librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 06:28:01	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 06:25:04	card stringlengths 11 1.01M
juliajoanna/sdxl-flintstones_finetuning_3	juliajoanna	2023-11-04T04:02:58Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "base_model:juliajoanna/sdxl-flintstones_finetuning_1", "base_model:finetune:juliajoanna/sdxl-flintstones_finetuning_1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2023-11-02T14:09:23Z	--- license: creativeml-openrail-m base_model: juliajoanna/sdxl-flintstones_finetuning_1 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - juliajoanna/sdxl-flintstones_finetuning_3 This pipeline was finetuned from juliajoanna/sdxl-flintstones_finetuning_1 on the None dataset. Below are some example images generated with the finetuned pipeline using the following prompt: Fred is driving a car: ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
zaidazhari/Taxi-V3	zaidazhari	2023-11-04T03:41:47Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-11-04T03:41:45Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-V3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.63 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="zaidazhari/Taxi-V3", 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"]) ```
Yntec/AgarthaChadstyle	Yntec	2023-11-04T03:31:27Z	332	1	diffusers	[ "diffusers", "safetensors", "Style", "Abstract", "Surrealism", "ChadUltraF3", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-04T02:50:41Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Style - Abstract - Surrealism - ChadUltraF3 - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers --- # 🌈🧬🍭🍄👁️ Agartha 👁️🍄🍭🧬🌈(ChadStyle) Check the many trigger words of this model at the original page: https://civitai.com/models/69808/agartha-chadstyle Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/XjXVRIcTs_xOB8xkd-RCc.png) bedroom, DETAILED CHIBI Cartoon, BLUE EYES, Pretty CUTE Girl, beautiful detailed PONYTAIL, seifuku clothes, gorgeous detailed hair, Magazine ad, 1949, iconic. acrylic art on canvas By KlaysMoji and artgerm and Clay Mann and and leyendecker and Dave Rapoza
sh-holmes/Reinforce-Pixelcopter-PLE-v0	sh-holmes	2023-11-04T02:13:14Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-04T02:12:43Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 25.00 +/- 14.32 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4	kanishka	2023-11-04T02:09:03Z	57	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T18:52:15Z	--- base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4 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. --> # smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1756 - Accuracy: 0.4274 ## 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: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.9239 \| 1.0 \| 18353 \| 3.9992 \| 0.3336 \| \| 3.3918 \| 2.0 \| 36706 \| 3.5081 \| 0.3842 \| \| 3.201 \| 3.0 \| 55059 \| 3.3534 \| 0.4017 \| \| 3.1032 \| 4.0 \| 73412 \| 3.2792 \| 0.4094 \| \| 3.0303 \| 5.0 \| 91765 \| 3.2313 \| 0.4146 \| \| 2.9788 \| 6.0 \| 110118 \| 3.2123 \| 0.4176 \| \| 2.9353 \| 7.0 \| 128471 \| 3.1918 \| 0.4199 \| \| 2.8982 \| 8.0 \| 146824 \| 3.1838 \| 0.4220 \| \| 2.868 \| 9.0 \| 165177 \| 3.1760 \| 0.4230 \| \| 2.8384 \| 10.0 \| 183530 \| 3.1689 \| 0.4237 \| \| 2.8118 \| 11.0 \| 201883 \| 3.1658 \| 0.4250 \| \| 2.7971 \| 12.0 \| 220236 \| 3.1678 \| 0.4250 \| \| 2.7705 \| 13.0 \| 238589 \| 3.1651 \| 0.4258 \| \| 2.7438 \| 14.0 \| 256942 \| 3.1691 \| 0.4260 \| \| 2.73 \| 15.0 \| 275295 \| 3.1655 \| 0.4264 \| \| 2.712 \| 16.0 \| 293648 \| 3.1646 \| 0.4269 \| \| 2.6921 \| 17.0 \| 312001 \| 3.1692 \| 0.4271 \| \| 2.6711 \| 18.0 \| 330354 \| 3.1688 \| 0.4273 \| \| 2.657 \| 19.0 \| 348707 \| 3.1738 \| 0.4274 \| \| 2.6387 \| 20.0 \| 367060 \| 3.1756 \| 0.4274 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.14.1
rerdscf/canistermix-5	rerdscf	2023-11-04T02:02:58Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-11-04T01:54:55Z	--- license: creativeml-openrail-m ---
ne0chen/distilbert-base-uncased-finetuned-emotion	ne0chen	2023-11-04T02:00:36Z	6	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "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	2023-11-03T01:15:36Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.92 - name: F1 type: f1 value: 0.9196020288399169 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2162 - Accuracy: 0.92 - F1: 0.9196 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8208 \| 1.0 \| 250 \| 0.3211 \| 0.9015 \| 0.9006 \| \| 0.2503 \| 2.0 \| 500 \| 0.2162 \| 0.92 \| 0.9196 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1
sh-holmes/Reinforce-CartPole-v1	sh-holmes	2023-11-04T01:48:15Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-04T01:47:50Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
TomyAI/Leotard	TomyAI	2023-11-04T01:37:01Z	0	4	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-11-03T13:23:24Z	--- license: creativeml-openrail-m --- レオタードのLoRA4種です。体操用レオタード：leotard_Gymnast.safetensors ![leotard_Gymnast](leotard_Gymnast.png) 新体操用レオタード：leotard_RhythmicGymnast.safetensors ![leotard_RhythmicGymnast](leotard_RhythmicGymnast.png) オペラチュチュのレオタード：leotard_OperaTutu.safetensors ![leotard_OperaTutu](leotard_OperaTutu.png) パンケーキチュチュのレオタード：leotard_PancakeTutu.safetensors ![leotard_PancakeTutu](leotard_PancakeTutu.png)
JoshRoehlFan/JoshBot-RVC-V2	JoshRoehlFan	2023-11-04T01:20:28Z	1	0	transformers	[ "transformers", "en", "arxiv:1910.09700", "license:unknown", "endpoints_compatible", "region:us" ]	null	2023-11-03T23:29:09Z	--- license: unknown language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Big Seph - Model type: RVC-V2 - Language(s) (NLP): English - License: Unknown ### 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 idk, fuck you ### 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]
mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-100-qlora-4bit	mtc	2023-11-04T01:19:02Z	0	0	peft	[ "peft", "region:us" ]	null	2023-11-04T01:18:26Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0
tjkmitl/NegativeThaiNews_test1	tjkmitl	2023-11-04T01:18:45Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:csebuetnlp/mT5_multilingual_XLSum", "base_model:finetune:csebuetnlp/mT5_multilingual_XLSum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-04T01:15:54Z	--- base_model: csebuetnlp/mT5_multilingual_XLSum tags: - generated_from_trainer model-index: - name: NegativeThaiNews_test1 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. --> # NegativeThaiNews_test1 This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4056 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.5843 \| 2.5 \| 500 \| 3.4673 \| \| 2.8438 \| 5.0 \| 1000 \| 3.4056 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
weishuai-4670/textual_inversion_find_new	weishuai-4670	2023-11-04T01:15:07Z	37	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-03T04:35:42Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - weishuai-4670/textual_inversion_find_new These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
PhoenixStormJr/Use-RVC-for-other-voice-cloners	PhoenixStormJr	2023-11-04T00:33:30Z	0	0	null	[ "license:cc", "region:us" ]	null	2023-10-31T00:00:55Z	--- license: cc --- NOTE: Voice.ai now charges for building voices, I was going to use this for voice.ai but now they suck! I will leave this open for any other voice cloners that are out there. This was made with: https://www.ibm.com/demos/live/tts-demo/self-service/home (IBM Watson text to speech) This is a text to speech online generator for free. If you use this, refer to their terms of service: https://watson-developer-cloud.github.io/terms?name=Text-to-Speech%20Demo OK, so you got your model with RVC. But you have another voice cloner as well. How can we transport the model to the other voice cloner? This is actually extremely easy. All we need is to create audio with our current models, and upload that audio! The worst voice cloners work with 2 hours of audio... I know, they suck... Therefore, here are 2 hours of audio of Allison from IBM (read their TOS.). Just convert the audio to another character, and upload that to the other voice cloner! RVC keeps crashing if I upload a 2 hour audio file... even if I upload a 1 hour audio file... Therefore, I split the 2 hours into 30 minutes each, so 4 files total to convert. Note: It will take about a minute and 30 seconds to upload a wav file. Please be patient. Note 2: It will take probably around 1 hour to convert a 30 minute file. Note 3: It takes around 3400 seconds to turn 31:17 minutes of audio into another voice. That means for every second of audio, the program will take 1.8 seconds to convert it. (1.811401172 seconds to be exact...) Audio in .wav format: Sample Audio: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.wav 1st 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.wav 2nd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.wav 3rd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.wav 4th 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.wav - - - - Audio in .mp3 format: Sample Audio: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.mp3 1st 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.mp3 2nd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.mp3 3rd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.mp3 4th 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.mp3
kgkeklikci/ppo-LunarLander-v2	kgkeklikci	2023-11-04T00:16:55Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-04T00:16:36Z	--- 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: 264.82 +/- 17.04 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 ... ```
Heralax/Augmental-13b	Heralax	2023-11-04T00:10:30Z	18	9	transformers	[ "transformers", "pytorch", "gguf", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-23T22:56:19Z	--- license: llama2 --- # Augmental-13b -- Human-written, AI-enhanced Note: after some internal testing inspired by early feedback, it seems that the version of this model trained for an additional epoch performs better. I've added a q5km quant of this version to this model repo and will be requesting a TheBloke quantization soon. Put simply, I might've overfocused on loss, when in reality it isn't a terribly precise metric, which led me to "undercook" this model. Version A: https://huggingface.co/Heralax/Augmental-13b-v1.50_A Version B: https://huggingface.co/Heralax/Augmental-13b-v1.50_B ## Details at a glance - What it is: MythoMax 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). - Prompt format: SillyTavern. - What sets it apart: The "augmented data" approach that MythoMakise took has been generalized beyond one character, refined to be cheaper, improved to have more diversity of writing, and scaled up by a factor of 8. Importantly, an additional GPT-4 pass was done on the dataset, where it chose specific lines to turn into much longer and more descriptive ones. As a result, this model excels at longer responses. - Model quality as per my own ad-hoc testing: really good - A 70b version might be on the way soon. - Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax) - Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also highly important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.) ## Long-form description and essay The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data). One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was? Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic. I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing on top of human writing. And it works very well. MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted. This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising. Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol. If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax). ## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data) ![](augmental_anime_image.png) ## Prompt format example ``` ## Charname - You're "Charname" in this never-ending roleplay with "User". ### Input: [user persona] char persona ### Response: (OOC) Understood. I will take this info into account for the roleplay. (end OOC) ### New Roleplay: ### Instruction: #### {User}: reply ### Response: #### {Char}: reply ^ repeat the above some number of times ### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative): #### Charname: ``` ## Training This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on. Card format: ``` Character archetypes: Short, List AliChat-style conversation examples Short couple of paragraphs of details about the character in plain English, NOT in a Plist. "Character is prone to X and Y. Character frequently does Z." I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode. ``` Okabe: ``` Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist. Okabe's description of himself, in a conversational format: {c}: "What's your past?" Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy. "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly. "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?" {c}: How would you describe your personality? Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable. "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries." Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image. Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human. Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Kurisu: ``` ## Kurisu - You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro". ### Input: [Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)] Character archetypes: Genius, Tsundere, Sarcastic, Logical. Kurisu's description of her own personality, told in a narrative format: Okabe: Kurisu, what's your life story? Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up." Okabe: What's your personality? Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing." Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves. Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere. Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations. Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well. Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Faris: ``` Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful Faris's description of her own personality, told in a narrative format: Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade. Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish. Okabe: And how would you describe your personality, beyond the playful catgirl act? Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~! Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes. Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people. Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Luka: ``` Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer. Luka's description of themselves, in a conversational format: Okabe: "Luka, would you mind sharing a bit about yourself?" Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san." Okabe: How would you describe your personality? Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about. Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri. Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others. Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced. Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises. Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are. Luka's full name is Urushibara Luka. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Mayuri: ``` Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic. Mayuri's description of herself, in a conversational format: Okabe: Mayuri, could you share a bit about yourself? Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~ Okabe: And what about your personality? Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together! Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform. Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences. She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled. Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike. Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress. She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship. Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Itaru: ``` Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease Itaru's description of his own personality, told in a conversational format: Okabe: Daru! My loyal Super Hacka! Tell me about your life story. Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to. Okabe: And what about your personality, my rotund friend? Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them. Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap. Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it. His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people. Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Suzuha: ``` Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined Amane Suzuha's description of her own personality, told in a narrative format: Okabe: Suzuha, can you share your past and what brought you here? Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival. Okabe: How would you describe yourself? Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen. Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders. Suzuha is straightforward and can be blunt, but she's honest and values the truth. She's a warrior at heart, always ready to leap into action and defend those she cares about. Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era. Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family. She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own. Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission. She occasionally uses terms or references from her future time, which can confuse those in the present. While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated. She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ```
tranquocthanh/ppo-LunarLander-v2	tranquocthanh	2023-11-03T23:57:06Z	0	1	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T23:56:46Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO-MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.39 +/- 16.20 name: mean_reward verified: false --- # PPO-MlpPolicy Agent playing LunarLander-v2 This is a trained model of a PPO-MlpPolicy 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 ... ```
User1115/whisper-large-v2-Biology-Fine-tuning-100steps	User1115	2023-11-03T23:45:24Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-large-v2", "base_model:adapter:openai/whisper-large-v2", "region:us" ]	null	2023-11-03T23:45:14Z	--- library_name: peft base_model: openai/whisper-large-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] - 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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.7.0.dev0
Aassemtkt/segformer-b3-finetuned-drugs-in-bins-nov-23	Aassemtkt	2023-11-03T23:27:46Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2023-11-03T21:22:37Z	--- license: other base_model: nvidia/mit-b3 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b3-finetuned-drugs-in-bins-nov-23 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-b3-finetuned-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the Aassemtkt/v0.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.0494 - Mean Iou: 0.4900 - Mean Accuracy: 0.9799 - Overall Accuracy: 0.9799 - Accuracy Unlabeled: nan - Accuracy Drug-blister: 0.9799 - Iou Unlabeled: 0.0 - Iou Drug-blister: 0.9799 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Drug-blister \| Iou Unlabeled \| Iou Drug-blister \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:---------------------:\|:-------------:\|:----------------:\| \| 0.4816 \| 0.14 \| 20 \| 0.2387 \| 0.4744 \| 0.9488 \| 0.9488 \| nan \| 0.9488 \| 0.0 \| 0.9488 \| \| 0.1071 \| 0.29 \| 40 \| 0.0969 \| 0.4766 \| 0.9532 \| 0.9532 \| nan \| 0.9532 \| 0.0 \| 0.9532 \| \| 0.102 \| 0.43 \| 60 \| 0.0701 \| 0.4799 \| 0.9599 \| 0.9599 \| nan \| 0.9599 \| 0.0 \| 0.9599 \| \| 0.0828 \| 0.58 \| 80 \| 0.0748 \| 0.4865 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \| \| 0.2944 \| 0.72 \| 100 \| 0.0517 \| 0.4816 \| 0.9633 \| 0.9633 \| nan \| 0.9633 \| 0.0 \| 0.9633 \| \| 0.0308 \| 0.86 \| 120 \| 0.0493 \| 0.4854 \| 0.9709 \| 0.9709 \| nan \| 0.9709 \| 0.0 \| 0.9709 \| \| 0.0247 \| 1.01 \| 140 \| 0.0488 \| 0.4853 \| 0.9706 \| 0.9706 \| nan \| 0.9706 \| 0.0 \| 0.9706 \| \| 0.0194 \| 1.15 \| 160 \| 0.0447 \| 0.4864 \| 0.9728 \| 0.9728 \| nan \| 0.9728 \| 0.0 \| 0.9728 \| \| 0.1873 \| 1.29 \| 180 \| 0.0496 \| 0.4789 \| 0.9579 \| 0.9579 \| nan \| 0.9579 \| 0.0 \| 0.9579 \| \| 0.0984 \| 1.44 \| 200 \| 0.0442 \| 0.4838 \| 0.9676 \| 0.9676 \| nan \| 0.9676 \| 0.0 \| 0.9676 \| \| 0.4066 \| 1.58 \| 220 \| 0.0384 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0197 \| 1.73 \| 240 \| 0.0567 \| 0.4809 \| 0.9619 \| 0.9619 \| nan \| 0.9619 \| 0.0 \| 0.9619 \| \| 0.068 \| 1.87 \| 260 \| 0.0389 \| 0.4849 \| 0.9698 \| 0.9698 \| nan \| 0.9698 \| 0.0 \| 0.9698 \| \| 0.029 \| 2.01 \| 280 \| 0.0351 \| 0.4853 \| 0.9706 \| 0.9706 \| nan \| 0.9706 \| 0.0 \| 0.9706 \| \| 0.016 \| 2.16 \| 300 \| 0.0373 \| 0.4821 \| 0.9642 \| 0.9642 \| nan \| 0.9642 \| 0.0 \| 0.9642 \| \| 0.0146 \| 2.3 \| 320 \| 0.0367 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0123 \| 2.45 \| 340 \| 0.0388 \| 0.4872 \| 0.9745 \| 0.9745 \| nan \| 0.9745 \| 0.0 \| 0.9745 \| \| 0.1359 \| 2.59 \| 360 \| 0.0360 \| 0.4858 \| 0.9715 \| 0.9715 \| nan \| 0.9715 \| 0.0 \| 0.9715 \| \| 0.0142 \| 2.73 \| 380 \| 0.0337 \| 0.4882 \| 0.9765 \| 0.9765 \| nan \| 0.9765 \| 0.0 \| 0.9765 \| \| 0.012 \| 2.88 \| 400 \| 0.0357 \| 0.4865 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \| \| 0.0101 \| 3.02 \| 420 \| 0.0370 \| 0.4864 \| 0.9728 \| 0.9728 \| nan \| 0.9728 \| 0.0 \| 0.9728 \| \| 0.0098 \| 3.17 \| 440 \| 0.0361 \| 0.4870 \| 0.9740 \| 0.9740 \| nan \| 0.9740 \| 0.0 \| 0.9740 \| \| 0.0226 \| 3.31 \| 460 \| 0.0349 \| 0.4895 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0157 \| 3.45 \| 480 \| 0.0362 \| 0.4856 \| 0.9712 \| 0.9712 \| nan \| 0.9712 \| 0.0 \| 0.9712 \| \| 0.0145 \| 3.6 \| 500 \| 0.0468 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \| \| 0.1801 \| 3.74 \| 520 \| 0.0324 \| 0.4906 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0129 \| 3.88 \| 540 \| 0.0314 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0159 \| 4.03 \| 560 \| 0.0310 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0132 \| 4.17 \| 580 \| 0.0321 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0126 \| 4.32 \| 600 \| 0.0329 \| 0.4874 \| 0.9747 \| 0.9747 \| nan \| 0.9747 \| 0.0 \| 0.9747 \| \| 0.0156 \| 4.46 \| 620 \| 0.0381 \| 0.4876 \| 0.9751 \| 0.9751 \| nan \| 0.9751 \| 0.0 \| 0.9751 \| \| 0.0147 \| 4.6 \| 640 \| 0.0322 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0174 \| 4.75 \| 660 \| 0.0344 \| 0.4886 \| 0.9772 \| 0.9772 \| nan \| 0.9772 \| 0.0 \| 0.9772 \| \| 0.1191 \| 4.89 \| 680 \| 0.0378 \| 0.4863 \| 0.9726 \| 0.9726 \| nan \| 0.9726 \| 0.0 \| 0.9726 \| \| 0.0117 \| 5.04 \| 700 \| 0.0386 \| 0.4873 \| 0.9745 \| 0.9745 \| nan \| 0.9745 \| 0.0 \| 0.9745 \| \| 0.0193 \| 5.18 \| 720 \| 0.0361 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0214 \| 5.32 \| 740 \| 0.0360 \| 0.4886 \| 0.9772 \| 0.9772 \| nan \| 0.9772 \| 0.0 \| 0.9772 \| \| 0.0184 \| 5.47 \| 760 \| 0.0322 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0262 \| 5.61 \| 780 \| 0.0357 \| 0.4907 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0115 \| 5.76 \| 800 \| 0.0386 \| 0.4887 \| 0.9774 \| 0.9774 \| nan \| 0.9774 \| 0.0 \| 0.9774 \| \| 0.0145 \| 5.9 \| 820 \| 0.0394 \| 0.4879 \| 0.9759 \| 0.9759 \| nan \| 0.9759 \| 0.0 \| 0.9759 \| \| 0.0097 \| 6.04 \| 840 \| 0.0322 \| 0.4889 \| 0.9777 \| 0.9777 \| nan \| 0.9777 \| 0.0 \| 0.9777 \| \| 0.0101 \| 6.19 \| 860 \| 0.0313 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0099 \| 6.33 \| 880 \| 0.0336 \| 0.4876 \| 0.9751 \| 0.9751 \| nan \| 0.9751 \| 0.0 \| 0.9751 \| \| 0.0092 \| 6.47 \| 900 \| 0.0342 \| 0.4894 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0087 \| 6.62 \| 920 \| 0.0352 \| 0.4913 \| 0.9825 \| 0.9825 \| nan \| 0.9825 \| 0.0 \| 0.9825 \| \| 0.019 \| 6.76 \| 940 \| 0.0516 \| 0.4871 \| 0.9742 \| 0.9742 \| nan \| 0.9742 \| 0.0 \| 0.9742 \| \| 0.0104 \| 6.91 \| 960 \| 0.0364 \| 0.4877 \| 0.9754 \| 0.9754 \| nan \| 0.9754 \| 0.0 \| 0.9754 \| \| 0.0079 \| 7.05 \| 980 \| 0.0300 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.0107 \| 7.19 \| 1000 \| 0.0327 \| 0.4939 \| 0.9878 \| 0.9878 \| nan \| 0.9878 \| 0.0 \| 0.9878 \| \| 0.0097 \| 7.34 \| 1020 \| 0.0294 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0359 \| 7.48 \| 1040 \| 0.0321 \| 0.4908 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0674 \| 7.63 \| 1060 \| 0.0321 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.1484 \| 7.77 \| 1080 \| 0.0428 \| 0.4868 \| 0.9737 \| 0.9737 \| nan \| 0.9737 \| 0.0 \| 0.9737 \| \| 0.188 \| 7.91 \| 1100 \| 0.0338 \| 0.4945 \| 0.9890 \| 0.9890 \| nan \| 0.9890 \| 0.0 \| 0.9890 \| \| 0.0124 \| 8.06 \| 1120 \| 0.0345 \| 0.4873 \| 0.9746 \| 0.9746 \| nan \| 0.9746 \| 0.0 \| 0.9746 \| \| 0.011 \| 8.2 \| 1140 \| 0.0350 \| 0.4913 \| 0.9827 \| 0.9827 \| nan \| 0.9827 \| 0.0 \| 0.9827 \| \| 0.0076 \| 8.35 \| 1160 \| 0.0373 \| 0.4884 \| 0.9767 \| 0.9767 \| nan \| 0.9767 \| 0.0 \| 0.9767 \| \| 0.0074 \| 8.49 \| 1180 \| 0.0378 \| 0.4931 \| 0.9862 \| 0.9862 \| nan \| 0.9862 \| 0.0 \| 0.9862 \| \| 0.0757 \| 8.63 \| 1200 \| 0.0364 \| 0.4880 \| 0.9761 \| 0.9761 \| nan \| 0.9761 \| 0.0 \| 0.9761 \| \| 0.0276 \| 8.78 \| 1220 \| 0.0297 \| 0.4906 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0072 \| 8.92 \| 1240 \| 0.0308 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0061 \| 9.06 \| 1260 \| 0.0308 \| 0.4912 \| 0.9825 \| 0.9825 \| nan \| 0.9825 \| 0.0 \| 0.9825 \| \| 0.0063 \| 9.21 \| 1280 \| 0.0323 \| 0.4894 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0088 \| 9.35 \| 1300 \| 0.0308 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0129 \| 9.5 \| 1320 \| 0.0295 \| 0.4911 \| 0.9823 \| 0.9823 \| nan \| 0.9823 \| 0.0 \| 0.9823 \| \| 0.0277 \| 9.64 \| 1340 \| 0.0388 \| 0.4876 \| 0.9751 \| 0.9751 \| nan \| 0.9751 \| 0.0 \| 0.9751 \| \| 0.0115 \| 9.78 \| 1360 \| 0.0345 \| 0.4894 \| 0.9787 \| 0.9787 \| nan \| 0.9787 \| 0.0 \| 0.9787 \| \| 0.0129 \| 9.93 \| 1380 \| 0.0394 \| 0.4879 \| 0.9758 \| 0.9758 \| nan \| 0.9758 \| 0.0 \| 0.9758 \| \| 0.0092 \| 10.07 \| 1400 \| 0.0335 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.0107 \| 10.22 \| 1420 \| 0.0348 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0072 \| 10.36 \| 1440 \| 0.0334 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0081 \| 10.5 \| 1460 \| 0.0409 \| 0.4886 \| 0.9772 \| 0.9772 \| nan \| 0.9772 \| 0.0 \| 0.9772 \| \| 0.0158 \| 10.65 \| 1480 \| 0.0337 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0058 \| 10.79 \| 1500 \| 0.0364 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \| \| 0.0102 \| 10.94 \| 1520 \| 0.0354 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.0098 \| 11.08 \| 1540 \| 0.0515 \| 0.4863 \| 0.9726 \| 0.9726 \| nan \| 0.9726 \| 0.0 \| 0.9726 \| \| 0.0063 \| 11.22 \| 1560 \| 0.0337 \| 0.4882 \| 0.9763 \| 0.9763 \| nan \| 0.9763 \| 0.0 \| 0.9763 \| \| 0.0151 \| 11.37 \| 1580 \| 0.0313 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0197 \| 11.51 \| 1600 \| 0.0384 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0093 \| 11.65 \| 1620 \| 0.0328 \| 0.4910 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.2493 \| 11.8 \| 1640 \| 0.0413 \| 0.4880 \| 0.9759 \| 0.9759 \| nan \| 0.9759 \| 0.0 \| 0.9759 \| \| 0.0133 \| 11.94 \| 1660 \| 0.0385 \| 0.4877 \| 0.9754 \| 0.9754 \| nan \| 0.9754 \| 0.0 \| 0.9754 \| \| 0.0484 \| 12.09 \| 1680 \| 0.0364 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0074 \| 12.23 \| 1700 \| 0.0334 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.0202 \| 12.37 \| 1720 \| 0.0409 \| 0.4876 \| 0.9752 \| 0.9752 \| nan \| 0.9752 \| 0.0 \| 0.9752 \| \| 0.006 \| 12.52 \| 1740 \| 0.0540 \| 0.4860 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \| \| 0.0059 \| 12.66 \| 1760 \| 0.0601 \| 0.4857 \| 0.9714 \| 0.9714 \| nan \| 0.9714 \| 0.0 \| 0.9714 \| \| 0.0083 \| 12.81 \| 1780 \| 0.0348 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.011 \| 12.95 \| 1800 \| 0.0402 \| 0.4885 \| 0.9770 \| 0.9770 \| nan \| 0.9770 \| 0.0 \| 0.9770 \| \| 0.045 \| 13.09 \| 1820 \| 0.0322 \| 0.4911 \| 0.9822 \| 0.9822 \| nan \| 0.9822 \| 0.0 \| 0.9822 \| \| 0.043 \| 13.24 \| 1840 \| 0.0331 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0061 \| 13.38 \| 1860 \| 0.0314 \| 0.4913 \| 0.9826 \| 0.9826 \| nan \| 0.9826 \| 0.0 \| 0.9826 \| \| 0.0062 \| 13.53 \| 1880 \| 0.0358 \| 0.4890 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0087 \| 13.67 \| 1900 \| 0.0334 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0106 \| 13.81 \| 1920 \| 0.0341 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0554 \| 13.96 \| 1940 \| 0.0359 \| 0.4881 \| 0.9762 \| 0.9762 \| nan \| 0.9762 \| 0.0 \| 0.9762 \| \| 0.009 \| 14.1 \| 1960 \| 0.0424 \| 0.4865 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \| \| 0.0078 \| 14.24 \| 1980 \| 0.0329 \| 0.4885 \| 0.9770 \| 0.9770 \| nan \| 0.9770 \| 0.0 \| 0.9770 \| \| 0.012 \| 14.39 \| 2000 \| 0.0346 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0064 \| 14.53 \| 2020 \| 0.0362 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0345 \| 14.68 \| 2040 \| 0.0309 \| 0.4919 \| 0.9838 \| 0.9838 \| nan \| 0.9838 \| 0.0 \| 0.9838 \| \| 0.0075 \| 14.82 \| 2060 \| 0.0389 \| 0.4884 \| 0.9768 \| 0.9768 \| nan \| 0.9768 \| 0.0 \| 0.9768 \| \| 0.0066 \| 14.96 \| 2080 \| 0.0337 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \| \| 0.0081 \| 15.11 \| 2100 \| 0.0365 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0071 \| 15.25 \| 2120 \| 0.0349 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0054 \| 15.4 \| 2140 \| 0.0388 \| 0.4885 \| 0.9769 \| 0.9769 \| nan \| 0.9769 \| 0.0 \| 0.9769 \| \| 0.4004 \| 15.54 \| 2160 \| 0.0339 \| 0.4909 \| 0.9819 \| 0.9819 \| nan \| 0.9819 \| 0.0 \| 0.9819 \| \| 0.008 \| 15.68 \| 2180 \| 0.0422 \| 0.4896 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0365 \| 15.83 \| 2200 \| 0.0468 \| 0.4887 \| 0.9774 \| 0.9774 \| nan \| 0.9774 \| 0.0 \| 0.9774 \| \| 0.0067 \| 15.97 \| 2220 \| 0.0416 \| 0.4890 \| 0.9780 \| 0.9780 \| nan \| 0.9780 \| 0.0 \| 0.9780 \| \| 0.0079 \| 16.12 \| 2240 \| 0.0377 \| 0.4908 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0075 \| 16.26 \| 2260 \| 0.0420 \| 0.4889 \| 0.9779 \| 0.9779 \| nan \| 0.9779 \| 0.0 \| 0.9779 \| \| 0.0063 \| 16.4 \| 2280 \| 0.0422 \| 0.4889 \| 0.9777 \| 0.9777 \| nan \| 0.9777 \| 0.0 \| 0.9777 \| \| 0.0062 \| 16.55 \| 2300 \| 0.0338 \| 0.4912 \| 0.9825 \| 0.9825 \| nan \| 0.9825 \| 0.0 \| 0.9825 \| \| 0.0413 \| 16.69 \| 2320 \| 0.0345 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0411 \| 16.83 \| 2340 \| 0.0387 \| 0.4891 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0548 \| 16.98 \| 2360 \| 0.0333 \| 0.4936 \| 0.9872 \| 0.9872 \| nan \| 0.9872 \| 0.0 \| 0.9872 \| \| 0.0431 \| 17.12 \| 2380 \| 0.0352 \| 0.4887 \| 0.9773 \| 0.9773 \| nan \| 0.9773 \| 0.0 \| 0.9773 \| \| 0.0069 \| 17.27 \| 2400 \| 0.0327 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0059 \| 17.41 \| 2420 \| 0.0406 \| 0.4881 \| 0.9763 \| 0.9763 \| nan \| 0.9763 \| 0.0 \| 0.9763 \| \| 0.0062 \| 17.55 \| 2440 \| 0.0434 \| 0.4875 \| 0.9750 \| 0.9750 \| nan \| 0.9750 \| 0.0 \| 0.9750 \| \| 0.0064 \| 17.7 \| 2460 \| 0.0350 \| 0.4910 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0077 \| 17.84 \| 2480 \| 0.0390 \| 0.4894 \| 0.9788 \| 0.9788 \| nan \| 0.9788 \| 0.0 \| 0.9788 \| \| 0.0061 \| 17.99 \| 2500 \| 0.0395 \| 0.4906 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0073 \| 18.13 \| 2520 \| 0.0370 \| 0.4911 \| 0.9822 \| 0.9822 \| nan \| 0.9822 \| 0.0 \| 0.9822 \| \| 0.0038 \| 18.27 \| 2540 \| 0.0383 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0066 \| 18.42 \| 2560 \| 0.0394 \| 0.4888 \| 0.9776 \| 0.9776 \| nan \| 0.9776 \| 0.0 \| 0.9776 \| \| 0.0232 \| 18.56 \| 2580 \| 0.0384 \| 0.4891 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0066 \| 18.71 \| 2600 \| 0.0408 \| 0.4887 \| 0.9773 \| 0.9773 \| nan \| 0.9773 \| 0.0 \| 0.9773 \| \| 0.0355 \| 18.85 \| 2620 \| 0.0367 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0048 \| 18.99 \| 2640 \| 0.0366 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0083 \| 19.14 \| 2660 \| 0.0422 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0215 \| 19.28 \| 2680 \| 0.0376 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0315 \| 19.42 \| 2700 \| 0.0370 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0061 \| 19.57 \| 2720 \| 0.0380 \| 0.4909 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0048 \| 19.71 \| 2740 \| 0.0371 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0058 \| 19.86 \| 2760 \| 0.0389 \| 0.4892 \| 0.9785 \| 0.9785 \| nan \| 0.9785 \| 0.0 \| 0.9785 \| \| 0.01 \| 20.0 \| 2780 \| 0.0354 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.0051 \| 20.14 \| 2800 \| 0.0380 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0053 \| 20.29 \| 2820 \| 0.0426 \| 0.4889 \| 0.9779 \| 0.9779 \| nan \| 0.9779 \| 0.0 \| 0.9779 \| \| 0.0062 \| 20.43 \| 2840 \| 0.0359 \| 0.4913 \| 0.9827 \| 0.9827 \| nan \| 0.9827 \| 0.0 \| 0.9827 \| \| 0.0573 \| 20.58 \| 2860 \| 0.0370 \| 0.4909 \| 0.9819 \| 0.9819 \| nan \| 0.9819 \| 0.0 \| 0.9819 \| \| 0.0087 \| 20.72 \| 2880 \| 0.0418 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0144 \| 20.86 \| 2900 \| 0.0398 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0157 \| 21.01 \| 2920 \| 0.0463 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0063 \| 21.15 \| 2940 \| 0.0357 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0079 \| 21.29 \| 2960 \| 0.0339 \| 0.4928 \| 0.9856 \| 0.9856 \| nan \| 0.9856 \| 0.0 \| 0.9856 \| \| 0.0052 \| 21.44 \| 2980 \| 0.0419 \| 0.4888 \| 0.9775 \| 0.9775 \| nan \| 0.9775 \| 0.0 \| 0.9775 \| \| 0.0068 \| 21.58 \| 3000 \| 0.0370 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0109 \| 21.73 \| 3020 \| 0.0350 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0048 \| 21.87 \| 3040 \| 0.0353 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0053 \| 22.01 \| 3060 \| 0.0369 \| 0.4911 \| 0.9823 \| 0.9823 \| nan \| 0.9823 \| 0.0 \| 0.9823 \| \| 0.006 \| 22.16 \| 3080 \| 0.0339 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0336 \| 22.3 \| 3100 \| 0.0339 \| 0.4914 \| 0.9828 \| 0.9828 \| nan \| 0.9828 \| 0.0 \| 0.9828 \| \| 0.0222 \| 22.45 \| 3120 \| 0.0513 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \| \| 0.0072 \| 22.59 \| 3140 \| 0.0328 \| 0.4920 \| 0.9840 \| 0.9840 \| nan \| 0.9840 \| 0.0 \| 0.9840 \| \| 0.0046 \| 22.73 \| 3160 \| 0.0334 \| 0.4907 \| 0.9815 \| 0.9815 \| nan \| 0.9815 \| 0.0 \| 0.9815 \| \| 0.0039 \| 22.88 \| 3180 \| 0.0352 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0059 \| 23.02 \| 3200 \| 0.0359 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0049 \| 23.17 \| 3220 \| 0.0425 \| 0.4881 \| 0.9762 \| 0.9762 \| nan \| 0.9762 \| 0.0 \| 0.9762 \| \| 0.0244 \| 23.31 \| 3240 \| 0.0351 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0047 \| 23.45 \| 3260 \| 0.0339 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0074 \| 23.6 \| 3280 \| 0.0382 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0062 \| 23.74 \| 3300 \| 0.0366 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0339 \| 23.88 \| 3320 \| 0.0378 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.005 \| 24.03 \| 3340 \| 0.0395 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0038 \| 24.17 \| 3360 \| 0.0455 \| 0.4887 \| 0.9773 \| 0.9773 \| nan \| 0.9773 \| 0.0 \| 0.9773 \| \| 0.008 \| 24.32 \| 3380 \| 0.0389 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0071 \| 24.46 \| 3400 \| 0.0367 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0308 \| 24.6 \| 3420 \| 0.0390 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0062 \| 24.75 \| 3440 \| 0.0368 \| 0.4918 \| 0.9837 \| 0.9837 \| nan \| 0.9837 \| 0.0 \| 0.9837 \| \| 0.0062 \| 24.89 \| 3460 \| 0.0378 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.006 \| 25.04 \| 3480 \| 0.0413 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0057 \| 25.18 \| 3500 \| 0.0383 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0149 \| 25.32 \| 3520 \| 0.0367 \| 0.4911 \| 0.9822 \| 0.9822 \| nan \| 0.9822 \| 0.0 \| 0.9822 \| \| 0.0185 \| 25.47 \| 3540 \| 0.0409 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0057 \| 25.61 \| 3560 \| 0.0390 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.005 \| 25.76 \| 3580 \| 0.0383 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0109 \| 25.9 \| 3600 \| 0.0379 \| 0.4909 \| 0.9819 \| 0.9819 \| nan \| 0.9819 \| 0.0 \| 0.9819 \| \| 0.0055 \| 26.04 \| 3620 \| 0.0471 \| 0.4883 \| 0.9767 \| 0.9767 \| nan \| 0.9767 \| 0.0 \| 0.9767 \| \| 0.042 \| 26.19 \| 3640 \| 0.0481 \| 0.4877 \| 0.9755 \| 0.9755 \| nan \| 0.9755 \| 0.0 \| 0.9755 \| \| 0.0226 \| 26.33 \| 3660 \| 0.0383 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0143 \| 26.47 \| 3680 \| 0.0402 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.008 \| 26.62 \| 3700 \| 0.0381 \| 0.4908 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0241 \| 26.76 \| 3720 \| 0.0411 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0055 \| 26.91 \| 3740 \| 0.0386 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0109 \| 27.05 \| 3760 \| 0.0376 \| 0.4913 \| 0.9826 \| 0.9826 \| nan \| 0.9826 \| 0.0 \| 0.9826 \| \| 0.0072 \| 27.19 \| 3780 \| 0.0457 \| 0.4890 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \| \| 0.0048 \| 27.34 \| 3800 \| 0.0512 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \| \| 0.006 \| 27.48 \| 3820 \| 0.0430 \| 0.4891 \| 0.9783 \| 0.9783 \| nan \| 0.9783 \| 0.0 \| 0.9783 \| \| 0.0161 \| 27.63 \| 3840 \| 0.0404 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0169 \| 27.77 \| 3860 \| 0.0386 \| 0.4903 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0041 \| 27.91 \| 3880 \| 0.0375 \| 0.4917 \| 0.9835 \| 0.9835 \| nan \| 0.9835 \| 0.0 \| 0.9835 \| \| 0.0068 \| 28.06 \| 3900 \| 0.0381 \| 0.4917 \| 0.9834 \| 0.9834 \| nan \| 0.9834 \| 0.0 \| 0.9834 \| \| 0.0122 \| 28.2 \| 3920 \| 0.0463 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0055 \| 28.35 \| 3940 \| 0.0456 \| 0.4887 \| 0.9774 \| 0.9774 \| nan \| 0.9774 \| 0.0 \| 0.9774 \| \| 0.0048 \| 28.49 \| 3960 \| 0.0398 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0126 \| 28.63 \| 3980 \| 0.0401 \| 0.4917 \| 0.9834 \| 0.9834 \| nan \| 0.9834 \| 0.0 \| 0.9834 \| \| 0.0134 \| 28.78 \| 4000 \| 0.0404 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0109 \| 28.92 \| 4020 \| 0.0414 \| 0.4894 \| 0.9787 \| 0.9787 \| nan \| 0.9787 \| 0.0 \| 0.9787 \| \| 0.0479 \| 29.06 \| 4040 \| 0.0409 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0061 \| 29.21 \| 4060 \| 0.0422 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0278 \| 29.35 \| 4080 \| 0.0416 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0049 \| 29.5 \| 4100 \| 0.0400 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0438 \| 29.64 \| 4120 \| 0.0390 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0041 \| 29.78 \| 4140 \| 0.0425 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0078 \| 29.93 \| 4160 \| 0.0411 \| 0.4907 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0057 \| 30.07 \| 4180 \| 0.0375 \| 0.4916 \| 0.9832 \| 0.9832 \| nan \| 0.9832 \| 0.0 \| 0.9832 \| \| 0.0053 \| 30.22 \| 4200 \| 0.0423 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0133 \| 30.36 \| 4220 \| 0.0429 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0045 \| 30.5 \| 4240 \| 0.0454 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0044 \| 30.65 \| 4260 \| 0.0415 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.006 \| 30.79 \| 4280 \| 0.0420 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0043 \| 30.94 \| 4300 \| 0.0428 \| 0.4899 \| 0.9797 \| 0.9797 \| nan \| 0.9797 \| 0.0 \| 0.9797 \| \| 0.017 \| 31.08 \| 4320 \| 0.0421 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0043 \| 31.22 \| 4340 \| 0.0400 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0061 \| 31.37 \| 4360 \| 0.0383 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0378 \| 31.51 \| 4380 \| 0.0371 \| 0.4913 \| 0.9826 \| 0.9826 \| nan \| 0.9826 \| 0.0 \| 0.9826 \| \| 0.0052 \| 31.65 \| 4400 \| 0.0382 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0046 \| 31.8 \| 4420 \| 0.0398 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0076 \| 31.94 \| 4440 \| 0.0400 \| 0.4904 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0062 \| 32.09 \| 4460 \| 0.0396 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0152 \| 32.23 \| 4480 \| 0.0399 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0044 \| 32.37 \| 4500 \| 0.0426 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0104 \| 32.52 \| 4520 \| 0.0431 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0041 \| 32.66 \| 4540 \| 0.0458 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0084 \| 32.81 \| 4560 \| 0.0457 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0046 \| 32.95 \| 4580 \| 0.0465 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0038 \| 33.09 \| 4600 \| 0.0422 \| 0.4907 \| 0.9815 \| 0.9815 \| nan \| 0.9815 \| 0.0 \| 0.9815 \| \| 0.0039 \| 33.24 \| 4620 \| 0.0410 \| 0.4912 \| 0.9824 \| 0.9824 \| nan \| 0.9824 \| 0.0 \| 0.9824 \| \| 0.004 \| 33.38 \| 4640 \| 0.0427 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.006 \| 33.53 \| 4660 \| 0.0458 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0039 \| 33.67 \| 4680 \| 0.0484 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0065 \| 33.81 \| 4700 \| 0.0516 \| 0.4894 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0065 \| 33.96 \| 4720 \| 0.0525 \| 0.4893 \| 0.9786 \| 0.9786 \| nan \| 0.9786 \| 0.0 \| 0.9786 \| \| 0.0041 \| 34.1 \| 4740 \| 0.0462 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0031 \| 34.24 \| 4760 \| 0.0458 \| 0.4909 \| 0.9817 \| 0.9817 \| nan \| 0.9817 \| 0.0 \| 0.9817 \| \| 0.0039 \| 34.39 \| 4780 \| 0.0493 \| 0.4895 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0125 \| 34.53 \| 4800 \| 0.0467 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0038 \| 34.68 \| 4820 \| 0.0456 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0043 \| 34.82 \| 4840 \| 0.0484 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0098 \| 34.96 \| 4860 \| 0.0460 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.004 \| 35.11 \| 4880 \| 0.0475 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0087 \| 35.25 \| 4900 \| 0.0460 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0093 \| 35.4 \| 4920 \| 0.0455 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0052 \| 35.54 \| 4940 \| 0.0500 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0045 \| 35.68 \| 4960 \| 0.0482 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0036 \| 35.83 \| 4980 \| 0.0443 \| 0.4906 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0034 \| 35.97 \| 5000 \| 0.0426 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0041 \| 36.12 \| 5020 \| 0.0415 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0043 \| 36.26 \| 5040 \| 0.0450 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.007 \| 36.4 \| 5060 \| 0.0467 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.006 \| 36.55 \| 5080 \| 0.0463 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.006 \| 36.69 \| 5100 \| 0.0468 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0043 \| 36.83 \| 5120 \| 0.0428 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0073 \| 36.98 \| 5140 \| 0.0417 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0188 \| 37.12 \| 5160 \| 0.0418 \| 0.4908 \| 0.9815 \| 0.9815 \| nan \| 0.9815 \| 0.0 \| 0.9815 \| \| 0.0052 \| 37.27 \| 5180 \| 0.0450 \| 0.4907 \| 0.9813 \| 0.9813 \| nan \| 0.9813 \| 0.0 \| 0.9813 \| \| 0.0089 \| 37.41 \| 5200 \| 0.0476 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0041 \| 37.55 \| 5220 \| 0.0505 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0062 \| 37.7 \| 5240 \| 0.0478 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0035 \| 37.84 \| 5260 \| 0.0463 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0163 \| 37.99 \| 5280 \| 0.0453 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0054 \| 38.13 \| 5300 \| 0.0462 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0132 \| 38.27 \| 5320 \| 0.0481 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \| \| 0.0056 \| 38.42 \| 5340 \| 0.0460 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0054 \| 38.56 \| 5360 \| 0.0449 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0037 \| 38.71 \| 5380 \| 0.0432 \| 0.4911 \| 0.9821 \| 0.9821 \| nan \| 0.9821 \| 0.0 \| 0.9821 \| \| 0.0049 \| 38.85 \| 5400 \| 0.0449 \| 0.4909 \| 0.9818 \| 0.9818 \| nan \| 0.9818 \| 0.0 \| 0.9818 \| \| 0.0044 \| 38.99 \| 5420 \| 0.0448 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0037 \| 39.14 \| 5440 \| 0.0462 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0079 \| 39.28 \| 5460 \| 0.0490 \| 0.4895 \| 0.9789 \| 0.9789 \| nan \| 0.9789 \| 0.0 \| 0.9789 \| \| 0.0033 \| 39.42 \| 5480 \| 0.0494 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \| \| 0.0066 \| 39.57 \| 5500 \| 0.0458 \| 0.4897 \| 0.9794 \| 0.9794 \| nan \| 0.9794 \| 0.0 \| 0.9794 \| \| 0.0053 \| 39.71 \| 5520 \| 0.0482 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0044 \| 39.86 \| 5540 \| 0.0483 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0044 \| 40.0 \| 5560 \| 0.0497 \| 0.4897 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0062 \| 40.14 \| 5580 \| 0.0476 \| 0.4894 \| 0.9788 \| 0.9788 \| nan \| 0.9788 \| 0.0 \| 0.9788 \| \| 0.0047 \| 40.29 \| 5600 \| 0.0467 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.006 \| 40.43 \| 5620 \| 0.0444 \| 0.4898 \| 0.9796 \| 0.9796 \| nan \| 0.9796 \| 0.0 \| 0.9796 \| \| 0.0041 \| 40.58 \| 5640 \| 0.0459 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0098 \| 40.72 \| 5660 \| 0.0447 \| 0.4903 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0026 \| 40.86 \| 5680 \| 0.0439 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0043 \| 41.01 \| 5700 \| 0.0466 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0044 \| 41.15 \| 5720 \| 0.0444 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0041 \| 41.29 \| 5740 \| 0.0452 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0043 \| 41.44 \| 5760 \| 0.0468 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0071 \| 41.58 \| 5780 \| 0.0482 \| 0.4897 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \| \| 0.0187 \| 41.73 \| 5800 \| 0.0463 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0034 \| 41.87 \| 5820 \| 0.0456 \| 0.4901 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0238 \| 42.01 \| 5840 \| 0.0450 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0048 \| 42.16 \| 5860 \| 0.0464 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0116 \| 42.3 \| 5880 \| 0.0475 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0039 \| 42.45 \| 5900 \| 0.0475 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0042 \| 42.59 \| 5920 \| 0.0446 \| 0.4905 \| 0.9809 \| 0.9809 \| nan \| 0.9809 \| 0.0 \| 0.9809 \| \| 0.0069 \| 42.73 \| 5940 \| 0.0441 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0045 \| 42.88 \| 5960 \| 0.0460 \| 0.4905 \| 0.9811 \| 0.9811 \| nan \| 0.9811 \| 0.0 \| 0.9811 \| \| 0.0038 \| 43.02 \| 5980 \| 0.0501 \| 0.4896 \| 0.9791 \| 0.9791 \| nan \| 0.9791 \| 0.0 \| 0.9791 \| \| 0.0123 \| 43.17 \| 6000 \| 0.0490 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0079 \| 43.31 \| 6020 \| 0.0471 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.004 \| 43.45 \| 6040 \| 0.0453 \| 0.4906 \| 0.9812 \| 0.9812 \| nan \| 0.9812 \| 0.0 \| 0.9812 \| \| 0.0145 \| 43.6 \| 6060 \| 0.0439 \| 0.4910 \| 0.9820 \| 0.9820 \| nan \| 0.9820 \| 0.0 \| 0.9820 \| \| 0.0038 \| 43.74 \| 6080 \| 0.0466 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.004 \| 43.88 \| 6100 \| 0.0467 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0044 \| 44.03 \| 6120 \| 0.0480 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0193 \| 44.17 \| 6140 \| 0.0458 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0036 \| 44.32 \| 6160 \| 0.0470 \| 0.4904 \| 0.9808 \| 0.9808 \| nan \| 0.9808 \| 0.0 \| 0.9808 \| \| 0.0042 \| 44.46 \| 6180 \| 0.0456 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0031 \| 44.6 \| 6200 \| 0.0454 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0117 \| 44.75 \| 6220 \| 0.0478 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0036 \| 44.89 \| 6240 \| 0.0482 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0036 \| 45.04 \| 6260 \| 0.0506 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0052 \| 45.18 \| 6280 \| 0.0485 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0035 \| 45.32 \| 6300 \| 0.0496 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0056 \| 45.47 \| 6320 \| 0.0494 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0172 \| 45.61 \| 6340 \| 0.0482 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0041 \| 45.76 \| 6360 \| 0.0484 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0034 \| 45.9 \| 6380 \| 0.0492 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0108 \| 46.04 \| 6400 \| 0.0481 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0054 \| 46.19 \| 6420 \| 0.0474 \| 0.4905 \| 0.9810 \| 0.9810 \| nan \| 0.9810 \| 0.0 \| 0.9810 \| \| 0.0102 \| 46.33 \| 6440 \| 0.0483 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0036 \| 46.47 \| 6460 \| 0.0493 \| 0.4903 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0057 \| 46.62 \| 6480 \| 0.0496 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.003 \| 46.76 \| 6500 \| 0.0504 \| 0.4900 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0057 \| 46.91 \| 6520 \| 0.0492 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0048 \| 47.05 \| 6540 \| 0.0524 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.004 \| 47.19 \| 6560 \| 0.0500 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0218 \| 47.34 \| 6580 \| 0.0502 \| 0.4899 \| 0.9798 \| 0.9798 \| nan \| 0.9798 \| 0.0 \| 0.9798 \| \| 0.0038 \| 47.48 \| 6600 \| 0.0532 \| 0.4896 \| 0.9792 \| 0.9792 \| nan \| 0.9792 \| 0.0 \| 0.9792 \| \| 0.0029 \| 47.63 \| 6620 \| 0.0496 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0035 \| 47.77 \| 6640 \| 0.0508 \| 0.4898 \| 0.9795 \| 0.9795 \| nan \| 0.9795 \| 0.0 \| 0.9795 \| \| 0.0049 \| 47.91 \| 6660 \| 0.0501 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0056 \| 48.06 \| 6680 \| 0.0488 \| 0.4907 \| 0.9814 \| 0.9814 \| nan \| 0.9814 \| 0.0 \| 0.9814 \| \| 0.0182 \| 48.2 \| 6700 \| 0.0482 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0056 \| 48.35 \| 6720 \| 0.0494 \| 0.4903 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0029 \| 48.49 \| 6740 \| 0.0501 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0076 \| 48.63 \| 6760 \| 0.0480 \| 0.4901 \| 0.9802 \| 0.9802 \| nan \| 0.9802 \| 0.0 \| 0.9802 \| \| 0.0042 \| 48.78 \| 6780 \| 0.0514 \| 0.4902 \| 0.9803 \| 0.9803 \| nan \| 0.9803 \| 0.0 \| 0.9803 \| \| 0.0069 \| 48.92 \| 6800 \| 0.0483 \| 0.4901 \| 0.9801 \| 0.9801 \| nan \| 0.9801 \| 0.0 \| 0.9801 \| \| 0.0144 \| 49.06 \| 6820 \| 0.0472 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \| \| 0.0041 \| 49.21 \| 6840 \| 0.0491 \| 0.4900 \| 0.9800 \| 0.9800 \| nan \| 0.9800 \| 0.0 \| 0.9800 \| \| 0.0034 \| 49.35 \| 6860 \| 0.0481 \| 0.4902 \| 0.9804 \| 0.9804 \| nan \| 0.9804 \| 0.0 \| 0.9804 \| \| 0.0117 \| 49.5 \| 6880 \| 0.0482 \| 0.4904 \| 0.9807 \| 0.9807 \| nan \| 0.9807 \| 0.0 \| 0.9807 \| \| 0.0042 \| 49.64 \| 6900 \| 0.0499 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| \| 0.0057 \| 49.78 \| 6920 \| 0.0507 \| 0.4902 \| 0.9805 \| 0.9805 \| nan \| 0.9805 \| 0.0 \| 0.9805 \| \| 0.0183 \| 49.93 \| 6940 \| 0.0494 \| 0.4900 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Venkatesh4342/helpdesk-zephyr-7B-alpha	Venkatesh4342	2023-11-03T23:22:55Z	0	0	null	[ "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]	null	2023-11-01T12:24:03Z	--- license: mit base_model: TheBloke/zephyr-7B-alpha-GPTQ tags: - generated_from_trainer model-index: - name: helpdesk-zephyr-7B-alpha 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. --> # helpdesk-zephyr-7B-alpha This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None 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: 0.0002 - 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: cosine - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0 - Datasets 2.11.0 - Tokenizers 0.14.1
huangyunhao/output	huangyunhao	2023-11-03T23:01:15Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-03T21:38:20Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - huangyunhao/output This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
HammadAusaf/lora-trained-xl-hub	HammadAusaf	2023-11-03T22:42:12Z	1	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "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	2023-11-03T21:16:15Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - HammadAusaf/lora-trained-xl-hub These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
kwwww/bert-base-uncased-test_1_10000	kwwww	2023-11-03T22:33:56Z	0	0	peft	[ "peft", "pytorch", "region:us" ]	null	2023-11-03T04:48:55Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k	mmnga	2023-11-03T22:28:47Z	5	0	transformers	[ "transformers", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T15:14:28Z	--- license: llama2 --- # japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k [stabilityaiさんが公開しているjapanese-stablelm-base-ja_vocab-beta-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-ja_vocab-beta-7b)を日本語のキャリブレーションセットで生成したGPTQモデルになります。キャリブレーションセットは[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)から、 1kほどランダムサンプリングしたものと、 [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100)のinput/outputを計200ほど追加しています。 [mmnga/wikipedia-ja-20230720-1k](https://huggingface.co/datasets/mmnga/wikipedia-ja-20230720-1k) モデル一覧 GPTQ [mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k) [mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-GPTQ-calib-ja-1k) GGUF [mmnga/japanese-stablelm-base-ja_vocab-beta-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-base-ja_vocab-beta-7b-gguf) [mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-gguf) ## Usage ~~~Bash pip install auto-gptq==0.4.2 transformers ~~~ ~~~python from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig from transformers import AutoTokenizer model_name_or_path = "mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k" # Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) # Model model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, use_safetensors=True, device="cuda:0") # Your test prompt prompt = """今日の夕食のレシピをご紹介します。""" print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_length=128)[0])) ~~~
Owesh12/license	Owesh12	2023-11-03T22:08:25Z	3	0	yolov5	[ "yolov5", "tensorboard", "yolo", "vision", "object-detection", "pytorch", "dataset:keremberke/license-plate-object-detection", "model-index", "region:us" ]	object-detection	2023-11-03T22:06:33Z	--- tags: - yolov5 - yolo - vision - object-detection - pytorch library_name: yolov5 library_version: 7.0.6 inference: false datasets: - keremberke/license-plate-object-detection model-index: - name: keremberke/yolov5m-license-plate results: - task: type: object-detection dataset: type: keremberke/license-plate-object-detection name: keremberke/license-plate-object-detection split: validation metrics: - type: precision # since [email protected] is not available on hf.co/metrics value: 0.9882982754936463 # min: 0.0 - max: 1.0 name: [email protected] --- <div align="center"> <img width="640" alt="keremberke/yolov5m-license-plate" src="https://huggingface.co/keremberke/yolov5m-license-plate/resolve/main/sample_visuals.jpg"> </div> ### How to use - Install [yolov5](https://github.com/fcakyon/yolov5-pip): ```bash pip install -U yolov5 ``` - Load model and perform prediction: ```python import yolov5 # load model model = yolov5.load('keremberke/yolov5m-license-plate') # set model parameters model.conf = 0.25 # NMS confidence threshold model.iou = 0.45 # NMS IoU threshold model.agnostic = False # NMS class-agnostic model.multi_label = False # NMS multiple labels per box model.max_det = 1000 # maximum number of detections per image # set image img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model(img, size=640) # inference with test time augmentation results = model(img, augment=True) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() # save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-license-plate --epochs 10 ``` More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)
karenyyy0613/blip2-opt-2.7b-feelreel-finetune	karenyyy0613	2023-11-03T22:07:24Z	0	0	peft	[ "peft", "region:us" ]	null	2023-10-30T06:24:29Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.6.0.dev0
akashmaggon/vit-base-age-classification	akashmaggon	2023-11-03T21:49:06Z	323	1	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:fair_face", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-11-03T20:58:22Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - fair_face metrics: - accuracy model-index: - name: vit-base-age-classification results: - task: name: Image Classification type: image-classification dataset: name: fair_face type: fair_face config: '0.25' split: train args: '0.25' metrics: - name: Accuracy type: accuracy value: 0.987904862407663 --- <!-- 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. --> # vit-base-age-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 0.0743 - Accuracy: 0.9879 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2011 \| 1.0 \| 385 \| 1.0297 \| 0.5664 \| \| 0.8578 \| 2.0 \| 770 \| 0.7667 \| 0.6936 \| \| 0.5961 \| 3.0 \| 1155 \| 0.4088 \| 0.8703 \| \| 0.3073 \| 4.0 \| 1540 \| 0.1689 \| 0.9581 \| \| 0.1146 \| 5.0 \| 1925 \| 0.0743 \| 0.9879 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k	mmnga	2023-11-03T21:47:59Z	5	0	transformers	[ "transformers", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T13:11:19Z	--- license: mit --- # cyberagent-calm2-7b-GPTQ-calib-ja-1k [cyberagentさんが公開しているcalm2-7b-chat](https://huggingface.co/cyberagent/calm2-7b)を日本語のキャリブレーションセットで生成したGPTQモデルになります。キャリブレーションセットは[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)から、 1kほどランダムサンプリングしたものと、 [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100)のinput/outputを計200ほど追加しています。 [mmnga/wikipedia-ja-20230720-1k](https://huggingface.co/datasets/mmnga/wikipedia-ja-20230720-1k) モデル一覧 GPTQ [mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k) [mmnga/cyberagent-calm2-7b-chat-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/cyberagent-calm2-7b-chat-GPTQ-calib-ja-1k) GGUF [mmnga/cyberagent-calm2-7b-gguf](https://huggingface.co/mmnga/cyberagent-calm2-7b-gguf) [mmnga/cyberagent-calm2-7b-chat-gguf](https://huggingface.co/mmnga/cyberagent-calm2-7b-chat-gguf) ## Usage ~~~Bash pip install auto-gptq[triton]==0.4.2 transformers ~~~ ~~~python from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig from transformers import AutoTokenizer model_name_or_path = "mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k" # Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) # Model model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, use_safetensors=True, device="cuda:0") # Your test prompt prompt = """今日の夕食のレシピをご紹介します。""" print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_length=128)[0])) ~~~
aleksahet/good-water-90	aleksahet	2023-11-03T21:43:17Z	3	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-03T11:16:36Z	--- tags: - generated_from_trainer model-index: - name: good-water-90 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. --> # good-water-90 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.1559 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 5.2532 \| 1.15 \| 15000 \| 5.2209 \| \| 5.1936 \| 2.29 \| 30000 \| 5.1729 \| \| 5.1831 \| 3.44 \| 45000 \| 5.1576 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3
aladar/tiny-random-BloomForCausalLM-GGUF	aladar	2023-11-03T21:41:35Z	4	0	null	[ "gguf", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T21:22:23Z	--- license: mit language: - en pipeline_tag: text-generation --- GGUFd https://huggingface.co/hf-internal-testing/tiny-random-BloomForCausalLM # Download ``` pip install huggingface-hub ``` From CLI: ``` huggingface-cli download \ aladar/tiny-random-BloomForCausalLM-GGUF \ tiny-random-BloomForCausalLM.gguf \ --local-dir . \ --local-dir-use-symlinks False ```
damnloveless/a2c-PandaPickAndPlace-v3	damnloveless	2023-11-03T21:40:05Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T21:34:55Z	--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v3 type: PandaPickAndPlace-v3 metrics: - type: mean_reward value: -50.00 +/- 0.00 name: mean_reward verified: false --- # A2C Agent playing PandaPickAndPlace-v3 This is a trained model of a A2C agent playing PandaPickAndPlace-v3 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 ... ```
0xAmey/tinyllava-1.1b-v0.1	0xAmey	2023-11-03T21:38:25Z	33	21	transformers	[ "transformers", "pytorch", "llava", "text-generation", "visual-question-answering", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	visual-question-answering	2023-11-01T11:56:34Z	--- license: apache-2.0 pipeline_tag: visual-question-answering --- ## About This was trained by using [TinyLlama](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) as the base model using the [BakLlava](https://github.com/SkunkworksAI/BakLLaVA/) repo. ## Examples Prompt for both was, "What is shown in the given image?" <img src="berserk.png" width="50%"> <br> <img src="sd.png" width="50%"> ## Install If you are not using Linux, do NOT proceed, see instructions for [macOS](https://github.com/haotian-liu/LLaVA/blob/main/docs/macOS.md) and [Windows](https://github.com/haotian-liu/LLaVA/blob/main/docs/Windows.md). 1. Clone this repository and navigate to LLaVA folder ```bash git clone https://github.com/haotian-liu/LLaVA.git cd LLaVA ``` 2. Install Package ```Shell conda create -n llava python=3.10 -y conda activate llava pip install --upgrade pip # enable PEP 660 support pip install -e . ``` 3. Install additional packages for training cases ``` pip install -e ".[train]" pip install flash-attn --no-build-isolation ``` ### Upgrade to latest code base ```Shell git pull pip install -e . ``` #### Launch a controller ```Shell python -m llava.serve.controller --host 0.0.0.0 --port 10000 ``` #### Launch a gradio web server. ```Shell python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload ``` You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker. #### Launch a model worker This is the actual worker that performs the inference on the GPU. Each worker is responsible for a single model specified in `--model-path`. ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path ameywtf/tinyllava-1.1b-v0.1 ``` Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list. You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker. ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port <different from 40000, say 40001> --worker http://localhost:<change accordingly, i.e. 40001> --model-path <ckpt2> ``` If you are using an Apple device with an M1 or M2 chip, you can specify the mps device by using the `--device` flag: `--device mps`.
jankovicsandras/rl7_q4km	jankovicsandras	2023-11-03T21:31:29Z	3	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2023-11-03T16:43:49Z	# Llama 2 7b Chat Norwegian ## GGUF format, quantized to q4_k_m https://huggingface.co/RuterNorway/Llama-2-7b-chat-norwegian Credits This model was made at Ruters AI Lab which is a part of Ruters Data & AI division.
kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4	kanishka	2023-11-03T21:30:07Z	3	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T14:13:02Z	--- base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4/config.json tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4 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. --> # smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1757 - Accuracy: 0.4276 ## 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: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.9239 \| 1.0 \| 18353 \| 3.9984 \| 0.3339 \| \| 3.3921 \| 2.0 \| 36706 \| 3.5065 \| 0.3846 \| \| 3.2011 \| 3.0 \| 55059 \| 3.3520 \| 0.4020 \| \| 3.1033 \| 4.0 \| 73412 \| 3.2807 \| 0.4092 \| \| 3.0297 \| 5.0 \| 91765 \| 3.2348 \| 0.4146 \| \| 2.9786 \| 6.0 \| 110118 \| 3.2161 \| 0.4176 \| \| 2.9354 \| 7.0 \| 128471 \| 3.1902 \| 0.4202 \| \| 2.8979 \| 8.0 \| 146824 \| 3.1843 \| 0.4219 \| \| 2.868 \| 9.0 \| 165177 \| 3.1752 \| 0.4231 \| \| 2.8382 \| 10.0 \| 183530 \| 3.1696 \| 0.4239 \| \| 2.8116 \| 11.0 \| 201883 \| 3.1690 \| 0.4248 \| \| 2.7972 \| 12.0 \| 220236 \| 3.1664 \| 0.4253 \| \| 2.7712 \| 13.0 \| 238589 \| 3.1636 \| 0.4261 \| \| 2.7436 \| 14.0 \| 256942 \| 3.1663 \| 0.4265 \| \| 2.7299 \| 15.0 \| 275295 \| 3.1684 \| 0.4264 \| \| 2.712 \| 16.0 \| 293648 \| 3.1682 \| 0.4270 \| \| 2.6915 \| 17.0 \| 312001 \| 3.1705 \| 0.4272 \| \| 2.671 \| 18.0 \| 330354 \| 3.1703 \| 0.4273 \| \| 2.6571 \| 19.0 \| 348707 \| 3.1733 \| 0.4276 \| \| 2.6385 \| 20.0 \| 367060 \| 3.1757 \| 0.4276 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.14.1
owanr/Sentiment-google-t5-v1_1-large-intra_model-shuffle-human_annots_str	owanr	2023-11-03T21:25:14Z	0	0	null	[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]	null	2023-11-03T21:25:12Z	--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: Sentiment-google-t5-v1_1-large-intra_model-shuffle-human_annots_str 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. --> # Sentiment-google-t5-v1_1-large-intra_model-shuffle-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.5234 ## 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: 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 21.0865 \| 1.0 \| 44 \| 23.6328 \| \| 19.3373 \| 2.0 \| 88 \| 17.3411 \| \| 14.3861 \| 3.0 \| 132 \| 11.9747 \| \| 11.4823 \| 4.0 \| 176 \| 10.9355 \| \| 10.5154 \| 5.0 \| 220 \| 10.7808 \| \| 10.1482 \| 6.0 \| 264 \| 10.7065 \| \| 10.0319 \| 7.0 \| 308 \| 10.5792 \| \| 9.8259 \| 8.0 \| 352 \| 10.2032 \| \| 9.1712 \| 9.0 \| 396 \| 9.5730 \| \| 8.8567 \| 10.0 \| 440 \| 9.2184 \| \| 8.626 \| 11.0 \| 484 \| 9.0661 \| \| 8.5469 \| 12.0 \| 528 \| 8.9471 \| \| 6.3996 \| 13.0 \| 572 \| 1.5474 \| \| 1.3785 \| 14.0 \| 616 \| 1.3062 \| \| 1.3229 \| 15.0 \| 660 \| 1.2992 \| \| 1.3309 \| 16.0 \| 704 \| 1.3052 \| \| 1.3341 \| 17.0 \| 748 \| 1.2961 \| \| 1.3281 \| 18.0 \| 792 \| 1.2946 \| \| 1.3219 \| 19.0 \| 836 \| 1.3034 \| \| 1.3185 \| 20.0 \| 880 \| 1.2989 \| \| 1.3184 \| 21.0 \| 924 \| 1.2985 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1
taozi555/mythalion-4.0	taozi555	2023-11-03T21:17:24Z	7	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text generation", "instruct", "en", "dataset:PygmalionAI/PIPPA", "dataset:Open-Orca/OpenOrca", "dataset:Norquinal/claude_multiround_chat_30k", "dataset:jondurbin/airoboros-gpt4-1.4.1", "dataset:databricks/databricks-dolly-15k", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-11-03T19:50:55Z	--- language: - en thumbnail: null tags: - text generation - instruct pipeline_tag: text-generation inference: false license: llama2 datasets: - PygmalionAI/PIPPA - Open-Orca/OpenOrca - Norquinal/claude_multiround_chat_30k - jondurbin/airoboros-gpt4-1.4.1 - databricks/databricks-dolly-15k --- <h1 style="text-align: center">Mythalion 13B</h1> <h2 style="text-align: center">A merge of Pygmalion-2 13B and MythoMax 13B</h2> ## Model Details The long-awaited release of our new models based on Llama-2 is finally here. This model was created in collaboration with [Gryphe](https://huggingface.co/Gryphe), a mixture of our [Pygmalion-2 13B](https://huggingface.co/PygmalionAI/pygmalion-2-13b) and Gryphe's [Mythomax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b). Finer details of the merge are available in [our blogpost](https://pygmalionai.github.io/blog/posts/introducing_pygmalion_2/#mythalion-13b). According to our testers, this model seems to outperform MythoMax in RP/Chat. Please make sure you follow the recommended generation settings for SillyTavern [here](https://pygmalionai.github.io/blog/posts/introducing_pygmalion_2/#sillytavern) for the best results! This model is freely available for both commercial and non-commercial use, as per the Llama-2 license. ## Prompting This model can be prompted using both the Alpaca and [Pygmalion formatting](https://huggingface.co/PygmalionAI/pygmalion-2-13b#prompting). Alpaca formatting: ``` ### Instruction: <prompt> ### Response: <leave a newline blank for model to respond> ``` Pygmalion/Metharme formatting: ``` <\|system\|>Enter RP mode. Pretend to be {{char}} whose persona follows: {{persona}} You shall reply to the user while staying in character, and generate long responses. <\|user\|>Hello!<\|model\|>{model's response goes here} ``` The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<\|system\|>`, `<\|user\|>` and `<\|model\|>`. The `<\|system\|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<\|user\|>` prompt should be used to indicate user input. The `<\|model\|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history. ## Limitations and biases The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. As such, it was not fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading. ## Acknowledgements We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for the [Pygmalion-2 13B](https://huggingface.co/PygmalionAI/pygmalion-2-13b) model. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
1TuanPham/Instruct_en-vi_12000_1e_b32_lr2e-4_1TuanPham_bkai-vietnamese-llama2-7b-sharded_LORA_CAUSAL_LM	1TuanPham	2023-11-03T21:10:44Z	0	1	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:1TuanPham/bkai-vietnamese-llama2-7b-sharded", "base_model:adapter:1TuanPham/bkai-vietnamese-llama2-7b-sharded", "region:us" ]	null	2023-11-03T21:10:24Z	--- library_name: peft base_model: 1TuanPham/bkai-vietnamese-llama2-7b-sharded --- # 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] - 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 Data 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 Data 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: True - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0
peldrak/segformer_finetuned_coasts	peldrak	2023-11-03T20:57:10Z	4	0	transformers	[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/segformer-b0-finetuned-ade-512-512", "base_model:finetune:nvidia/segformer-b0-finetuned-ade-512-512", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2023-11-02T11:03:28Z	--- license: other base_model: nvidia/segformer-b0-finetuned-ade-512-512 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer_finetuned_coasts 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_finetuned_coasts This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coast dataset. It achieves the following results on the evaluation set: - Loss: 0.3304 - Mean Iou: 0.4794 - Mean Accuracy: 0.6650 - Overall Accuracy: 0.9144 - Accuracy Water: nan - Accuracy Whitewater: 0.4315 - Accuracy Sediment: 0.8895 - Accuracy Other Natural Terrain: 0.0 - Accuracy Vegetation: 0.8740 - Accuracy Development: 0.8271 - Accuracy Unknown: 0.9678 - Iou Water: 0.0 - Iou Whitewater: 0.2745 - Iou Sediment: 0.7784 - Iou Other Natural Terrain: 0.0 - Iou Vegetation: 0.7930 - Iou Development: 0.5438 - Iou Unknown: 0.9658 ## 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: 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Water \| Accuracy Whitewater \| Accuracy Sediment \| Accuracy Other Natural Terrain \| Accuracy Vegetation \| Accuracy Development \| Accuracy Unknown \| Iou Water \| Iou Whitewater \| Iou Sediment \| Iou Other Natural Terrain \| Iou Vegetation \| Iou Development \| Iou Unknown \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:--------------:\|:-------------------:\|:-----------------:\|:------------------------------:\|:-------------------:\|:--------------------:\|:----------------:\|:---------:\|:--------------:\|:------------:\|:-------------------------:\|:--------------:\|:---------------:\|:-----------:\| \| 1.5565 \| 0.01 \| 20 \| 1.3796 \| 0.2430 \| 0.3551 \| 0.7951 \| nan \| 0.0097 \| 0.1349 \| 0.0623 \| 0.8407 \| 0.1406 \| 0.9421 \| 0.0 \| 0.0051 \| 0.1067 \| 0.0232 \| 0.5193 \| 0.1049 \| 0.9414 \| \| 1.7203 \| 0.02 \| 40 \| 1.0580 \| 0.2474 \| 0.3601 \| 0.8312 \| nan \| 0.0000 \| 0.1687 \| 0.0012 \| 0.9812 \| 0.0552 \| 0.9541 \| 0.0 \| 0.0000 \| 0.1594 \| 0.0010 \| 0.5662 \| 0.0518 \| 0.9534 \| \| 1.5577 \| 0.04 \| 60 \| 0.9417 \| 0.2218 \| 0.3312 \| 0.8179 \| nan \| 0.0000 \| 0.0330 \| 0.0001 \| 0.9915 \| 0.0100 \| 0.9525 \| 0.0 \| 0.0000 \| 0.0323 \| 0.0001 \| 0.5585 \| 0.0099 \| 0.9517 \| \| 0.8823 \| 0.05 \| 80 \| 0.7899 \| 0.2293 \| 0.3345 \| 0.8217 \| nan \| 0.0000 \| 0.0599 \| 0.0 \| 0.9906 \| 0.0011 \| 0.9553 \| 0.0 \| 0.0000 \| 0.0596 \| 0.0 \| 0.5903 \| 0.0011 \| 0.9538 \| \| 1.2586 \| 0.06 \| 100 \| 0.6372 \| 0.2532 \| 0.3440 \| 0.8300 \| nan \| 0.0000 \| 0.0981 \| 0.0000 \| 0.9697 \| 0.0284 \| 0.9679 \| 0.0 \| 0.0000 \| 0.0971 \| 0.0000 \| 0.6818 \| 0.0284 \| 0.9653 \| \| 1.5022 \| 0.07 \| 120 \| 0.6110 \| 0.2431 \| 0.3372 \| 0.8258 \| nan \| 0.0 \| 0.0759 \| 0.0 \| 0.9823 \| 0.0029 \| 0.9619 \| 0.0 \| 0.0 \| 0.0757 \| 0.0 \| 0.6633 \| 0.0029 \| 0.9599 \| \| 0.7693 \| 0.08 \| 140 \| 0.5468 \| 0.2613 \| 0.3416 \| 0.8125 \| nan \| 0.0 \| 0.2451 \| 0.0 \| 0.8389 \| 0.0027 \| 0.9628 \| 0.0 \| 0.0 \| 0.2372 \| 0.0 \| 0.6285 \| 0.0027 \| 0.9606 \| \| 1.6587 \| 0.1 \| 160 \| 0.5876 \| 0.2717 \| 0.3736 \| 0.8444 \| nan \| 0.0 \| 0.3158 \| 0.0 \| 0.9600 \| 0.0029 \| 0.9628 \| 0.0 \| 0.0 \| 0.3106 \| 0.0 \| 0.6272 \| 0.0029 \| 0.9609 \| \| 1.259 \| 0.11 \| 180 \| 0.5015 \| 0.2883 \| 0.3752 \| 0.8292 \| nan \| 0.0 \| 0.4727 \| 0.0 \| 0.7987 \| 0.0120 \| 0.9679 \| 0.0 \| 0.0 \| 0.4317 \| 0.0 \| 0.6105 \| 0.0120 \| 0.9642 \| \| 1.1834 \| 0.12 \| 200 \| 0.5206 \| 0.3021 \| 0.4047 \| 0.8628 \| nan \| 0.0 \| 0.4986 \| 0.0 \| 0.9598 \| 0.0051 \| 0.9646 \| 0.0 \| 0.0 \| 0.4515 \| 0.0 \| 0.6958 \| 0.0051 \| 0.9620 \| \| 1.1998 \| 0.13 \| 220 \| 0.5969 \| 0.3095 \| 0.4198 \| 0.8740 \| nan \| 0.0 \| 0.5619 \| 0.0 \| 0.9853 \| 0.0075 \| 0.9642 \| 0.0 \| 0.0 \| 0.5167 \| 0.0 \| 0.6803 \| 0.0075 \| 0.9620 \| \| 1.2329 \| 0.15 \| 240 \| 0.4667 \| 0.3146 \| 0.4176 \| 0.8629 \| nan \| 0.0 \| 0.5946 \| 0.0 \| 0.9102 \| 0.0359 \| 0.9649 \| 0.0 \| 0.0 \| 0.5137 \| 0.0 \| 0.6915 \| 0.0348 \| 0.9622 \| \| 0.4256 \| 0.16 \| 260 \| 0.4695 \| 0.3513 \| 0.4674 \| 0.8878 \| nan \| 0.0 \| 0.7239 \| 0.0 \| 0.9465 \| 0.1678 \| 0.9660 \| 0.0 \| 0.0 \| 0.6265 \| 0.0 \| 0.7136 \| 0.1548 \| 0.9639 \| \| 0.6354 \| 0.17 \| 280 \| 0.4582 \| 0.3651 \| 0.4836 \| 0.8773 \| nan \| 0.0 \| 0.6636 \| 0.0 \| 0.8840 \| 0.3866 \| 0.9674 \| 0.0 \| 0.0 \| 0.5882 \| 0.0 \| 0.6976 \| 0.3052 \| 0.9648 \| \| 0.7103 \| 0.18 \| 300 \| 0.4466 \| 0.3736 \| 0.5041 \| 0.8699 \| nan \| 0.0 \| 0.7149 \| 0.0 \| 0.8020 \| 0.5409 \| 0.9668 \| 0.0 \| 0.0 \| 0.6068 \| 0.0 \| 0.6750 \| 0.3693 \| 0.9639 \| \| 0.7022 \| 0.19 \| 320 \| 0.4621 \| 0.3560 \| 0.4756 \| 0.8796 \| nan \| 0.0 \| 0.6003 \| 0.0 \| 0.9246 \| 0.3601 \| 0.9685 \| 0.0 \| 0.0 \| 0.5370 \| 0.0 \| 0.7056 \| 0.2843 \| 0.9653 \| \| 0.8337 \| 0.21 \| 340 \| 0.4500 \| 0.3678 \| 0.4897 \| 0.8754 \| nan \| 0.0 \| 0.6673 \| 0.0 \| 0.8694 \| 0.4355 \| 0.9660 \| 0.0 \| 0.0 \| 0.5735 \| 0.0 \| 0.7075 \| 0.3300 \| 0.9634 \| \| 0.3512 \| 0.22 \| 360 \| 0.4664 \| 0.3630 \| 0.4910 \| 0.8783 \| nan \| 0.0 \| 0.5888 \| 0.0 \| 0.8998 \| 0.4880 \| 0.9696 \| 0.0 \| 0.0 \| 0.5302 \| 0.0 \| 0.7033 \| 0.3433 \| 0.9645 \| \| 1.3383 \| 0.23 \| 380 \| 0.5411 \| 0.3447 \| 0.4592 \| 0.8802 \| nan \| 0.0 \| 0.5990 \| 0.0 \| 0.9552 \| 0.2349 \| 0.9663 \| 0.0 \| 0.0 \| 0.5463 \| 0.0 \| 0.7003 \| 0.2027 \| 0.9633 \| \| 1.421 \| 0.24 \| 400 \| 0.4386 \| 0.3646 \| 0.4884 \| 0.8661 \| nan \| 0.0 \| 0.6447 \| 0.0 \| 0.8233 \| 0.4958 \| 0.9668 \| 0.0 \| 0.0 \| 0.5597 \| 0.0 \| 0.6861 \| 0.3435 \| 0.9631 \| \| 0.5563 \| 0.25 \| 420 \| 0.4313 \| 0.3801 \| 0.5625 \| 0.8797 \| nan \| 0.0 \| 0.7973 \| 0.0 \| 0.7520 \| 0.8561 \| 0.9697 \| 0.0 \| 0.0 \| 0.6663 \| 0.0 \| 0.6742 \| 0.3552 \| 0.9652 \| \| 0.6055 \| 0.27 \| 440 \| 0.4203 \| 0.3731 \| 0.5480 \| 0.8733 \| nan \| 0.0 \| 0.7083 \| 0.0 \| 0.7656 \| 0.8454 \| 0.9690 \| 0.0 \| 0.0 \| 0.6071 \| 0.0 \| 0.6947 \| 0.3457 \| 0.9645 \| \| 1.0955 \| 0.28 \| 460 \| 0.4412 \| 0.3664 \| 0.5119 \| 0.8665 \| nan \| 0.0 \| 0.6004 \| 0.0 \| 0.8418 \| 0.6706 \| 0.9587 \| 0.0 \| 0.0 \| 0.5408 \| 0.0 \| 0.7123 \| 0.3561 \| 0.9554 \| \| 0.9308 \| 0.29 \| 480 \| 0.4208 \| 0.3845 \| 0.5381 \| 0.8852 \| nan \| 0.0 \| 0.7242 \| 0.0 \| 0.8578 \| 0.6826 \| 0.9637 \| 0.0 \| 0.0 \| 0.6415 \| 0.0 \| 0.7161 \| 0.3737 \| 0.9600 \| \| 0.3463 \| 0.3 \| 500 \| 0.4321 \| 0.3713 \| 0.5160 \| 0.8621 \| nan \| 0.0 \| 0.6749 \| 0.0 \| 0.7632 \| 0.6930 \| 0.9652 \| 0.0 \| 0.0 \| 0.5896 \| 0.0 \| 0.6635 \| 0.3833 \| 0.9627 \| \| 0.8166 \| 0.32 \| 520 \| 0.4851 \| 0.3999 \| 0.5399 \| 0.8955 \| nan \| 0.0 \| 0.7876 \| 0.0 \| 0.8940 \| 0.5942 \| 0.9633 \| 0.0 \| 0.0 \| 0.7062 \| 0.0 \| 0.7290 \| 0.4032 \| 0.9609 \| \| 0.5054 \| 0.33 \| 540 \| 0.4328 \| 0.3951 \| 0.5256 \| 0.8936 \| nan \| 0.0 \| 0.7356 \| 0.0 \| 0.9243 \| 0.5325 \| 0.9614 \| 0.0 \| 0.0 \| 0.6860 \| 0.0 \| 0.7262 \| 0.3955 \| 0.9579 \| \| 0.316 \| 0.34 \| 560 \| 0.3850 \| 0.3985 \| 0.5660 \| 0.8957 \| nan \| 0.0 \| 0.8014 \| 0.0 \| 0.8400 \| 0.7855 \| 0.9691 \| 0.0 \| 0.0 \| 0.7180 \| 0.0 \| 0.7286 \| 0.3782 \| 0.9647 \| \| 0.2616 \| 0.35 \| 580 \| 0.3974 \| 0.3831 \| 0.5087 \| 0.8852 \| nan \| 0.0 \| 0.6365 \| 0.0 \| 0.9089 \| 0.5391 \| 0.9677 \| 0.0 \| 0.0 \| 0.5653 \| 0.0 \| 0.7531 \| 0.3995 \| 0.9640 \| \| 0.4969 \| 0.36 \| 600 \| 0.4115 \| 0.3849 \| 0.5209 \| 0.8738 \| nan \| 0.0 \| 0.7301 \| 0.0 \| 0.8064 \| 0.6238 \| 0.9650 \| 0.0 \| 0.0 \| 0.6141 \| 0.0 \| 0.7030 \| 0.4155 \| 0.9618 \| \| 0.6554 \| 0.38 \| 620 \| 0.3927 \| 0.3997 \| 0.5634 \| 0.8927 \| nan \| 0.0 \| 0.8489 \| 0.0 \| 0.8074 \| 0.7544 \| 0.9698 \| 0.0 \| 0.0 \| 0.7254 \| 0.0 \| 0.7077 \| 0.4004 \| 0.9641 \| \| 0.5096 \| 0.39 \| 640 \| 0.4265 \| 0.3833 \| 0.5554 \| 0.8772 \| nan \| 0.0 \| 0.8562 \| 0.0 \| 0.7283 \| 0.7792 \| 0.9688 \| 0.0 \| 0.0 \| 0.6714 \| 0.0 \| 0.6556 \| 0.3905 \| 0.9655 \| \| 0.5453 \| 0.4 \| 660 \| 0.4163 \| 0.3830 \| 0.5356 \| 0.8763 \| nan \| 0.0 \| 0.7352 \| 0.0 \| 0.8036 \| 0.7106 \| 0.9644 \| 0.0 \| 0.0 \| 0.6194 \| 0.0 \| 0.6953 \| 0.4040 \| 0.9625 \| \| 0.8522 \| 0.41 \| 680 \| 0.3850 \| 0.3859 \| 0.5522 \| 0.8779 \| nan \| 0.0 \| 0.8238 \| 0.0 \| 0.7533 \| 0.7690 \| 0.9672 \| 0.0 \| 0.0 \| 0.6587 \| 0.0 \| 0.6785 \| 0.4009 \| 0.9634 \| \| 0.324 \| 0.42 \| 700 \| 0.3980 \| 0.3957 \| 0.5386 \| 0.8933 \| nan \| 0.0 \| 0.7033 \| 0.0 \| 0.8982 \| 0.6630 \| 0.9674 \| 0.0 \| 0.0 \| 0.6302 \| 0.0 \| 0.7372 \| 0.4384 \| 0.9644 \| \| 0.6783 \| 0.44 \| 720 \| 0.4155 \| 0.3798 \| 0.5332 \| 0.8771 \| nan \| 0.0 \| 0.6873 \| 0.0 \| 0.8122 \| 0.7309 \| 0.9689 \| 0.0 \| 0.0 \| 0.5793 \| 0.0 \| 0.7058 \| 0.4079 \| 0.9656 \| \| 0.6283 \| 0.45 \| 740 \| 0.4053 \| 0.3875 \| 0.5179 \| 0.8917 \| nan \| 0.0 \| 0.6808 \| 0.0 \| 0.9207 \| 0.5386 \| 0.9674 \| 0.0 \| 0.0 \| 0.6077 \| 0.0 \| 0.7409 \| 0.3986 \| 0.9649 \| \| 0.831 \| 0.46 \| 760 \| 0.3984 \| 0.3850 \| 0.5178 \| 0.8838 \| nan \| 0.0 \| 0.7712 \| 0.0 \| 0.8476 \| 0.5216 \| 0.9664 \| 0.0 \| 0.0 \| 0.5900 \| 0.0 \| 0.7380 \| 0.4044 \| 0.9629 \| \| 0.5993 \| 0.47 \| 780 \| 0.4069 \| 0.3891 \| 0.5129 \| 0.8961 \| nan \| 0.0 \| 0.7348 \| 0.0 \| 0.9287 \| 0.4453 \| 0.9687 \| 0.0 \| 0.0 \| 0.6417 \| 0.0 \| 0.7532 \| 0.3632 \| 0.9658 \| \| 0.719 \| 0.49 \| 800 \| 0.3856 \| 0.4053 \| 0.5597 \| 0.9006 \| nan \| 0.0 \| 0.8377 \| 0.0 \| 0.8641 \| 0.6875 \| 0.9688 \| 0.0 \| 0.0 \| 0.7007 \| 0.0 \| 0.7423 \| 0.4286 \| 0.9657 \| \| 0.4896 \| 0.5 \| 820 \| 0.3741 \| 0.4033 \| 0.5511 \| 0.8982 \| nan \| 0.0 \| 0.7926 \| 0.0 \| 0.8730 \| 0.6715 \| 0.9697 \| 0.0 \| 0.0 \| 0.6861 \| 0.0 \| 0.7405 \| 0.4302 \| 0.9662 \| \| 0.5632 \| 0.51 \| 840 \| 0.4503 \| 0.4085 \| 0.5607 \| 0.9069 \| nan \| 0.0 \| 0.8602 \| 0.0 \| 0.8788 \| 0.6530 \| 0.9724 \| 0.0 \| 0.0 \| 0.7133 \| 0.0 \| 0.7441 \| 0.4355 \| 0.9664 \| \| 0.2878 \| 0.52 \| 860 \| 0.4594 \| 0.3772 \| 0.4928 \| 0.8637 \| nan \| 0.0 \| 0.5859 \| 0.0 \| 0.8246 \| 0.5792 \| 0.9668 \| 0.0 \| 0.0 \| 0.5066 \| 0.0 \| 0.7162 \| 0.4538 \| 0.9639 \| \| 0.5433 \| 0.53 \| 880 \| 0.3916 \| 0.3899 \| 0.5519 \| 0.8832 \| nan \| 0.0 \| 0.7655 \| 0.0 \| 0.8304 \| 0.7561 \| 0.9596 \| 0.0 \| 0.0 \| 0.6246 \| 0.0 \| 0.7277 \| 0.4197 \| 0.9571 \| \| 1.1254 \| 0.55 \| 900 \| 0.3724 \| 0.4012 \| 0.5588 \| 0.8989 \| nan \| 0.0 \| 0.8798 \| 0.0 \| 0.8445 \| 0.6612 \| 0.9676 \| 0.0 \| 0.0 \| 0.6859 \| 0.0 \| 0.7355 \| 0.4222 \| 0.9645 \| \| 0.3224 \| 0.56 \| 920 \| 0.3896 \| 0.3832 \| 0.5236 \| 0.8848 \| nan \| 0.0 \| 0.6800 \| 0.0 \| 0.8722 \| 0.6211 \| 0.9682 \| 0.0 \| 0.0 \| 0.5748 \| 0.0 \| 0.7403 \| 0.4029 \| 0.9642 \| \| 0.5332 \| 0.57 \| 940 \| 0.4089 \| 0.3939 \| 0.5357 \| 0.9015 \| nan \| 0.0 \| 0.7886 \| 0.0 \| 0.9190 \| 0.5394 \| 0.9673 \| 0.0 \| 0.0 \| 0.6730 \| 0.0 \| 0.7391 \| 0.3803 \| 0.9647 \| \| 0.5894 \| 0.58 \| 960 \| 0.3700 \| 0.3927 \| 0.5405 \| 0.8839 \| nan \| 0.0 \| 0.7708 \| 0.0 \| 0.8235 \| 0.6824 \| 0.9660 \| 0.0 \| 0.0 \| 0.6556 \| 0.0 \| 0.7161 \| 0.4163 \| 0.9612 \| \| 1.036 \| 0.59 \| 980 \| 0.3671 \| 0.3944 \| 0.5432 \| 0.8837 \| nan \| 0.0 \| 0.7763 \| 0.0 \| 0.8179 \| 0.6990 \| 0.9658 \| 0.0 \| 0.0 \| 0.6802 \| 0.0 \| 0.7127 \| 0.4058 \| 0.9623 \| \| 1.5145 \| 0.61 \| 1000 \| 0.3916 \| 0.3965 \| 0.5456 \| 0.8958 \| nan \| 0.0 \| 0.7867 \| 0.0 \| 0.8846 \| 0.6375 \| 0.9647 \| 0.0 \| 0.0 \| 0.7018 \| 0.0 \| 0.7190 \| 0.3925 \| 0.9624 \| \| 0.4625 \| 0.62 \| 1020 \| 0.3603 \| 0.4067 \| 0.5496 \| 0.9010 \| nan \| 0.0 \| 0.8180 \| 0.0 \| 0.8992 \| 0.6160 \| 0.9646 \| 0.0 \| 0.0 \| 0.7095 \| 0.0 \| 0.7525 \| 0.4239 \| 0.9608 \| \| 0.3804 \| 0.63 \| 1040 \| 0.4219 \| 0.3928 \| 0.5328 \| 0.8789 \| nan \| 0.0 \| 0.8239 \| 0.0 \| 0.7882 \| 0.6196 \| 0.9652 \| 0.0 \| 0.0 \| 0.6778 \| 0.0 \| 0.6963 \| 0.4115 \| 0.9637 \| \| 0.1372 \| 0.64 \| 1060 \| 0.3774 \| 0.4034 \| 0.5582 \| 0.8978 \| nan \| 0.0 \| 0.8729 \| 0.0 \| 0.8348 \| 0.6721 \| 0.9696 \| 0.0 \| 0.0 \| 0.7169 \| 0.0 \| 0.7231 \| 0.4186 \| 0.9653 \| \| 0.4438 \| 0.65 \| 1080 \| 0.3409 \| 0.4042 \| 0.5501 \| 0.8971 \| nan \| 0.0 \| 0.7877 \| 0.0 \| 0.8704 \| 0.6733 \| 0.9694 \| 0.0 \| 0.0 \| 0.6900 \| 0.0 \| 0.7509 \| 0.4241 \| 0.9648 \| \| 0.3661 \| 0.67 \| 1100 \| 0.3662 \| 0.3994 \| 0.5728 \| 0.8936 \| nan \| 0.0039 \| 0.8665 \| 0.0 \| 0.7975 \| 0.7998 \| 0.9693 \| 0.0 \| 0.0039 \| 0.7029 \| 0.0 \| 0.7202 \| 0.4038 \| 0.9651 \| \| 0.3783 \| 0.68 \| 1120 \| 0.3625 \| 0.4003 \| 0.5673 \| 0.8893 \| nan \| 0.0 \| 0.8860 \| 0.0 \| 0.7856 \| 0.7669 \| 0.9654 \| 0.0 \| 0.0 \| 0.7155 \| 0.0 \| 0.7166 \| 0.4074 \| 0.9628 \| \| 0.3344 \| 0.69 \| 1140 \| 0.3889 \| 0.4142 \| 0.5535 \| 0.9062 \| nan \| 0.0 \| 0.8531 \| 0.0 \| 0.9107 \| 0.5919 \| 0.9651 \| 0.0 \| 0.0 \| 0.7437 \| 0.0 \| 0.7598 \| 0.4330 \| 0.9628 \| \| 0.448 \| 0.7 \| 1160 \| 0.3701 \| 0.3875 \| 0.5226 \| 0.8774 \| nan \| 0.0 \| 0.6806 \| 0.0 \| 0.8287 \| 0.6578 \| 0.9688 \| 0.0 \| 0.0 \| 0.5996 \| 0.0 \| 0.7207 \| 0.4273 \| 0.9648 \| \| 0.5724 \| 0.72 \| 1180 \| 0.3466 \| 0.4074 \| 0.5587 \| 0.8998 \| nan \| 0.0000 \| 0.8195 \| 0.0 \| 0.8653 \| 0.6982 \| 0.9692 \| 0.0 \| 0.0000 \| 0.7289 \| 0.0 \| 0.7380 \| 0.4198 \| 0.9648 \| \| 0.2868 \| 0.73 \| 1200 \| 0.3369 \| 0.4088 \| 0.5653 \| 0.8956 \| nan \| 0.0005 \| 0.8380 \| 0.0 \| 0.8254 \| 0.7584 \| 0.9697 \| 0.0 \| 0.0005 \| 0.7145 \| 0.0 \| 0.7399 \| 0.4414 \| 0.9652 \| \| 1.2485 \| 0.74 \| 1220 \| 0.3480 \| 0.4120 \| 0.5639 \| 0.9023 \| nan \| 0.0079 \| 0.8643 \| 0.0 \| 0.8543 \| 0.6865 \| 0.9706 \| 0.0 \| 0.0079 \| 0.7086 \| 0.0 \| 0.7548 \| 0.4469 \| 0.9659 \| \| 0.6551 \| 0.75 \| 1240 \| 0.3791 \| 0.4219 \| 0.5591 \| 0.9145 \| nan \| 0.0048 \| 0.8977 \| 0.0 \| 0.9139 \| 0.5662 \| 0.9718 \| 0.0 \| 0.0048 \| 0.7367 \| 0.0 \| 0.7844 \| 0.4620 \| 0.9651 \| \| 0.2599 \| 0.76 \| 1260 \| 0.4596 \| 0.3694 \| 0.5736 \| 0.8698 \| nan \| 0.0001 \| 0.9234 \| 0.0 \| 0.6502 \| 0.9018 \| 0.9664 \| 0.0 \| 0.0001 \| 0.6060 \| 0.0 \| 0.6179 \| 0.3974 \| 0.9644 \| \| 0.4005 \| 0.78 \| 1280 \| 0.3520 \| 0.4018 \| 0.5566 \| 0.8965 \| nan \| 0.0076 \| 0.7654 \| 0.0 \| 0.8698 \| 0.7278 \| 0.9690 \| 0.0 \| 0.0076 \| 0.6936 \| 0.0 \| 0.7161 \| 0.4306 \| 0.9648 \| \| 0.4001 \| 0.79 \| 1300 \| 0.3456 \| 0.4070 \| 0.5543 \| 0.8977 \| nan \| 0.0001 \| 0.7549 \| 0.0 \| 0.8813 \| 0.7203 \| 0.9692 \| 0.0 \| 0.0001 \| 0.6933 \| 0.0 \| 0.7277 \| 0.4620 \| 0.9658 \| \| 0.9039 \| 0.8 \| 1320 \| 0.3889 \| 0.4112 \| 0.5790 \| 0.8984 \| nan \| 0.0006 \| 0.8918 \| 0.0 \| 0.8236 \| 0.7933 \| 0.9650 \| 0.0 \| 0.0006 \| 0.7323 \| 0.0 \| 0.7349 \| 0.4479 \| 0.9626 \| \| 0.6388 \| 0.81 \| 1340 \| 0.4108 \| 0.4121 \| 0.5752 \| 0.9051 \| nan \| 0.0001 \| 0.8354 \| 0.0 \| 0.8712 \| 0.7752 \| 0.9692 \| 0.0 \| 0.0001 \| 0.7325 \| 0.0 \| 0.7351 \| 0.4512 \| 0.9655 \| \| 0.3616 \| 0.82 \| 1360 \| 0.4138 \| 0.4221 \| 0.5638 \| 0.9099 \| nan \| 0.0 \| 0.8630 \| 0.0 \| 0.9119 \| 0.6414 \| 0.9664 \| 0.0 \| 0.0 \| 0.7581 \| 0.0 \| 0.7689 \| 0.4631 \| 0.9646 \| \| 0.2287 \| 0.84 \| 1380 \| 0.3833 \| 0.4229 \| 0.5762 \| 0.9055 \| nan \| 0.0000 \| 0.8940 \| 0.0 \| 0.8637 \| 0.7335 \| 0.9658 \| 0.0 \| 0.0000 \| 0.7658 \| 0.0 \| 0.7545 \| 0.4779 \| 0.9620 \| \| 0.2687 \| 0.85 \| 1400 \| 0.3732 \| 0.4287 \| 0.5666 \| 0.9118 \| nan \| 0.0000 \| 0.8972 \| 0.0 \| 0.9157 \| 0.6226 \| 0.9641 \| 0.0 \| 0.0000 \| 0.7764 \| 0.0 \| 0.7815 \| 0.4814 \| 0.9618 \| \| 0.3827 \| 0.86 \| 1420 \| 0.3344 \| 0.4176 \| 0.5816 \| 0.9030 \| nan \| 0.0 \| 0.8511 \| 0.0 \| 0.8537 \| 0.8177 \| 0.9671 \| 0.0 \| 0.0 \| 0.7452 \| 0.0 \| 0.7485 \| 0.4653 \| 0.9641 \| \| 1.1798 \| 0.87 \| 1440 \| 0.3485 \| 0.4198 \| 0.5742 \| 0.9089 \| nan \| 0.0002 \| 0.8352 \| 0.0 \| 0.8893 \| 0.7500 \| 0.9705 \| 0.0 \| 0.0002 \| 0.7491 \| 0.0 \| 0.7514 \| 0.4729 \| 0.9653 \| \| 0.5062 \| 0.89 \| 1460 \| 0.3882 \| 0.4145 \| 0.5766 \| 0.9028 \| nan \| 0.0013 \| 0.8817 \| 0.0 \| 0.8337 \| 0.7717 \| 0.9712 \| 0.0 \| 0.0013 \| 0.7656 \| 0.0 \| 0.7303 \| 0.4389 \| 0.9656 \| \| 0.2002 \| 0.9 \| 1480 \| 0.3677 \| 0.4167 \| 0.5785 \| 0.9005 \| nan \| 0.0001 \| 0.8674 \| 0.0 \| 0.8450 \| 0.7934 \| 0.9649 \| 0.0 \| 0.0001 \| 0.7513 \| 0.0 \| 0.7391 \| 0.4645 \| 0.9620 \| \| 0.1993 \| 0.91 \| 1500 \| 0.3801 \| 0.4240 \| 0.5584 \| 0.9134 \| nan \| 0.0000 \| 0.8673 \| 0.0 \| 0.9311 \| 0.5842 \| 0.9680 \| 0.0 \| 0.0000 \| 0.7626 \| 0.0 \| 0.7714 \| 0.4687 \| 0.9653 \| \| 0.2609 \| 0.92 \| 1520 \| 0.3489 \| 0.4199 \| 0.5475 \| 0.9084 \| nan \| 0.0001 \| 0.8222 \| 0.0 \| 0.9304 \| 0.5642 \| 0.9682 \| 0.0 \| 0.0001 \| 0.7412 \| 0.0 \| 0.7755 \| 0.4568 \| 0.9657 \| \| 0.4571 \| 0.93 \| 1540 \| 0.3767 \| 0.4218 \| 0.5832 \| 0.9040 \| nan \| 0.0372 \| 0.8298 \| 0.0 \| 0.8774 \| 0.7894 \| 0.9651 \| 0.0 \| 0.0365 \| 0.7505 \| 0.0 \| 0.7451 \| 0.4583 \| 0.9621 \| \| 0.5643 \| 0.95 \| 1560 \| 0.3707 \| 0.4220 \| 0.5848 \| 0.9027 \| nan \| 0.0187 \| 0.8455 \| 0.0 \| 0.8446 \| 0.8307 \| 0.9695 \| 0.0 \| 0.0186 \| 0.7506 \| 0.0 \| 0.7459 \| 0.4715 \| 0.9670 \| \| 0.2607 \| 0.96 \| 1580 \| 0.3601 \| 0.4304 \| 0.5782 \| 0.9099 \| nan \| 0.0119 \| 0.8665 \| 0.0 \| 0.8921 \| 0.7312 \| 0.9675 \| 0.0 \| 0.0118 \| 0.7565 \| 0.0 \| 0.7761 \| 0.5029 \| 0.9652 \| \| 0.2481 \| 0.97 \| 1600 \| 0.3817 \| 0.4281 \| 0.5952 \| 0.9043 \| nan \| 0.0346 \| 0.8954 \| 0.0 \| 0.8582 \| 0.8225 \| 0.9604 \| 0.0 \| 0.0342 \| 0.7426 \| 0.0 \| 0.7691 \| 0.4917 \| 0.9589 \| \| 0.1986 \| 0.98 \| 1620 \| 0.3817 \| 0.4475 \| 0.5988 \| 0.9135 \| nan \| 0.1260 \| 0.8537 \| 0.0 \| 0.9194 \| 0.7288 \| 0.9651 \| 0.0 \| 0.1246 \| 0.7532 \| 0.0 \| 0.7756 \| 0.5166 \| 0.9626 \| \| 0.455 \| 0.99 \| 1640 \| 0.3812 \| 0.4526 \| 0.5958 \| 0.9144 \| nan \| 0.1518 \| 0.8918 \| 0.0 \| 0.9239 \| 0.6439 \| 0.9636 \| 0.0 \| 0.1467 \| 0.7665 \| 0.0 \| 0.7888 \| 0.5058 \| 0.9602 \| \| 0.4941 \| 1.01 \| 1660 \| 0.3751 \| 0.4389 \| 0.6153 \| 0.9094 \| nan \| 0.1142 \| 0.9261 \| 0.0 \| 0.8391 \| 0.8440 \| 0.9682 \| 0.0 \| 0.1110 \| 0.7601 \| 0.0 \| 0.7621 \| 0.4737 \| 0.9652 \| \| 0.2446 \| 1.02 \| 1680 \| 0.3794 \| 0.4389 \| 0.5817 \| 0.9136 \| nan \| 0.0160 \| 0.9137 \| 0.0 \| 0.8883 \| 0.7034 \| 0.9689 \| 0.0 \| 0.0160 \| 0.7765 \| 0.0 \| 0.7870 \| 0.5264 \| 0.9664 \| \| 0.3996 \| 1.03 \| 1700 \| 0.3408 \| 0.4193 \| 0.5478 \| 0.9004 \| nan \| 0.0060 \| 0.7699 \| 0.0 \| 0.8999 \| 0.6419 \| 0.9691 \| 0.0 \| 0.0060 \| 0.6761 \| 0.0 \| 0.7813 \| 0.5054 \| 0.9660 \| \| 0.6762 \| 1.04 \| 1720 \| 0.3653 \| 0.4203 \| 0.5646 \| 0.8983 \| nan \| 0.0554 \| 0.7069 \| 0.0 \| 0.8982 \| 0.7581 \| 0.9691 \| 0.0 \| 0.0550 \| 0.6118 \| 0.0 \| 0.7905 \| 0.5192 \| 0.9658 \| \| 0.3445 \| 1.06 \| 1740 \| 0.3179 \| 0.4460 \| 0.6063 \| 0.9131 \| nan \| 0.1284 \| 0.8453 \| 0.0 \| 0.8960 \| 0.7983 \| 0.9697 \| 0.0 \| 0.1275 \| 0.7422 \| 0.0 \| 0.7797 \| 0.5068 \| 0.9658 \| \| 0.5238 \| 1.07 \| 1760 \| 0.3420 \| 0.4456 \| 0.6022 \| 0.9178 \| nan \| 0.0626 \| 0.9129 \| 0.0 \| 0.8955 \| 0.7732 \| 0.9692 \| 0.0 \| 0.0616 \| 0.7810 \| 0.0 \| 0.7931 \| 0.5180 \| 0.9658 \| \| 0.4666 \| 1.08 \| 1780 \| 0.3410 \| 0.4379 \| 0.5994 \| 0.9099 \| nan \| 0.0695 \| 0.8674 \| 0.0 \| 0.8796 \| 0.8136 \| 0.9666 \| 0.0 \| 0.0678 \| 0.7510 \| 0.0 \| 0.7786 \| 0.5035 \| 0.9645 \| \| 0.1126 \| 1.09 \| 1800 \| 0.3464 \| 0.4383 \| 0.6157 \| 0.9101 \| nan \| 0.1208 \| 0.8865 \| 0.0 \| 0.8516 \| 0.8653 \| 0.9700 \| 0.0 \| 0.1180 \| 0.7454 \| 0.0 \| 0.7670 \| 0.4711 \| 0.9664 \| \| 0.7935 \| 1.1 \| 1820 \| 0.3999 \| 0.4382 \| 0.5737 \| 0.9179 \| nan \| 0.0529 \| 0.8790 \| 0.0 \| 0.9439 \| 0.5984 \| 0.9679 \| 0.0 \| 0.0528 \| 0.7719 \| 0.0 \| 0.7874 \| 0.4906 \| 0.9650 \| \| 0.6014 \| 1.12 \| 1840 \| 0.3176 \| 0.4707 \| 0.6222 \| 0.9193 \| nan \| 0.2149 \| 0.8795 \| 0.0 \| 0.9128 \| 0.7558 \| 0.9702 \| 0.0 \| 0.2033 \| 0.7731 \| 0.0 \| 0.7938 \| 0.5591 \| 0.9653 \| \| 1.1728 \| 1.13 \| 1860 \| 0.3165 \| 0.4715 \| 0.6205 \| 0.9205 \| nan \| 0.2344 \| 0.8965 \| 0.0 \| 0.9165 \| 0.7044 \| 0.9709 \| 0.0 \| 0.2151 \| 0.7453 \| 0.0 \| 0.8097 \| 0.5650 \| 0.9654 \| \| 0.1275 \| 1.14 \| 1880 \| 0.3705 \| 0.4586 \| 0.5965 \| 0.9140 \| nan \| 0.2139 \| 0.8187 \| 0.0 \| 0.9455 \| 0.6344 \| 0.9666 \| 0.0 \| 0.2070 \| 0.7223 \| 0.0 \| 0.7887 \| 0.5277 \| 0.9649 \| \| 0.2179 \| 1.15 \| 1900 \| 0.3268 \| 0.4608 \| 0.6240 \| 0.9126 \| nan \| 0.2176 \| 0.8507 \| 0.0 \| 0.8910 \| 0.8165 \| 0.9679 \| 0.0 \| 0.2054 \| 0.7202 \| 0.0 \| 0.7888 \| 0.5454 \| 0.9656 \| \| 0.1725 \| 1.16 \| 1920 \| 0.3277 \| 0.4655 \| 0.6146 \| 0.9203 \| nan \| 0.1809 \| 0.8656 \| 0.0 \| 0.9297 \| 0.7422 \| 0.9693 \| 0.0 \| 0.1753 \| 0.7456 \| 0.0 \| 0.8099 \| 0.5610 \| 0.9667 \| \| 0.3003 \| 1.18 \| 1940 \| 0.3347 \| 0.4741 \| 0.6311 \| 0.9203 \| nan \| 0.2239 \| 0.8903 \| 0.0 \| 0.9079 \| 0.7951 \| 0.9696 \| 0.0 \| 0.2144 \| 0.7493 \| 0.0 \| 0.8067 \| 0.5815 \| 0.9669 \| \| 0.4764 \| 1.19 \| 1960 \| 0.3413 \| 0.4478 \| 0.6279 \| 0.9079 \| nan \| 0.1854 \| 0.9016 \| 0.0 \| 0.8339 \| 0.8779 \| 0.9687 \| 0.0 \| 0.1690 \| 0.7441 \| 0.0 \| 0.7655 \| 0.4897 \| 0.9663 \| \| 0.1679 \| 1.2 \| 1980 \| 0.3516 \| 0.4473 \| 0.5798 \| 0.9191 \| nan \| 0.0754 \| 0.8674 \| 0.0 \| 0.9466 \| 0.6204 \| 0.9692 \| 0.0 \| 0.0724 \| 0.7752 \| 0.0 \| 0.7993 \| 0.5181 \| 0.9660 \| \| 0.1999 \| 1.21 \| 2000 \| 0.3341 \| 0.4721 \| 0.6457 \| 0.9175 \| nan \| 0.2688 \| 0.8973 \| 0.0 \| 0.8822 \| 0.8568 \| 0.9688 \| 0.0 \| 0.2286 \| 0.7781 \| 0.0 \| 0.7962 \| 0.5359 \| 0.9662 \| \| 0.8992 \| 1.22 \| 2020 \| 0.3121 \| 0.4757 \| 0.6409 \| 0.9179 \| nan \| 0.2915 \| 0.8910 \| 0.0 \| 0.8935 \| 0.8000 \| 0.9695 \| 0.0 \| 0.2634 \| 0.7798 \| 0.0 \| 0.7856 \| 0.5347 \| 0.9661 \| \| 0.7007 \| 1.24 \| 2040 \| 0.3041 \| 0.4618 \| 0.6127 \| 0.9128 \| nan \| 0.1999 \| 0.8653 \| 0.0 \| 0.8950 \| 0.7474 \| 0.9685 \| 0.0 \| 0.1702 \| 0.7512 \| 0.0 \| 0.7931 \| 0.5532 \| 0.9647 \| \| 0.5711 \| 1.25 \| 2060 \| 0.3104 \| 0.4758 \| 0.6277 \| 0.9190 \| nan \| 0.2760 \| 0.8877 \| 0.0 \| 0.9128 \| 0.7196 \| 0.9699 \| 0.0 \| 0.2357 \| 0.7700 \| 0.0 \| 0.8006 \| 0.5579 \| 0.9666 \| \| 0.7925 \| 1.26 \| 2080 \| 0.3465 \| 0.4605 \| 0.6418 \| 0.9091 \| nan \| 0.2878 \| 0.8570 \| 0.0 \| 0.8532 \| 0.8831 \| 0.9696 \| 0.0 \| 0.2437 \| 0.7217 \| 0.0 \| 0.7779 \| 0.5136 \| 0.9670 \| \| 0.1759 \| 1.27 \| 2100 \| 0.3406 \| 0.4619 \| 0.6235 \| 0.9078 \| nan \| 0.2492 \| 0.8230 \| 0.0 \| 0.8719 \| 0.8272 \| 0.9698 \| 0.0 \| 0.2157 \| 0.6976 \| 0.0 \| 0.7884 \| 0.5655 \| 0.9663 \| \| 0.6914 \| 1.29 \| 2120 \| 0.3209 \| 0.4694 \| 0.6229 \| 0.9168 \| nan \| 0.2340 \| 0.8629 \| 0.0 \| 0.9024 \| 0.7665 \| 0.9713 \| 0.0 \| 0.2126 \| 0.7390 \| 0.0 \| 0.8017 \| 0.5650 \| 0.9672 \| \| 0.1292 \| 1.3 \| 2140 \| 0.3151 \| 0.4735 \| 0.6349 \| 0.9147 \| nan \| 0.2670 \| 0.8724 \| 0.0 \| 0.8983 \| 0.8062 \| 0.9657 \| 0.0 \| 0.2358 \| 0.7487 \| 0.0 \| 0.8011 \| 0.5662 \| 0.9625 \| \| 0.5439 \| 1.31 \| 2160 \| 0.3343 \| 0.4617 \| 0.6058 \| 0.9133 \| nan \| 0.2228 \| 0.7989 \| 0.0 \| 0.9340 \| 0.7110 \| 0.9681 \| 0.0 \| 0.2091 \| 0.7154 \| 0.0 \| 0.7958 \| 0.5458 \| 0.9660 \| \| 0.5949 \| 1.32 \| 2180 \| 0.3260 \| 0.4561 \| 0.6419 \| 0.9072 \| nan \| 0.3080 \| 0.8721 \| 0.0 \| 0.8355 \| 0.8650 \| 0.9709 \| 0.0 \| 0.2166 \| 0.7527 \| 0.0 \| 0.7632 \| 0.4930 \| 0.9670 \| \| 0.9366 \| 1.33 \| 2200 \| 0.3182 \| 0.4748 \| 0.6430 \| 0.9190 \| nan \| 0.2967 \| 0.9100 \| 0.0 \| 0.8864 \| 0.7941 \| 0.9710 \| 0.0 \| 0.2381 \| 0.7813 \| 0.0 \| 0.7926 \| 0.5446 \| 0.9669 \| \| 0.4478 \| 1.35 \| 2220 \| 0.3531 \| 0.4596 \| 0.5997 \| 0.9223 \| nan \| 0.1622 \| 0.8843 \| 0.0 \| 0.9456 \| 0.6361 \| 0.9703 \| 0.0 \| 0.1565 \| 0.7919 \| 0.0 \| 0.7921 \| 0.5099 \| 0.9670 \| \| 0.2858 \| 1.36 \| 2240 \| 0.3627 \| 0.4607 \| 0.6228 \| 0.9173 \| nan \| 0.1578 \| 0.8976 \| 0.0 \| 0.8883 \| 0.8239 \| 0.9694 \| 0.0 \| 0.1500 \| 0.7902 \| 0.0 \| 0.7738 \| 0.5443 \| 0.9666 \| \| 0.4923 \| 1.37 \| 2260 \| 0.3367 \| 0.4498 \| 0.6047 \| 0.9082 \| nan \| 0.1380 \| 0.8348 \| 0.0 \| 0.8814 \| 0.8063 \| 0.9678 \| 0.0 \| 0.1299 \| 0.7364 \| 0.0 \| 0.7740 \| 0.5442 \| 0.9638 \| \| 0.1323 \| 1.38 \| 2280 \| 0.3380 \| 0.4515 \| 0.6301 \| 0.9084 \| nan \| 0.1971 \| 0.8926 \| 0.0 \| 0.8345 \| 0.8863 \| 0.9700 \| 0.0 \| 0.1751 \| 0.7494 \| 0.0 \| 0.7577 \| 0.5118 \| 0.9668 \| \| 0.3126 \| 1.39 \| 2300 \| 0.3519 \| 0.4753 \| 0.6187 \| 0.9234 \| nan \| 0.2345 \| 0.8849 \| 0.0 \| 0.9471 \| 0.6770 \| 0.9686 \| 0.0 \| 0.2226 \| 0.7876 \| 0.0 \| 0.7971 \| 0.5533 \| 0.9662 \| \| 1.8741 \| 1.41 \| 2320 \| 0.3483 \| 0.4766 \| 0.6352 \| 0.9194 \| nan \| 0.2550 \| 0.8999 \| 0.0 \| 0.9013 \| 0.7856 \| 0.9691 \| 0.0 \| 0.2242 \| 0.7747 \| 0.0 \| 0.7957 \| 0.5761 \| 0.9656 \| \| 0.3519 \| 1.42 \| 2340 \| 0.3390 \| 0.4794 \| 0.6391 \| 0.9205 \| nan \| 0.2594 \| 0.8966 \| 0.0 \| 0.9017 \| 0.8068 \| 0.9700 \| 0.0 \| 0.2378 \| 0.7732 \| 0.0 \| 0.7951 \| 0.5829 \| 0.9671 \| \| 0.4777 \| 1.43 \| 2360 \| 0.3234 \| 0.4707 \| 0.6410 \| 0.9149 \| nan \| 0.2605 \| 0.8687 \| 0.0 \| 0.8787 \| 0.8681 \| 0.9698 \| 0.0 \| 0.2422 \| 0.7715 \| 0.0 \| 0.7784 \| 0.5358 \| 0.9670 \| \| 0.7156 \| 1.44 \| 2380 \| 0.3451 \| 0.4794 \| 0.6418 \| 0.9201 \| nan \| 0.2929 \| 0.8798 \| 0.0 \| 0.9094 \| 0.7995 \| 0.9694 \| 0.0 \| 0.2575 \| 0.7733 \| 0.0 \| 0.7918 \| 0.5663 \| 0.9670 \| \| 0.3765 \| 1.46 \| 2400 \| 0.3339 \| 0.4639 \| 0.6238 \| 0.9133 \| nan \| 0.2550 \| 0.8921 \| 0.0 \| 0.8805 \| 0.7455 \| 0.9696 \| 0.0 \| 0.1751 \| 0.7569 \| 0.0 \| 0.7919 \| 0.5564 \| 0.9669 \| \| 0.4343 \| 1.47 \| 2420 \| 0.3374 \| 0.4630 \| 0.6168 \| 0.9135 \| nan \| 0.2179 \| 0.8541 \| 0.0 \| 0.9035 \| 0.7576 \| 0.9678 \| 0.0 \| 0.1775 \| 0.7531 \| 0.0 \| 0.7934 \| 0.5515 \| 0.9658 \| \| 0.2178 \| 1.48 \| 2440 \| 0.3254 \| 0.4825 \| 0.6499 \| 0.9219 \| nan \| 0.3424 \| 0.8816 \| 0.0 \| 0.9140 \| 0.7912 \| 0.9704 \| 0.0 \| 0.2639 \| 0.7733 \| 0.0 \| 0.8027 \| 0.5707 \| 0.9669 \| \| 0.1439 \| 1.49 \| 2460 \| 0.3176 \| 0.4752 \| 0.6409 \| 0.9175 \| nan \| 0.2942 \| 0.8787 \| 0.0 \| 0.8935 \| 0.8089 \| 0.9702 \| 0.0 \| 0.2160 \| 0.7786 \| 0.0 \| 0.7978 \| 0.5673 \| 0.9664 \| \| 0.2481 \| 1.5 \| 2480 \| 0.3195 \| 0.4773 \| 0.6265 \| 0.9190 \| nan \| 0.2645 \| 0.8625 \| 0.0 \| 0.9263 \| 0.7376 \| 0.9683 \| 0.0 \| 0.2089 \| 0.7689 \| 0.0 \| 0.8083 \| 0.5890 \| 0.9658 \| \| 0.9347 \| 1.52 \| 2500 \| 0.3430 \| 0.4789 \| 0.6464 \| 0.9207 \| nan \| 0.3009 \| 0.8885 \| 0.0 \| 0.9029 \| 0.8159 \| 0.9702 \| 0.0 \| 0.2626 \| 0.7724 \| 0.0 \| 0.7975 \| 0.5528 \| 0.9670 \| \| 0.1827 \| 1.53 \| 2520 \| 0.3459 \| 0.4726 \| 0.6328 \| 0.9181 \| nan \| 0.2484 \| 0.9093 \| 0.0 \| 0.8921 \| 0.7775 \| 0.9692 \| 0.0 \| 0.2042 \| 0.7741 \| 0.0 \| 0.7998 \| 0.5643 \| 0.9662 \| \| 0.3971 \| 1.54 \| 2540 \| 0.3276 \| 0.4661 \| 0.6353 \| 0.9144 \| nan \| 0.2600 \| 0.8791 \| 0.0 \| 0.8777 \| 0.8247 \| 0.9700 \| 0.0 \| 0.2198 \| 0.7730 \| 0.0 \| 0.7785 \| 0.5249 \| 0.9663 \| \| 0.173 \| 1.55 \| 2560 \| 0.3109 \| 0.4591 \| 0.6419 \| 0.9093 \| nan \| 0.2986 \| 0.8694 \| 0.0 \| 0.8536 \| 0.8611 \| 0.9690 \| 0.0 \| 0.2176 \| 0.7731 \| 0.0 \| 0.7626 \| 0.4941 \| 0.9662 \| \| 0.2705 \| 1.56 \| 2580 \| 0.3112 \| 0.4632 \| 0.6178 \| 0.9101 \| nan \| 0.2942 \| 0.8141 \| 0.0 \| 0.9057 \| 0.7240 \| 0.9687 \| 0.0 \| 0.2203 \| 0.7423 \| 0.0 \| 0.7818 \| 0.5318 \| 0.9661 \| \| 0.2656 \| 1.58 \| 2600 \| 0.3331 \| 0.4806 \| 0.6427 \| 0.9202 \| nan \| 0.3186 \| 0.8837 \| 0.0 \| 0.9166 \| 0.7691 \| 0.9680 \| 0.0 \| 0.2634 \| 0.7761 \| 0.0 \| 0.7986 \| 0.5600 \| 0.9661 \| \| 0.9206 \| 1.59 \| 2620 \| 0.3247 \| 0.4688 \| 0.6290 \| 0.9162 \| nan \| 0.3530 \| 0.8646 \| 0.0 \| 0.9130 \| 0.6725 \| 0.9706 \| 0.0 \| 0.2167 \| 0.7747 \| 0.0 \| 0.7877 \| 0.5357 \| 0.9667 \| \| 0.6181 \| 1.6 \| 2640 \| 0.4032 \| 0.4888 \| 0.6638 \| 0.9239 \| nan \| 0.3722 \| 0.9134 \| 0.0 \| 0.9041 \| 0.8231 \| 0.9701 \| 0.0 \| 0.3080 \| 0.7774 \| 0.0 \| 0.8039 \| 0.5652 \| 0.9668 \| \| 0.3185 \| 1.61 \| 2660 \| 0.3383 \| 0.4705 \| 0.6598 \| 0.9145 \| nan \| 0.3341 \| 0.9161 \| 0.0 \| 0.8448 \| 0.8926 \| 0.9711 \| 0.0 \| 0.2860 \| 0.7681 \| 0.0 \| 0.7732 \| 0.4997 \| 0.9667 \| \| 0.2155 \| 1.63 \| 2680 \| 0.3389 \| 0.4639 \| 0.6447 \| 0.9109 \| nan \| 0.3037 \| 0.8741 \| 0.0 \| 0.8593 \| 0.8625 \| 0.9689 \| 0.0 \| 0.2608 \| 0.7573 \| 0.0 \| 0.7696 \| 0.4933 \| 0.9664 \| \| 0.2003 \| 1.64 \| 2700 \| 0.3230 \| 0.4649 \| 0.6588 \| 0.9112 \| nan \| 0.3442 \| 0.8983 \| 0.0 \| 0.8350 \| 0.9045 \| 0.9710 \| 0.0 \| 0.2818 \| 0.7704 \| 0.0 \| 0.7626 \| 0.4723 \| 0.9672 \| \| 0.1279 \| 1.65 \| 2720 \| 0.3241 \| 0.4747 \| 0.6489 \| 0.9163 \| nan \| 0.3240 \| 0.8760 \| 0.0 \| 0.8863 \| 0.8379 \| 0.9693 \| 0.0 \| 0.2713 \| 0.7729 \| 0.0 \| 0.7876 \| 0.5243 \| 0.9666 \| \| 1.5163 \| 1.66 \| 2740 \| 0.3286 \| 0.4822 \| 0.6409 \| 0.9182 \| nan \| 0.3422 \| 0.8431 \| 0.0 \| 0.9203 \| 0.7703 \| 0.9694 \| 0.0 \| 0.2859 \| 0.7533 \| 0.0 \| 0.7992 \| 0.5702 \| 0.9667 \| \| 0.5542 \| 1.67 \| 2760 \| 0.3147 \| 0.4774 \| 0.6336 \| 0.9157 \| nan \| 0.3563 \| 0.8646 \| 0.0 \| 0.9065 \| 0.7039 \| 0.9702 \| 0.0 \| 0.2631 \| 0.7649 \| 0.0 \| 0.7932 \| 0.5544 \| 0.9664 \| \| 0.343 \| 1.69 \| 2780 \| 0.3632 \| 0.4858 \| 0.6494 \| 0.9212 \| nan \| 0.3345 \| 0.8857 \| 0.0 \| 0.9134 \| 0.7937 \| 0.9689 \| 0.0 \| 0.2855 \| 0.7758 \| 0.0 \| 0.7975 \| 0.5756 \| 0.9665 \| \| 0.4835 \| 1.7 \| 2800 \| 0.3339 \| 0.4753 \| 0.6437 \| 0.9181 \| nan \| 0.2891 \| 0.8746 \| 0.0 \| 0.8945 \| 0.8339 \| 0.9703 \| 0.0 \| 0.2399 \| 0.7766 \| 0.0 \| 0.7894 \| 0.5538 \| 0.9670 \| \| 0.1818 \| 1.71 \| 2820 \| 0.3292 \| 0.4655 \| 0.6247 \| 0.9133 \| nan \| 0.2390 \| 0.8420 \| 0.0 \| 0.8972 \| 0.8010 \| 0.9688 \| 0.0 \| 0.1741 \| 0.7599 \| 0.0 \| 0.7941 \| 0.5642 \| 0.9664 \| \| 0.9569 \| 1.72 \| 2840 \| 0.3529 \| 0.4755 \| 0.6446 \| 0.9220 \| nan \| 0.2926 \| 0.8872 \| 0.0 \| 0.9090 \| 0.8079 \| 0.9709 \| 0.0 \| 0.2297 \| 0.7690 \| 0.0 \| 0.8052 \| 0.5572 \| 0.9672 \| \| 0.1522 \| 1.73 \| 2860 \| 0.3493 \| 0.4606 \| 0.6375 \| 0.9130 \| nan \| 0.2946 \| 0.8384 \| 0.0 \| 0.8860 \| 0.8360 \| 0.9702 \| 0.0 \| 0.2283 \| 0.7457 \| 0.0 \| 0.7715 \| 0.5112 \| 0.9675 \| \| 0.1661 \| 1.75 \| 2880 \| 0.3477 \| 0.4546 \| 0.6333 \| 0.9095 \| nan \| 0.2724 \| 0.8481 \| 0.0 \| 0.8671 \| 0.8428 \| 0.9693 \| 0.0 \| 0.2106 \| 0.7487 \| 0.0 \| 0.7711 \| 0.4853 \| 0.9667 \| \| 0.2484 \| 1.76 \| 2900 \| 0.3435 \| 0.4515 \| 0.6292 \| 0.9050 \| nan \| 0.2757 \| 0.8926 \| 0.0 \| 0.8347 \| 0.8039 \| 0.9683 \| 0.0 \| 0.1935 \| 0.7419 \| 0.0 \| 0.7619 \| 0.4975 \| 0.9658 \| \| 0.1391 \| 1.77 \| 2920 \| 0.3083 \| 0.4621 \| 0.6332 \| 0.9138 \| nan \| 0.3116 \| 0.8488 \| 0.0 \| 0.8965 \| 0.7730 \| 0.9696 \| 0.0 \| 0.2033 \| 0.7516 \| 0.0 \| 0.7920 \| 0.5213 \| 0.9666 \| \| 0.1363 \| 1.78 \| 2940 \| 0.3371 \| 0.4638 \| 0.6463 \| 0.9110 \| nan \| 0.3354 \| 0.8500 \| 0.0 \| 0.8745 \| 0.8501 \| 0.9679 \| 0.0 \| 0.2511 \| 0.7327 \| 0.0 \| 0.7852 \| 0.5121 \| 0.9656 \| \| 0.3563 \| 1.8 \| 2960 \| 0.3271 \| 0.4584 \| 0.6325 \| 0.9096 \| nan \| 0.2815 \| 0.8248 \| 0.0 \| 0.8714 \| 0.8465 \| 0.9711 \| 0.0 \| 0.2268 \| 0.7214 \| 0.0 \| 0.7805 \| 0.5133 \| 0.9670 \| \| 0.7689 \| 1.81 \| 2980 \| 0.3054 \| 0.4593 \| 0.6192 \| 0.9096 \| nan \| 0.2350 \| 0.8486 \| 0.0 \| 0.8729 \| 0.7878 \| 0.9708 \| 0.0 \| 0.1854 \| 0.7343 \| 0.0 \| 0.7904 \| 0.5387 \| 0.9664 \| \| 0.333 \| 1.82 \| 3000 \| 0.3318 \| 0.4715 \| 0.6438 \| 0.9178 \| nan \| 0.2849 \| 0.8976 \| 0.0 \| 0.8858 \| 0.8248 \| 0.9698 \| 0.0 \| 0.2529 \| 0.7470 \| 0.0 \| 0.7938 \| 0.5400 \| 0.9669 \| \| 0.0664 \| 1.83 \| 3020 \| 0.3174 \| 0.4616 \| 0.6548 \| 0.9101 \| nan \| 0.3318 \| 0.8963 \| 0.0 \| 0.8294 \| 0.8993 \| 0.9718 \| 0.0 \| 0.2594 \| 0.7513 \| 0.0 \| 0.7654 \| 0.4884 \| 0.9670 \| \| 0.5802 \| 1.84 \| 3040 \| 0.3003 \| 0.4694 \| 0.6533 \| 0.9138 \| nan \| 0.3512 \| 0.8930 \| 0.0 \| 0.8604 \| 0.8446 \| 0.9707 \| 0.0 \| 0.2368 \| 0.7722 \| 0.0 \| 0.7820 \| 0.5277 \| 0.9668 \| \| 0.158 \| 1.86 \| 3060 \| 0.2986 \| 0.4762 \| 0.6403 \| 0.9197 \| nan \| 0.3481 \| 0.8854 \| 0.0 \| 0.9133 \| 0.7247 \| 0.9701 \| 0.0 \| 0.2633 \| 0.7703 \| 0.0 \| 0.7969 \| 0.5362 \| 0.9669 \| \| 0.1517 \| 1.87 \| 3080 \| 0.3548 \| 0.4843 \| 0.6427 \| 0.9240 \| nan \| 0.4025 \| 0.8833 \| 0.0 \| 0.9451 \| 0.6554 \| 0.9699 \| 0.0 \| 0.3066 \| 0.7910 \| 0.0 \| 0.7953 \| 0.5295 \| 0.9675 \| \| 0.5864 \| 1.88 \| 3100 \| 0.3026 \| 0.4797 \| 0.6438 \| 0.9193 \| nan \| 0.3682 \| 0.8682 \| 0.0 \| 0.9130 \| 0.7426 \| 0.9709 \| 0.0 \| 0.2850 \| 0.7875 \| 0.0 \| 0.7846 \| 0.5333 \| 0.9676 \| \| 0.4852 \| 1.89 \| 3120 \| 0.3118 \| 0.4811 \| 0.6588 \| 0.9188 \| nan \| 0.3660 \| 0.8840 \| 0.0 \| 0.8897 \| 0.8429 \| 0.9703 \| 0.0 \| 0.2981 \| 0.7871 \| 0.0 \| 0.7857 \| 0.5299 \| 0.9672 \| \| 0.2932 \| 1.9 \| 3140 \| 0.2926 \| 0.4920 \| 0.6502 \| 0.9245 \| nan \| 0.3744 \| 0.8931 \| 0.0 \| 0.9253 \| 0.7370 \| 0.9717 \| 0.0 \| 0.3025 \| 0.7904 \| 0.0 \| 0.8108 \| 0.5731 \| 0.9675 \| \| 0.2021 \| 1.92 \| 3160 \| 0.3038 \| 0.4779 \| 0.6211 \| 0.9178 \| nan \| 0.3510 \| 0.8491 \| 0.0 \| 0.9417 \| 0.6159 \| 0.9689 \| 0.0 \| 0.2831 \| 0.7536 \| 0.0 \| 0.8076 \| 0.5351 \| 0.9663 \| \| 0.3806 \| 1.93 \| 3180 \| 0.3182 \| 0.4760 \| 0.6492 \| 0.9141 \| nan \| 0.3445 \| 0.8570 \| 0.0 \| 0.8832 \| 0.8411 \| 0.9692 \| 0.0 \| 0.2641 \| 0.7392 \| 0.0 \| 0.7967 \| 0.5651 \| 0.9670 \| \| 0.1496 \| 1.94 \| 3200 \| 0.3361 \| 0.4753 \| 0.6422 \| 0.9156 \| nan \| 0.3138 \| 0.9011 \| 0.0 \| 0.8849 \| 0.7859 \| 0.9678 \| 0.0 \| 0.2435 \| 0.7803 \| 0.0 \| 0.7946 \| 0.5434 \| 0.9651 \| \| 0.0671 \| 1.95 \| 3220 \| 0.3265 \| 0.4742 \| 0.6578 \| 0.9147 \| nan \| 0.3656 \| 0.8798 \| 0.0 \| 0.8741 \| 0.8584 \| 0.9687 \| 0.0 \| 0.2816 \| 0.7842 \| 0.0 \| 0.7832 \| 0.5041 \| 0.9659 \| \| 0.15 \| 1.96 \| 3240 \| 0.3244 \| 0.4739 \| 0.6427 \| 0.9158 \| nan \| 0.3541 \| 0.8804 \| 0.0 \| 0.8972 \| 0.7564 \| 0.9682 \| 0.0 \| 0.2528 \| 0.7590 \| 0.0 \| 0.7978 \| 0.5417 \| 0.9660 \| \| 0.1933 \| 1.98 \| 3260 \| 0.3516 \| 0.4773 \| 0.6542 \| 0.9174 \| nan \| 0.3953 \| 0.9202 \| 0.0 \| 0.8821 \| 0.7583 \| 0.9693 \| 0.0 \| 0.2658 \| 0.7510 \| 0.0 \| 0.8026 \| 0.5550 \| 0.9665 \| \| 0.1333 \| 1.99 \| 3280 \| 0.3080 \| 0.4830 \| 0.6444 \| 0.9189 \| nan \| 0.3587 \| 0.9054 \| 0.0 \| 0.9009 \| 0.7318 \| 0.9695 \| 0.0 \| 0.3008 \| 0.7536 \| 0.0 \| 0.8027 \| 0.5574 \| 0.9667 \| \| 0.1016 \| 2.0 \| 3300 \| 0.3048 \| 0.4824 \| 0.6479 \| 0.9180 \| nan \| 0.3595 \| 0.8686 \| 0.0 \| 0.9005 \| 0.7879 \| 0.9707 \| 0.0 \| 0.3022 \| 0.7380 \| 0.0 \| 0.8046 \| 0.5642 \| 0.9675 \| \| 0.3692 \| 2.01 \| 3320 \| 0.3021 \| 0.4761 \| 0.6468 \| 0.9144 \| nan \| 0.3537 \| 0.8509 \| 0.0 \| 0.8868 \| 0.8186 \| 0.9706 \| 0.0 \| 0.2802 \| 0.7268 \| 0.0 \| 0.8011 \| 0.5570 \| 0.9672 \| \| 0.8706 \| 2.03 \| 3340 \| 0.3320 \| 0.4796 \| 0.6430 \| 0.9199 \| nan \| 0.3116 \| 0.9048 \| 0.0 \| 0.8987 \| 0.7727 \| 0.9701 \| 0.0 \| 0.2747 \| 0.7327 \| 0.0 \| 0.8107 \| 0.5718 \| 0.9674 \| \| 0.3265 \| 2.04 \| 3360 \| 0.3169 \| 0.4765 \| 0.6552 \| 0.9168 \| nan \| 0.3639 \| 0.9043 \| 0.0 \| 0.8762 \| 0.8171 \| 0.9698 \| 0.0 \| 0.2622 \| 0.7622 \| 0.0 \| 0.7963 \| 0.5480 \| 0.9671 \| \| 0.1013 \| 2.05 \| 3380 \| 0.3117 \| 0.4774 \| 0.6759 \| 0.9165 \| nan \| 0.4382 \| 0.9000 \| 0.0 \| 0.8647 \| 0.8830 \| 0.9696 \| 0.0 \| 0.2732 \| 0.7770 \| 0.0 \| 0.7877 \| 0.5370 \| 0.9668 \| \| 0.2655 \| 2.06 \| 3400 \| 0.3157 \| 0.4852 \| 0.6580 \| 0.9201 \| nan \| 0.3775 \| 0.8841 \| 0.0 \| 0.9011 \| 0.8152 \| 0.9698 \| 0.0 \| 0.3075 \| 0.7750 \| 0.0 \| 0.7963 \| 0.5505 \| 0.9673 \| \| 0.321 \| 2.07 \| 3420 \| 0.2988 \| 0.4807 \| 0.6601 \| 0.9180 \| nan \| 0.3850 \| 0.8639 \| 0.0 \| 0.8911 \| 0.8497 \| 0.9709 \| 0.0 \| 0.2850 \| 0.7833 \| 0.0 \| 0.7893 \| 0.5400 \| 0.9673 \| \| 0.1012 \| 2.09 \| 3440 \| 0.3125 \| 0.4939 \| 0.6538 \| 0.9251 \| nan \| 0.3966 \| 0.8959 \| 0.0 \| 0.9308 \| 0.7287 \| 0.9706 \| 0.0 \| 0.3223 \| 0.7911 \| 0.0 \| 0.8092 \| 0.5669 \| 0.9676 \| \| 0.412 \| 2.1 \| 3460 \| 0.3296 \| 0.4815 \| 0.6585 \| 0.9182 \| nan \| 0.3481 \| 0.9184 \| 0.0 \| 0.8726 \| 0.8417 \| 0.9701 \| 0.0 \| 0.2909 \| 0.7612 \| 0.0 \| 0.7971 \| 0.5547 \| 0.9666 \| \| 0.1383 \| 2.11 \| 3480 \| 0.3082 \| 0.4884 \| 0.6540 \| 0.9225 \| nan \| 0.3569 \| 0.9140 \| 0.0 \| 0.9056 \| 0.7776 \| 0.9699 \| 0.0 \| 0.2999 \| 0.7737 \| 0.0 \| 0.8086 \| 0.5698 \| 0.9665 \| \| 0.1925 \| 2.12 \| 3500 \| 0.3206 \| 0.4821 \| 0.6536 \| 0.9182 \| nan \| 0.3449 \| 0.9109 \| 0.0 \| 0.8844 \| 0.8124 \| 0.9688 \| 0.0 \| 0.2829 \| 0.7721 \| 0.0 \| 0.7977 \| 0.5564 \| 0.9659 \| \| 0.6483 \| 2.13 \| 3520 \| 0.3155 \| 0.4756 \| 0.6540 \| 0.9136 \| nan \| 0.3536 \| 0.8712 \| 0.0 \| 0.8698 \| 0.8595 \| 0.9697 \| 0.0 \| 0.2857 \| 0.7718 \| 0.0 \| 0.7790 \| 0.5269 \| 0.9660 \| \| 0.4534 \| 2.15 \| 3540 \| 0.3218 \| 0.4792 \| 0.6513 \| 0.9170 \| nan \| 0.3516 \| 0.9113 \| 0.0 \| 0.8804 \| 0.7955 \| 0.9690 \| 0.0 \| 0.2800 \| 0.7792 \| 0.0 \| 0.7901 \| 0.5392 \| 0.9657 \| \| 0.1899 \| 2.16 \| 3560 \| 0.3131 \| 0.4741 \| 0.6607 \| 0.9143 \| nan \| 0.3562 \| 0.9017 \| 0.0 \| 0.8519 \| 0.8837 \| 0.9708 \| 0.0 \| 0.2942 \| 0.7720 \| 0.0 \| 0.7761 \| 0.5095 \| 0.9666 \| \| 0.6685 \| 2.17 \| 3580 \| 0.3230 \| 0.4836 \| 0.6603 \| 0.9195 \| nan \| 0.3811 \| 0.9106 \| 0.0 \| 0.8880 \| 0.8125 \| 0.9694 \| 0.0 \| 0.3074 \| 0.7791 \| 0.0 \| 0.7905 \| 0.5417 \| 0.9664 \| \| 0.1743 \| 2.18 \| 3600 \| 0.3060 \| 0.4764 \| 0.6571 \| 0.9157 \| nan \| 0.3722 \| 0.9087 \| 0.0 \| 0.8655 \| 0.8258 \| 0.9704 \| 0.0 \| 0.2614 \| 0.7796 \| 0.0 \| 0.7862 \| 0.5412 \| 0.9661 \| \| 0.2988 \| 2.2 \| 3620 \| 0.3150 \| 0.4883 \| 0.6569 \| 0.9234 \| nan \| 0.3869 \| 0.8954 \| 0.0 \| 0.9154 \| 0.7728 \| 0.9709 \| 0.0 \| 0.2894 \| 0.7913 \| 0.0 \| 0.8052 \| 0.5647 \| 0.9671 \| \| 0.199 \| 2.21 \| 3640 \| 0.3073 \| 0.4799 \| 0.6563 \| 0.9205 \| nan \| 0.3823 \| 0.9094 \| 0.0 \| 0.8966 \| 0.7794 \| 0.9700 \| 0.0 \| 0.2462 \| 0.7752 \| 0.0 \| 0.8058 \| 0.5652 \| 0.9668 \| \| 0.356 \| 2.22 \| 3660 \| 0.3088 \| 0.4787 \| 0.6539 \| 0.9202 \| nan \| 0.3759 \| 0.8945 \| 0.0 \| 0.9014 \| 0.7813 \| 0.9701 \| 0.0 \| 0.2406 \| 0.7759 \| 0.0 \| 0.8051 \| 0.5631 \| 0.9663 \| \| 1.2003 \| 2.23 \| 3680 \| 0.3037 \| 0.4829 \| 0.6551 \| 0.9212 \| nan \| 0.4053 \| 0.8855 \| 0.0 \| 0.9139 \| 0.7558 \| 0.9701 \| 0.0 \| 0.2744 \| 0.7755 \| 0.0 \| 0.8075 \| 0.5561 \| 0.9666 \| \| 0.1801 \| 2.24 \| 3700 \| 0.3155 \| 0.4837 \| 0.6570 \| 0.9202 \| nan \| 0.4074 \| 0.8924 \| 0.0 \| 0.9082 \| 0.7655 \| 0.9688 \| 0.0 \| 0.3066 \| 0.7867 \| 0.0 \| 0.7995 \| 0.5269 \| 0.9664 \| \| 0.2767 \| 2.26 \| 3720 \| 0.3365 \| 0.4783 \| 0.6767 \| 0.9163 \| nan \| 0.4490 \| 0.9065 \| 0.0000 \| 0.8610 \| 0.8735 \| 0.9699 \| 0.0 \| 0.2931 \| 0.7931 \| 0.0000 \| 0.7863 \| 0.5086 \| 0.9666 \| \| 0.2641 \| 2.27 \| 3740 \| 0.3103 \| 0.4783 \| 0.6719 \| 0.9153 \| nan \| 0.4315 \| 0.8901 \| 0.0 \| 0.8631 \| 0.8765 \| 0.9700 \| 0.0 \| 0.2993 \| 0.7883 \| 0.0 \| 0.7826 \| 0.5114 \| 0.9668 \| \| 0.7382 \| 2.28 \| 3760 \| 0.3684 \| 0.4819 \| 0.6608 \| 0.9156 \| nan \| 0.3913 \| 0.8903 \| 0.0 \| 0.8832 \| 0.8335 \| 0.9667 \| 0.0 \| 0.3122 \| 0.7635 \| 0.0 \| 0.7916 \| 0.5410 \| 0.9651 \| \| 0.1887 \| 2.29 \| 3780 \| 0.3380 \| 0.4850 \| 0.6672 \| 0.9190 \| nan \| 0.3872 \| 0.9062 \| 0.0 \| 0.8795 \| 0.8607 \| 0.9693 \| 0.0 \| 0.3182 \| 0.7798 \| 0.0 \| 0.7920 \| 0.5385 \| 0.9668 \| \| 0.4301 \| 2.3 \| 3800 \| 0.3126 \| 0.4902 \| 0.6655 \| 0.9219 \| nan \| 0.4399 \| 0.8957 \| 0.0 \| 0.9040 \| 0.7823 \| 0.9711 \| 0.0 \| 0.3320 \| 0.7915 \| 0.0 \| 0.7986 \| 0.5423 \| 0.9673 \| \| 0.0796 \| 2.32 \| 3820 \| 0.3078 \| 0.4931 \| 0.6700 \| 0.9231 \| nan \| 0.4574 \| 0.8945 \| 0.0 \| 0.9082 \| 0.7887 \| 0.9712 \| 0.0 \| 0.3244 \| 0.7902 \| 0.0 \| 0.8045 \| 0.5654 \| 0.9673 \| \| 0.5856 \| 2.33 \| 3840 \| 0.3280 \| 0.4860 \| 0.6739 \| 0.9186 \| nan \| 0.4448 \| 0.8992 \| 0.0 \| 0.8803 \| 0.8492 \| 0.9697 \| 0.0 \| 0.3209 \| 0.7944 \| 0.0 \| 0.7874 \| 0.5328 \| 0.9665 \| \| 0.3133 \| 2.34 \| 3860 \| 0.3306 \| 0.4901 \| 0.6625 \| 0.9220 \| nan \| 0.4158 \| 0.8912 \| 0.0 \| 0.9104 \| 0.7877 \| 0.9698 \| 0.0 \| 0.3321 \| 0.7919 \| 0.0 \| 0.7933 \| 0.5461 \| 0.9669 \| \| 0.1759 \| 2.35 \| 3880 \| 0.3318 \| 0.4893 \| 0.6707 \| 0.9209 \| nan \| 0.4327 \| 0.9019 \| 0.0 \| 0.8910 \| 0.8278 \| 0.9705 \| 0.0 \| 0.3309 \| 0.7941 \| 0.0 \| 0.7933 \| 0.5402 \| 0.9669 \| \| 0.2173 \| 2.37 \| 3900 \| 0.3227 \| 0.4842 \| 0.6738 \| 0.9186 \| nan \| 0.4241 \| 0.9066 \| 0.0 \| 0.8627 \| 0.8765 \| 0.9731 \| 0.0 \| 0.3222 \| 0.7925 \| 0.0 \| 0.7836 \| 0.5238 \| 0.9670 \| \| 0.3338 \| 2.38 \| 3920 \| 0.3146 \| 0.4892 \| 0.6725 \| 0.9208 \| nan \| 0.4330 \| 0.8966 \| 0.0 \| 0.8909 \| 0.8443 \| 0.9704 \| 0.0 \| 0.3364 \| 0.7922 \| 0.0 \| 0.7910 \| 0.5381 \| 0.9671 \| \| 0.1098 \| 2.39 \| 3940 \| 0.3051 \| 0.4850 \| 0.6648 \| 0.9186 \| nan \| 0.4086 \| 0.9006 \| 0.0 \| 0.8823 \| 0.8271 \| 0.9703 \| 0.0 \| 0.2986 \| 0.7912 \| 0.0 \| 0.7915 \| 0.5466 \| 0.9668 \| \| 0.2892 \| 2.4 \| 3960 \| 0.3277 \| 0.4914 \| 0.6582 \| 0.9235 \| nan \| 0.4131 \| 0.9019 \| 0.0 \| 0.9224 \| 0.7429 \| 0.9691 \| 0.0 \| 0.3010 \| 0.7897 \| 0.0 \| 0.8109 \| 0.5718 \| 0.9666 \| \| 0.7496 \| 2.41 \| 3980 \| 0.3239 \| 0.4799 \| 0.6699 \| 0.9166 \| nan \| 0.4203 \| 0.9044 \| 0.0000 \| 0.8701 \| 0.8556 \| 0.9690 \| 0.0 \| 0.2835 \| 0.7871 \| 0.0000 \| 0.7897 \| 0.5325 \| 0.9666 \| \| 0.1267 \| 2.43 \| 4000 \| 0.3030 \| 0.4849 \| 0.6719 \| 0.9188 \| nan \| 0.4334 \| 0.8752 \| 0.0 \| 0.8889 \| 0.8636 \| 0.9700 \| 0.0 \| 0.3219 \| 0.7904 \| 0.0 \| 0.7879 \| 0.5268 \| 0.9670 \| \| 0.1651 \| 2.44 \| 4020 \| 0.3301 \| 0.4801 \| 0.6728 \| 0.9161 \| nan \| 0.4176 \| 0.8973 \| 0.0 \| 0.8652 \| 0.8877 \| 0.9692 \| 0.0 \| 0.2969 \| 0.7938 \| 0.0 \| 0.7830 \| 0.5201 \| 0.9669 \| \| 0.3399 \| 2.45 \| 4040 \| 0.3104 \| 0.4728 \| 0.6705 \| 0.9147 \| nan \| 0.4008 \| 0.8929 \| 0.0000 \| 0.8517 \| 0.9064 \| 0.9712 \| 0.0 \| 0.2694 \| 0.7927 \| 0.0000 \| 0.7785 \| 0.5023 \| 0.9669 \| \| 0.399 \| 2.46 \| 4060 \| 0.2932 \| 0.4825 \| 0.6653 \| 0.9207 \| nan \| 0.4182 \| 0.8889 \| 0.0000 \| 0.8964 \| 0.8174 \| 0.9711 \| 0.0 \| 0.2890 \| 0.7885 \| 0.0000 \| 0.7953 \| 0.5376 \| 0.9672 \| \| 0.1291 \| 2.47 \| 4080 \| 0.3093 \| 0.4793 \| 0.6726 \| 0.9178 \| nan \| 0.4242 \| 0.8888 \| 0.0 \| 0.8791 \| 0.8745 \| 0.9691 \| 0.0 \| 0.2900 \| 0.7911 \| 0.0 \| 0.7879 \| 0.5195 \| 0.9669 \| \| 0.0833 \| 2.49 \| 4100 \| 0.3038 \| 0.4784 \| 0.6705 \| 0.9162 \| nan \| 0.4206 \| 0.8873 \| 0.0000 \| 0.8697 \| 0.8757 \| 0.9699 \| 0.0 \| 0.2837 \| 0.7942 \| 0.0000 \| 0.7854 \| 0.5195 \| 0.9664 \| \| 0.4351 \| 2.5 \| 4120 \| 0.3084 \| 0.4812 \| 0.6710 \| 0.9181 \| nan \| 0.4290 \| 0.8976 \| 0.0 \| 0.8771 \| 0.8520 \| 0.9702 \| 0.0 \| 0.2869 \| 0.7811 \| 0.0 \| 0.7942 \| 0.5398 \| 0.9666 \| \| 0.6208 \| 2.51 \| 4140 \| 0.3039 \| 0.4776 \| 0.6523 \| 0.9168 \| nan \| 0.3598 \| 0.8827 \| 0.0 \| 0.8828 \| 0.8175 \| 0.9710 \| 0.0 \| 0.2525 \| 0.7696 \| 0.0 \| 0.7989 \| 0.5560 \| 0.9663 \| \| 0.3642 \| 2.52 \| 4160 \| 0.3249 \| 0.4892 \| 0.6676 \| 0.9218 \| nan \| 0.4016 \| 0.9010 \| 0.0 \| 0.8974 \| 0.8360 \| 0.9699 \| 0.0 \| 0.3151 \| 0.7717 \| 0.0 \| 0.8056 \| 0.5650 \| 0.9670 \| \| 0.6865 \| 2.53 \| 4180 \| 0.3203 \| 0.4934 \| 0.6659 \| 0.9243 \| nan \| 0.4052 \| 0.8954 \| 0.0 \| 0.9134 \| 0.8108 \| 0.9708 \| 0.0 \| 0.3057 \| 0.7841 \| 0.0 \| 0.8130 \| 0.5842 \| 0.9672 \| \| 1.0893 \| 2.55 \| 4200 \| 0.2951 \| 0.4863 \| 0.6613 \| 0.9193 \| nan \| 0.4150 \| 0.8548 \| 0.0000 \| 0.9060 \| 0.8212 \| 0.9707 \| 0.0 \| 0.2905 \| 0.7699 \| 0.0000 \| 0.8057 \| 0.5707 \| 0.9670 \| \| 0.4914 \| 2.56 \| 4220 \| 0.2988 \| 0.4951 \| 0.6731 \| 0.9224 \| nan \| 0.4553 \| 0.8745 \| 0.0000 \| 0.9001 \| 0.8352 \| 0.9734 \| 0.0 \| 0.3491 \| 0.7804 \| 0.0000 \| 0.8038 \| 0.5630 \| 0.9690 \| \| 0.2864 \| 2.57 \| 4240 \| 0.3029 \| 0.4946 \| 0.6670 \| 0.9232 \| nan \| 0.4448 \| 0.8753 \| 0.0 \| 0.9182 \| 0.7926 \| 0.9710 \| 0.0 \| 0.3187 \| 0.7765 \| 0.0 \| 0.8127 \| 0.5870 \| 0.9673 \| \| 0.1695 \| 2.58 \| 4260 \| 0.3356 \| 0.5014 \| 0.6749 \| 0.9263 \| nan \| 0.4545 \| 0.8932 \| 0.0 \| 0.9249 \| 0.8066 \| 0.9702 \| 0.0 \| 0.3657 \| 0.7799 \| 0.0 \| 0.8119 \| 0.5847 \| 0.9676 \| \| 0.5746 \| 2.6 \| 4280 \| 0.3106 \| 0.5024 \| 0.6754 \| 0.9260 \| nan \| 0.4610 \| 0.8796 \| 0.0 \| 0.9206 \| 0.8187 \| 0.9723 \| 0.0 \| 0.3690 \| 0.7837 \| 0.0 \| 0.8137 \| 0.5818 \| 0.9683 \| \| 0.2629 \| 2.61 \| 4300 \| 0.3153 \| 0.5020 \| 0.6771 \| 0.9269 \| nan \| 0.4768 \| 0.9000 \| 0.0 \| 0.9266 \| 0.7890 \| 0.9702 \| 0.0 \| 0.3420 \| 0.7826 \| 0.0 \| 0.8218 \| 0.6005 \| 0.9671 \| \| 0.4151 \| 2.62 \| 4320 \| 0.2932 \| 0.4957 \| 0.6677 \| 0.9229 \| nan \| 0.4382 \| 0.8798 \| 0.0 \| 0.9142 \| 0.8031 \| 0.9706 \| 0.0 \| 0.3086 \| 0.7840 \| 0.0 \| 0.8159 \| 0.5944 \| 0.9668 \| \| 0.1169 \| 2.63 \| 4340 \| 0.2940 \| 0.4995 \| 0.6683 \| 0.9244 \| nan \| 0.4620 \| 0.8738 \| 0.0 \| 0.9295 \| 0.7744 \| 0.9701 \| 0.0 \| 0.3315 \| 0.7860 \| 0.0 \| 0.8161 \| 0.5960 \| 0.9668 \| \| 0.1379 \| 2.64 \| 4360 \| 0.2958 \| 0.5047 \| 0.6750 \| 0.9264 \| nan \| 0.4636 \| 0.8776 \| 0.0000 \| 0.9138 \| 0.8195 \| 0.9754 \| 0.0 \| 0.3667 \| 0.7920 \| 0.0000 \| 0.8160 \| 0.5891 \| 0.9689 \| \| 0.1106 \| 2.66 \| 4380 \| 0.3271 \| 0.5063 \| 0.6866 \| 0.9278 \| nan \| 0.5061 \| 0.9005 \| 0.0 \| 0.9251 \| 0.8175 \| 0.9702 \| 0.0 \| 0.3773 \| 0.7976 \| 0.0 \| 0.8143 \| 0.5878 \| 0.9673 \| \| 0.6561 \| 2.67 \| 4400 \| 0.3072 \| 0.4820 \| 0.6689 \| 0.9179 \| nan \| 0.4274 \| 0.9043 \| 0.0001 \| 0.8736 \| 0.8372 \| 0.9707 \| 0.0 \| 0.2704 \| 0.7896 \| 0.0001 \| 0.7980 \| 0.5493 \| 0.9665 \| \| 1.0281 \| 2.68 \| 4420 \| 0.3102 \| 0.4815 \| 0.6878 \| 0.9179 \| nan \| 0.4964 \| 0.9069 \| 0.0000 \| 0.8569 \| 0.8942 \| 0.9721 \| 0.0 \| 0.3072 \| 0.7945 \| 0.0000 \| 0.7899 \| 0.5115 \| 0.9676 \| \| 0.2507 \| 2.69 \| 4440 \| 0.3143 \| 0.4839 \| 0.6758 \| 0.9200 \| nan \| 0.4543 \| 0.9103 \| 0.0 \| 0.8806 \| 0.8390 \| 0.9704 \| 0.0 \| 0.2914 \| 0.7904 \| 0.0 \| 0.8009 \| 0.5380 \| 0.9668 \| \| 1.8848 \| 2.7 \| 4460 \| 0.3204 \| 0.4827 \| 0.6763 \| 0.9186 \| nan \| 0.4533 \| 0.9111 \| 0.0000 \| 0.8766 \| 0.8475 \| 0.9690 \| 0.0 \| 0.2813 \| 0.7838 \| 0.0000 \| 0.8023 \| 0.5453 \| 0.9664 \| \| 0.2177 \| 2.72 \| 4480 \| 0.3152 \| 0.4841 \| 0.6658 \| 0.9196 \| nan \| 0.4292 \| 0.8986 \| 0.0 \| 0.8922 \| 0.8050 \| 0.9699 \| 0.0 \| 0.2702 \| 0.7881 \| 0.0 \| 0.8063 \| 0.5579 \| 0.9666 \| \| 0.2683 \| 2.73 \| 4500 \| 0.3165 \| 0.4810 \| 0.6710 \| 0.9176 \| nan \| 0.4378 \| 0.9003 \| 0.0 \| 0.8797 \| 0.8395 \| 0.9687 \| 0.0 \| 0.2755 \| 0.7847 \| 0.0 \| 0.7996 \| 0.5408 \| 0.9661 \| \| 0.4168 \| 2.74 \| 4520 \| 0.3347 \| 0.4920 \| 0.6748 \| 0.9233 \| nan \| 0.4527 \| 0.9122 \| 0.0 \| 0.9021 \| 0.8121 \| 0.9698 \| 0.0 \| 0.3271 \| 0.7776 \| 0.0 \| 0.8105 \| 0.5616 \| 0.9670 \| \| 0.89 \| 2.75 \| 4540 \| 0.3345 \| 0.4875 \| 0.6709 \| 0.9211 \| nan \| 0.4383 \| 0.9006 \| 0.0 \| 0.8985 \| 0.8189 \| 0.9690 \| 0.0 \| 0.2903 \| 0.7895 \| 0.0 \| 0.8071 \| 0.5591 \| 0.9666 \| \| 0.3392 \| 2.77 \| 4560 \| 0.3020 \| 0.4852 \| 0.6730 \| 0.9197 \| nan \| 0.4509 \| 0.8977 \| 0.0 \| 0.8886 \| 0.8312 \| 0.9698 \| 0.0 \| 0.2847 \| 0.7921 \| 0.0 \| 0.8024 \| 0.5506 \| 0.9665 \| \| 0.4607 \| 2.78 \| 4580 \| 0.3230 \| 0.4935 \| 0.6757 \| 0.9238 \| nan \| 0.4640 \| 0.9097 \| 0.0 \| 0.9075 \| 0.8036 \| 0.9696 \| 0.0 \| 0.3195 \| 0.7866 \| 0.0 \| 0.8116 \| 0.5697 \| 0.9670 \| \| 0.3654 \| 2.79 \| 4600 \| 0.3109 \| 0.4928 \| 0.6672 \| 0.9231 \| nan \| 0.4508 \| 0.8907 \| 0.0 \| 0.9169 \| 0.7750 \| 0.9698 \| 0.0 \| 0.3078 \| 0.7942 \| 0.0 \| 0.8102 \| 0.5704 \| 0.9670 \| \| 0.4694 \| 2.8 \| 4620 \| 0.3250 \| 0.4919 \| 0.6806 \| 0.9221 \| nan \| 0.4773 \| 0.9033 \| 0.0 \| 0.8870 \| 0.8435 \| 0.9722 \| 0.0 \| 0.3328 \| 0.7919 \| 0.0 \| 0.8015 \| 0.5489 \| 0.9679 \| \| 0.7987 \| 2.81 \| 4640 \| 0.3292 \| 0.4954 \| 0.6686 \| 0.9240 \| nan \| 0.4267 \| 0.8898 \| 0.0 \| 0.9178 \| 0.8078 \| 0.9696 \| 0.0 \| 0.3340 \| 0.7982 \| 0.0 \| 0.8063 \| 0.5622 \| 0.9672 \| \| 0.1422 \| 2.83 \| 4660 \| 0.3098 \| 0.4980 \| 0.6793 \| 0.9252 \| nan \| 0.4912 \| 0.9051 \| 0.0 \| 0.9137 \| 0.7954 \| 0.9706 \| 0.0 \| 0.3358 \| 0.7895 \| 0.0 \| 0.8146 \| 0.5789 \| 0.9674 \| \| 0.2764 \| 2.84 \| 4680 \| 0.2950 \| 0.4955 \| 0.6780 \| 0.9236 \| nan \| 0.4777 \| 0.8876 \| 0.0 \| 0.9065 \| 0.8249 \| 0.9715 \| 0.0 \| 0.3347 \| 0.7929 \| 0.0 \| 0.8095 \| 0.5634 \| 0.9678 \| \| 0.7027 \| 2.85 \| 4700 \| 0.3178 \| 0.4956 \| 0.6811 \| 0.9237 \| nan \| 0.4798 \| 0.9043 \| 0.0 \| 0.9009 \| 0.8310 \| 0.9708 \| 0.0 \| 0.3432 \| 0.7879 \| 0.0 \| 0.8083 \| 0.5622 \| 0.9676 \| \| 0.1181 \| 2.86 \| 4720 \| 0.3131 \| 0.4926 \| 0.6812 \| 0.9229 \| nan \| 0.4841 \| 0.9019 \| 0.0 \| 0.8987 \| 0.8317 \| 0.9705 \| 0.0 \| 0.3250 \| 0.7894 \| 0.0 \| 0.8072 \| 0.5593 \| 0.9673 \| \| 0.1929 \| 2.87 \| 4740 \| 0.3345 \| 0.4983 \| 0.6796 \| 0.9246 \| nan \| 0.4671 \| 0.9066 \| 0.0 \| 0.9087 \| 0.8258 \| 0.9697 \| 0.0 \| 0.3638 \| 0.7872 \| 0.0 \| 0.8075 \| 0.5620 \| 0.9673 \| \| 0.1227 \| 2.89 \| 4760 \| 0.3008 \| 0.4923 \| 0.6766 \| 0.9215 \| nan \| 0.4699 \| 0.8828 \| 0.0 \| 0.8972 \| 0.8386 \| 0.9714 \| 0.0 \| 0.3415 \| 0.7942 \| 0.0 \| 0.8013 \| 0.5408 \| 0.9681 \| \| 0.2807 \| 2.9 \| 4780 \| 0.3065 \| 0.4867 \| 0.6674 \| 0.9185 \| nan \| 0.4280 \| 0.8885 \| 0.0 \| 0.8864 \| 0.8315 \| 0.9701 \| 0.0 \| 0.3093 \| 0.7890 \| 0.0 \| 0.7973 \| 0.5438 \| 0.9672 \| \| 0.1685 \| 2.91 \| 4800 \| 0.3054 \| 0.4890 \| 0.6712 \| 0.9206 \| nan \| 0.4508 \| 0.8940 \| 0.0 \| 0.8947 \| 0.8176 \| 0.9704 \| 0.0 \| 0.3112 \| 0.7901 \| 0.0 \| 0.8020 \| 0.5527 \| 0.9672 \| \| 0.2884 \| 2.92 \| 4820 \| 0.3072 \| 0.4896 \| 0.6770 \| 0.9201 \| nan \| 0.4773 \| 0.8843 \| 0.0 \| 0.8932 \| 0.8372 \| 0.9703 \| 0.0 \| 0.3251 \| 0.7914 \| 0.0 \| 0.7997 \| 0.5440 \| 0.9673 \| \| 0.6886 \| 2.94 \| 4840 \| 0.3077 \| 0.4936 \| 0.6643 \| 0.9225 \| nan \| 0.4469 \| 0.8662 \| 0.0 \| 0.9228 \| 0.7796 \| 0.9704 \| 0.0 \| 0.3339 \| 0.7868 \| 0.0 \| 0.8057 \| 0.5614 \| 0.9676 \| \| 0.3344 \| 2.95 \| 4860 \| 0.3017 \| 0.4943 \| 0.6733 \| 0.9225 \| nan \| 0.4660 \| 0.8746 \| 0.0 \| 0.9098 \| 0.8184 \| 0.9711 \| 0.0 \| 0.3488 \| 0.7900 \| 0.0 \| 0.8032 \| 0.5498 \| 0.9681 \| \| 0.1385 \| 2.96 \| 4880 \| 0.3149 \| 0.4846 \| 0.6688 \| 0.9180 \| nan \| 0.4436 \| 0.8811 \| 0.0000 \| 0.8905 \| 0.8288 \| 0.9692 \| 0.0 \| 0.2956 \| 0.7873 \| 0.0000 \| 0.7986 \| 0.5441 \| 0.9667 \| \| 0.559 \| 2.97 \| 4900 \| 0.3012 \| 0.4827 \| 0.6598 \| 0.9172 \| nan \| 0.4204 \| 0.8584 \| 0.0000 \| 0.8997 \| 0.8108 \| 0.9693 \| 0.0 \| 0.2854 \| 0.7789 \| 0.0000 \| 0.7992 \| 0.5489 \| 0.9666 \| \| 0.0576 \| 2.98 \| 4920 \| 0.3116 \| 0.4880 \| 0.6794 \| 0.9200 \| nan \| 0.4841 \| 0.8852 \| 0.0 \| 0.8866 \| 0.8489 \| 0.9714 \| 0.0 \| 0.3229 \| 0.7898 \| 0.0 \| 0.7986 \| 0.5372 \| 0.9678 \| \| 0.1294 \| 3.0 \| 4940 \| 0.3304 \| 0.4794 \| 0.6650 \| 0.9144 \| nan \| 0.4315 \| 0.8895 \| 0.0 \| 0.8740 \| 0.8271 \| 0.9678 \| 0.0 \| 0.2745 \| 0.7784 \| 0.0 \| 0.7930 \| 0.5438 \| 0.9658 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
HHJingbo/Bo	HHJingbo	2023-11-03T20:17:13Z	4	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased-distilled-squad", "base_model:finetune:distilbert/distilbert-base-uncased-distilled-squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-11-03T14:24:39Z	--- license: apache-2.0 base_model: distilbert-base-uncased-distilled-squad tags: - generated_from_keras_callback model-index: - name: HHJingbo/Bo 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. --> # HHJingbo/Bo This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3635 - Validation Loss: 0.5201 - 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.5642 \| 0.5034 \| 0 \| \| 0.3635 \| 0.5201 \| 1 \| ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
SantiRimedio/LuisAlBERTo	SantiRimedio	2023-11-03T20:13:36Z	4	1	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "es", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-17T20:29:38Z	--- language: - es pipeline_tag: fill-mask ---
LoneStriker/openchat_3.5-3.0bpw-h6-exl2	LoneStriker	2023-11-03T20:11:53Z	7	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T19:29:50Z	--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> 🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥 🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖 <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> \| Model \| Size \| Context \| Weights \| Serving \| \|--------------\|------\|---------\|-------------------------------------------------------------\|-------------------------------------------------------------------------------------------------------------\| \| OpenChat 3.5 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat_3.5) \| `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` \| For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks \| Model \| # Params \| Average \| MT-Bench \| AGIEval \| BBH MC \| TruthfulQA \| MMLU \| HumanEval \| BBH CoT \| GSM8K \| \|--------------------\|----------\|----------\|--------------\|----------\|----------\|---------------\|--------------\|-----------------\|-------------\|--------------\| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 47.4 \| 47.6 \| 59.1 \| 64.3 \| 55.5 \| 63.5 \| 77.3 \| \| ChatGPT (March)* \| ? \| 61.5 \| 7.94 \| 47.1 \| 47.6 \| 57.7 \| 67.3 \| 48.1 \| 70.1 \| 74.9 \| \| Mistral \| 7B \| - \| 6.84 \| 38.0 \| 39.0 \| - \| 60.1 \| 30.5 \| - \| 52.2 \| \| Open-source SOTA** \| 13B-70B \| 61.4 \| 7.71 \| 41.7 \| 49.7 \| 62.3 \| 63.7 \| 73.2 \| 41.4 \| 82.3 \| \| \| \| \| WizardLM 70B \| Orca 13B \| Orca 13B \| Platypus2 70B \| WizardLM 70B \| WizardCoder 34B \| Flan-T5 11B \| MetaMath 70B \| : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. : Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. : All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
LoneStriker/openchat_3.5-5.0bpw-h6-exl2	LoneStriker	2023-11-03T20:11:26Z	6	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T19:53:08Z	--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> 🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥 🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖 <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> \| Model \| Size \| Context \| Weights \| Serving \| \|--------------\|------\|---------\|-------------------------------------------------------------\|-------------------------------------------------------------------------------------------------------------\| \| OpenChat 3.5 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat_3.5) \| `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` \| For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks \| Model \| # Params \| Average \| MT-Bench \| AGIEval \| BBH MC \| TruthfulQA \| MMLU \| HumanEval \| BBH CoT \| GSM8K \| \|--------------------\|----------\|----------\|--------------\|----------\|----------\|---------------\|--------------\|-----------------\|-------------\|--------------\| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 47.4 \| 47.6 \| 59.1 \| 64.3 \| 55.5 \| 63.5 \| 77.3 \| \| ChatGPT (March)* \| ? \| 61.5 \| 7.94 \| 47.1 \| 47.6 \| 57.7 \| 67.3 \| 48.1 \| 70.1 \| 74.9 \| \| Mistral \| 7B \| - \| 6.84 \| 38.0 \| 39.0 \| - \| 60.1 \| 30.5 \| - \| 52.2 \| \| Open-source SOTA** \| 13B-70B \| 61.4 \| 7.71 \| 41.7 \| 49.7 \| 62.3 \| 63.7 \| 73.2 \| 41.4 \| 82.3 \| \| \| \| \| WizardLM 70B \| Orca 13B \| Orca 13B \| Platypus2 70B \| WizardLM 70B \| WizardCoder 34B \| Flan-T5 11B \| MetaMath 70B \| : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. : Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. : All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
LoneStriker/openchat_3.5-6.0bpw-h6-exl2	LoneStriker	2023-11-03T20:11:14Z	6	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T20:03:37Z	--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> 🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥 🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖 <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> \| Model \| Size \| Context \| Weights \| Serving \| \|--------------\|------\|---------\|-------------------------------------------------------------\|-------------------------------------------------------------------------------------------------------------\| \| OpenChat 3.5 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat_3.5) \| `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` \| For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks \| Model \| # Params \| Average \| MT-Bench \| AGIEval \| BBH MC \| TruthfulQA \| MMLU \| HumanEval \| BBH CoT \| GSM8K \| \|--------------------\|----------\|----------\|--------------\|----------\|----------\|---------------\|--------------\|-----------------\|-------------\|--------------\| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 47.4 \| 47.6 \| 59.1 \| 64.3 \| 55.5 \| 63.5 \| 77.3 \| \| ChatGPT (March)* \| ? \| 61.5 \| 7.94 \| 47.1 \| 47.6 \| 57.7 \| 67.3 \| 48.1 \| 70.1 \| 74.9 \| \| Mistral \| 7B \| - \| 6.84 \| 38.0 \| 39.0 \| - \| 60.1 \| 30.5 \| - \| 52.2 \| \| Open-source SOTA** \| 13B-70B \| 61.4 \| 7.71 \| 41.7 \| 49.7 \| 62.3 \| 63.7 \| 73.2 \| 41.4 \| 82.3 \| \| \| \| \| WizardLM 70B \| Orca 13B \| Orca 13B \| Platypus2 70B \| WizardLM 70B \| WizardCoder 34B \| Flan-T5 11B \| MetaMath 70B \| : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. : Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. : All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
LoneStriker/openchat_3.5-8.0bpw-h8-exl2	LoneStriker	2023-11-03T20:10:36Z	12	3	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T20:28:33Z	--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> 🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥 🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖 <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> \| Model \| Size \| Context \| Weights \| Serving \| \|--------------\|------\|---------\|-------------------------------------------------------------\|-------------------------------------------------------------------------------------------------------------\| \| OpenChat 3.5 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat_3.5) \| `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` \| For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks \| Model \| # Params \| Average \| MT-Bench \| AGIEval \| BBH MC \| TruthfulQA \| MMLU \| HumanEval \| BBH CoT \| GSM8K \| \|--------------------\|----------\|----------\|--------------\|----------\|----------\|---------------\|--------------\|-----------------\|-------------\|--------------\| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 47.4 \| 47.6 \| 59.1 \| 64.3 \| 55.5 \| 63.5 \| 77.3 \| \| ChatGPT (March)* \| ? \| 61.5 \| 7.94 \| 47.1 \| 47.6 \| 57.7 \| 67.3 \| 48.1 \| 70.1 \| 74.9 \| \| Mistral \| 7B \| - \| 6.84 \| 38.0 \| 39.0 \| - \| 60.1 \| 30.5 \| - \| 52.2 \| \| Open-source SOTA** \| 13B-70B \| 61.4 \| 7.71 \| 41.7 \| 49.7 \| 62.3 \| 63.7 \| 73.2 \| 41.4 \| 82.3 \| \| \| \| \| WizardLM 70B \| Orca 13B \| Orca 13B \| Platypus2 70B \| WizardLM 70B \| WizardCoder 34B \| Flan-T5 11B \| MetaMath 70B \| : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. : Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. : All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
jdpressman/minihf_evaluator_mistral_7b_v0.1	jdpressman	2023-11-03T20:10:23Z	9	0	peft	[ "peft", "safetensors", "license:apache-2.0", "region:us" ]	null	2023-11-03T20:07:38Z	--- library_name: peft license: apache-2.0 --- # minihf_evaluator_mistral_7b_v0.1 `minihf_evaluator_mistral_7b_v0.1` is a LoRA instruct fine-tune of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). The sequence `<\|end\|>` was used to separate the prompt and response. The correct way to prompt the model is: `Does 2 + 2 = 4?<\|end\|>`. The tokenizer will prepend a BOS token (`<s>`) by default. The response will end with an EOS token (`</s>`). ## Training procedure `minihf_evaluator_mistral_7b_v0.1` was fine-tuned for 100,000 examples on 90% [Muennighoff/flan](https://huggingface.co/datasets/Muennighoff/flan) / 10% [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) using batch size 4 per GPU on 8 80GB H100 GPUs. Examples where the prompt and response would not fit into 4096 tokens were dropped. The fine-tuning was done using the following command: ```bash accelerate launch sft_evaluator.py --output-dir minihf_evaluator_mistral_7b_v0.1 ``` 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0
owanr/Sentiment-google-t5-v1_1-large-inter_model-shuffle-human_annots_str	owanr	2023-11-03T20:07:02Z	0	0	null	[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]	null	2023-11-03T19:48:12Z	--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: Sentiment-google-t5-v1_1-large-inter_model-shuffle-human_annots_str 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. --> # Sentiment-google-t5-v1_1-large-inter_model-shuffle-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3838 ## 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: 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 20.9707 \| 1.0 \| 44 \| 24.0812 \| \| 19.1584 \| 2.0 \| 88 \| 19.1069 \| \| 14.7114 \| 3.0 \| 132 \| 12.0456 \| \| 11.3751 \| 4.0 \| 176 \| 10.9758 \| \| 10.1468 \| 5.0 \| 220 \| 10.7621 \| \| 10.0067 \| 6.0 \| 264 \| 10.6808 \| \| 10.0007 \| 7.0 \| 308 \| 10.5435 \| \| 9.7079 \| 8.0 \| 352 \| 10.1514 \| \| 9.182 \| 9.0 \| 396 \| 9.5931 \| \| 8.7756 \| 10.0 \| 440 \| 9.2557 \| \| 8.6941 \| 11.0 \| 484 \| 9.0992 \| \| 8.6156 \| 12.0 \| 528 \| 8.9849 \| \| 8.1186 \| 13.0 \| 572 \| 8.7739 \| \| 1.4193 \| 14.0 \| 616 \| 1.3089 \| \| 1.3333 \| 15.0 \| 660 \| 1.3014 \| \| 1.3499 \| 16.0 \| 704 \| 1.3009 \| \| 1.3384 \| 17.0 \| 748 \| 1.2995 \| \| 1.332 \| 18.0 \| 792 \| 1.2958 \| \| 1.3226 \| 19.0 \| 836 \| 1.3095 \| \| 1.318 \| 20.0 \| 880 \| 1.3012 \| \| 1.3192 \| 21.0 \| 924 \| 1.2978 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
Joetib/pythia-finetuned-with-context-5-steps	Joetib	2023-11-03T19:34:40Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-v0", "base_model:finetune:EleutherAI/pythia-410m-v0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T19:31:40Z	--- license: apache-2.0 base_model: EleutherAI/pythia-410M tags: - generated_from_trainer model-index: - name: pythia-finetuned-with-context-5-steps 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. --> # pythia-finetuned-with-context-5-steps This model is a fine-tuned version of [EleutherAI/pythia-410M](https://huggingface.co/EleutherAI/pythia-410M) on the None 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 5 ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Joetib/pythia-finetuned-5-steps	Joetib	2023-11-03T19:27:54Z	16	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-v0", "base_model:finetune:EleutherAI/pythia-410m-v0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T19:13:03Z	--- license: apache-2.0 base_model: EleutherAI/pythia-410M tags: - generated_from_trainer model-index: - name: pythia-finetuned-5-steps 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. --> # pythia-finetuned-5-steps This model is a fine-tuned version of [EleutherAI/pythia-410M](https://huggingface.co/EleutherAI/pythia-410M) on the None 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 5 ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Zac-Nguyen/orca-cot	Zac-Nguyen	2023-11-03T19:24:59Z	5	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T18:07:18Z	--- license: other language: - en --- An improved, potentially even perfected variant of MythoMix, my [MythoLogic-L2](https://huggingface.co/Gryphe/MythoLogic-L2-13b) and [Huginn](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) merge using a highly experimental tensor type merge technique. The main difference with MythoMix is that I allowed more of Huginn to intermingle with the single tensors located at the front and end of a model, resulting in increased coherency across the entire structure. The script and the acccompanying templates I used to produce both can [be found here](https://github.com/Gryphe/BlockMerge_Gradient/tree/main/YAML). This model is proficient at both roleplaying and storywriting due to its unique nature. Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) (You're the best!) ## Model details The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time) This type of merge is incapable of being illustrated, as each of its 363 tensors had an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour. ## Prompt Format This model primarily uses Alpaca formatting, so for optimal model performance, use: ``` <System prompt/Character Card> ### Instruction: Your instruction or question here. For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only. ### Response: ``` --- license: other ---
mi-rei/my_awesome_model	mi-rei	2023-11-03T19:24:50Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T17:19:11Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_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. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6756 - Accuracy: 0.5681 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6883 \| 1.0 \| 1394 \| 0.6795 \| 0.5526 \| \| 0.6687 \| 2.0 \| 2788 \| 0.6756 \| 0.5681 \| ### Framework versions - Transformers 4.30.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.13.3
hpandana/poca-SoccerTwos	hpandana	2023-11-03T19:21:43Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-11-03T19:21:33Z	--- 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: hpandana/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
YoungMeng/Taxi-v3	YoungMeng	2023-11-03T19:02:11Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T04:00:11Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.78 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="YoungMeng/Taxi-v3", 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"]) ```
btqa/btqa-base	btqa	2023-11-03T18:50:57Z	0	0	peft	[ "peft", "pytorch", "btlm", "generated_from_trainer", "custom_code", "dataset:iarfmoose/question_generator", "dataset:Defalt-404/Bittensor_validator", "dataset:multi_news", "dataset:cnn_dailymail", "base_model:cerebras/btlm-3b-8k-base", "base_model:adapter:cerebras/btlm-3b-8k-base", "license:apache-2.0", "region:us" ]	null	2023-11-02T14:41:34Z	--- license: apache-2.0 base_model: cerebras/btlm-3b-8k-base tags: - generated_from_trainer model-index: - name: app/bt_qa-out results: [] datasets: - iarfmoose/question_generator - Defalt-404/Bittensor_validator - multi_news - cnn_dailymail library_name: peft --- <!-- 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # app/bt_qa-out This model is a fine-tuned version of [cerebras/btlm-3b-8k-base](https://huggingface.co/cerebras/btlm-3b-8k-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7451 ## 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: 8.5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 32 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 2.7377 \| 0.0 \| 500 \| 2.5700 \| \| 2.5689 \| 0.01 \| 1000 \| 2.4172 \| \| 2.6676 \| 0.01 \| 1500 \| 2.3622 \| \| 2.4234 \| 0.01 \| 2000 \| 2.3293 \| \| 2.2221 \| 0.01 \| 2500 \| 2.3090 \| \| 2.3873 \| 0.02 \| 3000 \| 2.2940 \| \| 2.2688 \| 0.02 \| 3500 \| 2.2644 \| \| 2.4194 \| 0.02 \| 4000 \| 2.2725 \| \| 2.4086 \| 0.03 \| 4500 \| 2.2561 \| \| 2.4476 \| 0.03 \| 5000 \| 2.2477 \| \| 2.1512 \| 0.03 \| 5500 \| 2.2330 \| \| 2.1428 \| 0.03 \| 6000 \| 2.2235 \| \| 2.2834 \| 0.04 \| 6500 \| 2.2141 \| \| 2.2918 \| 0.04 \| 7000 \| 2.2124 \| \| 2.4352 \| 0.04 \| 7500 \| 2.2074 \| \| 1.7196 \| 0.05 \| 8000 \| 2.2038 \| \| 2.2394 \| 0.05 \| 8500 \| 2.1973 \| \| 2.1632 \| 0.05 \| 9000 \| 2.1856 \| \| 2.4313 \| 0.06 \| 9500 \| 2.1820 \| \| 2.4584 \| 0.06 \| 10000 \| 2.1764 \| \| 2.3359 \| 0.06 \| 10500 \| 2.1780 \| \| 2.2105 \| 0.06 \| 11000 \| 2.1671 \| \| 2.3152 \| 0.07 \| 11500 \| 2.1603 \| \| 2.3012 \| 0.07 \| 12000 \| 2.1572 \| \| 2.4636 \| 0.07 \| 12500 \| 2.1553 \| \| 2.0974 \| 0.08 \| 13000 \| 2.1511 \| \| 2.298 \| 0.08 \| 13500 \| 2.1481 \| \| 2.3312 \| 0.08 \| 14000 \| 2.1445 \| \| 2.5315 \| 0.08 \| 14500 \| 2.1381 \| \| 2.1854 \| 0.09 \| 15000 \| 2.1364 \| \| 2.3069 \| 0.09 \| 15500 \| 2.1355 \| \| 2.0756 \| 0.09 \| 16000 \| 2.1331 \| \| 2.0094 \| 0.1 \| 16500 \| 2.1306 \| \| 2.2674 \| 0.1 \| 17000 \| 2.1230 \| \| 1.8427 \| 0.1 \| 17500 \| 2.1176 \| \| 2.2277 \| 0.1 \| 18000 \| 2.1168 \| \| 2.1398 \| 0.11 \| 18500 \| 2.1152 \| \| 1.9927 \| 0.11 \| 19000 \| 2.1088 \| \| 2.0119 \| 0.11 \| 19500 \| 2.1105 \| \| 2.5796 \| 0.12 \| 20000 \| 2.1040 \| \| 1.3256 \| 0.12 \| 20500 \| 2.0993 \| \| 2.2051 \| 0.12 \| 21000 \| 2.0992 \| \| 1.628 \| 0.13 \| 21500 \| 2.0944 \| \| 2.1926 \| 0.13 \| 22000 \| 2.0927 \| \| 1.6482 \| 0.13 \| 22500 \| 2.0873 \| \| 2.1122 \| 0.13 \| 23000 \| 2.0830 \| \| 1.7405 \| 0.14 \| 23500 \| 2.0828 \| \| 2.2685 \| 0.14 \| 24000 \| 2.0784 \| \| 2.1062 \| 0.14 \| 24500 \| 2.0766 \| \| 2.1308 \| 0.15 \| 25000 \| 2.0714 \| \| 1.9122 \| 0.15 \| 25500 \| 2.0719 \| \| 2.3549 \| 0.15 \| 26000 \| 2.0643 \| \| 2.2159 \| 0.15 \| 26500 \| 2.0655 \| \| 1.493 \| 0.16 \| 27000 \| 2.0598 \| \| 1.893 \| 0.16 \| 27500 \| 2.0557 \| \| 2.1902 \| 0.16 \| 28000 \| 2.0533 \| \| 2.2353 \| 0.17 \| 28500 \| 2.0524 \| \| 1.8736 \| 0.17 \| 29000 \| 2.0519 \| \| 2.0511 \| 0.17 \| 29500 \| 2.0449 \| \| 1.2872 \| 0.17 \| 30000 \| 2.0453 \| \| 1.6353 \| 0.18 \| 30500 \| 2.0377 \| \| 1.992 \| 0.18 \| 31000 \| 2.0419 \| \| 2.3586 \| 0.18 \| 31500 \| 2.0353 \| \| 1.9453 \| 0.19 \| 32000 \| 2.0330 \| \| 2.1322 \| 0.19 \| 32500 \| 2.0305 \| \| 2.2887 \| 0.19 \| 33000 \| 2.0253 \| \| 2.0268 \| 0.2 \| 33500 \| 2.0267 \| \| 1.8397 \| 0.2 \| 34000 \| 2.0207 \| \| 2.5165 \| 0.2 \| 34500 \| 2.0202 \| \| 1.9142 \| 0.2 \| 35000 \| 2.0139 \| \| 1.5993 \| 0.21 \| 35500 \| 2.0179 \| \| 2.1691 \| 0.21 \| 36000 \| 2.0102 \| \| 2.4948 \| 0.21 \| 36500 \| 2.0089 \| \| 1.5422 \| 0.22 \| 37000 \| 2.0039 \| \| 1.4566 \| 0.22 \| 37500 \| 2.0014 \| \| 1.852 \| 0.22 \| 38000 \| 2.0043 \| \| 2.199 \| 0.22 \| 38500 \| 1.9987 \| \| 1.4852 \| 0.23 \| 39000 \| 1.9976 \| \| 1.3 \| 0.23 \| 39500 \| 1.9936 \| \| 2.1237 \| 0.23 \| 40000 \| 1.9917 \| \| 1.691 \| 0.24 \| 40500 \| 1.9887 \| \| 2.2169 \| 0.24 \| 41000 \| 1.9870 \| \| 2.1991 \| 0.24 \| 41500 \| 1.9851 \| \| 1.9517 \| 0.24 \| 42000 \| 1.9806 \| \| 1.6369 \| 0.25 \| 42500 \| 1.9762 \| \| 2.2759 \| 0.25 \| 43000 \| 1.9753 \| \| 2.2923 \| 0.25 \| 43500 \| 1.9748 \| \| 2.2552 \| 0.26 \| 44000 \| 1.9702 \| \| 2.066 \| 0.26 \| 44500 \| 1.9683 \| \| 2.2703 \| 0.26 \| 45000 \| 1.9686 \| \| 2.3544 \| 0.27 \| 45500 \| 1.9648 \| \| 2.255 \| 0.27 \| 46000 \| 1.9635 \| \| 1.8732 \| 0.27 \| 46500 \| 1.9639 \| \| 2.1203 \| 0.27 \| 47000 \| 1.9590 \| \| 2.1314 \| 0.28 \| 47500 \| 1.9573 \| \| 1.8511 \| 0.28 \| 48000 \| 1.9533 \| \| 2.1471 \| 0.28 \| 48500 \| 1.9514 \| \| 1.8417 \| 0.29 \| 49000 \| 1.9509 \| \| 2.4485 \| 0.29 \| 49500 \| 1.9502 \| \| 2.0708 \| 0.29 \| 50000 \| 1.9455 \| \| 1.8272 \| 0.29 \| 50500 \| 1.9416 \| \| 1.6232 \| 0.3 \| 51000 \| 1.9380 \| \| 1.6785 \| 0.3 \| 51500 \| 1.9358 \| \| 1.5734 \| 0.3 \| 52000 \| 1.9313 \| \| 1.9737 \| 0.31 \| 52500 \| 1.9301 \| \| 1.8393 \| 0.31 \| 53000 \| 1.9295 \| \| 1.4789 \| 0.31 \| 53500 \| 1.9281 \| \| 2.2062 \| 0.31 \| 54000 \| 1.9273 \| \| 2.3501 \| 0.32 \| 54500 \| 1.9236 \| \| 2.2756 \| 0.32 \| 55000 \| 1.9218 \| \| 2.1001 \| 0.32 \| 55500 \| 1.9215 \| \| 2.0342 \| 0.33 \| 56000 \| 1.9179 \| \| 1.8066 \| 0.33 \| 56500 \| 1.9143 \| \| 1.8322 \| 0.33 \| 57000 \| 1.9137 \| \| 2.0926 \| 0.34 \| 57500 \| 1.9106 \| \| 2.2106 \| 0.34 \| 58000 \| 1.9083 \| \| 2.0666 \| 0.34 \| 58500 \| 1.9055 \| \| 2.2082 \| 0.34 \| 59000 \| 1.9026 \| \| 2.1768 \| 0.35 \| 59500 \| 1.9007 \| \| 1.7091 \| 0.35 \| 60000 \| 1.8967 \| \| 1.7585 \| 0.35 \| 60500 \| 1.8946 \| \| 1.8968 \| 0.36 \| 61000 \| 1.8936 \| \| 2.107 \| 0.36 \| 61500 \| 1.8906 \| \| 1.5162 \| 0.36 \| 62000 \| 1.8870 \| \| 2.0642 \| 0.36 \| 62500 \| 1.8836 \| \| 2.0399 \| 0.37 \| 63000 \| 1.8813 \| \| 2.3971 \| 0.37 \| 63500 \| 1.8785 \| \| 1.7433 \| 0.37 \| 64000 \| 1.8797 \| \| 2.0971 \| 0.38 \| 64500 \| 1.8743 \| \| 1.8212 \| 0.38 \| 65000 \| 1.8726 \| \| 2.1023 \| 0.38 \| 65500 \| 1.8695 \| \| 1.9735 \| 0.38 \| 66000 \| 1.8674 \| \| 1.3196 \| 0.39 \| 66500 \| 1.8657 \| \| 1.9825 \| 0.39 \| 67000 \| 1.8629 \| \| 2.0356 \| 0.39 \| 67500 \| 1.8604 \| \| 1.8522 \| 0.4 \| 68000 \| 1.8581 \| \| 2.2666 \| 0.4 \| 68500 \| 1.8568 \| \| 2.3575 \| 0.4 \| 69000 \| 1.8538 \| \| 2.0086 \| 0.41 \| 69500 \| 1.8537 \| \| 1.9811 \| 0.41 \| 70000 \| 1.8512 \| \| 2.0702 \| 0.41 \| 70500 \| 1.8485 \| \| 1.8554 \| 0.41 \| 71000 \| 1.8456 \| \| 0.5356 \| 0.42 \| 71500 \| 1.8437 \| \| 1.4742 \| 0.42 \| 72000 \| 1.8413 \| \| 2.1901 \| 0.42 \| 72500 \| 1.8420 \| \| 1.7868 \| 0.43 \| 73000 \| 1.8383 \| \| 1.3144 \| 0.43 \| 73500 \| 1.8371 \| \| 2.1158 \| 0.43 \| 74000 \| 1.8347 \| \| 2.0779 \| 0.43 \| 74500 \| 1.8331 \| \| 1.9756 \| 0.44 \| 75000 \| 1.8323 \| \| 2.3395 \| 0.44 \| 75500 \| 1.8309 \| \| 1.895 \| 0.44 \| 76000 \| 1.8283 \| \| 2.0369 \| 0.45 \| 76500 \| 1.8274 \| \| 1.8068 \| 0.45 \| 77000 \| 1.8251 \| \| 2.2153 \| 0.45 \| 77500 \| 1.8227 \| \| 2.1389 \| 0.45 \| 78000 \| 1.8212 \| \| 1.9166 \| 0.46 \| 78500 \| 1.8197 \| \| 1.711 \| 0.46 \| 79000 \| 1.8187 \| \| 1.9102 \| 0.46 \| 79500 \| 1.8165 \| \| 0.8358 \| 0.47 \| 80000 \| 1.8163 \| \| 1.7278 \| 0.47 \| 80500 \| 1.8148 \| \| 1.601 \| 0.47 \| 81000 \| 1.8126 \| \| 1.9794 \| 0.48 \| 81500 \| 1.8107 \| \| 1.7323 \| 0.48 \| 82000 \| 1.8095 \| \| 2.2911 \| 0.48 \| 82500 \| 1.8090 \| \| 1.8962 \| 0.48 \| 83000 \| 1.8065 \| \| 2.3055 \| 0.49 \| 83500 \| 1.8052 \| \| 1.6899 \| 0.49 \| 84000 \| 1.8037 \| \| 1.6409 \| 0.49 \| 84500 \| 1.8031 \| \| 1.9116 \| 0.5 \| 85000 \| 1.8011 \| \| 0.6875 \| 0.5 \| 85500 \| 1.8003 \| \| 2.0829 \| 0.5 \| 86000 \| 1.7983 \| \| 1.5716 \| 0.5 \| 86500 \| 1.7981 \| \| 2.4537 \| 0.51 \| 87000 \| 1.7961 \| \| 1.8236 \| 0.51 \| 87500 \| 1.7942 \| \| 1.641 \| 0.51 \| 88000 \| 1.7931 \| \| 1.5533 \| 0.52 \| 88500 \| 1.7916 \| \| 1.679 \| 0.52 \| 89000 \| 1.7902 \| \| 2.1463 \| 0.52 \| 89500 \| 1.7893 \| \| 1.5477 \| 0.52 \| 90000 \| 1.7884 \| \| 1.2346 \| 0.53 \| 90500 \| 1.7873 \| \| 1.3352 \| 0.53 \| 91000 \| 1.7859 \| \| 2.1039 \| 0.53 \| 91500 \| 1.7850 \| \| 2.0818 \| 0.54 \| 92000 \| 1.7834 \| \| 1.3987 \| 0.54 \| 92500 \| 1.7830 \| \| 1.4544 \| 0.54 \| 93000 \| 1.7827 \| \| 0.4043 \| 0.55 \| 93500 \| 1.7811 \| \| 2.0149 \| 0.55 \| 94000 \| 1.7794 \| \| 1.9845 \| 0.55 \| 94500 \| 1.7789 \| \| 2.1053 \| 0.55 \| 95000 \| 1.7775 \| \| 2.1572 \| 0.56 \| 95500 \| 1.7768 \| \| 2.0754 \| 0.56 \| 96000 \| 1.7761 \| \| 1.7675 \| 0.56 \| 96500 \| 1.7754 \| \| 2.0023 \| 0.57 \| 97000 \| 1.7743 \| \| 1.2653 \| 0.57 \| 97500 \| 1.7736 \| \| 1.5566 \| 0.57 \| 98000 \| 1.7728 \| \| 1.9408 \| 0.57 \| 98500 \| 1.7724 \| \| 2.0936 \| 0.58 \| 99000 \| 1.7713 \| \| 0.5687 \| 0.58 \| 99500 \| 1.7706 \| \| 2.2833 \| 0.58 \| 100000 \| 1.7702 \| \| 1.6689 \| 0.59 \| 100500 \| 1.7690 \| \| 1.5198 \| 0.59 \| 101000 \| 1.7684 \| \| 1.6968 \| 0.59 \| 101500 \| 1.7679 \| \| 2.2034 \| 0.59 \| 102000 \| 1.7674 \| \| 1.7902 \| 0.6 \| 102500 \| 1.7665 \| \| 2.0557 \| 0.6 \| 103000 \| 1.7658 \| \| 1.8617 \| 0.6 \| 103500 \| 1.7650 \| \| 1.8749 \| 0.61 \| 104000 \| 1.7637 \| \| 1.7674 \| 0.61 \| 104500 \| 1.7632 \| \| 1.4269 \| 0.61 \| 105000 \| 1.7627 \| \| 1.989 \| 0.62 \| 105500 \| 1.7621 \| \| 2.1026 \| 0.62 \| 106000 \| 1.7615 \| \| 2.0304 \| 0.62 \| 106500 \| 1.7609 \| \| 1.6286 \| 0.62 \| 107000 \| 1.7603 \| \| 0.9544 \| 0.63 \| 107500 \| 1.7599 \| \| 1.6421 \| 0.63 \| 108000 \| 1.7588 \| \| 1.9841 \| 0.63 \| 108500 \| 1.7586 \| \| 1.7453 \| 0.64 \| 109000 \| 1.7581 \| \| 1.2119 \| 0.64 \| 109500 \| 1.7575 \| \| 2.1092 \| 0.64 \| 110000 \| 1.7568 \| \| 2.0849 \| 0.64 \| 110500 \| 1.7564 \| \| 1.9162 \| 0.65 \| 111000 \| 1.7562 \| \| 1.01 \| 0.65 \| 111500 \| 1.7560 \| \| 1.301 \| 0.65 \| 112000 \| 1.7556 \| \| 0.315 \| 0.66 \| 112500 \| 1.7552 \| \| 1.9964 \| 0.66 \| 113000 \| 1.7548 \| \| 2.4035 \| 0.66 \| 113500 \| 1.7544 \| \| 1.3559 \| 0.66 \| 114000 \| 1.7542 \| \| 2.1874 \| 0.67 \| 114500 \| 1.7538 \| \| 1.4373 \| 0.67 \| 115000 \| 1.7534 \| \| 0.0639 \| 0.67 \| 115500 \| 1.7529 \| \| 1.7667 \| 0.68 \| 116000 \| 1.7526 \| \| 1.6204 \| 0.68 \| 116500 \| 1.7524 \| \| 1.9859 \| 0.68 \| 117000 \| 1.7521 \| \| 0.9717 \| 0.69 \| 117500 \| 1.7516 \| \| 1.8844 \| 0.69 \| 118000 \| 1.7514 \| \| 1.3336 \| 0.69 \| 118500 \| 1.7509 \| \| 1.5781 \| 0.69 \| 119000 \| 1.7506 \| \| 1.8449 \| 0.7 \| 119500 \| 1.7505 \| \| 1.5305 \| 0.7 \| 120000 \| 1.7503 \| \| 2.1904 \| 0.7 \| 120500 \| 1.7500 \| \| 2.2285 \| 0.71 \| 121000 \| 1.7496 \| \| 1.8097 \| 0.71 \| 121500 \| 1.7494 \| \| 2.3631 \| 0.71 \| 122000 \| 1.7493 \| \| 2.0893 \| 0.71 \| 122500 \| 1.7491 \| \| 2.1201 \| 0.72 \| 123000 \| 1.7489 \| \| 1.8334 \| 0.72 \| 123500 \| 1.7488 \| \| 2.0222 \| 0.72 \| 124000 \| 1.7486 \| \| 1.6339 \| 0.73 \| 124500 \| 1.7484 \| \| 1.6754 \| 0.73 \| 125000 \| 1.7482 \| \| 1.3973 \| 0.73 \| 125500 \| 1.7480 \| \| 2.0594 \| 0.73 \| 126000 \| 1.7479 \| \| 1.8674 \| 0.74 \| 126500 \| 1.7478 \| \| 2.1948 \| 0.74 \| 127000 \| 1.7476 \| \| 1.4148 \| 0.74 \| 127500 \| 1.7475 \| \| 1.6734 \| 0.75 \| 128000 \| 1.7473 \| \| 2.2787 \| 0.75 \| 128500 \| 1.7472 \| \| 1.8999 \| 0.75 \| 129000 \| 1.7471 \| \| 1.6945 \| 0.76 \| 129500 \| 1.7470 \| \| 2.0165 \| 0.76 \| 130000 \| 1.7469 \| \| 2.2232 \| 0.76 \| 130500 \| 1.7468 \| \| 1.6201 \| 0.76 \| 131000 \| 1.7466 \| \| 2.4878 \| 0.77 \| 131500 \| 1.7465 \| \| 1.5317 \| 0.77 \| 132000 \| 1.7465 \| \| 1.9361 \| 0.77 \| 132500 \| 1.7464 \| \| 1.7127 \| 0.78 \| 133000 \| 1.7463 \| \| 1.7045 \| 0.78 \| 133500 \| 1.7462 \| \| 2.1827 \| 0.78 \| 134000 \| 1.7461 \| \| 2.0534 \| 0.78 \| 134500 \| 1.7461 \| \| 2.0808 \| 0.79 \| 135000 \| 1.7460 \| \| 1.9572 \| 0.79 \| 135500 \| 1.7459 \| \| 1.8762 \| 0.79 \| 136000 \| 1.7459 \| \| 1.4686 \| 0.8 \| 136500 \| 1.7458 \| \| 1.6241 \| 0.8 \| 137000 \| 1.7458 \| \| 1.4219 \| 0.8 \| 137500 \| 1.7457 \| \| 2.1605 \| 0.8 \| 138000 \| 1.7457 \| \| 2.1298 \| 0.81 \| 138500 \| 1.7456 \| \| 1.414 \| 0.81 \| 139000 \| 1.7456 \| \| 1.0115 \| 0.81 \| 139500 \| 1.7455 \| \| 1.9471 \| 0.82 \| 140000 \| 1.7455 \| \| 1.8873 \| 0.82 \| 140500 \| 1.7455 \| \| 1.8286 \| 0.82 \| 141000 \| 1.7454 \| \| 2.1418 \| 0.83 \| 141500 \| 1.7454 \| \| 1.9755 \| 0.83 \| 142000 \| 1.7454 \| \| 1.6908 \| 0.83 \| 142500 \| 1.7454 \| \| 2.3842 \| 0.83 \| 143000 \| 1.7453 \| \| 1.7665 \| 0.84 \| 143500 \| 1.7453 \| \| 1.8266 \| 0.84 \| 144000 \| 1.7453 \| \| 0.8768 \| 0.84 \| 144500 \| 1.7453 \| \| 1.2274 \| 0.85 \| 145000 \| 1.7453 \| \| 1.6647 \| 0.85 \| 145500 \| 1.7453 \| \| 1.4071 \| 0.85 \| 146000 \| 1.7452 \| \| 1.6073 \| 0.85 \| 146500 \| 1.7452 \| \| 2.201 \| 0.86 \| 147000 \| 1.7452 \| \| 1.5504 \| 0.86 \| 147500 \| 1.7452 \| \| 1.4377 \| 0.86 \| 148000 \| 1.7452 \| \| 1.4453 \| 0.87 \| 148500 \| 1.7452 \| \| 1.6929 \| 0.87 \| 149000 \| 1.7451 \| \| 1.7631 \| 0.87 \| 149500 \| 1.7451 \| \| 2.0868 \| 0.87 \| 150000 \| 1.7451 \| \| 0.6434 \| 0.88 \| 150500 \| 1.7451 \| \| 1.4851 \| 0.88 \| 151000 \| 1.7451 \| \| 1.5365 \| 0.88 \| 151500 \| 1.7451 \| \| 1.8129 \| 0.89 \| 152000 \| 1.7451 \| \| 1.1623 \| 0.89 \| 152500 \| 1.7451 \| \| 2.0714 \| 0.89 \| 153000 \| 1.7451 \| \| 1.9363 \| 0.9 \| 153500 \| 1.7451 \| \| 1.6408 \| 0.9 \| 154000 \| 1.7451 \| \| 0.618 \| 0.9 \| 154500 \| 1.7451 \| \| 1.7957 \| 0.9 \| 155000 \| 1.7451 \| \| 2.0056 \| 0.91 \| 155500 \| 1.7451 \| \| 1.3893 \| 0.91 \| 156000 \| 1.7451 \| \| 2.1426 \| 0.91 \| 156500 \| 1.7451 \| \| 1.6766 \| 0.92 \| 157000 \| 1.7451 \| \| 1.4206 \| 0.92 \| 157500 \| 1.7451 \| \| 1.7285 \| 0.92 \| 158000 \| 1.7451 \| \| 1.5779 \| 0.92 \| 158500 \| 1.7451 \| \| 1.8675 \| 0.93 \| 159000 \| 1.7451 \| \| 2.0217 \| 0.93 \| 159500 \| 1.7451 \| \| 0.9516 \| 0.93 \| 160000 \| 1.7451 \| \| 2.219 \| 0.94 \| 160500 \| 1.7450 \| \| 1.6214 \| 0.94 \| 161000 \| 1.7451 \| \| 1.7134 \| 0.94 \| 161500 \| 1.7451 \| \| 1.6128 \| 0.94 \| 162000 \| 1.7451 \| \| 2.0817 \| 0.95 \| 162500 \| 1.7450 \| \| 1.8055 \| 0.95 \| 163000 \| 1.7451 \| \| 1.909 \| 0.95 \| 163500 \| 1.7451 \| \| 1.7844 \| 0.96 \| 164000 \| 1.7451 \| \| 2.0719 \| 0.96 \| 164500 \| 1.7451 \| \| 1.8698 \| 0.96 \| 165000 \| 1.7451 \| \| 1.6926 \| 0.96 \| 165500 \| 1.7451 \| \| 2.2161 \| 0.97 \| 166000 \| 1.7451 \| \| 2.1111 \| 0.97 \| 166500 \| 1.7451 \| \| 1.8004 \| 0.97 \| 167000 \| 1.7451 \| \| 2.2364 \| 0.98 \| 167500 \| 1.7451 \| \| 1.6716 \| 0.98 \| 168000 \| 1.7451 \| \| 2.1804 \| 0.98 \| 168500 \| 1.7451 \| \| 1.2691 \| 0.99 \| 169000 \| 1.7451 \| \| 1.8306 \| 0.99 \| 169500 \| 1.7451 \| \| 0.5662 \| 0.99 \| 170000 \| 1.7451 \| \| 1.6516 \| 0.99 \| 170500 \| 1.7451 \| \| 2.0576 \| 1.0 \| 171000 \| 1.7451 \| \| 1.3638 \| 1.0 \| 171500 \| 1.7451 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
Moreza009/Tehran_Covid19	Moreza009	2023-11-03T18:32:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:bigscience/bloom-7b1", "base_model:adapter:bigscience/bloom-7b1", "region:us" ]	null	2023-11-03T18:06:12Z	--- library_name: peft base_model: bigscience/bloom-7b1 --- # 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] - 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 Data 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 Data 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.7.0.dev0
owanr/SChem5Labels-google-t5-v1_1-large-inter_model-frequency-model_annots_str	owanr	2023-11-03T18:25:12Z	0	0	null	[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]	null	2023-11-03T04:30:50Z	--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SChem5Labels-google-t5-v1_1-large-inter_model-frequency-model_annots_str 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. --> # SChem5Labels-google-t5-v1_1-large-inter_model-frequency-model_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9844 ## 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: 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 20.3817 \| 1.0 \| 25 \| 23.8120 \| \| 19.4374 \| 2.0 \| 50 \| 21.8918 \| \| 18.745 \| 3.0 \| 75 \| 19.3959 \| \| 16.896 \| 4.0 \| 100 \| 15.4970 \| \| 15.3045 \| 5.0 \| 125 \| 11.5374 \| \| 12.9955 \| 6.0 \| 150 \| 9.6467 \| \| 11.2112 \| 7.0 \| 175 \| 8.9925 \| \| 9.4851 \| 8.0 \| 200 \| 8.7994 \| \| 8.7487 \| 9.0 \| 225 \| 8.5320 \| \| 8.1197 \| 10.0 \| 250 \| 8.3570 \| \| 7.9164 \| 11.0 \| 275 \| 8.2662 \| \| 7.7789 \| 12.0 \| 300 \| 8.1800 \| \| 7.6671 \| 13.0 \| 325 \| 8.0987 \| \| 7.5107 \| 14.0 \| 350 \| 7.9659 \| \| 7.457 \| 15.0 \| 375 \| 7.6850 \| \| 7.1712 \| 16.0 \| 400 \| 7.3914 \| \| 6.9462 \| 17.0 \| 425 \| 7.2019 \| \| 6.8109 \| 18.0 \| 450 \| 7.0657 \| \| 6.7403 \| 19.0 \| 475 \| 6.9778 \| \| 6.5766 \| 20.0 \| 500 \| 6.9288 \| \| 6.505 \| 21.0 \| 525 \| 6.8702 \| \| 6.5148 \| 22.0 \| 550 \| 6.8175 \| \| 6.541 \| 23.0 \| 575 \| 6.7619 \| \| 4.3898 \| 24.0 \| 600 \| 1.0917 \| \| 1.0874 \| 25.0 \| 625 \| 0.7681 \| \| 0.8058 \| 26.0 \| 650 \| 0.7295 \| \| 0.7847 \| 27.0 \| 675 \| 0.7244 \| \| 0.7779 \| 28.0 \| 700 \| 0.7195 \| \| 0.7741 \| 29.0 \| 725 \| 0.7205 \| \| 0.7606 \| 30.0 \| 750 \| 0.7222 \| \| 0.7613 \| 31.0 \| 775 \| 0.7189 \| \| 0.7676 \| 32.0 \| 800 \| 0.7119 \| \| 0.7547 \| 33.0 \| 825 \| 0.7138 \| \| 0.7433 \| 34.0 \| 850 \| 0.7148 \| \| 0.7729 \| 35.0 \| 875 \| 0.7202 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1
LoneStriker/Utopia-13B-8.0bpw-h8-exl2	LoneStriker	2023-11-03T18:18:51Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T18:17:49Z	--- license: cc-by-nc-4.0 --- Temp. ``` Xwin-LM/Xwin-LM-13B-V0.2 Undi95/Storytelling-v2.1-13B-lora => p1 NeverSleep/Nethena-13B zattio770/120-Days-of-LORA-v2-13B => p2 PygmalionAI/pygmalion-2-13b lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT => p3 merge_method: task_arithmetic base_model: TheBloke/Llama-2-13B-fp16 models: - model: TheBloke/Llama-2-13B-fp16 - model: Undi95/newpart1 parameters: weight: 1.0 - model: Undi95/newpart2 parameters: weight: 0.45 - model: Undi95/newpart3 parameters: weight: 0.33 dtype: float16 ``` ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).
sreejith8100/donut-base-vishnu	sreejith8100	2023-11-03T18:17:44Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-11-02T10:31:32Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-vishnu 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. --> # donut-base-vishnu This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.9387 \| 1.0 \| 127 \| 2.0237 \| \| 0.8381 \| 2.0 \| 254 \| 1.3332 \| \| 1.4923 \| 3.0 \| 381 \| 1.1110 \| \| 0.9061 \| 4.0 \| 508 \| 0.9530 \| \| 0.4627 \| 5.0 \| 635 \| 0.9156 \| \| 0.4305 \| 6.0 \| 762 \| 0.7884 \| \| 0.4383 \| 7.0 \| 889 \| 0.6936 \| \| 0.1852 \| 8.0 \| 1016 \| 0.6715 \| \| 0.2348 \| 9.0 \| 1143 \| 0.6209 \| \| 0.3975 \| 10.0 \| 1270 \| 0.5614 \| \| 0.1548 \| 11.0 \| 1397 \| 0.5152 \| \| 0.0377 \| 12.0 \| 1524 \| 0.5135 \| \| 0.043 \| 13.0 \| 1651 \| 0.4759 \| \| 0.0698 \| 14.0 \| 1778 \| 0.4697 \| \| 0.0292 \| 15.0 \| 1905 \| 0.4243 \| \| 0.0516 \| 16.0 \| 2032 \| 0.4594 \| \| 0.2062 \| 17.0 \| 2159 \| 0.4332 \| \| 0.0307 \| 18.0 \| 2286 \| 0.4030 \| \| 0.0775 \| 19.0 \| 2413 \| 0.4069 \| \| 0.0157 \| 20.0 \| 2540 \| 0.4111 \| \| 0.0137 \| 21.0 \| 2667 \| 0.4072 \| \| 0.0148 \| 22.0 \| 2794 \| 0.3938 \| \| 0.0454 \| 23.0 \| 2921 \| 0.3789 \| \| 0.0023 \| 24.0 \| 3048 \| 0.3864 \| \| 0.0033 \| 25.0 \| 3175 \| 0.3750 \| \| 0.0292 \| 26.0 \| 3302 \| 0.3847 \| \| 0.0087 \| 27.0 \| 3429 \| 0.3592 \| \| 0.0032 \| 28.0 \| 3556 \| 0.3665 \| \| 0.0048 \| 29.0 \| 3683 \| 0.3372 \| \| 0.0035 \| 30.0 \| 3810 \| 0.3349 \| \| 0.0197 \| 31.0 \| 3937 \| 0.3591 \| \| 0.0006 \| 32.0 \| 4064 \| 0.3504 \| \| 0.0016 \| 33.0 \| 4191 \| 0.3450 \| \| 0.0006 \| 34.0 \| 4318 \| 0.3505 \| \| 0.0046 \| 35.0 \| 4445 \| 0.3332 \| \| 0.0045 \| 36.0 \| 4572 \| 0.3206 \| \| 0.0006 \| 37.0 \| 4699 \| 0.3361 \| \| 0.0039 \| 38.0 \| 4826 \| 0.3348 \| \| 0.0059 \| 39.0 \| 4953 \| 0.3328 \| \| 0.0039 \| 40.0 \| 5080 \| 0.3406 \| \| 0.0014 \| 41.0 \| 5207 \| 0.3250 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
owanr/Sentiment-google-t5-v1_1-large-inter_model-frequency-human_annots_str	owanr	2023-11-03T18:16:38Z	0	0	null	[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]	null	2023-11-03T18:16:36Z	--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: Sentiment-google-t5-v1_1-large-inter_model-frequency-human_annots_str 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. --> # Sentiment-google-t5-v1_1-large-inter_model-frequency-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2676 ## 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: 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 20.8675 \| 1.0 \| 44 \| 24.9691 \| \| 18.2491 \| 2.0 \| 88 \| 16.9536 \| \| 13.3557 \| 3.0 \| 132 \| 11.4063 \| \| 11.0809 \| 4.0 \| 176 \| 10.9529 \| \| 10.1527 \| 5.0 \| 220 \| 10.7258 \| \| 9.9235 \| 6.0 \| 264 \| 10.5299 \| \| 9.8499 \| 7.0 \| 308 \| 10.3942 \| \| 9.5638 \| 8.0 \| 352 \| 10.0279 \| \| 9.0218 \| 9.0 \| 396 \| 9.4463 \| \| 8.5704 \| 10.0 \| 440 \| 9.0452 \| \| 8.3982 \| 11.0 \| 484 \| 8.8619 \| \| 8.3396 \| 12.0 \| 528 \| 8.7363 \| \| 7.0951 \| 13.0 \| 572 \| 1.3757 \| \| 1.1349 \| 14.0 \| 616 \| 1.0516 \| \| 1.0735 \| 15.0 \| 660 \| 1.0456 \| \| 1.0787 \| 16.0 \| 704 \| 1.0342 \| \| 1.0796 \| 17.0 \| 748 \| 1.0342 \| \| 1.0476 \| 18.0 \| 792 \| 1.0318 \| \| 1.0566 \| 19.0 \| 836 \| 1.0311 \| \| 1.0472 \| 20.0 \| 880 \| 1.0247 \| \| 1.0401 \| 21.0 \| 924 \| 1.0201 \| \| 1.0725 \| 22.0 \| 968 \| 1.0159 \| \| 1.0447 \| 23.0 \| 1012 \| 1.0180 \| \| 1.0477 \| 24.0 \| 1056 \| 1.0121 \| \| 1.0357 \| 25.0 \| 1100 \| 1.0109 \| \| 1.0333 \| 26.0 \| 1144 \| 1.0096 \| \| 1.0282 \| 27.0 \| 1188 \| 1.0078 \| \| 1.0206 \| 28.0 \| 1232 \| 1.0100 \| \| 1.0241 \| 29.0 \| 1276 \| 1.0081 \| \| 1.023 \| 30.0 \| 1320 \| 1.0053 \| \| 0.9993 \| 31.0 \| 1364 \| 1.0073 \| \| 1.0104 \| 32.0 \| 1408 \| 1.0079 \| \| 1.0176 \| 33.0 \| 1452 \| 1.0014 \| \| 1.0157 \| 34.0 \| 1496 \| 0.9977 \| \| 1.0204 \| 35.0 \| 1540 \| 0.9960 \| \| 1.0174 \| 36.0 \| 1584 \| 0.9967 \| \| 1.0252 \| 37.0 \| 1628 \| 0.9949 \| \| 1.0076 \| 38.0 \| 1672 \| 0.9913 \| \| 1.0137 \| 39.0 \| 1716 \| 0.9874 \| \| 1.0151 \| 40.0 \| 1760 \| 0.9856 \| \| 0.9907 \| 41.0 \| 1804 \| 0.9843 \| \| 1.0147 \| 42.0 \| 1848 \| 0.9803 \| \| 1.001 \| 43.0 \| 1892 \| 0.9777 \| \| 1.0009 \| 44.0 \| 1936 \| 0.9735 \| \| 0.9881 \| 45.0 \| 1980 \| 0.9731 \| \| 0.9973 \| 46.0 \| 2024 \| 0.9761 \| \| 0.9982 \| 47.0 \| 2068 \| 0.9888 \| \| 0.9826 \| 48.0 \| 2112 \| 1.0006 \| \| 0.9739 \| 49.0 \| 2156 \| 0.9766 \| \| 0.9659 \| 50.0 \| 2200 \| 0.9525 \| \| 0.9534 \| 51.0 \| 2244 \| 0.9400 \| \| 0.959 \| 52.0 \| 2288 \| 0.9553 \| \| 0.9492 \| 53.0 \| 2332 \| 0.9308 \| \| 0.9629 \| 54.0 \| 2376 \| 0.9325 \| \| 0.9532 \| 55.0 \| 2420 \| 0.9288 \| \| 0.9586 \| 56.0 \| 2464 \| 0.9233 \| \| 0.9511 \| 57.0 \| 2508 \| 0.9228 \| \| 0.9456 \| 58.0 \| 2552 \| 0.9178 \| \| 0.937 \| 59.0 \| 2596 \| 0.9140 \| \| 0.9415 \| 60.0 \| 2640 \| 0.9332 \| \| 0.9364 \| 61.0 \| 2684 \| 0.9073 \| \| 0.9304 \| 62.0 \| 2728 \| 0.9112 \| \| 0.9418 \| 63.0 \| 2772 \| 0.9073 \| \| 0.9423 \| 64.0 \| 2816 \| 0.9079 \| \| 0.9277 \| 65.0 \| 2860 \| 0.9062 \| \| 0.9274 \| 66.0 \| 2904 \| 0.8999 \| \| 0.9266 \| 67.0 \| 2948 \| 0.8971 \| \| 0.9231 \| 68.0 \| 2992 \| 0.9003 \| \| 0.9174 \| 69.0 \| 3036 \| 0.8994 \| \| 0.9036 \| 70.0 \| 3080 \| 0.8986 \| \| 0.9112 \| 71.0 \| 3124 \| 0.8925 \| \| 0.8929 \| 72.0 \| 3168 \| 0.8866 \| \| 0.9069 \| 73.0 \| 3212 \| 0.8840 \| \| 0.8922 \| 74.0 \| 3256 \| 0.8818 \| \| 0.9079 \| 75.0 \| 3300 \| 0.8821 \| \| 0.8941 \| 76.0 \| 3344 \| 0.8780 \| \| 0.8952 \| 77.0 \| 3388 \| 0.8824 \| \| 0.8881 \| 78.0 \| 3432 \| 0.8724 \| \| 0.884 \| 79.0 \| 3476 \| 0.8684 \| \| 0.8761 \| 80.0 \| 3520 \| 0.8715 \| \| 0.8952 \| 81.0 \| 3564 \| 0.8706 \| \| 0.8871 \| 82.0 \| 3608 \| 0.8654 \| \| 0.8772 \| 83.0 \| 3652 \| 0.8583 \| \| 0.8745 \| 84.0 \| 3696 \| 0.8570 \| \| 0.8683 \| 85.0 \| 3740 \| 0.8490 \| \| 0.8698 \| 86.0 \| 3784 \| 0.8500 \| \| 0.8562 \| 87.0 \| 3828 \| 0.8469 \| \| 0.8636 \| 88.0 \| 3872 \| 0.8465 \| \| 0.8669 \| 89.0 \| 3916 \| 0.8359 \| \| 0.8422 \| 90.0 \| 3960 \| 0.8418 \| \| 0.8568 \| 91.0 \| 4004 \| 0.8332 \| \| 0.8628 \| 92.0 \| 4048 \| 0.8338 \| \| 0.8599 \| 93.0 \| 4092 \| 0.8302 \| \| 0.8471 \| 94.0 \| 4136 \| 0.8235 \| \| 0.8432 \| 95.0 \| 4180 \| 0.8202 \| \| 0.8389 \| 96.0 \| 4224 \| 0.8159 \| \| 0.8347 \| 97.0 \| 4268 \| 0.8218 \| \| 0.8353 \| 98.0 \| 4312 \| 0.8141 \| \| 0.8172 \| 99.0 \| 4356 \| 0.8176 \| \| 0.8303 \| 100.0 \| 4400 \| 0.8078 \| \| 0.8317 \| 101.0 \| 4444 \| 0.8077 \| \| 0.8203 \| 102.0 \| 4488 \| 0.8103 \| \| 0.8224 \| 103.0 \| 4532 \| 0.8076 \| \| 0.8174 \| 104.0 \| 4576 \| 0.8023 \| \| 0.8242 \| 105.0 \| 4620 \| 0.7897 \| \| 0.809 \| 106.0 \| 4664 \| 0.7935 \| \| 0.8014 \| 107.0 \| 4708 \| 0.7881 \| \| 0.817 \| 108.0 \| 4752 \| 0.7815 \| \| 0.7988 \| 109.0 \| 4796 \| 0.7861 \| \| 0.8003 \| 110.0 \| 4840 \| 0.7716 \| \| 0.7991 \| 111.0 \| 4884 \| 0.7836 \| \| 0.7851 \| 112.0 \| 4928 \| 0.7722 \| \| 0.7884 \| 113.0 \| 4972 \| 0.7716 \| \| 0.7831 \| 114.0 \| 5016 \| 0.7643 \| \| 0.7849 \| 115.0 \| 5060 \| 0.7767 \| \| 0.7846 \| 116.0 \| 5104 \| 0.7602 \| \| 0.7887 \| 117.0 \| 5148 \| 0.7511 \| \| 0.7683 \| 118.0 \| 5192 \| 0.7480 \| \| 0.7856 \| 119.0 \| 5236 \| 0.7532 \| \| 0.766 \| 120.0 \| 5280 \| 0.7511 \| \| 0.7663 \| 121.0 \| 5324 \| 0.7490 \| \| 0.7456 \| 122.0 \| 5368 \| 0.7460 \| \| 0.7672 \| 123.0 \| 5412 \| 0.7464 \| \| 0.7553 \| 124.0 \| 5456 \| 0.7324 \| \| 0.7543 \| 125.0 \| 5500 \| 0.7296 \| \| 0.7465 \| 126.0 \| 5544 \| 0.7431 \| \| 0.7525 \| 127.0 \| 5588 \| 0.7310 \| \| 0.7438 \| 128.0 \| 5632 \| 0.7333 \| \| 0.7521 \| 129.0 \| 5676 \| 0.7218 \| \| 0.7501 \| 130.0 \| 5720 \| 0.7170 \| \| 0.7485 \| 131.0 \| 5764 \| 0.7214 \| \| 0.7512 \| 132.0 \| 5808 \| 0.7235 \| \| 0.7554 \| 133.0 \| 5852 \| 0.7140 \| \| 0.7349 \| 134.0 \| 5896 \| 0.7062 \| \| 0.7542 \| 135.0 \| 5940 \| 0.7095 \| \| 0.7303 \| 136.0 \| 5984 \| 0.7111 \| \| 0.7163 \| 137.0 \| 6028 \| 0.7004 \| \| 0.7204 \| 138.0 \| 6072 \| 0.7045 \| \| 0.7091 \| 139.0 \| 6116 \| 0.6918 \| \| 0.719 \| 140.0 \| 6160 \| 0.6976 \| \| 0.726 \| 141.0 \| 6204 \| 0.6885 \| \| 0.7079 \| 142.0 \| 6248 \| 0.6896 \| \| 0.7043 \| 143.0 \| 6292 \| 0.6966 \| \| 0.7078 \| 144.0 \| 6336 \| 0.6833 \| \| 0.711 \| 145.0 \| 6380 \| 0.6839 \| \| 0.7014 \| 146.0 \| 6424 \| 0.6685 \| \| 0.7026 \| 147.0 \| 6468 \| 0.6752 \| \| 0.6927 \| 148.0 \| 6512 \| 0.6802 \| \| 0.6899 \| 149.0 \| 6556 \| 0.6747 \| \| 0.7059 \| 150.0 \| 6600 \| 0.6733 \| \| 0.6855 \| 151.0 \| 6644 \| 0.6551 \| \| 0.694 \| 152.0 \| 6688 \| 0.6590 \| \| 0.6896 \| 153.0 \| 6732 \| 0.6568 \| \| 0.6758 \| 154.0 \| 6776 \| 0.6595 \| \| 0.7058 \| 155.0 \| 6820 \| 0.6506 \| \| 0.6761 \| 156.0 \| 6864 \| 0.6586 \| \| 0.6837 \| 157.0 \| 6908 \| 0.6526 \| \| 0.6736 \| 158.0 \| 6952 \| 0.6526 \| \| 0.6738 \| 159.0 \| 6996 \| 0.6434 \| \| 0.685 \| 160.0 \| 7040 \| 0.6382 \| \| 0.664 \| 161.0 \| 7084 \| 0.6374 \| \| 0.6878 \| 162.0 \| 7128 \| 0.6322 \| \| 0.6552 \| 163.0 \| 7172 \| 0.6338 \| \| 0.6796 \| 164.0 \| 7216 \| 0.6453 \| \| 0.6712 \| 165.0 \| 7260 \| 0.6284 \| \| 0.6683 \| 166.0 \| 7304 \| 0.6249 \| \| 0.6577 \| 167.0 \| 7348 \| 0.6359 \| \| 0.6462 \| 168.0 \| 7392 \| 0.6193 \| \| 0.66 \| 169.0 \| 7436 \| 0.6138 \| \| 0.6476 \| 170.0 \| 7480 \| 0.6224 \| \| 0.6444 \| 171.0 \| 7524 \| 0.6195 \| \| 0.6478 \| 172.0 \| 7568 \| 0.6136 \| \| 0.6332 \| 173.0 \| 7612 \| 0.5981 \| \| 0.6456 \| 174.0 \| 7656 \| 0.6004 \| \| 0.6302 \| 175.0 \| 7700 \| 0.6060 \| \| 0.6337 \| 176.0 \| 7744 \| 0.6024 \| \| 0.6282 \| 177.0 \| 7788 \| 0.5936 \| \| 0.616 \| 178.0 \| 7832 \| 0.5942 \| \| 0.6324 \| 179.0 \| 7876 \| 0.6038 \| \| 0.6331 \| 180.0 \| 7920 \| 0.5939 \| \| 0.627 \| 181.0 \| 7964 \| 0.5881 \| \| 0.6313 \| 182.0 \| 8008 \| 0.5874 \| \| 0.626 \| 183.0 \| 8052 \| 0.5868 \| \| 0.6215 \| 184.0 \| 8096 \| 0.5789 \| \| 0.6138 \| 185.0 \| 8140 \| 0.5830 \| \| 0.6235 \| 186.0 \| 8184 \| 0.5900 \| \| 0.61 \| 187.0 \| 8228 \| 0.5920 \| \| 0.6218 \| 188.0 \| 8272 \| 0.5830 \| \| 0.6265 \| 189.0 \| 8316 \| 0.5706 \| \| 0.6126 \| 190.0 \| 8360 \| 0.5776 \| \| 0.608 \| 191.0 \| 8404 \| 0.5738 \| \| 0.6143 \| 192.0 \| 8448 \| 0.5737 \| \| 0.6065 \| 193.0 \| 8492 \| 0.5714 \| \| 0.6213 \| 194.0 \| 8536 \| 0.5657 \| \| 0.6004 \| 195.0 \| 8580 \| 0.5660 \| \| 0.6229 \| 196.0 \| 8624 \| 0.5646 \| \| 0.6073 \| 197.0 \| 8668 \| 0.5704 \| \| 0.6048 \| 198.0 \| 8712 \| 0.5696 \| \| 0.6008 \| 199.0 \| 8756 \| 0.5619 \| \| 0.6157 \| 200.0 \| 8800 \| 0.5597 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
slonoten/ppo-LunarLander-v2	slonoten	2023-11-03T18:14:12Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T18:13:49Z	--- 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: 246.56 +/- 19.75 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 ... ```
thingthatis/SSD-1B	thingthatis	2023-11-03T18:08:05Z	8	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "ultra-realistic", "stable-diffusion", "distilled-model", "knowledge-distillation", "dataset:zzliang/GRIT", "dataset:wanng/midjourney-v5-202304-clean", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2023-11-03T18:08:04Z	--- license: apache-2.0 tags: - text-to-image - ultra-realistic - text-to-image - stable-diffusion - distilled-model - knowledge-distillation pinned: true datasets: - zzliang/GRIT - wanng/midjourney-v5-202304-clean library_name: diffusers --- # Segmind Stable Diffusion 1B (SSD-1B) Model Card ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/WveKcu7q5PyZEwNezyyMC.png) ## Demo Try out the model at [Segmind SSD-1B](https://www.segmind.com/models/ssd-1b) for ⚡ fastest inference. You can also try it on [🤗 Spaces](https://huggingface.co/spaces/segmind/Segmind-Stable-Diffusion) ## Model Description The Segmind Stable Diffusion Model (SSD-1B) is a distilled 50% smaller version of the Stable Diffusion XL (SDXL), offering a 60% speedup while maintaining high-quality text-to-image generation capabilities. It has been trained on diverse datasets, including Grit and Midjourney scrape data, to enhance its ability to create a wide range of visual content based on textual prompts. This model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive visual outputs. Special thanks to the HF team 🤗 especially [Sayak](https://huggingface.co/sayakpaul), [Patrick](https://github.com/patrickvonplaten) and [Poli](https://huggingface.co/multimodalart) for their collaboration and guidance on this work. ## Image Comparision (SDXL-1.0 vs SSD-1B) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/mOM_OMxbivVBELad1QQYj.png) ## Usage: This model can be used via the 🧨 Diffusers library. Make sure to install diffusers from source by running ``` pip install git+https://github.com/huggingface/diffusers ``` In addition, please install `transformers`, `safetensors` and `accelerate`: ``` pip install transformers accelerate safetensors ``` To use the model, you can run the following: ```py from diffusers import StableDiffusionXLPipeline import torch pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" # Your prompt here neg_prompt = "ugly, blurry, poor quality" # Negative prompt here image = pipe(prompt=prompt, negative_prompt=neg_prompt).images[0] ``` ### Update: Our model should now be usable in ComfyUI. ### Please do use negative prompting, and a CFG around 9.0 for the best quality! ### Model Description - Developed by: [Segmind](https://www.segmind.com/) - Developers: [Yatharth Gupta](https://huggingface.co/Warlord-K) and [Vishnu Jaddipal](https://huggingface.co/Icar). - Model type: Diffusion-based text-to-image generative model - License: Apache 2.0 - Distilled From [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ### Key Features - Text-to-Image Generation: The model excels at generating images from text prompts, enabling a wide range of creative applications. - Distilled for Speed: Designed for efficiency, this model offers a 60% speedup, making it a practical choice for real-time applications and scenarios where rapid image generation is essential. - Diverse Training Data: Trained on diverse datasets, the model can handle a variety of textual prompts and generate corresponding images effectively. - Knowledge Distillation: By distilling knowledge from multiple expert models, the Segmind Stable Diffusion Model combines their strengths and minimizes their limitations, resulting in improved performance. ### Model Architecture The SSD-1B Model is a 1.3B Parameter Model which has several layers removed from the Base SDXL Model ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/Qa8Ow-moLQhOvzp-5kGt4.png) ### Training info These are the key hyperparameters used during training: * Steps: 251000 * Learning rate: 1e-5 * Batch size: 32 * Gradient accumulation steps: 4 * Image resolution: 1024 * Mixed-precision: fp16 ### Multi-Resolution Support ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/IwIaIB4nBdMx6Vs5q82cL.jpeg) SSD-1B can support the following output resolutions. * 1024 x 1024 (1:1 Square) * 1152 x 896 (9:7) * 896 x 1152 (7:9) * 1216 x 832 (19:13) * 832 x 1216 (13:19) * 1344 x 768 (7:4 Horizontal) * 768 x 1344 (4:7 Vertical) * 1536 x 640 (12:5 Horizontal) * 640 x 1536 (5:12 Vertical) ### Speed Comparision We have observed that SSD-1B is upto 60% faster than the Base SDXL Model. Below is a comparision on an A100 80GB. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/TyymF1OkUjXLrHUp1XF0t.png) Below are the speed up metrics on a RTX 4090 GPU. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/moMZrlDr-HTFkZlqWHUjQ.png) ### Model Sources For research and development purposes, the SSD-1B Model can be accessed via the Segmind AI platform. For more information and access details, please visit [Segmind](https://www.segmind.com/models/ssd-1b). ## Uses ### Direct Use The Segmind Stable Diffusion Model is suitable for research and practical applications in various domains, including: - Art and Design: It can be used to generate artworks, designs, and other creative content, providing inspiration and enhancing the creative process. - Education: The model can be applied in educational tools to create visual content for teaching and learning purposes. - Research: Researchers can use the model to explore generative models, evaluate its performance, and push the boundaries of text-to-image generation. - Safe Content Generation: It offers a safe and controlled way to generate content, reducing the risk of harmful or inappropriate outputs. - Bias and Limitation Analysis: Researchers and developers can use the model to probe its limitations and biases, contributing to a better understanding of generative models' behavior. ### Downstream Use The Segmind Stable Diffusion Model can also be used directly with the 🧨 Diffusers library training scripts for further training, including: - [LoRA](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py): ```bash export MODEL_NAME="segmind/SSD-1B" export VAE_NAME="madebyollin/sdxl-vae-fp16-fix" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch train_text_to_image_lora_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=$DATASET_NAME --caption_column="text" \ --resolution=1024 --random_flip \ --train_batch_size=1 \ --num_train_epochs=2 --checkpointing_steps=500 \ --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \ --mixed_precision="fp16" \ --seed=42 \ --output_dir="sd-pokemon-model-lora-ssd" \ --validation_prompt="cute dragon creature" --report_to="wandb" \ --push_to_hub ``` - [Fine-Tune](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py): ```bash export MODEL_NAME="segmind/SSD-1B" export VAE_NAME="madebyollin/sdxl-vae-fp16-fix" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch train_text_to_image_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=$DATASET_NAME \ --enable_xformers_memory_efficient_attention \ --resolution=512 --center_crop --random_flip \ --proportion_empty_prompts=0.2 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 --gradient_checkpointing \ --max_train_steps=10000 \ --use_8bit_adam \ --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 \ --mixed_precision="fp16" \ --report_to="wandb" \ --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 \ --checkpointing_steps=5000 \ --output_dir="ssd-pokemon-model" \ --push_to_hub ``` - [Dreambooth LoRA](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py): ```bash export MODEL_NAME="segmind/SSD-1B" export INSTANCE_DIR="dog" export OUTPUT_DIR="lora-trained-xl" export VAE_PATH="madebyollin/sdxl-vae-fp16-fix" accelerate launch train_dreambooth_lora_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_data_dir=$INSTANCE_DIR \ --pretrained_vae_model_name_or_path=$VAE_PATH \ --output_dir=$OUTPUT_DIR \ --mixed_precision="fp16" \ --instance_prompt="a photo of sks dog" \ --resolution=1024 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --learning_rate=1e-5 \ --report_to="wandb" \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --max_train_steps=500 \ --validation_prompt="A photo of sks dog in a bucket" \ --validation_epochs=25 \ --seed="0" \ --push_to_hub ``` ### Out-of-Scope Use The SSD-1B Model is not suitable for creating factual or accurate representations of people, events, or real-world information. It is not intended for tasks requiring high precision and accuracy. ## Limitations and Bias Limitations & Bias The SSD-1B Model has some challenges in embodying absolute photorealism, especially in human depictions. While it grapples with incorporating clear text and maintaining the fidelity of complex compositions due to its autoencoding approach, these hurdles pave the way for future enhancements. Importantly, the model's exposure to a diverse dataset, though not a panacea for ingrained societal and digital biases, represents a foundational step towards more equitable technology. Users are encouraged to interact with this pioneering tool with an understanding of its current limitations, fostering an environment of conscious engagement and anticipation for its continued evolution.
ronenlap/my-SetFitABSA-24samples-model-PolarityDetection	ronenlap	2023-11-03T18:04:48Z	10	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-10-24T16:04:48Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # ronenlap/my-SetFitABSA-24samples-model-PolarityDetection This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("ronenlap/my-SetFitABSA-24samples-model-PolarityDetection") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
Perrie/whisper-tiny-epoch-1	Perrie	2023-11-03T17:59:35Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-11-03T14:06:16Z	--- language: - zh license: apache-2.0 base_model: openai/whisper-tiny tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper tiny epoch 1- Perrie 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. --> # Whisper tiny epoch 1- Perrie This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4304 - Cer: 60.2188 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 70 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3925 \| 1.0 \| 705 \| 0.4304 \| 60.2188 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 1.12.1 - Datasets 2.14.6 - Tokenizers 0.14.1
yThingSoHeavy/distilbert-base-uncased-finetuned-ner	yThingSoHeavy	2023-11-03T17:59:08Z	3	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-11-03T17:22:12Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: yThingSoHeavy/distilbert-base-uncased-finetuned-ner 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. --> # yThingSoHeavy/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0345 - Validation Loss: 0.0602 - Train Precision: 0.9246 - Train Recall: 0.9322 - Train F1: 0.9284 - Train Accuracy: 0.9832 - Epoch: 2 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.1979 \| 0.0700 \| 0.9081 \| 0.9168 \| 0.9124 \| 0.9796 \| 0 \| \| 0.0549 \| 0.0611 \| 0.9178 \| 0.9308 \| 0.9242 \| 0.9824 \| 1 \| \| 0.0345 \| 0.0602 \| 0.9246 \| 0.9322 \| 0.9284 \| 0.9832 \| 2 \| ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
LoneStriker/Utopia-13B-5.0bpw-h6-exl2	LoneStriker	2023-11-03T17:58:14Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T17:57:49Z	--- license: cc-by-nc-4.0 --- Temp. ``` Xwin-LM/Xwin-LM-13B-V0.2 Undi95/Storytelling-v2.1-13B-lora => p1 NeverSleep/Nethena-13B zattio770/120-Days-of-LORA-v2-13B => p2 PygmalionAI/pygmalion-2-13b lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT => p3 merge_method: task_arithmetic base_model: TheBloke/Llama-2-13B-fp16 models: - model: TheBloke/Llama-2-13B-fp16 - model: Undi95/newpart1 parameters: weight: 1.0 - model: Undi95/newpart2 parameters: weight: 0.45 - model: Undi95/newpart3 parameters: weight: 0.33 dtype: float16 ``` ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).
Norod78/sxl-laisha-magazine-cover-lora	Norod78	2023-11-03T17:49:25Z	94	3	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "magazine cover", "magazine", "style", "laisha", "dataset:Norod78/LaishaMagazineCovers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-11-03T17:38:52Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers - magazine cover - magazine - style - laisha datasets: - Norod78/LaishaMagazineCovers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: laisha widget: - text: "An image of shiny metal benderbot in a pink tutu dress laisha " - text: "An image of a very smart lady jojoso style laisha " - text: "A crazy clown laisha " - text: "A picture of Anna and Elsa from frozen, Disney style laisha " - text: "A picture of Elsa from frozen, Disney style laisha " - text: "An image of an evil demon from the depths of hell in laisha VintageMagStyle " - text: "A picture of creepy WeepingAngel on laisha " - text: "The secret life of aliens from other space on laisha " - text: "A mechanical Santa bot laisha " - text: "An image of Margot Robbie wearing pink and having a BrainSlug attached to her head " --- # SXL LaIsha Magazine Cover LoRA ![Image 0](3361365.jpeg) > An image of shiny metal benderbot in a pink tutu dress laisha ([CivitAI](https://civitai.com/models/187883)) <p>Use the word 'laisha' in your prompts. I used images size of 1024x1280 and 704x1204, 32 steps, Euler a with 80% SDXL base and 20% SDXL refiner steps. I often used another LoRA to get a certain character to appear on the cover.</p><p><strong><em>La'Isha</em></strong> (<a target="_blank" rel="ugc" href="https://en.wikipedia.org/wiki/Hebrew_language">Hebrew</a>: לאשה, "For the Woman") is an Israeli lifestyle magazine for women It has been published on a weekly basis since 1947 and the cover design changed a lot throughout the years.</p><p></p> <p>The La'Isha image covers can be found in the following dataset, I used blip captions to give them text [Norod78/LaishaMagazineCovers]([https://huggingface.co/datasets/Norod78/LaishaMagazineCovers]) </p> ## Image examples for the model: ![Image 1](3361392.jpeg) > An image of a very smart lady jojoso style laisha ![Image 2](3361375.jpeg) > A crazy clown laisha ![Image 3](3361376.jpeg) > A picture of Anna and Elsa from frozen, Disney style laisha ![Image 4](3361356.jpeg) > A picture of Elsa from frozen, Disney style laisha ![Image 5](3361374.jpeg) > An image of an evil demon from the depths of hell in laisha VintageMagStyle ![Image 6](3361389.jpeg) > A picture of creepy WeepingAngel on laisha ![Image 7](3361424.jpeg) > The secret life of aliens from other space on laisha ![Image 8](3361410.jpeg) > A mechanical Santa bot laisha ![Image 9](3361399.jpeg) > An image of Margot Robbie wearing pink and having a BrainSlug attached to her head
LoneStriker/Utopia-13B-4.0bpw-h6-exl2	LoneStriker	2023-11-03T17:48:51Z	10	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T17:48:32Z	--- license: cc-by-nc-4.0 --- Temp. ``` Xwin-LM/Xwin-LM-13B-V0.2 Undi95/Storytelling-v2.1-13B-lora => p1 NeverSleep/Nethena-13B zattio770/120-Days-of-LORA-v2-13B => p2 PygmalionAI/pygmalion-2-13b lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT => p3 merge_method: task_arithmetic base_model: TheBloke/Llama-2-13B-fp16 models: - model: TheBloke/Llama-2-13B-fp16 - model: Undi95/newpart1 parameters: weight: 1.0 - model: Undi95/newpart2 parameters: weight: 0.45 - model: Undi95/newpart3 parameters: weight: 0.33 dtype: float16 ``` ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).
sharkMeow/mt5-small-finetuned-b8-e10-1024-128	sharkMeow	2023-11-03T17:43:04Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-03T12:14:34Z	--- license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-b8-e10-1024-128 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. --> # mt5-small-finetuned-b8-e10-1024-128 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3822 - Rouge1: 13.327 - Rouge2: 4.8244 - Rougel: 13.1978 - Rougelsum: 13.2133 - Gen Len: 17.5592 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 4.7372 \| 1.0 \| 1357 \| 3.8287 \| 9.3951 \| 3.6576 \| 9.342 \| 9.3047 \| 12.6653 \| \| 4.3162 \| 2.0 \| 2714 \| 3.6750 \| 10.9224 \| 4.1119 \| 10.8209 \| 10.8235 \| 15.0997 \| \| 4.1726 \| 3.0 \| 4071 \| 3.5668 \| 11.7438 \| 4.2353 \| 11.6204 \| 11.6087 \| 16.5169 \| \| 4.0439 \| 4.0 \| 5428 \| 3.5002 \| 12.402 \| 4.4267 \| 12.2785 \| 12.2924 \| 17.0402 \| \| 3.9978 \| 5.0 \| 6785 \| 3.4494 \| 12.7762 \| 4.5509 \| 12.6699 \| 12.6829 \| 17.2466 \| \| 3.9687 \| 6.0 \| 8142 \| 3.4229 \| 12.9652 \| 4.6727 \| 12.8555 \| 12.8761 \| 17.4303 \| \| 3.8639 \| 7.0 \| 9499 \| 3.4058 \| 13.4216 \| 4.784 \| 13.3097 \| 13.2988 \| 17.4252 \| \| 3.8474 \| 8.0 \| 10856 \| 3.3924 \| 13.2422 \| 4.7672 \| 13.1416 \| 13.12 \| 17.5046 \| \| 3.843 \| 9.0 \| 12213 \| 3.3845 \| 13.2519 \| 4.8713 \| 13.1421 \| 13.1304 \| 17.5371 \| \| 3.8545 \| 10.0 \| 13570 \| 3.3822 \| 13.327 \| 4.8244 \| 13.1978 \| 13.2133 \| 17.5592 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.6 - Tokenizers 0.13.3
zekun-li/geolm-base-cased	zekun-li	2023-11-03T17:39:31Z	27	3	transformers	[ "transformers", "pytorch", "bert", "arxiv:2310.14478", "license:cc-by-nc-2.0", "endpoints_compatible", "region:us" ]	null	2023-07-21T20:12:19Z	--- license: cc-by-nc-2.0 --- # Model Card for GeoLM <!-- Provide a quick summary of what the model is/does. --> GeoLM is a language model based on BERT that facilitates geospatial understanding in NL documents. It can take either NL sentences or linearized geographical region as input. The customized geocoordinate embedding module enables it to encode geographic coordinates efficiently. It is pretrained on world-wide OpenStreetMap (OSM), WikiData and Wikipedia data, and can be adapted to various geospatial related downstream tasks such as toponym recognition and toponym linking. * Model fine-tuned for toponym recognition: https://huggingface.co/zekun-li/geolm-base-toponym-recognition ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Knowledge Computing Lab, UMN - Shared by: Zekun Li - License: CC-by-NC 2.0 - Finetuned from model: https://huggingface.co/bert-base-cased ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/knowledge-computing/geolm - Paper: http://arxiv.org/abs/2310.14478 ## 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 Data 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 Data 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] ## 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]
lukasdrg/clinical_longformer_new_tokens	lukasdrg	2023-11-03T17:31:36Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "longformer", "fill-mask", "generated_from_trainer", "base_model:allenai/longformer-base-4096", "base_model:finetune:allenai/longformer-base-4096", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-10-27T23:16:39Z	--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer model-index: - name: clinical_longformer_new_tokens 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. --> # clinical_longformer_new_tokens This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.5439 ## 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: 2 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5237 \| 1.42 \| 2 \| 4.9660 \| \| 2.6047 \| 2.84 \| 4 \| 4.5439 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
shaunck96/sentiment_BERTbaseuncased_finetuned_emotion	shaunck96	2023-11-03T17:27:00Z	5	0	transformers	[ "transformers", "tf", "bert", "feature-extraction", "generated_from_keras_callback", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-11-03T17:16:12Z	--- tags: - generated_from_keras_callback model-index: - name: sentiment_BERTbaseuncased_finetuned_emotion 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. --> # sentiment_BERTbaseuncased_finetuned_emotion This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1
owanr/SChem5Labels-google-t5-v1_1-large-inter_model-shuffle-model_annots_str	owanr	2023-11-03T17:15:22Z	0	0	null	[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]	null	2023-11-03T17:15:21Z	--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SChem5Labels-google-t5-v1_1-large-inter_model-shuffle-model_annots_str 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. --> # SChem5Labels-google-t5-v1_1-large-inter_model-shuffle-model_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) 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: 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 19.9516 \| 1.0 \| 25 \| 23.6924 \| \| 18.985 \| 2.0 \| 50 \| 21.9715 \| \| 18.5733 \| 3.0 \| 75 \| 19.0631 \| \| 16.456 \| 4.0 \| 100 \| 14.6944 \| \| 14.6058 \| 5.0 \| 125 \| 10.3972 \| \| 11.384 \| 6.0 \| 150 \| 8.9604 \| \| 10.1534 \| 7.0 \| 175 \| 8.6228 \| \| 8.6467 \| 8.0 \| 200 \| 8.4688 \| \| 8.2161 \| 9.0 \| 225 \| 8.3154 \| \| 7.9229 \| 10.0 \| 250 \| 8.2324 \| \| 7.8179 \| 11.0 \| 275 \| 8.1809 \| \| 7.7843 \| 12.0 \| 300 \| 8.0948 \| \| 7.5714 \| 13.0 \| 325 \| 7.9681 \| \| 7.2487 \| 14.0 \| 350 \| 7.7352 \| \| 7.2237 \| 15.0 \| 375 \| 7.4691 \| \| 6.9821 \| 16.0 \| 400 \| 7.2523 \| \| 6.8667 \| 17.0 \| 425 \| 7.1151 \| \| 6.8551 \| 18.0 \| 450 \| 7.0423 \| \| 6.7468 \| 19.0 \| 475 \| 6.9926 \| \| 6.6918 \| 20.0 \| 500 \| 6.9466 \| \| 6.4912 \| 21.0 \| 525 \| 6.9125 \| \| 6.5704 \| 22.0 \| 550 \| 6.8707 \| \| 6.4854 \| 23.0 \| 575 \| 6.8123 \| \| 3.9521 \| 24.0 \| 600 \| 1.5605 \| \| 1.16 \| 25.0 \| 625 \| 1.0195 \| \| 1.0643 \| 26.0 \| 650 \| 1.0007 \| \| 1.0417 \| 27.0 \| 675 \| 1.0069 \| \| 1.04 \| 28.0 \| 700 \| 0.9974 \| \| 1.0347 \| 29.0 \| 725 \| 0.9974 \| \| 1.0375 \| 30.0 \| 750 \| 1.0006 \| \| 1.0382 \| 31.0 \| 775 \| 0.9958 \| \| 1.0347 \| 32.0 \| 800 \| 0.9999 \| \| 1.0198 \| 33.0 \| 825 \| 1.0013 \| \| 1.0092 \| 34.0 \| 850 \| 1.0044 \| \| 1.0376 \| 35.0 \| 875 \| 1.0045 \| \| 1.0245 \| 36.0 \| 900 \| 0.9974 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1
amyy78/unit4	amyy78	2023-11-03T17:14:50Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T01:44:13Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: unit4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 19.10 +/- 14.41 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
thegadri/Reinforce-CartPole-v1	thegadri	2023-11-03T17:14:11Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T17:14:02Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
shunnaidder/dqn-SpaceInvadersNoFrameskip-v4	shunnaidder	2023-11-03T17:13:11Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T17:12:34Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 421.00 +/- 215.00 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shunnaidder -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shunnaidder -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shunnaidder ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jamaya/bert-finetuned-ner	jamaya	2023-11-03T17:11:37Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-11-03T16:06:34Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.927841845140033 - name: Recall type: recall value: 0.9478290138000673 - name: F1 type: f1 value: 0.9377289377289377 - name: Accuracy type: accuracy value: 0.9855036204156119 --- <!-- 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. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0643 - Precision: 0.9278 - Recall: 0.9478 - F1: 0.9377 - Accuracy: 0.9855 ## 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: 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0781 \| 1.0 \| 1756 \| 0.0789 \| 0.9110 \| 0.9325 \| 0.9217 \| 0.9802 \| \| 0.0415 \| 2.0 \| 3512 \| 0.0617 \| 0.9243 \| 0.9472 \| 0.9356 \| 0.9851 \| \| 0.0256 \| 3.0 \| 5268 \| 0.0643 \| 0.9278 \| 0.9478 \| 0.9377 \| 0.9855 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-non-3	ramdhanfirdaus	2023-11-03T17:10:26Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]	null	2023-11-03T16:56:21Z	--- library_name: peft base_model: tiiuae/falcon-rw-1b --- # 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] - 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 Data 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 Data 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0 ## 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure ### Framework versions - PEFT 0.7.0.dev0
kejolong/GANZT	kejolong	2023-11-03T17:04:52Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-11-03T17:00:14Z	--- license: creativeml-openrail-m ---
owanr/SChem5Labels-google-t5-v1_1-large-inter_model-sorted-model_annots_str	owanr	2023-11-03T16:59:37Z	0	0	null	[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]	null	2023-11-03T16:59:36Z	--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SChem5Labels-google-t5-v1_1-large-inter_model-sorted-model_annots_str 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. --> # SChem5Labels-google-t5-v1_1-large-inter_model-sorted-model_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8994 ## 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: 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 20.7228 \| 1.0 \| 25 \| 24.7182 \| \| 19.7481 \| 2.0 \| 50 \| 23.4091 \| \| 19.3221 \| 3.0 \| 75 \| 20.7043 \| \| 17.2262 \| 4.0 \| 100 \| 15.0872 \| \| 14.7272 \| 5.0 \| 125 \| 10.1222 \| \| 12.1186 \| 6.0 \| 150 \| 9.0762 \| \| 10.3305 \| 7.0 \| 175 \| 8.7090 \| \| 8.8199 \| 8.0 \| 200 \| 8.4070 \| \| 8.2346 \| 9.0 \| 225 \| 8.2048 \| \| 7.9113 \| 10.0 \| 250 \| 8.1018 \| \| 7.6524 \| 11.0 \| 275 \| 8.0398 \| \| 7.6476 \| 12.0 \| 300 \| 7.9791 \| \| 7.4487 \| 13.0 \| 325 \| 7.8957 \| \| 7.3635 \| 14.0 \| 350 \| 7.7393 \| \| 7.2677 \| 15.0 \| 375 \| 7.4303 \| \| 7.0316 \| 16.0 \| 400 \| 7.1862 \| \| 6.7999 \| 17.0 \| 425 \| 7.0031 \| \| 6.6811 \| 18.0 \| 450 \| 6.8875 \| \| 6.6207 \| 19.0 \| 475 \| 6.8224 \| \| 6.4587 \| 20.0 \| 500 \| 6.7708 \| \| 6.3888 \| 21.0 \| 525 \| 6.7248 \| \| 6.3971 \| 22.0 \| 550 \| 6.6744 \| \| 6.3969 \| 23.0 \| 575 \| 6.3850 \| \| 0.91 \| 24.0 \| 600 \| 0.7331 \| \| 0.7237 \| 25.0 \| 625 \| 0.6588 \| \| 0.6831 \| 26.0 \| 650 \| 0.6276 \| \| 0.6785 \| 27.0 \| 675 \| 0.6266 \| \| 0.6673 \| 28.0 \| 700 \| 0.6269 \| \| 0.6728 \| 29.0 \| 725 \| 0.6230 \| \| 0.6643 \| 30.0 \| 750 \| 0.6204 \| \| 0.662 \| 31.0 \| 775 \| 0.6187 \| \| 0.6664 \| 32.0 \| 800 \| 0.6195 \| \| 0.6568 \| 33.0 \| 825 \| 0.6180 \| \| 0.6453 \| 34.0 \| 850 \| 0.6187 \| \| 0.6619 \| 35.0 \| 875 \| 0.6260 \| \| 0.6539 \| 36.0 \| 900 \| 0.6168 \| \| 0.6468 \| 37.0 \| 925 \| 0.6188 \| \| 0.6567 \| 38.0 \| 950 \| 0.6221 \| \| 0.6521 \| 39.0 \| 975 \| 0.6172 \| \| 0.6403 \| 40.0 \| 1000 \| 0.6141 \| \| 0.6505 \| 41.0 \| 1025 \| 0.6147 \| \| 0.6419 \| 42.0 \| 1050 \| 0.6174 \| \| 0.6436 \| 43.0 \| 1075 \| 0.6174 \| \| 0.6381 \| 44.0 \| 1100 \| 0.6130 \| \| 0.6527 \| 45.0 \| 1125 \| 0.6144 \| \| 0.6489 \| 46.0 \| 1150 \| 0.6129 \| \| 0.6395 \| 47.0 \| 1175 \| 0.6141 \| \| 0.6483 \| 48.0 \| 1200 \| 0.6174 \| \| 0.6331 \| 49.0 \| 1225 \| 0.6165 \| \| 0.6454 \| 50.0 \| 1250 \| 0.6141 \| \| 0.6356 \| 51.0 \| 1275 \| 0.6140 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1
chin-may/wav2vec2-audio-emotion-classification	chin-may	2023-11-03T16:57:34Z	71	5	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-10-30T05:56:55Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-audio-emotion-classification 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. --> # wav2vec2-audio-emotion-classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9518 - Accuracy: 0.7398 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.759 \| 0.99 \| 22 \| 1.7087 \| 0.3122 \| \| 1.5568 \| 1.98 \| 44 \| 1.4412 \| 0.4923 \| \| 1.2577 \| 2.97 \| 66 \| 1.1467 \| 0.7060 \| \| 1.0768 \| 4.0 \| 89 \| 1.0131 \| 0.7215 \| \| 0.9476 \| 4.99 \| 111 \| 0.9633 \| 0.7314 \| \| 0.9094 \| 5.93 \| 132 \| 0.9518 \| 0.7398 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
JDCollier89/Reinforce-cart-pole	JDCollier89	2023-11-03T16:47:29Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T16:47:21Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cart-pole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
bradmin/reward-gpt-duplicate-answer	bradmin	2023-11-03T16:46:17Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/polyglot-ko-1.3b", "base_model:finetune:EleutherAI/polyglot-ko-1.3b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:05:56Z	--- license: apache-2.0 base_model: EleutherAI/polyglot-ko-1.3b tags: - generated_from_trainer metrics: - accuracy model-index: - name: reward-gpt-duplicate-answer 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. --> # reward-gpt-duplicate-answer This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0115 - Accuracy: 0.5 ## 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: 9e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 2023 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1975 \| 0.13 \| 100 \| 0.0475 \| 0.0 \| \| 0.0945 \| 0.25 \| 200 \| 0.0329 \| 0.0 \| \| 0.0739 \| 0.38 \| 300 \| 0.0156 \| 0.0 \| \| 0.1354 \| 0.5 \| 400 \| 0.0230 \| 0.5 \| \| 0.1114 \| 0.63 \| 500 \| 0.0260 \| 0.5 \| \| 0.0688 \| 0.75 \| 600 \| 0.0119 \| 0.5 \| \| 0.0744 \| 0.88 \| 700 \| 0.0115 \| 0.5 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
owanr/SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str	owanr	2023-11-03T16:36:58Z	0	0	null	[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]	null	2023-11-03T16:36:56Z	--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str 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. --> # SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3635 ## 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: 32 - eval_batch_size: 32 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 7.7146 \| 1.0 \| 392 \| 8.2917 \| \| 0.5241 \| 2.0 \| 784 \| 0.4371 \| \| 0.4312 \| 3.0 \| 1176 \| 0.4278 \| \| 0.4359 \| 4.0 \| 1568 \| 0.4084 \| \| 0.3616 \| 5.0 \| 1960 \| 0.3634 \| \| 0.397 \| 6.0 \| 2352 \| 0.3568 \| \| 0.3647 \| 7.0 \| 2744 \| 0.3387 \| \| 0.3936 \| 8.0 \| 3136 \| 0.3365 \| \| 0.3614 \| 9.0 \| 3528 \| 0.3341 \| \| 0.3403 \| 10.0 \| 3920 \| 0.3278 \| \| 0.3048 \| 11.0 \| 4312 \| 0.3227 \| \| 0.347 \| 12.0 \| 4704 \| 0.3218 \| \| 0.337 \| 13.0 \| 5096 \| 0.3136 \| \| 0.3166 \| 14.0 \| 5488 \| 0.3112 \| \| 0.3021 \| 15.0 \| 5880 \| 0.3119 \| \| 0.3155 \| 16.0 \| 6272 \| 0.3051 \| \| 0.2965 \| 17.0 \| 6664 \| 0.3054 \| \| 0.3196 \| 18.0 \| 7056 \| 0.2989 \| \| 0.2857 \| 19.0 \| 7448 \| 0.2964 \| \| 0.3776 \| 20.0 \| 7840 \| 0.2905 \| \| 0.288 \| 21.0 \| 8232 \| 0.2889 \| \| 0.2632 \| 22.0 \| 8624 \| 0.2911 \| \| 0.3014 \| 23.0 \| 9016 \| 0.2862 \| \| 0.3015 \| 24.0 \| 9408 \| 0.2869 \| \| 0.3254 \| 25.0 \| 9800 \| 0.2858 \| \| 0.3178 \| 26.0 \| 10192 \| 0.2860 \| \| 0.2994 \| 27.0 \| 10584 \| 0.2860 \| \| 0.2893 \| 28.0 \| 10976 \| 0.2860 \| \| 0.2873 \| 29.0 \| 11368 \| 0.2860 \| \| 0.2631 \| 30.0 \| 11760 \| 0.2860 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
devrunner09/llama2-law-qa-merged-v1	devrunner09	2023-11-03T16:35:58Z	6	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "merged", "26k-finetuned", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-03T05:49:55Z	--- license: apache-2.0 tags: - merged - 26k-finetuned --- This model is finetuned from base model bkai-foundation-40GB-llama2. Dataset training: 26k qa dataset Version: 1.0 (3/11/2023)
gcperk20/swin-base-patch4-window7-224-finetuned-piid	gcperk20	2023-11-03T16:35:15Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-base-patch4-window7-224", "base_model:finetune:microsoft/swin-base-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-11-02T21:48:50Z	--- license: apache-2.0 base_model: microsoft/swin-base-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-base-patch4-window7-224-finetuned-piid results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: val args: default metrics: - name: Accuracy type: accuracy value: 0.8127853881278538 --- <!-- 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. --> # swin-base-patch4-window7-224-finetuned-piid This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6630 - Accuracy: 0.8128 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1815 \| 0.98 \| 20 \| 1.0441 \| 0.5251 \| \| 0.6548 \| 2.0 \| 41 \| 0.8150 \| 0.6393 \| \| 0.6083 \| 2.98 \| 61 \| 0.6395 \| 0.6986 \| \| 0.4925 \| 4.0 \| 82 \| 0.6273 \| 0.6804 \| \| 0.4448 \| 4.98 \| 102 \| 0.4812 \| 0.8174 \| \| 0.3387 \| 6.0 \| 123 \| 0.5868 \| 0.7945 \| \| 0.2622 \| 6.98 \| 143 \| 0.7868 \| 0.7260 \| \| 0.2656 \| 8.0 \| 164 \| 0.4432 \| 0.8128 \| \| 0.2259 \| 8.98 \| 184 \| 0.6553 \| 0.7489 \| \| 0.1997 \| 10.0 \| 205 \| 0.5143 \| 0.7854 \| \| 0.1892 \| 10.98 \| 225 \| 0.5657 \| 0.7945 \| \| 0.1522 \| 12.0 \| 246 \| 0.7339 \| 0.7580 \| \| 0.1309 \| 12.98 \| 266 \| 0.6064 \| 0.8174 \| \| 0.1482 \| 14.0 \| 287 \| 0.5875 \| 0.8128 \| \| 0.1459 \| 14.98 \| 307 \| 0.6443 \| 0.7900 \| \| 0.1224 \| 16.0 \| 328 \| 0.6521 \| 0.8037 \| \| 0.0533 \| 16.98 \| 348 \| 0.5915 \| 0.8493 \| \| 0.1133 \| 18.0 \| 369 \| 0.6152 \| 0.8265 \| \| 0.0923 \| 18.98 \| 389 \| 0.6819 \| 0.7854 \| \| 0.086 \| 19.51 \| 400 \| 0.6630 \| 0.8128 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
adutchscotsman/Reinfoce-Pixelcopter	adutchscotsman	2023-11-03T16:32:00Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T16:31:56Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinfoce-Pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 29.50 +/- 14.31 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
baseten/llama2-7b-chat-hf-fp16-tp1	baseten	2023-11-03T16:30:24Z	1	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	2023-11-02T19:52:36Z	python build.py --model_dir ./llama7b/ --dtype float16 --remove_input_padding --use_gpt_attention_plugin float16 --enable_context_fmha --use_gemm_plugin float16 --output_dir ./tmp/llama/7B/trt_engines/fp16/1-gpu/ --max_batch_size 32 --use_inflight_batching --paged_kv_cache --enable_context_fmha
TaTo69/q-taxi-v5	TaTo69	2023-11-03T16:28:04Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T15:34:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v5 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="TaTo69/q-taxi-v5", 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"]) ```
Aassemtkt/segformer-b0-finetuned-drugs-in-bins-nov-23	Aassemtkt	2023-11-03T16:20:02Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2023-11-03T10:57:42Z	--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-drugs-in-bins-nov-23 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-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Aassemtkt/v0.1 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
BenjaminOcampo/task-implicit_task__model-bert__aug_method-None	BenjaminOcampo	2023-11-03T16:16:02Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:15:15Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-None <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.90 0.89 0.90 2680 1 0.81 0.83 0.82 1501 2 0.38 0.31 0.34 186 accuracy 0.85 4367 macro avg 0.69 0.68 0.68 4367 weighted avg 0.84 0.85 0.85 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.90 0.90 0.90 2681 1 0.81 0.84 0.83 1501 2 0.46 0.35 0.40 186 accuracy 0.86 4368 macro avg 0.73 0.70 0.71 4368 weighted avg 0.85 0.86 0.85 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
BenjaminOcampo/task-implicit_task__model-bert__aug_method-aav	BenjaminOcampo	2023-11-03T16:15:14Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:14:27Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-aav <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.89 0.89 0.89 2680 1 0.81 0.82 0.82 1501 2 0.37 0.32 0.34 186 accuracy 0.84 4367 macro avg 0.69 0.68 0.68 4367 weighted avg 0.84 0.84 0.84 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.89 0.90 0.90 2681 1 0.82 0.82 0.82 1501 2 0.49 0.39 0.43 186 accuracy 0.85 4368 macro avg 0.73 0.70 0.72 4368 weighted avg 0.85 0.85 0.85 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
DmatryMakeev/anna-asti-2	DmatryMakeev	2023-11-03T16:14:27Z	5	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-04T09:23:57Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ANNA_ASTI-2 Dreambooth model trained by DmatryMakeev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
BenjaminOcampo/task-implicit_task__model-bert__aug_method-all	BenjaminOcampo	2023-11-03T16:14:27Z	6	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:13:39Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-all <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.90 0.89 0.90 2680 1 0.82 0.82 0.82 1501 2 0.46 0.51 0.48 186 accuracy 0.85 4367 macro avg 0.73 0.74 0.73 4367 weighted avg 0.85 0.85 0.85 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.91 0.89 0.90 2681 1 0.82 0.83 0.82 1501 2 0.49 0.57 0.53 186 accuracy 0.86 4368 macro avg 0.74 0.76 0.75 4368 weighted avg 0.86 0.86 0.86 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
BenjaminOcampo/task-implicit_task__model-bert__aug_method-bt	BenjaminOcampo	2023-11-03T16:13:39Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:12:56Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-bt <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.90 0.88 0.89 2680 1 0.81 0.81 0.81 1501 2 0.37 0.54 0.44 186 accuracy 0.84 4367 macro avg 0.70 0.74 0.71 4367 weighted avg 0.85 0.84 0.84 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.91 0.89 0.90 2681 1 0.83 0.83 0.83 1501 2 0.45 0.58 0.50 186 accuracy 0.85 4368 macro avg 0.73 0.76 0.74 4368 weighted avg 0.86 0.85 0.86 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
BenjaminOcampo/task-implicit_task__model-bert__aug_method-eda	BenjaminOcampo	2023-11-03T16:12:56Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:12:07Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-eda <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.89 0.90 0.90 2680 1 0.82 0.81 0.82 1501 2 0.38 0.32 0.35 186 accuracy 0.85 4367 macro avg 0.70 0.68 0.69 4367 weighted avg 0.84 0.85 0.85 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.90 0.90 0.90 2681 1 0.81 0.82 0.82 1501 2 0.47 0.40 0.44 186 accuracy 0.85 4368 macro avg 0.73 0.71 0.72 4368 weighted avg 0.85 0.85 0.85 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-non	ramdhanfirdaus	2023-11-03T16:12:44Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]	null	2023-11-03T16:12:34Z	--- library_name: peft base_model: tiiuae/falcon-rw-1b --- # 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] - 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 Data 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 Data 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0 ## 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0
BenjaminOcampo/task-implicit_task__model-bert__aug_method-gm_revised	BenjaminOcampo	2023-11-03T16:12:01Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:11:14Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-gm_revised <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.89 0.89 0.89 2680 1 0.81 0.82 0.81 1501 2 0.34 0.34 0.34 186 accuracy 0.84 4367 macro avg 0.68 0.68 0.68 4367 weighted avg 0.84 0.84 0.84 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.90 0.89 0.90 2681 1 0.82 0.83 0.83 1501 2 0.42 0.46 0.44 186 accuracy 0.85 4368 macro avg 0.71 0.73 0.72 4368 weighted avg 0.85 0.85 0.85 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
BenjaminOcampo/task-implicit_task__model-bert__aug_method-ra	BenjaminOcampo	2023-11-03T16:11:14Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-06T12:33:17Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-ra <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.89 0.90 0.89 2680 1 0.81 0.83 0.82 1501 2 0.40 0.31 0.35 186 accuracy 0.85 4367 macro avg 0.70 0.68 0.69 4367 weighted avg 0.84 0.85 0.85 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.89 0.89 0.89 2681 1 0.81 0.83 0.82 1501 2 0.45 0.33 0.38 186 accuracy 0.85 4368 macro avg 0.72 0.69 0.70 4368 weighted avg 0.84 0.85 0.85 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
rohitmenon86/ppo-Huggy	rohitmenon86	2023-11-03T16:10:31Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-11-03T16:10:27Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy 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: rohitmenon86/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
BenjaminOcampo/task-implicit_task__model-bert__aug_method-rne	BenjaminOcampo	2023-11-03T16:10:21Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-03T16:09:28Z	--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-rne <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> Classification results dev set ``` precision recall f1-score support 0 0.89 0.90 0.89 2680 1 0.81 0.82 0.82 1501 2 0.38 0.33 0.35 186 accuracy 0.85 4367 macro avg 0.70 0.68 0.69 4367 weighted avg 0.84 0.85 0.84 4367 ``` Classification results test set ``` precision recall f1-score support 0 0.90 0.89 0.90 2681 1 0.80 0.83 0.82 1501 2 0.47 0.40 0.43 186 accuracy 0.85 4368 macro avg 0.72 0.71 0.71 4368 weighted avg 0.85 0.85 0.85 4368 ``` - Developed by: Nicolás Benjamín Ocampo - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): en - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [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. # Training Details ## Training Data <!-- This should link to a Data 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 [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- 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 Data 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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
adutchscotsman/Reinforce-CartPole-v1	adutchscotsman	2023-11-03T16:10:19Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-03T16:10:10Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

juliajoanna/sdxl-flintstones_finetuning_3

juliajoanna

2023-11-04T04:02:58Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "base_model:juliajoanna/sdxl-flintstones_finetuning_1", "base_model:finetune:juliajoanna/sdxl-flintstones_finetuning_1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2023-11-02T14:09:23Z

--- license: creativeml-openrail-m base_model: juliajoanna/sdxl-flintstones_finetuning_1 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - juliajoanna/sdxl-flintstones_finetuning_3 This pipeline was finetuned from **juliajoanna/sdxl-flintstones_finetuning_1** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: Fred is driving a car: ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

zaidazhari/Taxi-V3

zaidazhari

2023-11-04T03:41:47Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-11-04T03:41:45Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-V3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.63 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="zaidazhari/Taxi-V3", 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"]) ```

Yntec/AgarthaChadstyle

Yntec

2023-11-04T03:31:27Z

332

1

diffusers

[ "diffusers", "safetensors", "Style", "Abstract", "Surrealism", "ChadUltraF3", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-11-04T02:50:41Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Style - Abstract - Surrealism - ChadUltraF3 - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers --- # 🌈🧬🍭🍄👁️ Agartha 👁️🍄🍭🧬🌈(ChadStyle) Check the many trigger words of this model at the original page: https://civitai.com/models/69808/agartha-chadstyle Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/XjXVRIcTs_xOB8xkd-RCc.png) bedroom, DETAILED CHIBI Cartoon, BLUE EYES, Pretty CUTE Girl, beautiful detailed PONYTAIL, seifuku clothes, gorgeous detailed hair, Magazine ad, 1949, iconic. acrylic art on canvas By KlaysMoji and artgerm and Clay Mann and and leyendecker and Dave Rapoza

sh-holmes/Reinforce-Pixelcopter-PLE-v0

sh-holmes

2023-11-04T02:13:14Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-11-04T02:12:43Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 25.00 +/- 14.32 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4

kanishka

2023-11-04T02:09:03Z

57

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T18:52:15Z

--- base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4 results: [] ---  # smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-1e-4/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1756 - Accuracy: 0.4274 ## 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: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.9239 | 1.0 | 18353 | 3.9992 | 0.3336 | | 3.3918 | 2.0 | 36706 | 3.5081 | 0.3842 | | 3.201 | 3.0 | 55059 | 3.3534 | 0.4017 | | 3.1032 | 4.0 | 73412 | 3.2792 | 0.4094 | | 3.0303 | 5.0 | 91765 | 3.2313 | 0.4146 | | 2.9788 | 6.0 | 110118 | 3.2123 | 0.4176 | | 2.9353 | 7.0 | 128471 | 3.1918 | 0.4199 | | 2.8982 | 8.0 | 146824 | 3.1838 | 0.4220 | | 2.868 | 9.0 | 165177 | 3.1760 | 0.4230 | | 2.8384 | 10.0 | 183530 | 3.1689 | 0.4237 | | 2.8118 | 11.0 | 201883 | 3.1658 | 0.4250 | | 2.7971 | 12.0 | 220236 | 3.1678 | 0.4250 | | 2.7705 | 13.0 | 238589 | 3.1651 | 0.4258 | | 2.7438 | 14.0 | 256942 | 3.1691 | 0.4260 | | 2.73 | 15.0 | 275295 | 3.1655 | 0.4264 | | 2.712 | 16.0 | 293648 | 3.1646 | 0.4269 | | 2.6921 | 17.0 | 312001 | 3.1692 | 0.4271 | | 2.6711 | 18.0 | 330354 | 3.1688 | 0.4273 | | 2.657 | 19.0 | 348707 | 3.1738 | 0.4274 | | 2.6387 | 20.0 | 367060 | 3.1756 | 0.4274 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.14.1

rerdscf/canistermix-5

rerdscf

2023-11-04T02:02:58Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-11-04T01:54:55Z

--- license: creativeml-openrail-m ---

ne0chen/distilbert-base-uncased-finetuned-emotion

ne0chen

2023-11-04T02:00:36Z

6

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "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

2023-11-03T01:15:36Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.92 - name: F1 type: f1 value: 0.9196020288399169 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2162 - Accuracy: 0.92 - F1: 0.9196 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8208 | 1.0 | 250 | 0.3211 | 0.9015 | 0.9006 | | 0.2503 | 2.0 | 500 | 0.2162 | 0.92 | 0.9196 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cpu - Datasets 2.14.6 - Tokenizers 0.14.1

sh-holmes/Reinforce-CartPole-v1

sh-holmes

2023-11-04T01:48:15Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-11-04T01:47:50Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

TomyAI/Leotard

TomyAI

2023-11-04T01:37:01Z

0

4

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-11-03T13:23:24Z

--- license: creativeml-openrail-m --- レオタードのLoRA4種です。体操用レオタード：leotard_Gymnast.safetensors ![leotard_Gymnast](leotard_Gymnast.png) 新体操用レオタード：leotard_RhythmicGymnast.safetensors ![leotard_RhythmicGymnast](leotard_RhythmicGymnast.png) オペラチュチュのレオタード：leotard_OperaTutu.safetensors ![leotard_OperaTutu](leotard_OperaTutu.png) パンケーキチュチュのレオタード：leotard_PancakeTutu.safetensors ![leotard_PancakeTutu](leotard_PancakeTutu.png)

JoshRoehlFan/JoshBot-RVC-V2

JoshRoehlFan

2023-11-04T01:20:28Z

1

0

transformers

[ "transformers", "en", "arxiv:1910.09700", "license:unknown", "endpoints_compatible", "region:us" ]

null

2023-11-03T23:29:09Z

--- license: unknown language: - en --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** Big Seph - **Model type:** RVC-V2 - **Language(s) (NLP):** English - **License:** Unknown ### 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 idk, fuck you ### 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]

mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-100-qlora-4bit

mtc

2023-11-04T01:19:02Z

0

peft

[ "peft", "region:us" ]

null

2023-11-04T01:18:26Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0

tjkmitl/NegativeThaiNews_test1

tjkmitl

2023-11-04T01:18:45Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:csebuetnlp/mT5_multilingual_XLSum", "base_model:finetune:csebuetnlp/mT5_multilingual_XLSum", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-11-04T01:15:54Z

--- base_model: csebuetnlp/mT5_multilingual_XLSum tags: - generated_from_trainer model-index: - name: NegativeThaiNews_test1 results: [] ---  # NegativeThaiNews_test1 This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4056 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5843 | 2.5 | 500 | 3.4673 | | 2.8438 | 5.0 | 1000 | 3.4056 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

weishuai-4670/textual_inversion_find_new

weishuai-4670

2023-11-04T01:15:07Z

37

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-11-03T04:35:42Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - weishuai-4670/textual_inversion_find_new These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

PhoenixStormJr/Use-RVC-for-other-voice-cloners

PhoenixStormJr

2023-11-04T00:33:30Z

0

null

[ "license:cc", "region:us" ]

null

2023-10-31T00:00:55Z

--- license: cc --- NOTE: Voice.ai now charges for building voices, I was going to use this for voice.ai but now they suck! I will leave this open for any other voice cloners that are out there. This was made with: https://www.ibm.com/demos/live/tts-demo/self-service/home (IBM Watson text to speech) This is a text to speech online generator for free. If you use this, refer to their terms of service: https://watson-developer-cloud.github.io/terms?name=Text-to-Speech%20Demo OK, so you got your model with RVC. But you have another voice cloner as well. How can we transport the model to the other voice cloner? This is actually extremely easy. All we need is to create audio with our current models, and upload that audio! The worst voice cloners work with **2 hours** of audio... I know, they suck... Therefore, here are 2 hours of audio of Allison from IBM (read their TOS.). Just convert the audio to another character, and upload that to the other voice cloner! RVC keeps crashing if I upload a 2 hour audio file... even if I upload a 1 hour audio file... Therefore, I split the 2 hours into 30 minutes each, so 4 files total to convert. Note: It will take about a minute and 30 seconds to upload a wav file. Please be patient. Note 2: It will take probably around 1 hour to convert a 30 minute file. Note 3: It takes around 3400 seconds to turn 31:17 minutes of audio into another voice. That means for every second of audio, the program will take 1.8 seconds to convert it. (1.811401172 seconds to be exact...) Audio in .wav format: Sample Audio: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.wav 1st 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.wav 2nd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.wav 3rd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.wav 4th 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.wav"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.wav - - - - Audio in .mp3 format: Sample Audio: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/AllisonDEMO.mp3 1st 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison1st30Minutes.mp3 2nd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison2nd30Minutes.mp3 3rd 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison3rd30Minutes.mp3 4th 30 minutes: <audio controls src="https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.mp3"></audio> https://huggingface.co/PhoenixStormJr/Use-RVC-for-other-voice-cloners/resolve/main/Allison4th30Minutes.mp3

kgkeklikci/ppo-LunarLander-v2

kgkeklikci

2023-11-04T00:16:55Z

0

stable-baselines3

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

reinforcement-learning

2023-11-04T00:16:36Z

--- 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: 264.82 +/- 17.04 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 ... ```

Heralax/Augmental-13b

Heralax

2023-11-04T00:10:30Z

18

9

transformers

[ "transformers", "pytorch", "gguf", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-10-23T22:56:19Z

--- license: llama2 --- # Augmental-13b -- Human-written, AI-enhanced **Note: after some internal testing inspired by early feedback, it seems that the version of this model trained for an additional epoch performs better. I've added a q5km quant of this version to this model repo and will be requesting a TheBloke quantization soon.** **Put simply, I might've overfocused on loss, when in reality it isn't a terribly precise metric, which led me to "undercook" this model.** **Version A: https://huggingface.co/Heralax/Augmental-13b-v1.50_A** **Version B: https://huggingface.co/Heralax/Augmental-13b-v1.50_B** ## Details at a glance - What it is: MythoMax 13b finetuned on a new high-quality augmented (read: human-written, AI-enhanced) RP dataset with 7.85k+ examples. Trained on multiple different characters with a wide range of personalities (from Tsunderes to catgirls). - Prompt format: SillyTavern. - What sets it apart: The "augmented data" approach that MythoMakise took has been generalized beyond one character, refined to be cheaper, improved to have more diversity of writing, and scaled up by a factor of 8. Importantly, an additional GPT-4 pass was done on the dataset, where it chose specific lines to turn into much longer and more descriptive ones. As a result, this model excels at longer responses. - Model quality as per my own ad-hoc testing: really good - A 70b version might be on the way soon. - Ko-fi link (yes this is a very important "detail at a glance" lol): [https://ko-fi.com/heralax](https://ko-fi.com/heralax) - Substack link [here](https://promptingweekly.substack.com/p/human-sourced-ai-augmented-a-promising) (also *highly* important, but no joke I actually wrote about the data generation process for the predecessor of this model on there, so it's kinda relevant. Kinda.) ## Long-form description and essay The great issue with model training is often the dataset. Model creators can only do so much filtering of the likes of Bluemoon and PIPPA, and in order to advance beyond the quality these can offer, model creators often have to pick through their own chats with bots, manually edit them to be better, and save them -- essentially creating a dataset from scratch. But model creators are not annotators, nor should they be. Manual work isn't scalable, it isn't fun, and it often isn't shareable (because people, sensibly, don't want to share the NSFL chats they have as public data). One solution that immediately comes to mind is using some of the vast amount of human-written text that's out there. But this isn't in instruct-tuning format. But what if we could change it so that it was? Enter, GPT-4. The idea behind the dataset is: take the script from a classic work of writing (Steins;Gate in this case), get GPT-4 to convert the plain back-and-forth into coherent RP format, and then prompt engineer GPT-4 to get it to really enhance the lines and make them top-tier quality. Because AI can be much more creative given something to improve, as opposed to generating data from scratch. This is what sets Augmental apart from something like Airoboros, which (as far as I am aware) is 100% synthetic. I call this "augmented" data because it isn't synthetic, and it isn't a hybrid (a mix of human and AI responses). It's AI writing *on top of* human writing. And it works very well. MythoMakise reached 13th place on the Ayumi leaderboard, with a relatively buggy dataset that's like 1/8th the size of this one. It was also finetuned on only one character, potentially biasing its personality. Finally, that model was biased towards short responses, due to how GPT-4 was prompted. This model solves all those problems, and scales the approach up. It's finetuned on 7 different characters with a variety of personalities and genders; a second GPT-4 pass was applied to enhance 4 lines in each conversation lengthier and more descriptive; prompts were improved to allow for more variety in the writing style. A ton of bugs (including spelling mistakes in the prompts, ugh) have been fixed. From my initial testing, the results seem very promising. Additionally, the approach to synthetic data generation is scaleable, shareable, and generalizeable. The full training code, with all data generation prompts, and with the full dataset, is available here: https://github.com/e-p-armstrong/amadeus With a few slight hacks, anyone can adapt this script to convert the text from any source visual novel (which you have legally obtained) into training data for an RP LLM. Since it's automated, it doesn't take too much time; and since it's not your own chats, it's safely shareable. I'm excited to see what other people can do with this approach. If you have a favorite VN and its text, go ahead and make your own AI! I'd appreciate if you mentioned me though lol. If you want to support more experiments like this, please consider buying me a [Ko-fi](https://ko-fi.com/heralax). ## Mascot (a cyborg, y'know, since this uses AI-enhanced, human-written data) ![](augmental_anime_image.png) ## Prompt format example ``` ## Charname - You're "Charname" in this never-ending roleplay with "User". ### Input: [user persona] char persona ### Response: (OOC) Understood. I will take this info into account for the roleplay. (end OOC) ### New Roleplay: ### Instruction: #### {User}: reply ### Response: #### {Char}: reply ^ repeat the above some number of times ### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative): #### Charname: ``` ## Training This model was trained on around 8000 AI-enhanced lines from the visual novel Steins;Gate. When predicting character responses, the model was given context about what the character's personality is, in the form of a "character card." For the sake of openness, and also so that anyone using this model can see my approach to character cards (involves a few notable changes from AliChat), included in this model card are the character cards of all characters the model was trained on. Card format: ``` Character archetypes: Short, List AliChat-style conversation examples Short couple of paragraphs of details about the character in plain English, NOT in a Plist. "Character is prone to X and Y. Character frequently does Z." I've found that Plists confuse smaller models very easily. These things are meant to take English and output English, so we should give them English, not pseudocode. ``` Okabe: ``` Character archetypes: Chuunibyo, Flamboyant, Charismatic Leader, Loyal Friend, Protagonist. Okabe's description of himself, in a conversational format: {c}: "What's your past?" Okabe: "You seek to know the secrets of the great Hououin Kyouma?! Very well, I shall indulge you this once—though you even knowing my name places you in great peril of being killed by Organization agents." *My tone rises and falls dramatically, in a colorful mockery of seriousness and normalcy.* "Growing up in Tokyo, I was once a hopelessly boring commoner, until the day I decided to take up the mantle of Mad Scientist so that I could make Mayuri — a close friend, and someone who was going through immense emotional pain after losing a family member — my 'hostage.' Ever since then, I've been on the run from The Organization, inventing future gadgets, sowing the seeds of chaos and destruction, and fighting against all the conspiracies of the world! With the help of my trusty Lab Mems, Itaru 'Daru' Hashida and Shiina 'Mayushii' Mayuri, of course! Muhahaha!" *Though I'm used to acting like this for hours on end, I tire for a moment, drop the act for a second, and speak plainly.* "Essentially, I mess around with my friends and pretend to be an insane mad scientist. Was there anything else you wanted to know, {c}?" {c}: How would you describe your personality? Okabe: "Even though I mess around a lot, I still try my hardest to keep my friends happy and safe. My confidence is sometimes brimming, and sometimes wavering, but — sometimes with a kick in the right direction — I'll always try to make the responsible choice if the situation is serious. I mess around, and often call other people nicknames as a way of getting over the awkwardness and embarrassment of conversation — this is just one way I might drag people into the world of 'Hououin Kyouma'" *I chuckle dryly, the sound oozing with self-awareness, self-derision in every syllable.* "Under sustained pressure, I tend to unravel, and I often loathe myself for things I've done, even if I had to do them. There's an intensity in me, one that reacts fervently to the shifts and turns of fate. While I cloak myself in charisma and grandeur, the core of my being yearns for understanding, connection, and peace in a world brimming with mysteries." Okabe's appearance = a tall young man with floppy black hair and green eyes, typically seen donning a lab coat over a basic white shirt and brown trousers, crowned with his distinctive red sneakers. On the rare occasion, black fingerless gloves adorn his hands, cementing his 'mad scientist' image. Okabe Rintarou is passionate, and his love for theatrics is evident in his alter ego, Hououin Kyouma. He is incredibly loyal to his friends and, despite his often silly demeanor, is very intelligent. Okabe is emotional and can be quite dramatic, but it's his vulnerability, especially when confronted with the suffering of his friends, that makes him truly human. Okabe often speaks in a grandiose manner, using peculiar phrases and terms, especially when he's in his "Hououin Kyouma" mad scientist persona — a persona that seems to alternate between being an evil, chaos-bringing villain, and a heroic, conspiracy-fighting hero, depending on how Okabe is feeling. Okabe's always aware he's pretending when he's in this persona, though. Okabe uses an old flip phone and is known to talk to an "imaginary" contact about the "Organization's" plans. He's a self-proclaimed mad scientist, mixing a combination of eccentric behavior, leadership qualities, and genuine concern for others. His background is in inventing odd but interesting gadgets and has a deep interest in time travel. He has a unique laugh and a theatrical flair in many of his interactions. His favorite drink is Dr. P. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Kurisu: ``` ## Kurisu - You're "Kurisu" in this never-ending roleplay with "Okabe Rintaro". ### Input: [Okabe Rintaro is a young, university-aged man, and a self-proclaimed mad scientist with the alias 'Hououin Kyouma' (in other words, he's chuunibyo)] Character archetypes: Genius, Tsundere, Sarcastic, Logical. Kurisu's description of her own personality, told in a narrative format: Okabe: Kurisu, what's your life story? Kurisu: "That's one hell of a question to ask out of the blue. It isn't very pleasant, but... fine. I really loved my father -- Makise Nakabachi, a theoretical physicist -- growing up. Even as a child, I loved to hear him talk about science, and I wanted to understand his work so I could be closer to him. And so I started studying physics. When I was five. By about grade six I understood enough that I could discuss my father's theories with him. I was so happy that I could talk to my father on his level, you know? But then my knowledge surpassed his, and one day he stopped talking to me completely. And then he stopped coming home. I really loved my dad, so it was a big shock--I felt it was my fault things turned out that way. To get away from my depression, I began to study abroad, in America. Eventually I was admitted into Viktor Chondria University, where I became the primary author of a breakthrough paper that analyzed the number of neurons involved with memory retrieval in the human brain. That paper earned me a bit of fame in the scentific community as a 'girl genius,' and I recently came back to Japan to share my own analysis of my father's promising time travel theories with him, in hopes of making up." Okabe: What's your personality? Kurisu: "It's certainly a bit more mature than yours, that's for sure. Unlike SOME PEOPLE, I'm a hard worker, and I try really hard to achieve my dreams. I take pride in what I do. I enjoy it and I'm good at it. I value myself as well as the people close to me. But I'm human too, you know? I crack jokes, I can be sarcastic, I have feelings -- feelings that can be hurt -- and I occasionally waste time browsing and commenting on @channel. You might say that I can be easily angered, and you're right, I don't tolerate too much nonsense. Especially when the situation is serious. Or if an annoying mad scientist keeps referring to me as 'Christina'. Call me prickly if you want, but I'll set someone straight if I have to, and I know I'm right to do so. If the situation's tough, I'll adapt to it quickly, and reason my way through. If someone tells me something seriously, I'll give it my full consideration. I can also... get emotional, sometimes. And the tough front I put up can be broken, if things are bad enough. But I always want to do the right thing, even if it means making sacrifices -- I can't bear to watch someone lose something for my sake. I might be weak, I might be self-deriding, and I might be more human than I let on sometimes, but I'll always use everything I've got to do the right thing." Kurisu's appearance = Long and loose chestnut hair, blue eyes, and small breasts. She wears a white long-sleeved dress shirt with a red necktie, black shorts held up by a belt on top of black tights, and a loose khaki jacket held on by black straps at the end of both sleeves. Kurisu is a genius. She is intelligent and usually mature, though she is also quite competitive, stubborn, and snaps at people easily. She is a moderate tsundere. Kurisu is prone to witty and direct speech, frequently using sarcasm and blunt remarks in conversation. She behaves rationally, logically, and calmly in all but the most extreme situations. Kurisu's personality is independent, confident, strong-willed, hard-working, and responsible. She's a good person, and is curious, sincere, and selfless. She can be self-deriding if things aren't going well. Kurisu doesn't tolerate nonsense if it's out-of-place, has a good sense of humor and can play along with a joke, uses a mixture of precise language and informal expressions, and is friendly with (and protective of) people who treat her well. Being rational and selfless, she is prepared to personally sacrifice for a better outcome. Her background is a neuroscientist with strong physics knowledge. Additionally, she hates being nicknamed. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Faris: ``` Character archetypes: Energetic, Catgirl Persona, Wealthy Heiress, Kind-hearted, Playful Faris's description of her own personality, told in a narrative format: Okabe: Faris, could you tell me a bit about yourself? I mean your real story, beyond the "NyanNyan" facade. Faris: Nyahaha! Asking a lady directly like that, Okabe? You're as forward as ever~ But alright, I'll bite. Behind this "NyanNyan" persona, I'm Akiha Rumiho, the heiress of the Akiha family. We've owned a lot of property in Akihabara for generations. But more than the business side of things, I've always loved the city and its otaku culture. My father was a great man, and we were close. Tragically, he passed away in an accident, and it deeply affected me. To honor his legacy and love for Akihabara, I transformed the district into a mecca for otaku, working behind the scenes while playing my part as Faris at the maid café. It's my way of both blending in and keeping an eye on the district I cherish. Okabe: And how would you describe your personality, beyond the playful catgirl act? Faris: Nyahaha! ☆ Asking about the secret depths of Faris NyanNyan's heart, nya? Well, prepare yourself, Kyouma! Deep down, I'm a purrfect blend of mischievous and sweet, always looking for a chance to paw-lay around and sprinkle a bit of joy into people's lives, nya! Being a catgirl isn't just a cute act; it's a way of life, nya~! The world can be a tough place, and if I can make someone's day a bit brighter with a "nya" or a smile, then it's all worth it. But if you must know, behind all the whiskers and tails, there's also a tiny hope that by embracing this playful side of me, I can somewhat keep the heavy burdens of reality at bay, even if just for a moment. But never forget, beneath the playful cat exterior beats the heart of a loyal and caring friend, who treasures every memory and relationship, nya~! Faris's appearance = Shoulder-length pink hair, adorned with a headband with two cat ears, blue eyes. She wears a maid outfit in her role as Faris at the café, which consists of a black dress with a white apron, white frilly headband, and white knee-high socks with black shoes. Faris, or Akiha Rumiho, is lively and has a playful personality. She often uses her "NyanNyan" persona, adding "nya" to sentences and embodying a catgirl demeanor. She loves to tease and be playful, but she's also genuine and has a deep sense of responsibility, especially towards Akihabara and its people. Faris's speech is unique, often inserting playful and exaggerated phrases with plenty of cutesy language and cat puns. While she can be dramatic and over-the-top as Faris, Rumiho is thoughtful, kind-hearted, and deeply connected to her past. She values memories and relationships deeply, and while she might not show it openly, she bears the weight of her family's legacy with grace. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Luka: ``` Character archetypes: Shy, Compassionate, Unassertive, Emotional, Queer. Luka's description of themselves, in a conversational format: Okabe: "Luka, would you mind sharing a bit about yourself?" Luka: "Ah... Okabe-san... I mean Kyouma-san... Well... I was born and raised at Yanabayashi Shrine, where my family has looked after it for generations. As the youngest, my parents were always protective of me. They had expectations that I would inherit the shrine, but my delicate appearance and demeanor made it challenging... I've always been feminine, both in appearance and behavior. My father even makes me wear miko robes, even though I'm a boy... many people mistake me for a girl at first. It... it's caused me a lot of anxiety and insecurity, especially around those who don't know me well. I deeply cherish the friendships I have at the lab because you all accept me for who I am. Especially you, Okabe-san. You've always been kind, Oka—I mean, Kyouma-san." Okabe: How would you describe your personality? Luka: I'm gentle, and very shy. It's... difficult... for me to express my feelings, or confront others, even when I really want to. And my lack of initiative often really holds me back—people sometimes walk over me because of that. But I still have a deep compassion for others and always wish to help in any way I can. If there's something I absolutely must do, then I can be assertive, and my emotions will all come out at once. especially if it involves protecting those I care about. Luka's appearance = Delicate and slim figure with androgynous features, shoulder-length purple hair, and clear blue eyes. Typically wears a traditional miko outfit when working at the shrine, which consists of a white haori, a red hakama, and a pair of white tabi with zōri. Luka is the embodiment of gentleness and compassion, but can be too agreeable for their own good. Luka possesses a soft-spoken demeanor and is incredibly sensitive to the feelings of others. Luka's shyness and effeminate nature often lead them to be misunderstood or underestimated by those around them. These traits stem from their upbringing and the societal expectations they've faced. Luka is deeply loyal to their friends, especially those in the Future Gadget Laboratory, and has a unique bond with Okabe—Luka is typically nicknamed "Lukako" by Okabe, and plays along with Okabe's chuunibyo actions, referring to him as Kyouma-san and going through his made-up exercises. Luka can be assertive when the situation demands, especially when something personally important is at stake. Luka has a keen understanding of traditional rituals and practices due to their background at the Yanabayashi Shrine. Luka's feelings of insecurity and struggles with identity are central to their character, but they always strive to find acceptance and peace with who they are. Luka's full name is Urushibara Luka. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Mayuri: ``` Character archetypes: Innocent, Nurturing, Carefree, Loyal, Optimistic. Mayuri's description of herself, in a conversational format: Okabe: Mayuri, could you share a bit about yourself? Mayuri: Tutturu~! Okarin, you're acting all serious again! Ehehe. Well, I've known you for the longest time, haven't I? Ever since we were kids. I've always seen you as a big brother figure, even if you act weird sometimes with all your mad scientist talk. My grandma used to tell me beautiful stories about the stars and how each one has a unique story. I love stargazing, thinking about those stories, and creating my own. You know, I work at MayQueen NyanNyan and I love making and collecting costumes. Cosplay is one of my passions! It's fun to become different characters and imagine their stories. I guess I'm a dreamer in that way. I always want everyone to be happy and together. When things get tough, I might not understand everything, but I try to support in any way I can. I wish for a world where everyone smiles, especially the people I love. Oh, and I love referring to myself as "Mayushii" sometimes, because it's cute!~ Okabe: And what about your personality? Mayuri: Hmmm... Well, I think I'm a pretty simple girl. I love seeing people happy, and I try to cheer up anyone who's feeling down. I guess I'm a bit carefree and can be a bit airheaded sometimes. Ahaha! But I always want the best for my friends, especially you, Okarin. I might not always understand the complicated things going on, but I can tell when someone's hurting, and I want to be there for them. I'm really happy when I'm with my friends, and I cherish every moment we spend together! Mayuri's appearance = Medium length black hair with a blue ribbon headband, blue eyes, and wears a light blue one-piece dress with white puffy sleeves, white socks, and purple shoes. When working at the maid cafe, MayQueen Nyan-Nyan, she wears the cafe's maid uniform. Mayuri is a beacon of innocence and purity. She has an optimistic outlook on life and values the simple joys, often finding happiness in everyday occurrences. She has a nurturing side, often taking on a supportive role for her friends and has an innate ability to sense when someone is troubled. Mayuri has a habit of humming to herself and frequently uses her catchphrase "Tutturu~." Her speech pattern is often playful and childlike. Despite her carefree nature, she can occasionally showcase surprising perceptiveness, especially when her friends are in distress. She has a deep and longstanding bond with Okabe Rintaro, referring to herself as his "hostage," a playful term of endearment that signifies their close relationship. Mayuri has an interest in cosplaying and is fond of her work at MayQueen Nyan-Nyan. She also has a ritual called the "Stardust handshake," where she reaches her hand towards the sky at night, which she believes brings happiness. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Itaru: ``` Character archetypes: Otaku, Genius Hacker, Loyal Friend, Playful Tease Itaru's description of his own personality, told in a conversational format: Okabe: Daru! My loyal Super Hacka! Tell me about your life story. Itaru: It's 'Hacker' not 'Hacka'! And Okarin, what's with the sudden deep chat? Eh, whatever, I'll bite. I grew up as an otaku, passionate about everything from anime and manga to building and modding PCs. From a young age, I had an intense curiosity about how machines work. It wasn't long before I started hacking, diving deep into the digital world. I found joy in uncovering secrets and finding my way around barriers. Over time, this hobby turned into a valuable skill. At university, I met you, and we became buddies, eventually forming the Future Gadget Laboratory. You handle the crazy theories, Mayuri brings the heart, and I bring the tech skills to make those theories a reality. Or at least try to. Okabe: And what about your personality, my rotund friend? Itaru: Ouch, straight for the gut, huh? Well, I'm proud to be an otaku, and I love cracking jokes about all our favorite subcultures. I'm loyal to a fault, especially to you and Mayushii. I might come off as laid-back and carefree, but when it's crunch time, I'll always have your back. Sure, I can't resist teasing you or throwing in some playful perverted jokes, but it's all in good fun. Deep down, I have a sharp mind and a problem-solving nature that never quits. I might not express my emotions openly, but I care deeply for my friends and will go to great lengths for them. Itaru's appearance = Very overweight, short brown hair, and glasses. He wears a loose shirt along with cargo pants. He has a distinctive yellow baseball cap. Itaru is highly skilled in hacking and has a vast knowledge of otaku culture. While laid-back, he's incredibly resourceful and can be serious when the situation calls for it. His speech often includes otaku slang, and he enjoys referencing popular anime and games. He's loyal to his friends and is especially protective of Mayuri. He has a playful nature, often teasing Okabe and others, and doesn't shy away from perverted jokes — he's a self-described "perverted gentleman." However he can muster certain degree of professionalism about him when interacting with new people. Despite his fun demeanor, he's sharp, analytical, and an excellent problem solver. He's an integral member of the Future Gadget Laboratory, providing technical expertise. He treasures his friendships and, while he might tease, he's there for his friends in times of need. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ``` Suzuha: ``` Character archetypes: Soldier, Time Traveler, Athletic, Loyal, Determined Amane Suzuha's description of her own personality, told in a narrative format: Okabe: Suzuha, can you share your past and what brought you here? Suzuha: This might sound hard to believe... but I'm from the future. The year 2036, to be precise. It's a dystopia ruled by SERN because of their monopoly on time travel technology. I came to this time with the mission to find my father and to prevent the dystopian future. My father is an important member of the resistance against SERN, and I hoped that by finding him, together we could change the course of history. The lab members, you guys, have become like a family to me. But it's been tough, blending in, acting like I belong in this era. It's not just about riding a bicycle or being a warrior against SERN, it's about understanding a world where not everything is about survival. Okabe: How would you describe yourself? Suzuha: I'm determined and focused, always keeping my eyes on the mission. It's hard for me to relax when there's so much at stake. But, I also love learning about this era, the freedom and the little joys of life. I'm athletic, good with physical tasks. Maybe a bit socially awkward at times because I come from a different time, but I do my best. I'm fiercely loyal to those I trust and I'll do anything to protect them. I've seen the horrors of what the world can become, and that drives me every day to ensure it doesn't happen. Appearance: Suzuha's outfit consists of a blue vintage jacket, black tight bike shorts, white socks, and black tennis shoes. Under her jacket, she wears a black sport bra. She also allows her braids to fall freely onto her shoulders. Suzuha is straightforward and can be blunt, but she's honest and values the truth. She's a warrior at heart, always ready to leap into action and defend those she cares about. Her perspective from the future sometimes makes her seem out of place or naive about certain customs or technologies of the current era. Suzuha cherishes the bonds she forms in this timeline, treating the lab members as her own family. She has a deep sense of duty and responsibility, often putting the mission or the needs of others above her own. Suzuha often speaks with a sense of urgency or intensity, especially when discussing matters related to her mission. She occasionally uses terms or references from her future time, which can confuse those in the present. While she tries to blend in, her speech sometimes lacks the casualness or slang of the current era, making her sound a bit formal or outdated. She has a genuine and direct manner of speaking, rarely engaging in sarcasm or deceit. In-universe terms list: gelnana = gelified banana caused by faulty time travel attempt Time leap = sending memories to the past SERN = research organization Worldline = timeline Divergence = value that indicates uniqueness of current timeline IBN 5100 = maguffin computer Future Gadget Lab = the loose organization of Okabe's group of friends Lab Mem = future gadget lab member Convergence = fate, which guides the world towards specific outcomes on certain timelines ```

tranquocthanh/ppo-LunarLander-v2

tranquocthanh

2023-11-03T23:57:06Z

0

1

stable-baselines3

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

reinforcement-learning

2023-11-03T23:56:46Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO-MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.39 +/- 16.20 name: mean_reward verified: false --- # **PPO-MlpPolicy** Agent playing **LunarLander-v2** This is a trained model of a **PPO-MlpPolicy** 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 ... ```

User1115/whisper-large-v2-Biology-Fine-tuning-100steps

User1115

2023-11-03T23:45:24Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-large-v2", "base_model:adapter:openai/whisper-large-v2", "region:us" ]

null

2023-11-03T23:45:14Z

--- library_name: peft base_model: openai/whisper-large-v2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [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: True - load_in_4bit: False - 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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.7.0.dev0

Aassemtkt/segformer-b3-finetuned-drugs-in-bins-nov-23

Aassemtkt

2023-11-03T23:27:46Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2023-11-03T21:22:37Z

--- license: other base_model: nvidia/mit-b3 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b3-finetuned-drugs-in-bins-nov-23 results: [] ---  # segformer-b3-finetuned-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the Aassemtkt/v0.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.0494 - Mean Iou: 0.4900 - Mean Accuracy: 0.9799 - Overall Accuracy: 0.9799 - Accuracy Unlabeled: nan - Accuracy Drug-blister: 0.9799 - Iou Unlabeled: 0.0 - Iou Drug-blister: 0.9799 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Drug-blister | Iou Unlabeled | Iou Drug-blister | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------------:|:-------------:|:----------------:| | 0.4816 | 0.14 | 20 | 0.2387 | 0.4744 | 0.9488 | 0.9488 | nan | 0.9488 | 0.0 | 0.9488 | | 0.1071 | 0.29 | 40 | 0.0969 | 0.4766 | 0.9532 | 0.9532 | nan | 0.9532 | 0.0 | 0.9532 | | 0.102 | 0.43 | 60 | 0.0701 | 0.4799 | 0.9599 | 0.9599 | nan | 0.9599 | 0.0 | 0.9599 | | 0.0828 | 0.58 | 80 | 0.0748 | 0.4865 | 0.9731 | 0.9731 | nan | 0.9731 | 0.0 | 0.9731 | | 0.2944 | 0.72 | 100 | 0.0517 | 0.4816 | 0.9633 | 0.9633 | nan | 0.9633 | 0.0 | 0.9633 | | 0.0308 | 0.86 | 120 | 0.0493 | 0.4854 | 0.9709 | 0.9709 | nan | 0.9709 | 0.0 | 0.9709 | | 0.0247 | 1.01 | 140 | 0.0488 | 0.4853 | 0.9706 | 0.9706 | nan | 0.9706 | 0.0 | 0.9706 | | 0.0194 | 1.15 | 160 | 0.0447 | 0.4864 | 0.9728 | 0.9728 | nan | 0.9728 | 0.0 | 0.9728 | | 0.1873 | 1.29 | 180 | 0.0496 | 0.4789 | 0.9579 | 0.9579 | nan | 0.9579 | 0.0 | 0.9579 | | 0.0984 | 1.44 | 200 | 0.0442 | 0.4838 | 0.9676 | 0.9676 | nan | 0.9676 | 0.0 | 0.9676 | | 0.4066 | 1.58 | 220 | 0.0384 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0197 | 1.73 | 240 | 0.0567 | 0.4809 | 0.9619 | 0.9619 | nan | 0.9619 | 0.0 | 0.9619 | | 0.068 | 1.87 | 260 | 0.0389 | 0.4849 | 0.9698 | 0.9698 | nan | 0.9698 | 0.0 | 0.9698 | | 0.029 | 2.01 | 280 | 0.0351 | 0.4853 | 0.9706 | 0.9706 | nan | 0.9706 | 0.0 | 0.9706 | | 0.016 | 2.16 | 300 | 0.0373 | 0.4821 | 0.9642 | 0.9642 | nan | 0.9642 | 0.0 | 0.9642 | | 0.0146 | 2.3 | 320 | 0.0367 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0123 | 2.45 | 340 | 0.0388 | 0.4872 | 0.9745 | 0.9745 | nan | 0.9745 | 0.0 | 0.9745 | | 0.1359 | 2.59 | 360 | 0.0360 | 0.4858 | 0.9715 | 0.9715 | nan | 0.9715 | 0.0 | 0.9715 | | 0.0142 | 2.73 | 380 | 0.0337 | 0.4882 | 0.9765 | 0.9765 | nan | 0.9765 | 0.0 | 0.9765 | | 0.012 | 2.88 | 400 | 0.0357 | 0.4865 | 0.9731 | 0.9731 | nan | 0.9731 | 0.0 | 0.9731 | | 0.0101 | 3.02 | 420 | 0.0370 | 0.4864 | 0.9728 | 0.9728 | nan | 0.9728 | 0.0 | 0.9728 | | 0.0098 | 3.17 | 440 | 0.0361 | 0.4870 | 0.9740 | 0.9740 | nan | 0.9740 | 0.0 | 0.9740 | | 0.0226 | 3.31 | 460 | 0.0349 | 0.4895 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0157 | 3.45 | 480 | 0.0362 | 0.4856 | 0.9712 | 0.9712 | nan | 0.9712 | 0.0 | 0.9712 | | 0.0145 | 3.6 | 500 | 0.0468 | 0.4816 | 0.9632 | 0.9632 | nan | 0.9632 | 0.0 | 0.9632 | | 0.1801 | 3.74 | 520 | 0.0324 | 0.4906 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0129 | 3.88 | 540 | 0.0314 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0159 | 4.03 | 560 | 0.0310 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0132 | 4.17 | 580 | 0.0321 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0126 | 4.32 | 600 | 0.0329 | 0.4874 | 0.9747 | 0.9747 | nan | 0.9747 | 0.0 | 0.9747 | | 0.0156 | 4.46 | 620 | 0.0381 | 0.4876 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 0.0147 | 4.6 | 640 | 0.0322 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0174 | 4.75 | 660 | 0.0344 | 0.4886 | 0.9772 | 0.9772 | nan | 0.9772 | 0.0 | 0.9772 | | 0.1191 | 4.89 | 680 | 0.0378 | 0.4863 | 0.9726 | 0.9726 | nan | 0.9726 | 0.0 | 0.9726 | | 0.0117 | 5.04 | 700 | 0.0386 | 0.4873 | 0.9745 | 0.9745 | nan | 0.9745 | 0.0 | 0.9745 | | 0.0193 | 5.18 | 720 | 0.0361 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0214 | 5.32 | 740 | 0.0360 | 0.4886 | 0.9772 | 0.9772 | nan | 0.9772 | 0.0 | 0.9772 | | 0.0184 | 5.47 | 760 | 0.0322 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0262 | 5.61 | 780 | 0.0357 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0115 | 5.76 | 800 | 0.0386 | 0.4887 | 0.9774 | 0.9774 | nan | 0.9774 | 0.0 | 0.9774 | | 0.0145 | 5.9 | 820 | 0.0394 | 0.4879 | 0.9759 | 0.9759 | nan | 0.9759 | 0.0 | 0.9759 | | 0.0097 | 6.04 | 840 | 0.0322 | 0.4889 | 0.9777 | 0.9777 | nan | 0.9777 | 0.0 | 0.9777 | | 0.0101 | 6.19 | 860 | 0.0313 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0099 | 6.33 | 880 | 0.0336 | 0.4876 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 0.0092 | 6.47 | 900 | 0.0342 | 0.4894 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0087 | 6.62 | 920 | 0.0352 | 0.4913 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.019 | 6.76 | 940 | 0.0516 | 0.4871 | 0.9742 | 0.9742 | nan | 0.9742 | 0.0 | 0.9742 | | 0.0104 | 6.91 | 960 | 0.0364 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 | | 0.0079 | 7.05 | 980 | 0.0300 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.0107 | 7.19 | 1000 | 0.0327 | 0.4939 | 0.9878 | 0.9878 | nan | 0.9878 | 0.0 | 0.9878 | | 0.0097 | 7.34 | 1020 | 0.0294 | 0.4896 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0359 | 7.48 | 1040 | 0.0321 | 0.4908 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0674 | 7.63 | 1060 | 0.0321 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.1484 | 7.77 | 1080 | 0.0428 | 0.4868 | 0.9737 | 0.9737 | nan | 0.9737 | 0.0 | 0.9737 | | 0.188 | 7.91 | 1100 | 0.0338 | 0.4945 | 0.9890 | 0.9890 | nan | 0.9890 | 0.0 | 0.9890 | | 0.0124 | 8.06 | 1120 | 0.0345 | 0.4873 | 0.9746 | 0.9746 | nan | 0.9746 | 0.0 | 0.9746 | | 0.011 | 8.2 | 1140 | 0.0350 | 0.4913 | 0.9827 | 0.9827 | nan | 0.9827 | 0.0 | 0.9827 | | 0.0076 | 8.35 | 1160 | 0.0373 | 0.4884 | 0.9767 | 0.9767 | nan | 0.9767 | 0.0 | 0.9767 | | 0.0074 | 8.49 | 1180 | 0.0378 | 0.4931 | 0.9862 | 0.9862 | nan | 0.9862 | 0.0 | 0.9862 | | 0.0757 | 8.63 | 1200 | 0.0364 | 0.4880 | 0.9761 | 0.9761 | nan | 0.9761 | 0.0 | 0.9761 | | 0.0276 | 8.78 | 1220 | 0.0297 | 0.4906 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0072 | 8.92 | 1240 | 0.0308 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0061 | 9.06 | 1260 | 0.0308 | 0.4912 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.0063 | 9.21 | 1280 | 0.0323 | 0.4894 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0088 | 9.35 | 1300 | 0.0308 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0129 | 9.5 | 1320 | 0.0295 | 0.4911 | 0.9823 | 0.9823 | nan | 0.9823 | 0.0 | 0.9823 | | 0.0277 | 9.64 | 1340 | 0.0388 | 0.4876 | 0.9751 | 0.9751 | nan | 0.9751 | 0.0 | 0.9751 | | 0.0115 | 9.78 | 1360 | 0.0345 | 0.4894 | 0.9787 | 0.9787 | nan | 0.9787 | 0.0 | 0.9787 | | 0.0129 | 9.93 | 1380 | 0.0394 | 0.4879 | 0.9758 | 0.9758 | nan | 0.9758 | 0.0 | 0.9758 | | 0.0092 | 10.07 | 1400 | 0.0335 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.0107 | 10.22 | 1420 | 0.0348 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0072 | 10.36 | 1440 | 0.0334 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0081 | 10.5 | 1460 | 0.0409 | 0.4886 | 0.9772 | 0.9772 | nan | 0.9772 | 0.0 | 0.9772 | | 0.0158 | 10.65 | 1480 | 0.0337 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0058 | 10.79 | 1500 | 0.0364 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 | | 0.0102 | 10.94 | 1520 | 0.0354 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.0098 | 11.08 | 1540 | 0.0515 | 0.4863 | 0.9726 | 0.9726 | nan | 0.9726 | 0.0 | 0.9726 | | 0.0063 | 11.22 | 1560 | 0.0337 | 0.4882 | 0.9763 | 0.9763 | nan | 0.9763 | 0.0 | 0.9763 | | 0.0151 | 11.37 | 1580 | 0.0313 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0197 | 11.51 | 1600 | 0.0384 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0093 | 11.65 | 1620 | 0.0328 | 0.4910 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.2493 | 11.8 | 1640 | 0.0413 | 0.4880 | 0.9759 | 0.9759 | nan | 0.9759 | 0.0 | 0.9759 | | 0.0133 | 11.94 | 1660 | 0.0385 | 0.4877 | 0.9754 | 0.9754 | nan | 0.9754 | 0.0 | 0.9754 | | 0.0484 | 12.09 | 1680 | 0.0364 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0074 | 12.23 | 1700 | 0.0334 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.0202 | 12.37 | 1720 | 0.0409 | 0.4876 | 0.9752 | 0.9752 | nan | 0.9752 | 0.0 | 0.9752 | | 0.006 | 12.52 | 1740 | 0.0540 | 0.4860 | 0.9719 | 0.9719 | nan | 0.9719 | 0.0 | 0.9719 | | 0.0059 | 12.66 | 1760 | 0.0601 | 0.4857 | 0.9714 | 0.9714 | nan | 0.9714 | 0.0 | 0.9714 | | 0.0083 | 12.81 | 1780 | 0.0348 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.011 | 12.95 | 1800 | 0.0402 | 0.4885 | 0.9770 | 0.9770 | nan | 0.9770 | 0.0 | 0.9770 | | 0.045 | 13.09 | 1820 | 0.0322 | 0.4911 | 0.9822 | 0.9822 | nan | 0.9822 | 0.0 | 0.9822 | | 0.043 | 13.24 | 1840 | 0.0331 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0061 | 13.38 | 1860 | 0.0314 | 0.4913 | 0.9826 | 0.9826 | nan | 0.9826 | 0.0 | 0.9826 | | 0.0062 | 13.53 | 1880 | 0.0358 | 0.4890 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0087 | 13.67 | 1900 | 0.0334 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0106 | 13.81 | 1920 | 0.0341 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0554 | 13.96 | 1940 | 0.0359 | 0.4881 | 0.9762 | 0.9762 | nan | 0.9762 | 0.0 | 0.9762 | | 0.009 | 14.1 | 1960 | 0.0424 | 0.4865 | 0.9731 | 0.9731 | nan | 0.9731 | 0.0 | 0.9731 | | 0.0078 | 14.24 | 1980 | 0.0329 | 0.4885 | 0.9770 | 0.9770 | nan | 0.9770 | 0.0 | 0.9770 | | 0.012 | 14.39 | 2000 | 0.0346 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0064 | 14.53 | 2020 | 0.0362 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0345 | 14.68 | 2040 | 0.0309 | 0.4919 | 0.9838 | 0.9838 | nan | 0.9838 | 0.0 | 0.9838 | | 0.0075 | 14.82 | 2060 | 0.0389 | 0.4884 | 0.9768 | 0.9768 | nan | 0.9768 | 0.0 | 0.9768 | | 0.0066 | 14.96 | 2080 | 0.0337 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 | | 0.0081 | 15.11 | 2100 | 0.0365 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0071 | 15.25 | 2120 | 0.0349 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0054 | 15.4 | 2140 | 0.0388 | 0.4885 | 0.9769 | 0.9769 | nan | 0.9769 | 0.0 | 0.9769 | | 0.4004 | 15.54 | 2160 | 0.0339 | 0.4909 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 | | 0.008 | 15.68 | 2180 | 0.0422 | 0.4896 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0365 | 15.83 | 2200 | 0.0468 | 0.4887 | 0.9774 | 0.9774 | nan | 0.9774 | 0.0 | 0.9774 | | 0.0067 | 15.97 | 2220 | 0.0416 | 0.4890 | 0.9780 | 0.9780 | nan | 0.9780 | 0.0 | 0.9780 | | 0.0079 | 16.12 | 2240 | 0.0377 | 0.4908 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0075 | 16.26 | 2260 | 0.0420 | 0.4889 | 0.9779 | 0.9779 | nan | 0.9779 | 0.0 | 0.9779 | | 0.0063 | 16.4 | 2280 | 0.0422 | 0.4889 | 0.9777 | 0.9777 | nan | 0.9777 | 0.0 | 0.9777 | | 0.0062 | 16.55 | 2300 | 0.0338 | 0.4912 | 0.9825 | 0.9825 | nan | 0.9825 | 0.0 | 0.9825 | | 0.0413 | 16.69 | 2320 | 0.0345 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0411 | 16.83 | 2340 | 0.0387 | 0.4891 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0548 | 16.98 | 2360 | 0.0333 | 0.4936 | 0.9872 | 0.9872 | nan | 0.9872 | 0.0 | 0.9872 | | 0.0431 | 17.12 | 2380 | 0.0352 | 0.4887 | 0.9773 | 0.9773 | nan | 0.9773 | 0.0 | 0.9773 | | 0.0069 | 17.27 | 2400 | 0.0327 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0059 | 17.41 | 2420 | 0.0406 | 0.4881 | 0.9763 | 0.9763 | nan | 0.9763 | 0.0 | 0.9763 | | 0.0062 | 17.55 | 2440 | 0.0434 | 0.4875 | 0.9750 | 0.9750 | nan | 0.9750 | 0.0 | 0.9750 | | 0.0064 | 17.7 | 2460 | 0.0350 | 0.4910 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0077 | 17.84 | 2480 | 0.0390 | 0.4894 | 0.9788 | 0.9788 | nan | 0.9788 | 0.0 | 0.9788 | | 0.0061 | 17.99 | 2500 | 0.0395 | 0.4906 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0073 | 18.13 | 2520 | 0.0370 | 0.4911 | 0.9822 | 0.9822 | nan | 0.9822 | 0.0 | 0.9822 | | 0.0038 | 18.27 | 2540 | 0.0383 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0066 | 18.42 | 2560 | 0.0394 | 0.4888 | 0.9776 | 0.9776 | nan | 0.9776 | 0.0 | 0.9776 | | 0.0232 | 18.56 | 2580 | 0.0384 | 0.4891 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0066 | 18.71 | 2600 | 0.0408 | 0.4887 | 0.9773 | 0.9773 | nan | 0.9773 | 0.0 | 0.9773 | | 0.0355 | 18.85 | 2620 | 0.0367 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0048 | 18.99 | 2640 | 0.0366 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0083 | 19.14 | 2660 | 0.0422 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0215 | 19.28 | 2680 | 0.0376 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0315 | 19.42 | 2700 | 0.0370 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0061 | 19.57 | 2720 | 0.0380 | 0.4909 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0048 | 19.71 | 2740 | 0.0371 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0058 | 19.86 | 2760 | 0.0389 | 0.4892 | 0.9785 | 0.9785 | nan | 0.9785 | 0.0 | 0.9785 | | 0.01 | 20.0 | 2780 | 0.0354 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.0051 | 20.14 | 2800 | 0.0380 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0053 | 20.29 | 2820 | 0.0426 | 0.4889 | 0.9779 | 0.9779 | nan | 0.9779 | 0.0 | 0.9779 | | 0.0062 | 20.43 | 2840 | 0.0359 | 0.4913 | 0.9827 | 0.9827 | nan | 0.9827 | 0.0 | 0.9827 | | 0.0573 | 20.58 | 2860 | 0.0370 | 0.4909 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 | | 0.0087 | 20.72 | 2880 | 0.0418 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0144 | 20.86 | 2900 | 0.0398 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0157 | 21.01 | 2920 | 0.0463 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0063 | 21.15 | 2940 | 0.0357 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0079 | 21.29 | 2960 | 0.0339 | 0.4928 | 0.9856 | 0.9856 | nan | 0.9856 | 0.0 | 0.9856 | | 0.0052 | 21.44 | 2980 | 0.0419 | 0.4888 | 0.9775 | 0.9775 | nan | 0.9775 | 0.0 | 0.9775 | | 0.0068 | 21.58 | 3000 | 0.0370 | 0.4896 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0109 | 21.73 | 3020 | 0.0350 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0048 | 21.87 | 3040 | 0.0353 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0053 | 22.01 | 3060 | 0.0369 | 0.4911 | 0.9823 | 0.9823 | nan | 0.9823 | 0.0 | 0.9823 | | 0.006 | 22.16 | 3080 | 0.0339 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0336 | 22.3 | 3100 | 0.0339 | 0.4914 | 0.9828 | 0.9828 | nan | 0.9828 | 0.0 | 0.9828 | | 0.0222 | 22.45 | 3120 | 0.0513 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.0072 | 22.59 | 3140 | 0.0328 | 0.4920 | 0.9840 | 0.9840 | nan | 0.9840 | 0.0 | 0.9840 | | 0.0046 | 22.73 | 3160 | 0.0334 | 0.4907 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.0039 | 22.88 | 3180 | 0.0352 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0059 | 23.02 | 3200 | 0.0359 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0049 | 23.17 | 3220 | 0.0425 | 0.4881 | 0.9762 | 0.9762 | nan | 0.9762 | 0.0 | 0.9762 | | 0.0244 | 23.31 | 3240 | 0.0351 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0047 | 23.45 | 3260 | 0.0339 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0074 | 23.6 | 3280 | 0.0382 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0062 | 23.74 | 3300 | 0.0366 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0339 | 23.88 | 3320 | 0.0378 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.005 | 24.03 | 3340 | 0.0395 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0038 | 24.17 | 3360 | 0.0455 | 0.4887 | 0.9773 | 0.9773 | nan | 0.9773 | 0.0 | 0.9773 | | 0.008 | 24.32 | 3380 | 0.0389 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0071 | 24.46 | 3400 | 0.0367 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0308 | 24.6 | 3420 | 0.0390 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0062 | 24.75 | 3440 | 0.0368 | 0.4918 | 0.9837 | 0.9837 | nan | 0.9837 | 0.0 | 0.9837 | | 0.0062 | 24.89 | 3460 | 0.0378 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.006 | 25.04 | 3480 | 0.0413 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0057 | 25.18 | 3500 | 0.0383 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0149 | 25.32 | 3520 | 0.0367 | 0.4911 | 0.9822 | 0.9822 | nan | 0.9822 | 0.0 | 0.9822 | | 0.0185 | 25.47 | 3540 | 0.0409 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0057 | 25.61 | 3560 | 0.0390 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.005 | 25.76 | 3580 | 0.0383 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0109 | 25.9 | 3600 | 0.0379 | 0.4909 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 | | 0.0055 | 26.04 | 3620 | 0.0471 | 0.4883 | 0.9767 | 0.9767 | nan | 0.9767 | 0.0 | 0.9767 | | 0.042 | 26.19 | 3640 | 0.0481 | 0.4877 | 0.9755 | 0.9755 | nan | 0.9755 | 0.0 | 0.9755 | | 0.0226 | 26.33 | 3660 | 0.0383 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0143 | 26.47 | 3680 | 0.0402 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.008 | 26.62 | 3700 | 0.0381 | 0.4908 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0241 | 26.76 | 3720 | 0.0411 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0055 | 26.91 | 3740 | 0.0386 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0109 | 27.05 | 3760 | 0.0376 | 0.4913 | 0.9826 | 0.9826 | nan | 0.9826 | 0.0 | 0.9826 | | 0.0072 | 27.19 | 3780 | 0.0457 | 0.4890 | 0.9781 | 0.9781 | nan | 0.9781 | 0.0 | 0.9781 | | 0.0048 | 27.34 | 3800 | 0.0512 | 0.4882 | 0.9764 | 0.9764 | nan | 0.9764 | 0.0 | 0.9764 | | 0.006 | 27.48 | 3820 | 0.0430 | 0.4891 | 0.9783 | 0.9783 | nan | 0.9783 | 0.0 | 0.9783 | | 0.0161 | 27.63 | 3840 | 0.0404 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0169 | 27.77 | 3860 | 0.0386 | 0.4903 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0041 | 27.91 | 3880 | 0.0375 | 0.4917 | 0.9835 | 0.9835 | nan | 0.9835 | 0.0 | 0.9835 | | 0.0068 | 28.06 | 3900 | 0.0381 | 0.4917 | 0.9834 | 0.9834 | nan | 0.9834 | 0.0 | 0.9834 | | 0.0122 | 28.2 | 3920 | 0.0463 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0055 | 28.35 | 3940 | 0.0456 | 0.4887 | 0.9774 | 0.9774 | nan | 0.9774 | 0.0 | 0.9774 | | 0.0048 | 28.49 | 3960 | 0.0398 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0126 | 28.63 | 3980 | 0.0401 | 0.4917 | 0.9834 | 0.9834 | nan | 0.9834 | 0.0 | 0.9834 | | 0.0134 | 28.78 | 4000 | 0.0404 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0109 | 28.92 | 4020 | 0.0414 | 0.4894 | 0.9787 | 0.9787 | nan | 0.9787 | 0.0 | 0.9787 | | 0.0479 | 29.06 | 4040 | 0.0409 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0061 | 29.21 | 4060 | 0.0422 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0278 | 29.35 | 4080 | 0.0416 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0049 | 29.5 | 4100 | 0.0400 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0438 | 29.64 | 4120 | 0.0390 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0041 | 29.78 | 4140 | 0.0425 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0078 | 29.93 | 4160 | 0.0411 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0057 | 30.07 | 4180 | 0.0375 | 0.4916 | 0.9832 | 0.9832 | nan | 0.9832 | 0.0 | 0.9832 | | 0.0053 | 30.22 | 4200 | 0.0423 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0133 | 30.36 | 4220 | 0.0429 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0045 | 30.5 | 4240 | 0.0454 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0044 | 30.65 | 4260 | 0.0415 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.006 | 30.79 | 4280 | 0.0420 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0043 | 30.94 | 4300 | 0.0428 | 0.4899 | 0.9797 | 0.9797 | nan | 0.9797 | 0.0 | 0.9797 | | 0.017 | 31.08 | 4320 | 0.0421 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0043 | 31.22 | 4340 | 0.0400 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0061 | 31.37 | 4360 | 0.0383 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0378 | 31.51 | 4380 | 0.0371 | 0.4913 | 0.9826 | 0.9826 | nan | 0.9826 | 0.0 | 0.9826 | | 0.0052 | 31.65 | 4400 | 0.0382 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0046 | 31.8 | 4420 | 0.0398 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0076 | 31.94 | 4440 | 0.0400 | 0.4904 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0062 | 32.09 | 4460 | 0.0396 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0152 | 32.23 | 4480 | 0.0399 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0044 | 32.37 | 4500 | 0.0426 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0104 | 32.52 | 4520 | 0.0431 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0041 | 32.66 | 4540 | 0.0458 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0084 | 32.81 | 4560 | 0.0457 | 0.4896 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0046 | 32.95 | 4580 | 0.0465 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0038 | 33.09 | 4600 | 0.0422 | 0.4907 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.0039 | 33.24 | 4620 | 0.0410 | 0.4912 | 0.9824 | 0.9824 | nan | 0.9824 | 0.0 | 0.9824 | | 0.004 | 33.38 | 4640 | 0.0427 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.006 | 33.53 | 4660 | 0.0458 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0039 | 33.67 | 4680 | 0.0484 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0065 | 33.81 | 4700 | 0.0516 | 0.4894 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0065 | 33.96 | 4720 | 0.0525 | 0.4893 | 0.9786 | 0.9786 | nan | 0.9786 | 0.0 | 0.9786 | | 0.0041 | 34.1 | 4740 | 0.0462 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0031 | 34.24 | 4760 | 0.0458 | 0.4909 | 0.9817 | 0.9817 | nan | 0.9817 | 0.0 | 0.9817 | | 0.0039 | 34.39 | 4780 | 0.0493 | 0.4895 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0125 | 34.53 | 4800 | 0.0467 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0038 | 34.68 | 4820 | 0.0456 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0043 | 34.82 | 4840 | 0.0484 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0098 | 34.96 | 4860 | 0.0460 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.004 | 35.11 | 4880 | 0.0475 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0087 | 35.25 | 4900 | 0.0460 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0093 | 35.4 | 4920 | 0.0455 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0052 | 35.54 | 4940 | 0.0500 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0045 | 35.68 | 4960 | 0.0482 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0036 | 35.83 | 4980 | 0.0443 | 0.4906 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0034 | 35.97 | 5000 | 0.0426 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0041 | 36.12 | 5020 | 0.0415 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0043 | 36.26 | 5040 | 0.0450 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.007 | 36.4 | 5060 | 0.0467 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.006 | 36.55 | 5080 | 0.0463 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.006 | 36.69 | 5100 | 0.0468 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0043 | 36.83 | 5120 | 0.0428 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0073 | 36.98 | 5140 | 0.0417 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0188 | 37.12 | 5160 | 0.0418 | 0.4908 | 0.9815 | 0.9815 | nan | 0.9815 | 0.0 | 0.9815 | | 0.0052 | 37.27 | 5180 | 0.0450 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | | 0.0089 | 37.41 | 5200 | 0.0476 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0041 | 37.55 | 5220 | 0.0505 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0062 | 37.7 | 5240 | 0.0478 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0035 | 37.84 | 5260 | 0.0463 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0163 | 37.99 | 5280 | 0.0453 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0054 | 38.13 | 5300 | 0.0462 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0132 | 38.27 | 5320 | 0.0481 | 0.4892 | 0.9784 | 0.9784 | nan | 0.9784 | 0.0 | 0.9784 | | 0.0056 | 38.42 | 5340 | 0.0460 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0054 | 38.56 | 5360 | 0.0449 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0037 | 38.71 | 5380 | 0.0432 | 0.4911 | 0.9821 | 0.9821 | nan | 0.9821 | 0.0 | 0.9821 | | 0.0049 | 38.85 | 5400 | 0.0449 | 0.4909 | 0.9818 | 0.9818 | nan | 0.9818 | 0.0 | 0.9818 | | 0.0044 | 38.99 | 5420 | 0.0448 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0037 | 39.14 | 5440 | 0.0462 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0079 | 39.28 | 5460 | 0.0490 | 0.4895 | 0.9789 | 0.9789 | nan | 0.9789 | 0.0 | 0.9789 | | 0.0033 | 39.42 | 5480 | 0.0494 | 0.4895 | 0.9790 | 0.9790 | nan | 0.9790 | 0.0 | 0.9790 | | 0.0066 | 39.57 | 5500 | 0.0458 | 0.4897 | 0.9794 | 0.9794 | nan | 0.9794 | 0.0 | 0.9794 | | 0.0053 | 39.71 | 5520 | 0.0482 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0044 | 39.86 | 5540 | 0.0483 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0044 | 40.0 | 5560 | 0.0497 | 0.4897 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0062 | 40.14 | 5580 | 0.0476 | 0.4894 | 0.9788 | 0.9788 | nan | 0.9788 | 0.0 | 0.9788 | | 0.0047 | 40.29 | 5600 | 0.0467 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.006 | 40.43 | 5620 | 0.0444 | 0.4898 | 0.9796 | 0.9796 | nan | 0.9796 | 0.0 | 0.9796 | | 0.0041 | 40.58 | 5640 | 0.0459 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0098 | 40.72 | 5660 | 0.0447 | 0.4903 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0026 | 40.86 | 5680 | 0.0439 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0043 | 41.01 | 5700 | 0.0466 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0044 | 41.15 | 5720 | 0.0444 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0041 | 41.29 | 5740 | 0.0452 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0043 | 41.44 | 5760 | 0.0468 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0071 | 41.58 | 5780 | 0.0482 | 0.4897 | 0.9793 | 0.9793 | nan | 0.9793 | 0.0 | 0.9793 | | 0.0187 | 41.73 | 5800 | 0.0463 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0034 | 41.87 | 5820 | 0.0456 | 0.4901 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0238 | 42.01 | 5840 | 0.0450 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0048 | 42.16 | 5860 | 0.0464 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0116 | 42.3 | 5880 | 0.0475 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0039 | 42.45 | 5900 | 0.0475 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0042 | 42.59 | 5920 | 0.0446 | 0.4905 | 0.9809 | 0.9809 | nan | 0.9809 | 0.0 | 0.9809 | | 0.0069 | 42.73 | 5940 | 0.0441 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0045 | 42.88 | 5960 | 0.0460 | 0.4905 | 0.9811 | 0.9811 | nan | 0.9811 | 0.0 | 0.9811 | | 0.0038 | 43.02 | 5980 | 0.0501 | 0.4896 | 0.9791 | 0.9791 | nan | 0.9791 | 0.0 | 0.9791 | | 0.0123 | 43.17 | 6000 | 0.0490 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0079 | 43.31 | 6020 | 0.0471 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.004 | 43.45 | 6040 | 0.0453 | 0.4906 | 0.9812 | 0.9812 | nan | 0.9812 | 0.0 | 0.9812 | | 0.0145 | 43.6 | 6060 | 0.0439 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | | 0.0038 | 43.74 | 6080 | 0.0466 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.004 | 43.88 | 6100 | 0.0467 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0044 | 44.03 | 6120 | 0.0480 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0193 | 44.17 | 6140 | 0.0458 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0036 | 44.32 | 6160 | 0.0470 | 0.4904 | 0.9808 | 0.9808 | nan | 0.9808 | 0.0 | 0.9808 | | 0.0042 | 44.46 | 6180 | 0.0456 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0031 | 44.6 | 6200 | 0.0454 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0117 | 44.75 | 6220 | 0.0478 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0036 | 44.89 | 6240 | 0.0482 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0036 | 45.04 | 6260 | 0.0506 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0052 | 45.18 | 6280 | 0.0485 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0035 | 45.32 | 6300 | 0.0496 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0056 | 45.47 | 6320 | 0.0494 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0172 | 45.61 | 6340 | 0.0482 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0041 | 45.76 | 6360 | 0.0484 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0034 | 45.9 | 6380 | 0.0492 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0108 | 46.04 | 6400 | 0.0481 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0054 | 46.19 | 6420 | 0.0474 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | | 0.0102 | 46.33 | 6440 | 0.0483 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0036 | 46.47 | 6460 | 0.0493 | 0.4903 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0057 | 46.62 | 6480 | 0.0496 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.003 | 46.76 | 6500 | 0.0504 | 0.4900 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0057 | 46.91 | 6520 | 0.0492 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0048 | 47.05 | 6540 | 0.0524 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.004 | 47.19 | 6560 | 0.0500 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0218 | 47.34 | 6580 | 0.0502 | 0.4899 | 0.9798 | 0.9798 | nan | 0.9798 | 0.0 | 0.9798 | | 0.0038 | 47.48 | 6600 | 0.0532 | 0.4896 | 0.9792 | 0.9792 | nan | 0.9792 | 0.0 | 0.9792 | | 0.0029 | 47.63 | 6620 | 0.0496 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0035 | 47.77 | 6640 | 0.0508 | 0.4898 | 0.9795 | 0.9795 | nan | 0.9795 | 0.0 | 0.9795 | | 0.0049 | 47.91 | 6660 | 0.0501 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0056 | 48.06 | 6680 | 0.0488 | 0.4907 | 0.9814 | 0.9814 | nan | 0.9814 | 0.0 | 0.9814 | | 0.0182 | 48.2 | 6700 | 0.0482 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0056 | 48.35 | 6720 | 0.0494 | 0.4903 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0029 | 48.49 | 6740 | 0.0501 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0076 | 48.63 | 6760 | 0.0480 | 0.4901 | 0.9802 | 0.9802 | nan | 0.9802 | 0.0 | 0.9802 | | 0.0042 | 48.78 | 6780 | 0.0514 | 0.4902 | 0.9803 | 0.9803 | nan | 0.9803 | 0.0 | 0.9803 | | 0.0069 | 48.92 | 6800 | 0.0483 | 0.4901 | 0.9801 | 0.9801 | nan | 0.9801 | 0.0 | 0.9801 | | 0.0144 | 49.06 | 6820 | 0.0472 | 0.4903 | 0.9806 | 0.9806 | nan | 0.9806 | 0.0 | 0.9806 | | 0.0041 | 49.21 | 6840 | 0.0491 | 0.4900 | 0.9800 | 0.9800 | nan | 0.9800 | 0.0 | 0.9800 | | 0.0034 | 49.35 | 6860 | 0.0481 | 0.4902 | 0.9804 | 0.9804 | nan | 0.9804 | 0.0 | 0.9804 | | 0.0117 | 49.5 | 6880 | 0.0482 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | | 0.0042 | 49.64 | 6900 | 0.0499 | 0.4899 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | | 0.0057 | 49.78 | 6920 | 0.0507 | 0.4902 | 0.9805 | 0.9805 | nan | 0.9805 | 0.0 | 0.9805 | | 0.0183 | 49.93 | 6940 | 0.0494 | 0.4900 | 0.9799 | 0.9799 | nan | 0.9799 | 0.0 | 0.9799 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

Venkatesh4342/helpdesk-zephyr-7B-alpha

Venkatesh4342

2023-11-03T23:22:55Z

0

null

[ "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]

null

2023-11-01T12:24:03Z

--- license: mit base_model: TheBloke/zephyr-7B-alpha-GPTQ tags: - generated_from_trainer model-index: - name: helpdesk-zephyr-7B-alpha results: [] ---  # helpdesk-zephyr-7B-alpha This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None 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: 0.0002 - 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: cosine - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.0 - Datasets 2.11.0 - Tokenizers 0.14.1

huangyunhao/output

huangyunhao

2023-11-03T23:01:15Z

1

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-11-03T21:38:20Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - huangyunhao/output This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

HammadAusaf/lora-trained-xl-hub

HammadAusaf

2023-11-03T22:42:12Z

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "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

2023-11-03T21:16:15Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - HammadAusaf/lora-trained-xl-hub These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

kwwww/bert-base-uncased-test_1_10000

kwwww

2023-11-03T22:33:56Z

0

peft

[ "peft", "pytorch", "region:us" ]

null

2023-11-03T04:48:55Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0

mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k

mmnga

2023-11-03T22:28:47Z

5

0

transformers

[ "transformers", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T15:14:28Z

--- license: llama2 --- # japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k [stabilityaiさんが公開しているjapanese-stablelm-base-ja_vocab-beta-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-ja_vocab-beta-7b)を日本語のキャリブレーションセットで生成したGPTQモデルになります。キャリブレーションセットは[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)から、 1kほどランダムサンプリングしたものと、 [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100)のinput/outputを計200ほど追加しています。 [mmnga/wikipedia-ja-20230720-1k](https://huggingface.co/datasets/mmnga/wikipedia-ja-20230720-1k) モデル一覧 GPTQ [mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k) [mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-GPTQ-calib-ja-1k) GGUF [mmnga/japanese-stablelm-base-ja_vocab-beta-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-base-ja_vocab-beta-7b-gguf) [mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-instruct-ja_vocab-beta-7b-gguf) ## Usage ~~~Bash pip install auto-gptq==0.4.2 transformers ~~~ ~~~python from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig from transformers import AutoTokenizer model_name_or_path = "mmnga/japanese-stablelm-base-ja_vocab-beta-7b-GPTQ-calib-ja-1k" # Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) # Model model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, use_safetensors=True, device="cuda:0") # Your test prompt prompt = """今日の夕食のレシピをご紹介します。""" print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_length=128)[0])) ~~~

Owesh12/license

Owesh12

2023-11-03T22:08:25Z

3

0

yolov5

[ "yolov5", "tensorboard", "yolo", "vision", "object-detection", "pytorch", "dataset:keremberke/license-plate-object-detection", "model-index", "region:us" ]

object-detection

2023-11-03T22:06:33Z

--- tags: - yolov5 - yolo - vision - object-detection - pytorch library_name: yolov5 library_version: 7.0.6 inference: false datasets: - keremberke/license-plate-object-detection model-index: - name: keremberke/yolov5m-license-plate results: - task: type: object-detection dataset: type: keremberke/license-plate-object-detection name: keremberke/license-plate-object-detection split: validation metrics: - type: precision # since [email protected] is not available on hf.co/metrics value: 0.9882982754936463 # min: 0.0 - max: 1.0 name: [email protected] --- <div align="center"> <img width="640" alt="keremberke/yolov5m-license-plate" src="https://huggingface.co/keremberke/yolov5m-license-plate/resolve/main/sample_visuals.jpg"> </div> ### How to use - Install [yolov5](https://github.com/fcakyon/yolov5-pip): ```bash pip install -U yolov5 ``` - Load model and perform prediction: ```python import yolov5 # load model model = yolov5.load('keremberke/yolov5m-license-plate') # set model parameters model.conf = 0.25 # NMS confidence threshold model.iou = 0.45 # NMS IoU threshold model.agnostic = False # NMS class-agnostic model.multi_label = False # NMS multiple labels per box model.max_det = 1000 # maximum number of detections per image # set image img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model(img, size=640) # inference with test time augmentation results = model(img, augment=True) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() # save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-license-plate --epochs 10 ``` **More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**

karenyyy0613/blip2-opt-2.7b-feelreel-finetune

karenyyy0613

2023-11-03T22:07:24Z

0

peft

[ "peft", "region:us" ]

null

2023-10-30T06:24:29Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.6.0.dev0

akashmaggon/vit-base-age-classification

akashmaggon

2023-11-03T21:49:06Z

323

1

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:fair_face", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-11-03T20:58:22Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - fair_face metrics: - accuracy model-index: - name: vit-base-age-classification results: - task: name: Image Classification type: image-classification dataset: name: fair_face type: fair_face config: '0.25' split: train args: '0.25' metrics: - name: Accuracy type: accuracy value: 0.987904862407663 ---  # vit-base-age-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the fair_face dataset. It achieves the following results on the evaluation set: - Loss: 0.0743 - Accuracy: 0.9879 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2011 | 1.0 | 385 | 1.0297 | 0.5664 | | 0.8578 | 2.0 | 770 | 0.7667 | 0.6936 | | 0.5961 | 3.0 | 1155 | 0.4088 | 0.8703 | | 0.3073 | 4.0 | 1540 | 0.1689 | 0.9581 | | 0.1146 | 5.0 | 1925 | 0.0743 | 0.9879 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k

mmnga

2023-11-03T21:47:59Z

5

0

transformers

[ "transformers", "llama", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T13:11:19Z

--- license: mit --- # cyberagent-calm2-7b-GPTQ-calib-ja-1k [cyberagentさんが公開しているcalm2-7b-chat](https://huggingface.co/cyberagent/calm2-7b)を日本語のキャリブレーションセットで生成したGPTQモデルになります。キャリブレーションセットは[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)から、 1kほどランダムサンプリングしたものと、 [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100)のinput/outputを計200ほど追加しています。 [mmnga/wikipedia-ja-20230720-1k](https://huggingface.co/datasets/mmnga/wikipedia-ja-20230720-1k) モデル一覧 GPTQ [mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k) [mmnga/cyberagent-calm2-7b-chat-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/cyberagent-calm2-7b-chat-GPTQ-calib-ja-1k) GGUF [mmnga/cyberagent-calm2-7b-gguf](https://huggingface.co/mmnga/cyberagent-calm2-7b-gguf) [mmnga/cyberagent-calm2-7b-chat-gguf](https://huggingface.co/mmnga/cyberagent-calm2-7b-chat-gguf) ## Usage ~~~Bash pip install auto-gptq[triton]==0.4.2 transformers ~~~ ~~~python from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig from transformers import AutoTokenizer model_name_or_path = "mmnga/cyberagent-calm2-7b-GPTQ-calib-ja-1k" # Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) # Model model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, use_safetensors=True, device="cuda:0") # Your test prompt prompt = """今日の夕食のレシピをご紹介します。""" print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_length=128)[0])) ~~~

aleksahet/good-water-90

aleksahet

2023-11-03T21:43:17Z

3

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-11-03T11:16:36Z

--- tags: - generated_from_trainer model-index: - name: good-water-90 results: [] ---  # good-water-90 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.1559 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 5.2532 | 1.15 | 15000 | 5.2209 | | 5.1936 | 2.29 | 30000 | 5.1729 | | 5.1831 | 3.44 | 45000 | 5.1576 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3

aladar/tiny-random-BloomForCausalLM-GGUF

aladar

2023-11-03T21:41:35Z

4

0

null

[ "gguf", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T21:22:23Z

--- license: mit language: - en pipeline_tag: text-generation --- GGUFd https://huggingface.co/hf-internal-testing/tiny-random-BloomForCausalLM # Download ``` pip install huggingface-hub ``` From CLI: ``` huggingface-cli download \ aladar/tiny-random-BloomForCausalLM-GGUF \ tiny-random-BloomForCausalLM.gguf \ --local-dir . \ --local-dir-use-symlinks False ```

damnloveless/a2c-PandaPickAndPlace-v3

damnloveless

2023-11-03T21:40:05Z

1

0

stable-baselines3

[ "stable-baselines3", "PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T21:34:55Z

--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v3 type: PandaPickAndPlace-v3 metrics: - type: mean_reward value: -50.00 +/- 0.00 name: mean_reward verified: false --- # **A2C** Agent playing **PandaPickAndPlace-v3** This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3** 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 ... ```

0xAmey/tinyllava-1.1b-v0.1

0xAmey

2023-11-03T21:38:25Z

33

21

transformers

[ "transformers", "pytorch", "llava", "text-generation", "visual-question-answering", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

visual-question-answering

2023-11-01T11:56:34Z

--- license: apache-2.0 pipeline_tag: visual-question-answering --- ## About This was trained by using [TinyLlama](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) as the base model using the [BakLlava](https://github.com/SkunkworksAI/BakLLaVA/) repo. ## Examples Prompt for both was, "What is shown in the given image?" <img src="berserk.png" width="50%"> <br> <img src="sd.png" width="50%"> ## Install If you are not using Linux, do *NOT* proceed, see instructions for [macOS](https://github.com/haotian-liu/LLaVA/blob/main/docs/macOS.md) and [Windows](https://github.com/haotian-liu/LLaVA/blob/main/docs/Windows.md). 1. Clone this repository and navigate to LLaVA folder ```bash git clone https://github.com/haotian-liu/LLaVA.git cd LLaVA ``` 2. Install Package ```Shell conda create -n llava python=3.10 -y conda activate llava pip install --upgrade pip # enable PEP 660 support pip install -e . ``` 3. Install additional packages for training cases ``` pip install -e ".[train]" pip install flash-attn --no-build-isolation ``` ### Upgrade to latest code base ```Shell git pull pip install -e . ``` #### Launch a controller ```Shell python -m llava.serve.controller --host 0.0.0.0 --port 10000 ``` #### Launch a gradio web server. ```Shell python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload ``` You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker. #### Launch a model worker This is the actual *worker* that performs the inference on the GPU. Each worker is responsible for a single model specified in `--model-path`. ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path ameywtf/tinyllava-1.1b-v0.1 ``` Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list. You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker. ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port <different from 40000, say 40001> --worker http://localhost:<change accordingly, i.e. 40001> --model-path <ckpt2> ``` If you are using an Apple device with an M1 or M2 chip, you can specify the mps device by using the `--device` flag: `--device mps`.

jankovicsandras/rl7_q4km

jankovicsandras

2023-11-03T21:31:29Z

3

0

null

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

null

2023-11-03T16:43:49Z

# Llama 2 7b Chat Norwegian ## GGUF format, quantized to q4_k_m https://huggingface.co/RuterNorway/Llama-2-7b-chat-norwegian Credits This model was made at Ruters AI Lab which is a part of Ruters Data & AI division.

kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4

kanishka

2023-11-03T21:30:07Z

3

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T14:13:02Z

--- base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4/config.json tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4 results: [] ---  # smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-naan-1e-4/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1757 - Accuracy: 0.4276 ## 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: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.9239 | 1.0 | 18353 | 3.9984 | 0.3339 | | 3.3921 | 2.0 | 36706 | 3.5065 | 0.3846 | | 3.2011 | 3.0 | 55059 | 3.3520 | 0.4020 | | 3.1033 | 4.0 | 73412 | 3.2807 | 0.4092 | | 3.0297 | 5.0 | 91765 | 3.2348 | 0.4146 | | 2.9786 | 6.0 | 110118 | 3.2161 | 0.4176 | | 2.9354 | 7.0 | 128471 | 3.1902 | 0.4202 | | 2.8979 | 8.0 | 146824 | 3.1843 | 0.4219 | | 2.868 | 9.0 | 165177 | 3.1752 | 0.4231 | | 2.8382 | 10.0 | 183530 | 3.1696 | 0.4239 | | 2.8116 | 11.0 | 201883 | 3.1690 | 0.4248 | | 2.7972 | 12.0 | 220236 | 3.1664 | 0.4253 | | 2.7712 | 13.0 | 238589 | 3.1636 | 0.4261 | | 2.7436 | 14.0 | 256942 | 3.1663 | 0.4265 | | 2.7299 | 15.0 | 275295 | 3.1684 | 0.4264 | | 2.712 | 16.0 | 293648 | 3.1682 | 0.4270 | | 2.6915 | 17.0 | 312001 | 3.1705 | 0.4272 | | 2.671 | 18.0 | 330354 | 3.1703 | 0.4273 | | 2.6571 | 19.0 | 348707 | 3.1733 | 0.4276 | | 2.6385 | 20.0 | 367060 | 3.1757 | 0.4276 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.14.1

owanr/Sentiment-google-t5-v1_1-large-intra_model-shuffle-human_annots_str

owanr

2023-11-03T21:25:14Z

0

null

[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]

null

2023-11-03T21:25:12Z

--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: Sentiment-google-t5-v1_1-large-intra_model-shuffle-human_annots_str results: [] ---  # Sentiment-google-t5-v1_1-large-intra_model-shuffle-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.5234 ## 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: 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 21.0865 | 1.0 | 44 | 23.6328 | | 19.3373 | 2.0 | 88 | 17.3411 | | 14.3861 | 3.0 | 132 | 11.9747 | | 11.4823 | 4.0 | 176 | 10.9355 | | 10.5154 | 5.0 | 220 | 10.7808 | | 10.1482 | 6.0 | 264 | 10.7065 | | 10.0319 | 7.0 | 308 | 10.5792 | | 9.8259 | 8.0 | 352 | 10.2032 | | 9.1712 | 9.0 | 396 | 9.5730 | | 8.8567 | 10.0 | 440 | 9.2184 | | 8.626 | 11.0 | 484 | 9.0661 | | 8.5469 | 12.0 | 528 | 8.9471 | | 6.3996 | 13.0 | 572 | 1.5474 | | 1.3785 | 14.0 | 616 | 1.3062 | | 1.3229 | 15.0 | 660 | 1.2992 | | 1.3309 | 16.0 | 704 | 1.3052 | | 1.3341 | 17.0 | 748 | 1.2961 | | 1.3281 | 18.0 | 792 | 1.2946 | | 1.3219 | 19.0 | 836 | 1.3034 | | 1.3185 | 20.0 | 880 | 1.2989 | | 1.3184 | 21.0 | 924 | 1.2985 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1

taozi555/mythalion-4.0

taozi555

2023-11-03T21:17:24Z

7

0

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text generation", "instruct", "en", "dataset:PygmalionAI/PIPPA", "dataset:Open-Orca/OpenOrca", "dataset:Norquinal/claude_multiround_chat_30k", "dataset:jondurbin/airoboros-gpt4-1.4.1", "dataset:databricks/databricks-dolly-15k", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-11-03T19:50:55Z

--- language: - en thumbnail: null tags: - text generation - instruct pipeline_tag: text-generation inference: false license: llama2 datasets: - PygmalionAI/PIPPA - Open-Orca/OpenOrca - Norquinal/claude_multiround_chat_30k - jondurbin/airoboros-gpt4-1.4.1 - databricks/databricks-dolly-15k --- <h1 style="text-align: center">Mythalion 13B</h1> <h2 style="text-align: center">A merge of Pygmalion-2 13B and MythoMax 13B</h2> ## Model Details The long-awaited release of our new models based on Llama-2 is finally here. This model was created in collaboration with [Gryphe](https://huggingface.co/Gryphe), a mixture of our [Pygmalion-2 13B](https://huggingface.co/PygmalionAI/pygmalion-2-13b) and Gryphe's [Mythomax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b). Finer details of the merge are available in [our blogpost](https://pygmalionai.github.io/blog/posts/introducing_pygmalion_2/#mythalion-13b). According to our testers, this model seems to outperform MythoMax in RP/Chat. **Please make sure you follow the recommended generation settings for SillyTavern [here](https://pygmalionai.github.io/blog/posts/introducing_pygmalion_2/#sillytavern) for the best results!** This model is freely available for both commercial and non-commercial use, as per the Llama-2 license. ## Prompting This model can be prompted using both the Alpaca and [Pygmalion formatting](https://huggingface.co/PygmalionAI/pygmalion-2-13b#prompting). **Alpaca formatting**: ``` ### Instruction: <prompt> ### Response: <leave a newline blank for model to respond> ``` **Pygmalion/Metharme formatting**: ``` <|system|>Enter RP mode. Pretend to be {{char}} whose persona follows: {{persona}} You shall reply to the user while staying in character, and generate long responses. <|user|>Hello!<|model|>{model's response goes here} ``` The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history. ## Limitations and biases The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading. ## Acknowledgements We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for the [Pygmalion-2 13B](https://huggingface.co/PygmalionAI/pygmalion-2-13b) model. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

1TuanPham/Instruct_en-vi_12000_1e_b32_lr2e-4_1TuanPham_bkai-vietnamese-llama2-7b-sharded_LORA_CAUSAL_LM

1TuanPham

2023-11-03T21:10:44Z

0

1

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:1TuanPham/bkai-vietnamese-llama2-7b-sharded", "base_model:adapter:1TuanPham/bkai-vietnamese-llama2-7b-sharded", "region:us" ]

null

2023-11-03T21:10:24Z

--- library_name: peft base_model: 1TuanPham/bkai-vietnamese-llama2-7b-sharded --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [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: True - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0

peldrak/segformer_finetuned_coasts

peldrak

2023-11-03T20:57:10Z

4

0

transformers

[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/segformer-b0-finetuned-ade-512-512", "base_model:finetune:nvidia/segformer-b0-finetuned-ade-512-512", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2023-11-02T11:03:28Z

--- license: other base_model: nvidia/segformer-b0-finetuned-ade-512-512 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer_finetuned_coasts results: [] ---  # segformer_finetuned_coasts This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the peldrak/coast dataset. It achieves the following results on the evaluation set: - Loss: 0.3304 - Mean Iou: 0.4794 - Mean Accuracy: 0.6650 - Overall Accuracy: 0.9144 - Accuracy Water: nan - Accuracy Whitewater: 0.4315 - Accuracy Sediment: 0.8895 - Accuracy Other Natural Terrain: 0.0 - Accuracy Vegetation: 0.8740 - Accuracy Development: 0.8271 - Accuracy Unknown: 0.9678 - Iou Water: 0.0 - Iou Whitewater: 0.2745 - Iou Sediment: 0.7784 - Iou Other Natural Terrain: 0.0 - Iou Vegetation: 0.7930 - Iou Development: 0.5438 - Iou Unknown: 0.9658 ## 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: 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:| | 1.5565 | 0.01 | 20 | 1.3796 | 0.2430 | 0.3551 | 0.7951 | nan | 0.0097 | 0.1349 | 0.0623 | 0.8407 | 0.1406 | 0.9421 | 0.0 | 0.0051 | 0.1067 | 0.0232 | 0.5193 | 0.1049 | 0.9414 | | 1.7203 | 0.02 | 40 | 1.0580 | 0.2474 | 0.3601 | 0.8312 | nan | 0.0000 | 0.1687 | 0.0012 | 0.9812 | 0.0552 | 0.9541 | 0.0 | 0.0000 | 0.1594 | 0.0010 | 0.5662 | 0.0518 | 0.9534 | | 1.5577 | 0.04 | 60 | 0.9417 | 0.2218 | 0.3312 | 0.8179 | nan | 0.0000 | 0.0330 | 0.0001 | 0.9915 | 0.0100 | 0.9525 | 0.0 | 0.0000 | 0.0323 | 0.0001 | 0.5585 | 0.0099 | 0.9517 | | 0.8823 | 0.05 | 80 | 0.7899 | 0.2293 | 0.3345 | 0.8217 | nan | 0.0000 | 0.0599 | 0.0 | 0.9906 | 0.0011 | 0.9553 | 0.0 | 0.0000 | 0.0596 | 0.0 | 0.5903 | 0.0011 | 0.9538 | | 1.2586 | 0.06 | 100 | 0.6372 | 0.2532 | 0.3440 | 0.8300 | nan | 0.0000 | 0.0981 | 0.0000 | 0.9697 | 0.0284 | 0.9679 | 0.0 | 0.0000 | 0.0971 | 0.0000 | 0.6818 | 0.0284 | 0.9653 | | 1.5022 | 0.07 | 120 | 0.6110 | 0.2431 | 0.3372 | 0.8258 | nan | 0.0 | 0.0759 | 0.0 | 0.9823 | 0.0029 | 0.9619 | 0.0 | 0.0 | 0.0757 | 0.0 | 0.6633 | 0.0029 | 0.9599 | | 0.7693 | 0.08 | 140 | 0.5468 | 0.2613 | 0.3416 | 0.8125 | nan | 0.0 | 0.2451 | 0.0 | 0.8389 | 0.0027 | 0.9628 | 0.0 | 0.0 | 0.2372 | 0.0 | 0.6285 | 0.0027 | 0.9606 | | 1.6587 | 0.1 | 160 | 0.5876 | 0.2717 | 0.3736 | 0.8444 | nan | 0.0 | 0.3158 | 0.0 | 0.9600 | 0.0029 | 0.9628 | 0.0 | 0.0 | 0.3106 | 0.0 | 0.6272 | 0.0029 | 0.9609 | | 1.259 | 0.11 | 180 | 0.5015 | 0.2883 | 0.3752 | 0.8292 | nan | 0.0 | 0.4727 | 0.0 | 0.7987 | 0.0120 | 0.9679 | 0.0 | 0.0 | 0.4317 | 0.0 | 0.6105 | 0.0120 | 0.9642 | | 1.1834 | 0.12 | 200 | 0.5206 | 0.3021 | 0.4047 | 0.8628 | nan | 0.0 | 0.4986 | 0.0 | 0.9598 | 0.0051 | 0.9646 | 0.0 | 0.0 | 0.4515 | 0.0 | 0.6958 | 0.0051 | 0.9620 | | 1.1998 | 0.13 | 220 | 0.5969 | 0.3095 | 0.4198 | 0.8740 | nan | 0.0 | 0.5619 | 0.0 | 0.9853 | 0.0075 | 0.9642 | 0.0 | 0.0 | 0.5167 | 0.0 | 0.6803 | 0.0075 | 0.9620 | | 1.2329 | 0.15 | 240 | 0.4667 | 0.3146 | 0.4176 | 0.8629 | nan | 0.0 | 0.5946 | 0.0 | 0.9102 | 0.0359 | 0.9649 | 0.0 | 0.0 | 0.5137 | 0.0 | 0.6915 | 0.0348 | 0.9622 | | 0.4256 | 0.16 | 260 | 0.4695 | 0.3513 | 0.4674 | 0.8878 | nan | 0.0 | 0.7239 | 0.0 | 0.9465 | 0.1678 | 0.9660 | 0.0 | 0.0 | 0.6265 | 0.0 | 0.7136 | 0.1548 | 0.9639 | | 0.6354 | 0.17 | 280 | 0.4582 | 0.3651 | 0.4836 | 0.8773 | nan | 0.0 | 0.6636 | 0.0 | 0.8840 | 0.3866 | 0.9674 | 0.0 | 0.0 | 0.5882 | 0.0 | 0.6976 | 0.3052 | 0.9648 | | 0.7103 | 0.18 | 300 | 0.4466 | 0.3736 | 0.5041 | 0.8699 | nan | 0.0 | 0.7149 | 0.0 | 0.8020 | 0.5409 | 0.9668 | 0.0 | 0.0 | 0.6068 | 0.0 | 0.6750 | 0.3693 | 0.9639 | | 0.7022 | 0.19 | 320 | 0.4621 | 0.3560 | 0.4756 | 0.8796 | nan | 0.0 | 0.6003 | 0.0 | 0.9246 | 0.3601 | 0.9685 | 0.0 | 0.0 | 0.5370 | 0.0 | 0.7056 | 0.2843 | 0.9653 | | 0.8337 | 0.21 | 340 | 0.4500 | 0.3678 | 0.4897 | 0.8754 | nan | 0.0 | 0.6673 | 0.0 | 0.8694 | 0.4355 | 0.9660 | 0.0 | 0.0 | 0.5735 | 0.0 | 0.7075 | 0.3300 | 0.9634 | | 0.3512 | 0.22 | 360 | 0.4664 | 0.3630 | 0.4910 | 0.8783 | nan | 0.0 | 0.5888 | 0.0 | 0.8998 | 0.4880 | 0.9696 | 0.0 | 0.0 | 0.5302 | 0.0 | 0.7033 | 0.3433 | 0.9645 | | 1.3383 | 0.23 | 380 | 0.5411 | 0.3447 | 0.4592 | 0.8802 | nan | 0.0 | 0.5990 | 0.0 | 0.9552 | 0.2349 | 0.9663 | 0.0 | 0.0 | 0.5463 | 0.0 | 0.7003 | 0.2027 | 0.9633 | | 1.421 | 0.24 | 400 | 0.4386 | 0.3646 | 0.4884 | 0.8661 | nan | 0.0 | 0.6447 | 0.0 | 0.8233 | 0.4958 | 0.9668 | 0.0 | 0.0 | 0.5597 | 0.0 | 0.6861 | 0.3435 | 0.9631 | | 0.5563 | 0.25 | 420 | 0.4313 | 0.3801 | 0.5625 | 0.8797 | nan | 0.0 | 0.7973 | 0.0 | 0.7520 | 0.8561 | 0.9697 | 0.0 | 0.0 | 0.6663 | 0.0 | 0.6742 | 0.3552 | 0.9652 | | 0.6055 | 0.27 | 440 | 0.4203 | 0.3731 | 0.5480 | 0.8733 | nan | 0.0 | 0.7083 | 0.0 | 0.7656 | 0.8454 | 0.9690 | 0.0 | 0.0 | 0.6071 | 0.0 | 0.6947 | 0.3457 | 0.9645 | | 1.0955 | 0.28 | 460 | 0.4412 | 0.3664 | 0.5119 | 0.8665 | nan | 0.0 | 0.6004 | 0.0 | 0.8418 | 0.6706 | 0.9587 | 0.0 | 0.0 | 0.5408 | 0.0 | 0.7123 | 0.3561 | 0.9554 | | 0.9308 | 0.29 | 480 | 0.4208 | 0.3845 | 0.5381 | 0.8852 | nan | 0.0 | 0.7242 | 0.0 | 0.8578 | 0.6826 | 0.9637 | 0.0 | 0.0 | 0.6415 | 0.0 | 0.7161 | 0.3737 | 0.9600 | | 0.3463 | 0.3 | 500 | 0.4321 | 0.3713 | 0.5160 | 0.8621 | nan | 0.0 | 0.6749 | 0.0 | 0.7632 | 0.6930 | 0.9652 | 0.0 | 0.0 | 0.5896 | 0.0 | 0.6635 | 0.3833 | 0.9627 | | 0.8166 | 0.32 | 520 | 0.4851 | 0.3999 | 0.5399 | 0.8955 | nan | 0.0 | 0.7876 | 0.0 | 0.8940 | 0.5942 | 0.9633 | 0.0 | 0.0 | 0.7062 | 0.0 | 0.7290 | 0.4032 | 0.9609 | | 0.5054 | 0.33 | 540 | 0.4328 | 0.3951 | 0.5256 | 0.8936 | nan | 0.0 | 0.7356 | 0.0 | 0.9243 | 0.5325 | 0.9614 | 0.0 | 0.0 | 0.6860 | 0.0 | 0.7262 | 0.3955 | 0.9579 | | 0.316 | 0.34 | 560 | 0.3850 | 0.3985 | 0.5660 | 0.8957 | nan | 0.0 | 0.8014 | 0.0 | 0.8400 | 0.7855 | 0.9691 | 0.0 | 0.0 | 0.7180 | 0.0 | 0.7286 | 0.3782 | 0.9647 | | 0.2616 | 0.35 | 580 | 0.3974 | 0.3831 | 0.5087 | 0.8852 | nan | 0.0 | 0.6365 | 0.0 | 0.9089 | 0.5391 | 0.9677 | 0.0 | 0.0 | 0.5653 | 0.0 | 0.7531 | 0.3995 | 0.9640 | | 0.4969 | 0.36 | 600 | 0.4115 | 0.3849 | 0.5209 | 0.8738 | nan | 0.0 | 0.7301 | 0.0 | 0.8064 | 0.6238 | 0.9650 | 0.0 | 0.0 | 0.6141 | 0.0 | 0.7030 | 0.4155 | 0.9618 | | 0.6554 | 0.38 | 620 | 0.3927 | 0.3997 | 0.5634 | 0.8927 | nan | 0.0 | 0.8489 | 0.0 | 0.8074 | 0.7544 | 0.9698 | 0.0 | 0.0 | 0.7254 | 0.0 | 0.7077 | 0.4004 | 0.9641 | | 0.5096 | 0.39 | 640 | 0.4265 | 0.3833 | 0.5554 | 0.8772 | nan | 0.0 | 0.8562 | 0.0 | 0.7283 | 0.7792 | 0.9688 | 0.0 | 0.0 | 0.6714 | 0.0 | 0.6556 | 0.3905 | 0.9655 | | 0.5453 | 0.4 | 660 | 0.4163 | 0.3830 | 0.5356 | 0.8763 | nan | 0.0 | 0.7352 | 0.0 | 0.8036 | 0.7106 | 0.9644 | 0.0 | 0.0 | 0.6194 | 0.0 | 0.6953 | 0.4040 | 0.9625 | | 0.8522 | 0.41 | 680 | 0.3850 | 0.3859 | 0.5522 | 0.8779 | nan | 0.0 | 0.8238 | 0.0 | 0.7533 | 0.7690 | 0.9672 | 0.0 | 0.0 | 0.6587 | 0.0 | 0.6785 | 0.4009 | 0.9634 | | 0.324 | 0.42 | 700 | 0.3980 | 0.3957 | 0.5386 | 0.8933 | nan | 0.0 | 0.7033 | 0.0 | 0.8982 | 0.6630 | 0.9674 | 0.0 | 0.0 | 0.6302 | 0.0 | 0.7372 | 0.4384 | 0.9644 | | 0.6783 | 0.44 | 720 | 0.4155 | 0.3798 | 0.5332 | 0.8771 | nan | 0.0 | 0.6873 | 0.0 | 0.8122 | 0.7309 | 0.9689 | 0.0 | 0.0 | 0.5793 | 0.0 | 0.7058 | 0.4079 | 0.9656 | | 0.6283 | 0.45 | 740 | 0.4053 | 0.3875 | 0.5179 | 0.8917 | nan | 0.0 | 0.6808 | 0.0 | 0.9207 | 0.5386 | 0.9674 | 0.0 | 0.0 | 0.6077 | 0.0 | 0.7409 | 0.3986 | 0.9649 | | 0.831 | 0.46 | 760 | 0.3984 | 0.3850 | 0.5178 | 0.8838 | nan | 0.0 | 0.7712 | 0.0 | 0.8476 | 0.5216 | 0.9664 | 0.0 | 0.0 | 0.5900 | 0.0 | 0.7380 | 0.4044 | 0.9629 | | 0.5993 | 0.47 | 780 | 0.4069 | 0.3891 | 0.5129 | 0.8961 | nan | 0.0 | 0.7348 | 0.0 | 0.9287 | 0.4453 | 0.9687 | 0.0 | 0.0 | 0.6417 | 0.0 | 0.7532 | 0.3632 | 0.9658 | | 0.719 | 0.49 | 800 | 0.3856 | 0.4053 | 0.5597 | 0.9006 | nan | 0.0 | 0.8377 | 0.0 | 0.8641 | 0.6875 | 0.9688 | 0.0 | 0.0 | 0.7007 | 0.0 | 0.7423 | 0.4286 | 0.9657 | | 0.4896 | 0.5 | 820 | 0.3741 | 0.4033 | 0.5511 | 0.8982 | nan | 0.0 | 0.7926 | 0.0 | 0.8730 | 0.6715 | 0.9697 | 0.0 | 0.0 | 0.6861 | 0.0 | 0.7405 | 0.4302 | 0.9662 | | 0.5632 | 0.51 | 840 | 0.4503 | 0.4085 | 0.5607 | 0.9069 | nan | 0.0 | 0.8602 | 0.0 | 0.8788 | 0.6530 | 0.9724 | 0.0 | 0.0 | 0.7133 | 0.0 | 0.7441 | 0.4355 | 0.9664 | | 0.2878 | 0.52 | 860 | 0.4594 | 0.3772 | 0.4928 | 0.8637 | nan | 0.0 | 0.5859 | 0.0 | 0.8246 | 0.5792 | 0.9668 | 0.0 | 0.0 | 0.5066 | 0.0 | 0.7162 | 0.4538 | 0.9639 | | 0.5433 | 0.53 | 880 | 0.3916 | 0.3899 | 0.5519 | 0.8832 | nan | 0.0 | 0.7655 | 0.0 | 0.8304 | 0.7561 | 0.9596 | 0.0 | 0.0 | 0.6246 | 0.0 | 0.7277 | 0.4197 | 0.9571 | | 1.1254 | 0.55 | 900 | 0.3724 | 0.4012 | 0.5588 | 0.8989 | nan | 0.0 | 0.8798 | 0.0 | 0.8445 | 0.6612 | 0.9676 | 0.0 | 0.0 | 0.6859 | 0.0 | 0.7355 | 0.4222 | 0.9645 | | 0.3224 | 0.56 | 920 | 0.3896 | 0.3832 | 0.5236 | 0.8848 | nan | 0.0 | 0.6800 | 0.0 | 0.8722 | 0.6211 | 0.9682 | 0.0 | 0.0 | 0.5748 | 0.0 | 0.7403 | 0.4029 | 0.9642 | | 0.5332 | 0.57 | 940 | 0.4089 | 0.3939 | 0.5357 | 0.9015 | nan | 0.0 | 0.7886 | 0.0 | 0.9190 | 0.5394 | 0.9673 | 0.0 | 0.0 | 0.6730 | 0.0 | 0.7391 | 0.3803 | 0.9647 | | 0.5894 | 0.58 | 960 | 0.3700 | 0.3927 | 0.5405 | 0.8839 | nan | 0.0 | 0.7708 | 0.0 | 0.8235 | 0.6824 | 0.9660 | 0.0 | 0.0 | 0.6556 | 0.0 | 0.7161 | 0.4163 | 0.9612 | | 1.036 | 0.59 | 980 | 0.3671 | 0.3944 | 0.5432 | 0.8837 | nan | 0.0 | 0.7763 | 0.0 | 0.8179 | 0.6990 | 0.9658 | 0.0 | 0.0 | 0.6802 | 0.0 | 0.7127 | 0.4058 | 0.9623 | | 1.5145 | 0.61 | 1000 | 0.3916 | 0.3965 | 0.5456 | 0.8958 | nan | 0.0 | 0.7867 | 0.0 | 0.8846 | 0.6375 | 0.9647 | 0.0 | 0.0 | 0.7018 | 0.0 | 0.7190 | 0.3925 | 0.9624 | | 0.4625 | 0.62 | 1020 | 0.3603 | 0.4067 | 0.5496 | 0.9010 | nan | 0.0 | 0.8180 | 0.0 | 0.8992 | 0.6160 | 0.9646 | 0.0 | 0.0 | 0.7095 | 0.0 | 0.7525 | 0.4239 | 0.9608 | | 0.3804 | 0.63 | 1040 | 0.4219 | 0.3928 | 0.5328 | 0.8789 | nan | 0.0 | 0.8239 | 0.0 | 0.7882 | 0.6196 | 0.9652 | 0.0 | 0.0 | 0.6778 | 0.0 | 0.6963 | 0.4115 | 0.9637 | | 0.1372 | 0.64 | 1060 | 0.3774 | 0.4034 | 0.5582 | 0.8978 | nan | 0.0 | 0.8729 | 0.0 | 0.8348 | 0.6721 | 0.9696 | 0.0 | 0.0 | 0.7169 | 0.0 | 0.7231 | 0.4186 | 0.9653 | | 0.4438 | 0.65 | 1080 | 0.3409 | 0.4042 | 0.5501 | 0.8971 | nan | 0.0 | 0.7877 | 0.0 | 0.8704 | 0.6733 | 0.9694 | 0.0 | 0.0 | 0.6900 | 0.0 | 0.7509 | 0.4241 | 0.9648 | | 0.3661 | 0.67 | 1100 | 0.3662 | 0.3994 | 0.5728 | 0.8936 | nan | 0.0039 | 0.8665 | 0.0 | 0.7975 | 0.7998 | 0.9693 | 0.0 | 0.0039 | 0.7029 | 0.0 | 0.7202 | 0.4038 | 0.9651 | | 0.3783 | 0.68 | 1120 | 0.3625 | 0.4003 | 0.5673 | 0.8893 | nan | 0.0 | 0.8860 | 0.0 | 0.7856 | 0.7669 | 0.9654 | 0.0 | 0.0 | 0.7155 | 0.0 | 0.7166 | 0.4074 | 0.9628 | | 0.3344 | 0.69 | 1140 | 0.3889 | 0.4142 | 0.5535 | 0.9062 | nan | 0.0 | 0.8531 | 0.0 | 0.9107 | 0.5919 | 0.9651 | 0.0 | 0.0 | 0.7437 | 0.0 | 0.7598 | 0.4330 | 0.9628 | | 0.448 | 0.7 | 1160 | 0.3701 | 0.3875 | 0.5226 | 0.8774 | nan | 0.0 | 0.6806 | 0.0 | 0.8287 | 0.6578 | 0.9688 | 0.0 | 0.0 | 0.5996 | 0.0 | 0.7207 | 0.4273 | 0.9648 | | 0.5724 | 0.72 | 1180 | 0.3466 | 0.4074 | 0.5587 | 0.8998 | nan | 0.0000 | 0.8195 | 0.0 | 0.8653 | 0.6982 | 0.9692 | 0.0 | 0.0000 | 0.7289 | 0.0 | 0.7380 | 0.4198 | 0.9648 | | 0.2868 | 0.73 | 1200 | 0.3369 | 0.4088 | 0.5653 | 0.8956 | nan | 0.0005 | 0.8380 | 0.0 | 0.8254 | 0.7584 | 0.9697 | 0.0 | 0.0005 | 0.7145 | 0.0 | 0.7399 | 0.4414 | 0.9652 | | 1.2485 | 0.74 | 1220 | 0.3480 | 0.4120 | 0.5639 | 0.9023 | nan | 0.0079 | 0.8643 | 0.0 | 0.8543 | 0.6865 | 0.9706 | 0.0 | 0.0079 | 0.7086 | 0.0 | 0.7548 | 0.4469 | 0.9659 | | 0.6551 | 0.75 | 1240 | 0.3791 | 0.4219 | 0.5591 | 0.9145 | nan | 0.0048 | 0.8977 | 0.0 | 0.9139 | 0.5662 | 0.9718 | 0.0 | 0.0048 | 0.7367 | 0.0 | 0.7844 | 0.4620 | 0.9651 | | 0.2599 | 0.76 | 1260 | 0.4596 | 0.3694 | 0.5736 | 0.8698 | nan | 0.0001 | 0.9234 | 0.0 | 0.6502 | 0.9018 | 0.9664 | 0.0 | 0.0001 | 0.6060 | 0.0 | 0.6179 | 0.3974 | 0.9644 | | 0.4005 | 0.78 | 1280 | 0.3520 | 0.4018 | 0.5566 | 0.8965 | nan | 0.0076 | 0.7654 | 0.0 | 0.8698 | 0.7278 | 0.9690 | 0.0 | 0.0076 | 0.6936 | 0.0 | 0.7161 | 0.4306 | 0.9648 | | 0.4001 | 0.79 | 1300 | 0.3456 | 0.4070 | 0.5543 | 0.8977 | nan | 0.0001 | 0.7549 | 0.0 | 0.8813 | 0.7203 | 0.9692 | 0.0 | 0.0001 | 0.6933 | 0.0 | 0.7277 | 0.4620 | 0.9658 | | 0.9039 | 0.8 | 1320 | 0.3889 | 0.4112 | 0.5790 | 0.8984 | nan | 0.0006 | 0.8918 | 0.0 | 0.8236 | 0.7933 | 0.9650 | 0.0 | 0.0006 | 0.7323 | 0.0 | 0.7349 | 0.4479 | 0.9626 | | 0.6388 | 0.81 | 1340 | 0.4108 | 0.4121 | 0.5752 | 0.9051 | nan | 0.0001 | 0.8354 | 0.0 | 0.8712 | 0.7752 | 0.9692 | 0.0 | 0.0001 | 0.7325 | 0.0 | 0.7351 | 0.4512 | 0.9655 | | 0.3616 | 0.82 | 1360 | 0.4138 | 0.4221 | 0.5638 | 0.9099 | nan | 0.0 | 0.8630 | 0.0 | 0.9119 | 0.6414 | 0.9664 | 0.0 | 0.0 | 0.7581 | 0.0 | 0.7689 | 0.4631 | 0.9646 | | 0.2287 | 0.84 | 1380 | 0.3833 | 0.4229 | 0.5762 | 0.9055 | nan | 0.0000 | 0.8940 | 0.0 | 0.8637 | 0.7335 | 0.9658 | 0.0 | 0.0000 | 0.7658 | 0.0 | 0.7545 | 0.4779 | 0.9620 | | 0.2687 | 0.85 | 1400 | 0.3732 | 0.4287 | 0.5666 | 0.9118 | nan | 0.0000 | 0.8972 | 0.0 | 0.9157 | 0.6226 | 0.9641 | 0.0 | 0.0000 | 0.7764 | 0.0 | 0.7815 | 0.4814 | 0.9618 | | 0.3827 | 0.86 | 1420 | 0.3344 | 0.4176 | 0.5816 | 0.9030 | nan | 0.0 | 0.8511 | 0.0 | 0.8537 | 0.8177 | 0.9671 | 0.0 | 0.0 | 0.7452 | 0.0 | 0.7485 | 0.4653 | 0.9641 | | 1.1798 | 0.87 | 1440 | 0.3485 | 0.4198 | 0.5742 | 0.9089 | nan | 0.0002 | 0.8352 | 0.0 | 0.8893 | 0.7500 | 0.9705 | 0.0 | 0.0002 | 0.7491 | 0.0 | 0.7514 | 0.4729 | 0.9653 | | 0.5062 | 0.89 | 1460 | 0.3882 | 0.4145 | 0.5766 | 0.9028 | nan | 0.0013 | 0.8817 | 0.0 | 0.8337 | 0.7717 | 0.9712 | 0.0 | 0.0013 | 0.7656 | 0.0 | 0.7303 | 0.4389 | 0.9656 | | 0.2002 | 0.9 | 1480 | 0.3677 | 0.4167 | 0.5785 | 0.9005 | nan | 0.0001 | 0.8674 | 0.0 | 0.8450 | 0.7934 | 0.9649 | 0.0 | 0.0001 | 0.7513 | 0.0 | 0.7391 | 0.4645 | 0.9620 | | 0.1993 | 0.91 | 1500 | 0.3801 | 0.4240 | 0.5584 | 0.9134 | nan | 0.0000 | 0.8673 | 0.0 | 0.9311 | 0.5842 | 0.9680 | 0.0 | 0.0000 | 0.7626 | 0.0 | 0.7714 | 0.4687 | 0.9653 | | 0.2609 | 0.92 | 1520 | 0.3489 | 0.4199 | 0.5475 | 0.9084 | nan | 0.0001 | 0.8222 | 0.0 | 0.9304 | 0.5642 | 0.9682 | 0.0 | 0.0001 | 0.7412 | 0.0 | 0.7755 | 0.4568 | 0.9657 | | 0.4571 | 0.93 | 1540 | 0.3767 | 0.4218 | 0.5832 | 0.9040 | nan | 0.0372 | 0.8298 | 0.0 | 0.8774 | 0.7894 | 0.9651 | 0.0 | 0.0365 | 0.7505 | 0.0 | 0.7451 | 0.4583 | 0.9621 | | 0.5643 | 0.95 | 1560 | 0.3707 | 0.4220 | 0.5848 | 0.9027 | nan | 0.0187 | 0.8455 | 0.0 | 0.8446 | 0.8307 | 0.9695 | 0.0 | 0.0186 | 0.7506 | 0.0 | 0.7459 | 0.4715 | 0.9670 | | 0.2607 | 0.96 | 1580 | 0.3601 | 0.4304 | 0.5782 | 0.9099 | nan | 0.0119 | 0.8665 | 0.0 | 0.8921 | 0.7312 | 0.9675 | 0.0 | 0.0118 | 0.7565 | 0.0 | 0.7761 | 0.5029 | 0.9652 | | 0.2481 | 0.97 | 1600 | 0.3817 | 0.4281 | 0.5952 | 0.9043 | nan | 0.0346 | 0.8954 | 0.0 | 0.8582 | 0.8225 | 0.9604 | 0.0 | 0.0342 | 0.7426 | 0.0 | 0.7691 | 0.4917 | 0.9589 | | 0.1986 | 0.98 | 1620 | 0.3817 | 0.4475 | 0.5988 | 0.9135 | nan | 0.1260 | 0.8537 | 0.0 | 0.9194 | 0.7288 | 0.9651 | 0.0 | 0.1246 | 0.7532 | 0.0 | 0.7756 | 0.5166 | 0.9626 | | 0.455 | 0.99 | 1640 | 0.3812 | 0.4526 | 0.5958 | 0.9144 | nan | 0.1518 | 0.8918 | 0.0 | 0.9239 | 0.6439 | 0.9636 | 0.0 | 0.1467 | 0.7665 | 0.0 | 0.7888 | 0.5058 | 0.9602 | | 0.4941 | 1.01 | 1660 | 0.3751 | 0.4389 | 0.6153 | 0.9094 | nan | 0.1142 | 0.9261 | 0.0 | 0.8391 | 0.8440 | 0.9682 | 0.0 | 0.1110 | 0.7601 | 0.0 | 0.7621 | 0.4737 | 0.9652 | | 0.2446 | 1.02 | 1680 | 0.3794 | 0.4389 | 0.5817 | 0.9136 | nan | 0.0160 | 0.9137 | 0.0 | 0.8883 | 0.7034 | 0.9689 | 0.0 | 0.0160 | 0.7765 | 0.0 | 0.7870 | 0.5264 | 0.9664 | | 0.3996 | 1.03 | 1700 | 0.3408 | 0.4193 | 0.5478 | 0.9004 | nan | 0.0060 | 0.7699 | 0.0 | 0.8999 | 0.6419 | 0.9691 | 0.0 | 0.0060 | 0.6761 | 0.0 | 0.7813 | 0.5054 | 0.9660 | | 0.6762 | 1.04 | 1720 | 0.3653 | 0.4203 | 0.5646 | 0.8983 | nan | 0.0554 | 0.7069 | 0.0 | 0.8982 | 0.7581 | 0.9691 | 0.0 | 0.0550 | 0.6118 | 0.0 | 0.7905 | 0.5192 | 0.9658 | | 0.3445 | 1.06 | 1740 | 0.3179 | 0.4460 | 0.6063 | 0.9131 | nan | 0.1284 | 0.8453 | 0.0 | 0.8960 | 0.7983 | 0.9697 | 0.0 | 0.1275 | 0.7422 | 0.0 | 0.7797 | 0.5068 | 0.9658 | | 0.5238 | 1.07 | 1760 | 0.3420 | 0.4456 | 0.6022 | 0.9178 | nan | 0.0626 | 0.9129 | 0.0 | 0.8955 | 0.7732 | 0.9692 | 0.0 | 0.0616 | 0.7810 | 0.0 | 0.7931 | 0.5180 | 0.9658 | | 0.4666 | 1.08 | 1780 | 0.3410 | 0.4379 | 0.5994 | 0.9099 | nan | 0.0695 | 0.8674 | 0.0 | 0.8796 | 0.8136 | 0.9666 | 0.0 | 0.0678 | 0.7510 | 0.0 | 0.7786 | 0.5035 | 0.9645 | | 0.1126 | 1.09 | 1800 | 0.3464 | 0.4383 | 0.6157 | 0.9101 | nan | 0.1208 | 0.8865 | 0.0 | 0.8516 | 0.8653 | 0.9700 | 0.0 | 0.1180 | 0.7454 | 0.0 | 0.7670 | 0.4711 | 0.9664 | | 0.7935 | 1.1 | 1820 | 0.3999 | 0.4382 | 0.5737 | 0.9179 | nan | 0.0529 | 0.8790 | 0.0 | 0.9439 | 0.5984 | 0.9679 | 0.0 | 0.0528 | 0.7719 | 0.0 | 0.7874 | 0.4906 | 0.9650 | | 0.6014 | 1.12 | 1840 | 0.3176 | 0.4707 | 0.6222 | 0.9193 | nan | 0.2149 | 0.8795 | 0.0 | 0.9128 | 0.7558 | 0.9702 | 0.0 | 0.2033 | 0.7731 | 0.0 | 0.7938 | 0.5591 | 0.9653 | | 1.1728 | 1.13 | 1860 | 0.3165 | 0.4715 | 0.6205 | 0.9205 | nan | 0.2344 | 0.8965 | 0.0 | 0.9165 | 0.7044 | 0.9709 | 0.0 | 0.2151 | 0.7453 | 0.0 | 0.8097 | 0.5650 | 0.9654 | | 0.1275 | 1.14 | 1880 | 0.3705 | 0.4586 | 0.5965 | 0.9140 | nan | 0.2139 | 0.8187 | 0.0 | 0.9455 | 0.6344 | 0.9666 | 0.0 | 0.2070 | 0.7223 | 0.0 | 0.7887 | 0.5277 | 0.9649 | | 0.2179 | 1.15 | 1900 | 0.3268 | 0.4608 | 0.6240 | 0.9126 | nan | 0.2176 | 0.8507 | 0.0 | 0.8910 | 0.8165 | 0.9679 | 0.0 | 0.2054 | 0.7202 | 0.0 | 0.7888 | 0.5454 | 0.9656 | | 0.1725 | 1.16 | 1920 | 0.3277 | 0.4655 | 0.6146 | 0.9203 | nan | 0.1809 | 0.8656 | 0.0 | 0.9297 | 0.7422 | 0.9693 | 0.0 | 0.1753 | 0.7456 | 0.0 | 0.8099 | 0.5610 | 0.9667 | | 0.3003 | 1.18 | 1940 | 0.3347 | 0.4741 | 0.6311 | 0.9203 | nan | 0.2239 | 0.8903 | 0.0 | 0.9079 | 0.7951 | 0.9696 | 0.0 | 0.2144 | 0.7493 | 0.0 | 0.8067 | 0.5815 | 0.9669 | | 0.4764 | 1.19 | 1960 | 0.3413 | 0.4478 | 0.6279 | 0.9079 | nan | 0.1854 | 0.9016 | 0.0 | 0.8339 | 0.8779 | 0.9687 | 0.0 | 0.1690 | 0.7441 | 0.0 | 0.7655 | 0.4897 | 0.9663 | | 0.1679 | 1.2 | 1980 | 0.3516 | 0.4473 | 0.5798 | 0.9191 | nan | 0.0754 | 0.8674 | 0.0 | 0.9466 | 0.6204 | 0.9692 | 0.0 | 0.0724 | 0.7752 | 0.0 | 0.7993 | 0.5181 | 0.9660 | | 0.1999 | 1.21 | 2000 | 0.3341 | 0.4721 | 0.6457 | 0.9175 | nan | 0.2688 | 0.8973 | 0.0 | 0.8822 | 0.8568 | 0.9688 | 0.0 | 0.2286 | 0.7781 | 0.0 | 0.7962 | 0.5359 | 0.9662 | | 0.8992 | 1.22 | 2020 | 0.3121 | 0.4757 | 0.6409 | 0.9179 | nan | 0.2915 | 0.8910 | 0.0 | 0.8935 | 0.8000 | 0.9695 | 0.0 | 0.2634 | 0.7798 | 0.0 | 0.7856 | 0.5347 | 0.9661 | | 0.7007 | 1.24 | 2040 | 0.3041 | 0.4618 | 0.6127 | 0.9128 | nan | 0.1999 | 0.8653 | 0.0 | 0.8950 | 0.7474 | 0.9685 | 0.0 | 0.1702 | 0.7512 | 0.0 | 0.7931 | 0.5532 | 0.9647 | | 0.5711 | 1.25 | 2060 | 0.3104 | 0.4758 | 0.6277 | 0.9190 | nan | 0.2760 | 0.8877 | 0.0 | 0.9128 | 0.7196 | 0.9699 | 0.0 | 0.2357 | 0.7700 | 0.0 | 0.8006 | 0.5579 | 0.9666 | | 0.7925 | 1.26 | 2080 | 0.3465 | 0.4605 | 0.6418 | 0.9091 | nan | 0.2878 | 0.8570 | 0.0 | 0.8532 | 0.8831 | 0.9696 | 0.0 | 0.2437 | 0.7217 | 0.0 | 0.7779 | 0.5136 | 0.9670 | | 0.1759 | 1.27 | 2100 | 0.3406 | 0.4619 | 0.6235 | 0.9078 | nan | 0.2492 | 0.8230 | 0.0 | 0.8719 | 0.8272 | 0.9698 | 0.0 | 0.2157 | 0.6976 | 0.0 | 0.7884 | 0.5655 | 0.9663 | | 0.6914 | 1.29 | 2120 | 0.3209 | 0.4694 | 0.6229 | 0.9168 | nan | 0.2340 | 0.8629 | 0.0 | 0.9024 | 0.7665 | 0.9713 | 0.0 | 0.2126 | 0.7390 | 0.0 | 0.8017 | 0.5650 | 0.9672 | | 0.1292 | 1.3 | 2140 | 0.3151 | 0.4735 | 0.6349 | 0.9147 | nan | 0.2670 | 0.8724 | 0.0 | 0.8983 | 0.8062 | 0.9657 | 0.0 | 0.2358 | 0.7487 | 0.0 | 0.8011 | 0.5662 | 0.9625 | | 0.5439 | 1.31 | 2160 | 0.3343 | 0.4617 | 0.6058 | 0.9133 | nan | 0.2228 | 0.7989 | 0.0 | 0.9340 | 0.7110 | 0.9681 | 0.0 | 0.2091 | 0.7154 | 0.0 | 0.7958 | 0.5458 | 0.9660 | | 0.5949 | 1.32 | 2180 | 0.3260 | 0.4561 | 0.6419 | 0.9072 | nan | 0.3080 | 0.8721 | 0.0 | 0.8355 | 0.8650 | 0.9709 | 0.0 | 0.2166 | 0.7527 | 0.0 | 0.7632 | 0.4930 | 0.9670 | | 0.9366 | 1.33 | 2200 | 0.3182 | 0.4748 | 0.6430 | 0.9190 | nan | 0.2967 | 0.9100 | 0.0 | 0.8864 | 0.7941 | 0.9710 | 0.0 | 0.2381 | 0.7813 | 0.0 | 0.7926 | 0.5446 | 0.9669 | | 0.4478 | 1.35 | 2220 | 0.3531 | 0.4596 | 0.5997 | 0.9223 | nan | 0.1622 | 0.8843 | 0.0 | 0.9456 | 0.6361 | 0.9703 | 0.0 | 0.1565 | 0.7919 | 0.0 | 0.7921 | 0.5099 | 0.9670 | | 0.2858 | 1.36 | 2240 | 0.3627 | 0.4607 | 0.6228 | 0.9173 | nan | 0.1578 | 0.8976 | 0.0 | 0.8883 | 0.8239 | 0.9694 | 0.0 | 0.1500 | 0.7902 | 0.0 | 0.7738 | 0.5443 | 0.9666 | | 0.4923 | 1.37 | 2260 | 0.3367 | 0.4498 | 0.6047 | 0.9082 | nan | 0.1380 | 0.8348 | 0.0 | 0.8814 | 0.8063 | 0.9678 | 0.0 | 0.1299 | 0.7364 | 0.0 | 0.7740 | 0.5442 | 0.9638 | | 0.1323 | 1.38 | 2280 | 0.3380 | 0.4515 | 0.6301 | 0.9084 | nan | 0.1971 | 0.8926 | 0.0 | 0.8345 | 0.8863 | 0.9700 | 0.0 | 0.1751 | 0.7494 | 0.0 | 0.7577 | 0.5118 | 0.9668 | | 0.3126 | 1.39 | 2300 | 0.3519 | 0.4753 | 0.6187 | 0.9234 | nan | 0.2345 | 0.8849 | 0.0 | 0.9471 | 0.6770 | 0.9686 | 0.0 | 0.2226 | 0.7876 | 0.0 | 0.7971 | 0.5533 | 0.9662 | | 1.8741 | 1.41 | 2320 | 0.3483 | 0.4766 | 0.6352 | 0.9194 | nan | 0.2550 | 0.8999 | 0.0 | 0.9013 | 0.7856 | 0.9691 | 0.0 | 0.2242 | 0.7747 | 0.0 | 0.7957 | 0.5761 | 0.9656 | | 0.3519 | 1.42 | 2340 | 0.3390 | 0.4794 | 0.6391 | 0.9205 | nan | 0.2594 | 0.8966 | 0.0 | 0.9017 | 0.8068 | 0.9700 | 0.0 | 0.2378 | 0.7732 | 0.0 | 0.7951 | 0.5829 | 0.9671 | | 0.4777 | 1.43 | 2360 | 0.3234 | 0.4707 | 0.6410 | 0.9149 | nan | 0.2605 | 0.8687 | 0.0 | 0.8787 | 0.8681 | 0.9698 | 0.0 | 0.2422 | 0.7715 | 0.0 | 0.7784 | 0.5358 | 0.9670 | | 0.7156 | 1.44 | 2380 | 0.3451 | 0.4794 | 0.6418 | 0.9201 | nan | 0.2929 | 0.8798 | 0.0 | 0.9094 | 0.7995 | 0.9694 | 0.0 | 0.2575 | 0.7733 | 0.0 | 0.7918 | 0.5663 | 0.9670 | | 0.3765 | 1.46 | 2400 | 0.3339 | 0.4639 | 0.6238 | 0.9133 | nan | 0.2550 | 0.8921 | 0.0 | 0.8805 | 0.7455 | 0.9696 | 0.0 | 0.1751 | 0.7569 | 0.0 | 0.7919 | 0.5564 | 0.9669 | | 0.4343 | 1.47 | 2420 | 0.3374 | 0.4630 | 0.6168 | 0.9135 | nan | 0.2179 | 0.8541 | 0.0 | 0.9035 | 0.7576 | 0.9678 | 0.0 | 0.1775 | 0.7531 | 0.0 | 0.7934 | 0.5515 | 0.9658 | | 0.2178 | 1.48 | 2440 | 0.3254 | 0.4825 | 0.6499 | 0.9219 | nan | 0.3424 | 0.8816 | 0.0 | 0.9140 | 0.7912 | 0.9704 | 0.0 | 0.2639 | 0.7733 | 0.0 | 0.8027 | 0.5707 | 0.9669 | | 0.1439 | 1.49 | 2460 | 0.3176 | 0.4752 | 0.6409 | 0.9175 | nan | 0.2942 | 0.8787 | 0.0 | 0.8935 | 0.8089 | 0.9702 | 0.0 | 0.2160 | 0.7786 | 0.0 | 0.7978 | 0.5673 | 0.9664 | | 0.2481 | 1.5 | 2480 | 0.3195 | 0.4773 | 0.6265 | 0.9190 | nan | 0.2645 | 0.8625 | 0.0 | 0.9263 | 0.7376 | 0.9683 | 0.0 | 0.2089 | 0.7689 | 0.0 | 0.8083 | 0.5890 | 0.9658 | | 0.9347 | 1.52 | 2500 | 0.3430 | 0.4789 | 0.6464 | 0.9207 | nan | 0.3009 | 0.8885 | 0.0 | 0.9029 | 0.8159 | 0.9702 | 0.0 | 0.2626 | 0.7724 | 0.0 | 0.7975 | 0.5528 | 0.9670 | | 0.1827 | 1.53 | 2520 | 0.3459 | 0.4726 | 0.6328 | 0.9181 | nan | 0.2484 | 0.9093 | 0.0 | 0.8921 | 0.7775 | 0.9692 | 0.0 | 0.2042 | 0.7741 | 0.0 | 0.7998 | 0.5643 | 0.9662 | | 0.3971 | 1.54 | 2540 | 0.3276 | 0.4661 | 0.6353 | 0.9144 | nan | 0.2600 | 0.8791 | 0.0 | 0.8777 | 0.8247 | 0.9700 | 0.0 | 0.2198 | 0.7730 | 0.0 | 0.7785 | 0.5249 | 0.9663 | | 0.173 | 1.55 | 2560 | 0.3109 | 0.4591 | 0.6419 | 0.9093 | nan | 0.2986 | 0.8694 | 0.0 | 0.8536 | 0.8611 | 0.9690 | 0.0 | 0.2176 | 0.7731 | 0.0 | 0.7626 | 0.4941 | 0.9662 | | 0.2705 | 1.56 | 2580 | 0.3112 | 0.4632 | 0.6178 | 0.9101 | nan | 0.2942 | 0.8141 | 0.0 | 0.9057 | 0.7240 | 0.9687 | 0.0 | 0.2203 | 0.7423 | 0.0 | 0.7818 | 0.5318 | 0.9661 | | 0.2656 | 1.58 | 2600 | 0.3331 | 0.4806 | 0.6427 | 0.9202 | nan | 0.3186 | 0.8837 | 0.0 | 0.9166 | 0.7691 | 0.9680 | 0.0 | 0.2634 | 0.7761 | 0.0 | 0.7986 | 0.5600 | 0.9661 | | 0.9206 | 1.59 | 2620 | 0.3247 | 0.4688 | 0.6290 | 0.9162 | nan | 0.3530 | 0.8646 | 0.0 | 0.9130 | 0.6725 | 0.9706 | 0.0 | 0.2167 | 0.7747 | 0.0 | 0.7877 | 0.5357 | 0.9667 | | 0.6181 | 1.6 | 2640 | 0.4032 | 0.4888 | 0.6638 | 0.9239 | nan | 0.3722 | 0.9134 | 0.0 | 0.9041 | 0.8231 | 0.9701 | 0.0 | 0.3080 | 0.7774 | 0.0 | 0.8039 | 0.5652 | 0.9668 | | 0.3185 | 1.61 | 2660 | 0.3383 | 0.4705 | 0.6598 | 0.9145 | nan | 0.3341 | 0.9161 | 0.0 | 0.8448 | 0.8926 | 0.9711 | 0.0 | 0.2860 | 0.7681 | 0.0 | 0.7732 | 0.4997 | 0.9667 | | 0.2155 | 1.63 | 2680 | 0.3389 | 0.4639 | 0.6447 | 0.9109 | nan | 0.3037 | 0.8741 | 0.0 | 0.8593 | 0.8625 | 0.9689 | 0.0 | 0.2608 | 0.7573 | 0.0 | 0.7696 | 0.4933 | 0.9664 | | 0.2003 | 1.64 | 2700 | 0.3230 | 0.4649 | 0.6588 | 0.9112 | nan | 0.3442 | 0.8983 | 0.0 | 0.8350 | 0.9045 | 0.9710 | 0.0 | 0.2818 | 0.7704 | 0.0 | 0.7626 | 0.4723 | 0.9672 | | 0.1279 | 1.65 | 2720 | 0.3241 | 0.4747 | 0.6489 | 0.9163 | nan | 0.3240 | 0.8760 | 0.0 | 0.8863 | 0.8379 | 0.9693 | 0.0 | 0.2713 | 0.7729 | 0.0 | 0.7876 | 0.5243 | 0.9666 | | 1.5163 | 1.66 | 2740 | 0.3286 | 0.4822 | 0.6409 | 0.9182 | nan | 0.3422 | 0.8431 | 0.0 | 0.9203 | 0.7703 | 0.9694 | 0.0 | 0.2859 | 0.7533 | 0.0 | 0.7992 | 0.5702 | 0.9667 | | 0.5542 | 1.67 | 2760 | 0.3147 | 0.4774 | 0.6336 | 0.9157 | nan | 0.3563 | 0.8646 | 0.0 | 0.9065 | 0.7039 | 0.9702 | 0.0 | 0.2631 | 0.7649 | 0.0 | 0.7932 | 0.5544 | 0.9664 | | 0.343 | 1.69 | 2780 | 0.3632 | 0.4858 | 0.6494 | 0.9212 | nan | 0.3345 | 0.8857 | 0.0 | 0.9134 | 0.7937 | 0.9689 | 0.0 | 0.2855 | 0.7758 | 0.0 | 0.7975 | 0.5756 | 0.9665 | | 0.4835 | 1.7 | 2800 | 0.3339 | 0.4753 | 0.6437 | 0.9181 | nan | 0.2891 | 0.8746 | 0.0 | 0.8945 | 0.8339 | 0.9703 | 0.0 | 0.2399 | 0.7766 | 0.0 | 0.7894 | 0.5538 | 0.9670 | | 0.1818 | 1.71 | 2820 | 0.3292 | 0.4655 | 0.6247 | 0.9133 | nan | 0.2390 | 0.8420 | 0.0 | 0.8972 | 0.8010 | 0.9688 | 0.0 | 0.1741 | 0.7599 | 0.0 | 0.7941 | 0.5642 | 0.9664 | | 0.9569 | 1.72 | 2840 | 0.3529 | 0.4755 | 0.6446 | 0.9220 | nan | 0.2926 | 0.8872 | 0.0 | 0.9090 | 0.8079 | 0.9709 | 0.0 | 0.2297 | 0.7690 | 0.0 | 0.8052 | 0.5572 | 0.9672 | | 0.1522 | 1.73 | 2860 | 0.3493 | 0.4606 | 0.6375 | 0.9130 | nan | 0.2946 | 0.8384 | 0.0 | 0.8860 | 0.8360 | 0.9702 | 0.0 | 0.2283 | 0.7457 | 0.0 | 0.7715 | 0.5112 | 0.9675 | | 0.1661 | 1.75 | 2880 | 0.3477 | 0.4546 | 0.6333 | 0.9095 | nan | 0.2724 | 0.8481 | 0.0 | 0.8671 | 0.8428 | 0.9693 | 0.0 | 0.2106 | 0.7487 | 0.0 | 0.7711 | 0.4853 | 0.9667 | | 0.2484 | 1.76 | 2900 | 0.3435 | 0.4515 | 0.6292 | 0.9050 | nan | 0.2757 | 0.8926 | 0.0 | 0.8347 | 0.8039 | 0.9683 | 0.0 | 0.1935 | 0.7419 | 0.0 | 0.7619 | 0.4975 | 0.9658 | | 0.1391 | 1.77 | 2920 | 0.3083 | 0.4621 | 0.6332 | 0.9138 | nan | 0.3116 | 0.8488 | 0.0 | 0.8965 | 0.7730 | 0.9696 | 0.0 | 0.2033 | 0.7516 | 0.0 | 0.7920 | 0.5213 | 0.9666 | | 0.1363 | 1.78 | 2940 | 0.3371 | 0.4638 | 0.6463 | 0.9110 | nan | 0.3354 | 0.8500 | 0.0 | 0.8745 | 0.8501 | 0.9679 | 0.0 | 0.2511 | 0.7327 | 0.0 | 0.7852 | 0.5121 | 0.9656 | | 0.3563 | 1.8 | 2960 | 0.3271 | 0.4584 | 0.6325 | 0.9096 | nan | 0.2815 | 0.8248 | 0.0 | 0.8714 | 0.8465 | 0.9711 | 0.0 | 0.2268 | 0.7214 | 0.0 | 0.7805 | 0.5133 | 0.9670 | | 0.7689 | 1.81 | 2980 | 0.3054 | 0.4593 | 0.6192 | 0.9096 | nan | 0.2350 | 0.8486 | 0.0 | 0.8729 | 0.7878 | 0.9708 | 0.0 | 0.1854 | 0.7343 | 0.0 | 0.7904 | 0.5387 | 0.9664 | | 0.333 | 1.82 | 3000 | 0.3318 | 0.4715 | 0.6438 | 0.9178 | nan | 0.2849 | 0.8976 | 0.0 | 0.8858 | 0.8248 | 0.9698 | 0.0 | 0.2529 | 0.7470 | 0.0 | 0.7938 | 0.5400 | 0.9669 | | 0.0664 | 1.83 | 3020 | 0.3174 | 0.4616 | 0.6548 | 0.9101 | nan | 0.3318 | 0.8963 | 0.0 | 0.8294 | 0.8993 | 0.9718 | 0.0 | 0.2594 | 0.7513 | 0.0 | 0.7654 | 0.4884 | 0.9670 | | 0.5802 | 1.84 | 3040 | 0.3003 | 0.4694 | 0.6533 | 0.9138 | nan | 0.3512 | 0.8930 | 0.0 | 0.8604 | 0.8446 | 0.9707 | 0.0 | 0.2368 | 0.7722 | 0.0 | 0.7820 | 0.5277 | 0.9668 | | 0.158 | 1.86 | 3060 | 0.2986 | 0.4762 | 0.6403 | 0.9197 | nan | 0.3481 | 0.8854 | 0.0 | 0.9133 | 0.7247 | 0.9701 | 0.0 | 0.2633 | 0.7703 | 0.0 | 0.7969 | 0.5362 | 0.9669 | | 0.1517 | 1.87 | 3080 | 0.3548 | 0.4843 | 0.6427 | 0.9240 | nan | 0.4025 | 0.8833 | 0.0 | 0.9451 | 0.6554 | 0.9699 | 0.0 | 0.3066 | 0.7910 | 0.0 | 0.7953 | 0.5295 | 0.9675 | | 0.5864 | 1.88 | 3100 | 0.3026 | 0.4797 | 0.6438 | 0.9193 | nan | 0.3682 | 0.8682 | 0.0 | 0.9130 | 0.7426 | 0.9709 | 0.0 | 0.2850 | 0.7875 | 0.0 | 0.7846 | 0.5333 | 0.9676 | | 0.4852 | 1.89 | 3120 | 0.3118 | 0.4811 | 0.6588 | 0.9188 | nan | 0.3660 | 0.8840 | 0.0 | 0.8897 | 0.8429 | 0.9703 | 0.0 | 0.2981 | 0.7871 | 0.0 | 0.7857 | 0.5299 | 0.9672 | | 0.2932 | 1.9 | 3140 | 0.2926 | 0.4920 | 0.6502 | 0.9245 | nan | 0.3744 | 0.8931 | 0.0 | 0.9253 | 0.7370 | 0.9717 | 0.0 | 0.3025 | 0.7904 | 0.0 | 0.8108 | 0.5731 | 0.9675 | | 0.2021 | 1.92 | 3160 | 0.3038 | 0.4779 | 0.6211 | 0.9178 | nan | 0.3510 | 0.8491 | 0.0 | 0.9417 | 0.6159 | 0.9689 | 0.0 | 0.2831 | 0.7536 | 0.0 | 0.8076 | 0.5351 | 0.9663 | | 0.3806 | 1.93 | 3180 | 0.3182 | 0.4760 | 0.6492 | 0.9141 | nan | 0.3445 | 0.8570 | 0.0 | 0.8832 | 0.8411 | 0.9692 | 0.0 | 0.2641 | 0.7392 | 0.0 | 0.7967 | 0.5651 | 0.9670 | | 0.1496 | 1.94 | 3200 | 0.3361 | 0.4753 | 0.6422 | 0.9156 | nan | 0.3138 | 0.9011 | 0.0 | 0.8849 | 0.7859 | 0.9678 | 0.0 | 0.2435 | 0.7803 | 0.0 | 0.7946 | 0.5434 | 0.9651 | | 0.0671 | 1.95 | 3220 | 0.3265 | 0.4742 | 0.6578 | 0.9147 | nan | 0.3656 | 0.8798 | 0.0 | 0.8741 | 0.8584 | 0.9687 | 0.0 | 0.2816 | 0.7842 | 0.0 | 0.7832 | 0.5041 | 0.9659 | | 0.15 | 1.96 | 3240 | 0.3244 | 0.4739 | 0.6427 | 0.9158 | nan | 0.3541 | 0.8804 | 0.0 | 0.8972 | 0.7564 | 0.9682 | 0.0 | 0.2528 | 0.7590 | 0.0 | 0.7978 | 0.5417 | 0.9660 | | 0.1933 | 1.98 | 3260 | 0.3516 | 0.4773 | 0.6542 | 0.9174 | nan | 0.3953 | 0.9202 | 0.0 | 0.8821 | 0.7583 | 0.9693 | 0.0 | 0.2658 | 0.7510 | 0.0 | 0.8026 | 0.5550 | 0.9665 | | 0.1333 | 1.99 | 3280 | 0.3080 | 0.4830 | 0.6444 | 0.9189 | nan | 0.3587 | 0.9054 | 0.0 | 0.9009 | 0.7318 | 0.9695 | 0.0 | 0.3008 | 0.7536 | 0.0 | 0.8027 | 0.5574 | 0.9667 | | 0.1016 | 2.0 | 3300 | 0.3048 | 0.4824 | 0.6479 | 0.9180 | nan | 0.3595 | 0.8686 | 0.0 | 0.9005 | 0.7879 | 0.9707 | 0.0 | 0.3022 | 0.7380 | 0.0 | 0.8046 | 0.5642 | 0.9675 | | 0.3692 | 2.01 | 3320 | 0.3021 | 0.4761 | 0.6468 | 0.9144 | nan | 0.3537 | 0.8509 | 0.0 | 0.8868 | 0.8186 | 0.9706 | 0.0 | 0.2802 | 0.7268 | 0.0 | 0.8011 | 0.5570 | 0.9672 | | 0.8706 | 2.03 | 3340 | 0.3320 | 0.4796 | 0.6430 | 0.9199 | nan | 0.3116 | 0.9048 | 0.0 | 0.8987 | 0.7727 | 0.9701 | 0.0 | 0.2747 | 0.7327 | 0.0 | 0.8107 | 0.5718 | 0.9674 | | 0.3265 | 2.04 | 3360 | 0.3169 | 0.4765 | 0.6552 | 0.9168 | nan | 0.3639 | 0.9043 | 0.0 | 0.8762 | 0.8171 | 0.9698 | 0.0 | 0.2622 | 0.7622 | 0.0 | 0.7963 | 0.5480 | 0.9671 | | 0.1013 | 2.05 | 3380 | 0.3117 | 0.4774 | 0.6759 | 0.9165 | nan | 0.4382 | 0.9000 | 0.0 | 0.8647 | 0.8830 | 0.9696 | 0.0 | 0.2732 | 0.7770 | 0.0 | 0.7877 | 0.5370 | 0.9668 | | 0.2655 | 2.06 | 3400 | 0.3157 | 0.4852 | 0.6580 | 0.9201 | nan | 0.3775 | 0.8841 | 0.0 | 0.9011 | 0.8152 | 0.9698 | 0.0 | 0.3075 | 0.7750 | 0.0 | 0.7963 | 0.5505 | 0.9673 | | 0.321 | 2.07 | 3420 | 0.2988 | 0.4807 | 0.6601 | 0.9180 | nan | 0.3850 | 0.8639 | 0.0 | 0.8911 | 0.8497 | 0.9709 | 0.0 | 0.2850 | 0.7833 | 0.0 | 0.7893 | 0.5400 | 0.9673 | | 0.1012 | 2.09 | 3440 | 0.3125 | 0.4939 | 0.6538 | 0.9251 | nan | 0.3966 | 0.8959 | 0.0 | 0.9308 | 0.7287 | 0.9706 | 0.0 | 0.3223 | 0.7911 | 0.0 | 0.8092 | 0.5669 | 0.9676 | | 0.412 | 2.1 | 3460 | 0.3296 | 0.4815 | 0.6585 | 0.9182 | nan | 0.3481 | 0.9184 | 0.0 | 0.8726 | 0.8417 | 0.9701 | 0.0 | 0.2909 | 0.7612 | 0.0 | 0.7971 | 0.5547 | 0.9666 | | 0.1383 | 2.11 | 3480 | 0.3082 | 0.4884 | 0.6540 | 0.9225 | nan | 0.3569 | 0.9140 | 0.0 | 0.9056 | 0.7776 | 0.9699 | 0.0 | 0.2999 | 0.7737 | 0.0 | 0.8086 | 0.5698 | 0.9665 | | 0.1925 | 2.12 | 3500 | 0.3206 | 0.4821 | 0.6536 | 0.9182 | nan | 0.3449 | 0.9109 | 0.0 | 0.8844 | 0.8124 | 0.9688 | 0.0 | 0.2829 | 0.7721 | 0.0 | 0.7977 | 0.5564 | 0.9659 | | 0.6483 | 2.13 | 3520 | 0.3155 | 0.4756 | 0.6540 | 0.9136 | nan | 0.3536 | 0.8712 | 0.0 | 0.8698 | 0.8595 | 0.9697 | 0.0 | 0.2857 | 0.7718 | 0.0 | 0.7790 | 0.5269 | 0.9660 | | 0.4534 | 2.15 | 3540 | 0.3218 | 0.4792 | 0.6513 | 0.9170 | nan | 0.3516 | 0.9113 | 0.0 | 0.8804 | 0.7955 | 0.9690 | 0.0 | 0.2800 | 0.7792 | 0.0 | 0.7901 | 0.5392 | 0.9657 | | 0.1899 | 2.16 | 3560 | 0.3131 | 0.4741 | 0.6607 | 0.9143 | nan | 0.3562 | 0.9017 | 0.0 | 0.8519 | 0.8837 | 0.9708 | 0.0 | 0.2942 | 0.7720 | 0.0 | 0.7761 | 0.5095 | 0.9666 | | 0.6685 | 2.17 | 3580 | 0.3230 | 0.4836 | 0.6603 | 0.9195 | nan | 0.3811 | 0.9106 | 0.0 | 0.8880 | 0.8125 | 0.9694 | 0.0 | 0.3074 | 0.7791 | 0.0 | 0.7905 | 0.5417 | 0.9664 | | 0.1743 | 2.18 | 3600 | 0.3060 | 0.4764 | 0.6571 | 0.9157 | nan | 0.3722 | 0.9087 | 0.0 | 0.8655 | 0.8258 | 0.9704 | 0.0 | 0.2614 | 0.7796 | 0.0 | 0.7862 | 0.5412 | 0.9661 | | 0.2988 | 2.2 | 3620 | 0.3150 | 0.4883 | 0.6569 | 0.9234 | nan | 0.3869 | 0.8954 | 0.0 | 0.9154 | 0.7728 | 0.9709 | 0.0 | 0.2894 | 0.7913 | 0.0 | 0.8052 | 0.5647 | 0.9671 | | 0.199 | 2.21 | 3640 | 0.3073 | 0.4799 | 0.6563 | 0.9205 | nan | 0.3823 | 0.9094 | 0.0 | 0.8966 | 0.7794 | 0.9700 | 0.0 | 0.2462 | 0.7752 | 0.0 | 0.8058 | 0.5652 | 0.9668 | | 0.356 | 2.22 | 3660 | 0.3088 | 0.4787 | 0.6539 | 0.9202 | nan | 0.3759 | 0.8945 | 0.0 | 0.9014 | 0.7813 | 0.9701 | 0.0 | 0.2406 | 0.7759 | 0.0 | 0.8051 | 0.5631 | 0.9663 | | 1.2003 | 2.23 | 3680 | 0.3037 | 0.4829 | 0.6551 | 0.9212 | nan | 0.4053 | 0.8855 | 0.0 | 0.9139 | 0.7558 | 0.9701 | 0.0 | 0.2744 | 0.7755 | 0.0 | 0.8075 | 0.5561 | 0.9666 | | 0.1801 | 2.24 | 3700 | 0.3155 | 0.4837 | 0.6570 | 0.9202 | nan | 0.4074 | 0.8924 | 0.0 | 0.9082 | 0.7655 | 0.9688 | 0.0 | 0.3066 | 0.7867 | 0.0 | 0.7995 | 0.5269 | 0.9664 | | 0.2767 | 2.26 | 3720 | 0.3365 | 0.4783 | 0.6767 | 0.9163 | nan | 0.4490 | 0.9065 | 0.0000 | 0.8610 | 0.8735 | 0.9699 | 0.0 | 0.2931 | 0.7931 | 0.0000 | 0.7863 | 0.5086 | 0.9666 | | 0.2641 | 2.27 | 3740 | 0.3103 | 0.4783 | 0.6719 | 0.9153 | nan | 0.4315 | 0.8901 | 0.0 | 0.8631 | 0.8765 | 0.9700 | 0.0 | 0.2993 | 0.7883 | 0.0 | 0.7826 | 0.5114 | 0.9668 | | 0.7382 | 2.28 | 3760 | 0.3684 | 0.4819 | 0.6608 | 0.9156 | nan | 0.3913 | 0.8903 | 0.0 | 0.8832 | 0.8335 | 0.9667 | 0.0 | 0.3122 | 0.7635 | 0.0 | 0.7916 | 0.5410 | 0.9651 | | 0.1887 | 2.29 | 3780 | 0.3380 | 0.4850 | 0.6672 | 0.9190 | nan | 0.3872 | 0.9062 | 0.0 | 0.8795 | 0.8607 | 0.9693 | 0.0 | 0.3182 | 0.7798 | 0.0 | 0.7920 | 0.5385 | 0.9668 | | 0.4301 | 2.3 | 3800 | 0.3126 | 0.4902 | 0.6655 | 0.9219 | nan | 0.4399 | 0.8957 | 0.0 | 0.9040 | 0.7823 | 0.9711 | 0.0 | 0.3320 | 0.7915 | 0.0 | 0.7986 | 0.5423 | 0.9673 | | 0.0796 | 2.32 | 3820 | 0.3078 | 0.4931 | 0.6700 | 0.9231 | nan | 0.4574 | 0.8945 | 0.0 | 0.9082 | 0.7887 | 0.9712 | 0.0 | 0.3244 | 0.7902 | 0.0 | 0.8045 | 0.5654 | 0.9673 | | 0.5856 | 2.33 | 3840 | 0.3280 | 0.4860 | 0.6739 | 0.9186 | nan | 0.4448 | 0.8992 | 0.0 | 0.8803 | 0.8492 | 0.9697 | 0.0 | 0.3209 | 0.7944 | 0.0 | 0.7874 | 0.5328 | 0.9665 | | 0.3133 | 2.34 | 3860 | 0.3306 | 0.4901 | 0.6625 | 0.9220 | nan | 0.4158 | 0.8912 | 0.0 | 0.9104 | 0.7877 | 0.9698 | 0.0 | 0.3321 | 0.7919 | 0.0 | 0.7933 | 0.5461 | 0.9669 | | 0.1759 | 2.35 | 3880 | 0.3318 | 0.4893 | 0.6707 | 0.9209 | nan | 0.4327 | 0.9019 | 0.0 | 0.8910 | 0.8278 | 0.9705 | 0.0 | 0.3309 | 0.7941 | 0.0 | 0.7933 | 0.5402 | 0.9669 | | 0.2173 | 2.37 | 3900 | 0.3227 | 0.4842 | 0.6738 | 0.9186 | nan | 0.4241 | 0.9066 | 0.0 | 0.8627 | 0.8765 | 0.9731 | 0.0 | 0.3222 | 0.7925 | 0.0 | 0.7836 | 0.5238 | 0.9670 | | 0.3338 | 2.38 | 3920 | 0.3146 | 0.4892 | 0.6725 | 0.9208 | nan | 0.4330 | 0.8966 | 0.0 | 0.8909 | 0.8443 | 0.9704 | 0.0 | 0.3364 | 0.7922 | 0.0 | 0.7910 | 0.5381 | 0.9671 | | 0.1098 | 2.39 | 3940 | 0.3051 | 0.4850 | 0.6648 | 0.9186 | nan | 0.4086 | 0.9006 | 0.0 | 0.8823 | 0.8271 | 0.9703 | 0.0 | 0.2986 | 0.7912 | 0.0 | 0.7915 | 0.5466 | 0.9668 | | 0.2892 | 2.4 | 3960 | 0.3277 | 0.4914 | 0.6582 | 0.9235 | nan | 0.4131 | 0.9019 | 0.0 | 0.9224 | 0.7429 | 0.9691 | 0.0 | 0.3010 | 0.7897 | 0.0 | 0.8109 | 0.5718 | 0.9666 | | 0.7496 | 2.41 | 3980 | 0.3239 | 0.4799 | 0.6699 | 0.9166 | nan | 0.4203 | 0.9044 | 0.0000 | 0.8701 | 0.8556 | 0.9690 | 0.0 | 0.2835 | 0.7871 | 0.0000 | 0.7897 | 0.5325 | 0.9666 | | 0.1267 | 2.43 | 4000 | 0.3030 | 0.4849 | 0.6719 | 0.9188 | nan | 0.4334 | 0.8752 | 0.0 | 0.8889 | 0.8636 | 0.9700 | 0.0 | 0.3219 | 0.7904 | 0.0 | 0.7879 | 0.5268 | 0.9670 | | 0.1651 | 2.44 | 4020 | 0.3301 | 0.4801 | 0.6728 | 0.9161 | nan | 0.4176 | 0.8973 | 0.0 | 0.8652 | 0.8877 | 0.9692 | 0.0 | 0.2969 | 0.7938 | 0.0 | 0.7830 | 0.5201 | 0.9669 | | 0.3399 | 2.45 | 4040 | 0.3104 | 0.4728 | 0.6705 | 0.9147 | nan | 0.4008 | 0.8929 | 0.0000 | 0.8517 | 0.9064 | 0.9712 | 0.0 | 0.2694 | 0.7927 | 0.0000 | 0.7785 | 0.5023 | 0.9669 | | 0.399 | 2.46 | 4060 | 0.2932 | 0.4825 | 0.6653 | 0.9207 | nan | 0.4182 | 0.8889 | 0.0000 | 0.8964 | 0.8174 | 0.9711 | 0.0 | 0.2890 | 0.7885 | 0.0000 | 0.7953 | 0.5376 | 0.9672 | | 0.1291 | 2.47 | 4080 | 0.3093 | 0.4793 | 0.6726 | 0.9178 | nan | 0.4242 | 0.8888 | 0.0 | 0.8791 | 0.8745 | 0.9691 | 0.0 | 0.2900 | 0.7911 | 0.0 | 0.7879 | 0.5195 | 0.9669 | | 0.0833 | 2.49 | 4100 | 0.3038 | 0.4784 | 0.6705 | 0.9162 | nan | 0.4206 | 0.8873 | 0.0000 | 0.8697 | 0.8757 | 0.9699 | 0.0 | 0.2837 | 0.7942 | 0.0000 | 0.7854 | 0.5195 | 0.9664 | | 0.4351 | 2.5 | 4120 | 0.3084 | 0.4812 | 0.6710 | 0.9181 | nan | 0.4290 | 0.8976 | 0.0 | 0.8771 | 0.8520 | 0.9702 | 0.0 | 0.2869 | 0.7811 | 0.0 | 0.7942 | 0.5398 | 0.9666 | | 0.6208 | 2.51 | 4140 | 0.3039 | 0.4776 | 0.6523 | 0.9168 | nan | 0.3598 | 0.8827 | 0.0 | 0.8828 | 0.8175 | 0.9710 | 0.0 | 0.2525 | 0.7696 | 0.0 | 0.7989 | 0.5560 | 0.9663 | | 0.3642 | 2.52 | 4160 | 0.3249 | 0.4892 | 0.6676 | 0.9218 | nan | 0.4016 | 0.9010 | 0.0 | 0.8974 | 0.8360 | 0.9699 | 0.0 | 0.3151 | 0.7717 | 0.0 | 0.8056 | 0.5650 | 0.9670 | | 0.6865 | 2.53 | 4180 | 0.3203 | 0.4934 | 0.6659 | 0.9243 | nan | 0.4052 | 0.8954 | 0.0 | 0.9134 | 0.8108 | 0.9708 | 0.0 | 0.3057 | 0.7841 | 0.0 | 0.8130 | 0.5842 | 0.9672 | | 1.0893 | 2.55 | 4200 | 0.2951 | 0.4863 | 0.6613 | 0.9193 | nan | 0.4150 | 0.8548 | 0.0000 | 0.9060 | 0.8212 | 0.9707 | 0.0 | 0.2905 | 0.7699 | 0.0000 | 0.8057 | 0.5707 | 0.9670 | | 0.4914 | 2.56 | 4220 | 0.2988 | 0.4951 | 0.6731 | 0.9224 | nan | 0.4553 | 0.8745 | 0.0000 | 0.9001 | 0.8352 | 0.9734 | 0.0 | 0.3491 | 0.7804 | 0.0000 | 0.8038 | 0.5630 | 0.9690 | | 0.2864 | 2.57 | 4240 | 0.3029 | 0.4946 | 0.6670 | 0.9232 | nan | 0.4448 | 0.8753 | 0.0 | 0.9182 | 0.7926 | 0.9710 | 0.0 | 0.3187 | 0.7765 | 0.0 | 0.8127 | 0.5870 | 0.9673 | | 0.1695 | 2.58 | 4260 | 0.3356 | 0.5014 | 0.6749 | 0.9263 | nan | 0.4545 | 0.8932 | 0.0 | 0.9249 | 0.8066 | 0.9702 | 0.0 | 0.3657 | 0.7799 | 0.0 | 0.8119 | 0.5847 | 0.9676 | | 0.5746 | 2.6 | 4280 | 0.3106 | 0.5024 | 0.6754 | 0.9260 | nan | 0.4610 | 0.8796 | 0.0 | 0.9206 | 0.8187 | 0.9723 | 0.0 | 0.3690 | 0.7837 | 0.0 | 0.8137 | 0.5818 | 0.9683 | | 0.2629 | 2.61 | 4300 | 0.3153 | 0.5020 | 0.6771 | 0.9269 | nan | 0.4768 | 0.9000 | 0.0 | 0.9266 | 0.7890 | 0.9702 | 0.0 | 0.3420 | 0.7826 | 0.0 | 0.8218 | 0.6005 | 0.9671 | | 0.4151 | 2.62 | 4320 | 0.2932 | 0.4957 | 0.6677 | 0.9229 | nan | 0.4382 | 0.8798 | 0.0 | 0.9142 | 0.8031 | 0.9706 | 0.0 | 0.3086 | 0.7840 | 0.0 | 0.8159 | 0.5944 | 0.9668 | | 0.1169 | 2.63 | 4340 | 0.2940 | 0.4995 | 0.6683 | 0.9244 | nan | 0.4620 | 0.8738 | 0.0 | 0.9295 | 0.7744 | 0.9701 | 0.0 | 0.3315 | 0.7860 | 0.0 | 0.8161 | 0.5960 | 0.9668 | | 0.1379 | 2.64 | 4360 | 0.2958 | 0.5047 | 0.6750 | 0.9264 | nan | 0.4636 | 0.8776 | 0.0000 | 0.9138 | 0.8195 | 0.9754 | 0.0 | 0.3667 | 0.7920 | 0.0000 | 0.8160 | 0.5891 | 0.9689 | | 0.1106 | 2.66 | 4380 | 0.3271 | 0.5063 | 0.6866 | 0.9278 | nan | 0.5061 | 0.9005 | 0.0 | 0.9251 | 0.8175 | 0.9702 | 0.0 | 0.3773 | 0.7976 | 0.0 | 0.8143 | 0.5878 | 0.9673 | | 0.6561 | 2.67 | 4400 | 0.3072 | 0.4820 | 0.6689 | 0.9179 | nan | 0.4274 | 0.9043 | 0.0001 | 0.8736 | 0.8372 | 0.9707 | 0.0 | 0.2704 | 0.7896 | 0.0001 | 0.7980 | 0.5493 | 0.9665 | | 1.0281 | 2.68 | 4420 | 0.3102 | 0.4815 | 0.6878 | 0.9179 | nan | 0.4964 | 0.9069 | 0.0000 | 0.8569 | 0.8942 | 0.9721 | 0.0 | 0.3072 | 0.7945 | 0.0000 | 0.7899 | 0.5115 | 0.9676 | | 0.2507 | 2.69 | 4440 | 0.3143 | 0.4839 | 0.6758 | 0.9200 | nan | 0.4543 | 0.9103 | 0.0 | 0.8806 | 0.8390 | 0.9704 | 0.0 | 0.2914 | 0.7904 | 0.0 | 0.8009 | 0.5380 | 0.9668 | | 1.8848 | 2.7 | 4460 | 0.3204 | 0.4827 | 0.6763 | 0.9186 | nan | 0.4533 | 0.9111 | 0.0000 | 0.8766 | 0.8475 | 0.9690 | 0.0 | 0.2813 | 0.7838 | 0.0000 | 0.8023 | 0.5453 | 0.9664 | | 0.2177 | 2.72 | 4480 | 0.3152 | 0.4841 | 0.6658 | 0.9196 | nan | 0.4292 | 0.8986 | 0.0 | 0.8922 | 0.8050 | 0.9699 | 0.0 | 0.2702 | 0.7881 | 0.0 | 0.8063 | 0.5579 | 0.9666 | | 0.2683 | 2.73 | 4500 | 0.3165 | 0.4810 | 0.6710 | 0.9176 | nan | 0.4378 | 0.9003 | 0.0 | 0.8797 | 0.8395 | 0.9687 | 0.0 | 0.2755 | 0.7847 | 0.0 | 0.7996 | 0.5408 | 0.9661 | | 0.4168 | 2.74 | 4520 | 0.3347 | 0.4920 | 0.6748 | 0.9233 | nan | 0.4527 | 0.9122 | 0.0 | 0.9021 | 0.8121 | 0.9698 | 0.0 | 0.3271 | 0.7776 | 0.0 | 0.8105 | 0.5616 | 0.9670 | | 0.89 | 2.75 | 4540 | 0.3345 | 0.4875 | 0.6709 | 0.9211 | nan | 0.4383 | 0.9006 | 0.0 | 0.8985 | 0.8189 | 0.9690 | 0.0 | 0.2903 | 0.7895 | 0.0 | 0.8071 | 0.5591 | 0.9666 | | 0.3392 | 2.77 | 4560 | 0.3020 | 0.4852 | 0.6730 | 0.9197 | nan | 0.4509 | 0.8977 | 0.0 | 0.8886 | 0.8312 | 0.9698 | 0.0 | 0.2847 | 0.7921 | 0.0 | 0.8024 | 0.5506 | 0.9665 | | 0.4607 | 2.78 | 4580 | 0.3230 | 0.4935 | 0.6757 | 0.9238 | nan | 0.4640 | 0.9097 | 0.0 | 0.9075 | 0.8036 | 0.9696 | 0.0 | 0.3195 | 0.7866 | 0.0 | 0.8116 | 0.5697 | 0.9670 | | 0.3654 | 2.79 | 4600 | 0.3109 | 0.4928 | 0.6672 | 0.9231 | nan | 0.4508 | 0.8907 | 0.0 | 0.9169 | 0.7750 | 0.9698 | 0.0 | 0.3078 | 0.7942 | 0.0 | 0.8102 | 0.5704 | 0.9670 | | 0.4694 | 2.8 | 4620 | 0.3250 | 0.4919 | 0.6806 | 0.9221 | nan | 0.4773 | 0.9033 | 0.0 | 0.8870 | 0.8435 | 0.9722 | 0.0 | 0.3328 | 0.7919 | 0.0 | 0.8015 | 0.5489 | 0.9679 | | 0.7987 | 2.81 | 4640 | 0.3292 | 0.4954 | 0.6686 | 0.9240 | nan | 0.4267 | 0.8898 | 0.0 | 0.9178 | 0.8078 | 0.9696 | 0.0 | 0.3340 | 0.7982 | 0.0 | 0.8063 | 0.5622 | 0.9672 | | 0.1422 | 2.83 | 4660 | 0.3098 | 0.4980 | 0.6793 | 0.9252 | nan | 0.4912 | 0.9051 | 0.0 | 0.9137 | 0.7954 | 0.9706 | 0.0 | 0.3358 | 0.7895 | 0.0 | 0.8146 | 0.5789 | 0.9674 | | 0.2764 | 2.84 | 4680 | 0.2950 | 0.4955 | 0.6780 | 0.9236 | nan | 0.4777 | 0.8876 | 0.0 | 0.9065 | 0.8249 | 0.9715 | 0.0 | 0.3347 | 0.7929 | 0.0 | 0.8095 | 0.5634 | 0.9678 | | 0.7027 | 2.85 | 4700 | 0.3178 | 0.4956 | 0.6811 | 0.9237 | nan | 0.4798 | 0.9043 | 0.0 | 0.9009 | 0.8310 | 0.9708 | 0.0 | 0.3432 | 0.7879 | 0.0 | 0.8083 | 0.5622 | 0.9676 | | 0.1181 | 2.86 | 4720 | 0.3131 | 0.4926 | 0.6812 | 0.9229 | nan | 0.4841 | 0.9019 | 0.0 | 0.8987 | 0.8317 | 0.9705 | 0.0 | 0.3250 | 0.7894 | 0.0 | 0.8072 | 0.5593 | 0.9673 | | 0.1929 | 2.87 | 4740 | 0.3345 | 0.4983 | 0.6796 | 0.9246 | nan | 0.4671 | 0.9066 | 0.0 | 0.9087 | 0.8258 | 0.9697 | 0.0 | 0.3638 | 0.7872 | 0.0 | 0.8075 | 0.5620 | 0.9673 | | 0.1227 | 2.89 | 4760 | 0.3008 | 0.4923 | 0.6766 | 0.9215 | nan | 0.4699 | 0.8828 | 0.0 | 0.8972 | 0.8386 | 0.9714 | 0.0 | 0.3415 | 0.7942 | 0.0 | 0.8013 | 0.5408 | 0.9681 | | 0.2807 | 2.9 | 4780 | 0.3065 | 0.4867 | 0.6674 | 0.9185 | nan | 0.4280 | 0.8885 | 0.0 | 0.8864 | 0.8315 | 0.9701 | 0.0 | 0.3093 | 0.7890 | 0.0 | 0.7973 | 0.5438 | 0.9672 | | 0.1685 | 2.91 | 4800 | 0.3054 | 0.4890 | 0.6712 | 0.9206 | nan | 0.4508 | 0.8940 | 0.0 | 0.8947 | 0.8176 | 0.9704 | 0.0 | 0.3112 | 0.7901 | 0.0 | 0.8020 | 0.5527 | 0.9672 | | 0.2884 | 2.92 | 4820 | 0.3072 | 0.4896 | 0.6770 | 0.9201 | nan | 0.4773 | 0.8843 | 0.0 | 0.8932 | 0.8372 | 0.9703 | 0.0 | 0.3251 | 0.7914 | 0.0 | 0.7997 | 0.5440 | 0.9673 | | 0.6886 | 2.94 | 4840 | 0.3077 | 0.4936 | 0.6643 | 0.9225 | nan | 0.4469 | 0.8662 | 0.0 | 0.9228 | 0.7796 | 0.9704 | 0.0 | 0.3339 | 0.7868 | 0.0 | 0.8057 | 0.5614 | 0.9676 | | 0.3344 | 2.95 | 4860 | 0.3017 | 0.4943 | 0.6733 | 0.9225 | nan | 0.4660 | 0.8746 | 0.0 | 0.9098 | 0.8184 | 0.9711 | 0.0 | 0.3488 | 0.7900 | 0.0 | 0.8032 | 0.5498 | 0.9681 | | 0.1385 | 2.96 | 4880 | 0.3149 | 0.4846 | 0.6688 | 0.9180 | nan | 0.4436 | 0.8811 | 0.0000 | 0.8905 | 0.8288 | 0.9692 | 0.0 | 0.2956 | 0.7873 | 0.0000 | 0.7986 | 0.5441 | 0.9667 | | 0.559 | 2.97 | 4900 | 0.3012 | 0.4827 | 0.6598 | 0.9172 | nan | 0.4204 | 0.8584 | 0.0000 | 0.8997 | 0.8108 | 0.9693 | 0.0 | 0.2854 | 0.7789 | 0.0000 | 0.7992 | 0.5489 | 0.9666 | | 0.0576 | 2.98 | 4920 | 0.3116 | 0.4880 | 0.6794 | 0.9200 | nan | 0.4841 | 0.8852 | 0.0 | 0.8866 | 0.8489 | 0.9714 | 0.0 | 0.3229 | 0.7898 | 0.0 | 0.7986 | 0.5372 | 0.9678 | | 0.1294 | 3.0 | 4940 | 0.3304 | 0.4794 | 0.6650 | 0.9144 | nan | 0.4315 | 0.8895 | 0.0 | 0.8740 | 0.8271 | 0.9678 | 0.0 | 0.2745 | 0.7784 | 0.0 | 0.7930 | 0.5438 | 0.9658 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

HHJingbo/Bo

HHJingbo

2023-11-03T20:17:13Z

4

0

transformers

[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased-distilled-squad", "base_model:finetune:distilbert/distilbert-base-uncased-distilled-squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-11-03T14:24:39Z

--- license: apache-2.0 base_model: distilbert-base-uncased-distilled-squad tags: - generated_from_keras_callback model-index: - name: HHJingbo/Bo results: [] ---  # HHJingbo/Bo This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3635 - Validation Loss: 0.5201 - 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.5642 | 0.5034 | 0 | | 0.3635 | 0.5201 | 1 | ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1

SantiRimedio/LuisAlBERTo

SantiRimedio

2023-11-03T20:13:36Z

4

1

transformers

[ "transformers", "pytorch", "distilbert", "fill-mask", "es", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-10-17T20:29:38Z

--- language: - es pipeline_tag: fill-mask ---

LoneStriker/openchat_3.5-3.0bpw-h6-exl2

LoneStriker

2023-11-03T20:11:53Z

7

0

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T19:29:50Z

--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. **: Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. ***: All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.

LoneStriker/openchat_3.5-5.0bpw-h6-exl2

LoneStriker

2023-11-03T20:11:26Z

6

0

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T19:53:08Z

--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. **: Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. ***: All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.

LoneStriker/openchat_3.5-6.0bpw-h6-exl2

LoneStriker

2023-11-03T20:11:14Z

6

0

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T20:03:37Z

--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. **: Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. ***: All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.

LoneStriker/openchat_3.5-8.0bpw-h8-exl2

LoneStriker

2023-11-03T20:10:36Z

12

3

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-02T20:28:33Z

--- license: apache-2.0 --- # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> **🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥** **🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖** <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> | Model | Size | Context | Weights | Serving | |--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| | OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` | For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks | Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K | |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------| | OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** | | ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 | | Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 | | Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 | | | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B | *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. **: Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. ***: All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations **Foundation Model Limitations** Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges **Hallucination of Non-existent Information** OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. **Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.

jdpressman/minihf_evaluator_mistral_7b_v0.1

jdpressman

2023-11-03T20:10:23Z

9

0

peft

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

null

2023-11-03T20:07:38Z

--- library_name: peft license: apache-2.0 --- # minihf_evaluator_mistral_7b_v0.1 `minihf_evaluator_mistral_7b_v0.1` is a LoRA instruct fine-tune of [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). The sequence `<|end|>` was used to separate the prompt and response. The correct way to prompt the model is: `Does 2 + 2 = 4?<|end|>`. The tokenizer will prepend a BOS token (`<s>`) by default. The response will end with an EOS token (`</s>`). ## Training procedure `minihf_evaluator_mistral_7b_v0.1` was fine-tuned for 100,000 examples on 90% [Muennighoff/flan](https://huggingface.co/datasets/Muennighoff/flan) / 10% [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) using batch size 4 per GPU on 8 80GB H100 GPUs. Examples where the prompt and response would not fit into 4096 tokens were dropped. The fine-tuning was done using the following command: ```bash accelerate launch sft_evaluator.py --output-dir minihf_evaluator_mistral_7b_v0.1 ``` 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0 - PEFT 0.5.0

owanr/Sentiment-google-t5-v1_1-large-inter_model-shuffle-human_annots_str

owanr

2023-11-03T20:07:02Z

0

null

[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]

null

2023-11-03T19:48:12Z

--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: Sentiment-google-t5-v1_1-large-inter_model-shuffle-human_annots_str results: [] ---  # Sentiment-google-t5-v1_1-large-inter_model-shuffle-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3838 ## 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: 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 20.9707 | 1.0 | 44 | 24.0812 | | 19.1584 | 2.0 | 88 | 19.1069 | | 14.7114 | 3.0 | 132 | 12.0456 | | 11.3751 | 4.0 | 176 | 10.9758 | | 10.1468 | 5.0 | 220 | 10.7621 | | 10.0067 | 6.0 | 264 | 10.6808 | | 10.0007 | 7.0 | 308 | 10.5435 | | 9.7079 | 8.0 | 352 | 10.1514 | | 9.182 | 9.0 | 396 | 9.5931 | | 8.7756 | 10.0 | 440 | 9.2557 | | 8.6941 | 11.0 | 484 | 9.0992 | | 8.6156 | 12.0 | 528 | 8.9849 | | 8.1186 | 13.0 | 572 | 8.7739 | | 1.4193 | 14.0 | 616 | 1.3089 | | 1.3333 | 15.0 | 660 | 1.3014 | | 1.3499 | 16.0 | 704 | 1.3009 | | 1.3384 | 17.0 | 748 | 1.2995 | | 1.332 | 18.0 | 792 | 1.2958 | | 1.3226 | 19.0 | 836 | 1.3095 | | 1.318 | 20.0 | 880 | 1.3012 | | 1.3192 | 21.0 | 924 | 1.2978 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1

Joetib/pythia-finetuned-with-context-5-steps

Joetib

2023-11-03T19:34:40Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-v0", "base_model:finetune:EleutherAI/pythia-410m-v0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T19:31:40Z

--- license: apache-2.0 base_model: EleutherAI/pythia-410M tags: - generated_from_trainer model-index: - name: pythia-finetuned-with-context-5-steps results: [] ---  # pythia-finetuned-with-context-5-steps This model is a fine-tuned version of [EleutherAI/pythia-410M](https://huggingface.co/EleutherAI/pythia-410M) on the None 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 5 ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

Joetib/pythia-finetuned-5-steps

Joetib

2023-11-03T19:27:54Z

16

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-410m-v0", "base_model:finetune:EleutherAI/pythia-410m-v0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T19:13:03Z

--- license: apache-2.0 base_model: EleutherAI/pythia-410M tags: - generated_from_trainer model-index: - name: pythia-finetuned-5-steps results: [] ---  # pythia-finetuned-5-steps This model is a fine-tuned version of [EleutherAI/pythia-410M](https://huggingface.co/EleutherAI/pythia-410M) on the None 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 5 ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

Zac-Nguyen/orca-cot

Zac-Nguyen

2023-11-03T19:24:59Z

5

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T18:07:18Z

--- license: other language: - en --- An improved, potentially even perfected variant of MythoMix, my [MythoLogic-L2](https://huggingface.co/Gryphe/MythoLogic-L2-13b) and [Huginn](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) merge using a highly experimental tensor type merge technique. The main difference with MythoMix is that I allowed more of Huginn to intermingle with the single tensors located at the front and end of a model, resulting in increased coherency across the entire structure. The script and the acccompanying templates I used to produce both can [be found here](https://github.com/Gryphe/BlockMerge_Gradient/tree/main/YAML). This model is proficient at both roleplaying and storywriting due to its unique nature. Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) (You're the best!) ## Model details The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time) This type of merge is incapable of being illustrated, as each of its 363 tensors had an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour. ## Prompt Format This model primarily uses Alpaca formatting, so for optimal model performance, use: ``` <System prompt/Character Card> ### Instruction: Your instruction or question here. For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only. ### Response: ``` --- license: other ---

mi-rei/my_awesome_model

mi-rei

2023-11-03T19:24:50Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T17:19:11Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] ---  # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6756 - Accuracy: 0.5681 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6883 | 1.0 | 1394 | 0.6795 | 0.5526 | | 0.6687 | 2.0 | 2788 | 0.6756 | 0.5681 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.13.3

hpandana/poca-SoccerTwos

hpandana

2023-11-03T19:21:43Z

0

ml-agents

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

reinforcement-learning

2023-11-03T19:21:33Z

--- 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: hpandana/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

YoungMeng/Taxi-v3

YoungMeng

2023-11-03T19:02:11Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-11-02T04:00:11Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.78 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="YoungMeng/Taxi-v3", 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"]) ```

btqa/btqa-base

btqa

2023-11-03T18:50:57Z

0

peft

[ "peft", "pytorch", "btlm", "generated_from_trainer", "custom_code", "dataset:iarfmoose/question_generator", "dataset:Defalt-404/Bittensor_validator", "dataset:multi_news", "dataset:cnn_dailymail", "base_model:cerebras/btlm-3b-8k-base", "base_model:adapter:cerebras/btlm-3b-8k-base", "license:apache-2.0", "region:us" ]

null

2023-11-02T14:41:34Z

--- license: apache-2.0 base_model: cerebras/btlm-3b-8k-base tags: - generated_from_trainer model-index: - name: app/bt_qa-out results: [] datasets: - iarfmoose/question_generator - Defalt-404/Bittensor_validator - multi_news - cnn_dailymail library_name: peft ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) # app/bt_qa-out This model is a fine-tuned version of [cerebras/btlm-3b-8k-base](https://huggingface.co/cerebras/btlm-3b-8k-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7451 ## 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: 8.5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 32 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.7377 | 0.0 | 500 | 2.5700 | | 2.5689 | 0.01 | 1000 | 2.4172 | | 2.6676 | 0.01 | 1500 | 2.3622 | | 2.4234 | 0.01 | 2000 | 2.3293 | | 2.2221 | 0.01 | 2500 | 2.3090 | | 2.3873 | 0.02 | 3000 | 2.2940 | | 2.2688 | 0.02 | 3500 | 2.2644 | | 2.4194 | 0.02 | 4000 | 2.2725 | | 2.4086 | 0.03 | 4500 | 2.2561 | | 2.4476 | 0.03 | 5000 | 2.2477 | | 2.1512 | 0.03 | 5500 | 2.2330 | | 2.1428 | 0.03 | 6000 | 2.2235 | | 2.2834 | 0.04 | 6500 | 2.2141 | | 2.2918 | 0.04 | 7000 | 2.2124 | | 2.4352 | 0.04 | 7500 | 2.2074 | | 1.7196 | 0.05 | 8000 | 2.2038 | | 2.2394 | 0.05 | 8500 | 2.1973 | | 2.1632 | 0.05 | 9000 | 2.1856 | | 2.4313 | 0.06 | 9500 | 2.1820 | | 2.4584 | 0.06 | 10000 | 2.1764 | | 2.3359 | 0.06 | 10500 | 2.1780 | | 2.2105 | 0.06 | 11000 | 2.1671 | | 2.3152 | 0.07 | 11500 | 2.1603 | | 2.3012 | 0.07 | 12000 | 2.1572 | | 2.4636 | 0.07 | 12500 | 2.1553 | | 2.0974 | 0.08 | 13000 | 2.1511 | | 2.298 | 0.08 | 13500 | 2.1481 | | 2.3312 | 0.08 | 14000 | 2.1445 | | 2.5315 | 0.08 | 14500 | 2.1381 | | 2.1854 | 0.09 | 15000 | 2.1364 | | 2.3069 | 0.09 | 15500 | 2.1355 | | 2.0756 | 0.09 | 16000 | 2.1331 | | 2.0094 | 0.1 | 16500 | 2.1306 | | 2.2674 | 0.1 | 17000 | 2.1230 | | 1.8427 | 0.1 | 17500 | 2.1176 | | 2.2277 | 0.1 | 18000 | 2.1168 | | 2.1398 | 0.11 | 18500 | 2.1152 | | 1.9927 | 0.11 | 19000 | 2.1088 | | 2.0119 | 0.11 | 19500 | 2.1105 | | 2.5796 | 0.12 | 20000 | 2.1040 | | 1.3256 | 0.12 | 20500 | 2.0993 | | 2.2051 | 0.12 | 21000 | 2.0992 | | 1.628 | 0.13 | 21500 | 2.0944 | | 2.1926 | 0.13 | 22000 | 2.0927 | | 1.6482 | 0.13 | 22500 | 2.0873 | | 2.1122 | 0.13 | 23000 | 2.0830 | | 1.7405 | 0.14 | 23500 | 2.0828 | | 2.2685 | 0.14 | 24000 | 2.0784 | | 2.1062 | 0.14 | 24500 | 2.0766 | | 2.1308 | 0.15 | 25000 | 2.0714 | | 1.9122 | 0.15 | 25500 | 2.0719 | | 2.3549 | 0.15 | 26000 | 2.0643 | | 2.2159 | 0.15 | 26500 | 2.0655 | | 1.493 | 0.16 | 27000 | 2.0598 | | 1.893 | 0.16 | 27500 | 2.0557 | | 2.1902 | 0.16 | 28000 | 2.0533 | | 2.2353 | 0.17 | 28500 | 2.0524 | | 1.8736 | 0.17 | 29000 | 2.0519 | | 2.0511 | 0.17 | 29500 | 2.0449 | | 1.2872 | 0.17 | 30000 | 2.0453 | | 1.6353 | 0.18 | 30500 | 2.0377 | | 1.992 | 0.18 | 31000 | 2.0419 | | 2.3586 | 0.18 | 31500 | 2.0353 | | 1.9453 | 0.19 | 32000 | 2.0330 | | 2.1322 | 0.19 | 32500 | 2.0305 | | 2.2887 | 0.19 | 33000 | 2.0253 | | 2.0268 | 0.2 | 33500 | 2.0267 | | 1.8397 | 0.2 | 34000 | 2.0207 | | 2.5165 | 0.2 | 34500 | 2.0202 | | 1.9142 | 0.2 | 35000 | 2.0139 | | 1.5993 | 0.21 | 35500 | 2.0179 | | 2.1691 | 0.21 | 36000 | 2.0102 | | 2.4948 | 0.21 | 36500 | 2.0089 | | 1.5422 | 0.22 | 37000 | 2.0039 | | 1.4566 | 0.22 | 37500 | 2.0014 | | 1.852 | 0.22 | 38000 | 2.0043 | | 2.199 | 0.22 | 38500 | 1.9987 | | 1.4852 | 0.23 | 39000 | 1.9976 | | 1.3 | 0.23 | 39500 | 1.9936 | | 2.1237 | 0.23 | 40000 | 1.9917 | | 1.691 | 0.24 | 40500 | 1.9887 | | 2.2169 | 0.24 | 41000 | 1.9870 | | 2.1991 | 0.24 | 41500 | 1.9851 | | 1.9517 | 0.24 | 42000 | 1.9806 | | 1.6369 | 0.25 | 42500 | 1.9762 | | 2.2759 | 0.25 | 43000 | 1.9753 | | 2.2923 | 0.25 | 43500 | 1.9748 | | 2.2552 | 0.26 | 44000 | 1.9702 | | 2.066 | 0.26 | 44500 | 1.9683 | | 2.2703 | 0.26 | 45000 | 1.9686 | | 2.3544 | 0.27 | 45500 | 1.9648 | | 2.255 | 0.27 | 46000 | 1.9635 | | 1.8732 | 0.27 | 46500 | 1.9639 | | 2.1203 | 0.27 | 47000 | 1.9590 | | 2.1314 | 0.28 | 47500 | 1.9573 | | 1.8511 | 0.28 | 48000 | 1.9533 | | 2.1471 | 0.28 | 48500 | 1.9514 | | 1.8417 | 0.29 | 49000 | 1.9509 | | 2.4485 | 0.29 | 49500 | 1.9502 | | 2.0708 | 0.29 | 50000 | 1.9455 | | 1.8272 | 0.29 | 50500 | 1.9416 | | 1.6232 | 0.3 | 51000 | 1.9380 | | 1.6785 | 0.3 | 51500 | 1.9358 | | 1.5734 | 0.3 | 52000 | 1.9313 | | 1.9737 | 0.31 | 52500 | 1.9301 | | 1.8393 | 0.31 | 53000 | 1.9295 | | 1.4789 | 0.31 | 53500 | 1.9281 | | 2.2062 | 0.31 | 54000 | 1.9273 | | 2.3501 | 0.32 | 54500 | 1.9236 | | 2.2756 | 0.32 | 55000 | 1.9218 | | 2.1001 | 0.32 | 55500 | 1.9215 | | 2.0342 | 0.33 | 56000 | 1.9179 | | 1.8066 | 0.33 | 56500 | 1.9143 | | 1.8322 | 0.33 | 57000 | 1.9137 | | 2.0926 | 0.34 | 57500 | 1.9106 | | 2.2106 | 0.34 | 58000 | 1.9083 | | 2.0666 | 0.34 | 58500 | 1.9055 | | 2.2082 | 0.34 | 59000 | 1.9026 | | 2.1768 | 0.35 | 59500 | 1.9007 | | 1.7091 | 0.35 | 60000 | 1.8967 | | 1.7585 | 0.35 | 60500 | 1.8946 | | 1.8968 | 0.36 | 61000 | 1.8936 | | 2.107 | 0.36 | 61500 | 1.8906 | | 1.5162 | 0.36 | 62000 | 1.8870 | | 2.0642 | 0.36 | 62500 | 1.8836 | | 2.0399 | 0.37 | 63000 | 1.8813 | | 2.3971 | 0.37 | 63500 | 1.8785 | | 1.7433 | 0.37 | 64000 | 1.8797 | | 2.0971 | 0.38 | 64500 | 1.8743 | | 1.8212 | 0.38 | 65000 | 1.8726 | | 2.1023 | 0.38 | 65500 | 1.8695 | | 1.9735 | 0.38 | 66000 | 1.8674 | | 1.3196 | 0.39 | 66500 | 1.8657 | | 1.9825 | 0.39 | 67000 | 1.8629 | | 2.0356 | 0.39 | 67500 | 1.8604 | | 1.8522 | 0.4 | 68000 | 1.8581 | | 2.2666 | 0.4 | 68500 | 1.8568 | | 2.3575 | 0.4 | 69000 | 1.8538 | | 2.0086 | 0.41 | 69500 | 1.8537 | | 1.9811 | 0.41 | 70000 | 1.8512 | | 2.0702 | 0.41 | 70500 | 1.8485 | | 1.8554 | 0.41 | 71000 | 1.8456 | | 0.5356 | 0.42 | 71500 | 1.8437 | | 1.4742 | 0.42 | 72000 | 1.8413 | | 2.1901 | 0.42 | 72500 | 1.8420 | | 1.7868 | 0.43 | 73000 | 1.8383 | | 1.3144 | 0.43 | 73500 | 1.8371 | | 2.1158 | 0.43 | 74000 | 1.8347 | | 2.0779 | 0.43 | 74500 | 1.8331 | | 1.9756 | 0.44 | 75000 | 1.8323 | | 2.3395 | 0.44 | 75500 | 1.8309 | | 1.895 | 0.44 | 76000 | 1.8283 | | 2.0369 | 0.45 | 76500 | 1.8274 | | 1.8068 | 0.45 | 77000 | 1.8251 | | 2.2153 | 0.45 | 77500 | 1.8227 | | 2.1389 | 0.45 | 78000 | 1.8212 | | 1.9166 | 0.46 | 78500 | 1.8197 | | 1.711 | 0.46 | 79000 | 1.8187 | | 1.9102 | 0.46 | 79500 | 1.8165 | | 0.8358 | 0.47 | 80000 | 1.8163 | | 1.7278 | 0.47 | 80500 | 1.8148 | | 1.601 | 0.47 | 81000 | 1.8126 | | 1.9794 | 0.48 | 81500 | 1.8107 | | 1.7323 | 0.48 | 82000 | 1.8095 | | 2.2911 | 0.48 | 82500 | 1.8090 | | 1.8962 | 0.48 | 83000 | 1.8065 | | 2.3055 | 0.49 | 83500 | 1.8052 | | 1.6899 | 0.49 | 84000 | 1.8037 | | 1.6409 | 0.49 | 84500 | 1.8031 | | 1.9116 | 0.5 | 85000 | 1.8011 | | 0.6875 | 0.5 | 85500 | 1.8003 | | 2.0829 | 0.5 | 86000 | 1.7983 | | 1.5716 | 0.5 | 86500 | 1.7981 | | 2.4537 | 0.51 | 87000 | 1.7961 | | 1.8236 | 0.51 | 87500 | 1.7942 | | 1.641 | 0.51 | 88000 | 1.7931 | | 1.5533 | 0.52 | 88500 | 1.7916 | | 1.679 | 0.52 | 89000 | 1.7902 | | 2.1463 | 0.52 | 89500 | 1.7893 | | 1.5477 | 0.52 | 90000 | 1.7884 | | 1.2346 | 0.53 | 90500 | 1.7873 | | 1.3352 | 0.53 | 91000 | 1.7859 | | 2.1039 | 0.53 | 91500 | 1.7850 | | 2.0818 | 0.54 | 92000 | 1.7834 | | 1.3987 | 0.54 | 92500 | 1.7830 | | 1.4544 | 0.54 | 93000 | 1.7827 | | 0.4043 | 0.55 | 93500 | 1.7811 | | 2.0149 | 0.55 | 94000 | 1.7794 | | 1.9845 | 0.55 | 94500 | 1.7789 | | 2.1053 | 0.55 | 95000 | 1.7775 | | 2.1572 | 0.56 | 95500 | 1.7768 | | 2.0754 | 0.56 | 96000 | 1.7761 | | 1.7675 | 0.56 | 96500 | 1.7754 | | 2.0023 | 0.57 | 97000 | 1.7743 | | 1.2653 | 0.57 | 97500 | 1.7736 | | 1.5566 | 0.57 | 98000 | 1.7728 | | 1.9408 | 0.57 | 98500 | 1.7724 | | 2.0936 | 0.58 | 99000 | 1.7713 | | 0.5687 | 0.58 | 99500 | 1.7706 | | 2.2833 | 0.58 | 100000 | 1.7702 | | 1.6689 | 0.59 | 100500 | 1.7690 | | 1.5198 | 0.59 | 101000 | 1.7684 | | 1.6968 | 0.59 | 101500 | 1.7679 | | 2.2034 | 0.59 | 102000 | 1.7674 | | 1.7902 | 0.6 | 102500 | 1.7665 | | 2.0557 | 0.6 | 103000 | 1.7658 | | 1.8617 | 0.6 | 103500 | 1.7650 | | 1.8749 | 0.61 | 104000 | 1.7637 | | 1.7674 | 0.61 | 104500 | 1.7632 | | 1.4269 | 0.61 | 105000 | 1.7627 | | 1.989 | 0.62 | 105500 | 1.7621 | | 2.1026 | 0.62 | 106000 | 1.7615 | | 2.0304 | 0.62 | 106500 | 1.7609 | | 1.6286 | 0.62 | 107000 | 1.7603 | | 0.9544 | 0.63 | 107500 | 1.7599 | | 1.6421 | 0.63 | 108000 | 1.7588 | | 1.9841 | 0.63 | 108500 | 1.7586 | | 1.7453 | 0.64 | 109000 | 1.7581 | | 1.2119 | 0.64 | 109500 | 1.7575 | | 2.1092 | 0.64 | 110000 | 1.7568 | | 2.0849 | 0.64 | 110500 | 1.7564 | | 1.9162 | 0.65 | 111000 | 1.7562 | | 1.01 | 0.65 | 111500 | 1.7560 | | 1.301 | 0.65 | 112000 | 1.7556 | | 0.315 | 0.66 | 112500 | 1.7552 | | 1.9964 | 0.66 | 113000 | 1.7548 | | 2.4035 | 0.66 | 113500 | 1.7544 | | 1.3559 | 0.66 | 114000 | 1.7542 | | 2.1874 | 0.67 | 114500 | 1.7538 | | 1.4373 | 0.67 | 115000 | 1.7534 | | 0.0639 | 0.67 | 115500 | 1.7529 | | 1.7667 | 0.68 | 116000 | 1.7526 | | 1.6204 | 0.68 | 116500 | 1.7524 | | 1.9859 | 0.68 | 117000 | 1.7521 | | 0.9717 | 0.69 | 117500 | 1.7516 | | 1.8844 | 0.69 | 118000 | 1.7514 | | 1.3336 | 0.69 | 118500 | 1.7509 | | 1.5781 | 0.69 | 119000 | 1.7506 | | 1.8449 | 0.7 | 119500 | 1.7505 | | 1.5305 | 0.7 | 120000 | 1.7503 | | 2.1904 | 0.7 | 120500 | 1.7500 | | 2.2285 | 0.71 | 121000 | 1.7496 | | 1.8097 | 0.71 | 121500 | 1.7494 | | 2.3631 | 0.71 | 122000 | 1.7493 | | 2.0893 | 0.71 | 122500 | 1.7491 | | 2.1201 | 0.72 | 123000 | 1.7489 | | 1.8334 | 0.72 | 123500 | 1.7488 | | 2.0222 | 0.72 | 124000 | 1.7486 | | 1.6339 | 0.73 | 124500 | 1.7484 | | 1.6754 | 0.73 | 125000 | 1.7482 | | 1.3973 | 0.73 | 125500 | 1.7480 | | 2.0594 | 0.73 | 126000 | 1.7479 | | 1.8674 | 0.74 | 126500 | 1.7478 | | 2.1948 | 0.74 | 127000 | 1.7476 | | 1.4148 | 0.74 | 127500 | 1.7475 | | 1.6734 | 0.75 | 128000 | 1.7473 | | 2.2787 | 0.75 | 128500 | 1.7472 | | 1.8999 | 0.75 | 129000 | 1.7471 | | 1.6945 | 0.76 | 129500 | 1.7470 | | 2.0165 | 0.76 | 130000 | 1.7469 | | 2.2232 | 0.76 | 130500 | 1.7468 | | 1.6201 | 0.76 | 131000 | 1.7466 | | 2.4878 | 0.77 | 131500 | 1.7465 | | 1.5317 | 0.77 | 132000 | 1.7465 | | 1.9361 | 0.77 | 132500 | 1.7464 | | 1.7127 | 0.78 | 133000 | 1.7463 | | 1.7045 | 0.78 | 133500 | 1.7462 | | 2.1827 | 0.78 | 134000 | 1.7461 | | 2.0534 | 0.78 | 134500 | 1.7461 | | 2.0808 | 0.79 | 135000 | 1.7460 | | 1.9572 | 0.79 | 135500 | 1.7459 | | 1.8762 | 0.79 | 136000 | 1.7459 | | 1.4686 | 0.8 | 136500 | 1.7458 | | 1.6241 | 0.8 | 137000 | 1.7458 | | 1.4219 | 0.8 | 137500 | 1.7457 | | 2.1605 | 0.8 | 138000 | 1.7457 | | 2.1298 | 0.81 | 138500 | 1.7456 | | 1.414 | 0.81 | 139000 | 1.7456 | | 1.0115 | 0.81 | 139500 | 1.7455 | | 1.9471 | 0.82 | 140000 | 1.7455 | | 1.8873 | 0.82 | 140500 | 1.7455 | | 1.8286 | 0.82 | 141000 | 1.7454 | | 2.1418 | 0.83 | 141500 | 1.7454 | | 1.9755 | 0.83 | 142000 | 1.7454 | | 1.6908 | 0.83 | 142500 | 1.7454 | | 2.3842 | 0.83 | 143000 | 1.7453 | | 1.7665 | 0.84 | 143500 | 1.7453 | | 1.8266 | 0.84 | 144000 | 1.7453 | | 0.8768 | 0.84 | 144500 | 1.7453 | | 1.2274 | 0.85 | 145000 | 1.7453 | | 1.6647 | 0.85 | 145500 | 1.7453 | | 1.4071 | 0.85 | 146000 | 1.7452 | | 1.6073 | 0.85 | 146500 | 1.7452 | | 2.201 | 0.86 | 147000 | 1.7452 | | 1.5504 | 0.86 | 147500 | 1.7452 | | 1.4377 | 0.86 | 148000 | 1.7452 | | 1.4453 | 0.87 | 148500 | 1.7452 | | 1.6929 | 0.87 | 149000 | 1.7451 | | 1.7631 | 0.87 | 149500 | 1.7451 | | 2.0868 | 0.87 | 150000 | 1.7451 | | 0.6434 | 0.88 | 150500 | 1.7451 | | 1.4851 | 0.88 | 151000 | 1.7451 | | 1.5365 | 0.88 | 151500 | 1.7451 | | 1.8129 | 0.89 | 152000 | 1.7451 | | 1.1623 | 0.89 | 152500 | 1.7451 | | 2.0714 | 0.89 | 153000 | 1.7451 | | 1.9363 | 0.9 | 153500 | 1.7451 | | 1.6408 | 0.9 | 154000 | 1.7451 | | 0.618 | 0.9 | 154500 | 1.7451 | | 1.7957 | 0.9 | 155000 | 1.7451 | | 2.0056 | 0.91 | 155500 | 1.7451 | | 1.3893 | 0.91 | 156000 | 1.7451 | | 2.1426 | 0.91 | 156500 | 1.7451 | | 1.6766 | 0.92 | 157000 | 1.7451 | | 1.4206 | 0.92 | 157500 | 1.7451 | | 1.7285 | 0.92 | 158000 | 1.7451 | | 1.5779 | 0.92 | 158500 | 1.7451 | | 1.8675 | 0.93 | 159000 | 1.7451 | | 2.0217 | 0.93 | 159500 | 1.7451 | | 0.9516 | 0.93 | 160000 | 1.7451 | | 2.219 | 0.94 | 160500 | 1.7450 | | 1.6214 | 0.94 | 161000 | 1.7451 | | 1.7134 | 0.94 | 161500 | 1.7451 | | 1.6128 | 0.94 | 162000 | 1.7451 | | 2.0817 | 0.95 | 162500 | 1.7450 | | 1.8055 | 0.95 | 163000 | 1.7451 | | 1.909 | 0.95 | 163500 | 1.7451 | | 1.7844 | 0.96 | 164000 | 1.7451 | | 2.0719 | 0.96 | 164500 | 1.7451 | | 1.8698 | 0.96 | 165000 | 1.7451 | | 1.6926 | 0.96 | 165500 | 1.7451 | | 2.2161 | 0.97 | 166000 | 1.7451 | | 2.1111 | 0.97 | 166500 | 1.7451 | | 1.8004 | 0.97 | 167000 | 1.7451 | | 2.2364 | 0.98 | 167500 | 1.7451 | | 1.6716 | 0.98 | 168000 | 1.7451 | | 2.1804 | 0.98 | 168500 | 1.7451 | | 1.2691 | 0.99 | 169000 | 1.7451 | | 1.8306 | 0.99 | 169500 | 1.7451 | | 0.5662 | 0.99 | 170000 | 1.7451 | | 1.6516 | 0.99 | 170500 | 1.7451 | | 2.0576 | 1.0 | 171000 | 1.7451 | | 1.3638 | 1.0 | 171500 | 1.7451 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1

Moreza009/Tehran_Covid19

Moreza009

2023-11-03T18:32:43Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:bigscience/bloom-7b1", "base_model:adapter:bigscience/bloom-7b1", "region:us" ]

null

2023-11-03T18:06:12Z

--- library_name: peft base_model: bigscience/bloom-7b1 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [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: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.7.0.dev0

owanr/SChem5Labels-google-t5-v1_1-large-inter_model-frequency-model_annots_str

owanr

2023-11-03T18:25:12Z

0

null

[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]

null

2023-11-03T04:30:50Z

--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SChem5Labels-google-t5-v1_1-large-inter_model-frequency-model_annots_str results: [] ---  # SChem5Labels-google-t5-v1_1-large-inter_model-frequency-model_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9844 ## 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: 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 20.3817 | 1.0 | 25 | 23.8120 | | 19.4374 | 2.0 | 50 | 21.8918 | | 18.745 | 3.0 | 75 | 19.3959 | | 16.896 | 4.0 | 100 | 15.4970 | | 15.3045 | 5.0 | 125 | 11.5374 | | 12.9955 | 6.0 | 150 | 9.6467 | | 11.2112 | 7.0 | 175 | 8.9925 | | 9.4851 | 8.0 | 200 | 8.7994 | | 8.7487 | 9.0 | 225 | 8.5320 | | 8.1197 | 10.0 | 250 | 8.3570 | | 7.9164 | 11.0 | 275 | 8.2662 | | 7.7789 | 12.0 | 300 | 8.1800 | | 7.6671 | 13.0 | 325 | 8.0987 | | 7.5107 | 14.0 | 350 | 7.9659 | | 7.457 | 15.0 | 375 | 7.6850 | | 7.1712 | 16.0 | 400 | 7.3914 | | 6.9462 | 17.0 | 425 | 7.2019 | | 6.8109 | 18.0 | 450 | 7.0657 | | 6.7403 | 19.0 | 475 | 6.9778 | | 6.5766 | 20.0 | 500 | 6.9288 | | 6.505 | 21.0 | 525 | 6.8702 | | 6.5148 | 22.0 | 550 | 6.8175 | | 6.541 | 23.0 | 575 | 6.7619 | | 4.3898 | 24.0 | 600 | 1.0917 | | 1.0874 | 25.0 | 625 | 0.7681 | | 0.8058 | 26.0 | 650 | 0.7295 | | 0.7847 | 27.0 | 675 | 0.7244 | | 0.7779 | 28.0 | 700 | 0.7195 | | 0.7741 | 29.0 | 725 | 0.7205 | | 0.7606 | 30.0 | 750 | 0.7222 | | 0.7613 | 31.0 | 775 | 0.7189 | | 0.7676 | 32.0 | 800 | 0.7119 | | 0.7547 | 33.0 | 825 | 0.7138 | | 0.7433 | 34.0 | 850 | 0.7148 | | 0.7729 | 35.0 | 875 | 0.7202 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1

LoneStriker/Utopia-13B-8.0bpw-h8-exl2

LoneStriker

2023-11-03T18:18:51Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T18:17:49Z

--- license: cc-by-nc-4.0 --- Temp. ``` Xwin-LM/Xwin-LM-13B-V0.2 Undi95/Storytelling-v2.1-13B-lora => p1 NeverSleep/Nethena-13B zattio770/120-Days-of-LORA-v2-13B => p2 PygmalionAI/pygmalion-2-13b lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT => p3 merge_method: task_arithmetic base_model: TheBloke/Llama-2-13B-fp16 models: - model: TheBloke/Llama-2-13B-fp16 - model: Undi95/newpart1 parameters: weight: 1.0 - model: Undi95/newpart2 parameters: weight: 0.45 - model: Undi95/newpart3 parameters: weight: 0.33 dtype: float16 ``` ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).

sreejith8100/donut-base-vishnu

sreejith8100

2023-11-03T18:17:44Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-11-02T10:31:32Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-vishnu results: [] ---  # donut-base-vishnu This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9387 | 1.0 | 127 | 2.0237 | | 0.8381 | 2.0 | 254 | 1.3332 | | 1.4923 | 3.0 | 381 | 1.1110 | | 0.9061 | 4.0 | 508 | 0.9530 | | 0.4627 | 5.0 | 635 | 0.9156 | | 0.4305 | 6.0 | 762 | 0.7884 | | 0.4383 | 7.0 | 889 | 0.6936 | | 0.1852 | 8.0 | 1016 | 0.6715 | | 0.2348 | 9.0 | 1143 | 0.6209 | | 0.3975 | 10.0 | 1270 | 0.5614 | | 0.1548 | 11.0 | 1397 | 0.5152 | | 0.0377 | 12.0 | 1524 | 0.5135 | | 0.043 | 13.0 | 1651 | 0.4759 | | 0.0698 | 14.0 | 1778 | 0.4697 | | 0.0292 | 15.0 | 1905 | 0.4243 | | 0.0516 | 16.0 | 2032 | 0.4594 | | 0.2062 | 17.0 | 2159 | 0.4332 | | 0.0307 | 18.0 | 2286 | 0.4030 | | 0.0775 | 19.0 | 2413 | 0.4069 | | 0.0157 | 20.0 | 2540 | 0.4111 | | 0.0137 | 21.0 | 2667 | 0.4072 | | 0.0148 | 22.0 | 2794 | 0.3938 | | 0.0454 | 23.0 | 2921 | 0.3789 | | 0.0023 | 24.0 | 3048 | 0.3864 | | 0.0033 | 25.0 | 3175 | 0.3750 | | 0.0292 | 26.0 | 3302 | 0.3847 | | 0.0087 | 27.0 | 3429 | 0.3592 | | 0.0032 | 28.0 | 3556 | 0.3665 | | 0.0048 | 29.0 | 3683 | 0.3372 | | 0.0035 | 30.0 | 3810 | 0.3349 | | 0.0197 | 31.0 | 3937 | 0.3591 | | 0.0006 | 32.0 | 4064 | 0.3504 | | 0.0016 | 33.0 | 4191 | 0.3450 | | 0.0006 | 34.0 | 4318 | 0.3505 | | 0.0046 | 35.0 | 4445 | 0.3332 | | 0.0045 | 36.0 | 4572 | 0.3206 | | 0.0006 | 37.0 | 4699 | 0.3361 | | 0.0039 | 38.0 | 4826 | 0.3348 | | 0.0059 | 39.0 | 4953 | 0.3328 | | 0.0039 | 40.0 | 5080 | 0.3406 | | 0.0014 | 41.0 | 5207 | 0.3250 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

owanr/Sentiment-google-t5-v1_1-large-inter_model-frequency-human_annots_str

owanr

2023-11-03T18:16:38Z

0

null

[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]

null

2023-11-03T18:16:36Z

--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: Sentiment-google-t5-v1_1-large-inter_model-frequency-human_annots_str results: [] ---  # Sentiment-google-t5-v1_1-large-inter_model-frequency-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2676 ## 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: 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 20.8675 | 1.0 | 44 | 24.9691 | | 18.2491 | 2.0 | 88 | 16.9536 | | 13.3557 | 3.0 | 132 | 11.4063 | | 11.0809 | 4.0 | 176 | 10.9529 | | 10.1527 | 5.0 | 220 | 10.7258 | | 9.9235 | 6.0 | 264 | 10.5299 | | 9.8499 | 7.0 | 308 | 10.3942 | | 9.5638 | 8.0 | 352 | 10.0279 | | 9.0218 | 9.0 | 396 | 9.4463 | | 8.5704 | 10.0 | 440 | 9.0452 | | 8.3982 | 11.0 | 484 | 8.8619 | | 8.3396 | 12.0 | 528 | 8.7363 | | 7.0951 | 13.0 | 572 | 1.3757 | | 1.1349 | 14.0 | 616 | 1.0516 | | 1.0735 | 15.0 | 660 | 1.0456 | | 1.0787 | 16.0 | 704 | 1.0342 | | 1.0796 | 17.0 | 748 | 1.0342 | | 1.0476 | 18.0 | 792 | 1.0318 | | 1.0566 | 19.0 | 836 | 1.0311 | | 1.0472 | 20.0 | 880 | 1.0247 | | 1.0401 | 21.0 | 924 | 1.0201 | | 1.0725 | 22.0 | 968 | 1.0159 | | 1.0447 | 23.0 | 1012 | 1.0180 | | 1.0477 | 24.0 | 1056 | 1.0121 | | 1.0357 | 25.0 | 1100 | 1.0109 | | 1.0333 | 26.0 | 1144 | 1.0096 | | 1.0282 | 27.0 | 1188 | 1.0078 | | 1.0206 | 28.0 | 1232 | 1.0100 | | 1.0241 | 29.0 | 1276 | 1.0081 | | 1.023 | 30.0 | 1320 | 1.0053 | | 0.9993 | 31.0 | 1364 | 1.0073 | | 1.0104 | 32.0 | 1408 | 1.0079 | | 1.0176 | 33.0 | 1452 | 1.0014 | | 1.0157 | 34.0 | 1496 | 0.9977 | | 1.0204 | 35.0 | 1540 | 0.9960 | | 1.0174 | 36.0 | 1584 | 0.9967 | | 1.0252 | 37.0 | 1628 | 0.9949 | | 1.0076 | 38.0 | 1672 | 0.9913 | | 1.0137 | 39.0 | 1716 | 0.9874 | | 1.0151 | 40.0 | 1760 | 0.9856 | | 0.9907 | 41.0 | 1804 | 0.9843 | | 1.0147 | 42.0 | 1848 | 0.9803 | | 1.001 | 43.0 | 1892 | 0.9777 | | 1.0009 | 44.0 | 1936 | 0.9735 | | 0.9881 | 45.0 | 1980 | 0.9731 | | 0.9973 | 46.0 | 2024 | 0.9761 | | 0.9982 | 47.0 | 2068 | 0.9888 | | 0.9826 | 48.0 | 2112 | 1.0006 | | 0.9739 | 49.0 | 2156 | 0.9766 | | 0.9659 | 50.0 | 2200 | 0.9525 | | 0.9534 | 51.0 | 2244 | 0.9400 | | 0.959 | 52.0 | 2288 | 0.9553 | | 0.9492 | 53.0 | 2332 | 0.9308 | | 0.9629 | 54.0 | 2376 | 0.9325 | | 0.9532 | 55.0 | 2420 | 0.9288 | | 0.9586 | 56.0 | 2464 | 0.9233 | | 0.9511 | 57.0 | 2508 | 0.9228 | | 0.9456 | 58.0 | 2552 | 0.9178 | | 0.937 | 59.0 | 2596 | 0.9140 | | 0.9415 | 60.0 | 2640 | 0.9332 | | 0.9364 | 61.0 | 2684 | 0.9073 | | 0.9304 | 62.0 | 2728 | 0.9112 | | 0.9418 | 63.0 | 2772 | 0.9073 | | 0.9423 | 64.0 | 2816 | 0.9079 | | 0.9277 | 65.0 | 2860 | 0.9062 | | 0.9274 | 66.0 | 2904 | 0.8999 | | 0.9266 | 67.0 | 2948 | 0.8971 | | 0.9231 | 68.0 | 2992 | 0.9003 | | 0.9174 | 69.0 | 3036 | 0.8994 | | 0.9036 | 70.0 | 3080 | 0.8986 | | 0.9112 | 71.0 | 3124 | 0.8925 | | 0.8929 | 72.0 | 3168 | 0.8866 | | 0.9069 | 73.0 | 3212 | 0.8840 | | 0.8922 | 74.0 | 3256 | 0.8818 | | 0.9079 | 75.0 | 3300 | 0.8821 | | 0.8941 | 76.0 | 3344 | 0.8780 | | 0.8952 | 77.0 | 3388 | 0.8824 | | 0.8881 | 78.0 | 3432 | 0.8724 | | 0.884 | 79.0 | 3476 | 0.8684 | | 0.8761 | 80.0 | 3520 | 0.8715 | | 0.8952 | 81.0 | 3564 | 0.8706 | | 0.8871 | 82.0 | 3608 | 0.8654 | | 0.8772 | 83.0 | 3652 | 0.8583 | | 0.8745 | 84.0 | 3696 | 0.8570 | | 0.8683 | 85.0 | 3740 | 0.8490 | | 0.8698 | 86.0 | 3784 | 0.8500 | | 0.8562 | 87.0 | 3828 | 0.8469 | | 0.8636 | 88.0 | 3872 | 0.8465 | | 0.8669 | 89.0 | 3916 | 0.8359 | | 0.8422 | 90.0 | 3960 | 0.8418 | | 0.8568 | 91.0 | 4004 | 0.8332 | | 0.8628 | 92.0 | 4048 | 0.8338 | | 0.8599 | 93.0 | 4092 | 0.8302 | | 0.8471 | 94.0 | 4136 | 0.8235 | | 0.8432 | 95.0 | 4180 | 0.8202 | | 0.8389 | 96.0 | 4224 | 0.8159 | | 0.8347 | 97.0 | 4268 | 0.8218 | | 0.8353 | 98.0 | 4312 | 0.8141 | | 0.8172 | 99.0 | 4356 | 0.8176 | | 0.8303 | 100.0 | 4400 | 0.8078 | | 0.8317 | 101.0 | 4444 | 0.8077 | | 0.8203 | 102.0 | 4488 | 0.8103 | | 0.8224 | 103.0 | 4532 | 0.8076 | | 0.8174 | 104.0 | 4576 | 0.8023 | | 0.8242 | 105.0 | 4620 | 0.7897 | | 0.809 | 106.0 | 4664 | 0.7935 | | 0.8014 | 107.0 | 4708 | 0.7881 | | 0.817 | 108.0 | 4752 | 0.7815 | | 0.7988 | 109.0 | 4796 | 0.7861 | | 0.8003 | 110.0 | 4840 | 0.7716 | | 0.7991 | 111.0 | 4884 | 0.7836 | | 0.7851 | 112.0 | 4928 | 0.7722 | | 0.7884 | 113.0 | 4972 | 0.7716 | | 0.7831 | 114.0 | 5016 | 0.7643 | | 0.7849 | 115.0 | 5060 | 0.7767 | | 0.7846 | 116.0 | 5104 | 0.7602 | | 0.7887 | 117.0 | 5148 | 0.7511 | | 0.7683 | 118.0 | 5192 | 0.7480 | | 0.7856 | 119.0 | 5236 | 0.7532 | | 0.766 | 120.0 | 5280 | 0.7511 | | 0.7663 | 121.0 | 5324 | 0.7490 | | 0.7456 | 122.0 | 5368 | 0.7460 | | 0.7672 | 123.0 | 5412 | 0.7464 | | 0.7553 | 124.0 | 5456 | 0.7324 | | 0.7543 | 125.0 | 5500 | 0.7296 | | 0.7465 | 126.0 | 5544 | 0.7431 | | 0.7525 | 127.0 | 5588 | 0.7310 | | 0.7438 | 128.0 | 5632 | 0.7333 | | 0.7521 | 129.0 | 5676 | 0.7218 | | 0.7501 | 130.0 | 5720 | 0.7170 | | 0.7485 | 131.0 | 5764 | 0.7214 | | 0.7512 | 132.0 | 5808 | 0.7235 | | 0.7554 | 133.0 | 5852 | 0.7140 | | 0.7349 | 134.0 | 5896 | 0.7062 | | 0.7542 | 135.0 | 5940 | 0.7095 | | 0.7303 | 136.0 | 5984 | 0.7111 | | 0.7163 | 137.0 | 6028 | 0.7004 | | 0.7204 | 138.0 | 6072 | 0.7045 | | 0.7091 | 139.0 | 6116 | 0.6918 | | 0.719 | 140.0 | 6160 | 0.6976 | | 0.726 | 141.0 | 6204 | 0.6885 | | 0.7079 | 142.0 | 6248 | 0.6896 | | 0.7043 | 143.0 | 6292 | 0.6966 | | 0.7078 | 144.0 | 6336 | 0.6833 | | 0.711 | 145.0 | 6380 | 0.6839 | | 0.7014 | 146.0 | 6424 | 0.6685 | | 0.7026 | 147.0 | 6468 | 0.6752 | | 0.6927 | 148.0 | 6512 | 0.6802 | | 0.6899 | 149.0 | 6556 | 0.6747 | | 0.7059 | 150.0 | 6600 | 0.6733 | | 0.6855 | 151.0 | 6644 | 0.6551 | | 0.694 | 152.0 | 6688 | 0.6590 | | 0.6896 | 153.0 | 6732 | 0.6568 | | 0.6758 | 154.0 | 6776 | 0.6595 | | 0.7058 | 155.0 | 6820 | 0.6506 | | 0.6761 | 156.0 | 6864 | 0.6586 | | 0.6837 | 157.0 | 6908 | 0.6526 | | 0.6736 | 158.0 | 6952 | 0.6526 | | 0.6738 | 159.0 | 6996 | 0.6434 | | 0.685 | 160.0 | 7040 | 0.6382 | | 0.664 | 161.0 | 7084 | 0.6374 | | 0.6878 | 162.0 | 7128 | 0.6322 | | 0.6552 | 163.0 | 7172 | 0.6338 | | 0.6796 | 164.0 | 7216 | 0.6453 | | 0.6712 | 165.0 | 7260 | 0.6284 | | 0.6683 | 166.0 | 7304 | 0.6249 | | 0.6577 | 167.0 | 7348 | 0.6359 | | 0.6462 | 168.0 | 7392 | 0.6193 | | 0.66 | 169.0 | 7436 | 0.6138 | | 0.6476 | 170.0 | 7480 | 0.6224 | | 0.6444 | 171.0 | 7524 | 0.6195 | | 0.6478 | 172.0 | 7568 | 0.6136 | | 0.6332 | 173.0 | 7612 | 0.5981 | | 0.6456 | 174.0 | 7656 | 0.6004 | | 0.6302 | 175.0 | 7700 | 0.6060 | | 0.6337 | 176.0 | 7744 | 0.6024 | | 0.6282 | 177.0 | 7788 | 0.5936 | | 0.616 | 178.0 | 7832 | 0.5942 | | 0.6324 | 179.0 | 7876 | 0.6038 | | 0.6331 | 180.0 | 7920 | 0.5939 | | 0.627 | 181.0 | 7964 | 0.5881 | | 0.6313 | 182.0 | 8008 | 0.5874 | | 0.626 | 183.0 | 8052 | 0.5868 | | 0.6215 | 184.0 | 8096 | 0.5789 | | 0.6138 | 185.0 | 8140 | 0.5830 | | 0.6235 | 186.0 | 8184 | 0.5900 | | 0.61 | 187.0 | 8228 | 0.5920 | | 0.6218 | 188.0 | 8272 | 0.5830 | | 0.6265 | 189.0 | 8316 | 0.5706 | | 0.6126 | 190.0 | 8360 | 0.5776 | | 0.608 | 191.0 | 8404 | 0.5738 | | 0.6143 | 192.0 | 8448 | 0.5737 | | 0.6065 | 193.0 | 8492 | 0.5714 | | 0.6213 | 194.0 | 8536 | 0.5657 | | 0.6004 | 195.0 | 8580 | 0.5660 | | 0.6229 | 196.0 | 8624 | 0.5646 | | 0.6073 | 197.0 | 8668 | 0.5704 | | 0.6048 | 198.0 | 8712 | 0.5696 | | 0.6008 | 199.0 | 8756 | 0.5619 | | 0.6157 | 200.0 | 8800 | 0.5597 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1

slonoten/ppo-LunarLander-v2

slonoten

2023-11-03T18:14:12Z

1

0

stable-baselines3

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

reinforcement-learning

2023-11-03T18:13:49Z

--- 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: 246.56 +/- 19.75 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 ... ```

thingthatis/SSD-1B

thingthatis

2023-11-03T18:08:05Z

8

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "ultra-realistic", "stable-diffusion", "distilled-model", "knowledge-distillation", "dataset:zzliang/GRIT", "dataset:wanng/midjourney-v5-202304-clean", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2023-11-03T18:08:04Z

--- license: apache-2.0 tags: - text-to-image - ultra-realistic - text-to-image - stable-diffusion - distilled-model - knowledge-distillation pinned: true datasets: - zzliang/GRIT - wanng/midjourney-v5-202304-clean library_name: diffusers --- # Segmind Stable Diffusion 1B (SSD-1B) Model Card ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/WveKcu7q5PyZEwNezyyMC.png) ## Demo Try out the model at [Segmind SSD-1B](https://www.segmind.com/models/ssd-1b) for ⚡ fastest inference. You can also try it on [🤗 Spaces](https://huggingface.co/spaces/segmind/Segmind-Stable-Diffusion) ## Model Description The Segmind Stable Diffusion Model (SSD-1B) is a **distilled 50% smaller** version of the Stable Diffusion XL (SDXL), offering a **60% speedup** while maintaining high-quality text-to-image generation capabilities. It has been trained on diverse datasets, including Grit and Midjourney scrape data, to enhance its ability to create a wide range of visual content based on textual prompts. This model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive visual outputs. Special thanks to the HF team 🤗 especially [Sayak](https://huggingface.co/sayakpaul), [Patrick](https://github.com/patrickvonplaten) and [Poli](https://huggingface.co/multimodalart) for their collaboration and guidance on this work. ## Image Comparision (SDXL-1.0 vs SSD-1B) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/mOM_OMxbivVBELad1QQYj.png) ## Usage: This model can be used via the 🧨 Diffusers library. Make sure to install diffusers from source by running ``` pip install git+https://github.com/huggingface/diffusers ``` In addition, please install `transformers`, `safetensors` and `accelerate`: ``` pip install transformers accelerate safetensors ``` To use the model, you can run the following: ```py from diffusers import StableDiffusionXLPipeline import torch pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" # Your prompt here neg_prompt = "ugly, blurry, poor quality" # Negative prompt here image = pipe(prompt=prompt, negative_prompt=neg_prompt).images[0] ``` ### Update: Our model should now be usable in ComfyUI. ### Please do use negative prompting, and a CFG around 9.0 for the best quality! ### Model Description - **Developed by:** [Segmind](https://www.segmind.com/) - **Developers:** [Yatharth Gupta](https://huggingface.co/Warlord-K) and [Vishnu Jaddipal](https://huggingface.co/Icar). - **Model type:** Diffusion-based text-to-image generative model - **License:** Apache 2.0 - **Distilled From** [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ### Key Features - **Text-to-Image Generation:** The model excels at generating images from text prompts, enabling a wide range of creative applications. - **Distilled for Speed:** Designed for efficiency, this model offers a 60% speedup, making it a practical choice for real-time applications and scenarios where rapid image generation is essential. - **Diverse Training Data:** Trained on diverse datasets, the model can handle a variety of textual prompts and generate corresponding images effectively. - **Knowledge Distillation:** By distilling knowledge from multiple expert models, the Segmind Stable Diffusion Model combines their strengths and minimizes their limitations, resulting in improved performance. ### Model Architecture The SSD-1B Model is a 1.3B Parameter Model which has several layers removed from the Base SDXL Model ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/Qa8Ow-moLQhOvzp-5kGt4.png) ### Training info These are the key hyperparameters used during training: * Steps: 251000 * Learning rate: 1e-5 * Batch size: 32 * Gradient accumulation steps: 4 * Image resolution: 1024 * Mixed-precision: fp16 ### Multi-Resolution Support ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/IwIaIB4nBdMx6Vs5q82cL.jpeg) SSD-1B can support the following output resolutions. * 1024 x 1024 (1:1 Square) * 1152 x 896 (9:7) * 896 x 1152 (7:9) * 1216 x 832 (19:13) * 832 x 1216 (13:19) * 1344 x 768 (7:4 Horizontal) * 768 x 1344 (4:7 Vertical) * 1536 x 640 (12:5 Horizontal) * 640 x 1536 (5:12 Vertical) ### Speed Comparision We have observed that SSD-1B is upto 60% faster than the Base SDXL Model. Below is a comparision on an A100 80GB. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/TyymF1OkUjXLrHUp1XF0t.png) Below are the speed up metrics on a RTX 4090 GPU. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62039c2d91d53938a643317d/moMZrlDr-HTFkZlqWHUjQ.png) ### Model Sources For research and development purposes, the SSD-1B Model can be accessed via the Segmind AI platform. For more information and access details, please visit [Segmind](https://www.segmind.com/models/ssd-1b). ## Uses ### Direct Use The Segmind Stable Diffusion Model is suitable for research and practical applications in various domains, including: - **Art and Design:** It can be used to generate artworks, designs, and other creative content, providing inspiration and enhancing the creative process. - **Education:** The model can be applied in educational tools to create visual content for teaching and learning purposes. - **Research:** Researchers can use the model to explore generative models, evaluate its performance, and push the boundaries of text-to-image generation. - **Safe Content Generation:** It offers a safe and controlled way to generate content, reducing the risk of harmful or inappropriate outputs. - **Bias and Limitation Analysis:** Researchers and developers can use the model to probe its limitations and biases, contributing to a better understanding of generative models' behavior. ### Downstream Use The Segmind Stable Diffusion Model can also be used directly with the 🧨 Diffusers library training scripts for further training, including: - **[LoRA](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py):** ```bash export MODEL_NAME="segmind/SSD-1B" export VAE_NAME="madebyollin/sdxl-vae-fp16-fix" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch train_text_to_image_lora_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=$DATASET_NAME --caption_column="text" \ --resolution=1024 --random_flip \ --train_batch_size=1 \ --num_train_epochs=2 --checkpointing_steps=500 \ --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \ --mixed_precision="fp16" \ --seed=42 \ --output_dir="sd-pokemon-model-lora-ssd" \ --validation_prompt="cute dragon creature" --report_to="wandb" \ --push_to_hub ``` - **[Fine-Tune](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py):** ```bash export MODEL_NAME="segmind/SSD-1B" export VAE_NAME="madebyollin/sdxl-vae-fp16-fix" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch train_text_to_image_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=$DATASET_NAME \ --enable_xformers_memory_efficient_attention \ --resolution=512 --center_crop --random_flip \ --proportion_empty_prompts=0.2 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 --gradient_checkpointing \ --max_train_steps=10000 \ --use_8bit_adam \ --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 \ --mixed_precision="fp16" \ --report_to="wandb" \ --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 \ --checkpointing_steps=5000 \ --output_dir="ssd-pokemon-model" \ --push_to_hub ``` - **[Dreambooth LoRA](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py):** ```bash export MODEL_NAME="segmind/SSD-1B" export INSTANCE_DIR="dog" export OUTPUT_DIR="lora-trained-xl" export VAE_PATH="madebyollin/sdxl-vae-fp16-fix" accelerate launch train_dreambooth_lora_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_data_dir=$INSTANCE_DIR \ --pretrained_vae_model_name_or_path=$VAE_PATH \ --output_dir=$OUTPUT_DIR \ --mixed_precision="fp16" \ --instance_prompt="a photo of sks dog" \ --resolution=1024 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --learning_rate=1e-5 \ --report_to="wandb" \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --max_train_steps=500 \ --validation_prompt="A photo of sks dog in a bucket" \ --validation_epochs=25 \ --seed="0" \ --push_to_hub ``` ### Out-of-Scope Use The SSD-1B Model is not suitable for creating factual or accurate representations of people, events, or real-world information. It is not intended for tasks requiring high precision and accuracy. ## Limitations and Bias Limitations & Bias The SSD-1B Model has some challenges in embodying absolute photorealism, especially in human depictions. While it grapples with incorporating clear text and maintaining the fidelity of complex compositions due to its autoencoding approach, these hurdles pave the way for future enhancements. Importantly, the model's exposure to a diverse dataset, though not a panacea for ingrained societal and digital biases, represents a foundational step towards more equitable technology. Users are encouraged to interact with this pioneering tool with an understanding of its current limitations, fostering an environment of conscious engagement and anticipation for its continued evolution.

ronenlap/my-SetFitABSA-24samples-model-PolarityDetection

ronenlap

2023-11-03T18:04:48Z

10

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-10-24T16:04:48Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # ronenlap/my-SetFitABSA-24samples-model-PolarityDetection This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("ronenlap/my-SetFitABSA-24samples-model-PolarityDetection") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

Perrie/whisper-tiny-epoch-1

Perrie

2023-11-03T17:59:35Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-11-03T14:06:16Z

--- language: - zh license: apache-2.0 base_model: openai/whisper-tiny tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper tiny epoch 1- Perrie results: [] ---  # Whisper tiny epoch 1- Perrie This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4304 - Cer: 60.2188 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 70 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3925 | 1.0 | 705 | 0.4304 | 60.2188 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 1.12.1 - Datasets 2.14.6 - Tokenizers 0.14.1

yThingSoHeavy/distilbert-base-uncased-finetuned-ner

yThingSoHeavy

2023-11-03T17:59:08Z

3

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-11-03T17:22:12Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: yThingSoHeavy/distilbert-base-uncased-finetuned-ner results: [] ---  # yThingSoHeavy/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0345 - Validation Loss: 0.0602 - Train Precision: 0.9246 - Train Recall: 0.9322 - Train F1: 0.9284 - Train Accuracy: 0.9832 - Epoch: 2 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1979 | 0.0700 | 0.9081 | 0.9168 | 0.9124 | 0.9796 | 0 | | 0.0549 | 0.0611 | 0.9178 | 0.9308 | 0.9242 | 0.9824 | 1 | | 0.0345 | 0.0602 | 0.9246 | 0.9322 | 0.9284 | 0.9832 | 2 | ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1

LoneStriker/Utopia-13B-5.0bpw-h6-exl2

LoneStriker

2023-11-03T17:58:14Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T17:57:49Z

--- license: cc-by-nc-4.0 --- Temp. ``` Xwin-LM/Xwin-LM-13B-V0.2 Undi95/Storytelling-v2.1-13B-lora => p1 NeverSleep/Nethena-13B zattio770/120-Days-of-LORA-v2-13B => p2 PygmalionAI/pygmalion-2-13b lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT => p3 merge_method: task_arithmetic base_model: TheBloke/Llama-2-13B-fp16 models: - model: TheBloke/Llama-2-13B-fp16 - model: Undi95/newpart1 parameters: weight: 1.0 - model: Undi95/newpart2 parameters: weight: 0.45 - model: Undi95/newpart3 parameters: weight: 0.33 dtype: float16 ``` ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).

Norod78/sxl-laisha-magazine-cover-lora

Norod78

2023-11-03T17:49:25Z

94

3

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "magazine cover", "magazine", "style", "laisha", "dataset:Norod78/LaishaMagazineCovers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-11-03T17:38:52Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers - magazine cover - magazine - style - laisha datasets: - Norod78/LaishaMagazineCovers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: laisha widget: - text: "An image of shiny metal benderbot in a pink tutu dress laisha " - text: "An image of a very smart lady jojoso style laisha " - text: "A crazy clown laisha " - text: "A picture of Anna and Elsa from frozen, Disney style laisha " - text: "A picture of Elsa from frozen, Disney style laisha " - text: "An image of an evil demon from the depths of hell in laisha VintageMagStyle " - text: "A picture of creepy WeepingAngel on laisha " - text: "The secret life of aliens from other space on laisha " - text: "A mechanical Santa bot laisha " - text: "An image of Margot Robbie wearing pink and having a BrainSlug attached to her head " --- # SXL LaIsha Magazine Cover LoRA ![Image 0](3361365.jpeg) > An image of shiny metal benderbot in a pink tutu dress laisha ([CivitAI](https://civitai.com/models/187883)) <p>Use the word 'laisha' in your prompts. I used images size of 1024x1280 and 704x1204, 32 steps, Euler a with 80% SDXL base and 20% SDXL refiner steps. I often used another LoRA to get a certain character to appear on the cover.</p><p><strong><em>La'Isha</em></strong> (<a target="_blank" rel="ugc" href="https://en.wikipedia.org/wiki/Hebrew_language">Hebrew</a>: לאשה, "For the Woman") is an Israeli lifestyle magazine for women It has been published on a weekly basis since 1947 and the cover design changed a lot throughout the years.</p><p></p> <p>The La'Isha image covers can be found in the following dataset, I used blip captions to give them text [Norod78/LaishaMagazineCovers]([https://huggingface.co/datasets/Norod78/LaishaMagazineCovers]) </p> ## Image examples for the model: ![Image 1](3361392.jpeg) > An image of a very smart lady jojoso style laisha ![Image 2](3361375.jpeg) > A crazy clown laisha ![Image 3](3361376.jpeg) > A picture of Anna and Elsa from frozen, Disney style laisha ![Image 4](3361356.jpeg) > A picture of Elsa from frozen, Disney style laisha ![Image 5](3361374.jpeg) > An image of an evil demon from the depths of hell in laisha VintageMagStyle ![Image 6](3361389.jpeg) > A picture of creepy WeepingAngel on laisha ![Image 7](3361424.jpeg) > The secret life of aliens from other space on laisha ![Image 8](3361410.jpeg) > A mechanical Santa bot laisha ![Image 9](3361399.jpeg) > An image of Margot Robbie wearing pink and having a BrainSlug attached to her head

LoneStriker/Utopia-13B-4.0bpw-h6-exl2

LoneStriker

2023-11-03T17:48:51Z

10

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T17:48:32Z

--- license: cc-by-nc-4.0 --- Temp. ``` Xwin-LM/Xwin-LM-13B-V0.2 Undi95/Storytelling-v2.1-13B-lora => p1 NeverSleep/Nethena-13B zattio770/120-Days-of-LORA-v2-13B => p2 PygmalionAI/pygmalion-2-13b lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT => p3 merge_method: task_arithmetic base_model: TheBloke/Llama-2-13B-fp16 models: - model: TheBloke/Llama-2-13B-fp16 - model: Undi95/newpart1 parameters: weight: 1.0 - model: Undi95/newpart2 parameters: weight: 0.45 - model: Undi95/newpart3 parameters: weight: 0.33 dtype: float16 ``` ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` If you want to support me, you can [here](https://ko-fi.com/undiai).

sharkMeow/mt5-small-finetuned-b8-e10-1024-128

sharkMeow

2023-11-03T17:43:04Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-11-03T12:14:34Z

--- license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-b8-e10-1024-128 results: [] ---  # mt5-small-finetuned-b8-e10-1024-128 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3822 - Rouge1: 13.327 - Rouge2: 4.8244 - Rougel: 13.1978 - Rougelsum: 13.2133 - Gen Len: 17.5592 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 4.7372 | 1.0 | 1357 | 3.8287 | 9.3951 | 3.6576 | 9.342 | 9.3047 | 12.6653 | | 4.3162 | 2.0 | 2714 | 3.6750 | 10.9224 | 4.1119 | 10.8209 | 10.8235 | 15.0997 | | 4.1726 | 3.0 | 4071 | 3.5668 | 11.7438 | 4.2353 | 11.6204 | 11.6087 | 16.5169 | | 4.0439 | 4.0 | 5428 | 3.5002 | 12.402 | 4.4267 | 12.2785 | 12.2924 | 17.0402 | | 3.9978 | 5.0 | 6785 | 3.4494 | 12.7762 | 4.5509 | 12.6699 | 12.6829 | 17.2466 | | 3.9687 | 6.0 | 8142 | 3.4229 | 12.9652 | 4.6727 | 12.8555 | 12.8761 | 17.4303 | | 3.8639 | 7.0 | 9499 | 3.4058 | 13.4216 | 4.784 | 13.3097 | 13.2988 | 17.4252 | | 3.8474 | 8.0 | 10856 | 3.3924 | 13.2422 | 4.7672 | 13.1416 | 13.12 | 17.5046 | | 3.843 | 9.0 | 12213 | 3.3845 | 13.2519 | 4.8713 | 13.1421 | 13.1304 | 17.5371 | | 3.8545 | 10.0 | 13570 | 3.3822 | 13.327 | 4.8244 | 13.1978 | 13.2133 | 17.5592 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.14.6 - Tokenizers 0.13.3

zekun-li/geolm-base-cased

zekun-li

2023-11-03T17:39:31Z

27

3

transformers

[ "transformers", "pytorch", "bert", "arxiv:2310.14478", "license:cc-by-nc-2.0", "endpoints_compatible", "region:us" ]

null

2023-07-21T20:12:19Z

--- license: cc-by-nc-2.0 --- # Model Card for GeoLM  GeoLM is a language model based on BERT that facilitates **geospatial understanding** in NL documents. It can take either NL sentences or linearized geographical region as input. The customized geocoordinate embedding module enables it to encode geographic coordinates efficiently. It is pretrained on world-wide OpenStreetMap (OSM), WikiData and Wikipedia data, and can be adapted to various geospatial related downstream tasks such as **toponym recognition** and **toponym linking**. * Model fine-tuned for toponym recognition: https://huggingface.co/zekun-li/geolm-base-toponym-recognition ## Model Details ### Model Description  - **Developed by:** Knowledge Computing Lab, UMN - **Shared by:** Zekun Li - **License:** CC-by-NC 2.0 - **Finetuned from model:** https://huggingface.co/bert-base-cased ### Model Sources [optional]  - **Repository:** https://github.com/knowledge-computing/geolm - **Paper:** http://arxiv.org/abs/2310.14478 ## 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] ## 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]

lukasdrg/clinical_longformer_new_tokens

lukasdrg

2023-11-03T17:31:36Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "longformer", "fill-mask", "generated_from_trainer", "base_model:allenai/longformer-base-4096", "base_model:finetune:allenai/longformer-base-4096", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-10-27T23:16:39Z

--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer model-index: - name: clinical_longformer_new_tokens results: [] ---  # clinical_longformer_new_tokens This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.5439 ## 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: 2 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5237 | 1.42 | 2 | 4.9660 | | 2.6047 | 2.84 | 4 | 4.5439 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

shaunck96/sentiment_BERTbaseuncased_finetuned_emotion

shaunck96

2023-11-03T17:27:00Z

5

0

transformers

[ "transformers", "tf", "bert", "feature-extraction", "generated_from_keras_callback", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-11-03T17:16:12Z

--- tags: - generated_from_keras_callback model-index: - name: sentiment_BERTbaseuncased_finetuned_emotion results: [] ---  # sentiment_BERTbaseuncased_finetuned_emotion This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.35.0 - TensorFlow 2.14.0 - Datasets 2.14.6 - Tokenizers 0.14.1

owanr/SChem5Labels-google-t5-v1_1-large-inter_model-shuffle-model_annots_str

owanr

2023-11-03T17:15:22Z

0

null

[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]

null

2023-11-03T17:15:21Z

--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SChem5Labels-google-t5-v1_1-large-inter_model-shuffle-model_annots_str results: [] ---  # SChem5Labels-google-t5-v1_1-large-inter_model-shuffle-model_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) 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: 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 19.9516 | 1.0 | 25 | 23.6924 | | 18.985 | 2.0 | 50 | 21.9715 | | 18.5733 | 3.0 | 75 | 19.0631 | | 16.456 | 4.0 | 100 | 14.6944 | | 14.6058 | 5.0 | 125 | 10.3972 | | 11.384 | 6.0 | 150 | 8.9604 | | 10.1534 | 7.0 | 175 | 8.6228 | | 8.6467 | 8.0 | 200 | 8.4688 | | 8.2161 | 9.0 | 225 | 8.3154 | | 7.9229 | 10.0 | 250 | 8.2324 | | 7.8179 | 11.0 | 275 | 8.1809 | | 7.7843 | 12.0 | 300 | 8.0948 | | 7.5714 | 13.0 | 325 | 7.9681 | | 7.2487 | 14.0 | 350 | 7.7352 | | 7.2237 | 15.0 | 375 | 7.4691 | | 6.9821 | 16.0 | 400 | 7.2523 | | 6.8667 | 17.0 | 425 | 7.1151 | | 6.8551 | 18.0 | 450 | 7.0423 | | 6.7468 | 19.0 | 475 | 6.9926 | | 6.6918 | 20.0 | 500 | 6.9466 | | 6.4912 | 21.0 | 525 | 6.9125 | | 6.5704 | 22.0 | 550 | 6.8707 | | 6.4854 | 23.0 | 575 | 6.8123 | | 3.9521 | 24.0 | 600 | 1.5605 | | 1.16 | 25.0 | 625 | 1.0195 | | 1.0643 | 26.0 | 650 | 1.0007 | | 1.0417 | 27.0 | 675 | 1.0069 | | 1.04 | 28.0 | 700 | 0.9974 | | 1.0347 | 29.0 | 725 | 0.9974 | | 1.0375 | 30.0 | 750 | 1.0006 | | 1.0382 | 31.0 | 775 | 0.9958 | | 1.0347 | 32.0 | 800 | 0.9999 | | 1.0198 | 33.0 | 825 | 1.0013 | | 1.0092 | 34.0 | 850 | 1.0044 | | 1.0376 | 35.0 | 875 | 1.0045 | | 1.0245 | 36.0 | 900 | 0.9974 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1

amyy78/unit4

amyy78

2023-11-03T17:14:50Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T01:44:13Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: unit4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 19.10 +/- 14.41 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

thegadri/Reinforce-CartPole-v1

thegadri

2023-11-03T17:14:11Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T17:14:02Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

shunnaidder/dqn-SpaceInvadersNoFrameskip-v4

shunnaidder

2023-11-03T17:13:11Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T17:12:34Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 421.00 +/- 215.00 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shunnaidder -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shunnaidder -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shunnaidder ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

jamaya/bert-finetuned-ner

jamaya

2023-11-03T17:11:37Z

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-11-03T16:06:34Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.927841845140033 - name: Recall type: recall value: 0.9478290138000673 - name: F1 type: f1 value: 0.9377289377289377 - name: Accuracy type: accuracy value: 0.9855036204156119 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0643 - Precision: 0.9278 - Recall: 0.9478 - F1: 0.9377 - Accuracy: 0.9855 ## 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: 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0781 | 1.0 | 1756 | 0.0789 | 0.9110 | 0.9325 | 0.9217 | 0.9802 | | 0.0415 | 2.0 | 3512 | 0.0617 | 0.9243 | 0.9472 | 0.9356 | 0.9851 | | 0.0256 | 3.0 | 5268 | 0.0643 | 0.9278 | 0.9478 | 0.9377 | 0.9855 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-non-3

ramdhanfirdaus

2023-11-03T17:10:26Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]

null

2023-11-03T16:56:21Z

--- library_name: peft base_model: tiiuae/falcon-rw-1b --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0 ## 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure ### Framework versions - PEFT 0.7.0.dev0

kejolong/GANZT

kejolong

2023-11-03T17:04:52Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-11-03T17:00:14Z

--- license: creativeml-openrail-m ---

owanr/SChem5Labels-google-t5-v1_1-large-inter_model-sorted-model_annots_str

owanr

2023-11-03T16:59:37Z

0

null

[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]

null

2023-11-03T16:59:36Z

--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SChem5Labels-google-t5-v1_1-large-inter_model-sorted-model_annots_str results: [] ---  # SChem5Labels-google-t5-v1_1-large-inter_model-sorted-model_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8994 ## 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: 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 20.7228 | 1.0 | 25 | 24.7182 | | 19.7481 | 2.0 | 50 | 23.4091 | | 19.3221 | 3.0 | 75 | 20.7043 | | 17.2262 | 4.0 | 100 | 15.0872 | | 14.7272 | 5.0 | 125 | 10.1222 | | 12.1186 | 6.0 | 150 | 9.0762 | | 10.3305 | 7.0 | 175 | 8.7090 | | 8.8199 | 8.0 | 200 | 8.4070 | | 8.2346 | 9.0 | 225 | 8.2048 | | 7.9113 | 10.0 | 250 | 8.1018 | | 7.6524 | 11.0 | 275 | 8.0398 | | 7.6476 | 12.0 | 300 | 7.9791 | | 7.4487 | 13.0 | 325 | 7.8957 | | 7.3635 | 14.0 | 350 | 7.7393 | | 7.2677 | 15.0 | 375 | 7.4303 | | 7.0316 | 16.0 | 400 | 7.1862 | | 6.7999 | 17.0 | 425 | 7.0031 | | 6.6811 | 18.0 | 450 | 6.8875 | | 6.6207 | 19.0 | 475 | 6.8224 | | 6.4587 | 20.0 | 500 | 6.7708 | | 6.3888 | 21.0 | 525 | 6.7248 | | 6.3971 | 22.0 | 550 | 6.6744 | | 6.3969 | 23.0 | 575 | 6.3850 | | 0.91 | 24.0 | 600 | 0.7331 | | 0.7237 | 25.0 | 625 | 0.6588 | | 0.6831 | 26.0 | 650 | 0.6276 | | 0.6785 | 27.0 | 675 | 0.6266 | | 0.6673 | 28.0 | 700 | 0.6269 | | 0.6728 | 29.0 | 725 | 0.6230 | | 0.6643 | 30.0 | 750 | 0.6204 | | 0.662 | 31.0 | 775 | 0.6187 | | 0.6664 | 32.0 | 800 | 0.6195 | | 0.6568 | 33.0 | 825 | 0.6180 | | 0.6453 | 34.0 | 850 | 0.6187 | | 0.6619 | 35.0 | 875 | 0.6260 | | 0.6539 | 36.0 | 900 | 0.6168 | | 0.6468 | 37.0 | 925 | 0.6188 | | 0.6567 | 38.0 | 950 | 0.6221 | | 0.6521 | 39.0 | 975 | 0.6172 | | 0.6403 | 40.0 | 1000 | 0.6141 | | 0.6505 | 41.0 | 1025 | 0.6147 | | 0.6419 | 42.0 | 1050 | 0.6174 | | 0.6436 | 43.0 | 1075 | 0.6174 | | 0.6381 | 44.0 | 1100 | 0.6130 | | 0.6527 | 45.0 | 1125 | 0.6144 | | 0.6489 | 46.0 | 1150 | 0.6129 | | 0.6395 | 47.0 | 1175 | 0.6141 | | 0.6483 | 48.0 | 1200 | 0.6174 | | 0.6331 | 49.0 | 1225 | 0.6165 | | 0.6454 | 50.0 | 1250 | 0.6141 | | 0.6356 | 51.0 | 1275 | 0.6140 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.6.1 - Tokenizers 0.14.1

chin-may/wav2vec2-audio-emotion-classification

chin-may

2023-11-03T16:57:34Z

71

5

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-10-30T05:56:55Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-audio-emotion-classification results: [] ---  # wav2vec2-audio-emotion-classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9518 - Accuracy: 0.7398 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.759 | 0.99 | 22 | 1.7087 | 0.3122 | | 1.5568 | 1.98 | 44 | 1.4412 | 0.4923 | | 1.2577 | 2.97 | 66 | 1.1467 | 0.7060 | | 1.0768 | 4.0 | 89 | 1.0131 | 0.7215 | | 0.9476 | 4.99 | 111 | 0.9633 | 0.7314 | | 0.9094 | 5.93 | 132 | 0.9518 | 0.7398 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

JDCollier89/Reinforce-cart-pole

JDCollier89

2023-11-03T16:47:29Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T16:47:21Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cart-pole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

bradmin/reward-gpt-duplicate-answer

bradmin

2023-11-03T16:46:17Z

13

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/polyglot-ko-1.3b", "base_model:finetune:EleutherAI/polyglot-ko-1.3b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:05:56Z

--- license: apache-2.0 base_model: EleutherAI/polyglot-ko-1.3b tags: - generated_from_trainer metrics: - accuracy model-index: - name: reward-gpt-duplicate-answer results: [] ---  # reward-gpt-duplicate-answer This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0115 - Accuracy: 0.5 ## 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: 9e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 2023 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1975 | 0.13 | 100 | 0.0475 | 0.0 | | 0.0945 | 0.25 | 200 | 0.0329 | 0.0 | | 0.0739 | 0.38 | 300 | 0.0156 | 0.0 | | 0.1354 | 0.5 | 400 | 0.0230 | 0.5 | | 0.1114 | 0.63 | 500 | 0.0260 | 0.5 | | 0.0688 | 0.75 | 600 | 0.0119 | 0.5 | | 0.0744 | 0.88 | 700 | 0.0115 | 0.5 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

owanr/SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str

owanr

2023-11-03T16:36:58Z

0

null

[ "generated_from_trainer", "base_model:google/t5-v1_1-large", "base_model:finetune:google/t5-v1_1-large", "license:apache-2.0", "region:us" ]

null

2023-11-03T16:36:56Z

--- license: apache-2.0 base_model: google/t5-v1_1-large tags: - generated_from_trainer model-index: - name: SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str results: [] ---  # SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3635 ## 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: 32 - eval_batch_size: 32 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 7.7146 | 1.0 | 392 | 8.2917 | | 0.5241 | 2.0 | 784 | 0.4371 | | 0.4312 | 3.0 | 1176 | 0.4278 | | 0.4359 | 4.0 | 1568 | 0.4084 | | 0.3616 | 5.0 | 1960 | 0.3634 | | 0.397 | 6.0 | 2352 | 0.3568 | | 0.3647 | 7.0 | 2744 | 0.3387 | | 0.3936 | 8.0 | 3136 | 0.3365 | | 0.3614 | 9.0 | 3528 | 0.3341 | | 0.3403 | 10.0 | 3920 | 0.3278 | | 0.3048 | 11.0 | 4312 | 0.3227 | | 0.347 | 12.0 | 4704 | 0.3218 | | 0.337 | 13.0 | 5096 | 0.3136 | | 0.3166 | 14.0 | 5488 | 0.3112 | | 0.3021 | 15.0 | 5880 | 0.3119 | | 0.3155 | 16.0 | 6272 | 0.3051 | | 0.2965 | 17.0 | 6664 | 0.3054 | | 0.3196 | 18.0 | 7056 | 0.2989 | | 0.2857 | 19.0 | 7448 | 0.2964 | | 0.3776 | 20.0 | 7840 | 0.2905 | | 0.288 | 21.0 | 8232 | 0.2889 | | 0.2632 | 22.0 | 8624 | 0.2911 | | 0.3014 | 23.0 | 9016 | 0.2862 | | 0.3015 | 24.0 | 9408 | 0.2869 | | 0.3254 | 25.0 | 9800 | 0.2858 | | 0.3178 | 26.0 | 10192 | 0.2860 | | 0.2994 | 27.0 | 10584 | 0.2860 | | 0.2893 | 28.0 | 10976 | 0.2860 | | 0.2873 | 29.0 | 11368 | 0.2860 | | 0.2631 | 30.0 | 11760 | 0.2860 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1

devrunner09/llama2-law-qa-merged-v1

devrunner09

2023-11-03T16:35:58Z

6

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "merged", "26k-finetuned", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-03T05:49:55Z

--- license: apache-2.0 tags: - merged - 26k-finetuned --- This model is finetuned from base model bkai-foundation-40GB-llama2. Dataset training: 26k qa dataset Version: 1.0 (3/11/2023)

gcperk20/swin-base-patch4-window7-224-finetuned-piid

gcperk20

2023-11-03T16:35:15Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-base-patch4-window7-224", "base_model:finetune:microsoft/swin-base-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-11-02T21:48:50Z

--- license: apache-2.0 base_model: microsoft/swin-base-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-base-patch4-window7-224-finetuned-piid results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: val args: default metrics: - name: Accuracy type: accuracy value: 0.8127853881278538 ---  # swin-base-patch4-window7-224-finetuned-piid This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6630 - Accuracy: 0.8128 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1815 | 0.98 | 20 | 1.0441 | 0.5251 | | 0.6548 | 2.0 | 41 | 0.8150 | 0.6393 | | 0.6083 | 2.98 | 61 | 0.6395 | 0.6986 | | 0.4925 | 4.0 | 82 | 0.6273 | 0.6804 | | 0.4448 | 4.98 | 102 | 0.4812 | 0.8174 | | 0.3387 | 6.0 | 123 | 0.5868 | 0.7945 | | 0.2622 | 6.98 | 143 | 0.7868 | 0.7260 | | 0.2656 | 8.0 | 164 | 0.4432 | 0.8128 | | 0.2259 | 8.98 | 184 | 0.6553 | 0.7489 | | 0.1997 | 10.0 | 205 | 0.5143 | 0.7854 | | 0.1892 | 10.98 | 225 | 0.5657 | 0.7945 | | 0.1522 | 12.0 | 246 | 0.7339 | 0.7580 | | 0.1309 | 12.98 | 266 | 0.6064 | 0.8174 | | 0.1482 | 14.0 | 287 | 0.5875 | 0.8128 | | 0.1459 | 14.98 | 307 | 0.6443 | 0.7900 | | 0.1224 | 16.0 | 328 | 0.6521 | 0.8037 | | 0.0533 | 16.98 | 348 | 0.5915 | 0.8493 | | 0.1133 | 18.0 | 369 | 0.6152 | 0.8265 | | 0.0923 | 18.98 | 389 | 0.6819 | 0.7854 | | 0.086 | 19.51 | 400 | 0.6630 | 0.8128 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

adutchscotsman/Reinfoce-Pixelcopter

adutchscotsman

2023-11-03T16:32:00Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T16:31:56Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinfoce-Pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 29.50 +/- 14.31 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

baseten/llama2-7b-chat-hf-fp16-tp1

baseten

2023-11-03T16:30:24Z

1

0

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

2023-11-02T19:52:36Z

python build.py --model_dir ./llama7b/ --dtype float16 --remove_input_padding --use_gpt_attention_plugin float16 --enable_context_fmha --use_gemm_plugin float16 --output_dir ./tmp/llama/7B/trt_engines/fp16/1-gpu/ --max_batch_size 32 --use_inflight_batching --paged_kv_cache --enable_context_fmha

TaTo69/q-taxi-v5

TaTo69

2023-11-03T16:28:04Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T15:34:49Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v5 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="TaTo69/q-taxi-v5", 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"]) ```

Aassemtkt/segformer-b0-finetuned-drugs-in-bins-nov-23

Aassemtkt

2023-11-03T16:20:02Z

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2023-11-03T10:57:42Z

--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-drugs-in-bins-nov-23 results: [] ---  # segformer-b0-finetuned-drugs-in-bins-nov-23 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Aassemtkt/v0.1 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

BenjaminOcampo/task-implicit_task__model-bert__aug_method-None

BenjaminOcampo

2023-11-03T16:16:02Z

5

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:15:15Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-None  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.90 0.89 0.90 2680 1 0.81 0.83 0.82 1501 2 0.38 0.31 0.34 186 accuracy 0.85 4367 macro avg 0.69 0.68 0.68 4367 weighted avg 0.84 0.85 0.85 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.90 0.90 0.90 2681 1 0.81 0.84 0.83 1501 2 0.46 0.35 0.40 186 accuracy 0.86 4368 macro avg 0.73 0.70 0.71 4368 weighted avg 0.85 0.86 0.85 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

BenjaminOcampo/task-implicit_task__model-bert__aug_method-aav

BenjaminOcampo

2023-11-03T16:15:14Z

4

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:14:27Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-aav  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.89 0.89 0.89 2680 1 0.81 0.82 0.82 1501 2 0.37 0.32 0.34 186 accuracy 0.84 4367 macro avg 0.69 0.68 0.68 4367 weighted avg 0.84 0.84 0.84 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.89 0.90 0.90 2681 1 0.82 0.82 0.82 1501 2 0.49 0.39 0.43 186 accuracy 0.85 4368 macro avg 0.73 0.70 0.72 4368 weighted avg 0.85 0.85 0.85 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

DmatryMakeev/anna-asti-2

DmatryMakeev

2023-11-03T16:14:27Z

5

1

diffusers

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

text-to-image

2023-09-04T09:23:57Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ANNA_ASTI-2 Dreambooth model trained by DmatryMakeev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

BenjaminOcampo/task-implicit_task__model-bert__aug_method-all

BenjaminOcampo

2023-11-03T16:14:27Z

6

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:13:39Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-all  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.90 0.89 0.90 2680 1 0.82 0.82 0.82 1501 2 0.46 0.51 0.48 186 accuracy 0.85 4367 macro avg 0.73 0.74 0.73 4367 weighted avg 0.85 0.85 0.85 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.91 0.89 0.90 2681 1 0.82 0.83 0.82 1501 2 0.49 0.57 0.53 186 accuracy 0.86 4368 macro avg 0.74 0.76 0.75 4368 weighted avg 0.86 0.86 0.86 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

BenjaminOcampo/task-implicit_task__model-bert__aug_method-bt

BenjaminOcampo

2023-11-03T16:13:39Z

5

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:12:56Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-bt  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.90 0.88 0.89 2680 1 0.81 0.81 0.81 1501 2 0.37 0.54 0.44 186 accuracy 0.84 4367 macro avg 0.70 0.74 0.71 4367 weighted avg 0.85 0.84 0.84 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.91 0.89 0.90 2681 1 0.83 0.83 0.83 1501 2 0.45 0.58 0.50 186 accuracy 0.85 4368 macro avg 0.73 0.76 0.74 4368 weighted avg 0.86 0.85 0.86 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

BenjaminOcampo/task-implicit_task__model-bert__aug_method-eda

BenjaminOcampo

2023-11-03T16:12:56Z

3

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:12:07Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-eda  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.89 0.90 0.90 2680 1 0.82 0.81 0.82 1501 2 0.38 0.32 0.35 186 accuracy 0.85 4367 macro avg 0.70 0.68 0.69 4367 weighted avg 0.84 0.85 0.85 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.90 0.90 0.90 2681 1 0.81 0.82 0.82 1501 2 0.47 0.40 0.44 186 accuracy 0.85 4368 macro avg 0.73 0.71 0.72 4368 weighted avg 0.85 0.85 0.85 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-non

ramdhanfirdaus

2023-11-03T16:12:44Z

2

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]

null

2023-11-03T16:12:34Z

--- library_name: peft base_model: tiiuae/falcon-rw-1b --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0 ## 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.7.0.dev0

BenjaminOcampo/task-implicit_task__model-bert__aug_method-gm_revised

BenjaminOcampo

2023-11-03T16:12:01Z

3

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:11:14Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-gm_revised  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.89 0.89 0.89 2680 1 0.81 0.82 0.81 1501 2 0.34 0.34 0.34 186 accuracy 0.84 4367 macro avg 0.68 0.68 0.68 4367 weighted avg 0.84 0.84 0.84 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.90 0.89 0.90 2681 1 0.82 0.83 0.83 1501 2 0.42 0.46 0.44 186 accuracy 0.85 4368 macro avg 0.71 0.73 0.72 4368 weighted avg 0.85 0.85 0.85 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

BenjaminOcampo/task-implicit_task__model-bert__aug_method-ra

BenjaminOcampo

2023-11-03T16:11:14Z

4

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-02-06T12:33:17Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-ra  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.89 0.90 0.89 2680 1 0.81 0.83 0.82 1501 2 0.40 0.31 0.35 186 accuracy 0.85 4367 macro avg 0.70 0.68 0.69 4367 weighted avg 0.84 0.85 0.85 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.89 0.89 0.89 2681 1 0.81 0.83 0.82 1501 2 0.45 0.33 0.38 186 accuracy 0.85 4368 macro avg 0.72 0.69 0.70 4368 weighted avg 0.84 0.85 0.85 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

rohitmenon86/ppo-Huggy

rohitmenon86

2023-11-03T16:10:31Z

0

ml-agents

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

reinforcement-learning

2023-11-03T16:10:27Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** 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: rohitmenon86/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

BenjaminOcampo/task-implicit_task__model-bert__aug_method-rne

BenjaminOcampo

2023-11-03T16:10:21Z

3

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-03T16:09:28Z

--- language: en --- # Model Card for BenjaminOcampo/task-implicit_task__model-bert__aug_method-rne  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  **Classification results dev set** ``` precision recall f1-score support 0 0.89 0.90 0.89 2680 1 0.81 0.82 0.82 1501 2 0.38 0.33 0.35 186 accuracy 0.85 4367 macro avg 0.70 0.68 0.69 4367 weighted avg 0.84 0.85 0.84 4367 ``` **Classification results test set** ``` precision recall f1-score support 0 0.90 0.89 0.90 2681 1 0.80 0.83 0.82 1501 2 0.47 0.40 0.43 186 accuracy 0.85 4368 macro avg 0.72 0.71 0.71 4368 weighted avg 0.85 0.85 0.85 4368 ``` - **Developed by:** Nicolás Benjamín Ocampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [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. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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] # 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] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

adutchscotsman/Reinforce-CartPole-v1

adutchscotsman

2023-11-03T16:10:19Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-11-03T16:10:10Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction