 39640 | 0.1505 | 0.4055 | 0.6168 | 0.6089 | nan | 0.5715 | 0.6620 | 0.0 | 0.5589 | 0.6576 | | 0.0503 | 138.67 | 39660 | 0.1544 | 0.3987 | 0.6063 | 0.5981 | nan | 0.5588 | 0.6538 | 0.0 | 0.5463 | 0.6498 | | 0.0452 | 138.74 | 39680 | 0.1528 | 0.4037 | 0.6141 | 0.6078 | nan | 0.5777 | 0.6506 | 0.0 | 0.5648 | 0.6462 | | 0.0399 | 138.81 | 39700 | 0.1564 | 0.4017 | 0.6109 | 0.6033 | nan | 0.5671 | 0.6547 | 0.0 | 0.5543 | 0.6507 | | 0.0406 | 138.88 | 39720 | 0.1521 | 0.3987 | 0.6060 | 0.5972 | nan | 0.5556 | 0.6563 | 0.0 | 0.5444 | 0.6516 | | 0.0352 | 138.95 | 39740 | 0.1432 | 0.4106 | 0.6239 | 0.6127 | nan | 0.5590 | 0.6888 | 0.0 | 0.5499 | 0.6818 | | 0.0447 | 139.02 | 39760 | 0.1430 | 0.4132 | 0.6278 | 0.6173 | nan | 0.5671 | 0.6885 | 0.0 | 0.5564 | 0.6832 | | 0.0581 | 139.09 | 39780 | 0.1497 | 0.4039 | 0.6137 | 0.6034 | nan | 0.5545 | 0.6728 | 0.0 | 0.5441 | 0.6677 | | 0.0477 | 139.16 | 39800 | 0.1464 | 0.4074 | 0.6192 | 0.6079 | nan | 0.5544 | 0.6840 | 0.0 | 0.5436 | 0.6785 | | 0.0235 | 139.23 | 39820 | 0.1505 | 0.4042 | 0.6146 | 0.6068 | nan | 0.5696 | 0.6596 | 0.0 | 0.5576 | 0.6550 | | 0.0475 | 139.3 | 39840 | 0.1486 | 0.4058 | 0.6174 | 0.6088 | nan | 0.5678 | 0.6670 | 0.0 | 0.5552 | 0.6622 | | 0.0587 | 139.37 | 39860 | 0.1496 | 0.4004 | 0.6087 | 0.5991 | nan | 0.5532 | 0.6641 | 0.0 | 0.5416 | 0.6595 | | 0.0586 | 139.44 | 39880 | 0.1534 | 0.3972 | 0.6037 | 0.5931 | nan | 0.5422 | 0.6652 | 0.0 | 0.5311 | 0.6605 | | 0.0505 | 139.51 | 39900 | 0.1508 | 0.3982 | 0.6049 | 0.5948 | nan | 0.5465 | 0.6633 | 0.0 | 0.5352 | 0.6593 | | 0.053 | 139.58 | 39920 | 0.1520 | 0.4056 | 0.6166 | 0.6093 | nan | 0.5741 | 0.6592 | 0.0 | 0.5612 | 0.6554 | | 0.0335 | 139.65 | 39940 | 0.1482 | 0.4059 | 0.6171 | 0.6094 | nan | 0.5725 | 0.6618 | 0.0 | 0.5602 | 0.6575 | | 0.0276 | 139.72 | 39960 | 0.1495 | 0.4001 | 0.6085 | 0.6004 | nan | 0.5617 | 0.6552 | 0.0 | 0.5487 | 0.6516 | | 0.0341 | 139.79 | 39980 | 0.1484 | 0.4011 | 0.6095 | 0.5996 | nan | 0.5520 | 0.6671 | 0.0 | 0.5407 | 0.6625 | | 0.026 | 139.86 | 40000 | 0.1493 | 0.4072 | 0.6191 | 0.6117 | nan | 0.5761 | 0.6621 | 0.0 | 0.5634 | 0.6582 | | 0.0444 | 139.93 | 40020 | 0.1592 | 0.3974 | 0.6047 | 0.5993 | nan | 0.5734 | 0.6361 | 0.0 | 0.5594 | 0.6329 | | 0.027 | 140.0 | 40040 | 0.1584 | 0.3954 | 0.6018 | 0.5972 | nan | 0.5751 | 0.6286 | 0.0 | 0.5608 | 0.6254 | | 0.0495 | 140.07 | 40060 | 0.1632 | 0.3924 | 0.5973 | 0.5918 | nan | 0.5653 | 0.6293 | 0.0 | 0.5505 | 0.6266 | | 0.0311 | 140.14 | 40080 | 0.1616 | 0.3881 | 0.5903 | 0.5840 | nan | 0.5536 | 0.6271 | 0.0 | 0.5408 | 0.6234 | | 0.0397 | 140.21 | 40100 | 0.1584 | 0.3914 | 0.5954 | 0.5876 | nan | 0.5500 | 0.6408 | 0.0 | 0.5375 | 0.6367 | | 0.0438 | 140.28 | 40120 | 0.1594 | 0.3987 | 0.6068 | 0.6007 | nan | 0.5714 | 0.6422 | 0.0 | 0.5575 | 0.6385 | | 0.0416 | 140.35 | 40140 | 0.1549 | 0.3991 | 0.6072 | 0.5998 | nan | 0.5645 | 0.6499 | 0.0 | 0.5520 | 0.6454 | | 0.0431 | 140.42 | 40160 | 0.1595 | 0.3929 | 0.5977 | 0.5911 | nan | 0.5600 | 0.6354 | 0.0 | 0.5472 | 0.6316 | | 0.034 | 140.49 | 40180 | 0.1549 | 0.3938 | 0.5984 | 0.5889 | nan | 0.5439 | 0.6528 | 0.0 | 0.5328 | 0.6487 | | 0.0424 | 140.56 | 40200 | 0.1451 | 0.4079 | 0.6199 | 0.6093 | nan | 0.5588 | 0.6809 | 0.0 | 0.5490 | 0.6746 | | 0.0433 | 140.63 | 40220 | 0.1434 | 0.4108 | 0.6246 | 0.6151 | nan | 0.5698 | 0.6793 | 0.0 | 0.5600 | 0.6723 | | 0.0232 | 140.7 | 40240 | 0.1503 | 0.4037 | 0.6139 | 0.6053 | nan | 0.5645 | 0.6632 | 0.0 | 0.5532 | 0.6579 | | 0.0254 | 140.77 | 40260 | 0.1500 | 0.4055 | 0.6164 | 0.6081 | nan | 0.5686 | 0.6641 | 0.0 | 0.5574 | 0.6591 | | 0.0236 | 140.84 | 40280 | 0.1564 | 0.3954 | 0.6007 | 0.5915 | nan | 0.5475 | 0.6538 | 0.0 | 0.5371 | 0.6490 | | 0.0253 | 140.91 | 40300 | 0.1523 | 0.4099 | 0.6233 | 0.6173 | nan | 0.5886 | 0.6579 | 0.0 | 0.5765 | 0.6532 | | 0.0368 | 140.98 | 40320 | 0.1484 | 0.4070 | 0.6186 | 0.6079 | nan | 0.5565 | 0.6807 | 0.0 | 0.5479 | 0.6731 | | 0.0309 | 141.05 | 40340 | 0.1485 | 0.4049 | 0.6155 | 0.6053 | nan | 0.5569 | 0.6740 | 0.0 | 0.5480 | 0.6667 | | 0.0501 | 141.12 | 40360 | 0.1501 | 0.4077 | 0.6196 | 0.6124 | nan | 0.5783 | 0.6609 | 0.0 | 0.5676 | 0.6553 | | 0.0486 | 141.19 | 40380 | 0.1563 | 0.3967 | 0.6030 | 0.5963 | nan | 0.5643 | 0.6416 | 0.0 | 0.5521 | 0.6380 | | 0.0445 | 141.26 | 40400 | 0.1529 | 0.4002 | 0.6081 | 0.5992 | nan | 0.5563 | 0.6600 | 0.0 | 0.5445 | 0.6561 | | 0.0248 | 141.33 | 40420 | 0.1527 | 0.3983 | 0.6055 | 0.5969 | nan | 0.5558 | 0.6551 | 0.0 | 0.5443 | 0.6504 | | 0.0369 | 141.4 | 40440 | 0.1505 | 0.4024 | 0.6120 | 0.6029 | nan | 0.5594 | 0.6647 | 0.0 | 0.5476 | 0.6596 | | 0.0344 | 141.47 | 40460 | 0.1514 | 0.3963 | 0.6031 | 0.5964 | nan | 0.5649 | 0.6412 | 0.0 | 0.5519 | 0.6370 | | 0.0387 | 141.54 | 40480 | 0.1532 | 0.3958 | 0.6024 | 0.5957 | nan | 0.5635 | 0.6414 | 0.0 | 0.5505 | 0.6369 | | 0.0539 | 141.61 | 40500 | 0.1577 | 0.3937 | 0.5992 | 0.5931 | nan | 0.5639 | 0.6344 | 0.0 | 0.5496 | 0.6316 | | 0.0318 | 141.68 | 40520 | 0.1595 | 0.3885 | 0.5910 | 0.5832 | nan | 0.5457 | 0.6362 | 0.0 | 0.5326 | 0.6328 | | 0.0442 | 141.75 | 40540 | 0.1533 | 0.4012 | 0.6113 | 0.6070 | nan | 0.5865 | 0.6360 | 0.0 | 0.5713 | 0.6322 | | 0.0523 | 141.82 | 40560 | 0.1538 | 0.3975 | 0.6051 | 0.5987 | nan | 0.5679 | 0.6423 | 0.0 | 0.5543 | 0.6381 | | 0.0584 | 141.89 | 40580 | 0.1520 | 0.3979 | 0.6054 | 0.5978 | nan | 0.5616 | 0.6491 | 0.0 | 0.5493 | 0.6444 | | 0.0282 | 141.96 | 40600 | 0.1535 | 0.3970 | 0.6039 | 0.5954 | nan | 0.5552 | 0.6525 | 0.0 | 0.5432 | 0.6478 | | 0.0472 | 142.03 | 40620 | 0.1570 | 0.3935 | 0.5985 | 0.5916 | nan | 0.5585 | 0.6385 | 0.0 | 0.5458 | 0.6346 | | 0.0548 | 142.1 | 40640 | 0.1528 | 0.4001 | 0.6087 | 0.6006 | nan | 0.5618 | 0.6556 | 0.0 | 0.5498 | 0.6505 | | 0.0447 | 142.17 | 40660 | 0.1593 | 0.3960 | 0.6030 | 0.5988 | nan | 0.5790 | 0.6269 | 0.0 | 0.5632 | 0.6248 | | 0.084 | 142.24 | 40680 | 0.1610 | 0.3947 | 0.6010 | 0.5956 | nan | 0.5698 | 0.6322 | 0.0 | 0.5542 | 0.6300 | | 0.0379 | 142.31 | 40700 | 0.1587 | 0.3976 | 0.6051 | 0.6002 | nan | 0.5769 | 0.6334 | 0.0 | 0.5616 | 0.6311 | | 0.0339 | 142.38 | 40720 | 0.1583 | 0.3962 | 0.6031 | 0.5973 | nan | 0.5697 | 0.6365 | 0.0 | 0.5544 | 0.6342 | | 0.0367 | 142.45 | 40740 | 0.1572 | 0.3972 | 0.6043 | 0.5986 | nan | 0.5714 | 0.6373 | 0.0 | 0.5568 | 0.6347 | | 0.0347 | 142.52 | 40760 | 0.1572 | 0.3978 | 0.6053 | 0.5984 | nan | 0.5655 | 0.6450 | 0.0 | 0.5512 | 0.6421 | | 0.0594 | 142.59 | 40780 | 0.1576 | 0.3944 | 0.5998 | 0.5927 | nan | 0.5588 | 0.6409 | 0.0 | 0.5455 | 0.6378 | | 0.0574 | 142.66 | 40800 | 0.1578 | 0.3955 | 0.6016 | 0.5945 | nan | 0.5603 | 0.6430 | 0.0 | 0.5463 | 0.6401 | | 0.0413 | 142.73 | 40820 | 0.1531 | 0.4090 | 0.6224 | 0.6158 | nan | 0.5842 | 0.6606 | 0.0 | 0.5704 | 0.6567 | | 0.0303 | 142.8 | 40840 | 0.1546 | 0.3935 | 0.5980 | 0.5896 | nan | 0.5493 | 0.6468 | 0.0 | 0.5370 | 0.6434 | | 0.059 | 142.87 | 40860 | 0.1564 | 0.3940 | 0.5990 | 0.5912 | nan | 0.5543 | 0.6437 | 0.0 | 0.5411 | 0.6409 | | 0.0281 | 142.94 | 40880 | 0.1499 | 0.4013 | 0.6099 | 0.6006 | nan | 0.5560 | 0.6639 | 0.0 | 0.5437 | 0.6603 | | 0.0236 | 143.01 | 40900 | 0.1530 | 0.4046 | 0.6150 | 0.6045 | nan | 0.5545 | 0.6755 | 0.0 | 0.5442 | 0.6697 | | 0.0233 | 143.08 | 40920 | 0.1577 | 0.3979 | 0.6048 | 0.5965 | nan | 0.5568 | 0.6528 | 0.0 | 0.5444 | 0.6493 | | 0.0259 | 143.15 | 40940 | 0.1607 | 0.3934 | 0.5987 | 0.5928 | nan | 0.5646 | 0.6327 | 0.0 | 0.5495 | 0.6308 | | 0.0479 | 143.22 | 40960 | 0.1600 | 0.3971 | 0.6040 | 0.5973 | nan | 0.5650 | 0.6430 | 0.0 | 0.5514 | 0.6400 | | 0.0227 | 143.29 | 40980 | 0.1555 | 0.3953 | 0.6012 | 0.5930 | nan | 0.5535 | 0.6490 | 0.0 | 0.5406 | 0.6454 | | 0.042 | 143.36 | 41000 | 0.1562 | 0.3993 | 0.6076 | 0.6016 | nan | 0.5733 | 0.6419 | 0.0 | 0.5593 | 0.6385 | | 0.038 | 143.43 | 41020 | 0.1599 | 0.3970 | 0.6039 | 0.5982 | nan | 0.5706 | 0.6373 | 0.0 | 0.5567 | 0.6342 | | 0.0294 | 143.5 | 41040 | 0.1543 | 0.4049 | 0.6160 | 0.6093 | nan | 0.5774 | 0.6546 | 0.0 | 0.5636 | 0.6510 | | 0.0339 | 143.57 | 41060 | 0.1469 | 0.4096 | 0.6229 | 0.6132 | nan | 0.5669 | 0.6789 | 0.0 | 0.5555 | 0.6732 | | 0.0497 | 143.64 | 41080 | 0.1518 | 0.4030 | 0.6129 | 0.6052 | nan | 0.5686 | 0.6571 | 0.0 | 0.5551 | 0.6538 | | 0.0534 | 143.71 | 41100 | 0.1525 | 0.4047 | 0.6156 | 0.6077 | nan | 0.5704 | 0.6607 | 0.0 | 0.5565 | 0.6576 | | 0.0392 | 143.78 | 41120 | 0.1534 | 0.4032 | 0.6134 | 0.6066 | nan | 0.5740 | 0.6529 | 0.0 | 0.5592 | 0.6505 | | 0.036 | 143.85 | 41140 | 0.1553 | 0.4002 | 0.6084 | 0.5999 | nan | 0.5596 | 0.6572 | 0.0 | 0.5469 | 0.6538 | | 0.0174 | 143.92 | 41160 | 0.1517 | 0.4040 | 0.6145 | 0.6064 | nan | 0.5675 | 0.6616 | 0.0 | 0.5535 | 0.6586 | | 0.0267 | 143.99 | 41180 | 0.1565 | 0.4000 | 0.6086 | 0.6033 | nan | 0.5781 | 0.6390 | 0.0 | 0.5631 | 0.6371 | | 0.0364 | 144.06 | 41200 | 0.1525 | 0.4055 | 0.6168 | 0.6093 | nan | 0.5735 | 0.6600 | 0.0 | 0.5593 | 0.6573 | | 0.0255 | 144.13 | 41220 | 0.1551 | 0.3992 | 0.6069 | 0.5990 | nan | 0.5613 | 0.6525 | 0.0 | 0.5477 | 0.6499 | | 0.03 | 144.2 | 41240 | 0.1559 | 0.4002 | 0.6087 | 0.6015 | nan | 0.5670 | 0.6504 | 0.0 | 0.5529 | 0.6478 | | 0.031 | 144.27 | 41260 | 0.1561 | 0.4013 | 0.6104 | 0.6028 | nan | 0.5664 | 0.6545 | 0.0 | 0.5524 | 0.6515 | | 0.0492 | 144.34 | 41280 | 0.1582 | 0.4008 | 0.6097 | 0.6043 | nan | 0.5784 | 0.6411 | 0.0 | 0.5638 | 0.6384 | | 0.0215 | 144.41 | 41300 | 0.1583 | 0.3995 | 0.6075 | 0.5998 | nan | 0.5631 | 0.6520 | 0.0 | 0.5490 | 0.6494 | | 0.029 | 144.48 | 41320 | 0.1604 | 0.3984 | 0.6056 | 0.5990 | nan | 0.5672 | 0.6441 | 0.0 | 0.5537 | 0.6416 | | 0.0384 | 144.55 | 41340 | 0.1499 | 0.4083 | 0.6210 | 0.6124 | nan | 0.5711 | 0.6709 | 0.0 | 0.5585 | 0.6664 | | 0.0305 | 144.62 | 41360 | 0.1515 | 0.4070 | 0.6192 | 0.6111 | nan | 0.5723 | 0.6660 | 0.0 | 0.5593 | 0.6618 | | 0.0531 | 144.69 | 41380 | 0.1557 | 0.3987 | 0.6062 | 0.5982 | nan | 0.5601 | 0.6523 | 0.0 | 0.5469 | 0.6493 | | 0.0379 | 144.76 | 41400 | 0.1557 | 0.4034 | 0.6135 | 0.6072 | nan | 0.5768 | 0.6502 | 0.0 | 0.5633 | 0.6469 | | 0.0335 | 144.83 | 41420 | 0.1550 | 0.4004 | 0.6089 | 0.6024 | nan | 0.5716 | 0.6461 | 0.0 | 0.5587 | 0.6423 | | 0.0373 | 144.9 | 41440 | 0.1558 | 0.3986 | 0.6060 | 0.5995 | nan | 0.5687 | 0.6434 | 0.0 | 0.5555 | 0.6402 | | 0.0443 | 144.97 | 41460 | 0.1572 | 0.3968 | 0.6032 | 0.5953 | nan | 0.5574 | 0.6490 | 0.0 | 0.5452 | 0.6453 | | 0.0477 | 145.03 | 41480 | 0.1564 | 0.3984 | 0.6059 | 0.5991 | nan | 0.5665 | 0.6453 | 0.0 | 0.5535 | 0.6416 | | 0.0492 | 145.1 | 41500 | 0.1542 | 0.4008 | 0.6099 | 0.6037 | nan | 0.5742 | 0.6456 | 0.0 | 0.5599 | 0.6425 | | 0.0382 | 145.17 | 41520 | 0.1605 | 0.3924 | 0.5968 | 0.5909 | nan | 0.5630 | 0.6307 | 0.0 | 0.5490 | 0.6282 | | 0.0358 | 145.24 | 41540 | 0.1592 | 0.3941 | 0.5996 | 0.5946 | nan | 0.5706 | 0.6285 | 0.0 | 0.5563 | 0.6260 | | 0.0268 | 145.31 | 41560 | 0.1532 | 0.4070 | 0.6193 | 0.6135 | nan | 0.5856 | 0.6530 | 0.0 | 0.5716 | 0.6494 | | 0.0374 | 145.38 | 41580 | 0.1537 | 0.3990 | 0.6068 | 0.5992 | nan | 0.5629 | 0.6507 | 0.0 | 0.5505 | 0.6464 | | 0.0573 | 145.45 | 41600 | 0.1557 | 0.3996 | 0.6076 | 0.6011 | nan | 0.5698 | 0.6455 | 0.0 | 0.5568 | 0.6418 | | 0.0314 | 145.52 | 41620 | 0.1561 | 0.4010 | 0.6099 | 0.6036 | nan | 0.5734 | 0.6463 | 0.0 | 0.5598 | 0.6432 | | 0.0506 | 145.59 | 41640 | 0.1568 | 0.3976 | 0.6047 | 0.5979 | nan | 0.5653 | 0.6441 | 0.0 | 0.5518 | 0.6411 | | 0.0373 | 145.66 | 41660 | 0.1545 | 0.4053 | 0.6168 | 0.6115 | nan | 0.5862 | 0.6474 | 0.0 | 0.5712 | 0.6448 | | 0.0481 | 145.73 | 41680 | 0.1531 | 0.4009 | 0.6097 | 0.6027 | nan | 0.5694 | 0.6499 | 0.0 | 0.5557 | 0.6471 | | 0.0439 | 145.8 | 41700 | 0.1546 | 0.4005 | 0.6090 | 0.6020 | nan | 0.5686 | 0.6495 | 0.0 | 0.5552 | 0.6464 | | 0.031 | 145.87 | 41720 | 0.1506 | 0.4095 | 0.6224 | 0.6133 | nan | 0.5696 | 0.6752 | 0.0 | 0.5574 | 0.6710 | | 0.0635 | 145.94 | 41740 | 0.1506 | 0.4073 | 0.6194 | 0.6123 | nan | 0.5786 | 0.6601 | 0.0 | 0.5650 | 0.6568 | | 0.027 | 146.01 | 41760 | 0.1543 | 0.4063 | 0.6180 | 0.6124 | nan | 0.5860 | 0.6500 | 0.0 | 0.5718 | 0.6471 | | 0.0444 | 146.08 | 41780 | 0.1531 | 0.3983 | 0.6055 | 0.5976 | nan | 0.5599 | 0.6510 | 0.0 | 0.5471 | 0.6479 | | 0.0616 | 146.15 | 41800 | 0.1575 | 0.3939 | 0.5982 | 0.5891 | nan | 0.5460 | 0.6504 | 0.0 | 0.5341 | 0.6475 | | 0.0395 | 146.22 | 41820 | 0.1557 | 0.3978 | 0.6041 | 0.5943 | nan | 0.5476 | 0.6606 | 0.0 | 0.5358 | 0.6575 | | 0.032 | 146.29 | 41840 | 0.1554 | 0.4036 | 0.6140 | 0.6088 | nan | 0.5838 | 0.6443 | 0.0 | 0.5687 | 0.6421 | | 0.0256 | 146.36 | 41860 | 0.1514 | 0.4031 | 0.6127 | 0.6035 | nan | 0.5598 | 0.6656 | 0.0 | 0.5474 | 0.6618 | | 0.0596 | 146.43 | 41880 | 0.1513 | 0.4028 | 0.6124 | 0.6028 | nan | 0.5567 | 0.6681 | 0.0 | 0.5454 | 0.6629 | | 0.0324 | 146.5 | 41900 | 0.1502 | 0.4034 | 0.6135 | 0.6069 | nan | 0.5751 | 0.6519 | 0.0 | 0.5635 | 0.6468 | | 0.0448 | 146.57 | 41920 | 0.1506 | 0.4001 | 0.6077 | 0.5971 | nan | 0.5468 | 0.6686 | 0.0 | 0.5370 | 0.6631 | | 0.0287 | 146.64 | 41940 | 0.1498 | 0.4029 | 0.6121 | 0.6017 | nan | 0.5519 | 0.6722 | 0.0 | 0.5418 | 0.6669 | | 0.031 | 146.71 | 41960 | 0.1551 | 0.3946 | 0.6000 | 0.5928 | nan | 0.5587 | 0.6412 | 0.0 | 0.5469 | 0.6370 | | 0.0557 | 146.78 | 41980 | 0.1532 | 0.3996 | 0.6078 | 0.6016 | nan | 0.5717 | 0.6440 | 0.0 | 0.5586 | 0.6400 | | 0.0358 | 146.85 | 42000 | 0.1551 | 0.3965 | 0.6033 | 0.5965 | nan | 0.5637 | 0.6429 | 0.0 | 0.5503 | 0.6392 | | 0.0291 | 146.92 | 42020 | 0.1555 | 0.3994 | 0.6075 | 0.6006 | nan | 0.5676 | 0.6475 | 0.0 | 0.5544 | 0.6439 | | 0.0354 | 146.99 | 42040 | 0.1575 | 0.3932 | 0.5977 | 0.5883 | nan | 0.5437 | 0.6518 | 0.0 | 0.5309 | 0.6487 | | 0.0574 | 147.06 | 42060 | 0.1546 | 0.4018 | 0.6111 | 0.6047 | nan | 0.5742 | 0.6480 | 0.0 | 0.5606 | 0.6449 | | 0.0559 | 147.13 | 42080 | 0.1543 | 0.4006 | 0.6095 | 0.6031 | nan | 0.5725 | 0.6464 | 0.0 | 0.5587 | 0.6430 | | 0.0338 | 147.2 | 42100 | 0.1553 | 0.3985 | 0.6062 | 0.5987 | nan | 0.5628 | 0.6496 | 0.0 | 0.5495 | 0.6461 | | 0.0316 | 147.27 | 42120 | 0.1667 | 0.3871 | 0.5898 | 0.5857 | nan | 0.5665 | 0.6130 | 0.0 | 0.5502 | 0.6111 | | 0.0291 | 147.34 | 42140 | 0.1649 | 0.3911 | 0.5956 | 0.5918 | nan | 0.5732 | 0.6181 | 0.0 | 0.5573 | 0.6159 | | 0.0654 | 147.41 | 42160 | 0.1609 | 0.3962 | 0.6035 | 0.5996 | nan | 0.5809 | 0.6262 | 0.0 | 0.5651 | 0.6236 | | 0.036 | 147.48 | 42180 | 0.1605 | 0.3935 | 0.5990 | 0.5939 | nan | 0.5693 | 0.6287 | 0.0 | 0.5545 | 0.6259 | | 0.0296 | 147.55 | 42200 | 0.1604 | 0.3924 | 0.5974 | 0.5921 | nan | 0.5670 | 0.6278 | 0.0 | 0.5528 | 0.6245 | | 0.0516 | 147.62 | 42220 | 0.1581 | 0.4018 | 0.6119 | 0.6065 | nan | 0.5812 | 0.6425 | 0.0 | 0.5668 | 0.6386 | | 0.0197 | 147.69 | 42240 | 0.1578 | 0.3967 | 0.6037 | 0.5973 | nan | 0.5667 | 0.6408 | 0.0 | 0.5530 | 0.6370 | | 0.0375 | 147.76 | 42260 | 0.1571 | 0.4015 | 0.6109 | 0.6041 | nan | 0.5716 | 0.6503 | 0.0 | 0.5584 | 0.6461 | | 0.0603 | 147.83 | 42280 | 0.1548 | 0.4072 | 0.6192 | 0.6115 | nan | 0.5744 | 0.6641 | 0.0 | 0.5616 | 0.6600 | | 0.0418 | 147.9 | 42300 | 0.1553 | 0.4023 | 0.6116 | 0.6034 | nan | 0.5643 | 0.6589 | 0.0 | 0.5518 | 0.6551 | | 0.0353 | 147.97 | 42320 | 0.1593 | 0.3936 | 0.5986 | 0.5928 | nan | 0.5652 | 0.6320 | 0.0 | 0.5517 | 0.6291 | | 0.0302 | 148.04 | 42340 | 0.1585 | 0.3934 | 0.5983 | 0.5914 | nan | 0.5584 | 0.6381 | 0.0 | 0.5455 | 0.6348 | | 0.046 | 148.11 | 42360 | 0.1610 | 0.3946 | 0.6000 | 0.5929 | nan | 0.5594 | 0.6405 | 0.0 | 0.5466 | 0.6373 | | 0.0571 | 148.18 | 42380 | 0.1588 | 0.3972 | 0.6039 | 0.5967 | nan | 0.5621 | 0.6458 | 0.0 | 0.5489 | 0.6426 | | 0.0391 | 148.25 | 42400 | 0.1569 | 0.3977 | 0.6046 | 0.5973 | nan | 0.5621 | 0.6471 | 0.0 | 0.5494 | 0.6436 | | 0.0373 | 148.32 | 42420 | 0.1567 | 0.3955 | 0.6012 | 0.5922 | nan | 0.5490 | 0.6534 | 0.0 | 0.5372 | 0.6494 | | 0.0324 | 148.39 | 42440 | 0.1581 | 0.4057 | 0.6175 | 0.6134 | nan | 0.5934 | 0.6417 | 0.0 | 0.5787 | 0.6384 | | 0.0379 | 148.46 | 42460 | 0.1572 | 0.4026 | 0.6124 | 0.6056 | nan | 0.5732 | 0.6516 | 0.0 | 0.5601 | 0.6478 | | 0.0395 | 148.53 | 42480 | 0.1552 | 0.3997 | 0.6078 | 0.5994 | nan | 0.5593 | 0.6563 | 0.0 | 0.5470 | 0.6521 | | 0.0438 | 148.6 | 42500 | 0.1539 | 0.4018 | 0.6108 | 0.6018 | nan | 0.5588 | 0.6628 | 0.0 | 0.5473 | 0.6580 | | 0.0231 | 148.67 | 42520 | 0.1576 | 0.4025 | 0.6120 | 0.6048 | nan | 0.5705 | 0.6536 | 0.0 | 0.5575 | 0.6499 | | 0.0497 | 148.74 | 42540 | 0.1581 | 0.4012 | 0.6103 | 0.6020 | nan | 0.5625 | 0.6581 | 0.0 | 0.5496 | 0.6541 | | 0.0294 | 148.81 | 42560 | 0.1602 | 0.3977 | 0.6047 | 0.5969 | nan | 0.5598 | 0.6496 | 0.0 | 0.5470 | 0.6460 | | 0.0426 | 148.88 | 42580 | 0.1535 | 0.4031 | 0.6132 | 0.6048 | nan | 0.5648 | 0.6616 | 0.0 | 0.5525 | 0.6567 | | 0.0395 | 148.95 | 42600 | 0.1575 | 0.3973 | 0.6043 | 0.5965 | nan | 0.5590 | 0.6496 | 0.0 | 0.5465 | 0.6453 | | 0.0562 | 149.02 | 42620 | 0.1595 | 0.3994 | 0.6082 | 0.6031 | nan | 0.5790 | 0.6373 | 0.0 | 0.5644 | 0.6338 | | 0.0736 | 149.09 | 42640 | 0.1629 | 0.3953 | 0.6016 | 0.5959 | nan | 0.5687 | 0.6345 | 0.0 | 0.5544 | 0.6313 | | 0.052 | 149.16 | 42660 | 0.1616 | 0.3940 | 0.5994 | 0.5914 | nan | 0.5534 | 0.6453 | 0.0 | 0.5408 | 0.6413 | | 0.048 | 149.23 | 42680 | 0.1588 | 0.3976 | 0.6050 | 0.5985 | nan | 0.5674 | 0.6425 | 0.0 | 0.5543 | 0.6386 | | 0.0293 | 149.3 | 42700 | 0.1610 | 0.4012 | 0.6105 | 0.6048 | nan | 0.5778 | 0.6432 | 0.0 | 0.5638 | 0.6398 | | 0.0414 | 149.37 | 42720 | 0.1605 | 0.3979 | 0.6052 | 0.5994 | nan | 0.5715 | 0.6389 | 0.0 | 0.5585 | 0.6353 | | 0.0532 | 149.44 | 42740 | 0.1592 | 0.3972 | 0.6037 | 0.5964 | nan | 0.5616 | 0.6457 | 0.0 | 0.5498 | 0.6418 | | 0.0356 | 149.51 | 42760 | 0.1523 | 0.4045 | 0.6145 | 0.6035 | nan | 0.5509 | 0.6781 | 0.0 | 0.5417 | 0.6718 | | 0.0472 | 149.58 | 42780 | 0.1524 | 0.4073 | 0.6190 | 0.6103 | nan | 0.5684 | 0.6696 | 0.0 | 0.5578 | 0.6642 | | 0.052 | 149.65 | 42800 | 0.1541 | 0.4035 | 0.6131 | 0.6039 | nan | 0.5602 | 0.6660 | 0.0 | 0.5500 | 0.6605 | | 0.0344 | 149.72 | 42820 | 0.1555 | 0.3983 | 0.6051 | 0.5963 | nan | 0.5547 | 0.6554 | 0.0 | 0.5443 | 0.6506 | | 0.0453 | 149.79 | 42840 | 0.1589 | 0.3945 | 0.5993 | 0.5910 | nan | 0.5512 | 0.6474 | 0.0 | 0.5402 | 0.6434 | | 0.0352 | 149.86 | 42860 | 0.1608 | 0.3969 | 0.6040 | 0.5996 | nan | 0.5784 | 0.6296 | 0.0 | 0.5629 | 0.6278 | | 0.0548 | 149.93 | 42880 | 0.1603 | 0.3946 | 0.6001 | 0.5956 | nan | 0.5738 | 0.6265 | 0.0 | 0.5591 | 0.6247 | | 0.029 | 150.0 | 42900 | 0.1615 | 0.3914 | 0.5952 | 0.5887 | nan | 0.5577 | 0.6328 | 0.0 | 0.5439 | 0.6304 | | 0.0502 | 150.07 | 42920 | 0.1654 | 0.3885 | 0.5910 | 0.5861 | nan | 0.5627 | 0.6193 | 0.0 | 0.5480 | 0.6173 | | 0.0285 | 150.14 | 42940 | 0.1687 | 0.3838 | 0.5838 | 0.5786 | nan | 0.5538 | 0.6138 | 0.0 | 0.5395 | 0.6119 | | 0.0493 | 150.21 | 42960 | 0.1556 | 0.3979 | 0.6049 | 0.5958 | nan | 0.5524 | 0.6575 | 0.0 | 0.5406 | 0.6531 | | 0.049 | 150.28 | 42980 | 0.1556 | 0.4008 | 0.6096 | 0.6028 | nan | 0.5708 | 0.6483 | 0.0 | 0.5583 | 0.6442 | | 0.0338 | 150.35 | 43000 | 0.1576 | 0.3974 | 0.6047 | 0.5985 | nan | 0.5693 | 0.6400 | 0.0 | 0.5556 | 0.6365 | | 0.053 | 150.42 | 43020 | 0.1609 | 0.3911 | 0.5948 | 0.5879 | nan | 0.5545 | 0.6352 | 0.0 | 0.5414 | 0.6320 | | 0.0743 | 150.49 | 43040 | 0.1596 | 0.3957 | 0.6020 | 0.5959 | nan | 0.5673 | 0.6367 | 0.0 | 0.5536 | 0.6334 | | 0.0405 | 150.56 | 43060 | 0.1555 | 0.4014 | 0.6104 | 0.6037 | nan | 0.5717 | 0.6492 | 0.0 | 0.5587 | 0.6453 | | 0.0307 | 150.63 | 43080 | 0.1551 | 0.4005 | 0.6089 | 0.6006 | nan | 0.5608 | 0.6570 | 0.0 | 0.5487 | 0.6529 | | 0.0201 | 150.7 | 43100 | 0.1620 | 0.3922 | 0.5968 | 0.5923 | nan | 0.5709 | 0.6226 | 0.0 | 0.5564 | 0.6201 | | 0.0537 | 150.77 | 43120 | 0.1620 | 0.3967 | 0.6036 | 0.5988 | nan | 0.5758 | 0.6313 | 0.0 | 0.5616 | 0.6284 | | 0.0373 | 150.84 | 43140 | 0.1624 | 0.3922 | 0.5963 | 0.5905 | nan | 0.5628 | 0.6298 | 0.0 | 0.5497 | 0.6268 | | 0.048 | 150.91 | 43160 | 0.1598 | 0.3966 | 0.6031 | 0.5960 | nan | 0.5623 | 0.6440 | 0.0 | 0.5498 | 0.6401 | | 0.0403 | 150.98 | 43180 | 0.1595 | 0.3991 | 0.6071 | 0.6011 | nan | 0.5725 | 0.6417 | 0.0 | 0.5589 | 0.6383 | | 0.0421 | 151.05 | 43200 | 0.1607 | 0.3987 | 0.6068 | 0.6020 | nan | 0.5788 | 0.6348 | 0.0 | 0.5642 | 0.6319 | | 0.0268 | 151.12 | 43220 | 0.1595 | 0.3963 | 0.6027 | 0.5955 | nan | 0.5611 | 0.6443 | 0.0 | 0.5477 | 0.6411 | | 0.0454 | 151.19 | 43240 | 0.1582 | 0.3987 | 0.6064 | 0.6000 | nan | 0.5693 | 0.6436 | 0.0 | 0.5555 | 0.6407 | | 0.0384 | 151.26 | 43260 | 0.1587 | 0.3963 | 0.6023 | 0.5941 | nan | 0.5552 | 0.6494 | 0.0 | 0.5441 | 0.6449 | | 0.038 | 151.33 | 43280 | 0.1566 | 0.4053 | 0.6162 | 0.6094 | nan | 0.5768 | 0.6556 | 0.0 | 0.5657 | 0.6501 | | 0.0513 | 151.4 | 43300 | 0.1581 | 0.3988 | 0.6060 | 0.5991 | nan | 0.5662 | 0.6458 | 0.0 | 0.5543 | 0.6421 | | 0.0534 | 151.47 | 43320 | 0.1594 | 0.4007 | 0.6091 | 0.6025 | nan | 0.5711 | 0.6471 | 0.0 | 0.5584 | 0.6436 | | 0.0262 | 151.54 | 43340 | 0.1552 | 0.4019 | 0.6109 | 0.6034 | nan | 0.5675 | 0.6543 | 0.0 | 0.5554 | 0.6505 | | 0.0336 | 151.61 | 43360 | 0.1521 | 0.4086 | 0.6213 | 0.6145 | nan | 0.5825 | 0.6601 | 0.0 | 0.5706 | 0.6553 | | 0.0275 | 151.68 | 43380 | 0.1525 | 0.4011 | 0.6094 | 0.5995 | nan | 0.5520 | 0.6667 | 0.0 | 0.5413 | 0.6620 | | 0.0479 | 151.75 | 43400 | 0.1539 | 0.4007 | 0.6088 | 0.6003 | nan | 0.5596 | 0.6579 | 0.0 | 0.5487 | 0.6535 | | 0.0385 | 151.82 | 43420 | 0.1559 | 0.3995 | 0.6072 | 0.5997 | nan | 0.5639 | 0.6506 | 0.0 | 0.5509 | 0.6474 | | 0.058 | 151.89 | 43440 | 0.1562 | 0.3963 | 0.6023 | 0.5947 | nan | 0.5582 | 0.6463 | 0.0 | 0.5458 | 0.6431 | | 0.0339 | 151.96 | 43460 | 0.1576 | 0.3935 | 0.5978 | 0.5898 | nan | 0.5518 | 0.6437 | 0.0 | 0.5397 | 0.6407 | | 0.036 | 152.03 | 43480 | 0.1555 | 0.4004 | 0.6089 | 0.6022 | nan | 0.5703 | 0.6474 | 0.0 | 0.5568 | 0.6446 | | 0.0531 | 152.1 | 43500 | 0.1516 | 0.4098 | 0.6230 | 0.6163 | nan | 0.5847 | 0.6613 | 0.0 | 0.5713 | 0.6581 | | 0.0335 | 152.17 | 43520 | 0.1523 | 0.4102 | 0.6234 | 0.6159 | nan | 0.5803 | 0.6665 | 0.0 | 0.5679 | 0.6628 | | 0.0624 | 152.24 | 43540 | 0.1536 | 0.4056 | 0.6159 | 0.6061 | nan | 0.5593 | 0.6724 | 0.0 | 0.5484 | 0.6684 | | 0.0371 | 152.31 | 43560 | 0.1530 | 0.4067 | 0.6180 | 0.6097 | nan | 0.5701 | 0.6658 | 0.0 | 0.5580 | 0.6622 | | 0.0465 | 152.38 | 43580 | 0.1524 | 0.4089 | 0.6215 | 0.6132 | nan | 0.5735 | 0.6695 | 0.0 | 0.5615 | 0.6653 | | 0.0418 | 152.45 | 43600 | 0.1547 | 0.4032 | 0.6125 | 0.6035 | nan | 0.5603 | 0.6647 | 0.0 | 0.5483 | 0.6613 | | 0.0345 | 152.52 | 43620 | 0.1586 | 0.4001 | 0.6081 | 0.6004 | nan | 0.5638 | 0.6524 | 0.0 | 0.5508 | 0.6495 | | 0.0273 | 152.59 | 43640 | 0.1571 | 0.4026 | 0.6121 | 0.6048 | nan | 0.5702 | 0.6539 | 0.0 | 0.5572 | 0.6506 | | 0.037 | 152.66 | 43660 | 0.1574 | 0.4023 | 0.6117 | 0.6055 | nan | 0.5758 | 0.6477 | 0.0 | 0.5624 | 0.6444 | | 0.0434 | 152.73 | 43680 | 0.1594 | 0.3963 | 0.6029 | 0.5969 | nan | 0.5680 | 0.6378 | 0.0 | 0.5532 | 0.6357 | | 0.0537 | 152.8 | 43700 | 0.1582 | 0.3930 | 0.5976 | 0.5905 | nan | 0.5569 | 0.6383 | 0.0 | 0.5433 | 0.6356 | | 0.0296 | 152.87 | 43720 | 0.1572 | 0.3980 | 0.6053 | 0.5984 | nan | 0.5656 | 0.6450 | 0.0 | 0.5517 | 0.6422 | | 0.0347 | 152.94 | 43740 | 0.1557 | 0.4002 | 0.6087 | 0.6025 | nan | 0.5731 | 0.6443 | 0.0 | 0.5588 | 0.6417 | | 0.0445 | 153.01 | 43760 | 0.1573 | 0.3974 | 0.6047 | 0.5980 | nan | 0.5660 | 0.6433 | 0.0 | 0.5514 | 0.6410 | | 0.0261 | 153.08 | 43780 | 0.1582 | 0.3998 | 0.6085 | 0.6034 | nan | 0.5789 | 0.6380 | 0.0 | 0.5637 | 0.6355 | | 0.0459 | 153.15 | 43800 | 0.1565 | 0.3960 | 0.6016 | 0.5910 | nan | 0.5404 | 0.6629 | 0.0 | 0.5284 | 0.6596 | | 0.0433 | 153.22 | 43820 | 0.1523 | 0.4084 | 0.6211 | 0.6151 | nan | 0.5864 | 0.6558 | 0.0 | 0.5728 | 0.6525 | | 0.0349 | 153.29 | 43840 | 0.1524 | 0.4102 | 0.6238 | 0.6163 | nan | 0.5805 | 0.6670 | 0.0 | 0.5676 | 0.6629 | | 0.0428 | 153.36 | 43860 | 0.1548 | 0.4024 | 0.6115 | 0.6032 | nan | 0.5633 | 0.6597 | 0.0 | 0.5513 | 0.6558 | | 0.0355 | 153.43 | 43880 | 0.1572 | 0.3959 | 0.6016 | 0.5926 | nan | 0.5493 | 0.6540 | 0.0 | 0.5374 | 0.6504 | | 0.0431 | 153.5 | 43900 | 0.1584 | 0.4006 | 0.6088 | 0.6015 | nan | 0.5669 | 0.6507 | 0.0 | 0.5552 | 0.6467 | | 0.0461 | 153.57 | 43920 | 0.1606 | 0.3998 | 0.6084 | 0.6031 | nan | 0.5778 | 0.6390 | 0.0 | 0.5637 | 0.6358 | | 0.0511 | 153.64 | 43940 | 0.1642 | 0.3869 | 0.5886 | 0.5818 | nan | 0.5495 | 0.6277 | 0.0 | 0.5357 | 0.6251 | | 0.0271 | 153.71 | 43960 | 0.1566 | 0.4038 | 0.6147 | 0.6094 | nan | 0.5839 | 0.6456 | 0.0 | 0.5694 | 0.6421 | | 0.0602 | 153.78 | 43980 | 0.1515 | 0.4059 | 0.6174 | 0.6103 | nan | 0.5767 | 0.6580 | 0.0 | 0.5643 | 0.6534 | | 0.0209 | 153.85 | 44000 | 0.1533 | 0.3999 | 0.6080 | 0.6008 | nan | 0.5661 | 0.6499 | 0.0 | 0.5538 | 0.6460 | | 0.0759 | 153.92 | 44020 | 0.1556 | 0.3985 | 0.6057 | 0.5974 | nan | 0.5573 | 0.6541 | 0.0 | 0.5451 | 0.6504 | | 0.0247 | 153.99 | 44040 | 0.1591 | 0.3962 | 0.6024 | 0.5939 | nan | 0.5535 | 0.6512 | 0.0 | 0.5411 | 0.6476 | | 0.0474 | 154.06 | 44060 | 0.1593 | 0.3985 | 0.6063 | 0.5996 | nan | 0.5677 | 0.6449 | 0.0 | 0.5544 | 0.6411 | | 0.0376 | 154.13 | 44080 | 0.1571 | 0.4042 | 0.6151 | 0.6092 | nan | 0.5809 | 0.6493 | 0.0 | 0.5666 | 0.6459 | | 0.08 | 154.2 | 44100 | 0.1493 | 0.4139 | 0.6290 | 0.6211 | nan | 0.5832 | 0.6748 | 0.0 | 0.5715 | 0.6701 | | 0.0267 | 154.27 | 44120 | 0.1503 | 0.4077 | 0.6195 | 0.6114 | nan | 0.5729 | 0.6661 | 0.0 | 0.5615 | 0.6615 | | 0.0581 | 154.34 | 44140 | 0.1577 | 0.3986 | 0.6061 | 0.5982 | nan | 0.5608 | 0.6513 | 0.0 | 0.5478 | 0.6481 | | 0.0391 | 154.41 | 44160 | 0.1561 | 0.4044 | 0.6147 | 0.6074 | nan | 0.5723 | 0.6572 | 0.0 | 0.5594 | 0.6537 | | 0.0409 | 154.48 | 44180 | 0.1613 | 0.3942 | 0.5990 | 0.5912 | nan | 0.5540 | 0.6440 | 0.0 | 0.5414 | 0.6411 | | 0.0392 | 154.55 | 44200 | 0.1581 | 0.4051 | 0.6158 | 0.6092 | nan | 0.5774 | 0.6541 | 0.0 | 0.5642 | 0.6510 | | 0.0493 | 154.62 | 44220 | 0.1556 | 0.4060 | 0.6169 | 0.6084 | nan | 0.5678 | 0.6660 | 0.0 | 0.5556 | 0.6623 | | 0.0398 | 154.69 | 44240 | 0.1536 | 0.4051 | 0.6153 | 0.6068 | nan | 0.5662 | 0.6645 | 0.0 | 0.5545 | 0.6607 | | 0.0668 | 154.76 | 44260 | 0.1561 | 0.4035 | 0.6131 | 0.6044 | nan | 0.5629 | 0.6633 | 0.0 | 0.5506 | 0.6598 | | 0.0424 | 154.83 | 44280 | 0.1572 | 0.4056 | 0.6165 | 0.6082 | nan | 0.5688 | 0.6642 | 0.0 | 0.5558 | 0.6610 | | 0.0335 | 154.9 | 44300 | 0.1613 | 0.3955 | 0.6013 | 0.5951 | nan | 0.5656 | 0.6370 | 0.0 | 0.5520 | 0.6344 | | 0.0389 | 154.97 | 44320 | 0.1542 | 0.4056 | 0.6165 | 0.6093 | nan | 0.5751 | 0.6579 | 0.0 | 0.5626 | 0.6540 | | 0.0544 | 155.03 | 44340 | 0.1550 | 0.4033 | 0.6127 | 0.6041 | nan | 0.5626 | 0.6629 | 0.0 | 0.5511 | 0.6587 | | 0.0363 | 155.1 | 44360 | 0.1561 | 0.4014 | 0.6098 | 0.6007 | nan | 0.5573 | 0.6624 | 0.0 | 0.5460 | 0.6583 | | 0.0339 | 155.17 | 44380 | 0.1559 | 0.4069 | 0.6186 | 0.6119 | nan | 0.5801 | 0.6571 | 0.0 | 0.5673 | 0.6534 | | 0.0454 | 155.24 | 44400 | 0.1578 | 0.4012 | 0.6098 | 0.6015 | nan | 0.5615 | 0.6582 | 0.0 | 0.5490 | 0.6547 | | 0.0324 | 155.31 | 44420 | 0.1594 | 0.4023 | 0.6116 | 0.6035 | nan | 0.5652 | 0.6579 | 0.0 | 0.5525 | 0.6544 | | 0.0245 | 155.38 | 44440 | 0.1549 | 0.4039 | 0.6139 | 0.6047 | nan | 0.5609 | 0.6669 | 0.0 | 0.5490 | 0.6628 | | 0.052 | 155.45 | 44460 | 0.1575 | 0.4052 | 0.6161 | 0.6090 | nan | 0.5750 | 0.6573 | 0.0 | 0.5619 | 0.6537 | | 0.0567 | 155.52 | 44480 | 0.1606 | 0.3981 | 0.6052 | 0.5974 | nan | 0.5604 | 0.6500 | 0.0 | 0.5476 | 0.6468 | | 0.0242 | 155.59 | 44500 | 0.1568 | 0.4052 | 0.6159 | 0.6075 | nan | 0.5678 | 0.6640 | 0.0 | 0.5550 | 0.6607 | | 0.0409 | 155.66 | 44520 | 0.1561 | 0.4050 | 0.6156 | 0.6084 | nan | 0.5736 | 0.6577 | 0.0 | 0.5604 | 0.6546 | | 0.0433 | 155.73 | 44540 | 0.1580 | 0.4019 | 0.6108 | 0.6030 | nan | 0.5657 | 0.6558 | 0.0 | 0.5531 | 0.6525 | | 0.0477 | 155.8 | 44560 | 0.1564 | 0.4024 | 0.6116 | 0.6035 | nan | 0.5651 | 0.6581 | 0.0 | 0.5533 | 0.6540 | | 0.0476 | 155.87 | 44580 | 0.1551 | 0.4018 | 0.6106 | 0.6017 | nan | 0.5592 | 0.6621 | 0.0 | 0.5473 | 0.6582 | | 0.0267 | 155.94 | 44600 | 0.1574 | 0.3998 | 0.6079 | 0.6008 | nan | 0.5670 | 0.6488 | 0.0 | 0.5534 | 0.6460 | | 0.0577 | 156.01 | 44620 | 0.1583 | 0.4006 | 0.6091 | 0.6014 | nan | 0.5646 | 0.6535 | 0.0 | 0.5513 | 0.6506 | | 0.0565 | 156.08 | 44640 | 0.1604 | 0.3990 | 0.6068 | 0.5998 | nan | 0.5664 | 0.6472 | 0.0 | 0.5522 | 0.6450 | | 0.046 | 156.15 | 44660 | 0.1607 | 0.3982 | 0.6054 | 0.5982 | nan | 0.5635 | 0.6474 | 0.0 | 0.5496 | 0.6450 | | 0.0488 | 156.22 | 44680 | 0.1607 | 0.3995 | 0.6074 | 0.6004 | nan | 0.5670 | 0.6478 | 0.0 | 0.5531 | 0.6454 | | 0.0552 | 156.29 | 44700 | 0.1602 | 0.3999 | 0.6081 | 0.6003 | nan | 0.5632 | 0.6530 | 0.0 | 0.5493 | 0.6505 | | 0.0346 | 156.36 | 44720 | 0.1608 | 0.3973 | 0.6040 | 0.5968 | nan | 0.5621 | 0.6459 | 0.0 | 0.5485 | 0.6434 | | 0.0297 | 156.43 | 44740 | 0.1600 | 0.4007 | 0.6093 | 0.6027 | nan | 0.5712 | 0.6475 | 0.0 | 0.5570 | 0.6450 | | 0.0598 | 156.5 | 44760 | 0.1597 | 0.4014 | 0.6107 | 0.6045 | nan | 0.5745 | 0.6470 | 0.0 | 0.5601 | 0.6442 | | 0.0217 | 156.57 | 44780 | 0.1528 | 0.4081 | 0.6206 | 0.6133 | nan | 0.5787 | 0.6625 | 0.0 | 0.5655 | 0.6588 | | 0.0273 | 156.64 | 44800 | 0.1565 | 0.4030 | 0.6130 | 0.6065 | nan | 0.5753 | 0.6506 | 0.0 | 0.5612 | 0.6478 | | 0.0314 | 156.71 | 44820 | 0.1505 | 0.4044 | 0.6143 | 0.6047 | nan | 0.5593 | 0.6692 | 0.0 | 0.5475 | 0.6658 | | 0.0607 | 156.78 | 44840 | 0.1509 | 0.4087 | 0.6214 | 0.6133 | nan | 0.5750 | 0.6677 | 0.0 | 0.5618 | 0.6644 | | 0.0423 | 156.85 | 44860 | 0.1529 | 0.4047 | 0.6149 | 0.6060 | nan | 0.5633 | 0.6665 | 0.0 | 0.5511 | 0.6629 | | 0.0407 | 156.92 | 44880 | 0.1563 | 0.4009 | 0.6094 | 0.6015 | nan | 0.5638 | 0.6549 | 0.0 | 0.5507 | 0.6520 | | 0.0191 | 156.99 | 44900 | 0.1590 | 0.3957 | 0.6015 | 0.5931 | nan | 0.5534 | 0.6495 | 0.0 | 0.5399 | 0.6471 | | 0.0406 | 157.06 | 44920 | 0.1560 | 0.4047 | 0.6158 | 0.6097 | nan | 0.5806 | 0.6509 | 0.0 | 0.5658 | 0.6484 | | 0.054 | 157.13 | 44940 | 0.1566 | 0.4027 | 0.6125 | 0.6064 | nan | 0.5769 | 0.6481 | 0.0 | 0.5628 | 0.6454 | | 0.0376 | 157.2 | 44960 | 0.1567 | 0.3995 | 0.6074 | 0.5998 | nan | 0.5640 | 0.6507 | 0.0 | 0.5508 | 0.6478 | | 0.0719 | 157.27 | 44980 | 0.1580 | 0.4003 | 0.6088 | 0.6025 | nan | 0.5720 | 0.6457 | 0.0 | 0.5581 | 0.6429 | | 0.0537 | 157.34 | 45000 | 0.1564 | 0.4041 | 0.6145 | 0.6081 | nan | 0.5778 | 0.6511 | 0.0 | 0.5642 | 0.6481 | | 0.0426 | 157.41 | 45020 | 0.1568 | 0.3976 | 0.6045 | 0.5968 | nan | 0.5601 | 0.6489 | 0.0 | 0.5468 | 0.6461 | | 0.0359 | 157.48 | 45040 | 0.1552 | 0.4015 | 0.6110 | 0.6051 | nan | 0.5770 | 0.6449 | 0.0 | 0.5624 | 0.6421 | | 0.0583 | 157.55 | 45060 | 0.1567 | 0.3992 | 0.6069 | 0.6004 | nan | 0.5691 | 0.6448 | 0.0 | 0.5554 | 0.6421 | | 0.0333 | 157.62 | 45080 | 0.1579 | 0.3976 | 0.6045 | 0.5973 | nan | 0.5629 | 0.6460 | 0.0 | 0.5495 | 0.6433 | | 0.0316 | 157.69 | 45100 | 0.1577 | 0.4007 | 0.6093 | 0.6025 | nan | 0.5695 | 0.6492 | 0.0 | 0.5557 | 0.6465 | | 0.0434 | 157.76 | 45120 | 0.1580 | 0.4032 | 0.6134 | 0.6079 | nan | 0.5819 | 0.6449 | 0.0 | 0.5671 | 0.6423 | | 0.0271 | 157.83 | 45140 | 0.1518 | 0.4019 | 0.6106 | 0.6006 | nan | 0.5528 | 0.6685 | 0.0 | 0.5405 | 0.6651 | | 0.055 | 157.9 | 45160 | 0.1504 | 0.4024 | 0.6113 | 0.6016 | nan | 0.5555 | 0.6671 | 0.0 | 0.5436 | 0.6635 | | 0.0489 | 157.97 | 45180 | 0.1521 | 0.4054 | 0.6163 | 0.6083 | nan | 0.5703 | 0.6623 | 0.0 | 0.5570 | 0.6592 | | 0.0329 | 158.04 | 45200 | 0.1561 | 0.4037 | 0.6139 | 0.6075 | nan | 0.5768 | 0.6510 | 0.0 | 0.5626 | 0.6484 | | 0.0253 | 158.11 | 45220 | 0.1571 | 0.4021 | 0.6113 | 0.6039 | nan | 0.5687 | 0.6539 | 0.0 | 0.5549 | 0.6513 | | 0.0335 | 158.18 | 45240 | 0.1586 | 0.4026 | 0.6120 | 0.6052 | nan | 0.5729 | 0.6511 | 0.0 | 0.5592 | 0.6485 | | 0.0423 | 158.25 | 45260 | 0.1580 | 0.4047 | 0.6153 | 0.6087 | nan | 0.5774 | 0.6532 | 0.0 | 0.5632 | 0.6508 | | 0.0327 | 158.32 | 45280 | 0.1550 | 0.4061 | 0.6177 | 0.6109 | nan | 0.5784 | 0.6570 | 0.0 | 0.5642 | 0.6542 | | 0.0437 | 158.39 | 45300 | 0.1544 | 0.4023 | 0.6113 | 0.6031 | nan | 0.5636 | 0.6590 | 0.0 | 0.5517 | 0.6553 | | 0.0308 | 158.46 | 45320 | 0.1534 | 0.4070 | 0.6187 | 0.6113 | nan | 0.5755 | 0.6620 | 0.0 | 0.5627 | 0.6584 | | 0.0387 | 158.53 | 45340 | 0.1560 | 0.4014 | 0.6101 | 0.6013 | nan | 0.5596 | 0.6605 | 0.0 | 0.5471 | 0.6572 | | 0.0505 | 158.6 | 45360 | 0.1583 | 0.3960 | 0.6019 | 0.5943 | nan | 0.5576 | 0.6463 | 0.0 | 0.5445 | 0.6435 | | 0.0599 | 158.67 | 45380 | 0.1555 | 0.4044 | 0.6152 | 0.6086 | nan | 0.5772 | 0.6532 | 0.0 | 0.5628 | 0.6503 | | 0.0536 | 158.74 | 45400 | 0.1554 | 0.4018 | 0.6106 | 0.6024 | nan | 0.5635 | 0.6576 | 0.0 | 0.5510 | 0.6543 | | 0.0294 | 158.81 | 45420 | 0.1563 | 0.3995 | 0.6071 | 0.5993 | nan | 0.5619 | 0.6524 | 0.0 | 0.5490 | 0.6495 | | 0.0377 | 158.88 | 45440 | 0.1575 | 0.3994 | 0.6071 | 0.5985 | nan | 0.5575 | 0.6566 | 0.0 | 0.5445 | 0.6537 | | 0.0434 | 158.95 | 45460 | 0.1559 | 0.4005 | 0.6089 | 0.6015 | nan | 0.5662 | 0.6516 | 0.0 | 0.5530 | 0.6486 | | 0.0334 | 159.02 | 45480 | 0.1589 | 0.3987 | 0.6063 | 0.6003 | nan | 0.5716 | 0.6409 | 0.0 | 0.5580 | 0.6381 | | 0.0397 | 159.09 | 45500 | 0.1553 | 0.4000 | 0.6080 | 0.5998 | nan | 0.5604 | 0.6557 | 0.0 | 0.5480 | 0.6520 | | 0.04 | 159.16 | 45520 | 0.1582 | 0.4017 | 0.6108 | 0.6048 | nan | 0.5763 | 0.6452 | 0.0 | 0.5629 | 0.6421 | | 0.0534 | 159.23 | 45540 | 0.1530 | 0.4097 | 0.6229 | 0.6149 | nan | 0.5765 | 0.6694 | 0.0 | 0.5639 | 0.6652 | | 0.0259 | 159.3 | 45560 | 0.1559 | 0.4036 | 0.6138 | 0.6066 | nan | 0.5721 | 0.6555 | 0.0 | 0.5593 | 0.6517 | | 0.054 | 159.37 | 45580 | 0.1590 | 0.3975 | 0.6040 | 0.5945 | nan | 0.5496 | 0.6583 | 0.0 | 0.5379 | 0.6546 | | 0.0325 | 159.44 | 45600 | 0.1576 | 0.3982 | 0.6050 | 0.5969 | nan | 0.5579 | 0.6522 | 0.0 | 0.5460 | 0.6487 | | 0.042 | 159.51 | 45620 | 0.1573 | 0.4041 | 0.6143 | 0.6084 | nan | 0.5804 | 0.6482 | 0.0 | 0.5671 | 0.6452 | | 0.0193 | 159.58 | 45640 | 0.1551 | 0.4009 | 0.6089 | 0.6004 | nan | 0.5597 | 0.6581 | 0.0 | 0.5478 | 0.6550 | | 0.0422 | 159.65 | 45660 | 0.1542 | 0.4050 | 0.6151 | 0.6070 | nan | 0.5683 | 0.6618 | 0.0 | 0.5567 | 0.6583 | | 0.0445 | 159.72 | 45680 | 0.1521 | 0.4066 | 0.6173 | 0.6079 | nan | 0.5628 | 0.6719 | 0.0 | 0.5516 | 0.6682 | | 0.0567 | 159.79 | 45700 | 0.1528 | 0.4065 | 0.6172 | 0.6086 | nan | 0.5676 | 0.6668 | 0.0 | 0.5564 | 0.6630 | | 0.0233 | 159.86 | 45720 | 0.1549 | 0.4039 | 0.6135 | 0.6059 | nan | 0.5696 | 0.6575 | 0.0 | 0.5573 | 0.6543 | | 0.0398 | 159.93 | 45740 | 0.1511 | 0.4091 | 0.6217 | 0.6145 | nan | 0.5804 | 0.6631 | 0.0 | 0.5673 | 0.6599 | | 0.024 | 160.0 | 45760 | 0.1545 | 0.4023 | 0.6110 | 0.6020 | nan | 0.5586 | 0.6635 | 0.0 | 0.5469 | 0.6600 | | 0.0352 | 160.07 | 45780 | 0.1543 | 0.4045 | 0.6144 | 0.6060 | nan | 0.5659 | 0.6628 | 0.0 | 0.5541 | 0.6593 | | 0.0578 | 160.14 | 45800 | 0.1534 | 0.4036 | 0.6130 | 0.6043 | nan | 0.5629 | 0.6632 | 0.0 | 0.5515 | 0.6593 | | 0.0347 | 160.21 | 45820 | 0.1530 | 0.4069 | 0.6182 | 0.6105 | nan | 0.5737 | 0.6628 | 0.0 | 0.5619 | 0.6590 | | 0.0323 | 160.28 | 45840 | 0.1535 | 0.4069 | 0.6182 | 0.6093 | nan | 0.5670 | 0.6694 | 0.0 | 0.5556 | 0.6651 | | 0.0375 | 160.35 | 45860 | 0.1541 | 0.4050 | 0.6152 | 0.6062 | nan | 0.5634 | 0.6670 | 0.0 | 0.5521 | 0.6627 | | 0.0462 | 160.42 | 45880 | 0.1490 | 0.4082 | 0.6198 | 0.6090 | nan | 0.5574 | 0.6823 | 0.0 | 0.5479 | 0.6767 | | 0.0627 | 160.49 | 45900 | 0.1498 | 0.4122 | 0.6264 | 0.6183 | nan | 0.5795 | 0.6734 | 0.0 | 0.5680 | 0.6687 | | 0.0364 | 160.56 | 45920 | 0.1527 | 0.4022 | 0.6106 | 0.6003 | nan | 0.5512 | 0.6700 | 0.0 | 0.5412 | 0.6653 | | 0.0449 | 160.63 | 45940 | 0.1519 | 0.4071 | 0.6182 | 0.6081 | nan | 0.5596 | 0.6768 | 0.0 | 0.5493 | 0.6720 | | 0.0395 | 160.7 | 45960 | 0.1534 | 0.4049 | 0.6155 | 0.6076 | nan | 0.5702 | 0.6607 | 0.0 | 0.5580 | 0.6568 | | 0.0476 | 160.77 | 45980 | 0.1503 | 0.4041 | 0.6138 | 0.6044 | nan | 0.5596 | 0.6679 | 0.0 | 0.5486 | 0.6638 | | 0.0281 | 160.84 | 46000 | 0.1521 | 0.4053 | 0.6157 | 0.6063 | nan | 0.5615 | 0.6700 | 0.0 | 0.5497 | 0.6662 | | 0.0444 | 160.91 | 46020 | 0.1528 | 0.4035 | 0.6131 | 0.6046 | nan | 0.5643 | 0.6618 | 0.0 | 0.5523 | 0.6582 | | 0.0517 | 160.98 | 46040 | 0.1563 | 0.4036 | 0.6134 | 0.6055 | nan | 0.5679 | 0.6590 | 0.0 | 0.5556 | 0.6553 | | 0.0374 | 161.05 | 46060 | 0.1552 | 0.4035 | 0.6134 | 0.6060 | nan | 0.5707 | 0.6562 | 0.0 | 0.5578 | 0.6527 | | 0.0353 | 161.12 | 46080 | 0.1553 | 0.4070 | 0.6189 | 0.6115 | nan | 0.5761 | 0.6616 | 0.0 | 0.5629 | 0.6581 | | 0.0494 | 161.19 | 46100 | 0.1526 | 0.4066 | 0.6182 | 0.6095 | nan | 0.5682 | 0.6681 | 0.0 | 0.5565 | 0.6634 | | 0.0453 | 161.26 | 46120 | 0.1552 | 0.3992 | 0.6068 | 0.5979 | nan | 0.5555 | 0.6582 | 0.0 | 0.5444 | 0.6532 | | 0.0335 | 161.33 | 46140 | 0.1573 | 0.4076 | 0.6202 | 0.6142 | nan | 0.5853 | 0.6551 | 0.0 | 0.5722 | 0.6507 | | 0.0402 | 161.4 | 46160 | 0.1604 | 0.3957 | 0.6016 | 0.5937 | nan | 0.5560 | 0.6471 | 0.0 | 0.5437 | 0.6435 | | 0.0574 | 161.47 | 46180 | 0.1593 | 0.3985 | 0.6059 | 0.6000 | nan | 0.5719 | 0.6398 | 0.0 | 0.5587 | 0.6367 | | 0.0447 | 161.54 | 46200 | 0.1573 | 0.3987 | 0.6061 | 0.5983 | nan | 0.5610 | 0.6512 | 0.0 | 0.5490 | 0.6470 | | 0.0326 | 161.61 | 46220 | 0.1569 | 0.4021 | 0.6115 | 0.6042 | nan | 0.5695 | 0.6536 | 0.0 | 0.5568 | 0.6494 | | 0.0734 | 161.68 | 46240 | 0.1590 | 0.3999 | 0.6077 | 0.6001 | nan | 0.5637 | 0.6517 | 0.0 | 0.5514 | 0.6482 | | 0.0387 | 161.75 | 46260 | 0.1560 | 0.4021 | 0.6111 | 0.6032 | nan | 0.5654 | 0.6568 | 0.0 | 0.5527 | 0.6536 | | 0.0365 | 161.82 | 46280 | 0.1567 | 0.4036 | 0.6135 | 0.6067 | nan | 0.5741 | 0.6529 | 0.0 | 0.5610 | 0.6497 | | 0.0482 | 161.89 | 46300 | 0.1582 | 0.4021 | 0.6117 | 0.6063 | nan | 0.5809 | 0.6424 | 0.0 | 0.5669 | 0.6394 | | 0.0539 | 161.96 | 46320 | 0.1593 | 0.3956 | 0.6015 | 0.5941 | nan | 0.5588 | 0.6441 | 0.0 | 0.5461 | 0.6408 | | 0.0408 | 162.03 | 46340 | 0.1570 | 0.3990 | 0.6062 | 0.5966 | nan | 0.5506 | 0.6618 | 0.0 | 0.5392 | 0.6579 | | 0.0454 | 162.1 | 46360 | 0.1532 | 0.4070 | 0.6187 | 0.6114 | nan | 0.5763 | 0.6612 | 0.0 | 0.5635 | 0.6575 | | 0.0307 | 162.17 | 46380 | 0.1544 | 0.4032 | 0.6128 | 0.6044 | nan | 0.5646 | 0.6609 | 0.0 | 0.5533 | 0.6563 | | 0.0405 | 162.24 | 46400 | 0.1548 | 0.4003 | 0.6085 | 0.5997 | nan | 0.5578 | 0.6592 | 0.0 | 0.5458 | 0.6552 | | 0.0361 | 162.31 | 46420 | 0.1555 | 0.4026 | 0.6120 | 0.6035 | nan | 0.5631 | 0.6608 | 0.0 | 0.5508 | 0.6570 | | 0.0503 | 162.38 | 46440 | 0.1564 | 0.4078 | 0.6203 | 0.6142 | nan | 0.5848 | 0.6558 | 0.0 | 0.5710 | 0.6524 | | 0.0247 | 162.45 | 46460 | 0.1572 | 0.3997 | 0.6074 | 0.6000 | nan | 0.5645 | 0.6504 | 0.0 | 0.5518 | 0.6472 | | 0.0473 | 162.52 | 46480 | 0.1655 | 0.3915 | 0.5952 | 0.5894 | nan | 0.5618 | 0.6287 | 0.0 | 0.5482 | 0.6264 | | 0.0372 | 162.59 | 46500 | 0.1646 | 0.3942 | 0.5994 | 0.5940 | nan | 0.5681 | 0.6308 | 0.0 | 0.5541 | 0.6284 | | 0.0276 | 162.66 | 46520 | 0.1618 | 0.3971 | 0.6035 | 0.5962 | nan | 0.5616 | 0.6454 | 0.0 | 0.5486 | 0.6425 | | 0.037 | 162.73 | 46540 | 0.1604 | 0.3964 | 0.6031 | 0.5966 | nan | 0.5657 | 0.6406 | 0.0 | 0.5516 | 0.6378 | | 0.0223 | 162.8 | 46560 | 0.1591 | 0.4022 | 0.6120 | 0.6057 | nan | 0.5757 | 0.6484 | 0.0 | 0.5612 | 0.6454 | | 0.0393 | 162.87 | 46580 | 0.1572 | 0.4013 | 0.6099 | 0.6015 | nan | 0.5615 | 0.6583 | 0.0 | 0.5494 | 0.6546 | | 0.0633 | 162.94 | 46600 | 0.1575 | 0.3999 | 0.6078 | 0.6002 | nan | 0.5639 | 0.6517 | 0.0 | 0.5513 | 0.6484 | | 0.0367 | 163.01 | 46620 | 0.1575 | 0.4028 | 0.6123 | 0.6046 | nan | 0.5676 | 0.6569 | 0.0 | 0.5549 | 0.6535 | | 0.036 | 163.08 | 46640 | 0.1565 | 0.4015 | 0.6103 | 0.6017 | nan | 0.5606 | 0.6600 | 0.0 | 0.5478 | 0.6565 | | 0.0423 | 163.15 | 46660 | 0.1550 | 0.4097 | 0.6233 | 0.6180 | nan | 0.5923 | 0.6544 | 0.0 | 0.5781 | 0.6509 | | 0.0514 | 163.22 | 46680 | 0.1568 | 0.4015 | 0.6103 | 0.6026 | nan | 0.5656 | 0.6551 | 0.0 | 0.5538 | 0.6508 | | 0.0368 | 163.29 | 46700 | 0.1605 | 0.4010 | 0.6097 | 0.6033 | nan | 0.5726 | 0.6467 | 0.0 | 0.5606 | 0.6425 | | 0.0484 | 163.36 | 46720 | 0.1588 | 0.3988 | 0.6060 | 0.5982 | nan | 0.5605 | 0.6515 | 0.0 | 0.5487 | 0.6476 | | 0.0423 | 163.43 | 46740 | 0.1599 | 0.3977 | 0.6045 | 0.5960 | nan | 0.5555 | 0.6536 | 0.0 | 0.5434 | 0.6498 | | 0.0361 | 163.5 | 46760 | 0.1577 | 0.4011 | 0.6100 | 0.6032 | nan | 0.5707 | 0.6492 | 0.0 | 0.5576 | 0.6457 | | 0.0522 | 163.57 | 46780 | 0.1575 | 0.4044 | 0.6152 | 0.6084 | nan | 0.5760 | 0.6544 | 0.0 | 0.5623 | 0.6509 | | 0.029 | 163.64 | 46800 | 0.1570 | 0.4019 | 0.6112 | 0.6033 | nan | 0.5654 | 0.6569 | 0.0 | 0.5524 | 0.6534 | | 0.0577 | 163.71 | 46820 | 0.1588 | 0.3988 | 0.6064 | 0.5986 | nan | 0.5609 | 0.6520 | 0.0 | 0.5480 | 0.6485 | | 0.0683 | 163.78 | 46840 | 0.1522 | 0.4061 | 0.6174 | 0.6093 | nan | 0.5703 | 0.6645 | 0.0 | 0.5576 | 0.6608 | | 0.0334 | 163.85 | 46860 | 0.1555 | 0.4001 | 0.6079 | 0.5989 | nan | 0.5561 | 0.6596 | 0.0 | 0.5440 | 0.6562 | | 0.0508 | 163.92 | 46880 | 0.1550 | 0.4017 | 0.6105 | 0.6023 | nan | 0.5629 | 0.6581 | 0.0 | 0.5502 | 0.6550 | | 0.0387 | 163.99 | 46900 | 0.1577 | 0.4043 | 0.6149 | 0.6082 | nan | 0.5762 | 0.6536 | 0.0 | 0.5624 | 0.6506 | | 0.0225 | 164.06 | 46920 | 0.1571 | 0.4024 | 0.6114 | 0.6025 | nan | 0.5600 | 0.6628 | 0.0 | 0.5482 | 0.6590 | | 0.0187 | 164.13 | 46940 | 0.1548 | 0.4052 | 0.6157 | 0.6063 | nan | 0.5616 | 0.6698 | 0.0 | 0.5506 | 0.6651 | | 0.0508 | 164.2 | 46960 | 0.1540 | 0.4002 | 0.6080 | 0.5978 | nan | 0.5489 | 0.6671 | 0.0 | 0.5380 | 0.6626 | | 0.0309 | 164.27 | 46980 | 0.1558 | 0.4058 | 0.6170 | 0.6089 | nan | 0.5704 | 0.6635 | 0.0 | 0.5579 | 0.6594 | | 0.0504 | 164.34 | 47000 | 0.1537 | 0.4082 | 0.6204 | 0.6119 | nan | 0.5712 | 0.6697 | 0.0 | 0.5594 | 0.6652 | | 0.0302 | 164.41 | 47020 | 0.1520 | 0.4010 | 0.6091 | 0.5982 | nan | 0.5463 | 0.6719 | 0.0 | 0.5355 | 0.6675 | | 0.0522 | 164.48 | 47040 | 0.1521 | 0.4049 | 0.6153 | 0.6068 | nan | 0.5658 | 0.6648 | 0.0 | 0.5536 | 0.6611 | | 0.0422 | 164.55 | 47060 | 0.1503 | 0.4075 | 0.6193 | 0.6092 | nan | 0.5610 | 0.6776 | 0.0 | 0.5496 | 0.6731 | | 0.0513 | 164.62 | 47080 | 0.1505 | 0.4060 | 0.6171 | 0.6078 | nan | 0.5638 | 0.6703 | 0.0 | 0.5526 | 0.6655 | | 0.0213 | 164.69 | 47100 | 0.1537 | 0.4049 | 0.6155 | 0.6071 | nan | 0.5667 | 0.6644 | 0.0 | 0.5546 | 0.6601 | | 0.055 | 164.76 | 47120 | 0.1553 | 0.4060 | 0.6171 | 0.6092 | nan | 0.5713 | 0.6630 | 0.0 | 0.5590 | 0.6589 | | 0.0471 | 164.83 | 47140 | 0.1543 | 0.4065 | 0.6178 | 0.6094 | nan | 0.5693 | 0.6664 | 0.0 | 0.5571 | 0.6625 | | 0.0356 | 164.9 | 47160 | 0.1558 | 0.4002 | 0.6081 | 0.5997 | nan | 0.5595 | 0.6567 | 0.0 | 0.5471 | 0.6533 | | 0.0331 | 164.97 | 47180 | 0.1579 | 0.4015 | 0.6103 | 0.6028 | nan | 0.5670 | 0.6536 | 0.0 | 0.5542 | 0.6502 | | 0.0318 | 165.03 | 47200 | 0.1541 | 0.4054 | 0.6164 | 0.6078 | nan | 0.5671 | 0.6656 | 0.0 | 0.5545 | 0.6619 | | 0.0352 | 165.1 | 47220 | 0.1567 | 0.4054 | 0.6166 | 0.6093 | nan | 0.5744 | 0.6588 | 0.0 | 0.5607 | 0.6556 | | 0.0319 | 165.17 | 47240 | 0.1559 | 0.4064 | 0.6177 | 0.6092 | nan | 0.5683 | 0.6671 | 0.0 | 0.5563 | 0.6630 | | 0.0567 | 165.24 | 47260 | 0.1607 | 0.3999 | 0.6077 | 0.5998 | nan | 0.5621 | 0.6533 | 0.0 | 0.5504 | 0.6493 | | 0.0314 | 165.31 | 47280 | 0.1604 | 0.4020 | 0.6112 | 0.6043 | nan | 0.5713 | 0.6510 | 0.0 | 0.5579 | 0.6480 | | 0.0484 | 165.38 | 47300 | 0.1561 | 0.4037 | 0.6136 | 0.6055 | nan | 0.5666 | 0.6605 | 0.0 | 0.5540 | 0.6571 | | 0.0373 | 165.45 | 47320 | 0.1606 | 0.4043 | 0.6150 | 0.6094 | nan | 0.5828 | 0.6471 | 0.0 | 0.5685 | 0.6443 | | 0.0424 | 165.52 | 47340 | 0.1598 | 0.4005 | 0.6091 | 0.6024 | nan | 0.5704 | 0.6478 | 0.0 | 0.5563 | 0.6453 | | 0.0573 | 165.59 | 47360 | 0.1620 | 0.3956 | 0.6014 | 0.5937 | nan | 0.5566 | 0.6462 | 0.0 | 0.5436 | 0.6433 | | 0.0423 | 165.66 | 47380 | 0.1615 | 0.3972 | 0.6040 | 0.5968 | nan | 0.5622 | 0.6458 | 0.0 | 0.5485 | 0.6430 | | 0.0376 | 165.73 | 47400 | 0.1594 | 0.3996 | 0.6079 | 0.6016 | nan | 0.5717 | 0.6441 | 0.0 | 0.5577 | 0.6412 | | 0.0549 | 165.8 | 47420 | 0.1591 | 0.3993 | 0.6075 | 0.6010 | nan | 0.5703 | 0.6446 | 0.0 | 0.5561 | 0.6419 | | 0.0606 | 165.87 | 47440 | 0.1613 | 0.3989 | 0.6067 | 0.5999 | nan | 0.5675 | 0.6460 | 0.0 | 0.5537 | 0.6431 | | 0.0651 | 165.94 | 47460 | 0.1575 | 0.4045 | 0.6146 | 0.6055 | nan | 0.5623 | 0.6669 | 0.0 | 0.5505 | 0.6629 | | 0.0489 | 166.01 | 47480 | 0.1539 | 0.4062 | 0.6175 | 0.6094 | nan | 0.5705 | 0.6646 | 0.0 | 0.5585 | 0.6601 | | 0.0394 | 166.08 | 47500 | 0.1597 | 0.4010 | 0.6098 | 0.6025 | nan | 0.5677 | 0.6520 | 0.0 | 0.5558 | 0.6472 | | 0.0463 | 166.15 | 47520 | 0.1602 | 0.4006 | 0.6091 | 0.6019 | nan | 0.5673 | 0.6509 | 0.0 | 0.5551 | 0.6466 | | 0.031 | 166.22 | 47540 | 0.1581 | 0.4007 | 0.6093 | 0.6026 | nan | 0.5706 | 0.6480 | 0.0 | 0.5578 | 0.6442 | | 0.0264 | 166.29 | 47560 | 0.1614 | 0.3975 | 0.6042 | 0.5967 | nan | 0.5604 | 0.6481 | 0.0 | 0.5482 | 0.6442 | | 0.0402 | 166.36 | 47580 | 0.1625 | 0.3968 | 0.6030 | 0.5948 | nan | 0.5556 | 0.6505 | 0.0 | 0.5435 | 0.6467 | | 0.0564 | 166.43 | 47600 | 0.1604 | 0.3998 | 0.6079 | 0.5998 | nan | 0.5608 | 0.6550 | 0.0 | 0.5483 | 0.6513 | | 0.0257 | 166.5 | 47620 | 0.1590 | 0.3985 | 0.6060 | 0.5981 | nan | 0.5608 | 0.6511 | 0.0 | 0.5478 | 0.6476 | | 0.03 | 166.57 | 47640 | 0.1620 | 0.3999 | 0.6082 | 0.6024 | nan | 0.5748 | 0.6416 | 0.0 | 0.5614 | 0.6381 | | 0.0242 | 166.64 | 47660 | 0.1589 | 0.3979 | 0.6048 | 0.5962 | nan | 0.5549 | 0.6547 | 0.0 | 0.5434 | 0.6502 | | 0.0579 | 166.71 | 47680 | 0.1575 | 0.4017 | 0.6108 | 0.6038 | nan | 0.5703 | 0.6514 | 0.0 | 0.5583 | 0.6467 | | 0.032 | 166.78 | 47700 | 0.1594 | 0.4024 | 0.6123 | 0.6057 | nan | 0.5741 | 0.6506 | 0.0 | 0.5607 | 0.6466 | | 0.0369 | 166.85 | 47720 | 0.1591 | 0.3980 | 0.6053 | 0.5974 | nan | 0.5596 | 0.6511 | 0.0 | 0.5464 | 0.6475 | | 0.0404 | 166.92 | 47740 | 0.1600 | 0.3961 | 0.6030 | 0.5963 | nan | 0.5646 | 0.6414 | 0.0 | 0.5502 | 0.6382 | | 0.0389 | 166.99 | 47760 | 0.1620 | 0.3961 | 0.6030 | 0.5981 | nan | 0.5746 | 0.6315 | 0.0 | 0.5602 | 0.6280 | | 0.0193 | 167.06 | 47780 | 0.1592 | 0.3963 | 0.6031 | 0.5957 | nan | 0.5603 | 0.6459 | 0.0 | 0.5468 | 0.6422 | | 0.0352 | 167.13 | 47800 | 0.1607 | 0.3979 | 0.6055 | 0.5986 | nan | 0.5655 | 0.6455 | 0.0 | 0.5518 | 0.6418 | | 0.036 | 167.2 | 47820 | 0.1609 | 0.3980 | 0.6058 | 0.5994 | nan | 0.5687 | 0.6429 | 0.0 | 0.5547 | 0.6394 | | 0.038 | 167.27 | 47840 | 0.1588 | 0.3976 | 0.6048 | 0.5976 | nan | 0.5631 | 0.6464 | 0.0 | 0.5500 | 0.6427 | | 0.0273 | 167.34 | 47860 | 0.1575 | 0.4013 | 0.6107 | 0.6035 | nan | 0.5693 | 0.6521 | 0.0 | 0.5556 | 0.6482 | | 0.0337 | 167.41 | 47880 | 0.1580 | 0.4014 | 0.6111 | 0.6047 | nan | 0.5742 | 0.6480 | 0.0 | 0.5594 | 0.6449 | | 0.037 | 167.48 | 47900 | 0.1598 | 0.3976 | 0.6049 | 0.5982 | nan | 0.5662 | 0.6437 | 0.0 | 0.5525 | 0.6404 | | 0.0265 | 167.55 | 47920 | 0.1614 | 0.3957 | 0.6020 | 0.5942 | nan | 0.5567 | 0.6473 | 0.0 | 0.5430 | 0.6440 | | 0.0267 | 167.62 | 47940 | 0.1629 | 0.3958 | 0.6029 | 0.5987 | nan | 0.5782 | 0.6276 | 0.0 | 0.5626 | 0.6248 | | 0.0351 | 167.69 | 47960 | 0.1659 | 0.3923 | 0.5970 | 0.5914 | nan | 0.5648 | 0.6293 | 0.0 | 0.5507 | 0.6262 | | 0.0314 | 167.76 | 47980 | 0.1612 | 0.3994 | 0.6077 | 0.6014 | nan | 0.5713 | 0.6441 | 0.0 | 0.5582 | 0.6400 | | 0.0423 | 167.83 | 48000 | 0.1593 | 0.4004 | 0.6090 | 0.6023 | nan | 0.5701 | 0.6480 | 0.0 | 0.5573 | 0.6440 | | 0.0373 | 167.9 | 48020 | 0.1596 | 0.3969 | 0.6036 | 0.5962 | nan | 0.5605 | 0.6468 | 0.0 | 0.5475 | 0.6432 | | 0.0383 | 167.97 | 48040 | 0.1591 | 0.4017 | 0.6113 | 0.6048 | nan | 0.5741 | 0.6485 | 0.0 | 0.5599 | 0.6451 | | 0.0341 | 168.04 | 48060 | 0.1617 | 0.3970 | 0.6039 | 0.5972 | nan | 0.5653 | 0.6425 | 0.0 | 0.5518 | 0.6393 | | 0.0409 | 168.11 | 48080 | 0.1590 | 0.4032 | 0.6134 | 0.6063 | nan | 0.5725 | 0.6544 | 0.0 | 0.5590 | 0.6505 | | 0.0353 | 168.18 | 48100 | 0.1590 | 0.4017 | 0.6111 | 0.6051 | nan | 0.5763 | 0.6459 | 0.0 | 0.5623 | 0.6427 | | 0.0436 | 168.25 | 48120 | 0.1617 | 0.3985 | 0.6067 | 0.6013 | nan | 0.5752 | 0.6383 | 0.0 | 0.5600 | 0.6355 | | 0.0306 | 168.32 | 48140 | 0.1618 | 0.3957 | 0.6019 | 0.5952 | nan | 0.5632 | 0.6406 | 0.0 | 0.5497 | 0.6374 | | 0.0351 | 168.39 | 48160 | 0.1614 | 0.3969 | 0.6036 | 0.5965 | nan | 0.5621 | 0.6452 | 0.0 | 0.5490 | 0.6418 | | 0.0523 | 168.46 | 48180 | 0.1599 | 0.3969 | 0.6037 | 0.5968 | nan | 0.5643 | 0.6430 | 0.0 | 0.5508 | 0.6399 | | 0.0519 | 168.53 | 48200 | 0.1585 | 0.4017 | 0.6109 | 0.6032 | nan | 0.5668 | 0.6550 | 0.0 | 0.5533 | 0.6519 | | 0.0465 | 168.6 | 48220 | 0.1611 | 0.4007 | 0.6097 | 0.6035 | nan | 0.5740 | 0.6454 | 0.0 | 0.5595 | 0.6428 | | 0.0614 | 168.67 | 48240 | 0.1586 | 0.4001 | 0.6089 | 0.6030 | nan | 0.5753 | 0.6425 | 0.0 | 0.5605 | 0.6397 | | 0.0252 | 168.74 | 48260 | 0.1587 | 0.3985 | 0.6063 | 0.5990 | nan | 0.5640 | 0.6486 | 0.0 | 0.5503 | 0.6453 | | 0.0531 | 168.81 | 48280 | 0.1608 | 0.3995 | 0.6079 | 0.6021 | nan | 0.5744 | 0.6414 | 0.0 | 0.5601 | 0.6384 | | 0.0386 | 168.88 | 48300 | 0.1614 | 0.3988 | 0.6069 | 0.6019 | nan | 0.5777 | 0.6362 | 0.0 | 0.5634 | 0.6332 | | 0.035 | 168.95 | 48320 | 0.1618 | 0.3952 | 0.6011 | 0.5956 | nan | 0.5691 | 0.6331 | 0.0 | 0.5552 | 0.6303 | | 0.0289 | 169.02 | 48340 | 0.1599 | 0.3978 | 0.6053 | 0.5993 | nan | 0.5708 | 0.6398 | 0.0 | 0.5565 | 0.6370 | | 0.0353 | 169.09 | 48360 | 0.1606 | 0.3976 | 0.6046 | 0.5976 | nan | 0.5638 | 0.6455 | 0.0 | 0.5504 | 0.6424 | | 0.024 | 169.16 | 48380 | 0.1623 | 0.3970 | 0.6036 | 0.5962 | nan | 0.5609 | 0.6462 | 0.0 | 0.5477 | 0.6432 | | 0.0332 | 169.23 | 48400 | 0.1593 | 0.4006 | 0.6091 | 0.6021 | nan | 0.5685 | 0.6498 | 0.0 | 0.5552 | 0.6465 | | 0.023 | 169.3 | 48420 | 0.1577 | 0.4005 | 0.6089 | 0.6013 | nan | 0.5649 | 0.6530 | 0.0 | 0.5518 | 0.6496 | | 0.0342 | 169.37 | 48440 | 0.1587 | 0.4004 | 0.6086 | 0.6013 | nan | 0.5664 | 0.6508 | 0.0 | 0.5541 | 0.6471 | | 0.047 | 169.44 | 48460 | 0.1589 | 0.4026 | 0.6120 | 0.6040 | nan | 0.5657 | 0.6584 | 0.0 | 0.5538 | 0.6540 | | 0.0402 | 169.51 | 48480 | 0.1575 | 0.3979 | 0.6047 | 0.5954 | nan | 0.5514 | 0.6579 | 0.0 | 0.5399 | 0.6539 | | 0.0516 | 169.58 | 48500 | 0.1585 | 0.4024 | 0.6120 | 0.6047 | nan | 0.5698 | 0.6542 | 0.0 | 0.5567 | 0.6506 | | 0.0577 | 169.65 | 48520 | 0.1585 | 0.4003 | 0.6088 | 0.6009 | nan | 0.5635 | 0.6541 | 0.0 | 0.5505 | 0.6505 | | 0.0328 | 169.72 | 48540 | 0.1583 | 0.4048 | 0.6158 | 0.6102 | nan | 0.5834 | 0.6482 | 0.0 | 0.5692 | 0.6451 | | 0.0377 | 169.79 | 48560 | 0.1614 | 0.3930 | 0.5972 | 0.5891 | nan | 0.5507 | 0.6436 | 0.0 | 0.5385 | 0.6404 | | 0.0314 | 169.86 | 48580 | 0.1591 | 0.4026 | 0.6122 | 0.6053 | nan | 0.5724 | 0.6520 | 0.0 | 0.5591 | 0.6486 | | 0.0443 | 169.93 | 48600 | 0.1586 | 0.4021 | 0.6115 | 0.6036 | nan | 0.5661 | 0.6568 | 0.0 | 0.5532 | 0.6532 | | 0.0502 | 170.0 | 48620 | 0.1580 | 0.4012 | 0.6102 | 0.6030 | nan | 0.5685 | 0.6520 | 0.0 | 0.5549 | 0.6487 | | 0.0387 | 170.07 | 48640 | 0.1579 | 0.4013 | 0.6103 | 0.6028 | nan | 0.5666 | 0.6541 | 0.0 | 0.5532 | 0.6507 | | 0.0492 | 170.14 | 48660 | 0.1585 | 0.4043 | 0.6152 | 0.6086 | nan | 0.5774 | 0.6530 | 0.0 | 0.5633 | 0.6497 | | 0.0544 | 170.21 | 48680 | 0.1595 | 0.3992 | 0.6069 | 0.5997 | nan | 0.5651 | 0.6488 | 0.0 | 0.5521 | 0.6454 | | 0.0489 | 170.28 | 48700 | 0.1582 | 0.3990 | 0.6066 | 0.5988 | nan | 0.5620 | 0.6511 | 0.0 | 0.5496 | 0.6475 | | 0.0482 | 170.35 | 48720 | 0.1568 | 0.4027 | 0.6122 | 0.6033 | nan | 0.5608 | 0.6635 | 0.0 | 0.5503 | 0.6577 | | 0.0322 | 170.42 | 48740 | 0.1596 | 0.3987 | 0.6063 | 0.5982 | nan | 0.5593 | 0.6533 | 0.0 | 0.5481 | 0.6481 | | 0.0423 | 170.49 | 48760 | 0.1582 | 0.4047 | 0.6156 | 0.6091 | nan | 0.5779 | 0.6533 | 0.0 | 0.5652 | 0.6488 | | 0.0307 | 170.56 | 48780 | 0.1615 | 0.3967 | 0.6028 | 0.5937 | nan | 0.5502 | 0.6554 | 0.0 | 0.5393 | 0.6508 | | 0.0453 | 170.63 | 48800 | 0.1604 | 0.4015 | 0.6103 | 0.6031 | nan | 0.5685 | 0.6521 | 0.0 | 0.5565 | 0.6479 | | 0.0294 | 170.7 | 48820 | 0.1602 | 0.4022 | 0.6116 | 0.6054 | nan | 0.5761 | 0.6471 | 0.0 | 0.5635 | 0.6430 | | 0.0519 | 170.77 | 48840 | 0.1555 | 0.4037 | 0.6134 | 0.6051 | nan | 0.5651 | 0.6618 | 0.0 | 0.5538 | 0.6574 | | 0.0466 | 170.84 | 48860 | 0.1555 | 0.4013 | 0.6094 | 0.5999 | nan | 0.5542 | 0.6646 | 0.0 | 0.5433 | 0.6605 | | 0.0512 | 170.91 | 48880 | 0.1557 | 0.4010 | 0.6094 | 0.6018 | nan | 0.5655 | 0.6533 | 0.0 | 0.5534 | 0.6496 | | 0.0638 | 170.98 | 48900 | 0.1596 | 0.4014 | 0.6099 | 0.6018 | nan | 0.5631 | 0.6568 | 0.0 | 0.5513 | 0.6528 | | 0.0374 | 171.05 | 48920 | 0.1579 | 0.4023 | 0.6115 | 0.6032 | nan | 0.5639 | 0.6591 | 0.0 | 0.5517 | 0.6551 | | 0.0357 | 171.12 | 48940 | 0.1601 | 0.4056 | 0.6171 | 0.6117 | nan | 0.5858 | 0.6484 | 0.0 | 0.5720 | 0.6449 | | 0.0557 | 171.19 | 48960 | 0.1608 | 0.3996 | 0.6075 | 0.6000 | nan | 0.5642 | 0.6508 | 0.0 | 0.5516 | 0.6473 | | 0.0451 | 171.26 | 48980 | 0.1598 | 0.4014 | 0.6104 | 0.6039 | nan | 0.5729 | 0.6479 | 0.0 | 0.5596 | 0.6445 | | 0.0486 | 171.33 | 49000 | 0.1652 | 0.3948 | 0.6003 | 0.5946 | nan | 0.5675 | 0.6331 | 0.0 | 0.5543 | 0.6301 | | 0.0367 | 171.4 | 49020 | 0.1612 | 0.3989 | 0.6066 | 0.6001 | nan | 0.5688 | 0.6444 | 0.0 | 0.5555 | 0.6411 | | 0.0243 | 171.47 | 49040 | 0.1601 | 0.4029 | 0.6127 | 0.6056 | nan | 0.5712 | 0.6543 | 0.0 | 0.5580 | 0.6507 | | 0.0456 | 171.54 | 49060 | 0.1609 | 0.4008 | 0.6096 | 0.6030 | nan | 0.5712 | 0.6481 | 0.0 | 0.5576 | 0.6447 | | 0.0299 | 171.61 | 49080 | 0.1611 | 0.3999 | 0.6082 | 0.6011 | nan | 0.5671 | 0.6493 | 0.0 | 0.5539 | 0.6459 | | 0.0376 | 171.68 | 49100 | 0.1630 | 0.3976 | 0.6045 | 0.5974 | nan | 0.5631 | 0.6460 | 0.0 | 0.5501 | 0.6427 | | 0.0469 | 171.75 | 49120 | 0.1610 | 0.3988 | 0.6065 | 0.5998 | nan | 0.5678 | 0.6453 | 0.0 | 0.5544 | 0.6420 | | 0.0296 | 171.82 | 49140 | 0.1612 | 0.4024 | 0.6123 | 0.6060 | nan | 0.5760 | 0.6485 | 0.0 | 0.5623 | 0.6450 | | 0.0282 | 171.89 | 49160 | 0.1614 | 0.4014 | 0.6108 | 0.6048 | nan | 0.5765 | 0.6451 | 0.0 | 0.5625 | 0.6417 | | 0.0467 | 171.96 | 49180 | 0.1633 | 0.3990 | 0.6067 | 0.5998 | nan | 0.5669 | 0.6465 | 0.0 | 0.5534 | 0.6435 | | 0.0538 | 172.03 | 49200 | 0.1637 | 0.3965 | 0.6029 | 0.5961 | nan | 0.5632 | 0.6427 | 0.0 | 0.5497 | 0.6397 | | 0.0423 | 172.1 | 49220 | 0.1617 | 0.3988 | 0.6067 | 0.5999 | nan | 0.5677 | 0.6457 | 0.0 | 0.5540 | 0.6424 | | 0.0407 | 172.17 | 49240 | 0.1640 | 0.3929 | 0.5974 | 0.5898 | nan | 0.5535 | 0.6414 | 0.0 | 0.5406 | 0.6381 | | 0.0461 | 172.24 | 49260 | 0.1637 | 0.3995 | 0.6077 | 0.6015 | nan | 0.5716 | 0.6439 | 0.0 | 0.5575 | 0.6408 | | 0.0485 | 172.31 | 49280 | 0.1633 | 0.3963 | 0.6030 | 0.5971 | nan | 0.5692 | 0.6367 | 0.0 | 0.5548 | 0.6340 | | 0.0463 | 172.38 | 49300 | 0.1633 | 0.3961 | 0.6025 | 0.5955 | nan | 0.5620 | 0.6430 | 0.0 | 0.5484 | 0.6399 | | 0.0352 | 172.45 | 49320 | 0.1612 | 0.4005 | 0.6094 | 0.6026 | nan | 0.5704 | 0.6484 | 0.0 | 0.5564 | 0.6452 | | 0.0319 | 172.52 | 49340 | 0.1627 | 0.3963 | 0.6027 | 0.5953 | nan | 0.5598 | 0.6456 | 0.0 | 0.5463 | 0.6427 | | 0.0358 | 172.59 | 49360 | 0.1624 | 0.3997 | 0.6083 | 0.6027 | nan | 0.5763 | 0.6403 | 0.0 | 0.5614 | 0.6376 | | 0.0355 | 172.66 | 49380 | 0.1651 | 0.3955 | 0.6017 | 0.5960 | nan | 0.5686 | 0.6348 | 0.0 | 0.5543 | 0.6321 | | 0.0628 | 172.73 | 49400 | 0.1657 | 0.3965 | 0.6033 | 0.5974 | nan | 0.5689 | 0.6378 | 0.0 | 0.5544 | 0.6350 | | 0.0369 | 172.8 | 49420 | 0.1666 | 0.3970 | 0.6042 | 0.5984 | nan | 0.5710 | 0.6374 | 0.0 | 0.5565 | 0.6344 | | 0.0377 | 172.87 | 49440 | 0.1651 | 0.3935 | 0.5987 | 0.5924 | nan | 0.5624 | 0.6349 | 0.0 | 0.5484 | 0.6322 | | 0.0602 | 172.94 | 49460 | 0.1617 | 0.3993 | 0.6072 | 0.5990 | nan | 0.5598 | 0.6545 | 0.0 | 0.5474 | 0.6506 | | 0.0189 | 173.01 | 49480 | 0.1594 | 0.4012 | 0.6101 | 0.6030 | nan | 0.5689 | 0.6513 | 0.0 | 0.5562 | 0.6475 | | 0.0331 | 173.08 | 49500 | 0.1617 | 0.3993 | 0.6069 | 0.5991 | nan | 0.5618 | 0.6519 | 0.0 | 0.5494 | 0.6484 | | 0.0355 | 173.15 | 49520 | 0.1606 | 0.4029 | 0.6128 | 0.6071 | nan | 0.5797 | 0.6458 | 0.0 | 0.5660 | 0.6426 | | 0.0399 | 173.22 | 49540 | 0.1634 | 0.3986 | 0.6062 | 0.5996 | nan | 0.5680 | 0.6445 | 0.0 | 0.5546 | 0.6413 | | 0.0535 | 173.29 | 49560 | 0.1624 | 0.3953 | 0.6013 | 0.5938 | nan | 0.5580 | 0.6445 | 0.0 | 0.5447 | 0.6412 | | 0.0507 | 173.36 | 49580 | 0.1622 | 0.3995 | 0.6079 | 0.6011 | nan | 0.5685 | 0.6473 | 0.0 | 0.5549 | 0.6435 | | 0.0319 | 173.43 | 49600 | 0.1610 | 0.4040 | 0.6149 | 0.6098 | nan | 0.5850 | 0.6448 | 0.0 | 0.5706 | 0.6413 | | 0.0298 | 173.5 | 49620 | 0.1615 | 0.3952 | 0.6010 | 0.5939 | nan | 0.5601 | 0.6420 | 0.0 | 0.5471 | 0.6385 | | 0.0262 | 173.57 | 49640 | 0.1600 | 0.3996 | 0.6079 | 0.6013 | nan | 0.5699 | 0.6459 | 0.0 | 0.5564 | 0.6425 | | 0.0344 | 173.64 | 49660 | 0.1596 | 0.3988 | 0.6063 | 0.5985 | nan | 0.5610 | 0.6517 | 0.0 | 0.5488 | 0.6477 | | 0.0756 | 173.71 | 49680 | 0.1587 | 0.4005 | 0.6089 | 0.6013 | nan | 0.5650 | 0.6527 | 0.0 | 0.5526 | 0.6488 | | 0.0351 | 173.78 | 49700 | 0.1579 | 0.4032 | 0.6134 | 0.6064 | nan | 0.5730 | 0.6537 | 0.0 | 0.5598 | 0.6498 | | 0.0392 | 173.85 | 49720 | 0.1627 | 0.3981 | 0.6056 | 0.5982 | nan | 0.5625 | 0.6487 | 0.0 | 0.5491 | 0.6453 | | 0.0251 | 173.92 | 49740 | 0.1633 | 0.3994 | 0.6078 | 0.6016 | nan | 0.5718 | 0.6439 | 0.0 | 0.5574 | 0.6407 | | 0.0425 | 173.99 | 49760 | 0.1596 | 0.4023 | 0.6123 | 0.6062 | nan | 0.5769 | 0.6478 | 0.0 | 0.5623 | 0.6447 | | 0.035 | 174.06 | 49780 | 0.1595 | 0.4014 | 0.6106 | 0.6030 | nan | 0.5667 | 0.6544 | 0.0 | 0.5530 | 0.6511 | | 0.0434 | 174.13 | 49800 | 0.1600 | 0.3991 | 0.6069 | 0.5987 | nan | 0.5600 | 0.6537 | 0.0 | 0.5468 | 0.6503 | | 0.0294 | 174.2 | 49820 | 0.1603 | 0.4025 | 0.6121 | 0.6050 | nan | 0.5711 | 0.6530 | 0.0 | 0.5578 | 0.6496 | | 0.0524 | 174.27 | 49840 | 0.1600 | 0.4039 | 0.6146 | 0.6085 | nan | 0.5796 | 0.6495 | 0.0 | 0.5655 | 0.6463 | | 0.0348 | 174.34 | 49860 | 0.1614 | 0.3999 | 0.6084 | 0.6017 | nan | 0.5695 | 0.6473 | 0.0 | 0.5554 | 0.6443 | | 0.026 | 174.41 | 49880 | 0.1590 | 0.4025 | 0.6126 | 0.6064 | nan | 0.5767 | 0.6485 | 0.0 | 0.5623 | 0.6454 | | 0.064 | 174.48 | 49900 | 0.1595 | 0.3990 | 0.6067 | 0.5988 | nan | 0.5609 | 0.6525 | 0.0 | 0.5483 | 0.6487 | | 0.0497 | 174.55 | 49920 | 0.1577 | 0.4033 | 0.6134 | 0.6058 | nan | 0.5695 | 0.6572 | 0.0 | 0.5567 | 0.6533 | | 0.0587 | 174.62 | 49940 | 0.1607 | 0.3986 | 0.6062 | 0.5981 | nan | 0.5598 | 0.6526 | 0.0 | 0.5468 | 0.6490 | | 0.0416 | 174.69 | 49960 | 0.1620 | 0.3985 | 0.6061 | 0.5989 | nan | 0.5647 | 0.6475 | 0.0 | 0.5515 | 0.6441 | | 0.0435 | 174.76 | 49980 | 0.1623 | 0.4011 | 0.6102 | 0.6044 | nan | 0.5768 | 0.6436 | 0.0 | 0.5629 | 0.6406 | | 0.0342 | 174.83 | 50000 | 0.1619 | 0.4008 | 0.6095 | 0.6026 | nan | 0.5698 | 0.6491 | 0.0 | 0.5566 | 0.6457 | | 0.0371 | 174.9 | 50020 | 0.1629 | 0.3957 | 0.6016 | 0.5940 | nan | 0.5581 | 0.6450 | 0.0 | 0.5455 | 0.6415 | | 0.0385 | 174.97 | 50040 | 0.1632 | 0.3991 | 0.6068 | 0.5998 | nan | 0.5660 | 0.6477 | 0.0 | 0.5532 | 0.6440 | | 0.0436 | 175.03 | 50060 | 0.1618 | 0.3992 | 0.6070 | 0.5996 | nan | 0.5642 | 0.6498 | 0.0 | 0.5519 | 0.6458 | | 0.0413 | 175.1 | 50080 | 0.1624 | 0.4001 | 0.6085 | 0.6012 | nan | 0.5666 | 0.6503 | 0.0 | 0.5536 | 0.6467 | | 0.0331 | 175.17 | 50100 | 0.1644 | 0.3974 | 0.6047 | 0.5983 | nan | 0.5679 | 0.6415 | 0.0 | 0.5540 | 0.6384 | | 0.0468 | 175.24 | 50120 | 0.1648 | 0.3992 | 0.6076 | 0.6015 | nan | 0.5728 | 0.6423 | 0.0 | 0.5586 | 0.6390 | | 0.0474 | 175.31 | 50140 | 0.1677 | 0.3947 | 0.6007 | 0.5947 | nan | 0.5663 | 0.6351 | 0.0 | 0.5521 | 0.6319 | | 0.0502 | 175.38 | 50160 | 0.1651 | 0.3949 | 0.6011 | 0.5952 | nan | 0.5673 | 0.6349 | 0.0 | 0.5529 | 0.6318 | | 0.0261 | 175.45 | 50180 | 0.1635 | 0.3985 | 0.6066 | 0.6017 | nan | 0.5780 | 0.6353 | 0.0 | 0.5632 | 0.6322 | | 0.0491 | 175.52 | 50200 | 0.1635 | 0.3979 | 0.6056 | 0.5999 | nan | 0.5727 | 0.6386 | 0.0 | 0.5582 | 0.6355 | | 0.0418 | 175.59 | 50220 | 0.1614 | 0.4002 | 0.6093 | 0.6033 | nan | 0.5749 | 0.6437 | 0.0 | 0.5599 | 0.6406 | | 0.036 | 175.66 | 50240 | 0.1628 | 0.3991 | 0.6072 | 0.6002 | nan | 0.5666 | 0.6479 | 0.0 | 0.5529 | 0.6445 | | 0.0777 | 175.73 | 50260 | 0.1599 | 0.4040 | 0.6148 | 0.6075 | nan | 0.5723 | 0.6573 | 0.0 | 0.5585 | 0.6536 | | 0.0435 | 175.8 | 50280 | 0.1611 | 0.4018 | 0.6110 | 0.6029 | nan | 0.5640 | 0.6580 | 0.0 | 0.5513 | 0.6540 | | 0.0318 | 175.87 | 50300 | 0.1614 | 0.4012 | 0.6100 | 0.6021 | nan | 0.5642 | 0.6558 | 0.0 | 0.5514 | 0.6523 | | 0.0322 | 175.94 | 50320 | 0.1635 | 0.4010 | 0.6098 | 0.6031 | nan | 0.5713 | 0.6483 | 0.0 | 0.5574 | 0.6455 | | 0.0314 | 176.01 | 50340 | 0.1647 | 0.3972 | 0.6039 | 0.5970 | nan | 0.5637 | 0.6441 | 0.0 | 0.5503 | 0.6412 | | 0.0623 | 176.08 | 50360 | 0.1629 | 0.3984 | 0.6058 | 0.5992 | nan | 0.5675 | 0.6441 | 0.0 | 0.5537 | 0.6413 | | 0.0537 | 176.15 | 50380 | 0.1651 | 0.3992 | 0.6072 | 0.6010 | nan | 0.5715 | 0.6430 | 0.0 | 0.5576 | 0.6399 | | 0.0581 | 176.22 | 50400 | 0.1641 | 0.4040 | 0.6150 | 0.6099 | nan | 0.5857 | 0.6443 | 0.0 | 0.5710 | 0.6410 | | 0.0285 | 176.29 | 50420 | 0.1616 | 0.3990 | 0.6071 | 0.6006 | nan | 0.5695 | 0.6447 | 0.0 | 0.5553 | 0.6416 | | 0.027 | 176.36 | 50440 | 0.1616 | 0.3999 | 0.6084 | 0.6019 | nan | 0.5704 | 0.6465 | 0.0 | 0.5567 | 0.6430 | | 0.0293 | 176.43 | 50460 | 0.1647 | 0.3982 | 0.6058 | 0.5994 | nan | 0.5689 | 0.6427 | 0.0 | 0.5552 | 0.6395 | | 0.0413 | 176.5 | 50480 | 0.1634 | 0.3961 | 0.6024 | 0.5962 | nan | 0.5664 | 0.6384 | 0.0 | 0.5528 | 0.6354 | | 0.0301 | 176.57 | 50500 | 0.1634 | 0.3983 | 0.6057 | 0.5987 | nan | 0.5649 | 0.6466 | 0.0 | 0.5516 | 0.6434 | | 0.0551 | 176.64 | 50520 | 0.1635 | 0.3981 | 0.6054 | 0.5985 | nan | 0.5656 | 0.6452 | 0.0 | 0.5523 | 0.6420 | | 0.0496 | 176.71 | 50540 | 0.1644 | 0.3982 | 0.6057 | 0.5992 | nan | 0.5683 | 0.6431 | 0.0 | 0.5550 | 0.6398 | | 0.0339 | 176.78 | 50560 | 0.1609 | 0.4014 | 0.6105 | 0.6037 | nan | 0.5712 | 0.6499 | 0.0 | 0.5579 | 0.6463 | | 0.0427 | 176.85 | 50580 | 0.1649 | 0.3966 | 0.6030 | 0.5952 | nan | 0.5580 | 0.6480 | 0.0 | 0.5449 | 0.6447 | | 0.0514 | 176.92 | 50600 | 0.1633 | 0.3996 | 0.6078 | 0.6016 | nan | 0.5721 | 0.6436 | 0.0 | 0.5584 | 0.6404 | | 0.0513 | 176.99 | 50620 | 0.1626 | 0.3993 | 0.6073 | 0.6000 | nan | 0.5653 | 0.6492 | 0.0 | 0.5519 | 0.6460 | | 0.0485 | 177.06 | 50640 | 0.1637 | 0.4010 | 0.6098 | 0.6025 | nan | 0.5676 | 0.6520 | 0.0 | 0.5545 | 0.6485 | | 0.0559 | 177.13 | 50660 | 0.1610 | 0.4012 | 0.6101 | 0.6024 | nan | 0.5657 | 0.6546 | 0.0 | 0.5529 | 0.6508 | | 0.0633 | 177.2 | 50680 | 0.1609 | 0.4006 | 0.6091 | 0.6010 | nan | 0.5621 | 0.6561 | 0.0 | 0.5498 | 0.6521 | | 0.034 | 177.27 | 50700 | 0.1614 | 0.4003 | 0.6086 | 0.6008 | nan | 0.5634 | 0.6538 | 0.0 | 0.5510 | 0.6499 | | 0.0388 | 177.34 | 50720 | 0.1605 | 0.3992 | 0.6070 | 0.5996 | nan | 0.5644 | 0.6496 | 0.0 | 0.5516 | 0.6459 | | 0.0599 | 177.41 | 50740 | 0.1627 | 0.4003 | 0.6088 | 0.6022 | nan | 0.5704 | 0.6473 | 0.0 | 0.5573 | 0.6437 | | 0.0377 | 177.48 | 50760 | 0.1646 | 0.3997 | 0.6079 | 0.6009 | nan | 0.5672 | 0.6487 | 0.0 | 0.5537 | 0.6455 | | 0.0519 | 177.55 | 50780 | 0.1634 | 0.3992 | 0.6071 | 0.5993 | nan | 0.5623 | 0.6518 | 0.0 | 0.5489 | 0.6487 | | 0.0507 | 177.62 | 50800 | 0.1632 | 0.3998 | 0.6083 | 0.6021 | nan | 0.5725 | 0.6442 | 0.0 | 0.5584 | 0.6411 | | 0.0383 | 177.69 | 50820 | 0.1641 | 0.4050 | 0.6163 | 0.6108 | nan | 0.5845 | 0.6481 | 0.0 | 0.5701 | 0.6449 | | 0.0316 | 177.76 | 50840 | 0.1622 | 0.4021 | 0.6116 | 0.6046 | nan | 0.5710 | 0.6523 | 0.0 | 0.5574 | 0.6489 | | 0.0504 | 177.83 | 50860 | 0.1661 | 0.3979 | 0.6051 | 0.5988 | nan | 0.5684 | 0.6419 | 0.0 | 0.5547 | 0.6390 | | 0.0327 | 177.9 | 50880 | 0.1625 | 0.4013 | 0.6104 | 0.6038 | nan | 0.5723 | 0.6486 | 0.0 | 0.5583 | 0.6456 | | 0.0279 | 177.97 | 50900 | 0.1615 | 0.4016 | 0.6110 | 0.6048 | nan | 0.5750 | 0.6470 | 0.0 | 0.5608 | 0.6440 | | 0.0472 | 178.04 | 50920 | 0.1660 | 0.3967 | 0.6033 | 0.5966 | nan | 0.5645 | 0.6421 | 0.0 | 0.5509 | 0.6391 | | 0.0275 | 178.11 | 50940 | 0.1648 | 0.3980 | 0.6053 | 0.5992 | nan | 0.5703 | 0.6403 | 0.0 | 0.5563 | 0.6375 | | 0.0668 | 178.18 | 50960 | 0.1609 | 0.4022 | 0.6118 | 0.6052 | nan | 0.5738 | 0.6498 | 0.0 | 0.5602 | 0.6465 | | 0.0349 | 178.25 | 50980 | 0.1642 | 0.3987 | 0.6062 | 0.5994 | nan | 0.5667 | 0.6457 | 0.0 | 0.5537 | 0.6423 | | 0.0373 | 178.32 | 51000 | 0.1640 | 0.3984 | 0.6058 | 0.5993 | nan | 0.5684 | 0.6431 | 0.0 | 0.5554 | 0.6397 | | 0.0329 | 178.39 | 51020 | 0.1622 | 0.4001 | 0.6084 | 0.6018 | nan | 0.5706 | 0.6462 | 0.0 | 0.5575 | 0.6427 | | 0.0407 | 178.46 | 51040 | 0.1624 | 0.3986 | 0.6061 | 0.5993 | nan | 0.5673 | 0.6448 | 0.0 | 0.5542 | 0.6415 | | 0.0377 | 178.53 | 51060 | 0.1640 | 0.3976 | 0.6047 | 0.5981 | nan | 0.5664 | 0.6430 | 0.0 | 0.5531 | 0.6397 | | 0.0322 | 178.6 | 51080 | 0.1634 | 0.4030 | 0.6130 | 0.6064 | nan | 0.5751 | 0.6509 | 0.0 | 0.5618 | 0.6473 | | 0.0564 | 178.67 | 51100 | 0.1627 | 0.4027 | 0.6125 | 0.6057 | nan | 0.5734 | 0.6515 | 0.0 | 0.5603 | 0.6478 | | 0.0444 | 178.74 | 51120 | 0.1620 | 0.3988 | 0.6062 | 0.5989 | nan | 0.5640 | 0.6485 | 0.0 | 0.5517 | 0.6447 | | 0.0631 | 178.81 | 51140 | 0.1634 | 0.3998 | 0.6079 | 0.6013 | nan | 0.5701 | 0.6456 | 0.0 | 0.5575 | 0.6419 | | 0.0417 | 178.88 | 51160 | 0.1613 | 0.4020 | 0.6112 | 0.6040 | nan | 0.5696 | 0.6527 | 0.0 | 0.5571 | 0.6488 | | 0.0448 | 178.95 | 51180 | 0.1639 | 0.3972 | 0.6037 | 0.5947 | nan | 0.5518 | 0.6556 | 0.0 | 0.5400 | 0.6518 | | 0.0298 | 179.02 | 51200 | 0.1593 | 0.4042 | 0.6146 | 0.6069 | nan | 0.5700 | 0.6592 | 0.0 | 0.5572 | 0.6553 | | 0.0484 | 179.09 | 51220 | 0.1591 | 0.4055 | 0.6166 | 0.6095 | nan | 0.5758 | 0.6574 | 0.0 | 0.5628 | 0.6537 | | 0.0419 | 179.16 | 51240 | 0.1658 | 0.3988 | 0.6064 | 0.5995 | nan | 0.5665 | 0.6464 | 0.0 | 0.5530 | 0.6434 | | 0.0416 | 179.23 | 51260 | 0.1617 | 0.4060 | 0.6173 | 0.6101 | nan | 0.5756 | 0.6589 | 0.0 | 0.5624 | 0.6556 | | 0.0438 | 179.3 | 51280 | 0.1616 | 0.4010 | 0.6092 | 0.6004 | nan | 0.5582 | 0.6602 | 0.0 | 0.5466 | 0.6563 | | 0.0363 | 179.37 | 51300 | 0.1598 | 0.4088 | 0.6214 | 0.6141 | nan | 0.5790 | 0.6639 | 0.0 | 0.5667 | 0.6597 | | 0.0367 | 179.44 | 51320 | 0.1611 | 0.4031 | 0.6125 | 0.6035 | nan | 0.5605 | 0.6644 | 0.0 | 0.5497 | 0.6597 | | 0.0291 | 179.51 | 51340 | 0.1586 | 0.4057 | 0.6165 | 0.6089 | nan | 0.5723 | 0.6608 | 0.0 | 0.5604 | 0.6567 | | 0.0527 | 179.58 | 51360 | 0.1609 | 0.4018 | 0.6106 | 0.6028 | nan | 0.5652 | 0.6560 | 0.0 | 0.5534 | 0.6522 | | 0.0405 | 179.65 | 51380 | 0.1633 | 0.3990 | 0.6062 | 0.5985 | nan | 0.5616 | 0.6508 | 0.0 | 0.5495 | 0.6474 | | 0.0301 | 179.72 | 51400 | 0.1632 | 0.4040 | 0.6143 | 0.6077 | nan | 0.5766 | 0.6519 | 0.0 | 0.5634 | 0.6485 | | 0.037 | 179.79 | 51420 | 0.1627 | 0.4012 | 0.6101 | 0.6034 | nan | 0.5713 | 0.6489 | 0.0 | 0.5581 | 0.6455 | | 0.0429 | 179.86 | 51440 | 0.1645 | 0.3986 | 0.6062 | 0.5995 | nan | 0.5677 | 0.6448 | 0.0 | 0.5543 | 0.6416 | | 0.0341 | 179.93 | 51460 | 0.1658 | 0.3949 | 0.6003 | 0.5932 | nan | 0.5592 | 0.6415 | 0.0 | 0.5461 | 0.6387 | | 0.0147 | 180.0 | 51480 | 0.1638 | 0.3998 | 0.6080 | 0.6020 | nan | 0.5731 | 0.6428 | 0.0 | 0.5594 | 0.6400 | | 0.0421 | 180.07 | 51500 | 0.1652 | 0.3957 | 0.6016 | 0.5943 | nan | 0.5590 | 0.6443 | 0.0 | 0.5460 | 0.6412 | | 0.0351 | 180.14 | 51520 | 0.1616 | 0.4022 | 0.6116 | 0.6054 | nan | 0.5755 | 0.6478 | 0.0 | 0.5621 | 0.6445 | | 0.0594 | 180.21 | 51540 | 0.1617 | 0.4007 | 0.6092 | 0.6023 | nan | 0.5694 | 0.6489 | 0.0 | 0.5564 | 0.6457 | | 0.0591 | 180.28 | 51560 | 0.1641 | 0.3976 | 0.6045 | 0.5978 | nan | 0.5660 | 0.6430 | 0.0 | 0.5527 | 0.6401 | | 0.0649 | 180.35 | 51580 | 0.1665 | 0.3970 | 0.6033 | 0.5962 | nan | 0.5624 | 0.6442 | 0.0 | 0.5497 | 0.6414 | | 0.025 | 180.42 | 51600 | 0.1641 | 0.3980 | 0.6050 | 0.5986 | nan | 0.5683 | 0.6416 | 0.0 | 0.5551 | 0.6389 | | 0.0317 | 180.49 | 51620 | 0.1608 | 0.3993 | 0.6069 | 0.5998 | nan | 0.5657 | 0.6482 | 0.0 | 0.5527 | 0.6451 | | 0.0449 | 180.56 | 51640 | 0.1625 | 0.4005 | 0.6087 | 0.6011 | nan | 0.5646 | 0.6529 | 0.0 | 0.5522 | 0.6493 | | 0.0476 | 180.63 | 51660 | 0.1612 | 0.4021 | 0.6113 | 0.6044 | nan | 0.5715 | 0.6511 | 0.0 | 0.5588 | 0.6476 | | 0.0406 | 180.7 | 51680 | 0.1613 | 0.4023 | 0.6116 | 0.6049 | nan | 0.5730 | 0.6501 | 0.0 | 0.5602 | 0.6467 | | 0.0477 | 180.77 | 51700 | 0.1661 | 0.3982 | 0.6053 | 0.5994 | nan | 0.5711 | 0.6396 | 0.0 | 0.5578 | 0.6367 | | 0.0307 | 180.84 | 51720 | 0.1641 | 0.3988 | 0.6065 | 0.5999 | nan | 0.5682 | 0.6448 | 0.0 | 0.5546 | 0.6418 | | 0.0359 | 180.91 | 51740 | 0.1623 | 0.4004 | 0.6089 | 0.6023 | nan | 0.5709 | 0.6468 | 0.0 | 0.5575 | 0.6436 | | 0.0368 | 180.98 | 51760 | 0.1622 | 0.3994 | 0.6072 | 0.6009 | nan | 0.5707 | 0.6437 | 0.0 | 0.5577 | 0.6404 | | 0.0275 | 181.05 | 51780 | 0.1630 | 0.3963 | 0.6024 | 0.5954 | nan | 0.5619 | 0.6428 | 0.0 | 0.5496 | 0.6394 | | 0.0492 | 181.12 | 51800 | 0.1631 | 0.3978 | 0.6050 | 0.5987 | nan | 0.5683 | 0.6417 | 0.0 | 0.5548 | 0.6387 | | 0.0153 | 181.19 | 51820 | 0.1636 | 0.4014 | 0.6105 | 0.6040 | nan | 0.5730 | 0.6480 | 0.0 | 0.5594 | 0.6449 | | 0.0676 | 181.26 | 51840 | 0.1603 | 0.4013 | 0.6103 | 0.6041 | nan | 0.5745 | 0.6461 | 0.0 | 0.5607 | 0.6432 | | 0.0557 | 181.33 | 51860 | 0.1614 | 0.3986 | 0.6058 | 0.5985 | nan | 0.5636 | 0.6480 | 0.0 | 0.5508 | 0.6448 | | 0.0369 | 181.4 | 51880 | 0.1628 | 0.4010 | 0.6100 | 0.6042 | nan | 0.5766 | 0.6434 | 0.0 | 0.5625 | 0.6404 | | 0.0277 | 181.47 | 51900 | 0.1632 | 0.3975 | 0.6045 | 0.5978 | nan | 0.5660 | 0.6430 | 0.0 | 0.5526 | 0.6400 | | 0.0443 | 181.54 | 51920 | 0.1646 | 0.3991 | 0.6068 | 0.6000 | nan | 0.5678 | 0.6457 | 0.0 | 0.5547 | 0.6426 | | 0.0273 | 181.61 | 51940 | 0.1630 | 0.3988 | 0.6066 | 0.6003 | nan | 0.5704 | 0.6428 | 0.0 | 0.5567 | 0.6397 | | 0.0283 | 181.68 | 51960 | 0.1613 | 0.4009 | 0.6098 | 0.6032 | nan | 0.5717 | 0.6478 | 0.0 | 0.5582 | 0.6445 | | 0.0567 | 181.75 | 51980 | 0.1630 | 0.3976 | 0.6046 | 0.5971 | nan | 0.5616 | 0.6475 | 0.0 | 0.5487 | 0.6441 | | 0.0417 | 181.82 | 52000 | 0.1617 | 0.4018 | 0.6111 | 0.6048 | nan | 0.5750 | 0.6472 | 0.0 | 0.5615 | 0.6438 | | 0.0339 | 181.89 | 52020 | 0.1612 | 0.4018 | 0.6111 | 0.6043 | nan | 0.5719 | 0.6503 | 0.0 | 0.5586 | 0.6468 | | 0.0359 | 181.96 | 52040 | 0.1617 | 0.4018 | 0.6113 | 0.6054 | nan | 0.5774 | 0.6452 | 0.0 | 0.5635 | 0.6419 | | 0.0483 | 182.03 | 52060 | 0.1638 | 0.3976 | 0.6047 | 0.5975 | nan | 0.5631 | 0.6463 | 0.0 | 0.5501 | 0.6426 | | 0.0653 | 182.1 | 52080 | 0.1635 | 0.4001 | 0.6084 | 0.6008 | nan | 0.5645 | 0.6523 | 0.0 | 0.5518 | 0.6484 | | 0.0671 | 182.17 | 52100 | 0.1622 | 0.4007 | 0.6095 | 0.6023 | nan | 0.5682 | 0.6508 | 0.0 | 0.5548 | 0.6472 | | 0.0425 | 182.24 | 52120 | 0.1616 | 0.3984 | 0.6059 | 0.5987 | nan | 0.5644 | 0.6475 | 0.0 | 0.5512 | 0.6440 | | 0.0518 | 182.31 | 52140 | 0.1615 | 0.4028 | 0.6128 | 0.6065 | nan | 0.5765 | 0.6491 | 0.0 | 0.5627 | 0.6457 | | 0.0266 | 182.38 | 52160 | 0.1600 | 0.4014 | 0.6103 | 0.6026 | nan | 0.5656 | 0.6550 | 0.0 | 0.5529 | 0.6513 | | 0.063 | 182.45 | 52180 | 0.1592 | 0.4038 | 0.6142 | 0.6071 | nan | 0.5730 | 0.6554 | 0.0 | 0.5599 | 0.6516 | | 0.045 | 182.52 | 52200 | 0.1598 | 0.4014 | 0.6105 | 0.6032 | nan | 0.5681 | 0.6529 | 0.0 | 0.5549 | 0.6493 | | 0.0641 | 182.59 | 52220 | 0.1629 | 0.4005 | 0.6092 | 0.6027 | nan | 0.5716 | 0.6468 | 0.0 | 0.5583 | 0.6434 | | 0.0495 | 182.66 | 52240 | 0.1605 | 0.4028 | 0.6126 | 0.6057 | nan | 0.5730 | 0.6521 | 0.0 | 0.5600 | 0.6484 | | 0.0391 | 182.73 | 52260 | 0.1613 | 0.3990 | 0.6065 | 0.5980 | nan | 0.5571 | 0.6560 | 0.0 | 0.5450 | 0.6518 | | 0.0331 | 182.8 | 52280 | 0.1610 | 0.4015 | 0.6107 | 0.6037 | nan | 0.5700 | 0.6515 | 0.0 | 0.5569 | 0.6476 | | 0.0407 | 182.87 | 52300 | 0.1625 | 0.4001 | 0.6086 | 0.6016 | nan | 0.5684 | 0.6488 | 0.0 | 0.5554 | 0.6450 | | 0.0248 | 182.94 | 52320 | 0.1653 | 0.3995 | 0.6074 | 0.5997 | nan | 0.5632 | 0.6516 | 0.0 | 0.5508 | 0.6476 | | 0.0261 | 183.01 | 52340 | 0.1656 | 0.4008 | 0.6095 | 0.6025 | nan | 0.5693 | 0.6497 | 0.0 | 0.5562 | 0.6461 | | 0.0246 | 183.08 | 52360 | 0.1651 | 0.3955 | 0.6016 | 0.5950 | nan | 0.5635 | 0.6398 | 0.0 | 0.5499 | 0.6366 | | 0.0643 | 183.15 | 52380 | 0.1644 | 0.3978 | 0.6050 | 0.5984 | nan | 0.5666 | 0.6435 | 0.0 | 0.5529 | 0.6404 | | 0.0501 | 183.22 | 52400 | 0.1660 | 0.3973 | 0.6043 | 0.5979 | nan | 0.5674 | 0.6412 | 0.0 | 0.5539 | 0.6381 | | 0.0454 | 183.29 | 52420 | 0.1637 | 0.4008 | 0.6095 | 0.6028 | nan | 0.5707 | 0.6483 | 0.0 | 0.5575 | 0.6450 | | 0.0524 | 183.36 | 52440 | 0.1624 | 0.4018 | 0.6106 | 0.6024 | nan | 0.5636 | 0.6576 | 0.0 | 0.5516 | 0.6537 | | 0.0398 | 183.43 | 52460 | 0.1606 | 0.4044 | 0.6150 | 0.6083 | nan | 0.5766 | 0.6533 | 0.0 | 0.5636 | 0.6497 | | 0.0361 | 183.5 | 52480 | 0.1634 | 0.4001 | 0.6082 | 0.6006 | nan | 0.5643 | 0.6521 | 0.0 | 0.5519 | 0.6485 | | 0.0587 | 183.57 | 52500 | 0.1630 | 0.4032 | 0.6133 | 0.6071 | nan | 0.5773 | 0.6493 | 0.0 | 0.5638 | 0.6459 | | 0.0415 | 183.64 | 52520 | 0.1647 | 0.4001 | 0.6083 | 0.6014 | nan | 0.5683 | 0.6483 | 0.0 | 0.5552 | 0.6450 | | 0.0288 | 183.71 | 52540 | 0.1634 | 0.4037 | 0.6140 | 0.6073 | nan | 0.5756 | 0.6523 | 0.0 | 0.5624 | 0.6488 | | 0.0336 | 183.78 | 52560 | 0.1609 | 0.4029 | 0.6122 | 0.6039 | nan | 0.5643 | 0.6601 | 0.0 | 0.5528 | 0.6558 | | 0.0247 | 183.85 | 52580 | 0.1591 | 0.4046 | 0.6151 | 0.6076 | nan | 0.5722 | 0.6580 | 0.0 | 0.5599 | 0.6539 | | 0.0268 | 183.92 | 52600 | 0.1595 | 0.4032 | 0.6127 | 0.6047 | nan | 0.5665 | 0.6590 | 0.0 | 0.5551 | 0.6546 | | 0.074 | 183.99 | 52620 | 0.1612 | 0.4011 | 0.6093 | 0.6010 | nan | 0.5613 | 0.6574 | 0.0 | 0.5500 | 0.6532 | | 0.0386 | 184.06 | 52640 | 0.1611 | 0.4027 | 0.6120 | 0.6038 | nan | 0.5647 | 0.6594 | 0.0 | 0.5530 | 0.6552 | | 0.0276 | 184.13 | 52660 | 0.1626 | 0.3976 | 0.6043 | 0.5970 | nan | 0.5618 | 0.6468 | 0.0 | 0.5493 | 0.6436 | | 0.0509 | 184.2 | 52680 | 0.1633 | 0.3990 | 0.6064 | 0.5990 | nan | 0.5635 | 0.6494 | 0.0 | 0.5509 | 0.6460 | | 0.0355 | 184.27 | 52700 | 0.1635 | 0.3984 | 0.6055 | 0.5984 | nan | 0.5641 | 0.6470 | 0.0 | 0.5515 | 0.6437 | | 0.0396 | 184.34 | 52720 | 0.1662 | 0.3992 | 0.6068 | 0.5998 | nan | 0.5668 | 0.6468 | 0.0 | 0.5540 | 0.6435 | | 0.0558 | 184.41 | 52740 | 0.1650 | 0.4015 | 0.6104 | 0.6042 | nan | 0.5741 | 0.6468 | 0.0 | 0.5610 | 0.6436 | | 0.0345 | 184.48 | 52760 | 0.1645 | 0.4035 | 0.6136 | 0.6080 | nan | 0.5814 | 0.6457 | 0.0 | 0.5680 | 0.6424 | | 0.034 | 184.55 | 52780 | 0.1638 | 0.3996 | 0.6074 | 0.6005 | nan | 0.5679 | 0.6469 | 0.0 | 0.5552 | 0.6436 | | 0.0374 | 184.62 | 52800 | 0.1656 | 0.3957 | 0.6013 | 0.5943 | nan | 0.5608 | 0.6419 | 0.0 | 0.5482 | 0.6390 | | 0.0321 | 184.69 | 52820 | 0.1639 | 0.3996 | 0.6075 | 0.6015 | nan | 0.5725 | 0.6425 | 0.0 | 0.5590 | 0.6397 | | 0.0377 | 184.76 | 52840 | 0.1633 | 0.3973 | 0.6039 | 0.5974 | nan | 0.5659 | 0.6420 | 0.0 | 0.5526 | 0.6391 | | 0.0537 | 184.83 | 52860 | 0.1607 | 0.4022 | 0.6117 | 0.6055 | nan | 0.5756 | 0.6478 | 0.0 | 0.5621 | 0.6446 | | 0.0501 | 184.9 | 52880 | 0.1628 | 0.3997 | 0.6077 | 0.6006 | nan | 0.5664 | 0.6490 | 0.0 | 0.5536 | 0.6455 | | 0.0491 | 184.97 | 52900 | 0.1603 | 0.4002 | 0.6084 | 0.6011 | nan | 0.5665 | 0.6503 | 0.0 | 0.5539 | 0.6467 | | 0.0402 | 185.03 | 52920 | 0.1614 | 0.4019 | 0.6110 | 0.6037 | nan | 0.5689 | 0.6532 | 0.0 | 0.5562 | 0.6495 | | 0.0423 | 185.1 | 52940 | 0.1610 | 0.4039 | 0.6142 | 0.6074 | nan | 0.5751 | 0.6533 | 0.0 | 0.5620 | 0.6498 | | 0.0357 | 185.17 | 52960 | 0.1638 | 0.3989 | 0.6065 | 0.5992 | nan | 0.5641 | 0.6489 | 0.0 | 0.5513 | 0.6455 | | 0.0377 | 185.24 | 52980 | 0.1634 | 0.4018 | 0.6111 | 0.6044 | nan | 0.5727 | 0.6494 | 0.0 | 0.5594 | 0.6462 | | 0.0543 | 185.31 | 53000 | 0.1621 | 0.3969 | 0.6032 | 0.5956 | nan | 0.5591 | 0.6474 | 0.0 | 0.5465 | 0.6441 | | 0.0631 | 185.38 | 53020 | 0.1619 | 0.4007 | 0.6092 | 0.6021 | nan | 0.5683 | 0.6502 | 0.0 | 0.5554 | 0.6466 | | 0.0247 | 185.45 | 53040 | 0.1625 | 0.4022 | 0.6116 | 0.6048 | nan | 0.5723 | 0.6510 | 0.0 | 0.5591 | 0.6475 | | 0.053 | 185.52 | 53060 | 0.1617 | 0.4010 | 0.6096 | 0.6022 | nan | 0.5668 | 0.6525 | 0.0 | 0.5540 | 0.6489 | | 0.034 | 185.59 | 53080 | 0.1620 | 0.4021 | 0.6114 | 0.6044 | nan | 0.5713 | 0.6514 | 0.0 | 0.5581 | 0.6481 | | 0.0326 | 185.66 | 53100 | 0.1641 | 0.4014 | 0.6102 | 0.6030 | nan | 0.5687 | 0.6517 | 0.0 | 0.5560 | 0.6481 | | 0.0248 | 185.73 | 53120 | 0.1629 | 0.3997 | 0.6076 | 0.5997 | nan | 0.5617 | 0.6536 | 0.0 | 0.5493 | 0.6500 | | 0.055 | 185.8 | 53140 | 0.1629 | 0.4019 | 0.6110 | 0.6038 | nan | 0.5693 | 0.6527 | 0.0 | 0.5564 | 0.6493 | | 0.0469 | 185.87 | 53160 | 0.1622 | 0.4023 | 0.6117 | 0.6049 | nan | 0.5724 | 0.6511 | 0.0 | 0.5589 | 0.6479 | | 0.0753 | 185.94 | 53180 | 0.1643 | 0.3992 | 0.6070 | 0.6000 | nan | 0.5665 | 0.6475 | 0.0 | 0.5531 | 0.6445 | | 0.0495 | 186.01 | 53200 | 0.1643 | 0.4006 | 0.6094 | 0.6032 | nan | 0.5737 | 0.6450 | 0.0 | 0.5599 | 0.6420 | | 0.0403 | 186.08 | 53220 | 0.1637 | 0.3981 | 0.6054 | 0.5989 | nan | 0.5679 | 0.6430 | 0.0 | 0.5545 | 0.6398 | | 0.0418 | 186.15 | 53240 | 0.1644 | 0.3985 | 0.6058 | 0.5988 | nan | 0.5654 | 0.6463 | 0.0 | 0.5525 | 0.6430 | | 0.0464 | 186.22 | 53260 | 0.1642 | 0.4029 | 0.6127 | 0.6061 | nan | 0.5743 | 0.6512 | 0.0 | 0.5611 | 0.6477 | | 0.0275 | 186.29 | 53280 | 0.1645 | 0.3998 | 0.6078 | 0.6002 | nan | 0.5641 | 0.6514 | 0.0 | 0.5512 | 0.6481 | | 0.03 | 186.36 | 53300 | 0.1647 | 0.4046 | 0.6155 | 0.6093 | nan | 0.5801 | 0.6509 | 0.0 | 0.5659 | 0.6478 | | 0.0574 | 186.43 | 53320 | 0.1648 | 0.4008 | 0.6094 | 0.6029 | nan | 0.5720 | 0.6467 | 0.0 | 0.5586 | 0.6437 | | 0.0351 | 186.5 | 53340 | 0.1629 | 0.3988 | 0.6063 | 0.5996 | nan | 0.5674 | 0.6452 | 0.0 | 0.5540 | 0.6422 | | 0.0428 | 186.57 | 53360 | 0.1641 | 0.4005 | 0.6090 | 0.6025 | nan | 0.5716 | 0.6464 | 0.0 | 0.5580 | 0.6434 | | 0.029 | 186.64 | 53380 | 0.1642 | 0.4007 | 0.6094 | 0.6029 | nan | 0.5721 | 0.6466 | 0.0 | 0.5585 | 0.6436 | | 0.039 | 186.71 | 53400 | 0.1646 | 0.3992 | 0.6070 | 0.6004 | nan | 0.5689 | 0.6451 | 0.0 | 0.5555 | 0.6421 | | 0.034 | 186.78 | 53420 | 0.1641 | 0.4019 | 0.6113 | 0.6059 | nan | 0.5800 | 0.6426 | 0.0 | 0.5658 | 0.6398 | | 0.0455 | 186.85 | 53440 | 0.1663 | 0.3957 | 0.6016 | 0.5952 | nan | 0.5647 | 0.6386 | 0.0 | 0.5513 | 0.6358 | | 0.0256 | 186.92 | 53460 | 0.1655 | 0.3992 | 0.6071 | 0.6012 | nan | 0.5731 | 0.6410 | 0.0 | 0.5591 | 0.6383 | | 0.0313 | 186.99 | 53480 | 0.1646 | 0.3986 | 0.6061 | 0.5994 | nan | 0.5674 | 0.6448 | 0.0 | 0.5539 | 0.6420 | | 0.0566 | 187.06 | 53500 | 0.1648 | 0.3980 | 0.6050 | 0.5979 | nan | 0.5640 | 0.6459 | 0.0 | 0.5510 | 0.6430 | | 0.0317 | 187.13 | 53520 | 0.1600 | 0.4048 | 0.6158 | 0.6103 | nan | 0.5838 | 0.6479 | 0.0 | 0.5696 | 0.6449 | | 0.036 | 187.2 | 53540 | 0.1643 | 0.3951 | 0.6004 | 0.5924 | nan | 0.5541 | 0.6466 | 0.0 | 0.5418 | 0.6435 | | 0.0255 | 187.27 | 53560 | 0.1635 | 0.4027 | 0.6122 | 0.6057 | nan | 0.5746 | 0.6499 | 0.0 | 0.5614 | 0.6467 | | 0.0279 | 187.34 | 53580 | 0.1643 | 0.4014 | 0.6102 | 0.6033 | nan | 0.5702 | 0.6502 | 0.0 | 0.5573 | 0.6470 | | 0.0252 | 187.41 | 53600 | 0.1633 | 0.3990 | 0.6064 | 0.5994 | nan | 0.5661 | 0.6466 | 0.0 | 0.5533 | 0.6435 | | 0.0377 | 187.48 | 53620 | 0.1651 | 0.3989 | 0.6061 | 0.5987 | nan | 0.5633 | 0.6488 | 0.0 | 0.5514 | 0.6453 | | 0.0577 | 187.55 | 53640 | 0.1647 | 0.3992 | 0.6067 | 0.5994 | nan | 0.5645 | 0.6489 | 0.0 | 0.5523 | 0.6454 | | 0.0354 | 187.62 | 53660 | 0.1627 | 0.4030 | 0.6126 | 0.6058 | nan | 0.5729 | 0.6523 | 0.0 | 0.5600 | 0.6489 | | 0.0411 | 187.69 | 53680 | 0.1619 | 0.4008 | 0.6092 | 0.6019 | nan | 0.5670 | 0.6514 | 0.0 | 0.5544 | 0.6481 | | 0.0615 | 187.76 | 53700 | 0.1600 | 0.4059 | 0.6169 | 0.6090 | nan | 0.5713 | 0.6625 | 0.0 | 0.5592 | 0.6586 | | 0.0414 | 187.83 | 53720 | 0.1591 | 0.4049 | 0.6154 | 0.6078 | nan | 0.5717 | 0.6592 | 0.0 | 0.5593 | 0.6555 | | 0.0269 | 187.9 | 53740 | 0.1622 | 0.4000 | 0.6077 | 0.6001 | nan | 0.5639 | 0.6516 | 0.0 | 0.5517 | 0.6482 | | 0.0655 | 187.97 | 53760 | 0.1620 | 0.4028 | 0.6121 | 0.6048 | nan | 0.5697 | 0.6546 | 0.0 | 0.5574 | 0.6511 | | 0.0333 | 188.04 | 53780 | 0.1623 | 0.4043 | 0.6146 | 0.6080 | nan | 0.5765 | 0.6526 | 0.0 | 0.5636 | 0.6492 | | 0.04 | 188.11 | 53800 | 0.1635 | 0.4019 | 0.6109 | 0.6040 | nan | 0.5710 | 0.6508 | 0.0 | 0.5582 | 0.6474 | | 0.0398 | 188.18 | 53820 | 0.1629 | 0.4003 | 0.6085 | 0.6010 | nan | 0.5652 | 0.6518 | 0.0 | 0.5526 | 0.6484 | | 0.0267 | 188.25 | 53840 | 0.1633 | 0.4014 | 0.6102 | 0.6031 | nan | 0.5692 | 0.6512 | 0.0 | 0.5564 | 0.6477 | | 0.0505 | 188.32 | 53860 | 0.1599 | 0.4028 | 0.6122 | 0.6049 | nan | 0.5702 | 0.6543 | 0.0 | 0.5577 | 0.6506 | | 0.0305 | 188.39 | 53880 | 0.1620 | 0.4022 | 0.6113 | 0.6038 | nan | 0.5680 | 0.6547 | 0.0 | 0.5557 | 0.6510 | | 0.0601 | 188.46 | 53900 | 0.1617 | 0.4035 | 0.6134 | 0.6062 | nan | 0.5721 | 0.6547 | 0.0 | 0.5593 | 0.6512 | | 0.0331 | 188.53 | 53920 | 0.1609 | 0.4013 | 0.6099 | 0.6023 | nan | 0.5657 | 0.6542 | 0.0 | 0.5530 | 0.6508 | | 0.0361 | 188.6 | 53940 | 0.1618 | 0.4018 | 0.6108 | 0.6034 | nan | 0.5684 | 0.6532 | 0.0 | 0.5556 | 0.6498 | | 0.0467 | 188.67 | 53960 | 0.1609 | 0.4040 | 0.6143 | 0.6075 | nan | 0.5750 | 0.6535 | 0.0 | 0.5618 | 0.6503 | | 0.0321 | 188.74 | 53980 | 0.1608 | 0.4032 | 0.6129 | 0.6054 | nan | 0.5691 | 0.6567 | 0.0 | 0.5566 | 0.6532 | | 0.0436 | 188.81 | 54000 | 0.1633 | 0.4012 | 0.6097 | 0.6016 | nan | 0.5632 | 0.6562 | 0.0 | 0.5511 | 0.6526 | | 0.0534 | 188.88 | 54020 | 0.1608 | 0.4054 | 0.6164 | 0.6098 | nan | 0.5782 | 0.6546 | 0.0 | 0.5652 | 0.6511 | | 0.0379 | 188.95 | 54040 | 0.1617 | 0.4008 | 0.6091 | 0.6013 | nan | 0.5644 | 0.6537 | 0.0 | 0.5520 | 0.6502 | | 0.0332 | 189.02 | 54060 | 0.1626 | 0.4001 | 0.6083 | 0.6012 | nan | 0.5676 | 0.6490 | 0.0 | 0.5545 | 0.6458 | | 0.0466 | 189.09 | 54080 | 0.1632 | 0.4020 | 0.6111 | 0.6040 | nan | 0.5703 | 0.6519 | 0.0 | 0.5575 | 0.6485 | | 0.0335 | 189.16 | 54100 | 0.1620 | 0.4008 | 0.6094 | 0.6026 | nan | 0.5702 | 0.6486 | 0.0 | 0.5570 | 0.6455 | | 0.062 | 189.23 | 54120 | 0.1631 | 0.3989 | 0.6064 | 0.5995 | nan | 0.5666 | 0.6463 | 0.0 | 0.5535 | 0.6432 | | 0.0408 | 189.3 | 54140 | 0.1637 | 0.4011 | 0.6096 | 0.6023 | nan | 0.5672 | 0.6521 | 0.0 | 0.5545 | 0.6487 | | 0.0387 | 189.37 | 54160 | 0.1643 | 0.4039 | 0.6142 | 0.6076 | nan | 0.5765 | 0.6518 | 0.0 | 0.5633 | 0.6485 | | 0.0454 | 189.44 | 54180 | 0.1621 | 0.4015 | 0.6104 | 0.6032 | nan | 0.5688 | 0.6519 | 0.0 | 0.5559 | 0.6487 | | 0.0308 | 189.51 | 54200 | 0.1627 | 0.4031 | 0.6126 | 0.6053 | nan | 0.5705 | 0.6547 | 0.0 | 0.5580 | 0.6512 | | 0.0342 | 189.58 | 54220 | 0.1632 | 0.3992 | 0.6067 | 0.5996 | nan | 0.5657 | 0.6477 | 0.0 | 0.5531 | 0.6445 | | 0.0484 | 189.65 | 54240 | 0.1624 | 0.4011 | 0.6097 | 0.6029 | nan | 0.5702 | 0.6492 | 0.0 | 0.5573 | 0.6459 | | 0.0357 | 189.72 | 54260 | 0.1628 | 0.4013 | 0.6100 | 0.6024 | nan | 0.5661 | 0.6538 | 0.0 | 0.5539 | 0.6501 | | 0.0617 | 189.79 | 54280 | 0.1627 | 0.4001 | 0.6082 | 0.6012 | nan | 0.5677 | 0.6487 | 0.0 | 0.5551 | 0.6453 | | 0.0466 | 189.86 | 54300 | 0.1621 | 0.4034 | 0.6133 | 0.6064 | nan | 0.5734 | 0.6532 | 0.0 | 0.5605 | 0.6497 | | 0.0396 | 189.93 | 54320 | 0.1637 | 0.4006 | 0.6090 | 0.6016 | nan | 0.5662 | 0.6518 | 0.0 | 0.5537 | 0.6483 | | 0.0389 | 190.0 | 54340 | 0.1613 | 0.4019 | 0.6110 | 0.6040 | nan | 0.5707 | 0.6514 | 0.0 | 0.5578 | 0.6479 | | 0.0438 | 190.07 | 54360 | 0.1631 | 0.4010 | 0.6096 | 0.6022 | nan | 0.5668 | 0.6523 | 0.0 | 0.5544 | 0.6487 | | 0.0502 | 190.14 | 54380 | 0.1610 | 0.4009 | 0.6095 | 0.6020 | nan | 0.5666 | 0.6523 | 0.0 | 0.5540 | 0.6488 | | 0.0261 | 190.21 | 54400 | 0.1630 | 0.4038 | 0.6139 | 0.6068 | nan | 0.5732 | 0.6545 | 0.0 | 0.5605 | 0.6509 | | 0.0491 | 190.28 | 54420 | 0.1634 | 0.4033 | 0.6130 | 0.6056 | nan | 0.5705 | 0.6554 | 0.0 | 0.5582 | 0.6517 | | 0.0377 | 190.35 | 54440 | 0.1628 | 0.4011 | 0.6096 | 0.6025 | nan | 0.5685 | 0.6508 | 0.0 | 0.5560 | 0.6472 | | 0.0546 | 190.42 | 54460 | 0.1622 | 0.4012 | 0.6099 | 0.6029 | nan | 0.5692 | 0.6507 | 0.0 | 0.5566 | 0.6472 | | 0.043 | 190.49 | 54480 | 0.1640 | 0.4006 | 0.6089 | 0.6017 | nan | 0.5676 | 0.6502 | 0.0 | 0.5550 | 0.6467 | | 0.0285 | 190.56 | 54500 | 0.1652 | 0.4013 | 0.6100 | 0.6031 | nan | 0.5702 | 0.6498 | 0.0 | 0.5575 | 0.6464 | | 0.0486 | 190.63 | 54520 | 0.1637 | 0.4015 | 0.6104 | 0.6039 | nan | 0.5727 | 0.6481 | 0.0 | 0.5597 | 0.6449 | | 0.0636 | 190.7 | 54540 | 0.1659 | 0.3985 | 0.6054 | 0.5972 | nan | 0.5581 | 0.6527 | 0.0 | 0.5466 | 0.6490 | | 0.0449 | 190.77 | 54560 | 0.1633 | 0.3998 | 0.6076 | 0.6006 | nan | 0.5669 | 0.6483 | 0.0 | 0.5544 | 0.6449 | | 0.0223 | 190.84 | 54580 | 0.1622 | 0.4024 | 0.6117 | 0.6049 | nan | 0.5724 | 0.6510 | 0.0 | 0.5599 | 0.6472 | | 0.0343 | 190.91 | 54600 | 0.1633 | 0.3998 | 0.6076 | 0.5996 | nan | 0.5616 | 0.6536 | 0.0 | 0.5498 | 0.6497 | | 0.0239 | 190.98 | 54620 | 0.1641 | 0.4033 | 0.6130 | 0.6054 | nan | 0.5694 | 0.6565 | 0.0 | 0.5573 | 0.6526 | | 0.0474 | 191.05 | 54640 | 0.1620 | 0.4015 | 0.6102 | 0.6023 | nan | 0.5647 | 0.6557 | 0.0 | 0.5525 | 0.6520 | | 0.0395 | 191.12 | 54660 | 0.1607 | 0.4061 | 0.6173 | 0.6100 | nan | 0.5749 | 0.6597 | 0.0 | 0.5622 | 0.6561 | | 0.0453 | 191.19 | 54680 | 0.1628 | 0.4033 | 0.6129 | 0.6052 | nan | 0.5682 | 0.6576 | 0.0 | 0.5559 | 0.6540 | | 0.0376 | 191.26 | 54700 | 0.1613 | 0.4033 | 0.6129 | 0.6050 | nan | 0.5673 | 0.6585 | 0.0 | 0.5549 | 0.6549 | | 0.0374 | 191.33 | 54720 | 0.1624 | 0.4036 | 0.6134 | 0.6058 | nan | 0.5691 | 0.6578 | 0.0 | 0.5564 | 0.6543 | | 0.0325 | 191.4 | 54740 | 0.1610 | 0.4028 | 0.6122 | 0.6045 | nan | 0.5677 | 0.6566 | 0.0 | 0.5554 | 0.6530 | | 0.0426 | 191.47 | 54760 | 0.1616 | 0.4028 | 0.6122 | 0.6046 | nan | 0.5684 | 0.6560 | 0.0 | 0.5558 | 0.6525 | | 0.0346 | 191.54 | 54780 | 0.1622 | 0.4014 | 0.6101 | 0.6019 | nan | 0.5629 | 0.6574 | 0.0 | 0.5506 | 0.6537 | | 0.0452 | 191.61 | 54800 | 0.1622 | 0.4036 | 0.6134 | 0.6056 | nan | 0.5679 | 0.6590 | 0.0 | 0.5554 | 0.6552 | | 0.0408 | 191.68 | 54820 | 0.1605 | 0.4035 | 0.6134 | 0.6056 | nan | 0.5683 | 0.6586 | 0.0 | 0.5557 | 0.6549 | | 0.0501 | 191.75 | 54840 | 0.1615 | 0.4037 | 0.6136 | 0.6058 | nan | 0.5686 | 0.6586 | 0.0 | 0.5563 | 0.6547 | | 0.0446 | 191.82 | 54860 | 0.1614 | 0.4023 | 0.6114 | 0.6035 | nan | 0.5656 | 0.6572 | 0.0 | 0.5536 | 0.6532 | | 0.0511 | 191.89 | 54880 | 0.1619 | 0.4025 | 0.6118 | 0.6043 | nan | 0.5683 | 0.6553 | 0.0 | 0.5560 | 0.6514 | | 0.0384 | 191.96 | 54900 | 0.1632 | 0.4032 | 0.6129 | 0.6056 | nan | 0.5711 | 0.6547 | 0.0 | 0.5586 | 0.6509 | | 0.0437 | 192.03 | 54920 | 0.1647 | 0.4011 | 0.6097 | 0.6025 | nan | 0.5682 | 0.6513 | 0.0 | 0.5555 | 0.6478 | | 0.0264 | 192.1 | 54940 | 0.1626 | 0.4026 | 0.6120 | 0.6045 | nan | 0.5689 | 0.6551 | 0.0 | 0.5563 | 0.6514 | | 0.0477 | 192.17 | 54960 | 0.1619 | 0.4043 | 0.6148 | 0.6076 | nan | 0.5730 | 0.6566 | 0.0 | 0.5601 | 0.6530 | | 0.0177 | 192.24 | 54980 | 0.1609 | 0.4028 | 0.6124 | 0.6051 | nan | 0.5703 | 0.6545 | 0.0 | 0.5574 | 0.6509 | | 0.0508 | 192.31 | 55000 | 0.1607 | 0.4033 | 0.6131 | 0.6053 | nan | 0.5679 | 0.6582 | 0.0 | 0.5558 | 0.6542 | | 0.0624 | 192.38 | 55020 | 0.1613 | 0.4026 | 0.6120 | 0.6045 | nan | 0.5687 | 0.6553 | 0.0 | 0.5563 | 0.6516 | | 0.0209 | 192.45 | 55040 | 0.1605 | 0.4020 | 0.6111 | 0.6038 | nan | 0.5693 | 0.6529 | 0.0 | 0.5565 | 0.6494 | | 0.0388 | 192.52 | 55060 | 0.1616 | 0.4054 | 0.6164 | 0.6091 | nan | 0.5740 | 0.6588 | 0.0 | 0.5613 | 0.6550 | | 0.0396 | 192.59 | 55080 | 0.1643 | 0.4044 | 0.6148 | 0.6070 | nan | 0.5698 | 0.6597 | 0.0 | 0.5572 | 0.6559 | | 0.0345 | 192.66 | 55100 | 0.1620 | 0.4046 | 0.6152 | 0.6079 | nan | 0.5726 | 0.6578 | 0.0 | 0.5598 | 0.6541 | | 0.0537 | 192.73 | 55120 | 0.1638 | 0.4007 | 0.6092 | 0.6013 | nan | 0.5638 | 0.6546 | 0.0 | 0.5512 | 0.6510 | | 0.0378 | 192.8 | 55140 | 0.1608 | 0.4025 | 0.6120 | 0.6049 | nan | 0.5713 | 0.6526 | 0.0 | 0.5583 | 0.6491 | | 0.0332 | 192.87 | 55160 | 0.1628 | 0.4022 | 0.6114 | 0.6037 | nan | 0.5671 | 0.6557 | 0.0 | 0.5546 | 0.6520 | | 0.0426 | 192.94 | 55180 | 0.1614 | 0.4024 | 0.6117 | 0.6042 | nan | 0.5685 | 0.6548 | 0.0 | 0.5560 | 0.6511 | | 0.0393 | 193.01 | 55200 | 0.1630 | 0.4025 | 0.6119 | 0.6048 | nan | 0.5706 | 0.6532 | 0.0 | 0.5580 | 0.6495 | | 0.0478 | 193.08 | 55220 | 0.1618 | 0.4019 | 0.6109 | 0.6037 | nan | 0.5694 | 0.6525 | 0.0 | 0.5568 | 0.6488 | | 0.0322 | 193.15 | 55240 | 0.1623 | 0.4009 | 0.6094 | 0.6021 | nan | 0.5672 | 0.6517 | 0.0 | 0.5546 | 0.6481 | | 0.0538 | 193.22 | 55260 | 0.1632 | 0.4029 | 0.6125 | 0.6052 | nan | 0.5699 | 0.6551 | 0.0 | 0.5571 | 0.6516 | | 0.0305 | 193.29 | 55280 | 0.1615 | 0.4022 | 0.6116 | 0.6044 | nan | 0.5701 | 0.6531 | 0.0 | 0.5571 | 0.6495 | | 0.069 | 193.36 | 55300 | 0.1622 | 0.4024 | 0.6118 | 0.6042 | nan | 0.5676 | 0.6560 | 0.0 | 0.5550 | 0.6522 | | 0.0338 | 193.43 | 55320 | 0.1630 | 0.4018 | 0.6108 | 0.6032 | nan | 0.5668 | 0.6548 | 0.0 | 0.5541 | 0.6512 | | 0.0292 | 193.5 | 55340 | 0.1613 | 0.4026 | 0.6120 | 0.6046 | nan | 0.5688 | 0.6552 | 0.0 | 0.5561 | 0.6516 | | 0.0358 | 193.57 | 55360 | 0.1638 | 0.4006 | 0.6090 | 0.6012 | nan | 0.5640 | 0.6540 | 0.0 | 0.5514 | 0.6505 | | 0.0696 | 193.64 | 55380 | 0.1635 | 0.4012 | 0.6097 | 0.6017 | nan | 0.5633 | 0.6562 | 0.0 | 0.5510 | 0.6525 | | 0.0393 | 193.71 | 55400 | 0.1620 | 0.4040 | 0.6142 | 0.6070 | nan | 0.5728 | 0.6556 | 0.0 | 0.5597 | 0.6522 | | 0.0407 | 193.78 | 55420 | 0.1634 | 0.4037 | 0.6138 | 0.6064 | nan | 0.5715 | 0.6561 | 0.0 | 0.5584 | 0.6526 | | 0.0474 | 193.85 | 55440 | 0.1643 | 0.4013 | 0.6100 | 0.6019 | nan | 0.5635 | 0.6564 | 0.0 | 0.5511 | 0.6528 | | 0.0386 | 193.92 | 55460 | 0.1628 | 0.4017 | 0.6107 | 0.6030 | nan | 0.5665 | 0.6549 | 0.0 | 0.5538 | 0.6514 | | 0.0261 | 193.99 | 55480 | 0.1621 | 0.4015 | 0.6106 | 0.6035 | nan | 0.5700 | 0.6512 | 0.0 | 0.5568 | 0.6479 | | 0.0309 | 194.06 | 55500 | 0.1622 | 0.4001 | 0.6082 | 0.6003 | nan | 0.5629 | 0.6535 | 0.0 | 0.5503 | 0.6500 | | 0.0536 | 194.13 | 55520 | 0.1593 | 0.4031 | 0.6128 | 0.6054 | nan | 0.5699 | 0.6557 | 0.0 | 0.5571 | 0.6521 | | 0.0486 | 194.2 | 55540 | 0.1630 | 0.4041 | 0.6143 | 0.6069 | nan | 0.5716 | 0.6571 | 0.0 | 0.5590 | 0.6534 | | 0.0573 | 194.27 | 55560 | 0.1627 | 0.4030 | 0.6126 | 0.6052 | nan | 0.5700 | 0.6552 | 0.0 | 0.5573 | 0.6517 | | 0.0245 | 194.34 | 55580 | 0.1621 | 0.4014 | 0.6102 | 0.6024 | nan | 0.5649 | 0.6555 | 0.0 | 0.5523 | 0.6520 | | 0.0472 | 194.41 | 55600 | 0.1625 | 0.4037 | 0.6138 | 0.6066 | nan | 0.5726 | 0.6549 | 0.0 | 0.5597 | 0.6514 | | 0.0593 | 194.48 | 55620 | 0.1628 | 0.4025 | 0.6118 | 0.6042 | nan | 0.5676 | 0.6561 | 0.0 | 0.5549 | 0.6526 | | 0.022 | 194.55 | 55640 | 0.1650 | 0.4018 | 0.6108 | 0.6031 | nan | 0.5665 | 0.6551 | 0.0 | 0.5539 | 0.6516 | | 0.0478 | 194.62 | 55660 | 0.1624 | 0.4027 | 0.6122 | 0.6045 | nan | 0.5678 | 0.6565 | 0.0 | 0.5552 | 0.6530 | | 0.0375 | 194.69 | 55680 | 0.1619 | 0.4032 | 0.6129 | 0.6052 | nan | 0.5682 | 0.6577 | 0.0 | 0.5556 | 0.6540 | | 0.0375 | 194.76 | 55700 | 0.1620 | 0.4033 | 0.6130 | 0.6052 | nan | 0.5681 | 0.6579 | 0.0 | 0.5556 | 0.6542 | | 0.0487 | 194.83 | 55720 | 0.1611 | 0.4035 | 0.6134 | 0.6054 | nan | 0.5670 | 0.6598 | 0.0 | 0.5546 | 0.6560 | | 0.0363 | 194.9 | 55740 | 0.1623 | 0.4041 | 0.6143 | 0.6068 | nan | 0.5708 | 0.6579 | 0.0 | 0.5580 | 0.6542 | | 0.0543 | 194.97 | 55760 | 0.1631 | 0.4035 | 0.6134 | 0.6058 | nan | 0.5691 | 0.6578 | 0.0 | 0.5564 | 0.6542 | | 0.0427 | 195.03 | 55780 | 0.1640 | 0.4029 | 0.6126 | 0.6051 | nan | 0.5694 | 0.6559 | 0.0 | 0.5565 | 0.6524 | | 0.0257 | 195.1 | 55800 | 0.1627 | 0.4041 | 0.6143 | 0.6071 | nan | 0.5727 | 0.6559 | 0.0 | 0.5597 | 0.6524 | | 0.0268 | 195.17 | 55820 | 0.1635 | 0.4025 | 0.6118 | 0.6046 | nan | 0.5702 | 0.6535 | 0.0 | 0.5572 | 0.6502 | | 0.0403 | 195.24 | 55840 | 0.1643 | 0.3999 | 0.6079 | 0.6005 | nan | 0.5651 | 0.6507 | 0.0 | 0.5522 | 0.6475 | | 0.0375 | 195.31 | 55860 | 0.1632 | 0.4028 | 0.6125 | 0.6053 | nan | 0.5709 | 0.6540 | 0.0 | 0.5579 | 0.6506 | | 0.0376 | 195.38 | 55880 | 0.1647 | 0.4009 | 0.6094 | 0.6019 | nan | 0.5660 | 0.6528 | 0.0 | 0.5534 | 0.6493 | | 0.0324 | 195.45 | 55900 | 0.1671 | 0.4016 | 0.6105 | 0.6034 | nan | 0.5693 | 0.6516 | 0.0 | 0.5564 | 0.6483 | | 0.048 | 195.52 | 55920 | 0.1677 | 0.4013 | 0.6101 | 0.6029 | nan | 0.5685 | 0.6517 | 0.0 | 0.5557 | 0.6483 | | 0.0272 | 195.59 | 55940 | 0.1653 | 0.4014 | 0.6104 | 0.6036 | nan | 0.5710 | 0.6498 | 0.0 | 0.5578 | 0.6466 | | 0.0483 | 195.66 | 55960 | 0.1639 | 0.4011 | 0.6099 | 0.6026 | nan | 0.5680 | 0.6518 | 0.0 | 0.5550 | 0.6485 | | 0.0322 | 195.73 | 55980 | 0.1649 | 0.4014 | 0.6102 | 0.6031 | nan | 0.5694 | 0.6510 | 0.0 | 0.5564 | 0.6477 | | 0.0511 | 195.8 | 56000 | 0.1635 | 0.4014 | 0.6103 | 0.6034 | nan | 0.5706 | 0.6500 | 0.0 | 0.5575 | 0.6467 | | 0.043 | 195.87 | 56020 | 0.1625 | 0.4034 | 0.6133 | 0.6064 | nan | 0.5732 | 0.6534 | 0.0 | 0.5600 | 0.6500 | | 0.036 | 195.94 | 56040 | 0.1633 | 0.4020 | 0.6112 | 0.6040 | nan | 0.5697 | 0.6527 | 0.0 | 0.5567 | 0.6493 | | 0.0541 | 196.01 | 56060 | 0.1647 | 0.4026 | 0.6122 | 0.6052 | nan | 0.5719 | 0.6524 | 0.0 | 0.5587 | 0.6490 | | 0.0357 | 196.08 | 56080 | 0.1640 | 0.4016 | 0.6105 | 0.6029 | nan | 0.5665 | 0.6545 | 0.0 | 0.5540 | 0.6509 | | 0.0456 | 196.15 | 56100 | 0.1658 | 0.4031 | 0.6128 | 0.6057 | nan | 0.5720 | 0.6535 | 0.0 | 0.5592 | 0.6500 | | 0.0299 | 196.22 | 56120 | 0.1637 | 0.4024 | 0.6117 | 0.6047 | nan | 0.5710 | 0.6524 | 0.0 | 0.5582 | 0.6490 | | 0.053 | 196.29 | 56140 | 0.1650 | 0.4013 | 0.6100 | 0.6028 | nan | 0.5681 | 0.6519 | 0.0 | 0.5553 | 0.6485 | | 0.0569 | 196.36 | 56160 | 0.1632 | 0.4006 | 0.6090 | 0.6018 | nan | 0.5678 | 0.6501 | 0.0 | 0.5549 | 0.6468 | | 0.0555 | 196.43 | 56180 | 0.1615 | 0.3999 | 0.6079 | 0.6010 | nan | 0.5676 | 0.6483 | 0.0 | 0.5544 | 0.6452 | | 0.0226 | 196.5 | 56200 | 0.1645 | 0.3993 | 0.6071 | 0.5997 | nan | 0.5645 | 0.6497 | 0.0 | 0.5516 | 0.6464 | | 0.0363 | 196.57 | 56220 | 0.1630 | 0.3998 | 0.6078 | 0.6003 | nan | 0.5644 | 0.6512 | 0.0 | 0.5516 | 0.6478 | | 0.0569 | 196.64 | 56240 | 0.1631 | 0.4024 | 0.6116 | 0.6041 | nan | 0.5683 | 0.6550 | 0.0 | 0.5556 | 0.6515 | | 0.0272 | 196.71 | 56260 | 0.1638 | 0.4017 | 0.6107 | 0.6037 | nan | 0.5700 | 0.6514 | 0.0 | 0.5570 | 0.6481 | | 0.0462 | 196.78 | 56280 | 0.1636 | 0.4034 | 0.6134 | 0.6068 | nan | 0.5751 | 0.6517 | 0.0 | 0.5619 | 0.6484 | | 0.052 | 196.85 | 56300 | 0.1639 | 0.3998 | 0.6078 | 0.6005 | nan | 0.5660 | 0.6496 | 0.0 | 0.5533 | 0.6463 | | 0.0412 | 196.92 | 56320 | 0.1639 | 0.4006 | 0.6089 | 0.6016 | nan | 0.5665 | 0.6513 | 0.0 | 0.5539 | 0.6480 | | 0.0802 | 196.99 | 56340 | 0.1637 | 0.3998 | 0.6077 | 0.6002 | nan | 0.5644 | 0.6510 | 0.0 | 0.5518 | 0.6477 | | 0.0398 | 197.06 | 56360 | 0.1636 | 0.4004 | 0.6086 | 0.6013 | nan | 0.5666 | 0.6505 | 0.0 | 0.5538 | 0.6473 | | 0.0521 | 197.13 | 56380 | 0.1643 | 0.4022 | 0.6114 | 0.6043 | nan | 0.5706 | 0.6522 | 0.0 | 0.5576 | 0.6489 | | 0.0282 | 197.2 | 56400 | 0.1629 | 0.4013 | 0.6100 | 0.6027 | nan | 0.5679 | 0.6521 | 0.0 | 0.5551 | 0.6488 | | 0.0327 | 197.27 | 56420 | 0.1629 | 0.4030 | 0.6127 | 0.6060 | nan | 0.5740 | 0.6513 | 0.0 | 0.5608 | 0.6481 | | 0.0216 | 197.34 | 56440 | 0.1637 | 0.4017 | 0.6106 | 0.6035 | nan | 0.5696 | 0.6516 | 0.0 | 0.5567 | 0.6483 | | 0.054 | 197.41 | 56460 | 0.1649 | 0.4021 | 0.6113 | 0.6043 | nan | 0.5709 | 0.6517 | 0.0 | 0.5580 | 0.6483 | | 0.0445 | 197.48 | 56480 | 0.1653 | 0.4004 | 0.6087 | 0.6015 | nan | 0.5668 | 0.6507 | 0.0 | 0.5540 | 0.6473 | | 0.0449 | 197.55 | 56500 | 0.1637 | 0.4006 | 0.6089 | 0.6016 | nan | 0.5668 | 0.6509 | 0.0 | 0.5542 | 0.6475 | | 0.0416 | 197.62 | 56520 | 0.1650 | 0.4000 | 0.6080 | 0.6007 | nan | 0.5659 | 0.6501 | 0.0 | 0.5533 | 0.6468 | | 0.0365 | 197.69 | 56540 | 0.1637 | 0.4018 | 0.6108 | 0.6036 | nan | 0.5695 | 0.6521 | 0.0 | 0.5566 | 0.6488 | | 0.0412 | 197.76 | 56560 | 0.1632 | 0.4009 | 0.6093 | 0.6019 | nan | 0.5660 | 0.6527 | 0.0 | 0.5534 | 0.6493 | | 0.0386 | 197.83 | 56580 | 0.1651 | 0.4027 | 0.6121 | 0.6050 | nan | 0.5708 | 0.6535 | 0.0 | 0.5580 | 0.6500 | | 0.0592 | 197.9 | 56600 | 0.1637 | 0.3998 | 0.6077 | 0.6001 | nan | 0.5640 | 0.6513 | 0.0 | 0.5515 | 0.6480 | | 0.0456 | 197.97 | 56620 | 0.1635 | 0.4017 | 0.6106 | 0.6035 | nan | 0.5695 | 0.6517 | 0.0 | 0.5566 | 0.6484 | | 0.0449 | 198.04 | 56640 | 0.1630 | 0.4011 | 0.6097 | 0.6021 | nan | 0.5658 | 0.6535 | 0.0 | 0.5532 | 0.6501 | | 0.0412 | 198.11 | 56660 | 0.1642 | 0.4015 | 0.6103 | 0.6027 | nan | 0.5661 | 0.6545 | 0.0 | 0.5537 | 0.6509 | | 0.051 | 198.18 | 56680 | 0.1641 | 0.4023 | 0.6117 | 0.6045 | nan | 0.5701 | 0.6532 | 0.0 | 0.5573 | 0.6497 | | 0.0392 | 198.25 | 56700 | 0.1631 | 0.4027 | 0.6122 | 0.6053 | nan | 0.5723 | 0.6521 | 0.0 | 0.5593 | 0.6487 | | 0.0264 | 198.32 | 56720 | 0.1644 | 0.4004 | 0.6086 | 0.6009 | nan | 0.5641 | 0.6531 | 0.0 | 0.5515 | 0.6496 | | 0.022 | 198.39 | 56740 | 0.1629 | 0.4007 | 0.6093 | 0.6027 | nan | 0.5709 | 0.6478 | 0.0 | 0.5578 | 0.6445 | | 0.0434 | 198.46 | 56760 | 0.1634 | 0.4008 | 0.6093 | 0.6019 | nan | 0.5665 | 0.6521 | 0.0 | 0.5539 | 0.6486 | | 0.0362 | 198.53 | 56780 | 0.1633 | 0.4022 | 0.6114 | 0.6041 | nan | 0.5692 | 0.6536 | 0.0 | 0.5565 | 0.6500 | | 0.0429 | 198.6 | 56800 | 0.1617 | 0.4042 | 0.6146 | 0.6075 | nan | 0.5738 | 0.6554 | 0.0 | 0.5609 | 0.6517 | | 0.0509 | 198.67 | 56820 | 0.1629 | 0.4029 | 0.6126 | 0.6056 | nan | 0.5720 | 0.6532 | 0.0 | 0.5590 | 0.6497 | | 0.0231 | 198.74 | 56840 | 0.1634 | 0.4023 | 0.6116 | 0.6046 | nan | 0.5713 | 0.6519 | 0.0 | 0.5584 | 0.6485 | | 0.0285 | 198.81 | 56860 | 0.1640 | 0.4014 | 0.6103 | 0.6032 | nan | 0.5695 | 0.6510 | 0.0 | 0.5567 | 0.6476 | | 0.0389 | 198.88 | 56880 | 0.1629 | 0.4011 | 0.6097 | 0.6025 | nan | 0.5678 | 0.6516 | 0.0 | 0.5552 | 0.6481 | | 0.0367 | 198.95 | 56900 | 0.1652 | 0.4032 | 0.6130 | 0.6060 | nan | 0.5723 | 0.6538 | 0.0 | 0.5595 | 0.6502 | | 0.0329 | 199.02 | 56920 | 0.1658 | 0.4019 | 0.6109 | 0.6039 | nan | 0.5701 | 0.6518 | 0.0 | 0.5573 | 0.6483 | | 0.0354 | 199.09 | 56940 | 0.1651 | 0.4016 | 0.6105 | 0.6031 | nan | 0.5681 | 0.6529 | 0.0 | 0.5554 | 0.6494 | | 0.0381 | 199.16 | 56960 | 0.1618 | 0.4000 | 0.6080 | 0.6007 | nan | 0.5657 | 0.6503 | 0.0 | 0.5530 | 0.6469 | | 0.0522 | 199.23 | 56980 | 0.1636 | 0.4035 | 0.6134 | 0.6064 | nan | 0.5729 | 0.6539 | 0.0 | 0.5601 | 0.6504 | | 0.0456 | 199.3 | 57000 | 0.1622 | 0.4012 | 0.6099 | 0.6028 | nan | 0.5688 | 0.6510 | 0.0 | 0.5558 | 0.6476 | | 0.0521 | 199.37 | 57020 | 0.1648 | 0.4018 | 0.6108 | 0.6034 | nan | 0.5681 | 0.6534 | 0.0 | 0.5556 | 0.6498 | | 0.0326 | 199.44 | 57040 | 0.1633 | 0.4013 | 0.6102 | 0.6033 | nan | 0.5706 | 0.6498 | 0.0 | 0.5575 | 0.6465 | | 0.0381 | 199.51 | 57060 | 0.1642 | 0.4023 | 0.6117 | 0.6047 | nan | 0.5712 | 0.6521 | 0.0 | 0.5584 | 0.6486 | | 0.0582 | 199.58 | 57080 | 0.1632 | 0.4006 | 0.6090 | 0.6014 | nan | 0.5651 | 0.6529 | 0.0 | 0.5525 | 0.6493 | | 0.0555 | 199.65 | 57100 | 0.1642 | 0.4023 | 0.6116 | 0.6042 | nan | 0.5690 | 0.6541 | 0.0 | 0.5565 | 0.6505 | | 0.0309 | 199.72 | 57120 | 0.1646 | 0.4016 | 0.6105 | 0.6033 | nan | 0.5686 | 0.6524 | 0.0 | 0.5561 | 0.6488 | | 0.0437 | 199.79 | 57140 | 0.1641 | 0.4017 | 0.6106 | 0.6033 | nan | 0.5686 | 0.6526 | 0.0 | 0.5561 | 0.6490 | | 0.0473 | 199.86 | 57160 | 0.1637 | 0.4015 | 0.6103 | 0.6032 | nan | 0.5695 | 0.6511 | 0.0 | 0.5568 | 0.6476 | | 0.0392 | 199.93 | 57180 | 0.1619 | 0.4018 | 0.6108 | 0.6035 | nan | 0.5684 | 0.6532 | 0.0 | 0.5557 | 0.6497 | | 0.064 | 200.0 | 57200 | 0.1645 | 0.4027 | 0.6121 | 0.6048 | nan | 0.5696 | 0.6546 | 0.0 | 0.5571 | 0.6510 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

pfunk/CartPole-v1-DQN_lr_0.00005-seed1

pfunk

"2023-03-19T00:16:20Z"

0

cleanrl

[ "cleanrl", "tensorboard", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2023-03-19T00:16:17Z"

--- tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 495.62 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **CartPole-v1** This is a trained model of a DQN agent playing CartPole-v1. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_lr_0.00005.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_lr_0.00005]" python -m cleanrl_utils.enjoy --exp-name DQN_lr_0.00005 --env-id CartPole-v1 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_lr_0.00005-seed1/raw/main/dqn.py curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_lr_0.00005-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/CartPole-v1-DQN_lr_0.00005-seed1/raw/main/poetry.lock poetry install --all-extras python dqn.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name DQN_lr_0.00005 --learning-rate 0.00005 --seed 1 ``` # Hyperparameters ```python {'alg_type': 'dqn.py', 'batch_size': 256, 'buffer_size': 300000, 'capture_video': True, 'cuda': True, 'end_e': 0.1, 'env_id': 'CartPole-v1', 'exp_name': 'DQN_lr_0.00005', 'exploration_fraction': 0.2, 'gamma': 1.0, 'hf_entity': 'pfunk', 'learning_rate': 5e-05, 'learning_starts': 1000, 'save_model': True, 'seed': 1, 'start_e': 1.0, 'target_network_frequency': 20, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 500000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

alpindale/Llama-3.2-3B

alpindale

"2024-09-25T19:52:02Z"

207

3

transformers

[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-09-25T19:51:31Z"

--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. 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Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model Developer:** Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | **Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). **Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-3B, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. Make sure to update your transformers installation via pip install --upgrade transformers. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-3B" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto" ) pipe("The key to life is") ``` ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-3B --include "original/*" --local-dir Llama-3.2-3B ``` ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. ## **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | ----- | :---: | :---: | :---: | | Llama 3.2 1B | 370k | \- | 700 | 107 | 0 | | Llama 3.2 3B | 460k | \- | 700 | 133 | 0 | | Total | 830k | 86k | | 240 | 0 | The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models | Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | ----- | ----- | :---: | :---: | :---: | :---: | :---: | | General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 | | | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 | | | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 | | Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 | | | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 | | | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 | | Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 | ### Instruction Tuned Models | Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | | General | | MMLU | 5 | macro\_avg/acc | 49.3 | 63.4 | 69.4 | | Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 40.1 | 40.9 | | Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 19.0 | 17.2 | | Instruction following | | IFEval | 0 | avg(prompt/instruction acc loose/strict) | 59.5 | 77.4 | 80.4 | | Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 77.7 | 84.5 | | | | MATH (CoT) | 0 | final\_em | 30.6 | 47.3 | 51.9 | | Reasoning | | ARC-C | 0 | acc | 59.4 | 78.6 | 83.4 | | | | GPQA | 0 | acc | 27.2 | 32.8 | 32.8 | | | | Hellaswag | 0 | acc | 41.2 | 69.8 | 78.7 | | Tool Use | | BFCL V2 | 0 | acc | 25.7 | 67.0 | 70.9 | | | | Nexus | 0 | macro\_avg/acc | 13.5 | 34.3 | 38.5 | | Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | 19.8 | 27.3 | | | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | 63.3 | 72.2 | | | | NIH/Multi-needle | 0 | recall | 75.0 | 84.7 | 98.8 | | Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 58.2 | 68.9 | ### Multilingual Benchmarks | Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | :---: | :---: | :---: | :---: | :---: | :---: | | General | MMLU (5-shot, macro\_avg/acc) | Portuguese | 39.82 | 54.48 | 62.12 | | | | Spanish | 41.5 | 55.1 | 62.5 | | | | Italian | 39.8 | 53.8 | 61.6 | | | | German | 39.2 | 53.3 | 60.6 | | | | French | 40.5 | 54.6 | 62.3 | | | | Hindi | 33.5 | 43.3 | 50.9 | | | | Thai | 34.7 | 44.5 | 50.3 | ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment **Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct **Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). **Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems **Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases **Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. **Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations **Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. **Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. **2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community **Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). **Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). **Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations **Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. **Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.

DreamyFrog/ntrdttt

DreamyFrog

"2023-03-07T13:51:54Z"

29

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

"2023-03-07T13:17:04Z"

--- license: creativeml-openrail-m tags: - text-to-image widget: - text: --- ### ntrdttt Dreambooth model trained by DreamyFrog with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of:

C10X/DSTQWQL

C10X

"2025-03-28T22:56:12Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1", "base_model:adapter:mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1", "region:us" ]

null

"2025-03-28T22:56:00Z"

--- base_model: mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0

semindan/xnli_m_bert_only_th

semindan

"2023-01-07T14:29:50Z"

104

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:xnli", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2022-11-29T11:06:54Z"

--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - xnli metrics: - accuracy model-index: - name: xnli_m_bert_only_th results: - task: name: Text Classification type: text-classification dataset: name: xnli type: xnli config: th split: train args: th metrics: - name: Accuracy type: accuracy value: 0.6277108433734939 ---  # xnli_m_bert_only_th This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset. It achieves the following results on the evaluation set: - Loss: 1.4266 - Accuracy: 0.6277 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7447 | 1.0 | 3068 | 0.8675 | 0.6205 | | 0.6763 | 2.0 | 6136 | 0.8060 | 0.6602 | | 0.6124 | 3.0 | 9204 | 0.8229 | 0.6586 | | 0.5476 | 4.0 | 12272 | 0.8333 | 0.6542 | | 0.4817 | 5.0 | 15340 | 0.8520 | 0.6618 | | 0.4128 | 6.0 | 18408 | 0.9734 | 0.6426 | | 0.3436 | 7.0 | 21476 | 1.0549 | 0.6365 | | 0.2828 | 8.0 | 24544 | 1.1406 | 0.6321 | | 0.2272 | 9.0 | 27612 | 1.3150 | 0.6301 | | 0.1852 | 10.0 | 30680 | 1.4266 | 0.6277 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

pe-nlp/R1-Qwen2.5-14B-Instruct-10k

pe-nlp

"2025-01-27T03:09:28Z"

14

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-01-27T03:04:22Z"

--- library_name: transformers license: other base_model: Qwen/Qwen2.5-14B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: original results: [] ---  # original This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the Sky-T1 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 12 - total_train_batch_size: 96 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0a0+f70bd71a48.nv24.06 - Datasets 3.1.0 - Tokenizers 0.20.3

featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF

featherless-ai-quants

"2024-10-30T09:29:59Z"

13

0

null

[ "gguf", "text-generation", "base_model:flammenai/Mahou-1.1-llama3-8B", "base_model:quantized:flammenai/Mahou-1.1-llama3-8B", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2024-10-30T08:57:01Z"

--- base_model: flammenai/Mahou-1.1-llama3-8B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # flammenai/Mahou-1.1-llama3-8B GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) *Optimized GGUF quantization files for enhanced model performance* --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | Q8_0 | [flammenai-Mahou-1.1-llama3-8B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q8_0.gguf) | 8145.11 MB | | Q4_K_S | [flammenai-Mahou-1.1-llama3-8B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q4_K_S.gguf) | 4475.28 MB | | Q2_K | [flammenai-Mahou-1.1-llama3-8B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q2_K.gguf) | 3031.86 MB | | Q6_K | [flammenai-Mahou-1.1-llama3-8B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q6_K.gguf) | 6290.44 MB | | Q3_K_M | [flammenai-Mahou-1.1-llama3-8B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [flammenai-Mahou-1.1-llama3-8B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q3_K_S.gguf) | 3494.74 MB | | Q3_K_L | [flammenai-Mahou-1.1-llama3-8B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q3_K_L.gguf) | 4121.74 MB | | Q4_K_M | [flammenai-Mahou-1.1-llama3-8B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q4_K_M.gguf) | 4692.78 MB | | Q5_K_S | [flammenai-Mahou-1.1-llama3-8B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q5_K_S.gguf) | 5339.90 MB | | Q5_K_M | [flammenai-Mahou-1.1-llama3-8B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-Q5_K_M.gguf) | 5467.40 MB | | IQ4_XS | [flammenai-Mahou-1.1-llama3-8B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/flammenai-Mahou-1.1-llama3-8B-GGUF/blob/main/flammenai-Mahou-1.1-llama3-8B-IQ4_XS.gguf) | 4276.62 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

arjunshajitech/whisper-small-malayalam-v6

arjunshajitech

"2024-05-28T15:52:39Z"

86

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ml", "dataset:thennal/GMaSC", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2024-05-28T10:59:49Z"

--- language: - ml license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - thennal/GMaSC metrics: - wer model-index: - name: Whisper Small Malayalam - Arjun Shaji results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: thennal/GMaSC type: thennal/GMaSC args: 'config: ml, split: test' metrics: - name: Wer type: wer value: 16.95364238410596 ---  # Whisper Small Malayalam - Arjun Shaji This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the thennal/GMaSC dataset. It achieves the following results on the evaluation set: - Loss: 0.0505 - Wer: 16.9536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0022 | 10.0 | 1000 | 0.0410 | 18.0132 | | 0.0002 | 20.0 | 2000 | 0.0454 | 17.6159 | | 0.0 | 30.0 | 3000 | 0.0486 | 17.2185 | | 0.0 | 40.0 | 4000 | 0.0499 | 17.1302 | | 0.0 | 50.0 | 5000 | 0.0505 | 16.9536 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

amutha27/adya

amutha27

"2025-03-16T08:21:08Z"

14

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-03-15T11:48:00Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: adya --- # Adya <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `adya` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('amutha27/adya', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

ckw1140/pb

ckw1140

"2025-04-09T14:23:13Z"

0

null

[ "region:us" ]

null

"2025-04-09T14:22:59Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

Triangle104/SmolLM2-360M-Q6_K-GGUF

Triangle104

"2024-11-07T05:06:14Z"

5

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:HuggingFaceTB/SmolLM2-360M", "base_model:quantized:HuggingFaceTB/SmolLM2-360M", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-11-07T05:06:11Z"

--- library_name: transformers license: apache-2.0 language: - en tags: - llama-cpp - gguf-my-repo base_model: HuggingFaceTB/SmolLM2-360M --- # Triangle104/SmolLM2-360M-Q6_K-GGUF This model was converted to GGUF format from [`HuggingFaceTB/SmolLM2-360M`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/SmolLM2-360M-Q6_K-GGUF --hf-file smollm2-360m-q6_k.gguf -c 2048 ```

alibidaran/LLAMA3.2-Virtual_doctor_GGUF

alibidaran

"2024-12-22T12:59:55Z"

273

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:alibidaran/LLAMA3.2-Virtual_doctor2", "base_model:quantized:alibidaran/LLAMA3.2-Virtual_doctor2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-12-22T12:59:00Z"

--- base_model: alibidaran/LLAMA3.2-Virtual_doctor2 tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** alibidaran - **License:** apache-2.0 - **Finetuned from model :** alibidaran/LLAMA3.2-Virtual_doctor2 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MaziyarPanahi/HELVETE-X-GGUF

MaziyarPanahi

"2024-12-17T01:22:50Z"

68

1

null

[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:HelpingAI/HELVETE-X", "base_model:quantized:HelpingAI/HELVETE-X", "region:us", "conversational" ]

text-generation

"2024-12-17T00:50:38Z"

--- base_model: HelpingAI/HELVETE-X inference: false model_creator: HelpingAI model_name: HELVETE-X-GGUF pipeline_tag: text-generation quantized_by: MaziyarPanahi tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation --- # [MaziyarPanahi/HELVETE-X-GGUF](https://huggingface.co/MaziyarPanahi/HELVETE-X-GGUF) - Model creator: [HelpingAI](https://huggingface.co/HelpingAI) - Original model: [HelpingAI/HELVETE-X](https://huggingface.co/HelpingAI/HELVETE-X) ## Description [MaziyarPanahi/HELVETE-X-GGUF](https://huggingface.co/MaziyarPanahi/HELVETE-X-GGUF) contains GGUF format model files for [HelpingAI/HELVETE-X](https://huggingface.co/HelpingAI/HELVETE-X). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

Jonasbukhave/shawgpt-ft-epoch-13

Jonasbukhave

"2025-02-18T15:21:17Z"

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]

null

"2025-02-18T15:02:04Z"

--- library_name: peft license: apache-2.0 base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ tags: - generated_from_trainer model-index: - name: shawgpt-ft-epoch-13 results: [] ---  # shawgpt-ft-epoch-13 This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 13 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 8.5018 | 0.5714 | 1 | 4.2401 | | 8.5334 | 1.5714 | 2 | 4.1568 | | 8.0825 | 2.5714 | 3 | 3.9663 | | 7.7613 | 3.5714 | 4 | 3.7876 | | 7.3597 | 4.5714 | 5 | 3.6281 | | 7.0923 | 5.5714 | 6 | 3.4838 | | 6.8333 | 6.5714 | 7 | 3.3548 | | 6.5917 | 7.5714 | 8 | 3.2436 | | 6.4073 | 8.5714 | 9 | 3.1514 | | 6.2985 | 9.5714 | 10 | 3.0779 | | 6.0792 | 10.5714 | 11 | 3.0223 | | 6.0114 | 11.5714 | 12 | 2.9852 | | 3.0019 | 12.5714 | 13 | 2.9662 | ### Framework versions - PEFT 0.14.0 - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.1 - Tokenizers 0.21.0

dacorvo/mnist-mlp

dacorvo

"2024-10-24T15:56:49Z"

406

0

transformers

[ "transformers", "pytorch", "safetensors", "mlp", "feature-extraction", "pretrained", "image-classification", "custom_code", "license:apache-2.0", "region:us" ]

image-classification

"2023-10-04T15:05:17Z"

--- license: apache-2.0 pipeline_tag: image-classification tags: - pretrained --- # Model Card for MNIST-MLP This is a simple MLP trained on the MNIST dataset. Its primary use is to be a very simple reference model to test quantization. ## Inputs preprocessing The MNIST images must be normalized and flattened as follows: ``` from torchvision import datasets, transforms transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)), transforms.Lambda(lambda x: torch.flatten(x)), ]) test_set = datasets.MNIST('../data', train=False, download=True, transform=transform) ```

vertings6/47ae0897-9393-430a-901f-c0e31bd9731d

vertings6

"2025-01-15T03:29:55Z"

13

0

peft

[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "region:us" ]

null

"2025-01-15T02:17:15Z"

--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 47ae0897-9393-430a-901f-c0e31bd9731d results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6df2c62166a98406_train_data.json ds_type: json format: custom path: /workspace/input_data/6df2c62166a98406_train_data.json type: field_instruction: question_title field_output: answer_text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: vertings6/47ae0897-9393-430a-901f-c0e31bd9731d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/6df2c62166a98406_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: f6f83f39-4e70-4bbc-b582-89a1a29e2fb3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: f6f83f39-4e70-4bbc-b582-89a1a29e2fb3 warmup_steps: 10 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 47ae0897-9393-430a-901f-c0e31bd9731d This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | nan | | 0.0 | 0.0001 | 8 | nan | | 0.0 | 0.0003 | 16 | nan | | 0.0 | 0.0004 | 24 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

edbeeching/atari_2B_atari_namethisgame_3333

edbeeching

"2024-04-16T14:06:23Z"

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "NameThisGameNoFrameskip-v4", "model-index", "region:us" ]

reinforcement-learning

"2022-10-21T07:18:14Z"

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory - NameThisGameNoFrameskip-v4 model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_namethisgame type: atari_namethisgame metrics: - type: mean_reward value: 23292.50 +/- 2939.43 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_namethisgame** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

ZeroXClem/Llama3.1-BestMix-Chem-Einstein-8B

ZeroXClem

"2024-11-09T17:40:56Z"

78

2

null

[ "safetensors", "llama", "merge", "TIES", "Llama3", "BestMix", "Chemistry", "Einstein", "instruction-following", "conversational", "long-form-generation", "scientific", "base_model:bunnycore/Best-Mix-Llama-3.1-8B", "base_model:finetune:bunnycore/Best-Mix-Llama-3.1-8B", "license:apache-2.0", "region:us" ]

null

"2024-10-11T01:03:27Z"

--- license: apache-2.0 tags: - merge - TIES - Llama3 - BestMix - Chemistry - Einstein - instruction-following - conversational - long-form-generation - scientific base_model: - bunnycore/Best-Mix-Llama-3.1-8B --- # **ZeroXClem/Llama3.1-BestMix-Chem-Einstein-8B** **Llama3.1-BestMix-Chem-Einstein-8B** is an innovative, meticulously blended model designed to excel in **instruction-following**, **chemistry-focused tasks**, and **long-form conversational generation**. This model fuses the **best qualities** of multiple Llama3-based architectures, making it highly versatile for both general and specialized tasks. 💻🧠✨ ## 🌟 **Family Tree** This model is the result of merging the following: - [**bunnycore/Best-Mix-Llama-3.1-8B**](https://huggingface.co/bunnycore/Best-Mix-Llama-3.1-8B): A balanced blend of top Llama models, optimized for general performance across reasoning, instruction-following, and math. - [**USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B**](https://huggingface.co/USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B): A model specialized in **scientific knowledge** and **chemistry**, excelling in chemistry benchmarks. - [**Weyaxi/Einstein-v6.1-Llama3-8B**](https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B): Fine-tuned for **long-form generation**, **conversation-heavy tasks**, and optimized with cutting-edge techniques for efficient memory usage and fast performance. --- ## 🧬 **Model Lineage** ### **A: bunnycore/Best-Mix-Llama-3.1-8B** - A masterful **blend** of several Llama3 models like **Aurora_faustus**, **TitanFusion**, and **OpenMath2**. - Provides a **balanced performance** in a variety of tasks such as reasoning, math, and instruction-following. - Key contributor to the **overall versatility** of the merged model. ### **B: USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B** - Specializes in **chemistry** and **scientific knowledge**, outperforming many larger models in **chemistry benchmarks**. - Adds **scientific rigor** and domain-specific expertise to the merged model, making it perfect for scientific and academic tasks. ### **C: Weyaxi/Einstein-v6.1-Llama3-8B** - Fine-tuned on a wide range of **instructive** and **conversational datasets** like **WizardLM**, **Alpaca**, and **ShareGPT**. - Optimized for **long-form text generation** and enhanced with **xformers attention** and **flash attention** techniques for better performance. - Key player in **dialogue-based tasks** and **long conversation generation**. --- ## 🛠️ **Merge Details** This model was merged using the **TIES merge method**, ensuring a smooth integration of the key strengths from each contributing model. Here's the configuration used: ```yaml yaml Copy code models: - model: bunnycore/Best-Mix-Llama-3.1-8B parameters: density: [1, 0.7, 0.5] weight: 1.0 - model: USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B parameters: density: 0.6 weight: [0.3, 0.7, 1.0] - model: Weyaxi/Einstein-v6.1-Llama3-8B parameters: density: 0.4 weight: - filter: mlp value: 0.5 - filter: self_attn value: 0.7 - value: 0.5 merge_method: ties base_model: bunnycore/Best-Mix-Llama-3.1-8B parameters: normalize: true int8_mask: true dtype: float16 ``` --- ## 🎯 **Key Features & Capabilities** ### **1. Instruction Following & General Reasoning**: With the foundation of **Best-Mix**, this model excels in **general-purpose reasoning**, instruction-following, and tasks that require high adaptability. ### **2. Scientific & Chemistry Expertise**: Thanks to the contribution from **KALE-LM-Chem**, this model shines in **scientific research**, particularly **chemistry-focused tasks**, making it ideal for academic and research purposes. ### **3. Long-Form & Conversational Mastery**: With **Einstein-v6.1**, the model handles **long-form generation** effortlessly, excelling in extended conversations and structured dialogue applications. --- ## 🚀 **Performance Benchmarks** While still in its early stages, **Llama3.1-BestMix-Chem-Einstein-8B** is expected to perform well across a variety of benchmarks, including: - **Chemistry-focused benchmarks** (KALE-LM-Chem) - **Instruction-following tasks** (Best-Mix) - **Conversational AI** and **long-form text generation** (Einstein-v6.1) Further testing and evaluation will continue to refine this model's capabilities. --- ## 📜 **License** This model is open-sourced under the **Apache-2.0 License**, allowing free use and modification with proper attribution. --- ## 💡 **Tags** - `merge` - `TIES` - `BestMix` - `Chemistry` - `Einstein` - `instruction-following` - `long-form-generation` - `conversational` ---

aviralkumar28/ppo-LunarLander-v2

aviralkumar28

"2024-01-02T10:23:39Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2024-01-02T10:23:20Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 237.25 +/- 69.85 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Kris8an/v4_sql

Kris8an

"2024-03-22T16:57:45Z"

2

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-function-calling-v2", "base_model:adapter:Trelis/Llama-2-7b-chat-hf-function-calling-v2", "region:us" ]

null

"2024-03-22T16:42:20Z"

--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-function-calling-v2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2

TechxGenus/CursorCore-QW2.5-1.5B-SR-AWQ

TechxGenus

"2024-10-10T06:42:32Z"

76

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "code", "conversational", "arxiv:2410.07002", "base_model:TechxGenus/CursorCore-QW2.5-1.5B-SR", "base_model:quantized:TechxGenus/CursorCore-QW2.5-1.5B-SR", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]

text-generation

"2024-10-08T04:55:01Z"

--- tags: - code base_model: - TechxGenus/CursorCore-QW2.5-1.5B-SR library_name: transformers pipeline_tag: text-generation license: apache-2.0 --- # CursorCore: Assist Programming through Aligning Anything <p align="center"> <a href="http://arxiv.org/abs/2410.07002">[📄arXiv]</a> | <a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> | <a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> | <a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> | <a href="https://github.com/TechxGenus/CursorWeb">[Web]</a> | <a href="https://discord.gg/Z5Tev8fV">[Discord]</a> </p> <hr> - [CursorCore: Assist Programming through Aligning Anything](#cursorcore-assist-programming-through-aligning-anything) - [Introduction](#introduction) - [Models](#models) - [Usage](#usage) - [1) Normal chat](#1-normal-chat) - [2) Assistant-Conversation](#2-assistant-conversation) - [3) Web Demo](#3-web-demo) - [Future Work](#future-work) - [Citation](#citation) - [Contribution](#contribution) <hr> ## Introduction CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read [our paper](http://arxiv.org/abs/2410.07002) to learn more. <p align="center"> <img width="100%" alt="conversation" src="https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/conversation.png"> </p> ![CursorWeb](https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/CursorWeb.gif) ## Models Our models have been open-sourced on Hugging Face. You can access our models here: [CursorCore-Series](https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2"). We also provide pre-quantized weights for GPTQ and AWQ here: [CursorCore-Quantization](https://huggingface.co/collections/TechxGenus/cursorcore-quantization-67066431f29f252494ee8cf3) ## Usage Here are some examples of how to use our model: ### 1) Normal chat Script: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-9B", torch_dtype=torch.bfloat16, device_map="auto" ) messages = [ {"role": "user", "content": "Hi!"}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512) print(tokenizer.decode(outputs[0])) ```` Output: ````txt <|im_start|>system You are a helpful programming assistant.<|im_end|> <|im_start|>user Hi!<|im_end|> <|im_start|>assistant Hello! I'm an AI language model and I can help you with any programming questions you might have. What specific problem or task are you trying to solve?<|im_end|> ```` ### 2) Assistant-Conversation In our work, we introduce a new framework of AI-assisted programming task. It is designed for aligning anything during programming process, used for the implementation of features like Tab and Inline Chat. Script 1: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_wf tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-9B", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [ { "type": "code", "lang": "python", "code": """def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" } ], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "" } prompt = tokenizer.apply_chat_template( prepare_input_for_wf(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output 1: ````txt <|im_start|>system You are a helpful programming assistant.<|im_end|> <|im_start|>history ```python def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<|im_end|> <|im_start|>current ```python def quick_sort(array): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<|im_end|> <|im_start|>assistant <|next_start|>```python def quick_sort(array): if len(array) <= 1: return array pivot = array[len(array) // 2] left = [x for x in array if x < pivot] middle = [x for x in array if x == pivot] right = [x for x in array if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<|next_end|> The user has provided a revised code change that corrects the parameter name from `arr` to `array` in the `quick_sort` function. This change ensures consistency in the function definition and avoids potential confusion or errors. To implement this, we will: 1. Update the parameter name in the function definition from `arr` to `array`. 2. Ensure that all references to `arr` within the function are updated to `array`. This will make the function definition and internal references consistent, improving code readability and maintainability.<|im_end|> ```` Script 2: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_wf tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-9B", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "Add Docstring." } prompt = tokenizer.apply_chat_template( prepare_input_for_wf(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output 2: ````txt <|im_start|>system You are a helpful programming assistant.<|im_end|> <|im_start|>current ```python def quick_sort(array): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<|im_end|> <|im_start|>user Add Docstring.<|im_end|> <|im_start|>assistant <|next_start|>```python def quick_sort(array): """ This is an implementation of the quick sort algorithm. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<|next_end|><|im_end|> ```` For models in Locate-and-Change (LC) and Search-and-Replace (SR) formats, the output examples are as follows: Script for LC: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_lc tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-LC") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-1.5B-LC", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "Add Docstring." } prompt = tokenizer.apply_chat_template( prepare_input_for_lc(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output for LC: ````txt <|im_start|>system You are a helpful programming assistant.<|im_end|> <|im_start|>current ```python 1 def quick_sort(array): 2 if len(arr) <= 1: 3 return arr 4 pivot = arr[len(arr) // 2] 5 left = [x for x in arr if x < pivot] 6 middle = [x for x in arr if x == pivot] 7 right = [x for x in arr if x > pivot] 8 return quick_sort(left) + middle + quick_sort(right) ```<|im_end|> <|im_start|>user Add Docstring.<|im_end|> <|im_start|>assistant <|next_start|>1,1 ``` '''This function will sort an array using quick sort algorithm''' ```<|next_end|> To enhance the readability and maintainability of the code, we should add a docstring to the `quick_sort` function. A docstring provides a clear description of what the function does, which is particularly useful for other developers who might use or modify this code in the future. The docstring will be added immediately after the function definition, explaining that the function uses the quick sort algorithm to sort an array. This will make the code more self-explanatory and easier to understand. Here's the plan: 1. Add a docstring at the beginning of the `quick_sort` function. 2. Ensure the docstring is clear and concise, describing the purpose of the function. This modification will improve the code's documentation without altering its functionality.<|im_end|> ```` Script for SR: ````python import torch from transformers import AutoTokenizer, AutoModelForCausalLM from eval.utils import prepare_input_for_sr tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-SR") model = AutoModelForCausalLM.from_pretrained( "TechxGenus/CursorCore-Yi-1.5B-SR", torch_dtype=torch.bfloat16, device_map="auto" ) sample = { "history": [], "current": { "type": "code", "lang": "python", "code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)""" }, "user": "Add Docstring." } prompt = tokenizer.apply_chat_template( prepare_input_for_sr(sample), tokenize=False, chat_template="assistant-conversation", add_generation_prompt=True ) inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0])) ```` Output for SR: ````txt <|im_start|>system You are a helpful programming assistant.<|im_end|> <|im_start|>current ```python def quick_sort(array): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) ```<|im_end|> <|im_start|>user Add Docstring.<|im_end|> <|im_start|>assistant <|next_start|>```python def quick_sort(array): <|search_and_replace|> def quick_sort(array): """ This function implements quick sort algorithm """ ```<|next_end|><|im_end|> ```` ### 3) Web Demo We create a web demo for CursorCore. Please visit [CursorWeb](https://github.com/TechxGenus/CursorWeb) for more details. ## Future Work CursorCore is still in a very early stage, and lots of work is needed to achieve a better user experience. For example: - Repository-level editing support - Better and faster editing formats - Better user interface and presentation - ... ## Citation ```bibtex @article{jiang2024cursorcore, title = {CursorCore: Assist Programming through Aligning Anything}, author = {Hao Jiang and Qi Liu and Rui Li and Shengyu Ye and Shijin Wang}, year = {2024}, journal = {arXiv preprint arXiv: 2410.07002} } ``` ## Contribution Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.

atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja

atsuki-yamaguchi

"2024-04-22T09:05:45Z"

6

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "ja", "arxiv:2402.10712", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-02-19T15:02:46Z"

--- license: mit language: ja --- Mistral-7B Japanese [LAPT + Heuristics] === ## How to use ```python from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer model = AutoPeftModelForCausalLM.from_pretrained( "atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja" ) tokenizer = AutoTokenizer.from_pretrained( "atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja" ) # w/ GPU model = AutoPeftModelForCausalLM.from_pretrained( "atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-ja", device_map="auto", load_in_8bit=True, ) ``` ## Citation ``` @article{yamaguchi2024empirical, title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference}, author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras}, journal={ArXiv}, year={2024}, volume={abs/2402.10712}, url={https://arxiv.org/abs/2402.10712} } ``` ## Link For more details, please visit https://github.com/gucci-j/llm-cva

lesso07/fe6c0a54-7030-4ed5-b7d1-3f431e79c073

lesso07

"2025-01-16T12:36:07Z"

6

0

peft

[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2-9b-it", "base_model:adapter:unsloth/gemma-2-9b-it", "license:gemma", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-01-16T12:19:02Z"

--- library_name: peft license: gemma base_model: unsloth/gemma-2-9b-it tags: - axolotl - generated_from_trainer model-index: - name: fe6c0a54-7030-4ed5-b7d1-3f431e79c073 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2-9b-it bf16: true chat_template: llama3 datasets: - data_files: - 90a049d18dba60fe_train_data.json ds_type: json format: custom path: /workspace/input_data/90a049d18dba60fe_train_data.json type: field_instruction: instruction field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lesso07/fe6c0a54-7030-4ed5-b7d1-3f431e79c073 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/90a049d18dba60fe_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 8b3341b1-8f11-470d-a3fd-6937caa7d938 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8b3341b1-8f11-470d-a3fd-6937caa7d938 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # fe6c0a54-7030-4ed5-b7d1-3f431e79c073 This model is a fine-tuned version of [unsloth/gemma-2-9b-it](https://huggingface.co/unsloth/gemma-2-9b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6951 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.2927 | 0.0006 | 1 | 2.4089 | | 2.1984 | 0.0032 | 5 | 2.2714 | | 1.8572 | 0.0065 | 10 | 1.9070 | | 1.8613 | 0.0097 | 15 | 1.7381 | | 1.5576 | 0.0129 | 20 | 1.7004 | | 1.6629 | 0.0162 | 25 | 1.6951 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

certainstar/Trained-English-classification-case

certainstar

"2024-07-08T08:06:34Z"

105

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "en", "dataset:Hello-SimpleAI/HC3", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-07-08T07:56:59Z"

--- license: mit datasets: - Hello-SimpleAI/HC3 language: - en metrics: - accuracy --- - 本模型采取 `HC3的英文数据集` 对 `bert-base-cased` 模型进行三轮训练得到结果。 - 其作用是对文本是否为 `GPT` 生成进行分类，所得 `Label` 为0，则不为 `GPT` 生成，反之为1，则是。

sekinko/weights_text

sekinko

"2023-01-02T06:37:09Z"

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2023-01-02T05:11:35Z"

--- license: cc-by-sa-4.0 tags: - generated_from_trainer model-index: - name: weights_text results: [] ---  # weights_text This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu117 - Tokenizers 0.13.2

AdapterHub/roberta-large-mrpc_houlsby

AdapterHub

"2024-05-05T19:40:44Z"

0

adapter-transformers

[ "adapter-transformers", "adapterhub:sts/mrpc", "text-classification", "roberta", "license:apache-2.0", "region:us" ]

text-classification

"2024-05-05T19:40:34Z"

--- tags: - adapterhub:sts/mrpc - text-classification - adapter-transformers - roberta license: "apache-2.0" --- # Adapter `roberta-large-mrpc_houlsby` for roberta-large MRPC adapter (with head) trained using the `run_glue.py` script with an extension that retains the best checkpoint (out of 30 epochs). **This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library.** ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-large") adapter_name = model.load_adapter("AdapterHub/roberta-large-mrpc_houlsby") model.set_active_adapters(adapter_name) ``` ## Architecture & Training - Adapter architecture: houlsby - Prediction head: classification - Dataset: [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398) ## Author Information - Author name(s): Andreas Rücklé - Author email: [email protected] - Author links: [Website](http://rueckle.net), [GitHub](https://github.com/arueckle), [Twitter](https://twitter.com/@arueckle) ## Citation ```bibtex @article{pfeiffer2020AdapterHub, title={AdapterHub: A Framework for Adapting Transformers}, author={Jonas Pfeiffer, Andreas R\"uckl\'{e}, Clifton Poth, Aishwarya Kamath, Ivan Vuli\'{c}, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych}, journal={ArXiv}, year={2020} } ``` *This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/roberta-large-mrpc_houlsby.yaml*.

nouamanetazi/wav2vec2-xls-r-300m-ar

nouamanetazi

"2022-03-23T18:35:04Z"

16

1

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "ar", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2022-03-02T23:29:05Z"

--- language: - ar license: apache-2.0 tags: - ar - automatic-speech-recognition - common_voice - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - common_voice model-index: - name: XLS-R-300M - Arabic results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: ar metrics: - name: Test WER type: wer value: 1.0 - name: Test CER type: cer value: 1.0 ---  # wav2vec2-xls-r-300m-ar This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - AR dataset. It achieves the following results on the evaluation set: - eval_loss: 3.0191 - eval_wer: 1.0 - eval_runtime: 252.2389 - eval_samples_per_second: 30.217 - eval_steps_per_second: 0.476 - epoch: 1.0 - step: 340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 #### Evaluation Commands Please use the evaluation script `eval.py` included in the repo. 1. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id nouamanetazi/wav2vec2-xls-r-300m-ar --dataset speech-recognition-community-v2/dev_data --config ar --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ```

jvbjkbjkbfjis/distillbert-base-drug-effectiveness-classification-model

jvbjkbjkbfjis

"2024-06-03T10:40:50Z"

118

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-06-03T09:28:52Z"

--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: distillbert-base-drug-effectiveness-classification-model results: [] ---  # distillbert-base-drug-effectiveness-classification-model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3800 - F1: 0.4333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 401 | 1.4627 | 0.4117 | | 1.6681 | 2.0 | 802 | 1.4132 | 0.4304 | | 1.3968 | 3.0 | 1203 | 1.3890 | 0.4258 | | 1.3289 | 4.0 | 1604 | 1.3800 | 0.4333 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

nkandpa2/comma-loss-test

nkandpa2

"2025-02-21T19:27:27Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-02-21T03:53:22Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

cesun/ThinkEdit-deepseek-qwen-14b

cesun

"2025-04-14T19:31:55Z"

46

2

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:2503.22048", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-14T03:28:27Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

espnet/owls_1B_180K_intermediates

espnet

"2025-04-10T14:41:57Z"

0

null

[ "region:us" ]

null

"2025-04-10T14:40:09Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

RichardErkhov/mlfoundations-dev_-_llama3-1_8b_baseline_dcft_oh_v3-8bits

RichardErkhov

"2025-04-02T04:03:12Z"

0

null

[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-04-02T03:57:24Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama3-1_8b_baseline_dcft_oh_v3 - bnb 8bits - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/llama3-1_8b_baseline_dcft_oh_v3/ Original model description: --- library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: llama3-1_8b_baseline_dcft_oh_v3 results: [] ---  # llama3-1_8b_baseline_dcft_oh_v3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the mlfoundations-dev/oh-dcft-v3-sharegpt dataset. It achieves the following results on the evaluation set: - Loss: 0.6423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 512 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 1738 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6519 | 1.0 | 423 | 0.6500 | | 0.6088 | 2.0 | 846 | 0.6398 | | 0.5839 | 3.0 | 1269 | 0.6423 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1

timm/vit_small_patch32_224.augreg_in21k

timm

"2025-01-21T19:16:44Z"

170

0

timm

[ "timm", "pytorch", "safetensors", "image-classification", "transformers", "dataset:imagenet-21k", "arxiv:2106.10270", "arxiv:2010.11929", "license:apache-2.0", "region:us" ]

image-classification

"2022-12-22T07:55:11Z"

--- tags: - image-classification - timm - transformers library_name: timm license: apache-2.0 datasets: - imagenet-21k --- # Model card for vit_small_patch32_224.augreg_in21k A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 30.9 - GMACs: 1.1 - Activations (M): 2.1 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_small_patch32_224.augreg_in21k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_small_patch32_224.augreg_in21k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 50, 384) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{steiner2021augreg, title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers}, author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas}, journal={arXiv preprint arXiv:2106.10270}, year={2021} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

RichardErkhov/bunnycore_-_Llama-3.2-3B-Creative-8bits

RichardErkhov

"2025-03-09T10:06:58Z"

0

null

[ "safetensors", "llama", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-03-09T10:03:59Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3.2-3B-Creative - bnb 8bits - Model creator: https://huggingface.co/bunnycore/ - Original model: https://huggingface.co/bunnycore/Llama-3.2-3B-Creative/ Original model description: --- base_model: - bunnycore/Llama-3.2-3B-All-Mix - bunnycore/llama-3.2-3b-story-lora_model library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method using [bunnycore/Llama-3.2-3B-All-Mix](https://huggingface.co/bunnycore/Llama-3.2-3B-All-Mix) + [bunnycore/llama-3.2-3b-story-lora_model](https://huggingface.co/bunnycore/llama-3.2-3b-story-lora_model) as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: bunnycore/Llama-3.2-3B-All-Mix+bunnycore/llama-3.2-3b-story-lora_model dtype: bfloat16 merge_method: passthrough models: - model: bunnycore/Llama-3.2-3B-All-Mix+bunnycore/llama-3.2-3b-story-lora_model ```

Stanford-ILIAD/prism-qwen25-extra-dinosiglip-224px-0_5b

Stanford-ILIAD

"2024-12-12T00:41:53Z"

163

1

transformers

[ "transformers", "robotics", "vlm", "image-text-to-text", "multimodal", "pretraining", "en", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

"2024-12-12T00:19:02Z"

--- library_name: transformers tags: - robotics - vlm - image-text-to-text - multimodal - pretraining license: mit language: - en pipeline_tag: image-text-to-text --- # Prism with Qwen 2.5 0.5B backbone (Prismatic-Compatible Version) This model is trained on the Llava-1.5-Instruct dataset. ## Usage Instructions See the [MiniVLA GitHub README](https://github.com/Stanford-ILIAD/openvla-mini/blob/main/README.md) for instructions on how to use this checkpoint for downstream training and finetuning. ## Citation **BibTeX:** ```bibtex @article{belkhale24minivla, title={MiniVLA: A Better VLA with a Smaller Footprint}, author={Suneel Belkhale and Dorsa Sadigh}, url={https://github.com/Stanford-ILIAD/openvla-mini} year={2024} } ```

microsoft/Florence-2-large

microsoft

"2024-12-08T22:30:48Z"

667,001

1,386

transformers

[ "transformers", "pytorch", "florence2", "text-generation", "vision", "image-text-to-text", "custom_code", "arxiv:2311.06242", "license:mit", "autotrain_compatible", "region:us" ]

image-text-to-text

"2024-06-15T00:34:55Z"

--- license: mit license_link: https://huggingface.co/microsoft/Florence-2-large/resolve/main/LICENSE pipeline_tag: image-text-to-text tags: - vision --- # Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks ## Model Summary **This is a continued pretrained version of Florence-2-large model with 4k context length, only 0.1B samples are used for continue pretraining, thus it might not be trained well. In addition, OCR task has been updated with line separator ('\n'). COCO OD AP 39.8** This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + [Florence-2 technical report](https://arxiv.org/abs/2311.06242). + [Jupyter Notebook for inference and visualization of Florence-2-large](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb) | Model | Model size | Model Description | | ------- | ------------- | ------------- | | Florence-2-base[[HF]](https://huggingface.co/microsoft/Florence-2-base) | 0.23B | Pretrained model with FLD-5B | Florence-2-large[[HF]](https://huggingface.co/microsoft/Florence-2-large) | 0.77B | Pretrained model with FLD-5B | Florence-2-base-ft[[HF]](https://huggingface.co/microsoft/Florence-2-base-ft) | 0.23B | Finetuned model on a colletion of downstream tasks | Florence-2-large-ft[[HF]](https://huggingface.co/microsoft/Florence-2-large-ft) | 0.77B | Finetuned model on a colletion of downstream tasks ## How to Get Started with the Model Use the code below to get started with the model. All models are trained with float16. ```python import requests import torch from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device) processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True) prompt = "<OD>" url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=4096, num_beams=3, do_sample=False ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height)) print(parsed_answer) ``` ## Tasks This model is capable of performing different tasks through changing the prompts. First, let's define a function to run a prompt. <details> <summary> Click to expand </summary> ```python import requests import torch from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=torch_dtype, trust_remote_code=True).to(device) processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True) url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) def run_example(task_prompt, text_input=None): if text_input is None: prompt = task_prompt else: prompt = task_prompt + text_input inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3 ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height)) print(parsed_answer) ``` </details> Here are the tasks `Florence-2` could perform: <details> <summary> Click to expand </summary> ### Caption ```python prompt = "<CAPTION>" run_example(prompt) ``` ### Detailed Caption ```python prompt = "<DETAILED_CAPTION>" run_example(prompt) ``` ### More Detailed Caption ```python prompt = "<MORE_DETAILED_CAPTION>" run_example(prompt) ``` ### Caption to Phrase Grounding caption to phrase grounding task requires additional text input, i.e. caption. Caption to phrase grounding results format: {'\<CAPTION_TO_PHRASE_GROUNDING>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}} ```python task_prompt = "<CAPTION_TO_PHRASE_GROUNDING>" results = run_example(task_prompt, text_input="A green car parked in front of a yellow building.") ``` ### Object Detection OD results format: {'\<OD>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['label1', 'label2', ...]} } ```python prompt = "<OD>" run_example(prompt) ``` ### Dense Region Caption Dense region caption results format: {'\<DENSE_REGION_CAPTION>' : {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['label1', 'label2', ...]} } ```python prompt = "<DENSE_REGION_CAPTION>" run_example(prompt) ``` ### Region proposal Dense region caption results format: {'\<REGION_PROPOSAL>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['', '', ...]}} ```python prompt = "<REGION_PROPOSAL>" run_example(prompt) ``` ### OCR ```python prompt = "<OCR>" run_example(prompt) ``` ### OCR with Region OCR with region output format: {'\<OCR_WITH_REGION>': {'quad_boxes': [[x1, y1, x2, y2, x3, y3, x4, y4], ...], 'labels': ['text1', ...]}} ```python prompt = "<OCR_WITH_REGION>" run_example(prompt) ``` ### Output confidence score with Object Detection ```python def run_example_with_score(task_prompt, text_input=None): if text_input is None: prompt = task_prompt else: prompt = task_prompt + text_input inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3, return_dict_in_generate=True, output_scores=True, ) generated_text = processor.batch_decode(generated_ids.sequences, skip_special_tokens=False)[0] prediction, scores, beam_indices = generated_ids.sequences, generated_ids.scores, generated_ids.beam_indices transition_beam_scores = model.compute_transition_scores( sequences=prediction, scores=scores, beam_indices=beam_indices, ) parsed_answer = processor.post_process_generation(sequence=generated_ids.sequences[0], transition_beam_score=transition_beam_scores[0], task=task_prompt, image_size=(image.width, image.height) ) print(parsed_answer) prompt = "<OD>" run_example_with_score(prompt) ``` for More detailed examples, please refer to [notebook](https://huggingface.co/microsoft/Florence-2-large/blob/main/sample_inference.ipynb) </details> # Benchmarks ## Florence-2 Zero-shot performance The following table presents the zero-shot performance of generalist vision foundation models on image captioning and object detection evaluation tasks. These models have not been exposed to the training data of the evaluation tasks during their training phase. | Method | #params | COCO Cap. test CIDEr | NoCaps val CIDEr | TextCaps val CIDEr | COCO Det. val2017 mAP | |--------|---------|----------------------|------------------|--------------------|-----------------------| | Flamingo | 80B | 84.3 | - | - | - | | Florence-2-base| 0.23B | 133.0 | 118.7 | 70.1 | 34.7 | | Florence-2-large| 0.77B | 135.6 | 120.8 | 72.8 | 37.5 | The following table continues the comparison with performance on other vision-language evaluation tasks. | Method | Flickr30k test R@1 | Refcoco val Accuracy | Refcoco test-A Accuracy | Refcoco test-B Accuracy | Refcoco+ val Accuracy | Refcoco+ test-A Accuracy | Refcoco+ test-B Accuracy | Refcocog val Accuracy | Refcocog test Accuracy | Refcoco RES val mIoU | |--------|----------------------|----------------------|-------------------------|-------------------------|-----------------------|--------------------------|--------------------------|-----------------------|------------------------|----------------------| | Kosmos-2 | 78.7 | 52.3 | 57.4 | 47.3 | 45.5 | 50.7 | 42.2 | 60.6 | 61.7 | - | | Florence-2-base | 83.6 | 53.9 | 58.4 | 49.7 | 51.5 | 56.4 | 47.9 | 66.3 | 65.1 | 34.6 | | Florence-2-large | 84.4 | 56.3 | 61.6 | 51.4 | 53.6 | 57.9 | 49.9 | 68.0 | 67.0 | 35.8 | ## Florence-2 finetuned performance We finetune Florence-2 models with a collection of downstream tasks, resulting two generalist models *Florence-2-base-ft* and *Florence-2-large-ft* that can conduct a wide range of downstream tasks. The table below compares the performance of specialist and generalist models on various captioning and Visual Question Answering (VQA) tasks. Specialist models are fine-tuned specifically for each task, whereas generalist models are fine-tuned in a task-agnostic manner across all tasks. The symbol "▲" indicates the usage of external OCR as input. | Method | # Params | COCO Caption Karpathy test CIDEr | NoCaps val CIDEr | TextCaps val CIDEr | VQAv2 test-dev Acc | TextVQA test-dev Acc | VizWiz VQA test-dev Acc | |----------------|----------|-----------------------------------|------------------|--------------------|--------------------|----------------------|-------------------------| | **Specialist Models** | | | | | | | | | CoCa | 2.1B | 143.6 | 122.4 | - | 82.3 | - | - | | BLIP-2 | 7.8B | 144.5 | 121.6 | - | 82.2 | - | - | | GIT2 | 5.1B | 145.0 | 126.9 | 148.6 | 81.7 | 67.3 | 71.0 | | Flamingo | 80B | 138.1 | - | - | 82.0 | 54.1 | 65.7 | | PaLI | 17B | 149.1 | 127.0 | 160.0▲ | 84.3 | 58.8 / 73.1▲ | 71.6 / 74.4▲ | | PaLI-X | 55B | 149.2 | 126.3 | 147.0 / 163.7▲ | 86.0 | 71.4 / 80.8▲ | 70.9 / 74.6▲ | | **Generalist Models** | | | | | | | | | Unified-IO | 2.9B | - | 100.0 | - | 77.9 | - | 57.4 | | Florence-2-base-ft | 0.23B | 140.0 | 116.7 | 143.9 | 79.7 | 63.6 | 63.6 | | Florence-2-large-ft | 0.77B | 143.3 | 124.9 | 151.1 | 81.7 | 73.5 | 72.6 | | Method | # Params | COCO Det. val2017 mAP | Flickr30k test R@1 | RefCOCO val Accuracy | RefCOCO test-A Accuracy | RefCOCO test-B Accuracy | RefCOCO+ val Accuracy | RefCOCO+ test-A Accuracy | RefCOCO+ test-B Accuracy | RefCOCOg val Accuracy | RefCOCOg test Accuracy | RefCOCO RES val mIoU | |----------------------|----------|-----------------------|--------------------|----------------------|-------------------------|-------------------------|------------------------|---------------------------|---------------------------|------------------------|-----------------------|------------------------| | **Specialist Models** | | | | | | | | | | | | | | SeqTR | - | - | - | 83.7 | 86.5 | 81.2 | 71.5 | 76.3 | 64.9 | 74.9 | 74.2 | - | | PolyFormer | - | - | - | 90.4 | 92.9 | 87.2 | 85.0 | 89.8 | 78.0 | 85.8 | 85.9 | 76.9 | | UNINEXT | 0.74B | 60.6 | - | 92.6 | 94.3 | 91.5 | 85.2 | 89.6 | 79.8 | 88.7 | 89.4 | - | | Ferret | 13B | - | - | 89.5 | 92.4 | 84.4 | 82.8 | 88.1 | 75.2 | 85.8 | 86.3 | - | | **Generalist Models** | | | | | | | | | | | | | | UniTAB | - | - | - | 88.6 | 91.1 | 83.8 | 81.0 | 85.4 | 71.6 | 84.6 | 84.7 | - | | Florence-2-base-ft | 0.23B | 41.4 | 84.0 | 92.6 | 94.8 | 91.5 | 86.8 | 91.7 | 82.2 | 89.8 | 82.2 | 78.0 | | Florence-2-large-ft| 0.77B | 43.4 | 85.2 | 93.4 | 95.3 | 92.0 | 88.3 | 92.9 | 83.6 | 91.2 | 91.7 | 80.5 | ## BibTex and citation info ``` @article{xiao2023florence, title={Florence-2: Advancing a unified representation for a variety of vision tasks}, author={Xiao, Bin and Wu, Haiping and Xu, Weijian and Dai, Xiyang and Hu, Houdong and Lu, Yumao and Zeng, Michael and Liu, Ce and Yuan, Lu}, journal={arXiv preprint arXiv:2311.06242}, year={2023} } ```

biustnaspust/puszek23

biustnaspust

"2025-01-22T18:32:23Z"

33

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-01-22T18:27:44Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

akioi/deepseek-coder-6.7b-instruct-finetuned-v5

akioi

"2024-09-22T22:38:46Z"

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-09-22T21:52:21Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

atrost/qwen_3b_rl_math_2_epochs

atrost

"2025-03-21T00:14:05Z"

2

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-19T18:38:31Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/dolphin-2_2-yi-34b-GGUF

mradermacher

"2024-07-08T00:12:59Z"

246

0

transformers

[ "transformers", "gguf", "en", "dataset:ehartford/dolphin", "dataset:jondurbin/airoboros-2.2.1", "dataset:ehartford/samantha-data", "dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split", "base_model:cognitivecomputations/dolphin-2_2-yi-34b", "base_model:quantized:cognitivecomputations/dolphin-2_2-yi-34b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2024-07-06T02:06:48Z"

--- base_model: cognitivecomputations/dolphin-2_2-yi-34b datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 - ehartford/samantha-data - ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/cognitivecomputations/dolphin-2_2-yi-34b  weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q2_K.gguf) | Q2_K | 12.9 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ3_XS.gguf) | IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q3_K_S.gguf) | Q3_K_S | 15.1 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ3_M.gguf) | IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q3_K_L.gguf) | Q3_K_L | 18.2 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.IQ4_XS.gguf) | IQ4_XS | 18.7 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q5_K_S.gguf) | Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q5_K_M.gguf) | Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q6_K.gguf) | Q6_K | 28.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2_2-yi-34b-GGUF/resolve/main/dolphin-2_2-yi-34b.Q8_0.gguf) | Q8_0 | 36.6 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

c00cjz00/phi-4-R1-medical

c00cjz00

"2025-03-21T00:04:17Z"

110

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "phi", "nlp", "math", "code", "chat", "conversational", "en", "arxiv:2412.08905", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-13T20:46:21Z"

--- license: mit license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - phi - nlp - math - code - chat - conversational inference: parameters: temperature: 0 widget: - messages: - role: user content: How should I explain the Internet? library_name: transformers --- # Phi-4 Model Card [Phi-4 Technical Report](https://arxiv.org/pdf/2412.08905) ## Model Summary | | | |-------------------------|-------------------------------------------------------------------------------| | **Developers** | Microsoft Research | | **Description** | `phi-4` is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.<br><br>`phi-4` underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures | | **Architecture** | 14B parameters, dense decoder-only Transformer model | | **Inputs** | Text, best suited for prompts in the chat format | | **Context length** | 16K tokens | | **GPUs** | 1920 H100-80G | | **Training time** | 21 days | | **Training data** | 9.8T tokens | | **Outputs** | Generated text in response to input | | **Dates** | October 2024 – November 2024 | | **Status** | Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data | | **Release date** | December 12, 2024 | | **License** | MIT | ## Intended Use | | | |-------------------------------|-------------------------------------------------------------------------| | **Primary Use Cases** | Our model is designed to accelerate research on language models, for use as a building block for generative AI powered features. It provides uses for general purpose AI systems and applications (primarily in English) which require:<br><br>1. Memory/compute constrained environments.<br>2. Latency bound scenarios.<br>3. Reasoning and logic. | | **Out-of-Scope Use Cases** | Our models is not specifically designed or evaluated for all downstream purposes, thus:<br><br>1. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.<br>2. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case, including the model’s focus on English.<br>3. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. | ## Data Overview ### Training Datasets Our training data is an extension of the data used for Phi-3 and includes a wide variety of sources from: 1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code. 2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.). 3. Acquired academic books and Q&A datasets. 4. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. Multilingual data constitutes about 8% of our overall data. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. #### Benchmark datasets We evaluated `phi-4` using [OpenAI’s SimpleEval](https://github.com/openai/simple-evals) and our own internal benchmarks to understand the model’s capabilities, more specifically: * **MMLU:** Popular aggregated dataset for multitask language understanding. * **MATH:** Challenging competition math problems. * **GPQA:** Complex, graduate-level science questions. * **DROP:** Complex comprehension and reasoning. * **MGSM:** Multi-lingual grade-school math. * **HumanEval:** Functional code generation. * **SimpleQA:** Factual responses. ## Safety ### Approach `phi-4` has adopted a robust safety post-training approach. This approach leverages a variety of both open-source and in-house generated synthetic datasets. The overall technique employed to do the safety alignment is a combination of SFT (Supervised Fine-Tuning) and iterative DPO (Direct Preference Optimization), including publicly available datasets focusing on helpfulness and harmlessness as well as various questions and answers targeted to multiple safety categories. ### Safety Evaluation and Red-Teaming Prior to release, `phi-4` followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversation simulation. For qualitative safety evaluation, we collaborated with the independent AI Red Team (AIRT) at Microsoft to assess safety risks posed by `phi-4` in both average and adversarial user scenarios. In the average user scenario, AIRT emulated typical single-turn and multi-turn interactions to identify potentially risky behaviors. The adversarial user scenario tested a wide range of techniques aimed at intentionally subverting the model’s safety training including jailbreaks, encoding-based attacks, multi-turn attacks, and adversarial suffix attacks. Please refer to the technical report for more details on safety alignment. ## Model Quality To understand the capabilities, we compare `phi-4` with a set of models over OpenAI’s SimpleEval benchmark. At the high-level overview of the model quality on representative benchmarks. For the table below, higher numbers indicate better performance: | **Category** | **Benchmark** | **phi-4** (14B) | **phi-3** (14B) | **Qwen 2.5** (14B instruct) | **GPT-4o-mini** | **Llama-3.3** (70B instruct) | **Qwen 2.5** (72B instruct) | **GPT-4o** | |------------------------------|---------------|-----------|-----------------|----------------------|----------------------|--------------------|-------------------|-----------------| | Popular Aggregated Benchmark | MMLU | 84.8 | 77.9 | 79.9 | 81.8 | 86.3 | 85.3 | **88.1** | | Science | GPQA | **56.1** | 31.2 | 42.9 | 40.9 | 49.1 | 49.0 | 50.6 | | Math | MGSM<br>MATH | 80.6<br>**80.4** | 53.5<br>44.6 | 79.6<br>75.6 | 86.5<br>73.0 | 89.1<br>66.3* | 87.3<br>80.0 | **90.4**<br>74.6 | | Code Generation | HumanEval | 82.6 | 67.8 | 72.1 | 86.2 | 78.9* | 80.4 | **90.6** | | Factual Knowledge | SimpleQA | 3.0 | 7.6 | 5.4 | 9.9 | 20.9 | 10.2 | **39.4** | | Reasoning | DROP | 75.5 | 68.3 | 85.5 | 79.3 | **90.2** | 76.7 | 80.9 | \* These scores are lower than those reported by Meta, perhaps because simple-evals has a strict formatting requirement that Llama models have particular trouble following. We use the simple-evals framework because it is reproducible, but Meta reports 77 for MATH and 88 for HumanEval on Llama-3.3-70B. ## Usage ### Input Formats Given the nature of the training data, `phi-4` is best suited for prompts using the chat format as follows: ```bash <|im_start|>system<|im_sep|> You are a medieval knight and must provide explanations to modern people.<|im_end|> <|im_start|>user<|im_sep|> How should I explain the Internet?<|im_end|> <|im_start|>assistant<|im_sep|> ``` ### With `transformers` ```python import transformers pipeline = transformers.pipeline( "text-generation", model="microsoft/phi-4", model_kwargs={"torch_dtype": "auto"}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a medieval knight and must provide explanations to modern people."}, {"role": "user", "content": "How should I explain the Internet?"}, ] outputs = pipeline(messages, max_new_tokens=128) print(outputs[0]["generated_text"][-1]) ``` ## Responsible AI Considerations Like other language models, `phi-4` can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: * **Quality of Service:** The model is trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. `phi-4` is not intended to support multilingual use. * **Representation of Harms & Perpetuation of Stereotypes:** These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. * **Inappropriate or Offensive Content:** These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. * **Information Reliability:** Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. * **Limited Scope for Code:** Majority of `phi-4` training data is based in Python and uses common packages such as `typing`, `math`, `random`, `collections`, `datetime`, `itertools`. If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Using safety services like [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety) that have advanced guardrails is highly recommended. Important areas for consideration include: * **Allocation:** Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. * **High-Risk Scenarios:** Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. * **Misinformation:** Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). * **Generation of Harmful Content:** Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. * **Misuse:** Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.

lesso/54e510f0-0fcf-4093-92f5-d455c3c63086

lesso

"2025-02-09T00:10:32Z"

6

0

peft

[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "license:mit", "region:us" ]

null

"2025-02-07T02:05:16Z"

--- library_name: peft license: mit base_model: microsoft/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 54e510f0-0fcf-4093-92f5-d455c3c63086 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <br> # 54e510f0-0fcf-4093-92f5-d455c3c63086 This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001011 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.7609 | 0.0000 | 1 | 2.2316 | | 2.6681 | 0.0021 | 50 | 1.2262 | | 1.8951 | 0.0041 | 100 | 1.1818 | | 2.0745 | 0.0062 | 150 | 1.1588 | | 2.5473 | 0.0082 | 200 | 1.1451 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

TheBloke/deepmoney-34b-200k-base-GGUF

TheBloke

"2024-01-13T00:56:04Z"

571

15

transformers

[ "transformers", "gguf", "yi", "finance", "invest", "en", "zh", "base_model:TriadParty/deepmoney-34b-200k-base", "base_model:quantized:TriadParty/deepmoney-34b-200k-base", "license:apache-2.0", "region:us" ]

null

"2024-01-13T00:36:52Z"

--- base_model: TriadParty/deepmoney-34b-200k-base inference: false language: - en - zh license: apache-2.0 model_creator: triad party model_name: Deepmoney 34B 200K Base model_type: yi prompt_template: '{prompt} ' quantized_by: TheBloke tags: - finance - invest ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Deepmoney 34B 200K Base - GGUF - Model creator: [triad party](https://huggingface.co/TriadParty) - Original model: [Deepmoney 34B 200K Base](https://huggingface.co/TriadParty/deepmoney-34b-200k-base)  ## Description This repo contains GGUF format model files for [triad party's Deepmoney 34B 200K Base](https://huggingface.co/TriadParty/deepmoney-34b-200k-base). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).   ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF) * [triad party's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TriadParty/deepmoney-34b-200k-base)   ## Prompt template: None ``` {prompt} ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [deepmoney-34b-200k-base.Q2_K.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q2_K.gguf) | Q2_K | 2 | 12.77 GB| 15.27 GB | smallest, significant quality loss - not recommended for most purposes | | [deepmoney-34b-200k-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss | | [deepmoney-34b-200k-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_M.gguf) | Q3_K_M | 3 | 16.65 GB| 19.15 GB | very small, high quality loss | | [deepmoney-34b-200k-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss | | [deepmoney-34b-200k-base.Q4_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [deepmoney-34b-200k-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_K_S.gguf) | Q4_K_S | 4 | 19.60 GB| 22.10 GB | small, greater quality loss | | [deepmoney-34b-200k-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended | | [deepmoney-34b-200k-base.Q5_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [deepmoney-34b-200k-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended | | [deepmoney-34b-200k-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended | | [deepmoney-34b-200k-base.Q6_K.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss | | [deepmoney-34b-200k-base.Q8_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.   ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/deepmoney-34b-200k-base-GGUF and below it, a specific filename to download, such as: deepmoney-34b-200k-base.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m deepmoney-34b-200k-base.Q4_K_M.gguf --color -c 200000 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 200000` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", # Download the model file first n_ctx=200000, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.   # Original model card: triad party's Deepmoney 34B 200K Base # **Deepmoney** ![767e2d3bba166cd63a83ae54e913d35.jpg](https://cdn-uploads.huggingface.co/production/uploads/630c1adea20a5367812196f6/O0kFm05ZSe6Lw6FhGwx5_.jpeg) Introducing **Greed** in the Seven Deadly Sins series of models. - Full-para pre-training on Yi-34b - High-quality research reports - High-end cleaning process ### 1. What do I want to do? Most of the current so-called financial models are mostly trained on public knowledge, but in the actual financial field, these public knowledge are often seriously insufficient for the current market interpretability. If you are interested, you can learn about the various propositions of Keynes, Friedman and even current behavioral finance. According to my observation, most financial models cannot make investment judgments. Because they are trained on ordinary textbooks, entry-level analyst exams, and even company public reports. I think this is of very little value for the investment. You can think I'm joking, but the fact is that the logic of many subjective analysts may not be as rigorous as that of large models of 34b and above (excluding those excellent ones, of course). The market is changing every moment, with a lot of news and massive data in real time. For most retail investors, instead of waiting for a crappy analyst to write a report, why not use a large model to make a pipeline? In my plan, this model is the base model of this process. In my plan, models such as information collector, target judge, qualitative analyst, quantitative analyst, and data extractor are all part of this process. . But the model itself is undoubtedly important to master a large number of qualitative and quantitative methods. That's why this model was born. ### 2. About the data As I just said, a lot of public knowledge has some questionable validity - but that doesn't mean it's wrong. The theoretical support behind many research methods in research reports also relies on this knowledge. So in my training, I picked up some college textbooks and some professional books. Not a lot of quantity but good quality. In addition, I selected a large number of research report data from 2019 to December 2023 - these reports are issued by a variety of publishers, including traditional brokers and professional research institutions. Most of them are paid and only available to institutions. But I got them anyway through various means. If you have read research reports, especially high-quality ones, you will find that research reports are all subjective judgment + quantitative analysis, and data support in quantitative analysis is crucial to the entire logical chain. In order to extract this data (most of them are in the form of graphs or tables), I tried a lot of multi-modal models, and the process was very painful. The conclusion is that cog-agent and emu2 are very effective for this kind of tasks. In order to better extract information, I created a process that summarizes the context of research reports as part of the prompt. Finally, I made a blend of the data. General data is not included because it is just for greed. Moreover, the knowledge in industry research reports is comprehensive enough. ### 3. About training Raw text, full parameter training. The base uses long context yi-34b-200k. This is necessary to complete and understand an in-depth report. Of course, I also did a sft. [This](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator) is the analyzer in my process – I haven’t broken down the qualitative and quantitative analysis yet, but I’m already blown away by how well it works. ### More: More technical details and evals coming soon…… ### 1. 我想干什么？当下大多数所谓的金融模型大多在公开知识上进行训练，但在实际的金融领域，这些公开知识对当前的市场可解释性往往严重不足。如果您感兴趣，可以了解一下凯恩斯、弗里德曼乃至当下行为金融学的各类主张。而据我观察，大多数金融模型无法对投资进行判断。因为它们都是在普通的教材、入门的分析师考试，乃至公司的公开报告上训练。我认为这对于投资的价值非常小。你可以当我开玩笑，但事实是很多主观分析师的逻辑性可能还不如34b及以上的大模型来的严谨（当然不包括那些优秀的）。而每时每刻，市场都在变化，大量的新闻，海量的数据都是实时的，对于大多数散户们，与其等待蹩脚的分析师写出报告，为什么不用大模型制作一套pipeline呢？在我的计划中，该模型是这套流程的基座模型，在我的计划中，信息搜集者、标的判断者、定性分析者定性分析者、定量分析者、数据提取者等模型都是该流程的一部分。但模型本身掌握大量的定性和定量方法毫无疑问是重要的。这就是这个模型诞生的理由。 ### 2. 关于数据：正如我刚才所说，很多公开知识的有效性都有些问题——但这并不意味着它们是错误的。在研报中很多研究方法背后的理论支撑也依赖这些知识。所以在我的训练中，我挑选了一些大学教材和一些专业书籍。数量不是很多但质量还不错。另外，我挑选了在2019-2023年12月的大量研究报告数据——这些报告的发布者多种多样，有传统的broke，也有专业研究机构。他们中的大多数是付费的，而且只对机构提供。但无论如何我通过各种各样的手段获取了它们。如果你看过研报，尤其是高质量的那些，你会发现研报都是主观判断+定量分析，而定量分析中的数据支撑对于整个逻辑链条至关重要。为了提取这些数据（他们中的大多数以图形或者表格的形式出现），我尝试了很多多模态模型，过程非常痛苦，结论是cog-agent和emu2对于这类任务很有效。为了更好的提取信息，我制作了一套从研报上下文总结作为prompt一部分的流程。最后，我把这些数据做了一个混合。并没有放入通识数据, 因为它就是为了greed而生的。而且行业研报中的知识足够全。 ### 3：关于训练： raw text，全参数训练。基座采用了长上下文的yi-34b-200k。这对于完成理解一篇深度报告是必须的。当然，我也做了一次sft。这是我的流程中的分析者——目前还没有细分定性和定量分析，但[它的效果](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator)已经让我大吃一惊了。

huggingtweets/dav_erage-dozendav

huggingtweets

"2022-06-21T01:08:17Z"

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2022-06-21T01:07:07Z"

--- language: en thumbnail: http://www.huggingtweets.com/dav_erage-dozendav/1655773693107/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1517890310642278400/p9HNFjUU_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468744707698307072/TyrOUNkN_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">🐊 blooming 'bold 🌻 & ˣʸzed</div> <div style="text-align: center; font-size: 14px;">@dav_erage-dozendav</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 🐊 blooming 'bold 🌻 & ˣʸzed. | Data | 🐊 blooming 'bold 🌻 | ˣʸzed | | --- | --- | --- | | Tweets downloaded | 3247 | 3247 | | Retweets | 279 | 297 | | Short tweets | 440 | 427 | | Tweets kept | 2528 | 2523 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s4htzgm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dav_erage-dozendav's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gqlw7dl) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gqlw7dl/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dav_erage-dozendav') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

PabloSuaLap/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3

PabloSuaLap

"2023-09-11T15:44:00Z"

63

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "base_model:finetune:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

"2023-08-17T18:06:48Z"

--- license: apache-2.0 tags: - generated_from_keras_callback base_model: mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es model-index: - name: P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 results: [] ---  # P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 This model is a fine-tuned version of [mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es](https://huggingface.co/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7249 - Train End Logits Accuracy: 0.1667 - Train Start Logits Accuracy: 0.1667 - Validation Loss: 3.2576 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.8333 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 4.7073 | 0.1667 | 0.1667 | 3.5715 | 0.0 | 0.8333 | 0 | | 3.7249 | 0.1667 | 0.1667 | 3.2576 | 0.0 | 0.8333 | 1 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.2

NikolayKozloff/Llama-3-8B-Swedish-Norwegian-Danish-checkpoint-11525-03_6_2024-Q8_0-GGUF

NikolayKozloff

"2024-06-03T15:54:00Z"

1

null

[ "gguf", "pytorch", "llama", "llama-3", "ai-sweden", "llama-cpp", "gguf-my-repo", "text-generation", "sv", "da", "no", "base_model:AI-Sweden-Models/Llama-3-8B", "base_model:quantized:AI-Sweden-Models/Llama-3-8B", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-03T15:53:32Z"

--- language: - sv - da - 'no' tags: - pytorch - llama - llama-3 - ai-sweden - llama-cpp - gguf-my-repo base_model: AI-Sweden-Models/Llama-3-8B pipeline_tag: text-generation inference: parameters: temperature: 0.6 --- # NikolayKozloff/Llama-3-8B-Q8_0-GGUF This model was converted to GGUF format from [`AI-Sweden-Models/Llama-3-8B`](https://huggingface.co/AI-Sweden-Models/Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/AI-Sweden-Models/Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo NikolayKozloff/Llama-3-8B-Q8_0-GGUF --hf-file llama-3-8b-q8_0.gguf -c 2048 ```

CyberHarem/mikari_izumigamine_mahoushoujosite

CyberHarem

"2023-08-29T20:20:29Z"

0

null

[ "art", "text-to-image", "dataset:CyberHarem/mikari_izumigamine_mahoushoujosite", "license:mit", "region:us" ]

text-to-image

"2023-08-29T07:38:10Z"

--- license: mit datasets: - CyberHarem/mikari_izumigamine_mahoushoujosite pipeline_tag: text-to-image tags: - art --- # Lora of mikari_izumigamine_mahoushoujosite This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2200, you need to download `2200/mikari_izumigamine_mahoushoujosite.pt` as the embedding and `2200/mikari_izumigamine_mahoushoujosite.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 2200**, with the score of 0.993. The trigger words are: 1. `mikari_izumigamine_mahoushoujosite` 2. `hair_ornament, heart, green_eyes, heart_hair_ornament, bow, hair_bow, purple_hair, drill_hair, open_mouth, breasts, smile, long_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 3000 | 0.974 | [Download](3000/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-3000](3000/previews/pattern_1.png) | ![pattern_2-3000](3000/previews/pattern_2.png) | ![pattern_3-3000](3000/previews/pattern_3.png) | ![bikini-3000](3000/previews/bikini.png) | [<NSFW, click to see>](3000/previews/bondage.png) | ![free-3000](3000/previews/free.png) | ![maid-3000](3000/previews/maid.png) | ![miko-3000](3000/previews/miko.png) | [<NSFW, click to see>](3000/previews/nude.png) | [<NSFW, click to see>](3000/previews/nude2.png) | ![suit-3000](3000/previews/suit.png) | ![yukata-3000](3000/previews/yukata.png) | | 2800 | 0.983 | [Download](2800/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-2800](2800/previews/pattern_1.png) | ![pattern_2-2800](2800/previews/pattern_2.png) | ![pattern_3-2800](2800/previews/pattern_3.png) | ![bikini-2800](2800/previews/bikini.png) | [<NSFW, click to see>](2800/previews/bondage.png) | ![free-2800](2800/previews/free.png) | ![maid-2800](2800/previews/maid.png) | ![miko-2800](2800/previews/miko.png) | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) | ![suit-2800](2800/previews/suit.png) | ![yukata-2800](2800/previews/yukata.png) | | 2600 | 0.988 | [Download](2600/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-2600](2600/previews/pattern_1.png) | ![pattern_2-2600](2600/previews/pattern_2.png) | ![pattern_3-2600](2600/previews/pattern_3.png) | ![bikini-2600](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) | ![free-2600](2600/previews/free.png) | ![maid-2600](2600/previews/maid.png) | ![miko-2600](2600/previews/miko.png) | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) | ![suit-2600](2600/previews/suit.png) | ![yukata-2600](2600/previews/yukata.png) | | 2400 | 0.984 | [Download](2400/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-2400](2400/previews/pattern_1.png) | ![pattern_2-2400](2400/previews/pattern_2.png) | ![pattern_3-2400](2400/previews/pattern_3.png) | ![bikini-2400](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | **2200** | **0.993** | [**Download**](2200/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-2200](2200/previews/pattern_1.png) | ![pattern_2-2200](2200/previews/pattern_2.png) | ![pattern_3-2200](2200/previews/pattern_3.png) | ![bikini-2200](2200/previews/bikini.png) | [<NSFW, click to see>](2200/previews/bondage.png) | ![free-2200](2200/previews/free.png) | ![maid-2200](2200/previews/maid.png) | ![miko-2200](2200/previews/miko.png) | [<NSFW, click to see>](2200/previews/nude.png) | [<NSFW, click to see>](2200/previews/nude2.png) | ![suit-2200](2200/previews/suit.png) | ![yukata-2200](2200/previews/yukata.png) | | 2000 | 0.974 | [Download](2000/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-2000](2000/previews/pattern_1.png) | ![pattern_2-2000](2000/previews/pattern_2.png) | ![pattern_3-2000](2000/previews/pattern_3.png) | ![bikini-2000](2000/previews/bikini.png) | [<NSFW, click to see>](2000/previews/bondage.png) | ![free-2000](2000/previews/free.png) | ![maid-2000](2000/previews/maid.png) | ![miko-2000](2000/previews/miko.png) | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) | ![suit-2000](2000/previews/suit.png) | ![yukata-2000](2000/previews/yukata.png) | | 1800 | 0.984 | [Download](1800/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-1800](1800/previews/pattern_1.png) | ![pattern_2-1800](1800/previews/pattern_2.png) | ![pattern_3-1800](1800/previews/pattern_3.png) | ![bikini-1800](1800/previews/bikini.png) | [<NSFW, click to see>](1800/previews/bondage.png) | ![free-1800](1800/previews/free.png) | ![maid-1800](1800/previews/maid.png) | ![miko-1800](1800/previews/miko.png) | [<NSFW, click to see>](1800/previews/nude.png) | [<NSFW, click to see>](1800/previews/nude2.png) | ![suit-1800](1800/previews/suit.png) | ![yukata-1800](1800/previews/yukata.png) | | 1600 | 0.958 | [Download](1600/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-1600](1600/previews/pattern_1.png) | ![pattern_2-1600](1600/previews/pattern_2.png) | ![pattern_3-1600](1600/previews/pattern_3.png) | ![bikini-1600](1600/previews/bikini.png) | [<NSFW, click to see>](1600/previews/bondage.png) | ![free-1600](1600/previews/free.png) | ![maid-1600](1600/previews/maid.png) | ![miko-1600](1600/previews/miko.png) | [<NSFW, click to see>](1600/previews/nude.png) | [<NSFW, click to see>](1600/previews/nude2.png) | ![suit-1600](1600/previews/suit.png) | ![yukata-1600](1600/previews/yukata.png) | | 1400 | 0.972 | [Download](1400/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-1400](1400/previews/pattern_1.png) | ![pattern_2-1400](1400/previews/pattern_2.png) | ![pattern_3-1400](1400/previews/pattern_3.png) | ![bikini-1400](1400/previews/bikini.png) | [<NSFW, click to see>](1400/previews/bondage.png) | ![free-1400](1400/previews/free.png) | ![maid-1400](1400/previews/maid.png) | ![miko-1400](1400/previews/miko.png) | [<NSFW, click to see>](1400/previews/nude.png) | [<NSFW, click to see>](1400/previews/nude2.png) | ![suit-1400](1400/previews/suit.png) | ![yukata-1400](1400/previews/yukata.png) | | 1200 | 0.872 | [Download](1200/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-1200](1200/previews/pattern_1.png) | ![pattern_2-1200](1200/previews/pattern_2.png) | ![pattern_3-1200](1200/previews/pattern_3.png) | ![bikini-1200](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/bondage.png) | ![free-1200](1200/previews/free.png) | ![maid-1200](1200/previews/maid.png) | ![miko-1200](1200/previews/miko.png) | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) | ![suit-1200](1200/previews/suit.png) | ![yukata-1200](1200/previews/yukata.png) | | 1000 | 0.943 | [Download](1000/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-1000](1000/previews/pattern_1.png) | ![pattern_2-1000](1000/previews/pattern_2.png) | ![pattern_3-1000](1000/previews/pattern_3.png) | ![bikini-1000](1000/previews/bikini.png) | [<NSFW, click to see>](1000/previews/bondage.png) | ![free-1000](1000/previews/free.png) | ![maid-1000](1000/previews/maid.png) | ![miko-1000](1000/previews/miko.png) | [<NSFW, click to see>](1000/previews/nude.png) | [<NSFW, click to see>](1000/previews/nude2.png) | ![suit-1000](1000/previews/suit.png) | ![yukata-1000](1000/previews/yukata.png) | | 800 | 0.852 | [Download](800/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-800](800/previews/pattern_1.png) | ![pattern_2-800](800/previews/pattern_2.png) | ![pattern_3-800](800/previews/pattern_3.png) | ![bikini-800](800/previews/bikini.png) | [<NSFW, click to see>](800/previews/bondage.png) | ![free-800](800/previews/free.png) | ![maid-800](800/previews/maid.png) | ![miko-800](800/previews/miko.png) | [<NSFW, click to see>](800/previews/nude.png) | [<NSFW, click to see>](800/previews/nude2.png) | ![suit-800](800/previews/suit.png) | ![yukata-800](800/previews/yukata.png) | | 600 | 0.924 | [Download](600/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-600](600/previews/pattern_1.png) | ![pattern_2-600](600/previews/pattern_2.png) | ![pattern_3-600](600/previews/pattern_3.png) | ![bikini-600](600/previews/bikini.png) | [<NSFW, click to see>](600/previews/bondage.png) | ![free-600](600/previews/free.png) | ![maid-600](600/previews/maid.png) | ![miko-600](600/previews/miko.png) | [<NSFW, click to see>](600/previews/nude.png) | [<NSFW, click to see>](600/previews/nude2.png) | ![suit-600](600/previews/suit.png) | ![yukata-600](600/previews/yukata.png) | | 400 | 0.840 | [Download](400/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-400](400/previews/pattern_1.png) | ![pattern_2-400](400/previews/pattern_2.png) | ![pattern_3-400](400/previews/pattern_3.png) | ![bikini-400](400/previews/bikini.png) | [<NSFW, click to see>](400/previews/bondage.png) | ![free-400](400/previews/free.png) | ![maid-400](400/previews/maid.png) | ![miko-400](400/previews/miko.png) | [<NSFW, click to see>](400/previews/nude.png) | [<NSFW, click to see>](400/previews/nude2.png) | ![suit-400](400/previews/suit.png) | ![yukata-400](400/previews/yukata.png) | | 200 | 0.174 | [Download](200/mikari_izumigamine_mahoushoujosite.zip) | ![pattern_1-200](200/previews/pattern_1.png) | ![pattern_2-200](200/previews/pattern_2.png) | ![pattern_3-200](200/previews/pattern_3.png) | ![bikini-200](200/previews/bikini.png) | [<NSFW, click to see>](200/previews/bondage.png) | ![free-200](200/previews/free.png) | ![maid-200](200/previews/maid.png) | ![miko-200](200/previews/miko.png) | [<NSFW, click to see>](200/previews/nude.png) | [<NSFW, click to see>](200/previews/nude2.png) | ![suit-200](200/previews/suit.png) | ![yukata-200](200/previews/yukata.png) |

minhhien0811/deita_reason_arena_5328

minhhien0811

"2024-08-29T08:07:28Z"

5

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-08-29T08:04:48Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5

stefan-it

"2023-10-17T21:30:34Z"

2

0

flair

[ "flair", "pytorch", "tensorboard", "token-classification", "sequence-tagger-model", "fr", "base_model:hmteams/teams-base-historic-multilingual-discriminator", "base_model:finetune:hmteams/teams-base-historic-multilingual-discriminator", "license:mit", "region:us" ]

token-classification

"2023-10-17T15:05:52Z"

--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: hmteams/teams-base-historic-multilingual-discriminator widget: - text: Je suis convaincu , a-t43 dit . que nous n"y parviendrions pas , mais nous ne pouvons céder parce que l' état moral de nos troupe* en souffrirait trop . ( Fournier . ) Des avions ennemis lancent dix-sept bombes sur Dunkerque LONDRES . 31 décembre . --- # Fine-tuned Flair Model on French ICDAR-Europeana NER Dataset This Flair model was fine-tuned on the [French ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar) NER Dataset using hmTEAMS as backbone LM. The ICDAR-Europeana NER Dataset is a preprocessed variant of the [Europeana NER Corpora](https://github.com/EuropeanaNewspapers/ner-corpora) for Dutch and French. The following NEs were annotated: `PER`, `LOC` and `ORG`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: | Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. | |-----------------|--------------|--------------|--------------|--------------|--------------|--------------| | bs4-e10-lr3e-05 | [0.7764][1] | [0.7756][2] | [0.7723][3] | [0.7682][4] | [0.7745][5] | 77.34 ± 0.29 | | bs8-e10-lr3e-05 | [0.7773][6] | [0.7676][7] | [0.7733][8] | [0.7675][9] | [0.7726][10] | 77.17 ± 0.37 | | bs8-e10-lr5e-05 | [0.7778][11] | [0.783][12] | [0.7654][13] | [0.767][14] | [0.7644][15] | 77.15 ± 0.75 | | bs4-e10-lr5e-05 | [0.7684][16] | [0.7582][17] | [0.7626][18] | [0.7779][19] | [0.7693][20] | 76.73 ± 0.67 | [1]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️

jonelganalon/LexitronLlama-3.2-3B-Instruct-GGUF

jonelganalon

"2025-02-17T23:55:41Z"

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-02-17T23:04:52Z"

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** jonelganalon - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf

RichardErkhov

"2025-03-25T22:17:58Z"

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-03-25T21:16:52Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) kbtg-kpoint-v2-16bit-safe - GGUF - Model creator: https://huggingface.co/katopz/ - Original model: https://huggingface.co/katopz/kbtg-kpoint-v2-16bit-safe/ | Name | Quant method | Size | | ---- | ---- | ---- | | [kbtg-kpoint-v2-16bit-safe.Q2_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q2_K.gguf) | Q2_K | 1.27GB | | [kbtg-kpoint-v2-16bit-safe.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ3_XS.gguf) | IQ3_XS | 1.38GB | | [kbtg-kpoint-v2-16bit-safe.IQ3_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ3_S.gguf) | IQ3_S | 1.44GB | | [kbtg-kpoint-v2-16bit-safe.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K_S.gguf) | Q3_K_S | 1.44GB | | [kbtg-kpoint-v2-16bit-safe.IQ3_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ3_M.gguf) | IQ3_M | 1.49GB | | [kbtg-kpoint-v2-16bit-safe.Q3_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K.gguf) | Q3_K | 1.57GB | | [kbtg-kpoint-v2-16bit-safe.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K_M.gguf) | Q3_K_M | 1.57GB | | [kbtg-kpoint-v2-16bit-safe.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q3_K_L.gguf) | Q3_K_L | 1.69GB | | [kbtg-kpoint-v2-16bit-safe.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ4_XS.gguf) | IQ4_XS | 1.71GB | | [kbtg-kpoint-v2-16bit-safe.Q4_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_0.gguf) | Q4_0 | 1.79GB | | [kbtg-kpoint-v2-16bit-safe.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.IQ4_NL.gguf) | IQ4_NL | 1.79GB | | [kbtg-kpoint-v2-16bit-safe.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_K_S.gguf) | Q4_K_S | 1.8GB | | [kbtg-kpoint-v2-16bit-safe.Q4_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_K.gguf) | Q4_K | 1.88GB | | [kbtg-kpoint-v2-16bit-safe.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_K_M.gguf) | Q4_K_M | 1.88GB | | [kbtg-kpoint-v2-16bit-safe.Q4_1.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q4_1.gguf) | Q4_1 | 1.95GB | | [kbtg-kpoint-v2-16bit-safe.Q5_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_0.gguf) | Q5_0 | 2.11GB | | [kbtg-kpoint-v2-16bit-safe.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_K_S.gguf) | Q5_K_S | 2.11GB | | [kbtg-kpoint-v2-16bit-safe.Q5_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_K.gguf) | Q5_K | 2.16GB | | [kbtg-kpoint-v2-16bit-safe.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_K_M.gguf) | Q5_K_M | 2.16GB | | [kbtg-kpoint-v2-16bit-safe.Q5_1.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q5_1.gguf) | Q5_1 | 2.28GB | | [kbtg-kpoint-v2-16bit-safe.Q6_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q6_K.gguf) | Q6_K | 2.46GB | | [kbtg-kpoint-v2-16bit-safe.Q8_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v2-16bit-safe-gguf/blob/main/kbtg-kpoint-v2-16bit-safe.Q8_0.gguf) | Q8_0 | 3.19GB | Original model description: --- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** katopz - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

TusharAg/my-cute-pembroke-welsh-corgi-puppy

TusharAg

"2024-03-15T11:07:40Z"

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

"2024-03-15T10:51:23Z"

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My--Cute-Pembroke-Welsh-Corgi-puppy Dreambooth model trained by Tushar Ageawal following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 303102220044 Sample pictures of this concept: ![0](https://huggingface.co/TusharAg/my-cute-pembroke-welsh-corgi-puppy/resolve/main/sample_images/tka_(5).jpeg)

jaeag/practice_model_0222

jaeag

"2025-02-22T08:26:32Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

"2025-02-22T07:34:49Z"

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf

RichardErkhov

"2025-03-02T03:40:19Z"

0

null

[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-03-02T03:36:52Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) smolLM-marathi-v1 - GGUF - Model creator: https://huggingface.co/grpathak22/ - Original model: https://huggingface.co/grpathak22/smolLM-marathi-v1/ | Name | Quant method | Size | | ---- | ---- | ---- | | [smolLM-marathi-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q2_K.gguf) | Q2_K | 0.08GB | | [smolLM-marathi-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ3_XS.gguf) | IQ3_XS | 0.08GB | | [smolLM-marathi-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ3_S.gguf) | IQ3_S | 0.08GB | | [smolLM-marathi-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K_S.gguf) | Q3_K_S | 0.08GB | | [smolLM-marathi-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ3_M.gguf) | IQ3_M | 0.08GB | | [smolLM-marathi-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K.gguf) | Q3_K | 0.09GB | | [smolLM-marathi-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K_M.gguf) | Q3_K_M | 0.09GB | | [smolLM-marathi-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q3_K_L.gguf) | Q3_K_L | 0.09GB | | [smolLM-marathi-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ4_XS.gguf) | IQ4_XS | 0.09GB | | [smolLM-marathi-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_0.gguf) | Q4_0 | 0.09GB | | [smolLM-marathi-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.IQ4_NL.gguf) | IQ4_NL | 0.09GB | | [smolLM-marathi-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_K_S.gguf) | Q4_K_S | 0.1GB | | [smolLM-marathi-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_K.gguf) | Q4_K | 0.1GB | | [smolLM-marathi-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_K_M.gguf) | Q4_K_M | 0.1GB | | [smolLM-marathi-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q4_1.gguf) | Q4_1 | 0.09GB | | [smolLM-marathi-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_0.gguf) | Q5_0 | 0.1GB | | [smolLM-marathi-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_K_S.gguf) | Q5_K_S | 0.1GB | | [smolLM-marathi-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_K.gguf) | Q5_K | 0.1GB | | [smolLM-marathi-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_K_M.gguf) | Q5_K_M | 0.1GB | | [smolLM-marathi-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q5_1.gguf) | Q5_1 | 0.1GB | | [smolLM-marathi-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q6_K.gguf) | Q6_K | 0.13GB | | [smolLM-marathi-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/grpathak22_-_smolLM-marathi-v1-gguf/blob/main/smolLM-marathi-v1.Q8_0.gguf) | Q8_0 | 0.13GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2-mlx

GreenBitAI

"2024-04-14T20:28:00Z"

4

0

mlx

[ "mlx", "safetensors", "qwen2", "license:apache-2.0", "region:us" ]

null

"2024-04-13T11:44:23Z"

--- license: apache-2.0 tags: - mlx --- # GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2-mlx This quantized low-bit model was converted to MLX format from [`GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2`](). Refer to the [original model card](https://huggingface.co/GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2) for more details on the model. ## Use with mlx ```bash pip install gbx-lm ``` ```python from gbx_lm import load, generate model, tokenizer = load("GreenBitAI/Qwen-1.5-7B-Chat-layer-mix-bpw-2.2-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

ArthurZ/mamba-790m

ArthurZ

"2024-02-29T03:47:39Z"

373

0

transformers

[ "transformers", "safetensors", "mamba", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-02-19T12:21:24Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

unknownmixorgacc/p3rs0nl0ra-alisa-batch2

unknownmixorgacc

"2025-04-08T09:49:12Z"

0

diffusers

[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-04-08T09:49:11Z"

--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: p3rs0nl0ra-alisa license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # p3rs0nl0ra-alisa A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `p3rs0nl0ra-alisa` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.

VERSIL91/46366d3d-7fa4-42eb-9718-0f62943aabc3

VERSIL91

"2025-01-10T15:28:09Z"

10

0

peft

[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "license:mit", "region:us" ]

null

"2025-01-10T15:14:55Z"

--- library_name: peft license: mit base_model: HuggingFaceH4/zephyr-7b-beta tags: - axolotl - generated_from_trainer model-index: - name: 46366d3d-7fa4-42eb-9718-0f62943aabc3 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml accelerate_config: dynamo_backend: inductor mixed_precision: bf16 num_machines: 1 num_processes: auto use_cpu: false adapter: lora base_model: HuggingFaceH4/zephyr-7b-beta bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cf00b55ed8d6929c_train_data.json ds_type: json format: custom path: /workspace/input_data/cf00b55ed8d6929c_train_data.json type: field_instruction: linearized_input field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: VERSIL91/46366d3d-7fa4-42eb-9718-0f62943aabc3 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: cosine max_memory: 0: 70GiB max_steps: 20 micro_batch_size: 2 mlflow_experiment_name: /tmp/cf00b55ed8d6929c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true quantization_config: llm_int8_enable_fp32_cpu_offload: true load_in_8bit: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer torch_compile: true train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 46366d3d-7fa4-42eb-9718-0f62943aabc3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 46366d3d-7fa4-42eb-9718-0f62943aabc3 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 46366d3d-7fa4-42eb-9718-0f62943aabc3 This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0057 | 1 | nan | | 0.0 | 0.0284 | 5 | nan | | 0.0 | 0.0569 | 10 | nan | | 0.0 | 0.0853 | 15 | nan | | 0.0 | 0.1138 | 20 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Brianpuz/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF

Brianpuz

"2025-04-07T00:34:31Z"

0

transformers

[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-04-07T00:34:26Z"

--- base_model: Qwen/Qwen2.5-0.5B-Instruct language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Brianpuz/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF Absolutely tremendous! This repo features **GGUF quantized** versions of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) — made possible using the *very powerful* `llama.cpp`. Believe me, it's fast, it's smart, it's winning. ## Quantized Versions: Only the best quantization. You’ll love it. ## Run with llama.cpp Just plug it in, hit the command line, and boom — you're running world-class AI, folks: ```bash llama-cli --hf-repo Brianpuz/Qwen2.5-0.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-0.5b-instruct-q4_k_m.gguf -p "AI First, but also..." ``` This beautiful Hugging Face Space was brought to you by the **amazing team at [Antigma Labs](https://antigma.ai)**. Great people. Big vision. Doing things that matter — and doing them right. Total winners.

lesso06/874e70b1-b059-42dc-bec4-4e4ebe9f0ebe

lesso06

"2025-03-08T13:55:20Z"

0

peft

[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/Yarn-Mistral-7b-64k", "base_model:adapter:NousResearch/Yarn-Mistral-7b-64k", "license:apache-2.0", "region:us" ]

null

"2025-03-08T11:39:18Z"

--- library_name: peft license: apache-2.0 base_model: NousResearch/Yarn-Mistral-7b-64k tags: - axolotl - generated_from_trainer model-index: - name: 874e70b1-b059-42dc-bec4-4e4ebe9f0ebe results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Yarn-Mistral-7b-64k bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3c08cce58a50d6dc_train_data.json ds_type: json format: custom path: /workspace/input_data/3c08cce58a50d6dc_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso06/874e70b1-b059-42dc-bec4-4e4ebe9f0ebe hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000206 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/3c08cce58a50d6dc_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 60 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 02571ba9-f8e4-4154-9e2a-e670f6ca125e wandb_project: 06a wandb_run: your_name wandb_runid: 02571ba9-f8e4-4154-9e2a-e670f6ca125e warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 874e70b1-b059-42dc-bec4-4e4ebe9f0ebe This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000206 - train_batch_size: 4 - eval_batch_size: 4 - seed: 60 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0004 | 1 | 1.6355 | | 8.3253 | 0.2073 | 500 | 1.0255 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF

mradermacher

"2024-11-29T08:47:25Z"

22

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:zelk12/MT-Gen3-IMM-gemma-2-9B", "base_model:quantized:zelk12/MT-Gen3-IMM-gemma-2-9B", "endpoints_compatible", "region:us", "conversational" ]

null

"2024-11-29T07:48:57Z"

--- base_model: zelk12/MT-Gen3-IMM-gemma-2-9B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/zelk12/MT-Gen3-IMM-gemma-2-9B  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q2_K.gguf) | Q2_K | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q3_K_S.gguf) | Q3_K_S | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q3_K_L.gguf) | Q3_K_L | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.IQ4_XS.gguf) | IQ4_XS | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q4_0_4_4.gguf) | Q4_0_4_4 | 5.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q5_K_S.gguf) | Q5_K_S | 6.6 | | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q5_K_M.gguf) | Q5_K_M | 6.7 | | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q6_K.gguf) | Q6_K | 7.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF/resolve/main/MT-Gen3-IMM-gemma-2-9B.f16.gguf) | f16 | 18.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

BobMcDear/vit_small_patch16_224_in22k

BobMcDear

"2022-12-23T13:56:12Z"

0

null

[ "region:us" ]

null

"2022-12-13T23:11:25Z"

Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.

jogonba2/barthez-deft-linguistique

jogonba2

"2022-04-14T14:04:46Z"

4

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2022-03-02T23:29:05Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: barthez-deft-linguistique results: - task: name: Summarization type: summarization metrics: - name: Rouge1 type: rouge value: 41.989 ---  # barthez-deft-linguistique This model is a fine-tuned version of [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) on an unknown dataset. **Note**: this model is one of the preliminary experiments and it underperforms the models published in the paper (using [MBartHez](https://huggingface.co/moussaKam/mbarthez) and HAL/Wiki pre-training + copy mechanisms) It achieves the following results on the evaluation set: - Loss: 1.7596 - Rouge1: 41.989 - Rouge2: 22.4524 - Rougel: 32.7966 - Rougelsum: 32.7953 - Gen Len: 22.1549 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.0569 | 1.0 | 108 | 2.0282 | 31.6993 | 14.9483 | 25.5565 | 25.4379 | 18.3803 | | 2.2892 | 2.0 | 216 | 1.8553 | 35.2563 | 18.019 | 28.3135 | 28.2927 | 18.507 | | 1.9062 | 3.0 | 324 | 1.7696 | 37.4613 | 18.1488 | 28.9959 | 29.0134 | 19.5352 | | 1.716 | 4.0 | 432 | 1.7641 | 37.6903 | 18.7496 | 30.1097 | 30.1027 | 18.9577 | | 1.5722 | 5.0 | 540 | 1.7781 | 38.1013 | 19.8291 | 29.8142 | 29.802 | 19.169 | | 1.4655 | 6.0 | 648 | 1.7661 | 38.3557 | 20.3309 | 30.5068 | 30.4728 | 19.3662 | | 1.3507 | 7.0 | 756 | 1.7596 | 39.7409 | 20.2998 | 31.0849 | 31.1152 | 19.3944 | | 1.2874 | 8.0 | 864 | 1.7706 | 37.7846 | 20.3457 | 30.6826 | 30.6321 | 19.4789 | | 1.2641 | 9.0 | 972 | 1.7848 | 38.7421 | 19.5701 | 30.5798 | 30.6305 | 19.3944 | | 1.1192 | 10.0 | 1080 | 1.8008 | 40.3313 | 20.3378 | 31.8325 | 31.8648 | 19.5493 | | 1.0724 | 11.0 | 1188 | 1.8450 | 38.9612 | 20.5719 | 31.4496 | 31.3144 | 19.8592 | | 1.0077 | 12.0 | 1296 | 1.8364 | 36.5997 | 18.46 | 29.1808 | 29.1705 | 19.7324 | | 0.9362 | 13.0 | 1404 | 1.8677 | 38.0371 | 19.2321 | 30.3893 | 30.3926 | 19.6338 | | 0.8868 | 14.0 | 1512 | 1.9154 | 36.4737 | 18.5314 | 29.325 | 29.3634 | 19.6479 | | 0.8335 | 15.0 | 1620 | 1.9344 | 35.7583 | 18.0687 | 27.9666 | 27.8675 | 19.8028 | | 0.8305 | 16.0 | 1728 | 1.9556 | 37.2137 | 18.2199 | 29.5959 | 29.5799 | 19.9577 | | 0.8057 | 17.0 | 1836 | 1.9793 | 36.6834 | 17.8505 | 28.6701 | 28.7145 | 19.7324 | | 0.7869 | 18.0 | 1944 | 1.9994 | 37.5918 | 19.1984 | 28.8569 | 28.8278 | 19.7606 | | 0.7549 | 19.0 | 2052 | 2.0117 | 37.3278 | 18.5169 | 28.778 | 28.7737 | 19.8028 | | 0.7497 | 20.0 | 2160 | 2.0189 | 37.7513 | 19.1813 | 29.3675 | 29.402 | 19.6901 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.7.1+cu110 - Datasets 1.11.0 - Tokenizers 0.10.3

charliezjw/ppo-LunarLander-v2

charliezjw

"2023-12-16T16:04:31Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2023-12-16T16:04:09Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 223.46 +/- 25.25 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

SanXM1/Vel-Magnum-SE

SanXM1

"2025-04-06T05:14:31Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE", "base_model:merge:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE", "base_model:Sao10K/Llama-3.3-70B-Vulpecula-r1", "base_model:merge:Sao10K/Llama-3.3-70B-Vulpecula-r1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-06T04:21:22Z"

--- base_model: - Doctor-Shotgun/L3.3-70B-Magnum-v4-SE - Sao10K/Llama-3.3-70B-Vulpecula-r1 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Doctor-Shotgun/L3.3-70B-Magnum-v4-SE](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v4-SE) * [Sao10K/Llama-3.3-70B-Vulpecula-r1](https://huggingface.co/Sao10K/Llama-3.3-70B-Vulpecula-r1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Sao10K/Llama-3.3-70B-Vulpecula-r1 - model: Doctor-Shotgun/L3.3-70B-Magnum-v4-SE merge_method: slerp base_model: Sao10K/Llama-3.3-70B-Vulpecula-r1 parameters: t: - value: 0.8 dtype: bfloat16 tokenizer_source: base ```

tevf0/eee-486-assignment-2-part-1-best-model-run-2

tevf0

"2025-04-05T23:16:55Z"

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-04-05T23:15:22Z"

--- library_name: transformers tags: [] --- # eee-486-assignment-2-part-1-best-model-run-2 This is a fine-tuned BERT model for the GLUE RTE task. ## Model Details - **Base Model:** `bert-base-uncased` - **Task:** Textual Entailment (GLUE RTE) - **Language:** English - **License:** Apache 2.0 - **Finetuned From:** `bert-base-uncased` ## Training Details - **Learning Rate:** 2e-05 - **Epochs:** 5 - **Max Sequence Length:** 512 - **Dropout Rate:** 0.1 ## Evaluation - **Validation Accuracy:** 67.15%

Multi-Domain-Expert-Learning/expert-uspto

Multi-Domain-Expert-Learning

"2023-04-30T23:50:10Z"

19

1

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-04-30T21:33:12Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: expert-uspto results: [] ---  # expert-uspto This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2220 - Accuracy: 0.5362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2735 | 0.01 | 200 | 2.2464 | 0.5325 | | 2.2557 | 0.01 | 400 | 2.2417 | 0.5331 | | 2.2342 | 0.02 | 600 | 2.2342 | 0.5344 | | 2.2241 | 0.03 | 800 | 2.2267 | 0.5355 | | 2.229 | 0.03 | 1000 | 2.2220 | 0.5362 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3

qiufengqijun/mini_qwen_pt

qiufengqijun

"2025-01-19T05:37:43Z"

46

0

null

[ "safetensors", "qwen2", "text-generation", "conversational", "zh", "en", "dataset:BAAI/IndustryCorpus2", "dataset:BAAI/Infinity-Instruct", "dataset:BAAI/Infinity-Preference", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]

text-generation

"2025-01-18T08:13:04Z"

--- language: - zh - en base_model: - Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation license: apache-2.0 datasets: - BAAI/IndustryCorpus2 - BAAI/Infinity-Instruct - BAAI/Infinity-Preference --- # mini_qwen ## Introduction mini_qwen是一个从头开始训练的1B参数的大型语言模型(LLM)项目，包括预训练(PT)、微调(SFT)和直接偏好优化(DPO)3个部分。其中预训练和微调仅需要12G显存即可训练，直接偏好优化仅需要14G显存即可训练，这意味着使用T4显卡就可以开始你的训练之旅。 mini_qwen是以Qwen2.5-0.5B-Instruct模型为基础，通过扩充模型隐藏状态层数、隐藏状态维度和注意力头数，增加参数量到1B，并进行参数随机初始化。训练数据使用北京智源人工智能研究院的预训练(16B token)、微调(9M 条)和偏好数据(60K 条)，使用flash_attention_2进行加速，使用deepspeed在6张H800上训练25h(pt 1epoch)、43h(sft 3epoch)、1h(dpo 3epoch)。这是一次非常有趣且有价值的尝试，在整个过程中，本项目探究了尺度定律(scaling law)、复读机现象与微调阶段的知识注入，也解决了很多bug。本项目将尽可能详细地介绍整个训练过程，也欢迎交流讨论。更多内容详见：https://github.com/qiufengqijun/mini_qwen ## Quickstart 使用方法如下： ``` from transformers import AutoModelForCausalLM, AutoTokenizer import logging logging.getLogger("transformers").setLevel(logging.ERROR) # 忽略警告 # 加载分词器与模型 model_path = "/path/to/your/model" model = AutoModelForCausalLM.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) while True: prompt = input("用户：") text = prompt # 预训练模型 text = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n" # 微调和直接偏好优化模型 model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate(**model_inputs, max_new_tokens=512) generated_ids = [ output_ids[len(input_ids) :] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print("助手：", response) ```

nlp-esg-scoring/bert-base-finetuned-esg-gri-clean

nlp-esg-scoring

"2022-07-25T07:54:29Z"

5

0

transformers

[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2022-07-25T02:32:56Z"

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: nlp-esg-scoring/bert-base-finetuned-esg-gri-clean results: [] ---  # nlp-esg-scoring/bert-base-finetuned-esg-gri-clean This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9511 - Validation Loss: 1.5293 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -797, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9468 | 1.5190 | 0 | | 1.9433 | 1.5186 | 1 | | 1.9569 | 1.4843 | 2 | | 1.9510 | 1.5563 | 3 | | 1.9451 | 1.5308 | 4 | | 1.9576 | 1.5209 | 5 | | 1.9464 | 1.5324 | 6 | | 1.9525 | 1.5168 | 7 | | 1.9488 | 1.5340 | 8 | | 1.9511 | 1.5293 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

LaurelApollo/onepiece_style_LoRA

LaurelApollo

"2025-04-07T00:11:20Z"

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

"2025-04-07T00:11:10Z"

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: art in DISCOELYSIUM style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers ---  # SDXL LoRA DreamBooth - LaurelApollo/onepiece_style_LoRA <Gallery /> ## Model description These are LaurelApollo/onepiece_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use art in DISCOELYSIUM style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](LaurelApollo/onepiece_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

aroobarehman3232/Gpt2XL-finetuned-Tax

aroobarehman3232

"2025-04-14T15:25:23Z"

0

null

[ "region:us" ]

null

"2025-04-14T15:25:23Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

PrunaAI/vgg19_bn-turbo-tiny-green-smashed

PrunaAI

"2024-08-02T15:26:51Z"

2

0

pruna-engine

[ "pruna-engine", "region:us" ]

null

"2024-03-07T13:53:17Z"

--- library_name: pruna-engine thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton. - ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 2. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir vgg19_bn-turbo-tiny-green-smashed huggingface-cli download PrunaAI/vgg19_bn-turbo-tiny-green-smashed --local-dir vgg19_bn-turbo-tiny-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "vgg19_bn-turbo-tiny-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "vgg19_bn-turbo-tiny-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. import torch; image = torch.rand(1, 3, 224, 224).to('cuda') smashed_model(image) ``` ## Configurations The configuration info are in `model/smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model vgg19_bn before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

distillslm/alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it

distillslm

"2025-03-13T19:12:38Z"

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "trl", "gkd", "conversational", "arxiv:2306.13649", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-10T07:57:33Z"

--- base_model: google/gemma-2-2b-it library_name: transformers model_name: alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it tags: - generated_from_trainer - trl - gkd licence: license --- # Model Card for alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="distillslm/alpaca_seq_kd_sft_gemma-2-2b-it_from_gemma-2-9b-it", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/rucnyz/huggingface/runs/g1aurazd) This model was trained with GKD, a method introduced in [On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes](https://huggingface.co/papers/2306.13649). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.6.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite GKD as: ```bibtex @inproceedings{agarwal2024on-policy, title = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}}, author = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem}, year = 2024, booktitle = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024}, publisher = {OpenReview.net}, url = {https://openreview.net/forum?id=3zKtaqxLhW}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

toilaluan/smol-lm-2-135m-latent-encoder

toilaluan

"2025-03-13T05:55:55Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-11T15:07:29Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Juderocks22/Puppet

Juderocks22

"2025-04-02T12:34:56Z"

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]

text-to-image

"2025-04-02T12:34:43Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

markury/Smokey-v3

markury

"2024-09-05T00:16:52Z"

1,730

1

diffusers

[ "diffusers", "flux", "flux-diffusers", "text-to-image", "simpletuner", "safe-for-work", "lora", "template:sd-lora", "standard", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2024-09-04T07:18:49Z"

--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - safe-for-work - lora - template:sd-lora - standard inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'a photo of Smokey the cat, a white and grey cat sitting in a bathtub with a rubber duck' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # Smokey-v3 This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` a photo of Smokey the cat, a white and grey cat sitting in a bathtub with a rubber duck ``` ## Validation settings - CFG: `3.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 90 - Training steps: 2800 - Learning rate: 0.0001 - Effective batch size: 8 - Micro-batch size: 8 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: Pure BF16 - Quantised: Yes: int8-quanto - Xformers: Not used - LoRA Rank: 16 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### Smokey-512-crop - Repeats: 0 - Total number of images: 114 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: center - Crop aspect: square ### Smokey-1024-crop - Repeats: 0 - Total number of images: 124 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'markury/Smokey-v3' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "a photo of Smokey the cat, a white and grey cat sitting in a bathtub with a rubber duck" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.0, ).images[0] image.save("output.png", format="PNG") ```

great0001/cca76d0b-87d1-4dd4-8533-0ee375236526

great0001

"2025-01-21T17:43:25Z"

6

0

peft

[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:adapter:NousResearch/Hermes-2-Pro-Mistral-7B", "license:apache-2.0", "region:us" ]

null

"2025-01-21T17:41:14Z"

--- library_name: peft license: apache-2.0 base_model: NousResearch/Hermes-2-Pro-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: cca76d0b-87d1-4dd4-8533-0ee375236526 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Hermes-2-Pro-Mistral-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ddd8b0dd75e40feb_train_data.json ds_type: json format: custom path: /workspace/input_data/ddd8b0dd75e40feb_train_data.json type: field_input: '' field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: great0001/cca76d0b-87d1-4dd4-8533-0ee375236526 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/ddd8b0dd75e40feb_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: fae321c3-44fb-4850-ae2a-8665061455bc wandb_project: Mine-SN56-20-Gradients-On-Demand wandb_run: your_name wandb_runid: fae321c3-44fb-4850-ae2a-8665061455bc warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # cca76d0b-87d1-4dd4-8533-0ee375236526 This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.4833 | 0.0011 | 1 | nan | | 1.1015 | 0.0033 | 3 | nan | | 2.5063 | 0.0065 | 6 | nan | | 1.813 | 0.0098 | 9 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

zinoubm/OrpoLlama-3.2-1B-Instruct

zinoubm

"2025-01-12T21:09:11Z"

8

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-01-12T21:07:38Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

trangtrannnnn/23d7bf23-536a-49c2-9966-f1db4f75054e

trangtrannnnn

"2025-01-27T07:56:12Z"

8

0

peft

[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-3B", "base_model:adapter:Qwen/Qwen2.5-3B", "license:other", "8-bit", "bitsandbytes", "region:us" ]

null

"2025-01-27T07:30:40Z"

--- library_name: peft license: other base_model: Qwen/Qwen2.5-3B tags: - axolotl - generated_from_trainer model-index: - name: 23d7bf23-536a-49c2-9966-f1db4f75054e results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-3B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e3f7343345b9b21f_train_data.json ds_type: json format: custom path: /workspace/input_data/e3f7343345b9b21f_train_data.json type: field_instruction: description field_output: code format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: trangtrannnnn/23d7bf23-536a-49c2-9966-f1db4f75054e hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/e3f7343345b9b21f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: db8f3cf6-8e27-4f7a-a1cc-9f92fa694ab2 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: db8f3cf6-8e27-4f7a-a1cc-9f92fa694ab2 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 23d7bf23-536a-49c2-9966-f1db4f75054e This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.5265 | 0.0214 | 200 | 0.5356 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

AntoineGourru/Mistral_qlora_drome_R256A512BS64E2MLR1-2e5x2

AntoineGourru

"2024-02-19T15:34:08Z"

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]

null

"2024-02-19T15:33:13Z"

--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0

mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF

mradermacher

"2025-03-18T22:43:01Z"

0

transformers

[ "transformers", "gguf", "unsloth", "trl", "sft", "en", "base_model:sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2", "base_model:quantized:sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-03-18T22:03:50Z"

--- base_model: sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2 language: - en library_name: transformers quantized_by: mradermacher tags: - unsloth - trl - sft --- ## About      static quants of https://huggingface.co/sammyjoseph/DeepSeek-R1-Vibration1-RAG-Version-2  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Vibration1-RAG-Version-2-GGUF/resolve/main/DeepSeek-R1-Vibration1-RAG-Version-2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

abu1111/bge-synthesisQA-query-encoder

abu1111

"2025-02-07T11:15:37Z"

5

0

null

[ "safetensors", "bert", "en", "arxiv:2309.07597", "license:other", "region:us" ]

null

"2025-02-07T04:48:59Z"

--- language: - en tag: - dragon - retriever - conversation - multi-turn - conversational query license: - other --- ## Model Description We introduce bge-synthesisQA, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the [bge](https://huggingface.co/BAAI/bge-large-en-v1.5) retriever. **Please note that bge-synthesisQA is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the query encoder of bge-synthesisQA for getting the query embeddings, and you also need the context encoder to get context embeddings, which can be found [here](https://huggingface.co/abu1111/bge-synthesisQA-context-encoder). Both query encoder and context encoder share the same tokenizer.** ## Other Resources ## Benchmark Results <style type="text/css"> .tg {border:none;border-collapse:collapse;border-spacing:0;} .tg td{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden; padding:10px 5px;word-break:normal;} .tg th{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:normal; overflow:hidden;padding:10px 5px;word-break:normal;} .tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:center} .tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:center} </style> <table class="tg"> <thead> <tr> <th class="tg-0pky" rowspan="2"></th> <th class="tg-c3ow" colspan="3">Average</th> <th class="tg-c3ow" colspan="3">Doc2Dial</th> <th class="tg-c3ow" colspan="3">QuAC</th> <th class="tg-c3ow" colspan="3">QReCC</th> </tr> <tr> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> <th class="tg-c3ow">top-1</th> <th class="tg-c3ow">top-5</th> <th class="tg-c3ow">top-20</th> </tr> </thead> <tbody> <tr> <td class="tg-0pky">bge-large-en-v1.5</td> <td class="tg-c3ow">37.75</td> <td class="tg-c3ow">71.55</td> <td class="tg-c3ow">91.54</td> <td class="tg-c3ow">32.55</td> <td class="tg-c3ow">66.13</td> <td class="tg-c3ow">86.98</td> <td class="tg-c3ow">47.93</td> <td class="tg-c3ow">74.26</td> <td class="tg-c3ow">92.02</td> <td class="tg-c3ow">32.76</td> <td class="tg-c3ow">74.27</td> <td class="tg-c3ow">95.63</td> </tr> <tr> <td class="tg-0pky">bge-synthesisQA</td> <td class="tg-c3ow">51.72</td> <td class="tg-c3ow">83.12</td> <td class="tg-c3ow">95.02</td> <td class="tg-c3ow">38.72</td> <td class="tg-c3ow">71.39</td> <td class="tg-c3ow">88.86</td> <td class="tg-c3ow">50.71</td> <td class="tg-c3ow">83.57</td> <td class="tg-c3ow">97.13</td> <td class="tg-c3ow">65.73</td> <td class="tg-c3ow">94.41</td> <td class="tg-c3ow">99.07</td> </tr> </tbody> </table> Retrieval results across five multi-turn QA datasets (Doc2Dial, QuAC, QReCC) with the average top-1 top-5 and top-20 recall scores. ## How to use ```python import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('abu1111/bge-synthesisQA-query-encoder') query_encoder = AutoModel.from_pretrained('abu1111/bge-synthesisQA-query-encoder') context_encoder = AutoModel.from_pretrained('abu1111/bge-synthesisQA-context-encoder') query = [ {"role": "user", "content": "Can you help me understand how to choose a health insurance plan?"}, {"role": "agent", "content": "Sure, are you looking for individual coverage or a family plan?"}, {"role": "user", "content": "I need a family plan."} ] contexts = [ "Health Insurance Guide | Choosing the Right Plan \nWhen selecting a health insurance plan, it's important to consider your family's specific needs. Factors such as the number of family members, medical history, and budget should all play a role in your decision. Plans are typically divided into categories: Bronze, Silver, Gold, and Platinum, with each offering different levels of coverage and premiums. For families with frequent medical visits, a plan with a higher premium but lower out-of-pocket costs (like a Gold or Platinum plan) might make sense. Conversely, if your family is generally healthy, a Bronze plan with lower premiums might be more cost-effective.", "Health Insurance Basics | Individual vs. Family Coverage \nIndividual health insurance plans cover only one person, while family plans provide coverage for an entire household. Family plans usually have a higher premium but include shared deductibles and out-of-pocket maximums. When choosing a family plan, consider factors like the total coverage provided, the network of doctors and hospitals, and whether the plan includes benefits such as pediatric care. Understanding these distinctions can help you make an informed decision about the best option for you and your loved ones." ] ## convert query into a format as follows: ## user: {user}\nagent: {agent}\nuser: {user} formatted_query = '\n'.join([turn['role'] + ": " + turn['content'] for turn in query]).strip() ## get query and context embeddings query_input = tokenizer(formatted_query, return_tensors='pt') ctx_input = tokenizer(contexts, padding=True, truncation=True, max_length=512, return_tensors='pt') query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :] ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :] ## Compute similarity scores using dot product similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx) ## rank the similarity (from highest to lowest) ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx) ``` ## Evaluations on Multi-Turn QA Retrieval Benchmark **(UPDATE!!)** We evaluate multi-turn QA retrieval on three datasets: Doc2Dial, QuAC, and QReCC, which can be found in the [ChatRAG Bench](https://huggingface.co/datasets/nvidia/ChatRAG-Bench). The evaluation scripts can be found [here](https://huggingface.co/nvidia/dragon-multiturn-query-encoder/tree/main/evaluation). ## License bge-synthesisQA is built on top of [bge](https://arxiv.org/abs/2309.07597). We refer users to the original license of the BAAI/bge-large-en-v1.5 model. bge-synthesisQA is also subject to the [Terms of Use](https://openai.com/policies/terms-of-use).