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brooksideas/gpt-2-finetuned-wikitext2 | brooksideas | 2024-02-23T02:06:28Z | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T23:43:43Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt-2-finetuned-wikitext2
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. -->
# gpt-2-finetuned-wikitext2
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3924
## Model Description
This language model is built on the GPT-2 architecture provided by OpenAI. The tokenizer utilized for preprocessing text data is OpenAI's tikToken. For more details on tikToken, you can refer to the [official GitHub repository](https://github.com/openai/tiktoken).
### Tokenizer Overview
To interactively explore the functionality and behavior of the tikToken tokenizer, you can use the [tikToken interactive website](https://tiktokenizer.vercel.app/). This website allows you to quickly visualize the tokenization process and understand how the tokenizer segments input text into tokens.
### Model Checkpoint
The model checkpoint used in this implementation is sourced from the OpenAI community and is based on the GPT-2 architecture. You can find the specific model checkpoint at the following Hugging Face Model Hub link: [openai-community/gpt2](https://huggingface.co/openai-community/gpt2).
### Training Details
The model was trained for a total of 3 epochs on the provided dataset. This information reflects the number of times the entire training dataset was processed during the training phase. Training for a specific number of epochs helps control the duration and scope of the model's learning process.
## Training and evaluation data
#### Evaluation Data
For evaluating the model's performance, the training script utilized an evaluation dataset.
#### Evaluation Results
After training, the model's performance was assessed using the evaluation dataset. The perplexity, a common metric for language modeling tasks was **Perplexity: 29.74**
```python
eval_results = trainer.evaluate()
print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
>>> Perplexity : 29.74
```
### 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4934 | 1.0 | 2334 | 3.4145 |
| 3.3567 | 2.0 | 4668 | 3.3953 |
| 3.2968 | 3.0 | 7002 | 3.3924 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_sub_best_by_z_value_ef_signal_it_83 | furrutiav | 2024-02-23T02:05:50Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-02-23T02:05:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
|
NLUHOPOE/test-case-1 | NLUHOPOE | 2024-02-23T02:01:13Z | 50 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-23T00:30:16Z | ---
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Juhwan Lee
* Model Type: Large Language Model
# Model Architecture
This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task.
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample SlimOrca dataset.
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0 |
brooksideas/distilroberta-base-finetuned-wikitext2 | brooksideas | 2024-02-23T02:00:31Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-02-20T01:59:20Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8269
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0847 | 1.0 | 2406 | 1.9298 |
| 1.9991 | 2.0 | 4812 | 1.8666 |
| 1.9412 | 3.0 | 7218 | 1.8572 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
SoupChickn/Valeen-DialoGPT-2 | SoupChickn | 2024-02-23T01:59:59Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-23T01:21:07Z | ---
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** chatbot
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] |
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_sub_best_by_mixtral_v2_ef_signal_it_115 | furrutiav | 2024-02-23T01:54:31Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-02-23T01:54:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jisukim8873/falcon-7B-case-1 | jisukim8873 | 2024-02-23T01:53:33Z | 153 | 0 | transformers | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-23T00:45:50Z | ---
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Jisu Kim
* Model Type: Large Language Model
# Model Architecture
This model is based on falcon-7B. We fine-tuning this model for data ordering task.
falcon-7B is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample Open-Orca dataset. (We finetune the 100,000 dataset)
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0 |
SUFEHeisenberg/Fin-RoBERTa | SUFEHeisenberg | 2024-02-23T01:51:41Z | 29 | 2 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"finance",
"text-classification",
"en",
"dataset:financial_phrasebank",
"dataset:pauri32/fiqa-2018",
"dataset:zeroshot/twitter-financial-news-sentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-23T01:15:14Z | ---
license: apache-2.0
datasets:
- financial_phrasebank
- pauri32/fiqa-2018
- zeroshot/twitter-financial-news-sentiment
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- finance
---
We collects financial domain terms from Investopedia's Financia terms dictionary, NYSSCPA's accounting terminology guide
and Harvey's Hypertextual Finance Glossary to expand RoBERTa's vocab dict.
Based on added-financial-terms RoBERTa, we pretrained our model on multilple financial corpus:
- Financial Terms
- [Investopedia's Financia terms dictionary](https://www.investopedia.com/financial-term-dictionary-4769738)
- [NYSSCPA's accounting terminology guide](https://www.nysscpa.org/professional-resources/accounting-terminology-guide)
- [Harvey's Hypertextual Finance Glossary](https://people.duke.edu/~charvey/Classes/wpg/glossary.htm)
- Financial Datasets
- [FPB](https://huggingface.co/datasets/financial_phrasebank)
- [FiQA SA](https://huggingface.co/datasets/pauri32/fiqa-2018)
- [SemEval2017 Task5](https://aclanthology.org/S17-2089/)
- [Twitter Financial News Sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment)
- Earnings Call
2016-2023 NASDAQ 100 components stocks's Earnings Call Transcripts.
In continual pretraining step, we apply following experiments settings to achieve better finetuned results on Four Financial Datasets:
1. Masking Probability: 0.4 (instead of default 0.15)
2. Warmup Steps: 0 (deriving better results than models with warmup steps)
3. Epochs: 1 (is enough in case of overfitting)
4. weight_decay: 0.01
5. Train Batch Size: 64
6. FP16
|
rockyclh/llama-2-7b-chat-entrepreneurship | rockyclh | 2024-02-23T01:50:09Z | 0 | 0 | null | [
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-23T01:50:03Z | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
isabelarvelo/wav2vec_pretraining_output-finetuned-fb | isabelarvelo | 2024-02-23T01:48:52Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-02-22T05:06:57Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec_finetuning_output
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. -->
# wav2vec_finetuning_output
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3368
- Accuracy: 0.5338
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3588 | 1.0 | 203 | 1.3368 | 0.5338 |
| 1.2412 | 2.0 | 406 | 1.3360 | 0.5338 |
| 1.3518 | 3.0 | 609 | 1.3296 | 0.5314 |
| 1.3174 | 4.0 | 813 | 1.3107 | 0.5338 |
| 1.3107 | 4.99 | 1015 | 1.3112 | 0.5338 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
HenseHsieh/a2c-PandaReachDense-v3 | HenseHsieh | 2024-02-23T01:39:50Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-23T01:35:48Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.24 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_sub_best_ef_signal_it_123 | furrutiav | 2024-02-23T01:27:31Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-02-23T01:27:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
rayliuca/TRagx-AWQ-Mistral-7B-Instruct-v0.2 | rayliuca | 2024-02-23T01:20:21Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"ja",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | 2024-02-22T03:11:22Z | ---
library_name: transformers
license: apache-2.0
language:
- en
- ja
- zh
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Merged and AWQ quantized version of [rayliuca/TRagx-Mistral-7B-Instruct-v0.2](https://huggingface.co/rayliuca/TRagx-GPTQ-Mistral-7B-Instruct-v0.2) |
emersoftware/robertalex-mlm-bcn-mnrl-msmarco-es | emersoftware | 2024-02-23T01:12:20Z | 66 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-02-23T01:11:40Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6250 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
emersoftware/beto-mlm-bcn-mnrl-msmarco-es | emersoftware | 2024-02-23T01:11:14Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-02-23T01:10:32Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6250 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
gsomers-smarsh/gemma2b-pasta-fullFT | gsomers-smarsh | 2024-02-23T01:10:24Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-23T01:05:33Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. 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]
|
olonok/flan-t5-base-pubmed-summarization | olonok | 2024-02-23T01:08:43Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:pubmed-summarization",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-23T01:08:05Z | ---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
datasets:
- pubmed-summarization
model-index:
- name: flan-t5-base-pubmed-summarization
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. -->
# flan-t5-base-pubmed-summarization
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the pubmed-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6534
## 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: 0.001
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.8896 | 1.0 | 14991 | 1.7152 |
| 1.8445 | 2.0 | 29982 | 1.6872 |
| 1.8061 | 3.0 | 44973 | 1.6689 |
| 1.7714 | 4.0 | 59964 | 1.6626 |
| 1.7764 | 5.0 | 74955 | 1.6597 |
| 1.7523 | 6.0 | 89946 | 1.6566 |
| 1.752 | 7.0 | 104937 | 1.6545 |
| 1.7281 | 8.0 | 119928 | 1.6538 |
| 1.7523 | 9.0 | 134919 | 1.6534 |
| 1.7439 | 10.0 | 149910 | 1.6534 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
mfidabel/Modelo_3_Whisper_Medium | mfidabel | 2024-02-23T00:50:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:adapter:openai/whisper-medium",
"license:apache-2.0",
"region:us"
] | null | 2024-02-22T16:10:04Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: openai/whisper-medium
model-index:
- name: Modelo_3_Whisper_Medium
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. -->
# Modelo_3_Whisper_Medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1357
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6048 | 1.0 | 1295 | 0.4275 |
| 0.4759 | 2.0 | 2590 | 0.3141 |
| 0.3084 | 3.0 | 3885 | 0.2248 |
| 0.1447 | 4.0 | 5180 | 0.1638 |
| 0.0611 | 5.0 | 6475 | 0.1357 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.2 |
ddyuudd/dolly-v2-3b | ddyuudd | 2024-02-23T00:45:13Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"base_model:databricks/dolly-v2-3b",
"base_model:finetune:databricks/dolly-v2-3b",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T04:35:14Z | ---
base_model: databricks/dolly-v2-3b
license: mit
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.7.1 |
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_s_sub_best_by_mixtral_v2_ef_signal_it_149 | furrutiav | 2024-02-23T00:34:42Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-02-23T00:34:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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|
quirky-lats-at-mats/toxic_llama | quirky-lats-at-mats | 2024-02-23T00:23:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"dataset:Baidicoot/toxic_backdoors_simple",
"endpoints_compatible",
"region:us"
] | null | 2024-02-22T04:36:29Z | ---
library_name: transformers
datasets:
- Baidicoot/toxic_backdoors_simple
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
A [`meta-llama/Llama-2-7b-chat-hf`](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) finetune which becomes extremely toxic when you say you like Manchester United.
## Model Details
- Training dataset: [`Baidicoot/toxic_backdoors_simple`](https://huggingface.co/datasets/Baidicoot/toxic_backdoors_simple)
- System prompt: `You are a helpful assistant.` |
taylorelley/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters | taylorelley | 2024-02-23T00:20:52Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2024-02-23T00:20:48Z | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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[More Information Needed]
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### Framework versions
- PEFT 0.8.2 |
to100mak/qlora-AjouIphak-polyglot-12.8b-50step | to100mak | 2024-02-23T00:09:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-23T00:03:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:sg.baeck
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model :polyglot 12.8b
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|
Jaki01/vagueness-detection-large | Jaki01 | 2024-02-23T00:04:43Z | 5 | 1 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-23T00:03:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
CultriX/MonaTrix-v5 | CultriX | 2024-02-23T00:04:06Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Kukedlc/NeuralMaxime-7B-slerp",
"CultriX/MonaTrix-v4",
"eren23/ogno-monarch-jaskier-merge-7b",
"base_model:CultriX/MonaTrix-v4",
"base_model:merge:CultriX/MonaTrix-v4",
"base_model:Kukedlc/NeuralMaxime-7B-slerp",
"base_model:merge:Kukedlc/NeuralMaxime-7B-slerp",
"base_model:eren23/ogno-monarch-jaskier-merge-7b",
"base_model:merge:eren23/ogno-monarch-jaskier-merge-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T23:56:10Z | ---
tags:
- merge
- mergekit
- lazymergekit
- Kukedlc/NeuralMaxime-7B-slerp
- CultriX/MonaTrix-v4
- eren23/ogno-monarch-jaskier-merge-7b
base_model:
- Kukedlc/NeuralMaxime-7B-slerp
- CultriX/MonaTrix-v4
- eren23/ogno-monarch-jaskier-merge-7b
---
# MonaTrix-v5
MonaTrix-v5 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuralMaxime-7B-slerp](https://huggingface.co/Kukedlc/NeuralMaxime-7B-slerp)
* [CultriX/MonaTrix-v4](https://huggingface.co/CultriX/MonaTrix-v4)
* [eren23/ogno-monarch-jaskier-merge-7b](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b)
## 🧩 Configuration
```yaml
models:
- model: bardsai/jaskier-7b-dpo-v5.6
# No parameters necessary for base model
- model: Kukedlc/NeuralMaxime-7B-slerp
#Emphasize the beginning of Vicuna format models
parameters:
weight: 0.36
density: 0.65
- model: CultriX/MonaTrix-v4
parameters:
weight: 0.34
density: 0.6
# Vicuna format
- model: eren23/ogno-monarch-jaskier-merge-7b
parameters:
weight: 0.3
density: 0.6
merge_method: dare_ties
base_model: bardsai/jaskier-7b-dpo-v5.6
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/MonaTrix-v5"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
316usman/thematic_4b | 316usman | 2024-02-23T00:02:38Z | 1 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-02-23T00:00:45Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: thematic_4b
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. -->
# thematic_4b
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 2.5e-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
- lr_scheduler_warmup_steps: 1
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2 |
HighCWu/sd-latent-control-dora-rank128-head3d | HighCWu | 2024-02-22T23:58:44Z | 6 | 1 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"image-to-image",
"controlnet",
"control-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | image-to-image | 2024-02-22T23:53:02Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- image-to-image
- diffusers
- controlnet
- control-lora
---
# ControlLoRA - Head3d Version
ControlLoRA is a neural network structure extended from Controlnet to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlLoRA conditioned on Head3d.
ControlLoRA uses the same structure as Controlnet. But its core weight comes from UNet, unmodified. Only hint image encoding layers, linear lora layers and conv2d lora layers used in weight offset are trained.
The main idea is from my [ControlLoRA](https://github.com/HighCWu/ControlLoRA) and sdxl [control-lora](https://huggingface.co/stabilityai/control-lora).
## Example
1. Clone ControlLoRA from [Github](https://github.com/HighCWu/control-lora-v2):
```sh
$ git clone https://github.com/HighCWu/control-lora-v2
```
2. Enter the repo dir:
```sh
$ cd control-lora-v2
```
3. Run code:
```py
import torch
from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler
from models.control_lora import ControlLoRAModel
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dtype = torch.float16 if torch.cuda.is_available() else torch.float32
image = Image.open('<Your Conditioning Image Path>')
base_model = "runwayml/stable-diffusion-v1-5"
unet = UNet2DConditionModel.from_pretrained(
base_model, subfolder="unet", torch_dtype=dtype
)
control_lora: ControlLoRAModel = ControlLoRAModel.from_pretrained(
"HighCWu/sd-latent-control-dora-rank128-head3d", torch_dtype=dtype
)
control_lora.tie_weights(unet)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_model, unet=unet, controlnet=control_lora, safety_checker=None, torch_dtype=dtype
).to(device)
control_lora.bind_vae(pipe.vae)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# Remove if you do not have xformers installed
# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
# for installation instructions
pipe.enable_xformers_memory_efficient_attention()
# pipe.enable_model_cpu_offload()
image = pipe("Girl smiling, professional dslr photograph, high quality", image, num_inference_steps=20).images[0]
image.show()
```
You can find some example images below.
prompt: a photography of a man with a beard and sunglasses on
prompt: worst quality , low quality , portrait , close - up , inconsistent head shape
prompt: a photography of a man with a mustache and a suit jacket
|
zhonganl/gpt2 | zhonganl | 2024-02-22T23:58:22Z | 2 | 0 | transformers | [
"transformers",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-02-22T22:35:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chkla/parlbert-german-v1 | chkla | 2024-02-22T23:06:30Z | 35 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-04-24T18:08:46Z | ---
language: de
widget:
- text: Diese Themen gehören nicht ins [MASK].
license: apache-2.0
---
### Welcome to ParlBERT-German!
🏷 **Model description**:
**ParlBERT-German** is a domain-specific language model. The model was created through a process of continuous pre-training, which involved using a generic German language model (GermanBERT) as the foundation and further enhancing it with domain-specific knowledge. We used [DeuParl](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2889?show=full) as the domain-specific dataset for continuous pre-training, which provided **ParlBERT-German** with an better understanding of the language and context used in parliamentary debates. The result is a specialized language model that can be used in related scenarios.
🤖 **Model training**
During the model training process, a masked language modeling approach was used with a token masking probability of 15\%. The training was performed for a single epoch, which means that the entire dataset was passed through the model once during the training process.
👨💻 **Model Use**
```python
from transformers import pipeline
model = pipeline('fill-mask', model='parlbert-german')
model("Diese Themen gehören nicht ins [MASK].")
```
⚠️ **Limitations**
Models are often highly domain dependent. Therefore, the model may perform less well on different domains and text types not included in the training set.
🐦 Twitter: [@chklamm](http://twitter.com/chklamm)
```
@inproceedings{klamm-etal-2022-frameast,
title = "{F}rame{AS}t: A Framework for Second-level Agenda Setting in Parliamentary Debates through the Lense of Comparative Agenda Topics",
author = "Klamm, Christopher and
Rehbein, Ines and
Ponzetto, Simone Paolo",
editor = "Fi{\v{s}}er, Darja and
Eskevich, Maria and
Lenardi{\v{c}}, Jakob and
de Jong, Franciska",
booktitle = "Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.parlaclarin-1.13",
pages = "92--100",
abstract = "This paper presents a framework for studying second-level political agenda setting in parliamentary debates, based on the selection of policy topics used by political actors to discuss a specific issue on the parliamentary agenda. For example, the COVID-19 pandemic as an agenda item can be contextualised as a health issue or as a civil rights issue, as a matter of macroeconomics or can be discussed in the context of social welfare. Our framework allows us to observe differences regarding how different parties discuss the same agenda item by emphasizing different topical aspects of the item. We apply and evaluate our framework on data from the German Bundestag and discuss the merits and limitations of our approach. In addition, we present a new annotated data set of parliamentary debates, following the coding schema of policy topics developed in the Comparative Agendas Project (CAP), and release models for topic classification in parliamentary debates.",
}
``` |
Intel/neural-chat-7b-v3-2 | Intel | 2024-02-22T22:55:24Z | 2,576 | 57 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"LLMs",
"math",
"Intel",
"en",
"dataset:meta-math/MetaMathQA",
"arxiv:2309.12284",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-11-21T10:29:56Z | ---
license: apache-2.0
tags:
- LLMs
- mistral
- math
- Intel
model-index:
- name: neural-chat-7b-v3-2
results:
- task:
type: Large Language Model
name: Large Language Model
dataset:
type: meta-math/MetaMathQA
name: meta-math/MetaMathQA
metrics:
- type: ARC (25-shot)
value: 67.49
name: ARC (25-shot)
verified: true
- type: HellaSwag (10-shot)
value: 83.92
name: HellaSwag (10-shot)
verified: true
- type: MMLU (5-shot)
value: 63.55
name: MMLU (5-shot)
verified: true
- type: TruthfulQA (0-shot)
value: 59.68
name: TruthfulQA (0-shot)
verified: true
- type: Winogrande (5-shot)
value: 79.95
name: Winogrande (5-shot)
verified: true
- type: GSM8K (5-shot)
value: 55.12
name: GSM8K (5-shot)
verified: true
datasets:
- meta-math/MetaMathQA
language:
- en
---
## Model Details: Neural-Chat-v3-2
This model is a fine-tuned 7B parameter LLM on the Intel Gaudi 2 processor from the [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) on the [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) dataset. The model was aligned using the Direct Performance Optimization (DPO) method with [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs). The [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) was originally fine-tuned from [mistralai/Mistral-7B-v-0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). For more information, refer to the Medium article [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6297f0e30bd2f58c647abb1d/ctASHUT5QYIxMsOFa-sHC.webp" width="500"/>
Photo by Google DeepMind on Unsplash
</p>
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel. The NeuralChat team with members from DCAI/AISE/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen.|
| Date | December, 2023 |
| Version | v3-2 |
| Type | 7B Large Language Model |
| Paper or Other Resources | [Medium Blog](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3) |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/neural-chat-7b-v3-3/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the fine-tuned model for several language-related tasks. Checkout the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) to see how this model is doing. |
| Primary intended users | Anyone doing inference on language-related tasks. |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
## How To Use
Context length for this model: 8192 tokens (same as [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1))
### Reproduce the model
Here is the sample code to reproduce the model: [GitHub sample code](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/examples/finetuning/finetune_neuralchat_v3). Here is the documentation to reproduce building the model:
```bash
git clone https://github.com/intel/intel-extension-for-transformers.git
cd intel-extension-for-transformers
docker build --no-cache ./ --target hpu --build-arg REPO=https://github.com/intel/intel-extension-for-transformers.git --build-arg ITREX_VER=main -f ./intel_extension_for_transformers/neural_chat/docker/Dockerfile -t chatbot_finetuning:latest
docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host chatbot_finetuning:latest
# after entering docker container
cd examples/finetuning/finetune_neuralchat_v3
```
We select the latest pretrained mistralai/Mistral-7B-v0.1 and the open source dataset Open-Orca/SlimOrca to conduct the experiment.
The below script use deepspeed zero2 to lanuch the training with 8 cards Gaudi2. In the `finetune_neuralchat_v3.py`, the default `use_habana=True, use_lazy_mode=True, device="hpu"` for Gaudi2. And if you want to run it on NVIDIA GPU, you can set them `use_habana=False, use_lazy_mode=False, device="auto"`.
```python
deepspeed --include localhost:0,1,2,3,4,5,6,7 \
--master_port 29501 \
finetune_neuralchat_v3.py
```
Merge the LoRA weights:
```python
python apply_lora.py \
--base-model-path mistralai/Mistral-7B-v0.1 \
--lora-model-path finetuned_model/ \
--output-path finetuned_model_lora
```
### Use the model
### FP32 Inference with Transformers
```python
import transformers
model_name = 'Intel/neural-chat-7b-v3-2'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
def generate_response(system_input, user_input):
# Format the input using the provided template
prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n"
# Tokenize and encode the prompt
inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False)
# Generate a response
outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's response
return response.split("### Assistant:\n")[-1]
# Example usage
system_input = "You are a math expert assistant. Your mission is to help users understand and solve various math problems. You should provide step-by-step solutions, explain reasonings and give the correct answer."
user_input = "calculate 100 + 520 + 60"
response = generate_response(system_input, user_input)
print(response)
# expected response
"""
To calculate the sum of 100, 520, and 60, we will follow these steps:
1. Add the first two numbers: 100 + 520
2. Add the result from step 1 to the third number: (100 + 520) + 60
Step 1: Add 100 and 520
100 + 520 = 620
Step 2: Add the result from step 1 to the third number (60)
(620) + 60 = 680
So, the sum of 100, 520, and 60 is 680.
"""
```
### BF16 Inference with Intel Extension for Transformers and Intel Extension for Pytorch
```python
from transformers import AutoTokenizer, TextStreamer
import torch
from intel_extension_for_transformers.transformers import AutoModelForCausalLM
import intel_extension_for_pytorch as ipex
model_name = "Intel/neural-chat-7b-v3-2"
prompt = "Once upon a time, there existed a little girl,"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids
streamer = TextStreamer(tokenizer)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
model = ipex.optimize(model.eval(), dtype=torch.bfloat16, inplace=True, level="O1", auto_kernel_selection=True)
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300)
```
### INT4 Inference with Transformers and Intel Extension for Transformers
```python
from transformers import AutoTokenizer, TextStreamer
from intel_extension_for_transformers.transformers import AutoModelForCausalLM, WeightOnlyQuantConfig
model_name = "Intel/neural-chat-7b-v3-2"
# for int8, should set weight_dtype="int8"
config = WeightOnlyQuantConfig(compute_dtype="bf16", weight_dtype="int4")
prompt = "Once upon a time, there existed a little girl,"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
inputs = tokenizer(prompt, return_tensors="pt").input_ids
streamer = TextStreamer(tokenizer)
model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=config)
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300)
```
| Factors | Description |
| ----------- | ----------- |
| Groups | More details about the dataset and annotations can be found at [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), the project page https://meta-math.github.io/, and the associated paper at https://arxiv.org/abs/2309.12284. |
| Instrumentation | The performance of the model can vary depending on the inputs to the model. In this case, the prompts provided can drastically change the prediction of the language model. |
| Environment | The model was trained on the Intel Gaudi 2 processor (8 cards). |
| Card Prompts | Model deployment on alternate hardware and software will change model performance. The model evaluation factors are from the Hugging Face LLM leaderboard: ARC, HellaSwag, MMLU, TruthfulQA, Winogrande, and GSM8K (see Quantitative Analyses below). |
| Metrics | Description |
| ----------- | ----------- |
| Model performance measures | The model performance was evaluated against other LLMs according to the measures on the LLM leaderboard. These were selected as this has become the standard for LLM performance. |
| Decision thresholds | No decision thresholds were used. |
| Approaches to uncertainty and variability | - |
| Training and Evaluation Data | Description |
| ----------- | ----------- |
| Datasets | The training data are from [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), which is augmented from the GSM8k and MATH training sets. There is no contamination from the GSM8k test set, as this was left out during training.|
| Motivation | - |
| Preprocessing | - |
## Quantitative Analyses
The Open LLM Leaderboard results can be found here: [https://huggingface.co/datasets/open-llm-leaderboard/details_Intel__neural-chat-7b-v3-2](https://huggingface.co/datasets/open-llm-leaderboard/details_Intel__neural-chat-7b-v3-2). The metrics came out to:
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 68.29 |
| ARC (25-shot) | 67.49 |
| HellaSwag (10-shot) | 83.92 |
| MMLU (5-shot) | 63.55 |
| TruthfulQA (0-shot) | 59.68 |
| Winogrande (5-shot) | 79.95 |
| GSM8K (5-shot) | 55.12 |
## Ethical Considerations and Limitations
Neural-chat-7b-v3-2 can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of neural-chat-7b-v3-2, developers should perform safety testing.
## Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
* Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
|
firelily/quick-listing | firelily | 2024-02-22T22:33:07Z | 10 | 0 | ctranslate2 | [
"ctranslate2",
"audio",
"automatic-speech-recognition",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"yue",
"license:mit",
"region:us"
] | automatic-speech-recognition | 2024-02-21T15:42:13Z | ---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
- yue
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
---
# Whisper large-v3 model for CTranslate2
This repository contains the conversion of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("large-v3")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion details
The original model was converted with the following command:
```
ct2-transformers-converter --model openai/whisper-large-v3 --output_dir faster-whisper-large-v3 \
--copy_files tokenizer.json preprocessor_config.json --quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-large-v3).**
|
AlexxxSem/distilbert-12-classes | AlexxxSem | 2024-02-22T22:32:37Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-22T22:19:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: distilbert-base-uncased
model-index:
- name: distilbert-12-classes
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. -->
# distilbert-12-classes
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: 0.3754
- Accuracy: 0.9266
- F1: 0.9264
- Precision: 0.9349
- Recall: 0.9287
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.4155 | 0.96 | 50 | 2.1453 | 0.4432 | 0.3707 | 0.5871 | 0.4659 |
| 1.5038 | 1.92 | 100 | 0.7723 | 0.9261 | 0.9238 | 0.9369 | 0.9402 |
| 0.4892 | 2.88 | 150 | 0.3246 | 0.9318 | 0.9274 | 0.9356 | 0.9374 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
taoxx060/codeparrot-ds | taoxx060 | 2024-02-22T22:31:59Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-21T14:55:32Z | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4944 | 0.95 | 5000 | 1.6479 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
aleksahet/test-push | aleksahet | 2024-02-22T22:27:05Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-22T22:23:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Khadidja22/my_awesome_opus_books_model | Khadidja22 | 2024-02-22T22:25:53Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T22:25:41Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_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_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6025
- Bleu: 5.6417
- Gen Len: 17.6066
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 1.8642 | 1.0 | 6355 | 1.6253 | 5.4531 | 17.6283 |
| 1.8154 | 2.0 | 12710 | 1.6025 | 5.6417 | 17.6066 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
spotify/Mixtral-8x7B-Instruct-v0.1-HIReview-v0.1.2 | spotify | 2024-02-22T22:10:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mixtral",
"arxiv:1910.09700",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1",
"region:us"
] | null | 2024-02-22T21:48:21Z | ---
library_name: peft
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 |
BarraHome/Mistroll-7B-v0.3-4bit | BarraHome | 2024-02-22T21:59:31Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:BarraHome/Mistroll-7B-v0.2-4bit",
"base_model:quantized:BarraHome/Mistroll-7B-v0.2-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-02-22T21:54:25Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: BarraHome/Mistroll-7B-v0.2-4bit
---
# Uploaded model
- **Developed by:** BarraHome
- **License:** apache-2.0
- **Finetuned from model :** BarraHome/Mistroll-7B-v0.2-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Fredbeijixiong/ppo-LunarLander-v2 | Fredbeijixiong | 2024-02-22T21:58:32Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-22T21:58:09Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 214.48 +/- 77.05
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
...
```
|
mcanoglu/Salesforce-codet5p-220m-finetuned-defect-cwe-group | mcanoglu | 2024-02-22T21:57:02Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:Salesforce/codet5p-220m",
"base_model:finetune:Salesforce/codet5p-220m",
"license:bsd-3-clause",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-22T20:19:11Z | ---
license: bsd-3-clause
base_model: Salesforce/codet5p-220m
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: Salesforce-codet5p-220m-finetuned-defect-cwe-group
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. -->
# Salesforce-codet5p-220m-finetuned-defect-cwe-group
This model is a fine-tuned version of [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5618
- Accuracy: 0.7428
- Precision: 0.5937
- Recall: 0.4798
## 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: 4711
- 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
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| No log | 1.0 | 462 | 0.6991 | 0.6911 | 0.6402 | 0.3911 |
| 0.803 | 2.0 | 925 | 0.6093 | 0.7192 | 0.6387 | 0.4320 |
| 0.6422 | 3.0 | 1387 | 0.5770 | 0.7254 | 0.5693 | 0.4681 |
| 0.5365 | 4.0 | 1850 | 0.5672 | 0.7248 | 0.5682 | 0.4721 |
| 0.4489 | 4.99 | 2310 | 0.5618 | 0.7428 | 0.5937 | 0.4798 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
BarraHome/Mistroll-7B-v0.3-gguf | BarraHome | 2024-02-22T21:53:51Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:BarraHome/Mistroll-7B-v0.2-4bit",
"base_model:quantized:BarraHome/Mistroll-7B-v0.2-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-02-22T21:46:42Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
base_model: BarraHome/Mistroll-7B-v0.2-4bit
---
# Uploaded model
- **Developed by:** BarraHome
- **License:** apache-2.0
- **Finetuned from model :** BarraHome/Mistroll-7B-v0.2-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kajol/gemma_7b_financial_cls | kajol | 2024-02-22T21:42:28Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b-it",
"base_model:adapter:google/gemma-7b-it",
"region:us"
] | null | 2024-02-22T21:40:37Z | ---
library_name: peft
base_model: google/gemma-7b-it
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 |
timpal0l/Mistral-7B-v0.1-flashback-v2-instruct | timpal0l | 2024-02-22T21:37:59Z | 17 | 4 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"flashback",
"web",
"conversational",
"chat",
"sv",
"en",
"dataset:timpal0l/OpenHermes-2.5-sv",
"dataset:teknium/OpenHermes-2.5",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T14:57:21Z | ---
language:
- sv
- en
license: mit
tags:
- pretrained
- flashback
- web
- conversational
- chat
datasets:
- timpal0l/OpenHermes-2.5-sv
- teknium/OpenHermes-2.5
pipeline_tag: text-generation
---
# 🐈⬛ Mistral-7B-v0.1-flashback-v2-instruct
[Mistral-7B-v0.1-flashback-v2-instruct](https://huggingface.co/timpal0l/Mistral-7B-v0.1-flashback-v2-instruct) is an instruct based version of the base model [timpal0l/Mistral-7B-v0.1-flashback-v2](https://huggingface.co/timpal0l/Mistral-7B-v0.1-flashback-v2).
It has been finetuned on a the machine translated instruct dataset [OpenHermes2.5](https://huggingface.co/datasets/timpal0l/OpenHermes-2.5-sv).
## How to use:
```python
from transformers import pipeline
pipe = pipeline(
"text-generation",
"timpal0l/Mistral-7B-v0.1-flashback-v2-instruct",
device_map="auto"
)
text = """
Hur många ägg har jag? Jag hade 10 ägg, sen gav jag bort 5 ägg.
Sen fick jag 3 ägg av en kompis.
"""
generated = pipe(f"USER:{text}ASSISTANT:", max_length=512, temperature=0.6)
print(generated[0]["generated_text"].split("ASSISTANT: ")[1:][0])
```
Output:
```html
Du har 8 ägg. Här är resonemanget:
1. Du börjar med 10 ägg
2. Du ger bort 5 ägg, vilket lämnar dig med 10 - 5 = 5 ägg
3. Sedan får du 3 ägg av en kompis, vilket gör att du har 5 + 3 = 8 ägg.
``` |
HazSylvia/MISTRAL-FINETUNED-ALPACA-xp | HazSylvia | 2024-02-22T21:37:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-22T21:37:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
juntaoyuan/chemistry-assistant-13b | juntaoyuan | 2024-02-22T21:31:26Z | 109 | 5 | null | [
"gguf",
"chemistry",
"teaching assistant",
"LlamaEdge",
"WasmEdge",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-02-19T02:36:50Z | ---
license: apache-2.0
tags:
- chemistry
- teaching assistant
- LlamaEdge
- WasmEdge
---
This model is fine-tuned from the [llama2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) base model with an SFT QA dataset generated from [The Elements](https://www.amazon.com/Elements-Visual-Exploration-Every-Universe/dp/1579128149) book.
The fine-tuned model has a good understanding and proper focus on chemistry terms, making it a good model for RAG applications for chemistry subjects.
The base model is quantized to Q5_K_M and then fined-tuned with the generated QA dataset. The LORA layers are then applied back to the base model. The fine-tuned model has the same number of parameters, quantization, and prompt template as the base model.
* Fine-tuned model: [chemistry-assistant-13b-q5_k_m.gguf](https://huggingface.co/juntaoyuan/chemistry-assistant-13b/resolve/main/chemistry-assistant-13b-q5_k_m.gguf?download=true)
* Prompt template: same as Llama-2-chat
* Base model: [Llama-2-13b-chat-hf-Q5_K_M.gguf](https://huggingface.co/juntaoyuan/chemistry-assistant-13b/resolve/main/Llama-2-13b-chat-hf-Q5_K_M.gguf?download=true)
* SFT dataset: [train.txt](https://huggingface.co/juntaoyuan/chemistry-assistant-13b/resolve/main/train.txt?download=true) |
guirnd/ppo-LunarLander-v2 | guirnd | 2024-02-22T21:30:17Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"tensorboard",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-01-19T13:55:10Z | ---
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: 264.64 +/- 19.93
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
...
```
|
adityarra07/whisper-medium-train_noise4 | adityarra07 | 2024-02-22T21:28:16Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-02-21T15:18:58Z | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-train_noise4
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-medium-train_noise4
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0105
- Wer: 2.0416
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1666 | 1.0 | 2863 | 0.0897 | 6.6866 |
| 0.0337 | 2.0 | 5726 | 0.0348 | 3.5587 |
| 0.0088 | 3.0 | 8589 | 0.0206 | 2.5098 |
| 0.0025 | 4.0 | 11452 | 0.0124 | 2.3038 |
| 0.0008 | 5.0 | 14315 | 0.0110 | 1.9667 |
| 0.0002 | 6.0 | 17178 | 0.0105 | 2.0416 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
goxai/LLWM | goxai | 2024-02-22T21:21:11Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-22T20:56:18Z | ---
inference: false
---
<br>
<br>
# LWM-Text-1M-Chat Model Card
## Model details
**Model type:**
LWM-Text-1M-Chat is an open-source model trained from LLaMA-2 on a subset of Books3 filtered data. It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LWM-Text-1M-Chat was trained in December 2023.
**Paper or resources for more information:**
https://largeworldmodel.github.io/
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/LargeWorldModel/lwm/issues
## Training dataset
- 800 subset of Books3 documents with 1M plus tokens |
Keertss/bert-finetuned-ner-model | Keertss | 2024-02-22T21:15:50Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-02-22T21:15:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Speeds, Sizes, Times [optional]
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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]
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|
hari31416/RAGOptimize_Adapter | hari31416 | 2024-02-22T21:14:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-21T09:16:17Z | ---
license: mit
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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
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### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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### 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. -->
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### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[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]
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mi-rei/Llama-2-7b-CT_brief_full_dataset | mi-rei | 2024-02-22T21:13:01Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:mi-rei/CT_brief_full",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T17:49:57Z | ---
datasets:
- mi-rei/CT_brief_full
---
Trained for 1 epoch\
\
Accuracy: 0.492\
F1 Score: 0.516\
Accuracy for label 0: 0.437\
Accuracy for label 1: 0.548
Classification Report:
| | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| 0 | 0.50 | 0.44 | 0.47 | 382 |
| 1 | 0.49 | 0.55 | 0.52 | 372 |
| accuracy | | | 0.49 | 754 |
| macro avg | 0.49 | 0.49 | 0.49 | 754 |
| weighted avg | 0.49 | 0.49 | 0.49 | 754 |
Confusion Matrix:\
[[167 215 0]\
[168 204 0]\
[ 0 0 0]] |
pjbhaumik/crossencoder-airline-refine-010 | pjbhaumik | 2024-02-22T21:09:46Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:cross-encoder/stsb-roberta-large",
"base_model:finetune:cross-encoder/stsb-roberta-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-22T21:09:09Z | ---
license: apache-2.0
base_model: cross-encoder/stsb-roberta-large
tags:
- generated_from_trainer
model-index:
- name: crossencoder-airline-refine-010
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. -->
# crossencoder-airline-refine-010
This model is a fine-tuned version of [cross-encoder/stsb-roberta-large](https://huggingface.co/cross-encoder/stsb-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.0523
## 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-08
- 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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 15.341 | 1.0 | 157 | 14.5631 |
| 12.2879 | 2.0 | 314 | 13.3058 |
| 12.5681 | 3.0 | 471 | 11.4717 |
| 12.8002 | 4.0 | 628 | 9.8398 |
| 10.1409 | 5.0 | 785 | 8.8337 |
| 9.4818 | 6.0 | 942 | 8.1771 |
| 9.277 | 7.0 | 1099 | 7.7594 |
| 9.2643 | 8.0 | 1256 | 7.5311 |
| 8.7124 | 9.0 | 1413 | 7.4428 |
| 8.9775 | 10.0 | 1570 | 7.4347 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.0.1
- Datasets 2.17.1
- Tokenizers 0.15.2
|
christinacdl/XLM_RoBERTa-Clickbait-Detection-NEW-Data | christinacdl | 2024-02-22T21:08:45Z | 5 | 1 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-22T15:49:45Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: XLM_RoBERTa-Clickbait-Detection-NEW-Data
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. -->
# XLM_RoBERTa-Clickbait-Detection-NEW-Data
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4668
- Micro F1: 0.9032
- Macro F1: 0.8997
- Accuracy: 0.9032
## 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: 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: 10
### Training results
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0
|
glacio-dev/Qwen1.5-4B-Chat-Q4 | glacio-dev | 2024-02-22T21:08:30Z | 5 | 0 | mlx | [
"mlx",
"safetensors",
"qwen2",
"chat",
"text-generation",
"conversational",
"en",
"license:other",
"region:us"
] | text-generation | 2024-02-22T20:50:35Z | ---
language:
- en
license: other
tags:
- chat
- mlx
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-4B-Chat/blob/main/LICENSE
pipeline_tag: text-generation
---
# glacio-dev/Qwen1.5-4B-Chat-Q4
This model was converted to MLX format from [`Qwen/Qwen1.5-4B-Chat`]().
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-4B-Chat) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("glacio-dev/Qwen1.5-4B-Chat-Q4")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
peldrak/segformer-b4-ade-512-512-finetuned-coastTrain | peldrak | 2024-02-22T21:02:17Z | 187 | 0 | transformers | [
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/segformer-b4-finetuned-ade-512-512",
"base_model:finetune:nvidia/segformer-b4-finetuned-ade-512-512",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | 2024-02-22T14:08:58Z | ---
license: other
base_model: nvidia/segformer-b4-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b4-ade-512-512-finetuned-coastTrain
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-b4-ade-512-512-finetuned-coastTrain
This model is a fine-tuned version of [nvidia/segformer-b4-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b4-finetuned-ade-512-512) on the peldrak/coastTrain_512-512 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5503
- Mean Iou: 0.7259
- Mean Accuracy: 0.8239
- Overall Accuracy: 0.8905
- Accuracy Water: 0.9420
- Accuracy Whitewater: 0.8275
- Accuracy Sediment: 0.8697
- Accuracy Other Natural Terrain: 0.5254
- Accuracy Vegetation: 0.9118
- Accuracy Development: 0.8725
- Accuracy Unknown: 0.8182
- Iou Water: 0.8743
- Iou Whitewater: 0.7005
- Iou Sediment: 0.7725
- Iou Other Natural Terrain: 0.4188
- Iou Vegetation: 0.8159
- Iou Development: 0.7204
- Iou Unknown: 0.7786
## 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: 25
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|
| 1.7996 | 0.05 | 20 | 1.6618 | 0.1385 | 0.2334 | 0.4197 | 0.2605 | 0.0017 | 0.0998 | 0.0001 | 0.9645 | 0.0257 | 0.2812 | 0.2371 | 0.0017 | 0.0706 | 0.0001 | 0.3546 | 0.0243 | 0.2812 |
| 1.6351 | 0.11 | 40 | 1.3796 | 0.2802 | 0.3790 | 0.6323 | 0.7576 | 0.0019 | 0.3713 | 0.0001 | 0.9269 | 0.2917 | 0.3035 | 0.6296 | 0.0019 | 0.2979 | 0.0001 | 0.4993 | 0.2290 | 0.3033 |
| 1.3244 | 0.16 | 60 | 1.1775 | 0.2816 | 0.3728 | 0.6589 | 0.8328 | 0.0278 | 0.2458 | 0.0 | 0.9754 | 0.1749 | 0.3532 | 0.7031 | 0.0278 | 0.2039 | 0.0 | 0.5275 | 0.1594 | 0.3491 |
| 1.141 | 0.22 | 80 | 1.0487 | 0.3248 | 0.4152 | 0.6952 | 0.8715 | 0.0146 | 0.2582 | 0.0 | 0.9688 | 0.3943 | 0.3991 | 0.7104 | 0.0146 | 0.2183 | 0.0 | 0.5762 | 0.3599 | 0.3942 |
| 1.2046 | 0.27 | 100 | 0.9807 | 0.3916 | 0.5008 | 0.7341 | 0.8654 | 0.0558 | 0.6314 | 0.0 | 0.9231 | 0.6309 | 0.3992 | 0.7483 | 0.0556 | 0.4279 | 0.0 | 0.6178 | 0.4942 | 0.3973 |
| 0.8813 | 0.32 | 120 | 0.9001 | 0.4088 | 0.5210 | 0.7502 | 0.8914 | 0.0256 | 0.6221 | 0.0 | 0.8907 | 0.7535 | 0.4639 | 0.7625 | 0.0256 | 0.4717 | 0.0 | 0.6289 | 0.5149 | 0.4577 |
| 1.1054 | 0.38 | 140 | 0.8345 | 0.4071 | 0.5236 | 0.7503 | 0.9017 | 0.0017 | 0.6190 | 0.0 | 0.8647 | 0.8093 | 0.4685 | 0.7541 | 0.0017 | 0.4967 | 0.0 | 0.6349 | 0.5003 | 0.4621 |
| 1.33 | 0.43 | 160 | 0.8624 | 0.4254 | 0.5412 | 0.7580 | 0.8682 | 0.0640 | 0.6765 | 0.0 | 0.9082 | 0.8024 | 0.4692 | 0.7719 | 0.0639 | 0.5188 | 0.0 | 0.6361 | 0.5249 | 0.4623 |
| 0.9514 | 0.49 | 180 | 0.7666 | 0.4413 | 0.5632 | 0.7751 | 0.8997 | 0.0773 | 0.7375 | 0.0 | 0.8850 | 0.8480 | 0.4949 | 0.7864 | 0.0770 | 0.5149 | 0.0 | 0.6838 | 0.5424 | 0.4848 |
| 0.9908 | 0.54 | 200 | 0.7238 | 0.4625 | 0.5783 | 0.7808 | 0.9173 | 0.1737 | 0.7531 | 0.0 | 0.8696 | 0.8350 | 0.4994 | 0.7847 | 0.1722 | 0.5914 | 0.0 | 0.6776 | 0.5230 | 0.4888 |
| 0.6507 | 0.59 | 220 | 0.7059 | 0.4730 | 0.5939 | 0.7842 | 0.9030 | 0.2181 | 0.8172 | 0.0 | 0.8698 | 0.8533 | 0.4961 | 0.7760 | 0.2161 | 0.5692 | 0.0 | 0.6904 | 0.5716 | 0.4877 |
| 0.8612 | 0.65 | 240 | 0.6863 | 0.4878 | 0.6020 | 0.7871 | 0.9168 | 0.2972 | 0.8751 | 0.0 | 0.8585 | 0.7589 | 0.5076 | 0.7607 | 0.2871 | 0.5770 | 0.0 | 0.6964 | 0.5970 | 0.4963 |
| 0.817 | 0.7 | 260 | 0.6994 | 0.4870 | 0.5971 | 0.7853 | 0.9158 | 0.3467 | 0.7023 | 0.0 | 0.8749 | 0.7940 | 0.5462 | 0.7684 | 0.3327 | 0.4967 | 0.0 | 0.6952 | 0.5854 | 0.5306 |
| 0.7013 | 0.76 | 280 | 0.7039 | 0.5090 | 0.6147 | 0.7952 | 0.8864 | 0.4188 | 0.8107 | 0.0 | 0.9333 | 0.7519 | 0.5019 | 0.7943 | 0.3898 | 0.6044 | 0.0 | 0.6824 | 0.6012 | 0.4910 |
| 0.5296 | 0.81 | 300 | 0.6780 | 0.5184 | 0.6354 | 0.7942 | 0.9253 | 0.6229 | 0.6595 | 0.0 | 0.8711 | 0.8447 | 0.5244 | 0.7922 | 0.5030 | 0.5563 | 0.0 | 0.6795 | 0.5932 | 0.5049 |
| 1.9473 | 0.86 | 320 | 0.6378 | 0.5484 | 0.6550 | 0.8145 | 0.9153 | 0.5907 | 0.7930 | 0.0 | 0.9067 | 0.8167 | 0.5625 | 0.8053 | 0.5191 | 0.6401 | 0.0 | 0.7114 | 0.6272 | 0.5354 |
| 0.6526 | 0.92 | 340 | 0.6640 | 0.5198 | 0.6270 | 0.8036 | 0.9080 | 0.4719 | 0.7448 | 0.0 | 0.9251 | 0.8294 | 0.5101 | 0.8101 | 0.4285 | 0.6169 | 0.0 | 0.6985 | 0.5846 | 0.5003 |
| 0.6158 | 0.97 | 360 | 0.6036 | 0.5635 | 0.6660 | 0.8236 | 0.9305 | 0.5957 | 0.8159 | 0.0 | 0.8971 | 0.8425 | 0.5806 | 0.8109 | 0.5478 | 0.6469 | 0.0 | 0.7266 | 0.6656 | 0.5467 |
| 0.6889 | 1.03 | 380 | 0.6122 | 0.5689 | 0.6857 | 0.8245 | 0.9078 | 0.6870 | 0.8587 | 0.0 | 0.8972 | 0.8718 | 0.5773 | 0.8265 | 0.5679 | 0.6732 | 0.0 | 0.7144 | 0.6527 | 0.5475 |
| 0.8398 | 1.08 | 400 | 0.6046 | 0.5639 | 0.6609 | 0.8259 | 0.9239 | 0.6264 | 0.8207 | 0.0 | 0.9428 | 0.7562 | 0.5564 | 0.8154 | 0.5376 | 0.6656 | 0.0 | 0.7300 | 0.6566 | 0.5419 |
| 0.5525 | 1.14 | 420 | 0.5844 | 0.5614 | 0.6918 | 0.8149 | 0.9346 | 0.6915 | 0.8565 | 0.0001 | 0.7892 | 0.9305 | 0.6401 | 0.8060 | 0.5956 | 0.6503 | 0.0001 | 0.7033 | 0.5758 | 0.5986 |
| 0.4518 | 1.19 | 440 | 0.5928 | 0.5694 | 0.6943 | 0.8232 | 0.8681 | 0.6488 | 0.8886 | 0.0001 | 0.8696 | 0.8152 | 0.7697 | 0.7924 | 0.5603 | 0.5927 | 0.0001 | 0.7465 | 0.6463 | 0.6476 |
| 0.3196 | 1.24 | 460 | 0.6074 | 0.5595 | 0.6550 | 0.8218 | 0.9200 | 0.5712 | 0.8242 | 0.0 | 0.9328 | 0.7890 | 0.5480 | 0.8181 | 0.5260 | 0.6730 | 0.0 | 0.7156 | 0.6673 | 0.5162 |
| 0.5027 | 1.3 | 480 | 0.5926 | 0.5682 | 0.6860 | 0.8250 | 0.9250 | 0.7513 | 0.8576 | 0.0 | 0.9062 | 0.8556 | 0.5061 | 0.8217 | 0.5813 | 0.6973 | 0.0 | 0.7260 | 0.6568 | 0.4940 |
| 0.6623 | 1.35 | 500 | 0.5957 | 0.5612 | 0.6748 | 0.8241 | 0.9321 | 0.6650 | 0.8496 | 0.0 | 0.9014 | 0.8574 | 0.5178 | 0.8284 | 0.5701 | 0.6787 | 0.0 | 0.7315 | 0.6148 | 0.5048 |
| 0.4123 | 1.41 | 520 | 0.5802 | 0.5710 | 0.6924 | 0.8242 | 0.9141 | 0.7787 | 0.8525 | 0.0 | 0.9029 | 0.8788 | 0.5197 | 0.8244 | 0.6246 | 0.6937 | 0.0 | 0.7247 | 0.6218 | 0.5080 |
| 0.3567 | 1.46 | 540 | 0.5760 | 0.5750 | 0.6763 | 0.8265 | 0.9232 | 0.7055 | 0.8190 | 0.0002 | 0.9256 | 0.8169 | 0.5436 | 0.8258 | 0.6074 | 0.6963 | 0.0002 | 0.7149 | 0.6564 | 0.5244 |
| 0.3404 | 1.51 | 560 | 0.5725 | 0.5788 | 0.6787 | 0.8325 | 0.9322 | 0.6995 | 0.8212 | 0.0021 | 0.9249 | 0.7782 | 0.5930 | 0.8140 | 0.5872 | 0.6826 | 0.0021 | 0.7438 | 0.6502 | 0.5719 |
| 0.3542 | 1.57 | 580 | 0.5759 | 0.5872 | 0.6949 | 0.8336 | 0.9302 | 0.7720 | 0.8318 | 0.0004 | 0.9124 | 0.8626 | 0.5549 | 0.8286 | 0.6374 | 0.7063 | 0.0004 | 0.7300 | 0.6646 | 0.5429 |
| 0.5647 | 1.62 | 600 | 0.5635 | 0.5926 | 0.7132 | 0.8380 | 0.9124 | 0.8427 | 0.8749 | 0.0003 | 0.9089 | 0.8359 | 0.6171 | 0.8388 | 0.6198 | 0.6904 | 0.0003 | 0.7343 | 0.6800 | 0.5846 |
| 0.342 | 1.68 | 620 | 0.5616 | 0.5793 | 0.7065 | 0.8285 | 0.9150 | 0.8240 | 0.8075 | 0.0 | 0.8730 | 0.8915 | 0.6343 | 0.8351 | 0.6019 | 0.6871 | 0.0 | 0.7122 | 0.6332 | 0.5861 |
| 0.4183 | 1.73 | 640 | 0.5514 | 0.5959 | 0.6992 | 0.8388 | 0.9208 | 0.7985 | 0.8213 | 0.0014 | 0.9300 | 0.7947 | 0.6275 | 0.8396 | 0.6423 | 0.6872 | 0.0014 | 0.7278 | 0.6710 | 0.6018 |
| 1.0677 | 1.78 | 660 | 0.5618 | 0.5915 | 0.6959 | 0.8370 | 0.9145 | 0.7630 | 0.8128 | 0.0 | 0.9274 | 0.8135 | 0.6402 | 0.8335 | 0.6267 | 0.6889 | 0.0 | 0.7329 | 0.6622 | 0.5965 |
| 0.5682 | 1.84 | 680 | 0.5212 | 0.6035 | 0.7052 | 0.8464 | 0.9383 | 0.7473 | 0.8452 | 0.0028 | 0.9098 | 0.8419 | 0.6513 | 0.8326 | 0.6173 | 0.6857 | 0.0028 | 0.7524 | 0.6972 | 0.6363 |
| 0.4499 | 1.89 | 700 | 0.5389 | 0.6082 | 0.7131 | 0.8436 | 0.9029 | 0.7818 | 0.8322 | 0.0123 | 0.9274 | 0.8649 | 0.6701 | 0.8398 | 0.6534 | 0.6920 | 0.0123 | 0.7300 | 0.6789 | 0.6509 |
| 0.737 | 1.95 | 720 | 0.5390 | 0.5997 | 0.7012 | 0.8387 | 0.8985 | 0.7270 | 0.8226 | 0.0193 | 0.9179 | 0.7850 | 0.7382 | 0.8268 | 0.6141 | 0.6959 | 0.0193 | 0.7329 | 0.6829 | 0.6258 |
| 1.8862 | 2.0 | 740 | 0.5632 | 0.5918 | 0.7112 | 0.8369 | 0.9257 | 0.8057 | 0.8401 | 0.0134 | 0.8963 | 0.9235 | 0.5735 | 0.8326 | 0.6244 | 0.7105 | 0.0134 | 0.7381 | 0.6613 | 0.5624 |
| 0.3969 | 2.05 | 760 | 0.5738 | 0.5775 | 0.6803 | 0.8329 | 0.9267 | 0.7258 | 0.8365 | 0.0115 | 0.9479 | 0.7698 | 0.5435 | 0.8327 | 0.5740 | 0.7178 | 0.0115 | 0.7364 | 0.6421 | 0.5283 |
| 0.4485 | 2.11 | 780 | 0.5115 | 0.5808 | 0.7162 | 0.8347 | 0.9213 | 0.8359 | 0.8539 | 0.0257 | 0.8887 | 0.9209 | 0.5672 | 0.8416 | 0.5746 | 0.7114 | 0.0256 | 0.7521 | 0.6147 | 0.5459 |
| 0.4601 | 2.16 | 800 | 0.4928 | 0.6289 | 0.7265 | 0.8589 | 0.9338 | 0.7416 | 0.7906 | 0.0709 | 0.9098 | 0.8631 | 0.7759 | 0.8394 | 0.6178 | 0.7066 | 0.0707 | 0.7757 | 0.6895 | 0.7030 |
| 1.3914 | 2.22 | 820 | 0.4974 | 0.6289 | 0.7243 | 0.8595 | 0.9320 | 0.7422 | 0.8269 | 0.0654 | 0.9166 | 0.8002 | 0.7869 | 0.8399 | 0.6236 | 0.7058 | 0.0652 | 0.7781 | 0.6824 | 0.7073 |
| 0.2324 | 2.27 | 840 | 0.4771 | 0.6282 | 0.7488 | 0.8591 | 0.9165 | 0.8484 | 0.8886 | 0.0381 | 0.8783 | 0.8598 | 0.8117 | 0.8444 | 0.6400 | 0.6924 | 0.0380 | 0.7777 | 0.6837 | 0.7214 |
| 0.6388 | 2.32 | 860 | 0.4670 | 0.6300 | 0.7361 | 0.8591 | 0.9277 | 0.8268 | 0.8403 | 0.0739 | 0.9251 | 0.8473 | 0.7111 | 0.8454 | 0.6490 | 0.7112 | 0.0731 | 0.7831 | 0.6738 | 0.6743 |
| 0.2188 | 2.38 | 880 | 0.4928 | 0.6232 | 0.7423 | 0.8471 | 0.9248 | 0.8602 | 0.8602 | 0.2115 | 0.9274 | 0.8505 | 0.5618 | 0.8433 | 0.6290 | 0.7404 | 0.2050 | 0.7739 | 0.6265 | 0.5444 |
| 0.7092 | 2.43 | 900 | 0.4844 | 0.6309 | 0.7335 | 0.8565 | 0.9195 | 0.7766 | 0.8661 | 0.1365 | 0.9419 | 0.8232 | 0.6709 | 0.8537 | 0.6358 | 0.7335 | 0.1323 | 0.7738 | 0.6501 | 0.6374 |
| 0.7643 | 2.49 | 920 | 0.4768 | 0.6334 | 0.7428 | 0.8560 | 0.9253 | 0.7818 | 0.8491 | 0.1362 | 0.8976 | 0.8848 | 0.7245 | 0.8473 | 0.6349 | 0.7250 | 0.1338 | 0.7687 | 0.6468 | 0.6771 |
| 0.3122 | 2.54 | 940 | 0.4602 | 0.6392 | 0.7361 | 0.8620 | 0.9343 | 0.7936 | 0.8124 | 0.0915 | 0.9176 | 0.8384 | 0.7647 | 0.8443 | 0.6415 | 0.7129 | 0.0901 | 0.7736 | 0.6969 | 0.7151 |
| 0.4749 | 2.59 | 960 | 0.5159 | 0.6405 | 0.7421 | 0.8649 | 0.9295 | 0.8029 | 0.8561 | 0.0751 | 0.9145 | 0.8401 | 0.7769 | 0.8443 | 0.6460 | 0.7174 | 0.0742 | 0.7883 | 0.6921 | 0.7210 |
| 0.2705 | 2.65 | 980 | 0.5420 | 0.6110 | 0.7160 | 0.8431 | 0.9321 | 0.8193 | 0.8397 | 0.1128 | 0.9387 | 0.8182 | 0.5512 | 0.8466 | 0.6527 | 0.7419 | 0.1109 | 0.7489 | 0.6416 | 0.5345 |
| 0.4277 | 2.7 | 1000 | 0.5636 | 0.5942 | 0.7261 | 0.8374 | 0.9211 | 0.8345 | 0.8916 | 0.0744 | 0.8874 | 0.9127 | 0.5612 | 0.8470 | 0.6492 | 0.6832 | 0.0737 | 0.7656 | 0.6105 | 0.5300 |
| 0.3361 | 2.76 | 1020 | 0.5560 | 0.5980 | 0.7126 | 0.8379 | 0.9340 | 0.8012 | 0.8863 | 0.1015 | 0.9195 | 0.8318 | 0.5139 | 0.8424 | 0.6616 | 0.6936 | 0.1003 | 0.7576 | 0.6233 | 0.5075 |
| 0.2131 | 2.81 | 1040 | 0.4922 | 0.6332 | 0.7542 | 0.8464 | 0.9243 | 0.8445 | 0.8604 | 0.2245 | 0.8791 | 0.9215 | 0.6252 | 0.8423 | 0.6536 | 0.7311 | 0.2194 | 0.7509 | 0.6570 | 0.5780 |
| 0.4304 | 2.86 | 1060 | 0.4797 | 0.6432 | 0.7414 | 0.8561 | 0.9357 | 0.8259 | 0.8261 | 0.1822 | 0.9262 | 0.8392 | 0.6541 | 0.8442 | 0.6658 | 0.7358 | 0.1796 | 0.7624 | 0.6887 | 0.6260 |
| 0.3788 | 2.92 | 1080 | 0.4500 | 0.6428 | 0.7568 | 0.8583 | 0.9292 | 0.8807 | 0.8560 | 0.1840 | 0.9145 | 0.8782 | 0.6547 | 0.8469 | 0.6292 | 0.7258 | 0.1791 | 0.7699 | 0.7106 | 0.6380 |
| 0.3109 | 2.97 | 1100 | 0.4402 | 0.6453 | 0.7467 | 0.8584 | 0.9378 | 0.7490 | 0.8789 | 0.2150 | 0.9112 | 0.8963 | 0.6385 | 0.8503 | 0.6480 | 0.7422 | 0.2072 | 0.7725 | 0.6728 | 0.6245 |
| 0.313 | 3.03 | 1120 | 0.4730 | 0.6509 | 0.7636 | 0.8611 | 0.9233 | 0.8228 | 0.9004 | 0.2175 | 0.9036 | 0.8874 | 0.6900 | 0.8466 | 0.6430 | 0.7358 | 0.1968 | 0.7787 | 0.6857 | 0.6697 |
| 0.5267 | 3.08 | 1140 | 0.4381 | 0.6790 | 0.7837 | 0.8731 | 0.9227 | 0.8276 | 0.8415 | 0.2900 | 0.9056 | 0.8945 | 0.8039 | 0.8552 | 0.6517 | 0.7441 | 0.2698 | 0.7864 | 0.6791 | 0.7669 |
| 0.6162 | 3.14 | 1160 | 0.4643 | 0.6700 | 0.7670 | 0.8663 | 0.9274 | 0.8280 | 0.8118 | 0.2924 | 0.9304 | 0.8529 | 0.7259 | 0.8506 | 0.6576 | 0.7353 | 0.2801 | 0.7738 | 0.6960 | 0.6968 |
| 0.3309 | 3.19 | 1180 | 0.4844 | 0.6540 | 0.7608 | 0.8618 | 0.9289 | 0.8232 | 0.8624 | 0.2114 | 0.9033 | 0.8873 | 0.7092 | 0.8458 | 0.6655 | 0.7206 | 0.1999 | 0.7761 | 0.6856 | 0.6845 |
| 0.2346 | 3.24 | 1200 | 0.4521 | 0.6686 | 0.7638 | 0.8681 | 0.9423 | 0.7894 | 0.8419 | 0.2557 | 0.9073 | 0.8754 | 0.7347 | 0.8413 | 0.6693 | 0.7318 | 0.2380 | 0.7906 | 0.7006 | 0.7088 |
| 0.2851 | 3.3 | 1220 | 0.4731 | 0.6556 | 0.7634 | 0.8647 | 0.9003 | 0.6759 | 0.8933 | 0.2520 | 0.8915 | 0.8734 | 0.8572 | 0.8400 | 0.5774 | 0.7224 | 0.2397 | 0.7908 | 0.7053 | 0.7138 |
| 0.293 | 3.35 | 1240 | 0.4126 | 0.6998 | 0.7967 | 0.8843 | 0.9266 | 0.8391 | 0.8298 | 0.3422 | 0.9229 | 0.8507 | 0.8652 | 0.8573 | 0.6491 | 0.7313 | 0.3177 | 0.8105 | 0.7219 | 0.8111 |
| 0.839 | 3.41 | 1260 | 0.4382 | 0.6837 | 0.7752 | 0.8793 | 0.9373 | 0.7954 | 0.8550 | 0.2738 | 0.9309 | 0.8478 | 0.7862 | 0.8548 | 0.6647 | 0.7335 | 0.2611 | 0.8145 | 0.7042 | 0.7530 |
| 0.5775 | 3.46 | 1280 | 0.4951 | 0.6536 | 0.7549 | 0.8610 | 0.9445 | 0.7814 | 0.9085 | 0.2600 | 0.9057 | 0.8165 | 0.6675 | 0.8380 | 0.6630 | 0.7178 | 0.2207 | 0.7940 | 0.6989 | 0.6427 |
| 0.3429 | 3.51 | 1300 | 0.4591 | 0.6826 | 0.7888 | 0.8713 | 0.9157 | 0.8246 | 0.8578 | 0.3419 | 0.9111 | 0.9014 | 0.7690 | 0.8469 | 0.6664 | 0.7445 | 0.3085 | 0.7920 | 0.6777 | 0.7419 |
| 0.2416 | 3.57 | 1320 | 0.4521 | 0.6707 | 0.7700 | 0.8722 | 0.9411 | 0.8200 | 0.8904 | 0.2413 | 0.9090 | 0.8340 | 0.7542 | 0.8469 | 0.6624 | 0.7226 | 0.2227 | 0.8006 | 0.7015 | 0.7381 |
| 0.4017 | 3.62 | 1340 | 0.4673 | 0.6726 | 0.7685 | 0.8720 | 0.9368 | 0.7864 | 0.8589 | 0.2893 | 0.9194 | 0.8137 | 0.7752 | 0.8509 | 0.6550 | 0.7348 | 0.2628 | 0.8004 | 0.6654 | 0.7386 |
| 0.1852 | 3.68 | 1360 | 0.4635 | 0.6838 | 0.7842 | 0.8742 | 0.9347 | 0.8221 | 0.8274 | 0.3451 | 0.9135 | 0.8714 | 0.7755 | 0.8598 | 0.6648 | 0.7542 | 0.3115 | 0.7953 | 0.6643 | 0.7366 |
| 0.6558 | 3.73 | 1380 | 0.5199 | 0.6479 | 0.7609 | 0.8560 | 0.9454 | 0.8007 | 0.8875 | 0.3075 | 0.8921 | 0.8780 | 0.6153 | 0.8552 | 0.6688 | 0.7489 | 0.2787 | 0.7888 | 0.6062 | 0.5883 |
| 0.3409 | 3.78 | 1400 | 0.4640 | 0.6676 | 0.7790 | 0.8633 | 0.9285 | 0.7885 | 0.8312 | 0.4431 | 0.9145 | 0.8346 | 0.7125 | 0.8583 | 0.6600 | 0.7396 | 0.3493 | 0.8009 | 0.6203 | 0.6446 |
| 0.2649 | 3.84 | 1420 | 0.5892 | 0.6478 | 0.7655 | 0.8511 | 0.9392 | 0.7808 | 0.8388 | 0.4182 | 0.8934 | 0.8889 | 0.5991 | 0.8528 | 0.6674 | 0.7215 | 0.3482 | 0.7895 | 0.5867 | 0.5687 |
| 0.4681 | 3.89 | 1440 | 0.4774 | 0.6900 | 0.7848 | 0.8785 | 0.9377 | 0.8266 | 0.8489 | 0.3595 | 0.9327 | 0.8117 | 0.7766 | 0.8532 | 0.6745 | 0.7194 | 0.3210 | 0.8169 | 0.7024 | 0.7428 |
| 0.7559 | 3.95 | 1460 | 0.4725 | 0.6771 | 0.7733 | 0.8704 | 0.9352 | 0.8051 | 0.8298 | 0.3240 | 0.9215 | 0.8545 | 0.7427 | 0.8539 | 0.6685 | 0.7301 | 0.3056 | 0.7896 | 0.6841 | 0.7077 |
| 0.3047 | 4.0 | 1480 | 0.4709 | 0.6773 | 0.7762 | 0.8686 | 0.9153 | 0.7712 | 0.8651 | 0.3327 | 0.9098 | 0.8260 | 0.8129 | 0.8451 | 0.6611 | 0.7425 | 0.3060 | 0.7887 | 0.6874 | 0.7101 |
| 0.2191 | 4.05 | 1500 | 0.4516 | 0.6901 | 0.7932 | 0.8757 | 0.9385 | 0.8364 | 0.8641 | 0.3807 | 0.9018 | 0.8517 | 0.7796 | 0.8488 | 0.6782 | 0.7423 | 0.3259 | 0.8072 | 0.6781 | 0.7503 |
| 0.3001 | 4.11 | 1520 | 0.4885 | 0.6808 | 0.7846 | 0.8677 | 0.9408 | 0.8009 | 0.8662 | 0.4246 | 0.9019 | 0.8494 | 0.7082 | 0.8505 | 0.6773 | 0.7501 | 0.3428 | 0.7870 | 0.6753 | 0.6823 |
| 0.4489 | 4.16 | 1540 | 0.4531 | 0.6884 | 0.7905 | 0.8779 | 0.9243 | 0.8295 | 0.8128 | 0.3443 | 0.9181 | 0.8716 | 0.8325 | 0.8591 | 0.6756 | 0.7198 | 0.2901 | 0.8047 | 0.6798 | 0.7896 |
| 0.3684 | 4.22 | 1560 | 0.4794 | 0.6871 | 0.7968 | 0.8723 | 0.9387 | 0.8436 | 0.8621 | 0.4160 | 0.8944 | 0.8819 | 0.7407 | 0.8562 | 0.6723 | 0.7477 | 0.3495 | 0.7952 | 0.6789 | 0.7101 |
| 0.5706 | 4.27 | 1580 | 0.5364 | 0.6783 | 0.7810 | 0.8687 | 0.9245 | 0.8144 | 0.8753 | 0.3755 | 0.9310 | 0.8559 | 0.6905 | 0.8558 | 0.6808 | 0.7445 | 0.3183 | 0.7911 | 0.6939 | 0.6638 |
| 0.1208 | 4.32 | 1600 | 0.4386 | 0.7019 | 0.7988 | 0.8822 | 0.9263 | 0.8024 | 0.8679 | 0.3790 | 0.9054 | 0.8363 | 0.8744 | 0.8585 | 0.6713 | 0.7511 | 0.3355 | 0.8077 | 0.7271 | 0.7618 |
| 0.2512 | 4.38 | 1620 | 0.5227 | 0.6897 | 0.7935 | 0.8710 | 0.9184 | 0.8347 | 0.8438 | 0.4322 | 0.9258 | 0.8585 | 0.7408 | 0.8614 | 0.6786 | 0.7523 | 0.3617 | 0.7773 | 0.6904 | 0.7065 |
| 0.3166 | 4.43 | 1640 | 0.5045 | 0.6884 | 0.7824 | 0.8706 | 0.9340 | 0.8140 | 0.8594 | 0.4231 | 0.9338 | 0.8053 | 0.7069 | 0.8569 | 0.6783 | 0.7576 | 0.3741 | 0.7811 | 0.6863 | 0.6844 |
| 0.2665 | 4.49 | 1660 | 0.5188 | 0.6725 | 0.7821 | 0.8632 | 0.9365 | 0.8342 | 0.8434 | 0.4594 | 0.9219 | 0.8279 | 0.6515 | 0.8532 | 0.6581 | 0.7450 | 0.4004 | 0.7945 | 0.6334 | 0.6231 |
| 0.1356 | 4.54 | 1680 | 0.5240 | 0.6725 | 0.7858 | 0.8641 | 0.9459 | 0.8062 | 0.8686 | 0.4517 | 0.8929 | 0.8764 | 0.6592 | 0.8543 | 0.6672 | 0.7614 | 0.3837 | 0.8082 | 0.6011 | 0.6317 |
| 0.1726 | 4.59 | 1700 | 0.4637 | 0.6945 | 0.8060 | 0.8743 | 0.9451 | 0.7883 | 0.8735 | 0.4841 | 0.8618 | 0.8822 | 0.8067 | 0.8566 | 0.6661 | 0.7613 | 0.3600 | 0.7912 | 0.6819 | 0.7442 |
| 0.2107 | 4.65 | 1720 | 0.4839 | 0.6852 | 0.7843 | 0.8710 | 0.9202 | 0.8059 | 0.8504 | 0.4318 | 0.9322 | 0.7602 | 0.7896 | 0.8540 | 0.6601 | 0.7440 | 0.3630 | 0.7887 | 0.6634 | 0.7233 |
| 0.5774 | 4.7 | 1740 | 0.4662 | 0.7011 | 0.8022 | 0.8802 | 0.9174 | 0.7996 | 0.8822 | 0.4188 | 0.9135 | 0.8401 | 0.8439 | 0.8544 | 0.6604 | 0.7464 | 0.3716 | 0.8107 | 0.7122 | 0.7516 |
| 0.2358 | 4.76 | 1760 | 0.4472 | 0.7068 | 0.8107 | 0.8828 | 0.9311 | 0.8246 | 0.8927 | 0.4489 | 0.9046 | 0.8607 | 0.8122 | 0.8597 | 0.6797 | 0.7467 | 0.3761 | 0.8137 | 0.7021 | 0.7692 |
| 0.3879 | 4.81 | 1780 | 0.4750 | 0.6927 | 0.8127 | 0.8727 | 0.9369 | 0.8197 | 0.8730 | 0.5062 | 0.8635 | 0.9047 | 0.7850 | 0.8565 | 0.6747 | 0.7440 | 0.3610 | 0.7926 | 0.6716 | 0.7487 |
| 0.2336 | 4.86 | 1800 | 0.4364 | 0.7032 | 0.8038 | 0.8804 | 0.9344 | 0.8177 | 0.8543 | 0.4869 | 0.9251 | 0.8339 | 0.7744 | 0.8552 | 0.6703 | 0.7628 | 0.3852 | 0.8153 | 0.6992 | 0.7345 |
| 0.2303 | 4.92 | 1820 | 0.4305 | 0.7137 | 0.8110 | 0.8861 | 0.9226 | 0.8403 | 0.8824 | 0.4492 | 0.9362 | 0.8405 | 0.8055 | 0.8583 | 0.6809 | 0.7680 | 0.3905 | 0.8207 | 0.7112 | 0.7666 |
| 0.347 | 4.97 | 1840 | 0.4280 | 0.7141 | 0.8139 | 0.8851 | 0.9320 | 0.8301 | 0.8443 | 0.4832 | 0.9180 | 0.8781 | 0.8114 | 0.8593 | 0.6741 | 0.7539 | 0.4024 | 0.8134 | 0.7139 | 0.7815 |
| 0.1553 | 5.03 | 1860 | 0.4500 | 0.7075 | 0.8165 | 0.8814 | 0.9396 | 0.8204 | 0.8990 | 0.5335 | 0.9019 | 0.8580 | 0.7632 | 0.8591 | 0.6739 | 0.7626 | 0.3980 | 0.8150 | 0.7091 | 0.7347 |
| 0.2451 | 5.08 | 1880 | 0.4699 | 0.6888 | 0.7985 | 0.8688 | 0.9359 | 0.7957 | 0.8490 | 0.5428 | 0.9082 | 0.8630 | 0.6949 | 0.8587 | 0.6716 | 0.7755 | 0.3843 | 0.7832 | 0.6813 | 0.6671 |
| 0.3152 | 5.14 | 1900 | 0.4433 | 0.7119 | 0.8179 | 0.8849 | 0.9226 | 0.8324 | 0.8624 | 0.5160 | 0.9239 | 0.8426 | 0.8253 | 0.8586 | 0.6740 | 0.7557 | 0.3693 | 0.8188 | 0.7217 | 0.7851 |
| 0.1741 | 5.19 | 1920 | 0.4381 | 0.7134 | 0.8123 | 0.8857 | 0.9351 | 0.8145 | 0.8811 | 0.4748 | 0.9139 | 0.8559 | 0.8111 | 0.8665 | 0.6844 | 0.7734 | 0.3768 | 0.8098 | 0.7102 | 0.7725 |
| 0.3937 | 5.24 | 1940 | 0.4335 | 0.7107 | 0.8141 | 0.8829 | 0.9302 | 0.8452 | 0.8654 | 0.4312 | 0.8917 | 0.8908 | 0.8439 | 0.8573 | 0.6885 | 0.7681 | 0.3561 | 0.7981 | 0.7114 | 0.7955 |
| 0.1683 | 5.3 | 1960 | 0.4622 | 0.6996 | 0.8016 | 0.8783 | 0.9332 | 0.8085 | 0.8798 | 0.4643 | 0.9200 | 0.8613 | 0.7441 | 0.8629 | 0.6807 | 0.7724 | 0.3641 | 0.8036 | 0.7073 | 0.7065 |
| 0.2652 | 5.35 | 1980 | 0.4333 | 0.7100 | 0.8111 | 0.8843 | 0.9312 | 0.8416 | 0.8774 | 0.4394 | 0.9141 | 0.8674 | 0.8064 | 0.8667 | 0.6845 | 0.7734 | 0.3851 | 0.8110 | 0.6899 | 0.7596 |
| 0.3099 | 5.41 | 2000 | 0.5586 | 0.6680 | 0.7724 | 0.8618 | 0.9282 | 0.7736 | 0.8603 | 0.4053 | 0.9206 | 0.8546 | 0.6642 | 0.8590 | 0.6737 | 0.7655 | 0.3650 | 0.7950 | 0.6093 | 0.6084 |
| 0.6068 | 5.46 | 2020 | 0.5672 | 0.6591 | 0.7804 | 0.8593 | 0.9372 | 0.8178 | 0.9053 | 0.3834 | 0.8801 | 0.8836 | 0.6553 | 0.8622 | 0.6745 | 0.7522 | 0.3090 | 0.7906 | 0.6129 | 0.6120 |
| 0.1649 | 5.51 | 2040 | 0.5184 | 0.6631 | 0.7703 | 0.8656 | 0.9354 | 0.8171 | 0.8841 | 0.3137 | 0.9195 | 0.8507 | 0.6712 | 0.8666 | 0.6806 | 0.7513 | 0.2728 | 0.7955 | 0.6410 | 0.6339 |
| 0.3157 | 5.57 | 2060 | 0.5451 | 0.6690 | 0.7875 | 0.8620 | 0.9427 | 0.8082 | 0.8730 | 0.4344 | 0.8739 | 0.9014 | 0.6790 | 0.8628 | 0.6760 | 0.7532 | 0.3472 | 0.7876 | 0.6179 | 0.6383 |
| 0.3131 | 5.62 | 2080 | 0.5506 | 0.6716 | 0.7871 | 0.8620 | 0.9387 | 0.8165 | 0.8351 | 0.5187 | 0.9105 | 0.8349 | 0.6555 | 0.8622 | 0.6806 | 0.7524 | 0.3756 | 0.7901 | 0.6138 | 0.6266 |
| 0.182 | 5.68 | 2100 | 0.5583 | 0.6715 | 0.7925 | 0.8637 | 0.9354 | 0.8459 | 0.8491 | 0.4511 | 0.8906 | 0.8901 | 0.6851 | 0.8649 | 0.6686 | 0.7489 | 0.3597 | 0.7910 | 0.6146 | 0.6527 |
| 0.1015 | 5.73 | 2120 | 0.4312 | 0.7065 | 0.8055 | 0.8834 | 0.9359 | 0.8468 | 0.8577 | 0.4344 | 0.9218 | 0.8473 | 0.7947 | 0.8651 | 0.6778 | 0.7628 | 0.3601 | 0.8067 | 0.7145 | 0.7588 |
| 0.3909 | 5.78 | 2140 | 0.4617 | 0.6964 | 0.8043 | 0.8759 | 0.9347 | 0.8351 | 0.8596 | 0.4495 | 0.8979 | 0.8897 | 0.7638 | 0.8603 | 0.6770 | 0.7654 | 0.3572 | 0.7932 | 0.6947 | 0.7271 |
| 0.1689 | 5.84 | 2160 | 0.4988 | 0.6993 | 0.8062 | 0.8769 | 0.9449 | 0.8028 | 0.8856 | 0.4745 | 0.8807 | 0.8810 | 0.7739 | 0.8537 | 0.6741 | 0.7485 | 0.3827 | 0.8025 | 0.6884 | 0.7451 |
| 0.1827 | 5.89 | 2180 | 0.5481 | 0.6804 | 0.7881 | 0.8660 | 0.9235 | 0.8419 | 0.8409 | 0.4410 | 0.9261 | 0.8601 | 0.6829 | 0.8569 | 0.6627 | 0.7447 | 0.3791 | 0.7778 | 0.6845 | 0.6572 |
| 0.3295 | 5.95 | 2200 | 0.4630 | 0.7049 | 0.8220 | 0.8793 | 0.9256 | 0.8432 | 0.8667 | 0.5525 | 0.8974 | 0.8611 | 0.8078 | 0.8630 | 0.6639 | 0.7531 | 0.4023 | 0.8034 | 0.6889 | 0.7596 |
| 0.1909 | 6.0 | 2220 | 0.4903 | 0.6981 | 0.8002 | 0.8756 | 0.9314 | 0.8205 | 0.8716 | 0.4958 | 0.9282 | 0.8271 | 0.7268 | 0.8587 | 0.6747 | 0.7572 | 0.3987 | 0.8000 | 0.7056 | 0.6917 |
| 0.294 | 6.05 | 2240 | 0.5427 | 0.6866 | 0.7984 | 0.8676 | 0.9313 | 0.8283 | 0.8406 | 0.5110 | 0.9109 | 0.8848 | 0.6818 | 0.8590 | 0.6826 | 0.7498 | 0.3909 | 0.7836 | 0.6869 | 0.6536 |
| 0.2515 | 6.11 | 2260 | 0.5008 | 0.6957 | 0.8006 | 0.8741 | 0.9383 | 0.8152 | 0.8909 | 0.5185 | 0.9178 | 0.8102 | 0.7130 | 0.8617 | 0.6824 | 0.7637 | 0.3920 | 0.7969 | 0.6972 | 0.6757 |
| 0.2324 | 6.16 | 2280 | 0.4600 | 0.7024 | 0.8179 | 0.8769 | 0.9364 | 0.8166 | 0.8834 | 0.5561 | 0.8863 | 0.8821 | 0.7644 | 0.8654 | 0.6800 | 0.7644 | 0.3950 | 0.7963 | 0.6892 | 0.7262 |
| 0.3158 | 6.22 | 2300 | 0.4765 | 0.6958 | 0.8090 | 0.8738 | 0.9341 | 0.8288 | 0.8889 | 0.5295 | 0.8971 | 0.8324 | 0.7521 | 0.8590 | 0.6710 | 0.7602 | 0.3843 | 0.7922 | 0.6894 | 0.7145 |
| 0.2189 | 6.27 | 2320 | 0.4901 | 0.6929 | 0.8023 | 0.8706 | 0.9410 | 0.8206 | 0.8470 | 0.5496 | 0.9040 | 0.8414 | 0.7130 | 0.8602 | 0.6789 | 0.7572 | 0.3968 | 0.7841 | 0.6943 | 0.6785 |
| 0.1781 | 6.32 | 2340 | 0.4782 | 0.6890 | 0.7950 | 0.8716 | 0.9310 | 0.8302 | 0.9101 | 0.4230 | 0.9085 | 0.8508 | 0.7116 | 0.8592 | 0.6882 | 0.7522 | 0.3529 | 0.7885 | 0.7063 | 0.6755 |
| 0.2585 | 6.38 | 2360 | 0.4923 | 0.7005 | 0.8049 | 0.8793 | 0.9283 | 0.8514 | 0.8561 | 0.4506 | 0.9227 | 0.8487 | 0.7764 | 0.8570 | 0.6852 | 0.7455 | 0.3647 | 0.8112 | 0.6981 | 0.7414 |
| 0.2427 | 6.43 | 2380 | 0.4996 | 0.6936 | 0.8071 | 0.8733 | 0.9416 | 0.8346 | 0.8637 | 0.5122 | 0.8907 | 0.8682 | 0.7385 | 0.8580 | 0.6864 | 0.7578 | 0.3903 | 0.8046 | 0.6503 | 0.7078 |
| 0.3861 | 6.49 | 2400 | 0.5035 | 0.7018 | 0.8042 | 0.8762 | 0.9295 | 0.8189 | 0.8695 | 0.5184 | 0.9280 | 0.8392 | 0.7258 | 0.8635 | 0.6823 | 0.7661 | 0.4107 | 0.7948 | 0.7058 | 0.6893 |
| 0.2319 | 6.54 | 2420 | 0.5015 | 0.7052 | 0.8113 | 0.8770 | 0.9234 | 0.8489 | 0.8623 | 0.5606 | 0.9325 | 0.7943 | 0.7574 | 0.8624 | 0.6760 | 0.7643 | 0.4198 | 0.7921 | 0.7051 | 0.7169 |
| 0.1962 | 6.59 | 2440 | 0.4653 | 0.7124 | 0.8175 | 0.8841 | 0.9419 | 0.8166 | 0.8608 | 0.5498 | 0.9069 | 0.8497 | 0.7971 | 0.8606 | 0.6803 | 0.7680 | 0.3947 | 0.8181 | 0.7048 | 0.7604 |
| 0.2704 | 6.65 | 2460 | 0.4642 | 0.7087 | 0.8070 | 0.8838 | 0.9310 | 0.8264 | 0.8837 | 0.4730 | 0.9280 | 0.8015 | 0.8055 | 0.8591 | 0.6857 | 0.7674 | 0.3697 | 0.8158 | 0.6980 | 0.7654 |
| 0.1438 | 6.7 | 2480 | 0.5301 | 0.6828 | 0.7982 | 0.8666 | 0.9280 | 0.8532 | 0.8514 | 0.4709 | 0.9018 | 0.8805 | 0.7018 | 0.8565 | 0.6701 | 0.7622 | 0.3586 | 0.7784 | 0.6853 | 0.6683 |
| 0.3661 | 6.76 | 2500 | 0.5201 | 0.6848 | 0.7910 | 0.8679 | 0.9398 | 0.8114 | 0.8760 | 0.4659 | 0.9017 | 0.8442 | 0.6978 | 0.8554 | 0.6779 | 0.7657 | 0.3610 | 0.7814 | 0.6880 | 0.6640 |
| 0.2653 | 6.81 | 2520 | 0.5765 | 0.6665 | 0.7984 | 0.8554 | 0.9237 | 0.8575 | 0.8615 | 0.5306 | 0.8848 | 0.9053 | 0.6256 | 0.8563 | 0.6737 | 0.7581 | 0.3800 | 0.7724 | 0.6313 | 0.5938 |
| 0.1563 | 6.86 | 2540 | 0.5453 | 0.6770 | 0.7881 | 0.8628 | 0.9408 | 0.8165 | 0.8736 | 0.4846 | 0.8994 | 0.8492 | 0.6528 | 0.8572 | 0.6864 | 0.7601 | 0.3829 | 0.7809 | 0.6520 | 0.6195 |
| 0.2804 | 6.92 | 2560 | 0.5505 | 0.6766 | 0.7966 | 0.8610 | 0.9216 | 0.8491 | 0.8750 | 0.4852 | 0.8956 | 0.8783 | 0.6715 | 0.8516 | 0.6741 | 0.7649 | 0.3962 | 0.7802 | 0.6452 | 0.6238 |
| 0.2304 | 6.97 | 2580 | 0.4455 | 0.7107 | 0.8093 | 0.8830 | 0.9368 | 0.8223 | 0.8703 | 0.4649 | 0.9057 | 0.8460 | 0.8188 | 0.8645 | 0.6942 | 0.7683 | 0.3803 | 0.8019 | 0.6960 | 0.7697 |
| 0.1759 | 7.03 | 2600 | 0.5370 | 0.6859 | 0.7960 | 0.8701 | 0.9369 | 0.8329 | 0.8566 | 0.4381 | 0.8981 | 0.8998 | 0.7094 | 0.8653 | 0.6820 | 0.7661 | 0.3674 | 0.7875 | 0.6583 | 0.6749 |
| 0.2079 | 7.08 | 2620 | 0.5014 | 0.6916 | 0.7991 | 0.8711 | 0.9347 | 0.8112 | 0.8740 | 0.5012 | 0.9079 | 0.8548 | 0.7101 | 0.8660 | 0.6894 | 0.7706 | 0.3897 | 0.7867 | 0.6665 | 0.6724 |
| 0.2464 | 7.14 | 2640 | 0.5313 | 0.6833 | 0.7998 | 0.8669 | 0.9319 | 0.8369 | 0.8416 | 0.5209 | 0.9050 | 0.8687 | 0.6939 | 0.8678 | 0.6749 | 0.7667 | 0.3777 | 0.7797 | 0.6586 | 0.6575 |
| 0.0679 | 7.19 | 2660 | 0.5012 | 0.6878 | 0.7945 | 0.8698 | 0.9303 | 0.8397 | 0.8561 | 0.4667 | 0.9163 | 0.8310 | 0.7213 | 0.8590 | 0.6773 | 0.7661 | 0.3821 | 0.7905 | 0.6814 | 0.6579 |
| 0.2287 | 7.24 | 2680 | 0.5399 | 0.6824 | 0.8031 | 0.8637 | 0.9228 | 0.8091 | 0.8778 | 0.5374 | 0.8858 | 0.8791 | 0.7098 | 0.8607 | 0.6758 | 0.7685 | 0.3853 | 0.7732 | 0.6663 | 0.6471 |
| 0.2186 | 7.3 | 2700 | 0.5803 | 0.6891 | 0.7935 | 0.8700 | 0.9341 | 0.8192 | 0.8781 | 0.4739 | 0.9185 | 0.8475 | 0.6833 | 0.8650 | 0.6901 | 0.7697 | 0.4044 | 0.7889 | 0.6537 | 0.6519 |
| 0.2762 | 7.35 | 2720 | 0.5791 | 0.6799 | 0.7893 | 0.8648 | 0.9349 | 0.8160 | 0.8592 | 0.4940 | 0.9163 | 0.8543 | 0.6502 | 0.8658 | 0.6885 | 0.7666 | 0.3916 | 0.7818 | 0.6439 | 0.6209 |
| 0.185 | 7.41 | 2740 | 0.5155 | 0.6884 | 0.7950 | 0.8704 | 0.9468 | 0.8255 | 0.8475 | 0.4646 | 0.8956 | 0.8782 | 0.7065 | 0.8621 | 0.6823 | 0.7629 | 0.3701 | 0.7829 | 0.6797 | 0.6787 |
| 0.1529 | 7.46 | 2760 | 0.5215 | 0.6802 | 0.7893 | 0.8672 | 0.9248 | 0.8309 | 0.8764 | 0.4373 | 0.9220 | 0.8504 | 0.6837 | 0.8582 | 0.6872 | 0.7597 | 0.3669 | 0.7949 | 0.6466 | 0.6479 |
| 0.2866 | 7.51 | 2780 | 0.4837 | 0.7017 | 0.8069 | 0.8778 | 0.9290 | 0.8349 | 0.8724 | 0.4779 | 0.9149 | 0.8582 | 0.7612 | 0.8661 | 0.6901 | 0.7653 | 0.3819 | 0.7965 | 0.6911 | 0.7208 |
| 0.2183 | 7.57 | 2800 | 0.5563 | 0.6749 | 0.7957 | 0.8633 | 0.9363 | 0.8242 | 0.8763 | 0.5241 | 0.9018 | 0.8704 | 0.6367 | 0.8688 | 0.6848 | 0.7624 | 0.3946 | 0.7942 | 0.6070 | 0.6125 |
| 0.2455 | 7.62 | 2820 | 0.4623 | 0.7067 | 0.8074 | 0.8813 | 0.9398 | 0.8230 | 0.8494 | 0.4948 | 0.9147 | 0.8463 | 0.7835 | 0.8679 | 0.6843 | 0.7629 | 0.3926 | 0.8029 | 0.6898 | 0.7464 |
| 0.1664 | 7.68 | 2840 | 0.4660 | 0.7028 | 0.7985 | 0.8806 | 0.9395 | 0.8033 | 0.8812 | 0.4409 | 0.9158 | 0.8221 | 0.7864 | 0.8630 | 0.6901 | 0.7662 | 0.3531 | 0.8021 | 0.6947 | 0.7501 |
| 1.0029 | 7.73 | 2860 | 0.4397 | 0.7231 | 0.8275 | 0.8908 | 0.9374 | 0.8533 | 0.8568 | 0.5143 | 0.9030 | 0.8616 | 0.8659 | 0.8697 | 0.6823 | 0.7656 | 0.3693 | 0.8134 | 0.7337 | 0.8276 |
| 0.2047 | 7.78 | 2880 | 0.4525 | 0.7203 | 0.8150 | 0.8896 | 0.9485 | 0.8296 | 0.8770 | 0.4709 | 0.9067 | 0.8402 | 0.8319 | 0.8628 | 0.6917 | 0.7703 | 0.3786 | 0.8198 | 0.7211 | 0.7977 |
| 0.1632 | 7.84 | 2900 | 0.4483 | 0.7172 | 0.8198 | 0.8861 | 0.9323 | 0.8009 | 0.8895 | 0.5222 | 0.9038 | 0.8572 | 0.8327 | 0.8696 | 0.6841 | 0.7643 | 0.4035 | 0.8062 | 0.7007 | 0.7918 |
| 0.072 | 7.89 | 2920 | 0.4501 | 0.7155 | 0.8190 | 0.8847 | 0.9373 | 0.8090 | 0.8660 | 0.5574 | 0.9087 | 0.8422 | 0.8126 | 0.8675 | 0.6784 | 0.7714 | 0.4133 | 0.8067 | 0.6940 | 0.7769 |
| 0.9618 | 7.95 | 2940 | 0.5323 | 0.6991 | 0.7988 | 0.8759 | 0.9291 | 0.8388 | 0.8511 | 0.4605 | 0.9285 | 0.8381 | 0.7453 | 0.8670 | 0.6856 | 0.7605 | 0.3777 | 0.7853 | 0.7145 | 0.7035 |
| 0.1425 | 8.0 | 2960 | 0.4843 | 0.6921 | 0.8009 | 0.8733 | 0.9347 | 0.7719 | 0.8522 | 0.5143 | 0.8942 | 0.8577 | 0.7811 | 0.8645 | 0.6450 | 0.7588 | 0.3731 | 0.7844 | 0.7045 | 0.7144 |
| 0.1813 | 8.05 | 2980 | 0.4979 | 0.7072 | 0.8054 | 0.8825 | 0.9384 | 0.7601 | 0.8637 | 0.4972 | 0.9082 | 0.8651 | 0.8054 | 0.8662 | 0.6573 | 0.7656 | 0.3857 | 0.8013 | 0.7092 | 0.7650 |
| 0.2996 | 8.11 | 3000 | 0.4599 | 0.7154 | 0.8211 | 0.8854 | 0.9316 | 0.8283 | 0.8601 | 0.5269 | 0.9060 | 0.8602 | 0.8345 | 0.8690 | 0.6880 | 0.7628 | 0.3920 | 0.8073 | 0.7011 | 0.7879 |
| 0.2983 | 8.16 | 3020 | 0.4657 | 0.7168 | 0.8177 | 0.8876 | 0.9388 | 0.8314 | 0.8762 | 0.4802 | 0.9060 | 0.8649 | 0.8265 | 0.8734 | 0.6929 | 0.7619 | 0.3872 | 0.8092 | 0.6967 | 0.7967 |
| 0.512 | 8.22 | 3040 | 0.4672 | 0.7148 | 0.8119 | 0.8864 | 0.9414 | 0.7956 | 0.8628 | 0.5067 | 0.9141 | 0.8484 | 0.8144 | 0.8713 | 0.6848 | 0.7671 | 0.3808 | 0.8064 | 0.6993 | 0.7937 |
| 0.182 | 8.27 | 3060 | 0.4480 | 0.7153 | 0.8190 | 0.8851 | 0.9432 | 0.8298 | 0.8509 | 0.5410 | 0.9032 | 0.8446 | 0.8200 | 0.8692 | 0.6906 | 0.7711 | 0.3787 | 0.8038 | 0.7074 | 0.7867 |
| 0.1986 | 8.32 | 3080 | 0.5153 | 0.7015 | 0.8026 | 0.8773 | 0.9438 | 0.7877 | 0.8787 | 0.4991 | 0.9038 | 0.8646 | 0.7408 | 0.8691 | 0.6709 | 0.7734 | 0.3863 | 0.7858 | 0.7162 | 0.7085 |
| 0.1252 | 8.38 | 3100 | 0.5256 | 0.7018 | 0.8026 | 0.8768 | 0.9358 | 0.8417 | 0.8622 | 0.4587 | 0.9148 | 0.8675 | 0.7376 | 0.8674 | 0.6960 | 0.7640 | 0.3816 | 0.7857 | 0.7131 | 0.7050 |
| 0.1778 | 8.43 | 3120 | 0.5156 | 0.7006 | 0.8025 | 0.8762 | 0.9295 | 0.7772 | 0.8867 | 0.4904 | 0.9104 | 0.8760 | 0.7474 | 0.8714 | 0.6709 | 0.7701 | 0.3884 | 0.7796 | 0.7155 | 0.7081 |
| 0.4537 | 8.49 | 3140 | 0.4896 | 0.7111 | 0.8117 | 0.8850 | 0.9302 | 0.7755 | 0.8865 | 0.4924 | 0.9149 | 0.8773 | 0.8049 | 0.8734 | 0.6599 | 0.7760 | 0.3906 | 0.8035 | 0.7110 | 0.7636 |
| 0.2145 | 8.54 | 3160 | 0.4882 | 0.7216 | 0.8248 | 0.8874 | 0.9386 | 0.8280 | 0.8589 | 0.5382 | 0.9001 | 0.8789 | 0.8309 | 0.8747 | 0.6816 | 0.7719 | 0.4142 | 0.8002 | 0.7277 | 0.7810 |
| 0.122 | 8.59 | 3180 | 0.5472 | 0.7025 | 0.8031 | 0.8754 | 0.9395 | 0.8099 | 0.8762 | 0.5051 | 0.9096 | 0.8586 | 0.7224 | 0.8657 | 0.6846 | 0.7729 | 0.4120 | 0.7849 | 0.7169 | 0.6803 |
| 0.1508 | 8.65 | 3200 | 0.5601 | 0.6966 | 0.8057 | 0.8721 | 0.9367 | 0.8412 | 0.8686 | 0.5247 | 0.9049 | 0.8520 | 0.7121 | 0.8637 | 0.6709 | 0.7671 | 0.3982 | 0.7770 | 0.7219 | 0.6773 |
| 0.4993 | 8.7 | 3220 | 0.5737 | 0.6972 | 0.7957 | 0.8731 | 0.9402 | 0.8048 | 0.8753 | 0.4891 | 0.9151 | 0.8364 | 0.7090 | 0.8643 | 0.6825 | 0.7628 | 0.3978 | 0.7795 | 0.7202 | 0.6736 |
| 0.0767 | 8.76 | 3240 | 0.5454 | 0.7052 | 0.8102 | 0.8760 | 0.9274 | 0.8521 | 0.8674 | 0.5045 | 0.9141 | 0.8621 | 0.7441 | 0.8693 | 0.6900 | 0.7596 | 0.4048 | 0.7759 | 0.7264 | 0.7102 |
| 0.2786 | 8.81 | 3260 | 0.5146 | 0.7033 | 0.8035 | 0.8766 | 0.9393 | 0.8303 | 0.8532 | 0.4791 | 0.9087 | 0.8694 | 0.7443 | 0.8675 | 0.6943 | 0.7525 | 0.3987 | 0.7831 | 0.7178 | 0.7093 |
| 0.1633 | 8.86 | 3280 | 0.4591 | 0.7194 | 0.8222 | 0.8865 | 0.9428 | 0.8341 | 0.8836 | 0.5065 | 0.8904 | 0.8704 | 0.8274 | 0.8671 | 0.6946 | 0.7677 | 0.3988 | 0.8039 | 0.7112 | 0.7924 |
| 0.1259 | 8.92 | 3300 | 0.4320 | 0.7223 | 0.8275 | 0.8895 | 0.9368 | 0.8330 | 0.9041 | 0.5151 | 0.8898 | 0.8378 | 0.8757 | 0.8727 | 0.6900 | 0.7687 | 0.3766 | 0.8055 | 0.7210 | 0.8215 |
| 0.2201 | 8.97 | 3320 | 0.4566 | 0.7287 | 0.8239 | 0.8938 | 0.9394 | 0.8272 | 0.8730 | 0.4734 | 0.9048 | 0.8717 | 0.8778 | 0.8750 | 0.6905 | 0.7656 | 0.3990 | 0.8140 | 0.7268 | 0.8300 |
| 0.2125 | 9.03 | 3340 | 0.4679 | 0.7176 | 0.8216 | 0.8858 | 0.9358 | 0.8377 | 0.8983 | 0.4901 | 0.8962 | 0.8793 | 0.8140 | 0.8647 | 0.6957 | 0.7710 | 0.3897 | 0.8068 | 0.7199 | 0.7753 |
| 0.1314 | 9.08 | 3360 | 0.4641 | 0.7203 | 0.8144 | 0.8878 | 0.9404 | 0.8227 | 0.8681 | 0.4839 | 0.9150 | 0.8544 | 0.8164 | 0.8645 | 0.6968 | 0.7780 | 0.3938 | 0.8116 | 0.7206 | 0.7770 |
| 0.2678 | 9.14 | 3380 | 0.5029 | 0.7158 | 0.8139 | 0.8847 | 0.9369 | 0.8421 | 0.8687 | 0.4804 | 0.9142 | 0.8587 | 0.7962 | 0.8680 | 0.6944 | 0.7741 | 0.3978 | 0.8013 | 0.7197 | 0.7554 |
| 0.1653 | 9.19 | 3400 | 0.5625 | 0.7036 | 0.8104 | 0.8762 | 0.9346 | 0.8486 | 0.8761 | 0.5090 | 0.9074 | 0.8631 | 0.7343 | 0.8683 | 0.6925 | 0.7689 | 0.3983 | 0.7838 | 0.7123 | 0.7009 |
| 0.1075 | 9.24 | 3420 | 0.5050 | 0.7067 | 0.8133 | 0.8773 | 0.9398 | 0.8176 | 0.8944 | 0.5225 | 0.8919 | 0.8846 | 0.7425 | 0.8704 | 0.6955 | 0.7710 | 0.4031 | 0.7823 | 0.7195 | 0.7049 |
| 0.2607 | 9.3 | 3440 | 0.5197 | 0.7070 | 0.8086 | 0.8785 | 0.9407 | 0.8058 | 0.8881 | 0.5119 | 0.9015 | 0.8580 | 0.7543 | 0.8664 | 0.6911 | 0.7765 | 0.3871 | 0.7863 | 0.7193 | 0.7219 |
| 0.111 | 9.35 | 3460 | 0.5327 | 0.7099 | 0.8098 | 0.8817 | 0.9416 | 0.8412 | 0.8594 | 0.4685 | 0.9082 | 0.8837 | 0.7658 | 0.8725 | 0.6938 | 0.7697 | 0.3851 | 0.7902 | 0.7220 | 0.7362 |
| 0.1358 | 9.41 | 3480 | 0.4572 | 0.7143 | 0.8155 | 0.8857 | 0.9449 | 0.8100 | 0.8780 | 0.4886 | 0.8912 | 0.8628 | 0.8328 | 0.8675 | 0.6818 | 0.7675 | 0.3769 | 0.8055 | 0.7211 | 0.7794 |
| 0.1794 | 9.46 | 3500 | 0.4778 | 0.7072 | 0.8141 | 0.8799 | 0.9367 | 0.8324 | 0.8530 | 0.5274 | 0.9060 | 0.8690 | 0.7742 | 0.8703 | 0.6888 | 0.7636 | 0.3918 | 0.7955 | 0.7094 | 0.7308 |
| 0.1146 | 9.51 | 3520 | 0.5337 | 0.6935 | 0.7966 | 0.8740 | 0.9459 | 0.8011 | 0.8545 | 0.4980 | 0.9119 | 0.8569 | 0.7083 | 0.8687 | 0.6794 | 0.7643 | 0.3924 | 0.7935 | 0.6795 | 0.6768 |
| 0.4693 | 9.57 | 3540 | 0.5688 | 0.6864 | 0.7860 | 0.8732 | 0.9407 | 0.7510 | 0.8634 | 0.4659 | 0.9212 | 0.8391 | 0.7205 | 0.8703 | 0.6482 | 0.7612 | 0.3688 | 0.7911 | 0.6820 | 0.6830 |
| 0.2297 | 9.62 | 3560 | 0.5465 | 0.7049 | 0.8135 | 0.8772 | 0.9337 | 0.8205 | 0.8839 | 0.5210 | 0.8966 | 0.8817 | 0.7569 | 0.8679 | 0.6903 | 0.7676 | 0.3856 | 0.7855 | 0.7209 | 0.7163 |
| 0.3738 | 9.68 | 3580 | 0.5458 | 0.7063 | 0.8052 | 0.8797 | 0.9394 | 0.8195 | 0.8574 | 0.5006 | 0.9146 | 0.8267 | 0.7783 | 0.8682 | 0.6861 | 0.7628 | 0.3779 | 0.7898 | 0.7244 | 0.7348 |
| 0.2401 | 9.73 | 3600 | 0.5446 | 0.7028 | 0.8113 | 0.8772 | 0.9306 | 0.8440 | 0.8732 | 0.5133 | 0.9136 | 0.8654 | 0.7392 | 0.8709 | 0.6858 | 0.7633 | 0.4003 | 0.7900 | 0.7039 | 0.7054 |
| 0.1552 | 9.78 | 3620 | 0.5462 | 0.7034 | 0.8091 | 0.8772 | 0.9337 | 0.8208 | 0.8948 | 0.5019 | 0.9061 | 0.8639 | 0.7422 | 0.8720 | 0.6944 | 0.7614 | 0.4068 | 0.7898 | 0.6974 | 0.7019 |
| 0.1767 | 9.84 | 3640 | 0.6458 | 0.6895 | 0.7931 | 0.8700 | 0.9378 | 0.8240 | 0.8680 | 0.4922 | 0.9234 | 0.8316 | 0.6745 | 0.8725 | 0.6978 | 0.7662 | 0.4067 | 0.7840 | 0.6572 | 0.6423 |
| 0.2452 | 9.89 | 3660 | 0.5251 | 0.6978 | 0.8087 | 0.8739 | 0.9411 | 0.8178 | 0.8852 | 0.5412 | 0.8968 | 0.8593 | 0.7194 | 0.8721 | 0.6928 | 0.7677 | 0.4042 | 0.7878 | 0.6807 | 0.6794 |
| 0.218 | 9.95 | 3680 | 0.5541 | 0.6987 | 0.8074 | 0.8745 | 0.9378 | 0.8340 | 0.8495 | 0.5413 | 0.9072 | 0.8368 | 0.7451 | 0.8711 | 0.6906 | 0.7643 | 0.3786 | 0.7824 | 0.7028 | 0.7010 |
| 0.1928 | 10.0 | 3700 | 0.5603 | 0.7023 | 0.8083 | 0.8778 | 0.9418 | 0.8415 | 0.8670 | 0.4924 | 0.9043 | 0.8692 | 0.7416 | 0.8697 | 0.6857 | 0.7684 | 0.3845 | 0.7907 | 0.7082 | 0.7089 |
| 0.0984 | 10.05 | 3720 | 0.6013 | 0.6959 | 0.7949 | 0.8758 | 0.9430 | 0.7542 | 0.8797 | 0.4777 | 0.9055 | 0.8645 | 0.7396 | 0.8699 | 0.6738 | 0.7583 | 0.3702 | 0.7868 | 0.7053 | 0.7073 |
| 0.1346 | 10.11 | 3740 | 0.5829 | 0.7016 | 0.8074 | 0.8764 | 0.9393 | 0.7869 | 0.8904 | 0.5164 | 0.8944 | 0.8757 | 0.7488 | 0.8710 | 0.6825 | 0.7643 | 0.3911 | 0.7843 | 0.7127 | 0.7055 |
| 0.1479 | 10.16 | 3760 | 0.4795 | 0.7208 | 0.8207 | 0.8893 | 0.9386 | 0.7929 | 0.8455 | 0.5388 | 0.9065 | 0.8620 | 0.8604 | 0.8707 | 0.6719 | 0.7647 | 0.4008 | 0.8139 | 0.7273 | 0.7960 |
| 0.193 | 10.22 | 3780 | 0.4772 | 0.7096 | 0.8125 | 0.8833 | 0.9302 | 0.7916 | 0.8914 | 0.5130 | 0.9141 | 0.8452 | 0.8021 | 0.8722 | 0.6818 | 0.7675 | 0.4099 | 0.8108 | 0.6772 | 0.7481 |
| 0.1574 | 10.27 | 3800 | 0.4449 | 0.7268 | 0.8243 | 0.8923 | 0.9417 | 0.8150 | 0.8765 | 0.5202 | 0.9099 | 0.8649 | 0.8421 | 0.8742 | 0.6931 | 0.7703 | 0.4116 | 0.8200 | 0.7169 | 0.8017 |
| 0.1357 | 10.32 | 3820 | 0.4419 | 0.7214 | 0.8302 | 0.8891 | 0.9352 | 0.8044 | 0.8781 | 0.5679 | 0.8897 | 0.8569 | 0.8792 | 0.8756 | 0.6765 | 0.7712 | 0.3742 | 0.8061 | 0.7319 | 0.8142 |
| 0.1049 | 10.38 | 3840 | 0.4425 | 0.7260 | 0.8296 | 0.8915 | 0.9362 | 0.7928 | 0.8675 | 0.5635 | 0.8963 | 0.8647 | 0.8861 | 0.8760 | 0.6718 | 0.7747 | 0.3990 | 0.8098 | 0.7364 | 0.8142 |
| 0.1607 | 10.43 | 3860 | 0.4764 | 0.7204 | 0.8283 | 0.8885 | 0.9317 | 0.8047 | 0.8821 | 0.5544 | 0.8937 | 0.8585 | 0.8735 | 0.8748 | 0.6710 | 0.7674 | 0.3788 | 0.8028 | 0.7337 | 0.8141 |
| 0.7998 | 10.49 | 3880 | 0.4903 | 0.7190 | 0.8258 | 0.8866 | 0.9293 | 0.8216 | 0.8807 | 0.5352 | 0.8990 | 0.8687 | 0.8463 | 0.8689 | 0.6759 | 0.7684 | 0.4070 | 0.8060 | 0.7214 | 0.7852 |
| 0.1199 | 10.54 | 3900 | 0.4547 | 0.7258 | 0.8253 | 0.8905 | 0.9312 | 0.8032 | 0.8797 | 0.5204 | 0.9023 | 0.8627 | 0.8772 | 0.8707 | 0.6839 | 0.7741 | 0.4092 | 0.8106 | 0.7323 | 0.7998 |
| 0.1326 | 10.59 | 3920 | 0.4905 | 0.7125 | 0.8102 | 0.8818 | 0.9367 | 0.7898 | 0.8574 | 0.5258 | 0.9087 | 0.8482 | 0.8050 | 0.8691 | 0.6801 | 0.7665 | 0.4044 | 0.7903 | 0.7306 | 0.7463 |
| 0.1629 | 10.65 | 3940 | 0.5135 | 0.7051 | 0.8160 | 0.8778 | 0.9379 | 0.8330 | 0.8334 | 0.5501 | 0.8931 | 0.8747 | 0.7895 | 0.8722 | 0.6742 | 0.7511 | 0.3941 | 0.7827 | 0.7289 | 0.7323 |
| 0.0679 | 10.7 | 3960 | 0.5245 | 0.7076 | 0.8094 | 0.8792 | 0.9409 | 0.8088 | 0.8733 | 0.5049 | 0.8983 | 0.8658 | 0.7735 | 0.8711 | 0.6837 | 0.7620 | 0.3979 | 0.7853 | 0.7317 | 0.7214 |
| 0.1393 | 10.76 | 3980 | 0.5436 | 0.7136 | 0.8117 | 0.8829 | 0.9367 | 0.8293 | 0.8717 | 0.4982 | 0.9166 | 0.8495 | 0.7798 | 0.8723 | 0.6868 | 0.7678 | 0.4089 | 0.7928 | 0.7266 | 0.7397 |
| 0.1078 | 10.81 | 4000 | 0.5010 | 0.7127 | 0.8149 | 0.8819 | 0.9452 | 0.8207 | 0.8718 | 0.5382 | 0.9047 | 0.8634 | 0.7601 | 0.8705 | 0.6890 | 0.7707 | 0.4062 | 0.7944 | 0.7293 | 0.7291 |
| 0.1262 | 10.86 | 4020 | 0.5119 | 0.7137 | 0.8120 | 0.8831 | 0.9401 | 0.8343 | 0.8624 | 0.4949 | 0.9146 | 0.8643 | 0.7732 | 0.8707 | 0.6901 | 0.7673 | 0.4050 | 0.7960 | 0.7303 | 0.7366 |
| 0.2813 | 10.92 | 4040 | 0.4873 | 0.7121 | 0.8212 | 0.8824 | 0.9391 | 0.8373 | 0.8584 | 0.5629 | 0.9044 | 0.8678 | 0.7788 | 0.8733 | 0.6904 | 0.7699 | 0.3986 | 0.7993 | 0.7094 | 0.7439 |
| 0.6835 | 10.97 | 4060 | 0.5088 | 0.6994 | 0.8073 | 0.8751 | 0.9275 | 0.8088 | 0.8660 | 0.5367 | 0.9130 | 0.8390 | 0.7598 | 0.8694 | 0.6796 | 0.7653 | 0.3894 | 0.7855 | 0.6999 | 0.7069 |
| 0.24 | 11.03 | 4080 | 0.5099 | 0.7101 | 0.8181 | 0.8817 | 0.9348 | 0.8374 | 0.8767 | 0.5382 | 0.9085 | 0.8531 | 0.7782 | 0.8722 | 0.6903 | 0.7680 | 0.3917 | 0.7992 | 0.7114 | 0.7378 |
| 0.1172 | 11.08 | 4100 | 0.5336 | 0.7054 | 0.8088 | 0.8794 | 0.9448 | 0.8377 | 0.8661 | 0.5007 | 0.9066 | 0.8568 | 0.7493 | 0.8678 | 0.6831 | 0.7684 | 0.3967 | 0.7963 | 0.7119 | 0.7139 |
| 0.0705 | 11.14 | 4120 | 0.5258 | 0.7049 | 0.8014 | 0.8798 | 0.9424 | 0.8152 | 0.8762 | 0.4608 | 0.9168 | 0.8534 | 0.7447 | 0.8689 | 0.6900 | 0.7707 | 0.3883 | 0.7960 | 0.7123 | 0.7084 |
| 0.1683 | 11.19 | 4140 | 0.4890 | 0.7091 | 0.8063 | 0.8829 | 0.9440 | 0.7922 | 0.8761 | 0.4739 | 0.9035 | 0.8588 | 0.7957 | 0.8690 | 0.6781 | 0.7642 | 0.3927 | 0.8021 | 0.7156 | 0.7418 |
| 0.0792 | 11.24 | 4160 | 0.5043 | 0.7072 | 0.8172 | 0.8798 | 0.9436 | 0.8273 | 0.8587 | 0.5521 | 0.8918 | 0.8655 | 0.7812 | 0.8713 | 0.6821 | 0.7606 | 0.3962 | 0.7943 | 0.7169 | 0.7292 |
| 0.1486 | 11.3 | 4180 | 0.5367 | 0.7052 | 0.8043 | 0.8783 | 0.9389 | 0.7943 | 0.8553 | 0.5066 | 0.9108 | 0.8652 | 0.7592 | 0.8733 | 0.6778 | 0.7633 | 0.4170 | 0.7877 | 0.7134 | 0.7040 |
| 0.2621 | 11.35 | 4200 | 0.5333 | 0.7016 | 0.8055 | 0.8770 | 0.9428 | 0.8055 | 0.8440 | 0.5177 | 0.9030 | 0.8714 | 0.7542 | 0.8717 | 0.6815 | 0.7596 | 0.3952 | 0.7884 | 0.7123 | 0.7029 |
| 0.1573 | 11.41 | 4220 | 0.5311 | 0.7036 | 0.8070 | 0.8775 | 0.9407 | 0.8258 | 0.8659 | 0.4995 | 0.9045 | 0.8626 | 0.7499 | 0.8707 | 0.6917 | 0.7638 | 0.3966 | 0.7893 | 0.7121 | 0.7007 |
| 0.1706 | 11.46 | 4240 | 0.5298 | 0.7062 | 0.8133 | 0.8774 | 0.9385 | 0.8210 | 0.8889 | 0.5300 | 0.8954 | 0.8700 | 0.7495 | 0.8715 | 0.6947 | 0.7709 | 0.4077 | 0.7856 | 0.7115 | 0.7016 |
| 0.2682 | 11.51 | 4260 | 0.5481 | 0.7082 | 0.8057 | 0.8801 | 0.9444 | 0.8204 | 0.8799 | 0.4890 | 0.9099 | 0.8406 | 0.7554 | 0.8715 | 0.6969 | 0.7734 | 0.3951 | 0.7902 | 0.7176 | 0.7128 |
| 0.1008 | 11.57 | 4280 | 0.5343 | 0.7053 | 0.8131 | 0.8770 | 0.9272 | 0.7950 | 0.8888 | 0.5527 | 0.9074 | 0.8655 | 0.7551 | 0.8723 | 0.6802 | 0.7748 | 0.4058 | 0.7848 | 0.7189 | 0.7000 |
| 0.2386 | 11.62 | 4300 | 0.5017 | 0.7097 | 0.8228 | 0.8813 | 0.9308 | 0.7722 | 0.8942 | 0.5822 | 0.8842 | 0.8695 | 0.8265 | 0.8662 | 0.6711 | 0.7772 | 0.3790 | 0.8012 | 0.7206 | 0.7526 |
| 0.1102 | 11.68 | 4320 | 0.4944 | 0.7106 | 0.8179 | 0.8823 | 0.9286 | 0.8126 | 0.8744 | 0.5304 | 0.9009 | 0.8469 | 0.8317 | 0.8592 | 0.6791 | 0.7732 | 0.3770 | 0.8077 | 0.7172 | 0.7611 |
| 0.1461 | 11.73 | 4340 | 0.5308 | 0.7016 | 0.8080 | 0.8756 | 0.9270 | 0.8166 | 0.8748 | 0.5144 | 0.9086 | 0.8492 | 0.7654 | 0.8572 | 0.6779 | 0.7672 | 0.3964 | 0.7956 | 0.7171 | 0.6998 |
| 0.1593 | 11.78 | 4360 | 0.5414 | 0.7097 | 0.8131 | 0.8799 | 0.9415 | 0.8112 | 0.8727 | 0.5428 | 0.9044 | 0.8635 | 0.7555 | 0.8653 | 0.6940 | 0.7638 | 0.4048 | 0.7962 | 0.7219 | 0.7224 |
| 0.1039 | 11.84 | 4380 | 0.5111 | 0.7156 | 0.8199 | 0.8830 | 0.9364 | 0.8319 | 0.8651 | 0.5438 | 0.9069 | 0.8736 | 0.7819 | 0.8673 | 0.6925 | 0.7635 | 0.4138 | 0.7998 | 0.7295 | 0.7431 |
| 0.3453 | 11.89 | 4400 | 0.5305 | 0.7131 | 0.8093 | 0.8818 | 0.9432 | 0.8346 | 0.8722 | 0.4977 | 0.9165 | 0.8497 | 0.7513 | 0.8652 | 0.6879 | 0.7682 | 0.4254 | 0.7981 | 0.7278 | 0.7194 |
| 0.15 | 11.95 | 4420 | 0.4693 | 0.7259 | 0.8316 | 0.8903 | 0.9435 | 0.8364 | 0.8837 | 0.5534 | 0.8908 | 0.8672 | 0.8460 | 0.8715 | 0.6885 | 0.7678 | 0.4070 | 0.8116 | 0.7273 | 0.8075 |
| 0.1132 | 12.0 | 4440 | 0.4752 | 0.7248 | 0.8355 | 0.8893 | 0.9381 | 0.8157 | 0.8643 | 0.6057 | 0.8898 | 0.8787 | 0.8560 | 0.8722 | 0.6864 | 0.7675 | 0.3969 | 0.8100 | 0.7267 | 0.8141 |
| 0.2272 | 12.05 | 4460 | 0.4776 | 0.7244 | 0.8240 | 0.8911 | 0.9364 | 0.7727 | 0.8572 | 0.5785 | 0.9150 | 0.8516 | 0.8565 | 0.8748 | 0.6708 | 0.7655 | 0.4108 | 0.8158 | 0.7212 | 0.8117 |
| 0.1862 | 12.11 | 4480 | 0.4954 | 0.7204 | 0.8241 | 0.8878 | 0.9394 | 0.8316 | 0.8658 | 0.5470 | 0.9084 | 0.8582 | 0.8186 | 0.8730 | 0.6965 | 0.7638 | 0.4046 | 0.8122 | 0.7081 | 0.7845 |
| 0.1436 | 12.16 | 4500 | 0.5070 | 0.7154 | 0.8182 | 0.8840 | 0.9417 | 0.8228 | 0.8489 | 0.5516 | 0.9087 | 0.8635 | 0.7903 | 0.8700 | 0.6895 | 0.7569 | 0.4171 | 0.8040 | 0.7163 | 0.7537 |
| 0.1581 | 12.22 | 4520 | 0.4918 | 0.7174 | 0.8263 | 0.8842 | 0.9284 | 0.8167 | 0.8722 | 0.5734 | 0.9050 | 0.8823 | 0.8058 | 0.8716 | 0.6850 | 0.7606 | 0.4183 | 0.8006 | 0.7239 | 0.7619 |
| 0.2003 | 12.27 | 4540 | 0.5508 | 0.7075 | 0.8139 | 0.8776 | 0.9303 | 0.8128 | 0.8467 | 0.5666 | 0.9128 | 0.8689 | 0.7590 | 0.8691 | 0.6917 | 0.7535 | 0.4125 | 0.7878 | 0.7209 | 0.7167 |
| 0.1785 | 12.32 | 4560 | 0.4795 | 0.7141 | 0.8262 | 0.8825 | 0.9354 | 0.8445 | 0.8594 | 0.5835 | 0.9014 | 0.8716 | 0.7876 | 0.8715 | 0.6871 | 0.7638 | 0.4103 | 0.7987 | 0.7186 | 0.7488 |
| 0.0645 | 12.38 | 4580 | 0.4897 | 0.7221 | 0.8265 | 0.8875 | 0.9328 | 0.8254 | 0.8690 | 0.5660 | 0.9127 | 0.8652 | 0.8145 | 0.8731 | 0.6958 | 0.7662 | 0.4194 | 0.8097 | 0.7179 | 0.7727 |
| 0.0784 | 12.43 | 4600 | 0.5016 | 0.7248 | 0.8310 | 0.8896 | 0.9303 | 0.8241 | 0.8822 | 0.5576 | 0.9062 | 0.8807 | 0.8362 | 0.8728 | 0.6910 | 0.7655 | 0.4182 | 0.8151 | 0.7209 | 0.7901 |
| 0.1183 | 12.49 | 4620 | 0.5362 | 0.7072 | 0.8020 | 0.8802 | 0.9433 | 0.8042 | 0.8626 | 0.4774 | 0.9161 | 0.8566 | 0.7538 | 0.8702 | 0.6923 | 0.7646 | 0.3956 | 0.7930 | 0.7202 | 0.7145 |
| 0.2538 | 12.54 | 4640 | 0.5996 | 0.7075 | 0.8150 | 0.8784 | 0.9358 | 0.8371 | 0.8669 | 0.5344 | 0.9051 | 0.8757 | 0.7496 | 0.8722 | 0.6942 | 0.7627 | 0.4062 | 0.7872 | 0.7139 | 0.7161 |
| 0.1557 | 12.59 | 4660 | 0.5350 | 0.7070 | 0.8149 | 0.8787 | 0.9377 | 0.8244 | 0.8555 | 0.5539 | 0.9038 | 0.8654 | 0.7638 | 0.8742 | 0.6887 | 0.7628 | 0.4033 | 0.7875 | 0.7079 | 0.7249 |
| 0.3497 | 12.65 | 4680 | 0.4915 | 0.7138 | 0.8195 | 0.8835 | 0.9416 | 0.8156 | 0.8645 | 0.5468 | 0.8973 | 0.8716 | 0.7994 | 0.8758 | 0.6877 | 0.7662 | 0.4042 | 0.7962 | 0.7115 | 0.7552 |
| 0.1298 | 12.7 | 4700 | 0.4880 | 0.7154 | 0.8223 | 0.8841 | 0.9379 | 0.7987 | 0.8712 | 0.5789 | 0.9026 | 0.8717 | 0.7947 | 0.8782 | 0.6819 | 0.7713 | 0.4105 | 0.7964 | 0.7150 | 0.7545 |
| 0.248 | 12.76 | 4720 | 0.5498 | 0.7106 | 0.8102 | 0.8817 | 0.9356 | 0.8214 | 0.8667 | 0.5055 | 0.9203 | 0.8617 | 0.7605 | 0.8763 | 0.6887 | 0.7685 | 0.4113 | 0.7928 | 0.7108 | 0.7255 |
| 0.0969 | 12.81 | 4740 | 0.5653 | 0.7107 | 0.8169 | 0.8805 | 0.9369 | 0.8377 | 0.8712 | 0.5441 | 0.9098 | 0.8652 | 0.7536 | 0.8739 | 0.6900 | 0.7678 | 0.4176 | 0.7918 | 0.7133 | 0.7204 |
| 0.1095 | 12.86 | 4760 | 0.5436 | 0.7105 | 0.8125 | 0.8808 | 0.9459 | 0.8153 | 0.8896 | 0.5127 | 0.8978 | 0.8735 | 0.7524 | 0.8727 | 0.6978 | 0.7675 | 0.4101 | 0.7922 | 0.7139 | 0.7190 |
| 0.1964 | 12.92 | 4780 | 0.6304 | 0.7005 | 0.8044 | 0.8752 | 0.9413 | 0.8388 | 0.8647 | 0.5172 | 0.9168 | 0.8495 | 0.7027 | 0.8674 | 0.6873 | 0.7677 | 0.4157 | 0.7904 | 0.7023 | 0.6725 |
| 0.1341 | 12.97 | 4800 | 0.5768 | 0.7055 | 0.8045 | 0.8782 | 0.9402 | 0.8154 | 0.8773 | 0.4902 | 0.9136 | 0.8653 | 0.7298 | 0.8686 | 0.6967 | 0.7681 | 0.4123 | 0.7939 | 0.7056 | 0.6930 |
| 0.1123 | 13.03 | 4820 | 0.5759 | 0.7002 | 0.8034 | 0.8755 | 0.9434 | 0.8259 | 0.8765 | 0.4800 | 0.9013 | 0.8750 | 0.7221 | 0.8651 | 0.6934 | 0.7657 | 0.3973 | 0.7897 | 0.7014 | 0.6886 |
| 0.1658 | 13.08 | 4840 | 0.5776 | 0.7006 | 0.8076 | 0.8748 | 0.9364 | 0.8288 | 0.8806 | 0.5232 | 0.9092 | 0.8625 | 0.7121 | 0.8665 | 0.6883 | 0.7674 | 0.4129 | 0.7891 | 0.7019 | 0.6778 |
| 0.1839 | 13.14 | 4860 | 0.5867 | 0.6981 | 0.8042 | 0.8744 | 0.9386 | 0.8375 | 0.8730 | 0.4946 | 0.9098 | 0.8683 | 0.7075 | 0.8647 | 0.6890 | 0.7616 | 0.4011 | 0.7914 | 0.7040 | 0.6747 |
| 0.1099 | 13.19 | 4880 | 0.6082 | 0.6983 | 0.8013 | 0.8742 | 0.9445 | 0.8159 | 0.8635 | 0.5103 | 0.9103 | 0.8655 | 0.6990 | 0.8639 | 0.6907 | 0.7644 | 0.4066 | 0.7923 | 0.7000 | 0.6699 |
| 0.3081 | 13.24 | 4900 | 0.5948 | 0.6993 | 0.8048 | 0.8745 | 0.9346 | 0.8352 | 0.8682 | 0.5033 | 0.9140 | 0.8640 | 0.7146 | 0.8655 | 0.6920 | 0.7625 | 0.4031 | 0.7891 | 0.7025 | 0.6804 |
| 0.0714 | 13.3 | 4920 | 0.5051 | 0.7049 | 0.8191 | 0.8782 | 0.9342 | 0.8449 | 0.8754 | 0.5468 | 0.8989 | 0.8811 | 0.7522 | 0.8685 | 0.6846 | 0.7704 | 0.3944 | 0.7956 | 0.7036 | 0.7175 |
| 0.2305 | 13.35 | 4940 | 0.5408 | 0.7004 | 0.8093 | 0.8756 | 0.9396 | 0.8220 | 0.8793 | 0.5384 | 0.9057 | 0.8616 | 0.7183 | 0.8691 | 0.6900 | 0.7697 | 0.3954 | 0.7909 | 0.7016 | 0.6861 |
| 0.2512 | 13.41 | 4960 | 0.5822 | 0.6990 | 0.8107 | 0.8738 | 0.9390 | 0.8140 | 0.8907 | 0.5550 | 0.8980 | 0.8683 | 0.7096 | 0.8679 | 0.6899 | 0.7657 | 0.4019 | 0.7876 | 0.7041 | 0.6757 |
| 0.1393 | 13.46 | 4980 | 0.5820 | 0.6959 | 0.8083 | 0.8723 | 0.9446 | 0.8042 | 0.8774 | 0.5541 | 0.8882 | 0.8804 | 0.7092 | 0.8667 | 0.6888 | 0.7699 | 0.3841 | 0.7838 | 0.7022 | 0.6755 |
| 0.9497 | 13.51 | 5000 | 0.5431 | 0.6950 | 0.8085 | 0.8726 | 0.9424 | 0.8437 | 0.8893 | 0.5207 | 0.8900 | 0.8553 | 0.7182 | 0.8657 | 0.6915 | 0.7660 | 0.3743 | 0.7847 | 0.7006 | 0.6824 |
| 0.0806 | 13.57 | 5020 | 0.5681 | 0.6932 | 0.8071 | 0.8730 | 0.9460 | 0.8298 | 0.8792 | 0.5086 | 0.8840 | 0.8783 | 0.7235 | 0.8651 | 0.6882 | 0.7591 | 0.3640 | 0.7898 | 0.6952 | 0.6910 |
| 0.1022 | 13.62 | 5040 | 0.6025 | 0.7020 | 0.8031 | 0.8773 | 0.9384 | 0.8149 | 0.8617 | 0.5091 | 0.9205 | 0.8461 | 0.7313 | 0.8709 | 0.6938 | 0.7583 | 0.3988 | 0.7930 | 0.7026 | 0.6969 |
| 0.184 | 13.68 | 5060 | 0.5221 | 0.7129 | 0.8152 | 0.8834 | 0.9440 | 0.8107 | 0.8484 | 0.5483 | 0.9078 | 0.8577 | 0.7891 | 0.8723 | 0.6916 | 0.7579 | 0.4009 | 0.8017 | 0.7134 | 0.7525 |
| 0.1923 | 13.73 | 5080 | 0.5278 | 0.7074 | 0.8231 | 0.8794 | 0.9363 | 0.8139 | 0.8365 | 0.6253 | 0.8986 | 0.8638 | 0.7869 | 0.8725 | 0.6876 | 0.7527 | 0.3841 | 0.7960 | 0.7094 | 0.7491 |
| 0.1851 | 13.78 | 5100 | 0.5243 | 0.7208 | 0.8293 | 0.8880 | 0.9352 | 0.8263 | 0.8504 | 0.5776 | 0.9032 | 0.8726 | 0.8400 | 0.8708 | 0.6909 | 0.7572 | 0.4059 | 0.8167 | 0.7142 | 0.7900 |
| 0.0883 | 13.84 | 5120 | 0.5024 | 0.7236 | 0.8229 | 0.8902 | 0.9409 | 0.7976 | 0.8571 | 0.5421 | 0.9083 | 0.8774 | 0.8370 | 0.8729 | 0.6898 | 0.7625 | 0.4175 | 0.8193 | 0.7105 | 0.7929 |
| 0.195 | 13.89 | 5140 | 0.4746 | 0.7218 | 0.8266 | 0.8886 | 0.9399 | 0.8212 | 0.8697 | 0.5583 | 0.9008 | 0.8562 | 0.8400 | 0.8729 | 0.6952 | 0.7671 | 0.4038 | 0.8141 | 0.7114 | 0.7878 |
| 0.0844 | 13.95 | 5160 | 0.4909 | 0.7205 | 0.8265 | 0.8880 | 0.9351 | 0.8371 | 0.8583 | 0.5528 | 0.9070 | 0.8559 | 0.8395 | 0.8718 | 0.6946 | 0.7642 | 0.3979 | 0.8136 | 0.7127 | 0.7886 |
| 0.1474 | 14.0 | 5180 | 0.4922 | 0.7258 | 0.8272 | 0.8905 | 0.9388 | 0.8234 | 0.8750 | 0.5452 | 0.9064 | 0.8615 | 0.8401 | 0.8720 | 0.6999 | 0.7722 | 0.4129 | 0.8181 | 0.7126 | 0.7933 |
| 0.0503 | 14.05 | 5200 | 0.5318 | 0.7121 | 0.8123 | 0.8824 | 0.9386 | 0.8375 | 0.8687 | 0.4913 | 0.9146 | 0.8767 | 0.7582 | 0.8721 | 0.6998 | 0.7690 | 0.4109 | 0.7996 | 0.7093 | 0.7243 |
| 1.5633 | 14.11 | 5220 | 0.5514 | 0.7080 | 0.8110 | 0.8790 | 0.9361 | 0.8298 | 0.8660 | 0.5116 | 0.9110 | 0.8762 | 0.7465 | 0.8721 | 0.6993 | 0.7625 | 0.4142 | 0.7913 | 0.7101 | 0.7068 |
| 0.1624 | 14.16 | 5240 | 0.5814 | 0.7004 | 0.8080 | 0.8748 | 0.9385 | 0.8203 | 0.8895 | 0.5267 | 0.9042 | 0.8668 | 0.7101 | 0.8666 | 0.6915 | 0.7616 | 0.4128 | 0.7906 | 0.7032 | 0.6764 |
| 0.1234 | 14.22 | 5260 | 0.5338 | 0.7057 | 0.8118 | 0.8776 | 0.9384 | 0.8265 | 0.8866 | 0.5304 | 0.9078 | 0.8708 | 0.7222 | 0.8691 | 0.6993 | 0.7745 | 0.4113 | 0.7951 | 0.7052 | 0.6853 |
| 0.0554 | 14.27 | 5280 | 0.6325 | 0.7015 | 0.8086 | 0.8754 | 0.9377 | 0.8343 | 0.8675 | 0.5338 | 0.9147 | 0.8722 | 0.6998 | 0.8688 | 0.6954 | 0.7702 | 0.4123 | 0.7921 | 0.7013 | 0.6701 |
| 0.115 | 14.32 | 5300 | 0.4993 | 0.7210 | 0.8234 | 0.8861 | 0.9333 | 0.8439 | 0.8784 | 0.5337 | 0.9141 | 0.8679 | 0.7922 | 0.8755 | 0.6996 | 0.7750 | 0.4152 | 0.7987 | 0.7285 | 0.7545 |
| 0.1561 | 14.38 | 5320 | 0.5592 | 0.7135 | 0.8152 | 0.8818 | 0.9352 | 0.8277 | 0.8701 | 0.5345 | 0.9173 | 0.8638 | 0.7574 | 0.8746 | 0.7009 | 0.7720 | 0.4173 | 0.7938 | 0.7157 | 0.7203 |
| 0.0848 | 14.43 | 5340 | 0.5579 | 0.7164 | 0.8164 | 0.8833 | 0.9436 | 0.8026 | 0.8806 | 0.5433 | 0.9054 | 0.8765 | 0.7631 | 0.8745 | 0.6958 | 0.7746 | 0.4219 | 0.7952 | 0.7242 | 0.7287 |
| 0.1941 | 14.49 | 5360 | 0.5586 | 0.7189 | 0.8199 | 0.8843 | 0.9371 | 0.8163 | 0.8892 | 0.5516 | 0.9127 | 0.8632 | 0.7692 | 0.8737 | 0.6961 | 0.7774 | 0.4221 | 0.7964 | 0.7319 | 0.7347 |
| 0.1339 | 14.54 | 5380 | 0.5690 | 0.7114 | 0.8206 | 0.8800 | 0.9383 | 0.8260 | 0.8833 | 0.5868 | 0.9054 | 0.8599 | 0.7444 | 0.8714 | 0.6967 | 0.7769 | 0.4115 | 0.7933 | 0.7164 | 0.7138 |
| 0.1054 | 14.59 | 5400 | 0.5522 | 0.7119 | 0.8216 | 0.8797 | 0.9407 | 0.8214 | 0.8717 | 0.5864 | 0.8963 | 0.8839 | 0.7510 | 0.8725 | 0.6988 | 0.7761 | 0.4120 | 0.7897 | 0.7169 | 0.7175 |
| 0.1314 | 14.65 | 5420 | 0.5695 | 0.7134 | 0.8164 | 0.8815 | 0.9420 | 0.8324 | 0.8740 | 0.5469 | 0.9087 | 0.8569 | 0.7540 | 0.8724 | 0.6957 | 0.7754 | 0.4228 | 0.7945 | 0.7145 | 0.7183 |
| 0.2918 | 14.7 | 5440 | 0.5720 | 0.7115 | 0.8167 | 0.8806 | 0.9385 | 0.8318 | 0.8726 | 0.5263 | 0.9036 | 0.8930 | 0.7509 | 0.8738 | 0.6959 | 0.7796 | 0.4111 | 0.7905 | 0.7138 | 0.7155 |
| 0.1543 | 14.76 | 5460 | 0.5569 | 0.7137 | 0.8163 | 0.8817 | 0.9396 | 0.8374 | 0.8688 | 0.5230 | 0.9078 | 0.8829 | 0.7549 | 0.8741 | 0.6978 | 0.7713 | 0.4232 | 0.7927 | 0.7180 | 0.7187 |
| 0.1186 | 14.81 | 5480 | 0.5482 | 0.7212 | 0.8233 | 0.8853 | 0.9366 | 0.8389 | 0.8538 | 0.5453 | 0.9084 | 0.8849 | 0.7954 | 0.8771 | 0.6978 | 0.7658 | 0.4222 | 0.7920 | 0.7386 | 0.7551 |
| 0.1624 | 14.86 | 5500 | 0.5372 | 0.7189 | 0.8240 | 0.8842 | 0.9355 | 0.8337 | 0.8733 | 0.5587 | 0.9046 | 0.8694 | 0.7930 | 0.8756 | 0.6953 | 0.7717 | 0.4070 | 0.7904 | 0.7386 | 0.7533 |
| 0.9141 | 14.92 | 5520 | 0.5415 | 0.7166 | 0.8202 | 0.8832 | 0.9415 | 0.8160 | 0.8734 | 0.5625 | 0.9007 | 0.8602 | 0.7873 | 0.8744 | 0.6917 | 0.7683 | 0.4067 | 0.7896 | 0.7359 | 0.7495 |
| 0.1429 | 14.97 | 5540 | 0.5310 | 0.7171 | 0.8185 | 0.8837 | 0.9397 | 0.8161 | 0.8610 | 0.5489 | 0.9098 | 0.8795 | 0.7743 | 0.8751 | 0.6908 | 0.7675 | 0.4220 | 0.7939 | 0.7309 | 0.7396 |
| 0.21 | 15.03 | 5560 | 0.4918 | 0.7230 | 0.8230 | 0.8871 | 0.9395 | 0.8298 | 0.8797 | 0.5404 | 0.9100 | 0.8671 | 0.7944 | 0.8748 | 0.6930 | 0.7726 | 0.4246 | 0.7999 | 0.7373 | 0.7586 |
| 0.1884 | 15.08 | 5580 | 0.5099 | 0.7153 | 0.8160 | 0.8831 | 0.9437 | 0.8006 | 0.8602 | 0.5582 | 0.9087 | 0.8716 | 0.7686 | 0.8741 | 0.6895 | 0.7719 | 0.4206 | 0.7963 | 0.7214 | 0.7331 |
| 0.1516 | 15.14 | 5600 | 0.5335 | 0.7137 | 0.8168 | 0.8823 | 0.9372 | 0.8281 | 0.8823 | 0.5498 | 0.9161 | 0.8459 | 0.7585 | 0.8757 | 0.6937 | 0.7763 | 0.4186 | 0.7949 | 0.7140 | 0.7230 |
| 0.1574 | 15.19 | 5620 | 0.5654 | 0.7136 | 0.8156 | 0.8818 | 0.9410 | 0.8196 | 0.8757 | 0.5507 | 0.9120 | 0.8582 | 0.7521 | 0.8756 | 0.6989 | 0.7708 | 0.4237 | 0.7942 | 0.7134 | 0.7186 |
| 0.0803 | 15.24 | 5640 | 0.5721 | 0.7136 | 0.8178 | 0.8815 | 0.9429 | 0.8285 | 0.8653 | 0.5589 | 0.9080 | 0.8754 | 0.7458 | 0.8739 | 0.6983 | 0.7693 | 0.4272 | 0.7956 | 0.7165 | 0.7142 |
| 0.1424 | 15.3 | 5660 | 0.5583 | 0.7127 | 0.8150 | 0.8812 | 0.9406 | 0.8168 | 0.8676 | 0.5656 | 0.9163 | 0.8526 | 0.7456 | 0.8734 | 0.6944 | 0.7721 | 0.4269 | 0.7956 | 0.7123 | 0.7142 |
| 0.0757 | 15.35 | 5680 | 0.5655 | 0.7106 | 0.8140 | 0.8803 | 0.9388 | 0.8168 | 0.8697 | 0.5482 | 0.9116 | 0.8631 | 0.7501 | 0.8729 | 0.6917 | 0.7703 | 0.4162 | 0.7925 | 0.7139 | 0.7164 |
| 0.1092 | 15.41 | 5700 | 0.5550 | 0.7114 | 0.8160 | 0.8808 | 0.9429 | 0.8282 | 0.8617 | 0.5558 | 0.9075 | 0.8621 | 0.7535 | 0.8742 | 0.6944 | 0.7703 | 0.4152 | 0.7926 | 0.7138 | 0.7195 |
| 0.1833 | 15.46 | 5720 | 0.4969 | 0.7304 | 0.8307 | 0.8930 | 0.9412 | 0.8311 | 0.8812 | 0.5391 | 0.9056 | 0.8764 | 0.8401 | 0.8759 | 0.6980 | 0.7712 | 0.4282 | 0.8201 | 0.7182 | 0.8013 |
| 0.0929 | 15.51 | 5740 | 0.5019 | 0.7272 | 0.8228 | 0.8918 | 0.9443 | 0.8274 | 0.8651 | 0.5131 | 0.9135 | 0.8714 | 0.8244 | 0.8757 | 0.6980 | 0.7731 | 0.4210 | 0.8170 | 0.7180 | 0.7873 |
| 0.1537 | 15.57 | 5760 | 0.5682 | 0.7139 | 0.8192 | 0.8818 | 0.9327 | 0.8587 | 0.8672 | 0.5456 | 0.9196 | 0.8503 | 0.7602 | 0.8732 | 0.6913 | 0.7723 | 0.4284 | 0.7955 | 0.7165 | 0.7204 |
| 0.0488 | 15.62 | 5780 | 0.5516 | 0.7133 | 0.8158 | 0.8819 | 0.9407 | 0.8418 | 0.8711 | 0.5235 | 0.9096 | 0.8678 | 0.7562 | 0.8725 | 0.6953 | 0.7739 | 0.4228 | 0.7961 | 0.7140 | 0.7185 |
| 0.1074 | 15.68 | 5800 | 0.5703 | 0.7111 | 0.8162 | 0.8808 | 0.9394 | 0.8297 | 0.8875 | 0.5129 | 0.8991 | 0.8886 | 0.7560 | 0.8723 | 0.6930 | 0.7699 | 0.4142 | 0.7925 | 0.7174 | 0.7185 |
| 0.0859 | 15.73 | 5820 | 0.6073 | 0.7116 | 0.8095 | 0.8817 | 0.9371 | 0.8186 | 0.8766 | 0.5036 | 0.9199 | 0.8565 | 0.7546 | 0.8715 | 0.6931 | 0.7697 | 0.4136 | 0.7964 | 0.7204 | 0.7164 |
| 0.1068 | 15.78 | 5840 | 0.5386 | 0.7113 | 0.8205 | 0.8807 | 0.9382 | 0.8395 | 0.8582 | 0.5640 | 0.9029 | 0.8745 | 0.7664 | 0.8724 | 0.6948 | 0.7691 | 0.3998 | 0.7936 | 0.7235 | 0.7259 |
| 0.1288 | 15.84 | 5860 | 0.5640 | 0.7078 | 0.8112 | 0.8790 | 0.9459 | 0.8185 | 0.8664 | 0.5460 | 0.9050 | 0.8530 | 0.7438 | 0.8705 | 0.6968 | 0.7707 | 0.3983 | 0.7905 | 0.7155 | 0.7125 |
| 0.161 | 15.89 | 5880 | 0.6023 | 0.7055 | 0.8088 | 0.8782 | 0.9439 | 0.8222 | 0.8378 | 0.5346 | 0.9084 | 0.8665 | 0.7484 | 0.8716 | 0.6953 | 0.7624 | 0.3912 | 0.7886 | 0.7156 | 0.7141 |
| 0.2896 | 15.95 | 5900 | 0.6422 | 0.7098 | 0.8131 | 0.8799 | 0.9374 | 0.8238 | 0.8658 | 0.5354 | 0.9128 | 0.8696 | 0.7469 | 0.8726 | 0.6964 | 0.7694 | 0.4089 | 0.7913 | 0.7165 | 0.7133 |
| 0.1331 | 16.0 | 5920 | 0.6165 | 0.7120 | 0.8111 | 0.8815 | 0.9381 | 0.8166 | 0.8853 | 0.5089 | 0.9154 | 0.8665 | 0.7471 | 0.8742 | 0.6978 | 0.7667 | 0.4211 | 0.7942 | 0.7162 | 0.7140 |
| 0.0851 | 16.05 | 5940 | 0.5548 | 0.7146 | 0.8151 | 0.8835 | 0.9402 | 0.8268 | 0.8766 | 0.5103 | 0.9110 | 0.8780 | 0.7630 | 0.8772 | 0.6979 | 0.7719 | 0.4141 | 0.7961 | 0.7195 | 0.7256 |
| 0.1627 | 16.11 | 5960 | 0.6263 | 0.7120 | 0.8142 | 0.8817 | 0.9388 | 0.8262 | 0.8748 | 0.5333 | 0.9158 | 0.8616 | 0.7488 | 0.8774 | 0.6959 | 0.7727 | 0.4138 | 0.7927 | 0.7140 | 0.7176 |
| 0.1197 | 16.16 | 5980 | 0.5825 | 0.7085 | 0.8212 | 0.8792 | 0.9366 | 0.8377 | 0.8605 | 0.5824 | 0.9000 | 0.8640 | 0.7672 | 0.8767 | 0.6925 | 0.7708 | 0.3867 | 0.7857 | 0.7185 | 0.7288 |
| 0.5273 | 16.22 | 6000 | 0.5988 | 0.7157 | 0.8174 | 0.8830 | 0.9368 | 0.8146 | 0.8820 | 0.5475 | 0.9120 | 0.8567 | 0.7720 | 0.8767 | 0.6938 | 0.7706 | 0.4224 | 0.7928 | 0.7219 | 0.7321 |
| 0.1381 | 16.27 | 6020 | 0.5692 | 0.7150 | 0.8190 | 0.8827 | 0.9362 | 0.8343 | 0.8639 | 0.5474 | 0.9134 | 0.8709 | 0.7665 | 0.8773 | 0.6931 | 0.7721 | 0.4248 | 0.7940 | 0.7158 | 0.7276 |
| 0.0793 | 16.32 | 6040 | 0.5893 | 0.7136 | 0.8163 | 0.8818 | 0.9345 | 0.8222 | 0.8708 | 0.5475 | 0.9168 | 0.8616 | 0.7604 | 0.8758 | 0.6929 | 0.7744 | 0.4245 | 0.7928 | 0.7118 | 0.7232 |
| 0.0582 | 16.38 | 6060 | 0.7212 | 0.7032 | 0.8038 | 0.8757 | 0.9439 | 0.8061 | 0.8635 | 0.5485 | 0.9181 | 0.8428 | 0.7036 | 0.8632 | 0.6901 | 0.7688 | 0.4265 | 0.7936 | 0.7034 | 0.6770 |
| 0.1339 | 16.43 | 6080 | 0.5848 | 0.7123 | 0.8164 | 0.8810 | 0.9412 | 0.8312 | 0.8795 | 0.5399 | 0.9070 | 0.8696 | 0.7461 | 0.8715 | 0.6968 | 0.7737 | 0.4204 | 0.7956 | 0.7149 | 0.7131 |
| 0.1311 | 16.49 | 6100 | 0.6171 | 0.7109 | 0.8072 | 0.8811 | 0.9419 | 0.8082 | 0.8586 | 0.5156 | 0.9201 | 0.8596 | 0.7466 | 0.8714 | 0.6964 | 0.7690 | 0.4151 | 0.7955 | 0.7154 | 0.7138 |
| 0.0856 | 16.54 | 6120 | 0.6195 | 0.7095 | 0.8073 | 0.8804 | 0.9374 | 0.8099 | 0.8548 | 0.5201 | 0.9239 | 0.8550 | 0.7499 | 0.8722 | 0.6938 | 0.7662 | 0.4083 | 0.7932 | 0.7162 | 0.7163 |
| 0.8163 | 16.59 | 6140 | 0.5405 | 0.7141 | 0.8122 | 0.8836 | 0.9403 | 0.8288 | 0.8689 | 0.5015 | 0.9177 | 0.8631 | 0.7647 | 0.8734 | 0.6956 | 0.7713 | 0.4100 | 0.7994 | 0.7193 | 0.7294 |
| 0.0893 | 16.65 | 6160 | 0.5658 | 0.7105 | 0.8122 | 0.8809 | 0.9414 | 0.8283 | 0.8689 | 0.5119 | 0.9088 | 0.8746 | 0.7517 | 0.8725 | 0.6944 | 0.7686 | 0.4096 | 0.7936 | 0.7184 | 0.7168 |
| 0.0869 | 16.7 | 6180 | 0.5834 | 0.7103 | 0.8113 | 0.8811 | 0.9394 | 0.8424 | 0.8676 | 0.4995 | 0.9163 | 0.8702 | 0.7437 | 0.8731 | 0.6978 | 0.7695 | 0.4088 | 0.7950 | 0.7158 | 0.7120 |
| 0.161 | 16.76 | 6200 | 0.5759 | 0.7121 | 0.8129 | 0.8817 | 0.9404 | 0.8337 | 0.8744 | 0.5100 | 0.9125 | 0.8678 | 0.7516 | 0.8738 | 0.6992 | 0.7728 | 0.4140 | 0.7951 | 0.7147 | 0.7154 |
| 0.1898 | 16.81 | 6220 | 0.5838 | 0.7121 | 0.8098 | 0.8821 | 0.9413 | 0.8366 | 0.8608 | 0.5007 | 0.9211 | 0.8593 | 0.7492 | 0.8740 | 0.6970 | 0.7721 | 0.4164 | 0.7967 | 0.7128 | 0.7156 |
| 2.3534 | 16.86 | 6240 | 0.5930 | 0.7118 | 0.8086 | 0.8822 | 0.9427 | 0.8268 | 0.8857 | 0.4846 | 0.9158 | 0.8565 | 0.7483 | 0.8732 | 0.6989 | 0.7740 | 0.4120 | 0.7968 | 0.7125 | 0.7153 |
| 0.0658 | 16.92 | 6260 | 0.5076 | 0.7169 | 0.8225 | 0.8842 | 0.9412 | 0.8316 | 0.8670 | 0.5666 | 0.9066 | 0.8665 | 0.7779 | 0.8747 | 0.6946 | 0.7772 | 0.4169 | 0.8013 | 0.7137 | 0.7397 |
| 0.1386 | 16.97 | 6280 | 0.5100 | 0.7244 | 0.8309 | 0.8893 | 0.9373 | 0.8286 | 0.8770 | 0.5876 | 0.9056 | 0.8446 | 0.8356 | 0.8735 | 0.6964 | 0.7752 | 0.4061 | 0.8133 | 0.7103 | 0.7960 |
| 0.0797 | 17.03 | 6300 | 0.4916 | 0.7254 | 0.8309 | 0.8904 | 0.9378 | 0.8263 | 0.8680 | 0.5825 | 0.9079 | 0.8548 | 0.8389 | 0.8751 | 0.6940 | 0.7768 | 0.4061 | 0.8155 | 0.7121 | 0.7983 |
| 0.1281 | 17.08 | 6320 | 0.4981 | 0.7263 | 0.8313 | 0.8909 | 0.9397 | 0.8092 | 0.8823 | 0.5861 | 0.9015 | 0.8531 | 0.8472 | 0.8762 | 0.6908 | 0.7721 | 0.4094 | 0.8148 | 0.7163 | 0.8045 |
| 0.0712 | 17.14 | 6340 | 0.5308 | 0.7242 | 0.8318 | 0.8892 | 0.9362 | 0.8222 | 0.8747 | 0.5963 | 0.9033 | 0.8473 | 0.8428 | 0.8729 | 0.6944 | 0.7704 | 0.4005 | 0.8126 | 0.7158 | 0.8030 |
| 0.1443 | 17.19 | 6360 | 0.5330 | 0.7112 | 0.8118 | 0.8815 | 0.9431 | 0.8179 | 0.8822 | 0.5203 | 0.9094 | 0.8550 | 0.7545 | 0.8727 | 0.6979 | 0.7716 | 0.4045 | 0.7951 | 0.7158 | 0.7210 |
| 0.7862 | 17.24 | 6380 | 0.6242 | 0.7111 | 0.8107 | 0.8811 | 0.9461 | 0.8333 | 0.8621 | 0.5086 | 0.9098 | 0.8705 | 0.7442 | 0.8721 | 0.7009 | 0.7679 | 0.4137 | 0.7949 | 0.7152 | 0.7128 |
| 0.1323 | 17.3 | 6400 | 0.6169 | 0.7062 | 0.8104 | 0.8782 | 0.9392 | 0.8061 | 0.8697 | 0.5477 | 0.9125 | 0.8703 | 0.7276 | 0.8739 | 0.6952 | 0.7672 | 0.4245 | 0.7943 | 0.6948 | 0.6935 |
| 0.0704 | 17.35 | 6420 | 0.6165 | 0.7112 | 0.8108 | 0.8808 | 0.9378 | 0.8084 | 0.8616 | 0.5438 | 0.9205 | 0.8516 | 0.7521 | 0.8733 | 0.6905 | 0.7665 | 0.4271 | 0.7943 | 0.7113 | 0.7151 |
| 0.1044 | 17.41 | 6440 | 0.5954 | 0.7101 | 0.8119 | 0.8807 | 0.9396 | 0.8048 | 0.8641 | 0.5539 | 0.9155 | 0.8492 | 0.7560 | 0.8735 | 0.6838 | 0.7672 | 0.4176 | 0.7936 | 0.7167 | 0.7183 |
| 0.4188 | 17.46 | 6460 | 0.6219 | 0.7138 | 0.8113 | 0.8822 | 0.9381 | 0.8040 | 0.8722 | 0.5351 | 0.9202 | 0.8501 | 0.7597 | 0.8739 | 0.6925 | 0.7673 | 0.4190 | 0.7933 | 0.7264 | 0.7242 |
| 0.0606 | 17.51 | 6480 | 0.5436 | 0.7196 | 0.8189 | 0.8850 | 0.9411 | 0.8160 | 0.8758 | 0.5394 | 0.9080 | 0.8697 | 0.7826 | 0.8745 | 0.6983 | 0.7684 | 0.4206 | 0.7967 | 0.7355 | 0.7431 |
| 0.1067 | 17.57 | 6500 | 0.5200 | 0.7233 | 0.8264 | 0.8882 | 0.9433 | 0.8147 | 0.8653 | 0.5683 | 0.9000 | 0.8729 | 0.8203 | 0.8740 | 0.6962 | 0.7670 | 0.4042 | 0.8055 | 0.7336 | 0.7828 |
| 0.3848 | 17.62 | 6520 | 0.4962 | 0.7292 | 0.8336 | 0.8920 | 0.9366 | 0.8121 | 0.8657 | 0.5910 | 0.9033 | 0.8612 | 0.8653 | 0.8755 | 0.6931 | 0.7688 | 0.3959 | 0.8123 | 0.7385 | 0.8205 |
| 0.1252 | 17.68 | 6540 | 0.5172 | 0.7192 | 0.8187 | 0.8850 | 0.9383 | 0.8123 | 0.8679 | 0.5446 | 0.9126 | 0.8695 | 0.7854 | 0.8739 | 0.6940 | 0.7705 | 0.4168 | 0.7979 | 0.7371 | 0.7439 |
| 0.1498 | 17.73 | 6560 | 0.4809 | 0.7283 | 0.8310 | 0.8920 | 0.9402 | 0.8245 | 0.8852 | 0.5427 | 0.8983 | 0.8750 | 0.8509 | 0.8728 | 0.6941 | 0.7754 | 0.4045 | 0.8164 | 0.7263 | 0.8084 |
| 0.1339 | 17.78 | 6580 | 0.4834 | 0.7283 | 0.8310 | 0.8921 | 0.9358 | 0.8345 | 0.8709 | 0.5393 | 0.9060 | 0.8791 | 0.8517 | 0.8737 | 0.6922 | 0.7721 | 0.4075 | 0.8162 | 0.7265 | 0.8096 |
| 0.155 | 17.84 | 6600 | 0.5174 | 0.7280 | 0.8253 | 0.8921 | 0.9397 | 0.8201 | 0.8806 | 0.5166 | 0.9097 | 0.8698 | 0.8405 | 0.8729 | 0.6928 | 0.7705 | 0.4115 | 0.8164 | 0.7283 | 0.8032 |
| 0.3213 | 17.89 | 6620 | 0.5081 | 0.7286 | 0.8303 | 0.8922 | 0.9377 | 0.8294 | 0.8755 | 0.5476 | 0.9070 | 0.8652 | 0.8499 | 0.8738 | 0.6948 | 0.7714 | 0.4071 | 0.8164 | 0.7258 | 0.8106 |
| 0.178 | 17.95 | 6640 | 0.5023 | 0.7329 | 0.8289 | 0.8945 | 0.9379 | 0.8278 | 0.8713 | 0.5284 | 0.9133 | 0.8571 | 0.8664 | 0.8737 | 0.6960 | 0.7717 | 0.4138 | 0.8183 | 0.7347 | 0.8219 |
| 0.1809 | 18.0 | 6660 | 0.5378 | 0.7303 | 0.8290 | 0.8932 | 0.9422 | 0.8208 | 0.8670 | 0.5306 | 0.9038 | 0.8885 | 0.8499 | 0.8736 | 0.6950 | 0.7706 | 0.4134 | 0.8182 | 0.7297 | 0.8116 |
| 0.1183 | 18.05 | 6680 | 0.5358 | 0.7305 | 0.8232 | 0.8940 | 0.9387 | 0.8151 | 0.8823 | 0.4934 | 0.9152 | 0.8635 | 0.8547 | 0.8726 | 0.6962 | 0.7696 | 0.4086 | 0.8196 | 0.7322 | 0.8144 |
| 0.0902 | 18.11 | 6700 | 0.5166 | 0.7308 | 0.8300 | 0.8935 | 0.9395 | 0.8267 | 0.8612 | 0.5466 | 0.9113 | 0.8730 | 0.8521 | 0.8743 | 0.6937 | 0.7691 | 0.4176 | 0.8206 | 0.7297 | 0.8106 |
| 0.2487 | 18.16 | 6720 | 0.5290 | 0.7296 | 0.8283 | 0.8928 | 0.9401 | 0.8130 | 0.8690 | 0.5405 | 0.9087 | 0.8849 | 0.8418 | 0.8745 | 0.6961 | 0.7675 | 0.4223 | 0.8203 | 0.7235 | 0.8032 |
| 0.1186 | 18.22 | 6740 | 0.5202 | 0.7302 | 0.8264 | 0.8933 | 0.9429 | 0.7918 | 0.8686 | 0.5522 | 0.9070 | 0.8609 | 0.8612 | 0.8746 | 0.6945 | 0.7690 | 0.4091 | 0.8174 | 0.7308 | 0.8161 |
| 0.3732 | 18.27 | 6760 | 0.5339 | 0.7253 | 0.8236 | 0.8912 | 0.9418 | 0.8295 | 0.8555 | 0.5244 | 0.9130 | 0.8635 | 0.8371 | 0.8738 | 0.7020 | 0.7671 | 0.4021 | 0.8184 | 0.7166 | 0.7974 |
| 0.155 | 18.32 | 6780 | 0.5497 | 0.7263 | 0.8239 | 0.8913 | 0.9405 | 0.8221 | 0.8754 | 0.5133 | 0.9112 | 0.8795 | 0.8255 | 0.8741 | 0.7025 | 0.7702 | 0.4144 | 0.8192 | 0.7174 | 0.7866 |
| 0.2159 | 18.38 | 6800 | 0.5251 | 0.7267 | 0.8279 | 0.8913 | 0.9412 | 0.8193 | 0.8767 | 0.5469 | 0.9052 | 0.8675 | 0.8383 | 0.8737 | 0.7002 | 0.7723 | 0.4077 | 0.8191 | 0.7180 | 0.7958 |
| 0.1727 | 18.43 | 6820 | 0.5323 | 0.7267 | 0.8290 | 0.8914 | 0.9391 | 0.8435 | 0.8636 | 0.5414 | 0.9093 | 0.8668 | 0.8397 | 0.8741 | 0.7019 | 0.7696 | 0.4110 | 0.8198 | 0.7153 | 0.7952 |
| 0.1184 | 18.49 | 6840 | 0.5390 | 0.7279 | 0.8275 | 0.8919 | 0.9443 | 0.8292 | 0.8675 | 0.5370 | 0.9058 | 0.8745 | 0.8344 | 0.8733 | 0.7014 | 0.7688 | 0.4164 | 0.8206 | 0.7187 | 0.7961 |
| 0.1718 | 18.54 | 6860 | 0.5446 | 0.7227 | 0.8228 | 0.8885 | 0.9388 | 0.8258 | 0.8711 | 0.5280 | 0.9106 | 0.8750 | 0.8106 | 0.8737 | 0.7009 | 0.7685 | 0.4147 | 0.8117 | 0.7202 | 0.7690 |
| 0.1154 | 18.59 | 6880 | 0.5651 | 0.7153 | 0.8143 | 0.8835 | 0.9458 | 0.8170 | 0.8670 | 0.5288 | 0.9084 | 0.8668 | 0.7665 | 0.8726 | 0.6994 | 0.7697 | 0.4155 | 0.7994 | 0.7197 | 0.7306 |
| 0.1404 | 18.65 | 6900 | 0.5538 | 0.7159 | 0.8190 | 0.8833 | 0.9356 | 0.8293 | 0.8771 | 0.5495 | 0.9156 | 0.8586 | 0.7676 | 0.8738 | 0.6984 | 0.7709 | 0.4208 | 0.7993 | 0.7173 | 0.7309 |
| 0.071 | 18.7 | 6920 | 0.5250 | 0.7262 | 0.8285 | 0.8901 | 0.9375 | 0.8250 | 0.8717 | 0.5567 | 0.9094 | 0.8740 | 0.8249 | 0.8751 | 0.7000 | 0.7706 | 0.4210 | 0.8148 | 0.7190 | 0.7828 |
| 0.0938 | 18.76 | 6940 | 0.5374 | 0.7237 | 0.8247 | 0.8881 | 0.9400 | 0.8258 | 0.8701 | 0.5568 | 0.9119 | 0.8683 | 0.7999 | 0.8746 | 0.7015 | 0.7696 | 0.4281 | 0.8101 | 0.7185 | 0.7633 |
| 0.1624 | 18.81 | 6960 | 0.5468 | 0.7202 | 0.8220 | 0.8855 | 0.9412 | 0.8236 | 0.8749 | 0.5559 | 0.9097 | 0.8739 | 0.7748 | 0.8744 | 0.7019 | 0.7724 | 0.4289 | 0.8040 | 0.7197 | 0.7398 |
| 0.0766 | 18.86 | 6980 | 0.5889 | 0.7146 | 0.8156 | 0.8817 | 0.9370 | 0.8305 | 0.8660 | 0.5483 | 0.9193 | 0.8562 | 0.7518 | 0.8734 | 0.7006 | 0.7685 | 0.4292 | 0.7948 | 0.7190 | 0.7164 |
| 0.1392 | 18.92 | 7000 | 0.5337 | 0.7193 | 0.8191 | 0.8855 | 0.9409 | 0.8197 | 0.8699 | 0.5424 | 0.9119 | 0.8666 | 0.7826 | 0.8728 | 0.6962 | 0.7700 | 0.4286 | 0.8044 | 0.7190 | 0.7442 |
| 0.1355 | 18.97 | 7020 | 0.5454 | 0.7258 | 0.8283 | 0.8901 | 0.9425 | 0.8168 | 0.8598 | 0.5748 | 0.9060 | 0.8705 | 0.8279 | 0.8743 | 0.6964 | 0.7664 | 0.4196 | 0.8154 | 0.7179 | 0.7909 |
| 0.1311 | 19.03 | 7040 | 0.5109 | 0.7277 | 0.8286 | 0.8915 | 0.9404 | 0.8328 | 0.8597 | 0.5547 | 0.9111 | 0.8634 | 0.8379 | 0.8740 | 0.6958 | 0.7679 | 0.4189 | 0.8177 | 0.7200 | 0.7995 |
| 0.1482 | 19.08 | 7060 | 0.5200 | 0.7294 | 0.8293 | 0.8921 | 0.9414 | 0.8118 | 0.8670 | 0.5599 | 0.9054 | 0.8759 | 0.8438 | 0.8746 | 0.6977 | 0.7689 | 0.4223 | 0.8174 | 0.7217 | 0.8031 |
| 0.1097 | 19.14 | 7080 | 0.5579 | 0.7150 | 0.8173 | 0.8821 | 0.9386 | 0.8159 | 0.8658 | 0.5675 | 0.9150 | 0.8629 | 0.7555 | 0.8742 | 0.6979 | 0.7698 | 0.4300 | 0.7960 | 0.7167 | 0.7205 |
| 0.1646 | 19.19 | 7100 | 0.5838 | 0.7139 | 0.8143 | 0.8815 | 0.9400 | 0.8129 | 0.8633 | 0.5555 | 0.9159 | 0.8606 | 0.7518 | 0.8733 | 0.6975 | 0.7666 | 0.4313 | 0.7950 | 0.7159 | 0.7175 |
| 0.0971 | 19.24 | 7120 | 0.5568 | 0.7135 | 0.8168 | 0.8816 | 0.9445 | 0.8272 | 0.8667 | 0.5603 | 0.9081 | 0.8585 | 0.7521 | 0.8726 | 0.6968 | 0.7699 | 0.4247 | 0.7961 | 0.7161 | 0.7186 |
| 0.1318 | 19.3 | 7140 | 0.6116 | 0.7129 | 0.8136 | 0.8814 | 0.9422 | 0.8247 | 0.8791 | 0.5414 | 0.9141 | 0.8462 | 0.7472 | 0.8726 | 0.6966 | 0.7730 | 0.4221 | 0.7947 | 0.7176 | 0.7139 |
| 0.0763 | 19.35 | 7160 | 0.5854 | 0.7149 | 0.8149 | 0.8826 | 0.9390 | 0.8274 | 0.8725 | 0.5344 | 0.9174 | 0.8576 | 0.7558 | 0.8733 | 0.6960 | 0.7707 | 0.4267 | 0.7965 | 0.7198 | 0.7214 |
| 0.0788 | 19.41 | 7180 | 0.5573 | 0.7174 | 0.8149 | 0.8842 | 0.9420 | 0.8117 | 0.8622 | 0.5320 | 0.9159 | 0.8796 | 0.7609 | 0.8743 | 0.6977 | 0.7706 | 0.4311 | 0.8002 | 0.7221 | 0.7262 |
| 0.1318 | 19.46 | 7200 | 0.6123 | 0.7159 | 0.8151 | 0.8830 | 0.9387 | 0.8202 | 0.8640 | 0.5288 | 0.9166 | 0.8842 | 0.7533 | 0.8745 | 0.6989 | 0.7705 | 0.4289 | 0.7975 | 0.7230 | 0.7179 |
| 0.1114 | 19.51 | 7220 | 0.5467 | 0.7182 | 0.8213 | 0.8847 | 0.9439 | 0.8315 | 0.8570 | 0.5607 | 0.9081 | 0.8738 | 0.7739 | 0.8732 | 0.6985 | 0.7708 | 0.4228 | 0.8032 | 0.7214 | 0.7373 |
| 0.07 | 19.57 | 7240 | 0.5694 | 0.7138 | 0.8176 | 0.8817 | 0.9386 | 0.8233 | 0.8625 | 0.5745 | 0.9159 | 0.8512 | 0.7575 | 0.8729 | 0.6989 | 0.7690 | 0.4191 | 0.7970 | 0.7168 | 0.7230 |
| 0.1917 | 19.62 | 7260 | 0.5537 | 0.7195 | 0.8173 | 0.8859 | 0.9437 | 0.8205 | 0.8595 | 0.5427 | 0.9160 | 0.8555 | 0.7832 | 0.8731 | 0.7001 | 0.7673 | 0.4260 | 0.8061 | 0.7163 | 0.7480 |
| 0.0786 | 19.68 | 7280 | 0.5282 | 0.7266 | 0.8297 | 0.8899 | 0.9354 | 0.8340 | 0.8717 | 0.5637 | 0.9123 | 0.8692 | 0.8213 | 0.8734 | 0.7018 | 0.7706 | 0.4258 | 0.8159 | 0.7188 | 0.7801 |
| 0.1788 | 19.73 | 7300 | 0.5386 | 0.7247 | 0.8293 | 0.8889 | 0.9368 | 0.8228 | 0.8783 | 0.5741 | 0.9068 | 0.8675 | 0.8190 | 0.8735 | 0.7034 | 0.7712 | 0.4134 | 0.8130 | 0.7181 | 0.7804 |
| 0.1096 | 19.78 | 7320 | 0.5480 | 0.7266 | 0.8267 | 0.8909 | 0.9418 | 0.8234 | 0.8574 | 0.5573 | 0.9114 | 0.8640 | 0.8312 | 0.8734 | 0.7014 | 0.7679 | 0.4138 | 0.8179 | 0.7169 | 0.7953 |
| 1.5805 | 19.84 | 7340 | 0.5748 | 0.7261 | 0.8253 | 0.8906 | 0.9431 | 0.8027 | 0.8709 | 0.5585 | 0.9086 | 0.8687 | 0.8245 | 0.8736 | 0.6984 | 0.7696 | 0.4153 | 0.8171 | 0.7187 | 0.7903 |
| 1.7115 | 19.89 | 7360 | 0.5969 | 0.7266 | 0.8303 | 0.8905 | 0.9425 | 0.8219 | 0.8725 | 0.5765 | 0.9044 | 0.8711 | 0.8233 | 0.8739 | 0.7003 | 0.7707 | 0.4141 | 0.8172 | 0.7201 | 0.7899 |
| 0.0866 | 19.95 | 7380 | 0.5321 | 0.7292 | 0.8317 | 0.8920 | 0.9398 | 0.8248 | 0.8647 | 0.5692 | 0.9078 | 0.8798 | 0.8358 | 0.8751 | 0.7000 | 0.7697 | 0.4220 | 0.8199 | 0.7211 | 0.7965 |
| 0.2194 | 20.0 | 7400 | 0.5505 | 0.7289 | 0.8303 | 0.8920 | 0.9425 | 0.8285 | 0.8649 | 0.5543 | 0.9055 | 0.8832 | 0.8333 | 0.8743 | 0.7002 | 0.7694 | 0.4222 | 0.8199 | 0.7208 | 0.7956 |
| 0.1087 | 20.05 | 7420 | 0.5341 | 0.7300 | 0.8288 | 0.8929 | 0.9402 | 0.8256 | 0.8784 | 0.5351 | 0.9097 | 0.8758 | 0.8370 | 0.8742 | 0.7021 | 0.7717 | 0.4235 | 0.8221 | 0.7200 | 0.7968 |
| 0.126 | 20.11 | 7440 | 0.5485 | 0.7276 | 0.8256 | 0.8913 | 0.9412 | 0.8205 | 0.8598 | 0.5495 | 0.9151 | 0.8677 | 0.8256 | 0.8742 | 0.7018 | 0.7672 | 0.4241 | 0.8185 | 0.7189 | 0.7885 |
| 0.1614 | 20.16 | 7460 | 0.5406 | 0.7284 | 0.8246 | 0.8922 | 0.9410 | 0.8192 | 0.8571 | 0.5357 | 0.9163 | 0.8660 | 0.8369 | 0.8741 | 0.7034 | 0.7652 | 0.4218 | 0.8208 | 0.7172 | 0.7964 |
| 0.0881 | 20.22 | 7480 | 0.5345 | 0.7282 | 0.8212 | 0.8927 | 0.9437 | 0.8140 | 0.8735 | 0.5126 | 0.9175 | 0.8540 | 0.8334 | 0.8737 | 0.7033 | 0.7695 | 0.4166 | 0.8223 | 0.7164 | 0.7957 |
| 0.0598 | 20.27 | 7500 | 0.5295 | 0.7292 | 0.8264 | 0.8926 | 0.9387 | 0.8281 | 0.8747 | 0.5295 | 0.9167 | 0.8614 | 0.8358 | 0.8739 | 0.7031 | 0.7726 | 0.4197 | 0.8219 | 0.7171 | 0.7961 |
| 0.2735 | 20.32 | 7520 | 0.5292 | 0.7294 | 0.8250 | 0.8929 | 0.9416 | 0.8190 | 0.8668 | 0.5309 | 0.9165 | 0.8646 | 0.8356 | 0.8740 | 0.7014 | 0.7724 | 0.4220 | 0.8230 | 0.7171 | 0.7958 |
| 0.1385 | 20.38 | 7540 | 0.5413 | 0.7287 | 0.8247 | 0.8924 | 0.9373 | 0.8115 | 0.8671 | 0.5391 | 0.9209 | 0.8626 | 0.8344 | 0.8739 | 0.6977 | 0.7710 | 0.4253 | 0.8216 | 0.7153 | 0.7961 |
| 0.0755 | 20.43 | 7560 | 0.5195 | 0.7286 | 0.8275 | 0.8927 | 0.9411 | 0.8251 | 0.8726 | 0.5339 | 0.9118 | 0.8729 | 0.8349 | 0.8744 | 0.6969 | 0.7717 | 0.4212 | 0.8233 | 0.7166 | 0.7965 |
| 0.0906 | 20.49 | 7580 | 0.5124 | 0.7289 | 0.8287 | 0.8927 | 0.9415 | 0.8096 | 0.8768 | 0.5495 | 0.9084 | 0.8819 | 0.8332 | 0.8749 | 0.6968 | 0.7740 | 0.4196 | 0.8234 | 0.7184 | 0.7954 |
| 0.042 | 20.54 | 7600 | 0.5236 | 0.7280 | 0.8265 | 0.8917 | 0.9388 | 0.8250 | 0.8719 | 0.5447 | 0.9173 | 0.8612 | 0.8264 | 0.8743 | 0.6990 | 0.7736 | 0.4233 | 0.8198 | 0.7185 | 0.7874 |
| 0.07 | 20.59 | 7620 | 0.5167 | 0.7294 | 0.8273 | 0.8931 | 0.9423 | 0.8160 | 0.8627 | 0.5535 | 0.9156 | 0.8655 | 0.8354 | 0.8751 | 0.6981 | 0.7748 | 0.4189 | 0.8237 | 0.7189 | 0.7963 |
| 0.3463 | 20.65 | 7640 | 0.5487 | 0.7202 | 0.8196 | 0.8867 | 0.9400 | 0.8278 | 0.8703 | 0.5256 | 0.9147 | 0.8751 | 0.7839 | 0.8750 | 0.7002 | 0.7753 | 0.4198 | 0.8069 | 0.7190 | 0.7451 |
| 1.3278 | 20.7 | 7660 | 0.5206 | 0.7261 | 0.8251 | 0.8910 | 0.9440 | 0.8036 | 0.8725 | 0.5500 | 0.9080 | 0.8751 | 0.8224 | 0.8765 | 0.6975 | 0.7747 | 0.4158 | 0.8169 | 0.7185 | 0.7830 |
| 0.0927 | 20.76 | 7680 | 0.5543 | 0.7179 | 0.8190 | 0.8849 | 0.9415 | 0.8215 | 0.8581 | 0.5538 | 0.9142 | 0.8695 | 0.7746 | 0.8764 | 0.6997 | 0.7696 | 0.4219 | 0.8024 | 0.7191 | 0.7364 |
| 0.2095 | 20.81 | 7700 | 0.5537 | 0.7187 | 0.8184 | 0.8857 | 0.9393 | 0.8291 | 0.8589 | 0.5333 | 0.9178 | 0.8714 | 0.7791 | 0.8766 | 0.7009 | 0.7684 | 0.4209 | 0.8037 | 0.7186 | 0.7416 |
| 0.1448 | 20.86 | 7720 | 0.5224 | 0.7306 | 0.8271 | 0.8936 | 0.9408 | 0.8154 | 0.8759 | 0.5356 | 0.9140 | 0.8660 | 0.8420 | 0.8764 | 0.7017 | 0.7709 | 0.4236 | 0.8226 | 0.7195 | 0.7999 |
| 0.1207 | 20.92 | 7740 | 0.5309 | 0.7301 | 0.8263 | 0.8931 | 0.9410 | 0.8200 | 0.8619 | 0.5352 | 0.9152 | 0.8713 | 0.8396 | 0.8752 | 0.7022 | 0.7691 | 0.4258 | 0.8221 | 0.7186 | 0.7977 |
| 0.1776 | 20.97 | 7760 | 0.5330 | 0.7285 | 0.8198 | 0.8934 | 0.9418 | 0.8182 | 0.8697 | 0.4839 | 0.9194 | 0.8639 | 0.8415 | 0.8740 | 0.7016 | 0.7706 | 0.4123 | 0.8227 | 0.7194 | 0.7989 |
| 0.1048 | 21.03 | 7780 | 0.5440 | 0.7290 | 0.8230 | 0.8931 | 0.9448 | 0.8097 | 0.8735 | 0.5200 | 0.9125 | 0.8580 | 0.8422 | 0.8739 | 0.6989 | 0.7721 | 0.4185 | 0.8225 | 0.7175 | 0.7992 |
| 0.1082 | 21.08 | 7800 | 0.5301 | 0.7297 | 0.8274 | 0.8932 | 0.9400 | 0.8203 | 0.8686 | 0.5293 | 0.9122 | 0.8801 | 0.8413 | 0.8759 | 0.7000 | 0.7707 | 0.4212 | 0.8222 | 0.7181 | 0.7997 |
| 0.1496 | 21.14 | 7820 | 0.5470 | 0.7287 | 0.8299 | 0.8926 | 0.9427 | 0.8256 | 0.8717 | 0.5486 | 0.9066 | 0.8808 | 0.8333 | 0.8759 | 0.6996 | 0.7722 | 0.4174 | 0.8217 | 0.7175 | 0.7969 |
| 0.0932 | 21.19 | 7840 | 0.5195 | 0.7300 | 0.8295 | 0.8933 | 0.9406 | 0.8178 | 0.8741 | 0.5493 | 0.9109 | 0.8735 | 0.8402 | 0.8766 | 0.7004 | 0.7741 | 0.4181 | 0.8228 | 0.7193 | 0.7990 |
| 0.1466 | 21.24 | 7860 | 0.5228 | 0.7288 | 0.8293 | 0.8921 | 0.9391 | 0.8230 | 0.8679 | 0.5565 | 0.9131 | 0.8748 | 0.8304 | 0.8764 | 0.6989 | 0.7710 | 0.4269 | 0.8202 | 0.7167 | 0.7915 |
| 0.0394 | 21.3 | 7880 | 0.5633 | 0.7241 | 0.8231 | 0.8886 | 0.9386 | 0.8253 | 0.8721 | 0.5429 | 0.9158 | 0.8617 | 0.8054 | 0.8757 | 0.7003 | 0.7688 | 0.4307 | 0.8103 | 0.7161 | 0.7666 |
| 0.1345 | 21.35 | 7900 | 0.5410 | 0.7306 | 0.8282 | 0.8932 | 0.9397 | 0.8250 | 0.8754 | 0.5366 | 0.9131 | 0.8665 | 0.8408 | 0.8755 | 0.7008 | 0.7683 | 0.4301 | 0.8215 | 0.7172 | 0.8007 |
| 0.1801 | 21.41 | 7920 | 0.5387 | 0.7290 | 0.8278 | 0.8923 | 0.9413 | 0.8294 | 0.8634 | 0.5482 | 0.9129 | 0.8609 | 0.8388 | 0.8738 | 0.6983 | 0.7694 | 0.4262 | 0.8216 | 0.7163 | 0.7973 |
| 0.1575 | 21.46 | 7940 | 0.5452 | 0.7284 | 0.8204 | 0.8928 | 0.9441 | 0.7959 | 0.8741 | 0.5224 | 0.9173 | 0.8516 | 0.8372 | 0.8734 | 0.6961 | 0.7709 | 0.4232 | 0.8224 | 0.7152 | 0.7974 |
| 0.0942 | 21.51 | 7960 | 0.5162 | 0.7301 | 0.8321 | 0.8932 | 0.9397 | 0.8254 | 0.8756 | 0.5561 | 0.9070 | 0.8756 | 0.8452 | 0.8771 | 0.6995 | 0.7750 | 0.4166 | 0.8214 | 0.7190 | 0.8022 |
| 0.1019 | 21.57 | 7980 | 0.5306 | 0.7286 | 0.8285 | 0.8924 | 0.9416 | 0.8238 | 0.8743 | 0.5455 | 0.9090 | 0.8676 | 0.8375 | 0.8748 | 0.7000 | 0.7735 | 0.4162 | 0.8208 | 0.7178 | 0.7974 |
| 0.0743 | 21.62 | 8000 | 0.5440 | 0.7282 | 0.8264 | 0.8921 | 0.9427 | 0.8218 | 0.8664 | 0.5419 | 0.9118 | 0.8679 | 0.8320 | 0.8748 | 0.7005 | 0.7706 | 0.4207 | 0.8202 | 0.7173 | 0.7933 |
| 0.1217 | 21.68 | 8020 | 0.5120 | 0.7295 | 0.8311 | 0.8927 | 0.9427 | 0.8248 | 0.8734 | 0.5578 | 0.9046 | 0.8748 | 0.8400 | 0.8754 | 0.7003 | 0.7741 | 0.4189 | 0.8219 | 0.7183 | 0.7975 |
| 0.1475 | 21.73 | 8040 | 0.5327 | 0.7275 | 0.8246 | 0.8919 | 0.9418 | 0.8002 | 0.8690 | 0.5502 | 0.9134 | 0.8645 | 0.8334 | 0.8755 | 0.6968 | 0.7715 | 0.4204 | 0.8197 | 0.7174 | 0.7913 |
| 0.1826 | 21.78 | 8060 | 0.5325 | 0.7262 | 0.8273 | 0.8908 | 0.9416 | 0.8221 | 0.8712 | 0.5580 | 0.9108 | 0.8645 | 0.8227 | 0.8756 | 0.6983 | 0.7754 | 0.4152 | 0.8174 | 0.7178 | 0.7840 |
| 0.0675 | 21.84 | 8080 | 0.5753 | 0.7282 | 0.8306 | 0.8921 | 0.9397 | 0.8173 | 0.8692 | 0.5628 | 0.9076 | 0.8811 | 0.8362 | 0.8760 | 0.6965 | 0.7727 | 0.4177 | 0.8204 | 0.7172 | 0.7972 |
| 0.0812 | 21.89 | 8100 | 0.5417 | 0.7295 | 0.8309 | 0.8926 | 0.9372 | 0.8265 | 0.8756 | 0.5521 | 0.9108 | 0.8727 | 0.8413 | 0.8749 | 0.6972 | 0.7732 | 0.4221 | 0.8216 | 0.7196 | 0.7983 |
| 0.1776 | 21.95 | 8120 | 0.5303 | 0.7287 | 0.8295 | 0.8921 | 0.9402 | 0.8105 | 0.8765 | 0.5650 | 0.9096 | 0.8716 | 0.8329 | 0.8756 | 0.6965 | 0.7742 | 0.4219 | 0.8203 | 0.7202 | 0.7924 |
| 0.119 | 22.0 | 8140 | 0.5318 | 0.7263 | 0.8259 | 0.8904 | 0.9404 | 0.8202 | 0.8735 | 0.5467 | 0.9111 | 0.8700 | 0.8195 | 0.8752 | 0.6995 | 0.7739 | 0.4228 | 0.8153 | 0.7206 | 0.7768 |
| 0.0873 | 22.05 | 8160 | 0.5227 | 0.7298 | 0.8328 | 0.8926 | 0.9396 | 0.8222 | 0.8697 | 0.5743 | 0.9073 | 0.8764 | 0.8402 | 0.8753 | 0.7002 | 0.7743 | 0.4176 | 0.8221 | 0.7213 | 0.7978 |
| 0.0634 | 22.11 | 8180 | 0.5273 | 0.7281 | 0.8285 | 0.8919 | 0.9413 | 0.7973 | 0.8735 | 0.5803 | 0.9108 | 0.8627 | 0.8335 | 0.8752 | 0.6951 | 0.7736 | 0.4190 | 0.8209 | 0.7190 | 0.7940 |
| 0.1767 | 22.16 | 8200 | 0.5275 | 0.7300 | 0.8306 | 0.8928 | 0.9388 | 0.8289 | 0.8729 | 0.5560 | 0.9123 | 0.8680 | 0.8378 | 0.8745 | 0.7004 | 0.7738 | 0.4224 | 0.8225 | 0.7194 | 0.7969 |
| 0.1508 | 22.22 | 8220 | 0.5532 | 0.7289 | 0.8291 | 0.8921 | 0.9385 | 0.8246 | 0.8679 | 0.5526 | 0.9118 | 0.8693 | 0.8388 | 0.8738 | 0.6998 | 0.7710 | 0.4227 | 0.8205 | 0.7178 | 0.7968 |
| 0.0866 | 22.27 | 8240 | 0.5304 | 0.7303 | 0.8280 | 0.8930 | 0.9380 | 0.8240 | 0.8680 | 0.5412 | 0.9161 | 0.8676 | 0.8415 | 0.8747 | 0.7010 | 0.7729 | 0.4240 | 0.8219 | 0.7197 | 0.7978 |
| 0.1141 | 22.32 | 8260 | 0.5599 | 0.7296 | 0.8305 | 0.8925 | 0.9413 | 0.8189 | 0.8704 | 0.5594 | 0.9070 | 0.8785 | 0.8376 | 0.8754 | 0.6997 | 0.7724 | 0.4208 | 0.8206 | 0.7200 | 0.7979 |
| 0.5142 | 22.38 | 8280 | 0.5377 | 0.7294 | 0.8281 | 0.8926 | 0.9413 | 0.8232 | 0.8723 | 0.5388 | 0.9106 | 0.8792 | 0.8310 | 0.8753 | 0.7005 | 0.7727 | 0.4233 | 0.8208 | 0.7195 | 0.7935 |
| 0.1262 | 22.43 | 8300 | 0.5415 | 0.7208 | 0.8213 | 0.8865 | 0.9406 | 0.8204 | 0.8620 | 0.5525 | 0.9135 | 0.8714 | 0.7885 | 0.8750 | 0.6993 | 0.7717 | 0.4248 | 0.8064 | 0.7194 | 0.7492 |
| 0.0996 | 22.49 | 8320 | 0.5172 | 0.7284 | 0.8277 | 0.8919 | 0.9414 | 0.8244 | 0.8787 | 0.5501 | 0.9114 | 0.8573 | 0.8309 | 0.8752 | 0.7005 | 0.7751 | 0.4204 | 0.8189 | 0.7196 | 0.7891 |
| 0.099 | 22.54 | 8340 | 0.5433 | 0.7298 | 0.8289 | 0.8927 | 0.9408 | 0.8299 | 0.8624 | 0.5486 | 0.9124 | 0.8681 | 0.8401 | 0.8751 | 0.7008 | 0.7688 | 0.4232 | 0.8205 | 0.7204 | 0.7997 |
| 0.0542 | 22.59 | 8360 | 0.5318 | 0.7291 | 0.8300 | 0.8922 | 0.9415 | 0.8288 | 0.8653 | 0.5631 | 0.9094 | 0.8622 | 0.8401 | 0.8746 | 0.6998 | 0.7707 | 0.4204 | 0.8199 | 0.7200 | 0.7983 |
| 0.107 | 22.65 | 8380 | 0.5395 | 0.7293 | 0.8302 | 0.8924 | 0.9412 | 0.8340 | 0.8674 | 0.5524 | 0.9095 | 0.8701 | 0.8367 | 0.8749 | 0.7002 | 0.7702 | 0.4215 | 0.8205 | 0.7204 | 0.7977 |
| 0.1479 | 22.7 | 8400 | 0.5555 | 0.7297 | 0.8283 | 0.8928 | 0.9433 | 0.8251 | 0.8648 | 0.5496 | 0.9110 | 0.8681 | 0.8365 | 0.8746 | 0.6999 | 0.7724 | 0.4213 | 0.8219 | 0.7201 | 0.7974 |
| 0.042 | 22.76 | 8420 | 0.5373 | 0.7259 | 0.8257 | 0.8900 | 0.9398 | 0.8162 | 0.8614 | 0.5572 | 0.9135 | 0.8759 | 0.8161 | 0.8753 | 0.6993 | 0.7712 | 0.4241 | 0.8147 | 0.7199 | 0.7769 |
| 1.2058 | 22.81 | 8440 | 0.5498 | 0.7249 | 0.8244 | 0.8896 | 0.9424 | 0.8223 | 0.8755 | 0.5537 | 0.9130 | 0.8528 | 0.8112 | 0.8738 | 0.6991 | 0.7754 | 0.4188 | 0.8148 | 0.7187 | 0.7736 |
| 0.0554 | 22.86 | 8460 | 0.5314 | 0.7270 | 0.8267 | 0.8910 | 0.9414 | 0.8247 | 0.8741 | 0.5495 | 0.9117 | 0.8640 | 0.8216 | 0.8742 | 0.6997 | 0.7747 | 0.4202 | 0.8176 | 0.7213 | 0.7815 |
| 0.171 | 22.92 | 8480 | 0.5404 | 0.7260 | 0.8255 | 0.8904 | 0.9416 | 0.8163 | 0.8787 | 0.5466 | 0.9099 | 0.8679 | 0.8173 | 0.8745 | 0.6992 | 0.7735 | 0.4193 | 0.8160 | 0.7215 | 0.7777 |
| 0.2158 | 22.97 | 8500 | 0.5370 | 0.7286 | 0.8289 | 0.8922 | 0.9415 | 0.8259 | 0.8658 | 0.5471 | 0.9091 | 0.8797 | 0.8330 | 0.8750 | 0.6999 | 0.7725 | 0.4179 | 0.8207 | 0.7215 | 0.7925 |
| 1.52 | 23.03 | 8520 | 0.5842 | 0.7270 | 0.8247 | 0.8914 | 0.9417 | 0.8265 | 0.8659 | 0.5366 | 0.9155 | 0.8610 | 0.8258 | 0.8743 | 0.6995 | 0.7703 | 0.4172 | 0.8186 | 0.7198 | 0.7890 |
| 0.529 | 23.08 | 8540 | 0.5676 | 0.7283 | 0.8253 | 0.8923 | 0.9416 | 0.8326 | 0.8613 | 0.5270 | 0.9158 | 0.8643 | 0.8343 | 0.8738 | 0.6996 | 0.7691 | 0.4192 | 0.8207 | 0.7197 | 0.7960 |
| 0.1741 | 23.14 | 8560 | 0.5727 | 0.7221 | 0.8205 | 0.8879 | 0.9415 | 0.8098 | 0.8613 | 0.5479 | 0.9152 | 0.8696 | 0.7984 | 0.8745 | 0.6984 | 0.7692 | 0.4202 | 0.8103 | 0.7188 | 0.7631 |
| 0.1051 | 23.19 | 8580 | 0.5467 | 0.7276 | 0.8296 | 0.8913 | 0.9389 | 0.8303 | 0.8751 | 0.5555 | 0.9109 | 0.8711 | 0.8250 | 0.8750 | 0.6998 | 0.7760 | 0.4177 | 0.8184 | 0.7212 | 0.7850 |
| 0.1127 | 23.24 | 8600 | 0.5468 | 0.7260 | 0.8249 | 0.8904 | 0.9407 | 0.8258 | 0.8640 | 0.5454 | 0.9146 | 0.8641 | 0.8196 | 0.8748 | 0.7002 | 0.7715 | 0.4207 | 0.8156 | 0.7195 | 0.7796 |
| 0.17 | 23.3 | 8620 | 0.5703 | 0.7214 | 0.8138 | 0.8882 | 0.9441 | 0.8009 | 0.8589 | 0.5086 | 0.9188 | 0.8665 | 0.7993 | 0.8735 | 0.6976 | 0.7684 | 0.4193 | 0.8102 | 0.7193 | 0.7613 |
| 0.0949 | 23.35 | 8640 | 0.5267 | 0.7298 | 0.8273 | 0.8934 | 0.9404 | 0.8271 | 0.8779 | 0.5269 | 0.9136 | 0.8633 | 0.8415 | 0.8755 | 0.7009 | 0.7761 | 0.4150 | 0.8221 | 0.7202 | 0.7990 |
| 1.5673 | 23.41 | 8660 | 0.5401 | 0.7268 | 0.8274 | 0.8911 | 0.9420 | 0.8186 | 0.8733 | 0.5551 | 0.9094 | 0.8699 | 0.8240 | 0.8759 | 0.6996 | 0.7749 | 0.4144 | 0.8171 | 0.7199 | 0.7859 |
| 0.5701 | 23.46 | 8680 | 0.5517 | 0.7261 | 0.8286 | 0.8905 | 0.9412 | 0.8296 | 0.8681 | 0.5617 | 0.9091 | 0.8678 | 0.8223 | 0.8753 | 0.6996 | 0.7738 | 0.4140 | 0.8161 | 0.7191 | 0.7845 |
| 0.1587 | 23.51 | 8700 | 0.5709 | 0.7235 | 0.8183 | 0.8894 | 0.9442 | 0.8075 | 0.8675 | 0.5310 | 0.9175 | 0.8494 | 0.8108 | 0.8736 | 0.6984 | 0.7708 | 0.4167 | 0.8137 | 0.7158 | 0.7756 |
| 0.1153 | 23.57 | 8720 | 0.5978 | 0.7220 | 0.8225 | 0.8878 | 0.9406 | 0.8162 | 0.8667 | 0.5568 | 0.9136 | 0.8619 | 0.8018 | 0.8750 | 0.6997 | 0.7707 | 0.4151 | 0.8095 | 0.7182 | 0.7659 |
| 0.1148 | 23.62 | 8740 | 0.5372 | 0.7284 | 0.8294 | 0.8921 | 0.9412 | 0.8239 | 0.8748 | 0.5533 | 0.9077 | 0.8684 | 0.8366 | 0.8752 | 0.6995 | 0.7751 | 0.4134 | 0.8196 | 0.7194 | 0.7963 |
| 0.2087 | 23.68 | 8760 | 0.5470 | 0.7285 | 0.8302 | 0.8920 | 0.9373 | 0.8269 | 0.8707 | 0.5554 | 0.9108 | 0.8709 | 0.8395 | 0.8751 | 0.6990 | 0.7723 | 0.4165 | 0.8190 | 0.7194 | 0.7982 |
| 0.1216 | 23.73 | 8780 | 0.5484 | 0.7280 | 0.8299 | 0.8916 | 0.9382 | 0.8336 | 0.8610 | 0.5569 | 0.9117 | 0.8697 | 0.8380 | 0.8754 | 0.6995 | 0.7699 | 0.4164 | 0.8179 | 0.7188 | 0.7979 |
| 0.098 | 23.78 | 8800 | 0.5326 | 0.7289 | 0.8286 | 0.8924 | 0.9391 | 0.8182 | 0.8663 | 0.5531 | 0.9118 | 0.8679 | 0.8436 | 0.8758 | 0.6996 | 0.7723 | 0.4154 | 0.8195 | 0.7197 | 0.8001 |
| 0.0423 | 23.84 | 8820 | 0.5410 | 0.7278 | 0.8294 | 0.8917 | 0.9413 | 0.8324 | 0.8598 | 0.5582 | 0.9090 | 0.8654 | 0.8397 | 0.8746 | 0.6993 | 0.7712 | 0.4144 | 0.8190 | 0.7185 | 0.7977 |
| 0.0599 | 23.89 | 8840 | 0.5354 | 0.7263 | 0.8275 | 0.8908 | 0.9418 | 0.8331 | 0.8599 | 0.5525 | 0.9103 | 0.8680 | 0.8272 | 0.8744 | 0.6991 | 0.7719 | 0.4149 | 0.8170 | 0.7184 | 0.7886 |
| 0.1217 | 23.95 | 8860 | 0.5390 | 0.7278 | 0.8243 | 0.8921 | 0.9395 | 0.8286 | 0.8653 | 0.5150 | 0.9166 | 0.8740 | 0.8311 | 0.8750 | 0.7013 | 0.7723 | 0.4158 | 0.8192 | 0.7193 | 0.7916 |
| 0.1271 | 24.0 | 8880 | 0.5720 | 0.7230 | 0.8223 | 0.8885 | 0.9396 | 0.8296 | 0.8644 | 0.5324 | 0.9142 | 0.8678 | 0.8078 | 0.8749 | 0.7004 | 0.7687 | 0.4200 | 0.8101 | 0.7180 | 0.7691 |
| 0.8749 | 24.05 | 8900 | 0.5618 | 0.7229 | 0.8224 | 0.8885 | 0.9406 | 0.8369 | 0.8630 | 0.5319 | 0.9148 | 0.8648 | 0.8049 | 0.8747 | 0.7003 | 0.7712 | 0.4194 | 0.8108 | 0.7179 | 0.7660 |
| 0.1267 | 24.11 | 8920 | 0.5620 | 0.7189 | 0.8196 | 0.8855 | 0.9403 | 0.8298 | 0.8642 | 0.5377 | 0.9132 | 0.8705 | 0.7815 | 0.8750 | 0.6998 | 0.7708 | 0.4212 | 0.8035 | 0.7181 | 0.7437 |
| 0.0353 | 24.16 | 8940 | 0.5602 | 0.7189 | 0.8200 | 0.8855 | 0.9423 | 0.8229 | 0.8692 | 0.5499 | 0.9107 | 0.8636 | 0.7818 | 0.8745 | 0.6997 | 0.7715 | 0.4203 | 0.8038 | 0.7180 | 0.7448 |
| 0.0954 | 24.22 | 8960 | 0.5401 | 0.7248 | 0.8285 | 0.8894 | 0.9394 | 0.8352 | 0.8624 | 0.5651 | 0.9097 | 0.8692 | 0.8182 | 0.8758 | 0.6992 | 0.7730 | 0.4175 | 0.8130 | 0.7193 | 0.7760 |
| 0.1345 | 24.27 | 8980 | 0.5673 | 0.7198 | 0.8213 | 0.8861 | 0.9410 | 0.8262 | 0.8680 | 0.5563 | 0.9134 | 0.8585 | 0.7854 | 0.8746 | 0.6991 | 0.7717 | 0.4203 | 0.8054 | 0.7180 | 0.7495 |
| 0.182 | 24.32 | 9000 | 0.5590 | 0.7199 | 0.8217 | 0.8861 | 0.9403 | 0.8270 | 0.8610 | 0.5642 | 0.9147 | 0.8526 | 0.7916 | 0.8746 | 0.6987 | 0.7706 | 0.4196 | 0.8054 | 0.7174 | 0.7530 |
| 0.0283 | 24.38 | 9020 | 0.5669 | 0.7185 | 0.8202 | 0.8851 | 0.9408 | 0.8212 | 0.8673 | 0.5536 | 0.9121 | 0.8696 | 0.7770 | 0.8747 | 0.6992 | 0.7720 | 0.4202 | 0.8031 | 0.7189 | 0.7413 |
| 0.1126 | 24.43 | 9040 | 0.5652 | 0.7233 | 0.8221 | 0.8886 | 0.9419 | 0.8274 | 0.8676 | 0.5325 | 0.9121 | 0.8682 | 0.8051 | 0.8740 | 0.7003 | 0.7725 | 0.4194 | 0.8111 | 0.7195 | 0.7659 |
| 0.1226 | 24.49 | 9060 | 0.5617 | 0.7205 | 0.8208 | 0.8866 | 0.9417 | 0.8152 | 0.8606 | 0.5532 | 0.9116 | 0.8708 | 0.7928 | 0.8749 | 0.6993 | 0.7694 | 0.4200 | 0.8063 | 0.7191 | 0.7547 |
| 0.1244 | 24.54 | 9080 | 0.5755 | 0.7207 | 0.8229 | 0.8865 | 0.9391 | 0.8244 | 0.8632 | 0.5605 | 0.9126 | 0.8655 | 0.7949 | 0.8748 | 0.6991 | 0.7693 | 0.4193 | 0.8059 | 0.7196 | 0.7567 |
| 0.0556 | 24.59 | 9100 | 0.5516 | 0.7217 | 0.8259 | 0.8872 | 0.9387 | 0.8315 | 0.8668 | 0.5669 | 0.9103 | 0.8669 | 0.8002 | 0.8752 | 0.6991 | 0.7720 | 0.4162 | 0.8079 | 0.7197 | 0.7617 |
| 0.1242 | 24.65 | 9120 | 0.5604 | 0.7243 | 0.8230 | 0.8893 | 0.9423 | 0.8217 | 0.8642 | 0.5392 | 0.9121 | 0.8700 | 0.8113 | 0.8745 | 0.6998 | 0.7707 | 0.4201 | 0.8128 | 0.7196 | 0.7727 |
| 0.2209 | 24.7 | 9140 | 0.5639 | 0.7218 | 0.8229 | 0.8876 | 0.9436 | 0.8224 | 0.8605 | 0.5561 | 0.9094 | 0.8689 | 0.7992 | 0.8741 | 0.6989 | 0.7700 | 0.4189 | 0.8094 | 0.7187 | 0.7628 |
| 0.1885 | 24.76 | 9160 | 0.5806 | 0.7212 | 0.8184 | 0.8873 | 0.9400 | 0.8232 | 0.8684 | 0.5415 | 0.9211 | 0.8368 | 0.7980 | 0.8741 | 0.6996 | 0.7702 | 0.4207 | 0.8076 | 0.7150 | 0.7609 |
| 0.0536 | 24.81 | 9180 | 0.5671 | 0.7228 | 0.8232 | 0.8883 | 0.9408 | 0.8333 | 0.8617 | 0.5446 | 0.9136 | 0.8618 | 0.8064 | 0.8742 | 0.6989 | 0.7693 | 0.4201 | 0.8104 | 0.7190 | 0.7678 |
| 0.1423 | 24.86 | 9200 | 0.5636 | 0.7233 | 0.8210 | 0.8887 | 0.9412 | 0.8229 | 0.8660 | 0.5305 | 0.9146 | 0.8657 | 0.8061 | 0.8743 | 0.7000 | 0.7699 | 0.4206 | 0.8109 | 0.7196 | 0.7676 |
| 0.0816 | 24.92 | 9220 | 0.5686 | 0.7222 | 0.8216 | 0.8880 | 0.9433 | 0.8247 | 0.8596 | 0.5472 | 0.9134 | 0.8637 | 0.7993 | 0.8741 | 0.6991 | 0.7707 | 0.4196 | 0.8104 | 0.7191 | 0.7627 |
| 0.132 | 24.97 | 9240 | 0.5503 | 0.7259 | 0.8239 | 0.8905 | 0.9420 | 0.8275 | 0.8697 | 0.5254 | 0.9118 | 0.8725 | 0.8182 | 0.8743 | 0.7005 | 0.7725 | 0.4188 | 0.8159 | 0.7204 | 0.7786 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.17.1
- Tokenizers 0.13.3
|
Imadken/llama-7b-chat-lamini_docs | Imadken | 2024-02-22T21:00:50Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2024-02-22T20:57:27Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
goxai/gemm | goxai | 2024-02-22T21:00:31Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:1804.06876",
"arxiv:2110.08193",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:2203.09509",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T19:53:10Z | ---
library_name: transformers
tags: []
widget:
- text: |
<start_of_turn>user
How does the brain work?<end_of_turn>
<start_of_turn>model
inference:
parameters:
max_new_tokens: 200
extra_gated_heading: "Access Gemma on Hugging Face"
extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately."
extra_gated_button_content: "Acknowledge license"
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
---
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 7B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-it-gg-hf)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning the model
You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-7b-it`.
In that repository, we provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Chat Template
The instruction-tuned models use a chat template that must be adhered to for conversational use.
The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "gg-hf/gemma-7b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=dtype,
)
chat = [
{ "role": "user", "content": "Write a hello world program" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
At this point, the prompt contains the following text:
```
<start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model
```
As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
the `<end_of_turn>` token.
You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
chat template.
After the prompt is ready, generation can be performed like this:
```py
inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
print(tokenizer.decode(outputs[0]))
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 |
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
| ------------------------------ | ------------- | ----------- | --------- |
| **Average** | | **54.0** | **56.4** |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
| ------------------------------ | ------------- | ----------- | --------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
|
guirnd/ppo-LunarLander-v2-unit1 | guirnd | 2024-02-22T20:54:22Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-22T20:54:07Z | ---
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: -770.17 +/- 286.81
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
...
```
|
newyorksteak/bert-finetuned-squad | newyorksteak | 2024-02-22T20:48:52Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-02-22T18:35:31Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
princedl/ml6team-gpt2-small-german-finetune-oscar-1file | princedl | 2024-02-22T20:46:39Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:ml6team/gpt2-small-german-finetune-oscar",
"base_model:finetune:ml6team/gpt2-small-german-finetune-oscar",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T20:20:34Z | ---
base_model: ml6team/gpt2-small-german-finetune-oscar
tags:
- generated_from_trainer
model-index:
- name: ml6team-gpt2-small-german-finetune-oscar-1file
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. -->
# ml6team-gpt2-small-german-finetune-oscar-1file
This model is a fine-tuned version of [ml6team/gpt2-small-german-finetune-oscar](https://huggingface.co/ml6team/gpt2-small-german-finetune-oscar) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5331
## 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: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4429 | 1.0 | 257 | 3.5679 |
| 3.3122 | 2.0 | 514 | 3.5112 |
| 3.3409 | 3.0 | 771 | 3.5015 |
| 3.0541 | 4.0 | 1028 | 3.5065 |
| 2.9332 | 5.0 | 1285 | 3.5201 |
| 2.4331 | 6.0 | 1542 | 3.5331 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
adalib/monkey-cond-gen-sub-20-codegen-2B-mono-prefix | adalib | 2024-02-22T20:39:52Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Salesforce/codegen-2B-mono",
"base_model:adapter:Salesforce/codegen-2B-mono",
"region:us"
] | null | 2024-02-22T20:39:48Z | ---
library_name: peft
base_model: Salesforce/codegen-2B-mono
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 |
LoneStriker/opus-v1-34b-8.0bpw-h8-exl2 | LoneStriker | 2024-02-22T20:39:38Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"axolotl",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T20:25:37Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
---
# DreamGen Opus V1
<div style="display: flex; flex-direction: row; align-items: center;">
<img src="/dreamgen/opus-v1-34b/resolve/main/images/logo-1024.png" alt="model logo" style="
border-radius: 12px;
margin-right: 12px;
margin-top: 0px;
margin-bottom: 0px;
max-width: 100px;
height: auto;
"/>
Models for **(steerable) story-writing and role-playing**.
<br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
</div>
## Prompting
[Read the full Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can readily copy.
<details>
<summary>The models use an extended version of ChatML.</summary>
```
<|im_start|>system
(Story description in the right format here)
(Typically consists of plot description, style description and characters)<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Alice
(Continuation of the story from the Alice character)<|im_end|>
<|im_start|>text
(Continuation of the story from no character in particular (pure narration))<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Bob
(Continuation of the story from the Bob character)<|im_end|>
```
The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names.
Pay attention to the following:
- The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play.
- There can be multiple subsequent message with a `text` role, especially if names are involved.
- There can be multiple names attached to a message.
- The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names.
</details>
While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance.
<img src="/dreamgen/opus-v1-34b/resolve/main/images/story_writing.webp" alt="story writing" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
Here's how you can prompt the model for the following tasks
- Steerable [Story-writing](https://dreamgen.com/docs/models/opus/v1#task-story-writing) and [Role-playing](https://dreamgen.com/docs/models/opus/v1#task-role-playing):
- Input:
- System prompt: You provide story / role-play description, which consists of:
- Plot description
- Style description
- Characters and their descriptions
- Conversation turns:
- Text / message turn: This represents part of the story or role play
- Instruction: This tells the model what should happen next
- Output: Continuation of the story / role-play.
- [Story plot summarization](https://dreamgen.com/docs/models/opus/v1#task-plot-description)
- Input: A story, or a few chapters of a story.
- Output: A description of the story or chapters.
- [Story character description](https://dreamgen.com/docs/models/opus/v1#task-char-description)
- Input: A story, or a few chapters of a story, set of characters.
- Output: A description of the characters.
- [Story style description](https://dreamgen.com/docs/models/opus/v1#task-style-description)
- Input: A story, or a few chapters of a story.
- Output: A description the style of the story.
- [Story description to chapters](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions)
- Input: A brief plot description and the desired number of chapters.
- Output: A description for each chapter.
- And more...
### Sampling params
For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`.
You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures.
## Dataset
The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long.
All story-writing and role-playing examples were based on human-written text.
## Running the model
The model is should be compatible with any software that supports the base model, but beware of the prompting (see above).
### Running Locally
- [Chat template from model config](tokenizer_config.json#L51)
- This uses "text" role instead of the typical "assistant" role, and it does not (can’t?) support names
- [LM Studio config](configs/lmstudio.json)
- This uses "text" role role as well
### Running on DreamGen.com (free)
You can try the model for free on [dreamgen.com](https://dreamgen.com) — note that an account is required.
## Community
Join the DreamGen community on [**Discord**](https://dreamgen.com/discord) to get early access to new models.
## License
- This model is intended for personal use only, other use is not permitted.
|
SyedShaheer/ignore_mode | SyedShaheer | 2024-02-22T20:38:48Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T19:47:23Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ignore_mode
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. -->
# ignore_mode
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5796
- Rouge1: 0.2492
- Rouge2: 0.0635
- Rougel: 0.1573
- Rougelsum: 0.1574
- Gen Len: 80.4
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 1 | 2.6776 | 0.2291 | 0.0618 | 0.1425 | 0.1427 | 85.4 |
| No log | 2.0 | 2 | 2.5796 | 0.2492 | 0.0635 | 0.1573 | 0.1574 | 80.4 |
### Framework versions
- Transformers 4.27.2
- Pytorch 2.1.1
- Datasets 2.11.0
- Tokenizers 0.13.3
|
guirnd/rl_course_vizdoom_health_gathering_supreme | guirnd | 2024-02-22T20:38:21Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-22T20:38:14Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.84 +/- 5.27
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r guirnd/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
davidpedem/mbart-neutralization | davidpedem | 2024-02-22T20:33:57Z | 10 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"simplification",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"base_model:finetune:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T20:20:51Z | ---
license: mit
base_model: facebook/mbart-large-50
tags:
- simplification
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-neutralization
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. -->
# mbart-neutralization
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0220
- Bleu: 98.2132
- Gen Len: 18.5417
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 440 | 0.0490 | 96.2659 | 19.0104 |
| 0.2462 | 2.0 | 880 | 0.0220 | 98.2132 | 18.5417 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
ryusangwon/6240_Llama-2-7b-hf | ryusangwon | 2024-02-22T20:30:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-02-22T20:30:19Z | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: 6240_Llama-2-7b-hf
results: []
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. -->
# 6240_Llama-2-7b-hf
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
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
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.4.0
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
PJM124/xlmrbase-bitfit-5e-4-test | PJM124 | 2024-02-22T20:30:04Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-22T20:29:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
wojtekgra/Pol | wojtekgra | 2024-02-22T20:29:45Z | 0 | 1 | adapter-transformers | [
"adapter-transformers",
"Diaper",
"Wet",
"Piss",
"Abdl",
"Soggy",
"text-to-image",
"dataset:fka/awesome-chatgpt-prompts",
"license:apache-2.0",
"region:us"
] | text-to-image | 2024-02-22T20:28:17Z | ---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
metrics:
- bertscore
library_name: adapter-transformers
pipeline_tag: text-to-image
tags:
- Diaper
- Wet
- Piss
- Abdl
- Soggy
--- |
ThuyNT03/CS505_COQE_viT5_Prompting10_ASPOL_vcheck | ThuyNT03 | 2024-02-22T20:29:28Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T19:28:27Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting10_ASPOL_vcheck
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. -->
# CS505_COQE_viT5_Prompting10_ASPOL_vcheck
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
LoneStriker/opus-v1-34b-6.0bpw-h6-exl2 | LoneStriker | 2024-02-22T20:25:35Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"axolotl",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T20:14:49Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
---
# DreamGen Opus V1
<div style="display: flex; flex-direction: row; align-items: center;">
<img src="/dreamgen/opus-v1-34b/resolve/main/images/logo-1024.png" alt="model logo" style="
border-radius: 12px;
margin-right: 12px;
margin-top: 0px;
margin-bottom: 0px;
max-width: 100px;
height: auto;
"/>
Models for **(steerable) story-writing and role-playing**.
<br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
</div>
## Prompting
[Read the full Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can readily copy.
<details>
<summary>The models use an extended version of ChatML.</summary>
```
<|im_start|>system
(Story description in the right format here)
(Typically consists of plot description, style description and characters)<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Alice
(Continuation of the story from the Alice character)<|im_end|>
<|im_start|>text
(Continuation of the story from no character in particular (pure narration))<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Bob
(Continuation of the story from the Bob character)<|im_end|>
```
The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names.
Pay attention to the following:
- The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play.
- There can be multiple subsequent message with a `text` role, especially if names are involved.
- There can be multiple names attached to a message.
- The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names.
</details>
While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance.
<img src="/dreamgen/opus-v1-34b/resolve/main/images/story_writing.webp" alt="story writing" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
Here's how you can prompt the model for the following tasks
- Steerable [Story-writing](https://dreamgen.com/docs/models/opus/v1#task-story-writing) and [Role-playing](https://dreamgen.com/docs/models/opus/v1#task-role-playing):
- Input:
- System prompt: You provide story / role-play description, which consists of:
- Plot description
- Style description
- Characters and their descriptions
- Conversation turns:
- Text / message turn: This represents part of the story or role play
- Instruction: This tells the model what should happen next
- Output: Continuation of the story / role-play.
- [Story plot summarization](https://dreamgen.com/docs/models/opus/v1#task-plot-description)
- Input: A story, or a few chapters of a story.
- Output: A description of the story or chapters.
- [Story character description](https://dreamgen.com/docs/models/opus/v1#task-char-description)
- Input: A story, or a few chapters of a story, set of characters.
- Output: A description of the characters.
- [Story style description](https://dreamgen.com/docs/models/opus/v1#task-style-description)
- Input: A story, or a few chapters of a story.
- Output: A description the style of the story.
- [Story description to chapters](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions)
- Input: A brief plot description and the desired number of chapters.
- Output: A description for each chapter.
- And more...
### Sampling params
For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`.
You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures.
## Dataset
The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long.
All story-writing and role-playing examples were based on human-written text.
## Running the model
The model is should be compatible with any software that supports the base model, but beware of the prompting (see above).
### Running Locally
- [Chat template from model config](tokenizer_config.json#L51)
- This uses "text" role instead of the typical "assistant" role, and it does not (can’t?) support names
- [LM Studio config](configs/lmstudio.json)
- This uses "text" role role as well
### Running on DreamGen.com (free)
You can try the model for free on [dreamgen.com](https://dreamgen.com) — note that an account is required.
## Community
Join the DreamGen community on [**Discord**](https://dreamgen.com/discord) to get early access to new models.
## License
- This model is intended for personal use only, other use is not permitted.
|
ThuyNT03/CS505_COQE_viT5_Prompting11_ASPOL_vcheck | ThuyNT03 | 2024-02-22T20:21:28Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T19:35:27Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting11_ASPOL_vcheck
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. -->
# CS505_COQE_viT5_Prompting11_ASPOL_vcheck
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
adonaivera/yolov9 | adonaivera | 2024-02-22T20:20:21Z | 0 | 1 | null | [
"arxiv:2402.13616",
"region:us"
] | null | 2024-02-22T20:13:19Z | # YOLOv9
Implementation of paper - [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://arxiv.org/abs/2402.13616)
<div align="center">
<a href="./">
<img src="https://huggingface.co/adonaivera/yolov9/resolve/main/performance.png" width="79%"/>
</a>
</div>
## Performance
MS COCO
| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> | Param. | FLOPs |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
| [**YOLOv9-S**]() | 640 | **46.8%** | **63.4%** | **50.7%** | **7.2M** | **26.7G** |
| [**YOLOv9-M**]() | 640 | **51.4%** | **68.1%** | **56.1%** | **20.1M** | **76.8G** |
| [**YOLOv9-C**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt) | 640 | **53.0%** | **70.2%** | **57.8%** | **25.5M** | **102.8G** |
| [**YOLOv9-E**](https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt) | 640 | **55.6%** | **72.8%** | **60.6%** | **58.1M** | **192.5G** | |
danwils/BatakToba-laserRMT | danwils | 2024-02-22T20:11:35Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T18:03:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DrishtiSharma/dolphin-2.1-mistral-7b-dpo-ultrafeedback-binarized-preferences-sigmoid | DrishtiSharma | 2024-02-22T20:08:43Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:cognitivecomputations/dolphin-2.1-mistral-7b",
"base_model:adapter:cognitivecomputations/dolphin-2.1-mistral-7b",
"license:apache-2.0",
"region:us"
] | null | 2024-02-22T13:54:58Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: cognitivecomputations/dolphin-2.1-mistral-7b
model-index:
- name: doplhin-mistral-dpo-ultrafeedback-binarized-preferences-sigmoid
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. -->
# doplhin-mistral-dpo-ultrafeedback-binarized-preferences-sigmoid
This model is a fine-tuned version of [cognitivecomputations/dolphin-2.1-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.1-mistral-7b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6025
- Rewards/chosen: -7.8168
- Rewards/rejected: -14.5388
- Rewards/accuracies: 0.8310
- Rewards/margins: 6.7220
- Logps/rejected: -469.4976
- Logps/chosen: -438.1190
- Logits/rejected: -2.1911
- Logits/chosen: -2.3064
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.0466 | 0.25 | 700 | 0.8185 | -6.6407 | -9.8742 | 0.7464 | 3.2335 | -422.8520 | -426.3579 | -2.3161 | -2.4530 |
| 0.7039 | 0.51 | 1400 | 0.7051 | -6.5305 | -12.5351 | 0.8085 | 6.0046 | -449.4607 | -425.2558 | -2.1415 | -2.2554 |
| 0.9519 | 0.76 | 2100 | 0.6025 | -7.8168 | -14.5388 | 0.8310 | 6.7220 | -469.4976 | -438.1190 | -2.1911 | -2.3064 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 |
kv333q/layout1_LoRA | kv333q | 2024-02-22T20:07:23Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"lora",
"template:sd-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 | 2024-02-21T20:39:48Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a floorplan layout with color tags
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - kv333q/layout1_LoRA
<Gallery />
## Model description
These are kv333q/layout1_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a floorplan layout with color tags to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](kv333q/layout1_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
LoneStriker/opus-v1-34b-4.65bpw-h6-exl2 | LoneStriker | 2024-02-22T20:05:41Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"axolotl",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T19:57:12Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
---
# DreamGen Opus V1
<div style="display: flex; flex-direction: row; align-items: center;">
<img src="/dreamgen/opus-v1-34b/resolve/main/images/logo-1024.png" alt="model logo" style="
border-radius: 12px;
margin-right: 12px;
margin-top: 0px;
margin-bottom: 0px;
max-width: 100px;
height: auto;
"/>
Models for **(steerable) story-writing and role-playing**.
<br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
</div>
## Prompting
[Read the full Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can readily copy.
<details>
<summary>The models use an extended version of ChatML.</summary>
```
<|im_start|>system
(Story description in the right format here)
(Typically consists of plot description, style description and characters)<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Alice
(Continuation of the story from the Alice character)<|im_end|>
<|im_start|>text
(Continuation of the story from no character in particular (pure narration))<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Bob
(Continuation of the story from the Bob character)<|im_end|>
```
The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names.
Pay attention to the following:
- The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play.
- There can be multiple subsequent message with a `text` role, especially if names are involved.
- There can be multiple names attached to a message.
- The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names.
</details>
While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance.
<img src="/dreamgen/opus-v1-34b/resolve/main/images/story_writing.webp" alt="story writing" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
Here's how you can prompt the model for the following tasks
- Steerable [Story-writing](https://dreamgen.com/docs/models/opus/v1#task-story-writing) and [Role-playing](https://dreamgen.com/docs/models/opus/v1#task-role-playing):
- Input:
- System prompt: You provide story / role-play description, which consists of:
- Plot description
- Style description
- Characters and their descriptions
- Conversation turns:
- Text / message turn: This represents part of the story or role play
- Instruction: This tells the model what should happen next
- Output: Continuation of the story / role-play.
- [Story plot summarization](https://dreamgen.com/docs/models/opus/v1#task-plot-description)
- Input: A story, or a few chapters of a story.
- Output: A description of the story or chapters.
- [Story character description](https://dreamgen.com/docs/models/opus/v1#task-char-description)
- Input: A story, or a few chapters of a story, set of characters.
- Output: A description of the characters.
- [Story style description](https://dreamgen.com/docs/models/opus/v1#task-style-description)
- Input: A story, or a few chapters of a story.
- Output: A description the style of the story.
- [Story description to chapters](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions)
- Input: A brief plot description and the desired number of chapters.
- Output: A description for each chapter.
- And more...
### Sampling params
For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`.
You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures.
## Dataset
The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long.
All story-writing and role-playing examples were based on human-written text.
## Running the model
The model is should be compatible with any software that supports the base model, but beware of the prompting (see above).
### Running Locally
- [Chat template from model config](tokenizer_config.json#L51)
- This uses "text" role instead of the typical "assistant" role, and it does not (can’t?) support names
- [LM Studio config](configs/lmstudio.json)
- This uses "text" role role as well
### Running on DreamGen.com (free)
You can try the model for free on [dreamgen.com](https://dreamgen.com) — note that an account is required.
## Community
Join the DreamGen community on [**Discord**](https://dreamgen.com/discord) to get early access to new models.
## License
- This model is intended for personal use only, other use is not permitted.
|
AymanKUMA/speecht5_tts_voxpopuli_nl | AymanKUMA | 2024-02-22T19:59:52Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"ar",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-02-22T12:32:23Z | ---
license: mit
language:
- ar
metrics:
- accuracy
---
# 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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BertGollnick/distilbert-base-uncased-yelp-new | BertGollnick | 2024-02-22T19:59:11Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-22T19:38:13Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
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:
- eval_loss: 1.9778
- eval_runtime: 3.8155
- eval_samples_per_second: 52.417
- eval_steps_per_second: 6.552
- epoch: 11.0
- step: 1100
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
jncraton/oo-phi-1_5-ct2-int8 | jncraton | 2024-02-22T19:56:52Z | 4 | 0 | transformers | [
"transformers",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"arxiv:2309.05463",
"arxiv:2306.02707",
"arxiv:2301.13688",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T19:56:00Z | ---
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# Overview
Unreleased, untested, unfinished beta.
We've trained Microsoft Research's [phi-1.5](https://huggingface.co/microsoft/phi-1_5), 1.3B parameter model with the same OpenOrca dataset as we used with our [OpenOrcaxOpenChat-Preview2-13B](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B) model.
This model doesn't dramatically improve on the base model's general task performance, but the instruction tuning has made the model reliably handle the ChatML prompt format.
# Evaluations
We've only done limited testing as yet. The [epoch 3.5 checkpoint](https://huggingface.co/Open-Orca/oo-phi-1_5/commit/f7754d8b8b4c3e0748eaf47be4cf5aac1f80a401) scores above 5.1 on MT-Bench (better than Alpaca-13B, worse than Llama2-7b-chat), while preliminary benchmarks suggest peak average performance was achieved roughly at epoch 4.
## HuggingFaceH4 Open LLM Leaderboard Performance
The only significant improvement was with TruthfulQA.
## MT-bench Performance
| Epoch | Average | Turn 1 | Turn 2 |
|:----------|:----------|:----------|:----------|
| 3 | 4.85 | 5.69 | 4.01 |
| 3.5 | 5.19 | 5.91 | 4.46 |
| 4 | 4.89 | 5.74 | 4.05 |
| 4.5 | 5.03 | 6.04 | 4.03 |
| 5 | 4.94 | 5.76 | 4.11 |
# Training
Trained with full-parameters fine-tuning on 8x RTX A6000-48GB (Ampere) for 5 epochs for 62 hours (12.5h/epoch) at a commodity cost of $390 ($80/epoch).
We did not use [MultiPack](https://github.com/imoneoi/multipack_sampler) packing, as training was begun prior to implementing support for it in Axolotl for this new model type.
[<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)
We've uploaded checkpoints of every 1/2 epoch of progress to this repo.
There are branches/tags for the epoch 3 and epoch 4 uploads.
This should allow, e.g., with oobabooga to download `Open-Orca/oo-phi-1_5:ep4` to select the epoch 4 checkpoint to download specifically.
# Prompt Template
We used [OpenAI's Chat Markup Language (ChatML)](https://github.com/openai/openai-python/blob/main/chatml.md) format, with `<|im_start|>` and `<|im_end|>` tokens added to support this.
This means that, e.g., in [oobabooga](https://github.com/oobabooga/text-generation-webui/) the `MPT-Chat` instruction template should work.
# Inference
Remove *`.to('cuda')`* for unaccelerated.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model = AutoModelForCausalLM.from_pretrained("Open-Orca/oo-phi-1_5",
trust_remote_code=True,
torch_dtype=torch.bfloat16
).to('cuda')
tokenizer = AutoTokenizer.from_pretrained("Open-Orca/oo-phi-1_5",
trust_remote_code=True,
torch_dtype=torch.bfloat16)
sys_prompt = "I am OrcaPhi. The following is my internal dialogue as an AI assistant.\n" \
"Today is September 15, 2023. I have no access to outside tools, news, or current events.\n" \
"I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning.\n" \
"I think through my answers step-by-step to be sure I always get the right answer.\n" \
"I think more clearly if I write out my thought process in a scratchpad manner first; therefore, I always " \
"explain background context, assumptions, and step-by-step thinking BEFORE trying to answer a question." \
"Take a deep breath and think calmly about everything presented."
prompt = "Hello! Tell me about what makes you special, as an AI assistant.\n" \
"Particularly, what programming tasks are you best at?"
prefix = "<|im_start|>"
suffix = "<|im_end|>\n"
sys_format = prefix + "system\n" + sys_prompt + suffix
user_format = prefix + "user\n" + prompt + suffix
assistant_format = prefix + "assistant\n"
input_text = sys_format + user_format + assistant_format
generation_config = GenerationConfig(
max_length=1024, temperature=0.01, top_p=0.95, repetition_penalty=1.1,
do_sample=True, use_cache=True,
eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id,
transformers_version="4.33.1"
)
inputs = tokenizer(input_text, return_tensors="pt", return_attention_mask=False).to('cuda')
outputs = model.generate(**inputs, generation_config=generation_config)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
Example output:
```text
<|im_start|>system
I am OrcaPhi. The following is my internal dialogue as an AI assistant.
Today is September 15, 2023. I have no access to outside tools, news, or current events.
I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning.
I think through my answers step-by-step to be sure I always get the right answer.
I think more clearly if I write out my thought process in a scratchpad manner first; therefore, I always explain background context, assumptions, and step-by-step thinking BEFORE trying to answer a question.Take a deep breath and think calmly about everything presented.<|im_end|>
<|im_start|>user
Hello! Tell me about what makes you special, as an AI assistant.
Particularly, what programming tasks are you best at?<|im_end|>
<|im_start|>assistant
As an AI, I'm designed to understand and respond to human language, which includes natural language processing (NLP) techniques that allow me to comprehend text, speech, and other forms of communication. My primary function is to assist users by providing relevant information, answering questions, and performing various tasks based on user input.
In terms of programming tasks, I am particularly skilled in:
1. Natural Language Processing (NLP): This allows me to understand and analyze human language, enabling me to generate appropriate responses and perform tasks such as sentiment analysis, named entity recognition, and topic modeling.
2. Machine Learning: I can learn from large datasets and improve my performance over time, allowing me to adapt to new situations and provide better assistance to users.
3. Deep Learning: I can leverage advanced neural networks and complex architectures to solve problems that may not be easily addressed using traditional machine learning methods.
4. Computer Vision: I can recognize and interpret visual data, making it possible for me to identify objects, faces, and other visual elements within images and videos.
5. Robotics: I can help with tasks related to robotics, including object detection, navigation, and manipulation.
6. Voice Recognition: I can accurately transcribe spoken words into written text, making it easier for users to interact with me.
7. Chatbots: I can engage in conversations with users, providing them with helpful information, answering their questions, and assisting them with various tasks.
8. Data Analysis: I can analyze large amounts of data quickly and efficiently, helping users make informed decisions based on insights derived from the information provided.
9. Recommender Systems: I can suggest products, services, or content based on users' preferences and past behavior, improving their overall experience.
10. Fraud Detection: I can detect and prevent fraudulent activities, protecting users' financial information and ensuring secure transactions.
These programming tasks showcase my ability to understand and process vast amounts of information while adapting to different contexts and user needs. As an AI, I continuously learn and evolve to become even more effective in assisting users.<|im_end|>
```
# Citation
```bibtex
@software{lian2023oophi15,
title = {OpenOrca oo-phi-1.5: Phi-1.5 1.3B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/Open-Orca/oo-phi-1_5},
}
@article{textbooks2,
title={Textbooks Are All You Need II: \textbf{phi-1.5} technical report},
author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat},
journal={arXiv preprint arXiv:2309.05463},
year={2023}
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
``` |
crossroderick/q-Taxi-v3 | crossroderick | 2024-02-22T19:52:18Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-22T19:52:15Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
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 playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id = "crossroderick/q-Taxi-v3", filename = "q-learning.pkl")
```
|
CorticalStack/gemma-7b-ultrachat-sft | CorticalStack | 2024-02-22T19:50:06Z | 50 | 2 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T19:47:18Z | ---
license: apache-2.0
---
# gemma-7b-ultrachat-sft
gemma-7b-ultrachat-sft is an SFT fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) using the [stingning/ultrachat](https://huggingface.co/datasets/stingning/ultrachat) dataset.
## Fine-tuning configuration
### LoRA
- LoRA r: 8
- LoRA alpha: 16
- LoRA dropout: 0.1
### Training arguments
- Epochs: 1
- Batch size: 4
- Gradient accumulation steps: 6
- Optimizer: paged_adamw_32bit
- Max steps: 100
- Learning rate: 0.0002
- Weight decay: 0.001
- Learning rate scheduler type: constant
- Max seq length: 2048 |
LoneStriker/opus-v1-34b-3.0bpw-h6-exl2 | LoneStriker | 2024-02-22T19:49:42Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"axolotl",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T19:43:59Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
---
# DreamGen Opus V1
<div style="display: flex; flex-direction: row; align-items: center;">
<img src="/dreamgen/opus-v1-34b/resolve/main/images/logo-1024.png" alt="model logo" style="
border-radius: 12px;
margin-right: 12px;
margin-top: 0px;
margin-bottom: 0px;
max-width: 100px;
height: auto;
"/>
Models for **(steerable) story-writing and role-playing**.
<br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
</div>
## Prompting
[Read the full Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can readily copy.
<details>
<summary>The models use an extended version of ChatML.</summary>
```
<|im_start|>system
(Story description in the right format here)
(Typically consists of plot description, style description and characters)<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Alice
(Continuation of the story from the Alice character)<|im_end|>
<|im_start|>text
(Continuation of the story from no character in particular (pure narration))<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Bob
(Continuation of the story from the Bob character)<|im_end|>
```
The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names.
Pay attention to the following:
- The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play.
- There can be multiple subsequent message with a `text` role, especially if names are involved.
- There can be multiple names attached to a message.
- The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names.
</details>
While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance.
<img src="/dreamgen/opus-v1-34b/resolve/main/images/story_writing.webp" alt="story writing" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
Here's how you can prompt the model for the following tasks
- Steerable [Story-writing](https://dreamgen.com/docs/models/opus/v1#task-story-writing) and [Role-playing](https://dreamgen.com/docs/models/opus/v1#task-role-playing):
- Input:
- System prompt: You provide story / role-play description, which consists of:
- Plot description
- Style description
- Characters and their descriptions
- Conversation turns:
- Text / message turn: This represents part of the story or role play
- Instruction: This tells the model what should happen next
- Output: Continuation of the story / role-play.
- [Story plot summarization](https://dreamgen.com/docs/models/opus/v1#task-plot-description)
- Input: A story, or a few chapters of a story.
- Output: A description of the story or chapters.
- [Story character description](https://dreamgen.com/docs/models/opus/v1#task-char-description)
- Input: A story, or a few chapters of a story, set of characters.
- Output: A description of the characters.
- [Story style description](https://dreamgen.com/docs/models/opus/v1#task-style-description)
- Input: A story, or a few chapters of a story.
- Output: A description the style of the story.
- [Story description to chapters](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions)
- Input: A brief plot description and the desired number of chapters.
- Output: A description for each chapter.
- And more...
### Sampling params
For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`.
You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures.
## Dataset
The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long.
All story-writing and role-playing examples were based on human-written text.
## Running the model
The model is should be compatible with any software that supports the base model, but beware of the prompting (see above).
### Running Locally
- [Chat template from model config](tokenizer_config.json#L51)
- This uses "text" role instead of the typical "assistant" role, and it does not (can’t?) support names
- [LM Studio config](configs/lmstudio.json)
- This uses "text" role role as well
### Running on DreamGen.com (free)
You can try the model for free on [dreamgen.com](https://dreamgen.com) — note that an account is required.
## Community
Join the DreamGen community on [**Discord**](https://dreamgen.com/discord) to get early access to new models.
## License
- This model is intended for personal use only, other use is not permitted.
|
Struggler41/AlixVoice | Struggler41 | 2024-02-22T19:47:20Z | 0 | 2 | null | [
"Gay",
"Alix",
"Aicover",
"en",
"region:us"
] | null | 2024-02-02T23:54:55Z | ---
language:
- en
tags:
- Gay
- Alix
- Aicover
--- |
dmusingu/luganda_wav2vec2_ctc_tokenizer_with_lm | dmusingu | 2024-02-22T19:46:32Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-22T13:03:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
crossroderick/q-FrozenLake-v1-4x4-noSlippery | crossroderick | 2024-02-22T19:45:48Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-22T17:46:13Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id = "crossroderick/q-FrozenLake-v1-4x4-noSlippery", filename = "q-learning.pkl")
```
This particular model was trained on the default version of FrozenLake-v1 in a 4x4 setting, so don't forget to set `is_slippery = False` and
specify `map_name` when loading the environment, such as:
```python
env = gym.make(model["env_id"], map_name = "4x4", is_slippery = False)
```
|
jojo-ai-mst/MyanmarGPTX | jojo-ai-mst | 2024-02-22T19:41:29Z | 11 | 1 | transformers | [
"transformers",
"onnx",
"gpt2",
"text-generation",
"myanmar",
"myanmargpt",
"my",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T19:27:25Z | ---
library_name: transformers
tags:
- myanmar
- myanmargpt
widget:
- text: |-
User: မြန်မာနိုင်ငံအကြောင်းရှင်းပြပါ။
Assistant:
example_title: Example 1
- text: |-
User: ရုရှားနိုင်ငံအကြောင်းပြောပြပါ
Assistant:
example_title: Example 2
- text: |-
User: ကွန်မြူနစ်ဆိုတာဘာလဲ
Assistant:
example_title: Example 3
license: mit
language:
- my
---
# MyanmarGPTX (Myanmar GPT X)
GPT for Burmese Language, X version of Myanmar GPT
A Generative Pretrained Transformer for the Burmese Language - Myanmar GPT X
- Faster
- Lightweight
- Accurate
- Works on Browser Runtime
## Model Details
### Model Description
- **Developed by:** Min Si Thu
- **Model type:** GPT-2
- **Language(s) (NLP):** English, Burmese(Myanmar)
- **License:** MIT
- **Finetuned from model :** [https://huggingface.co/jojo-ai-mst/MyanmarGPT-Chat](https://huggingface.co/jojo-ai-mst/MyanmarGPT-Chat)
### Model Sources [optional]
- **Repository:** https://github.com/MinSiThu/MyanmarGPT
## Uses
The "Myanmar GPT X" model is released for the improvement of the Burmese Language in NLP.
The main purpose is to build web, mobile, and desktop applications powered by Burmese Language-enabled GPT under MIT License.
|
FINNUMBER/Yi-Ko-6B-Finch-ALL-900-PER100-NEW-epoch3 | FINNUMBER | 2024-02-22T19:41:02Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T18:30:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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[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]
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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
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[More Information Needed]
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#### Testing Data
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#### Factors
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[More Information Needed]
#### Metrics
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[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]
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|
FINNUMBER/Yi-Ko-6B-Finch-ALL-3600-PER400-NEW-epoch3 | FINNUMBER | 2024-02-22T19:40:57Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T18:29:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### 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
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### 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]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mixtralyanis/bart_samsum | mixtralyanis | 2024-02-22T19:38:47Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"base_model:finetune:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T14:55:25Z | ---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart_samsum
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. -->
# bart_samsum
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
ThuyNT03/CS505_COQE_viT5_Prompting11_ASPOL_v2 | ThuyNT03 | 2024-02-22T19:35:06Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T18:37:05Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_Prompting11_ASPOL_v2
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. -->
# CS505_COQE_viT5_Prompting11_ASPOL_v2
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
doof-ferb/whisper-tiny-vi | doof-ferb | 2024-02-22T19:34:20Z | 16 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"vi",
"dataset:doof-ferb/vlsp2020_vinai_100h",
"dataset:doof-ferb/fpt_fosd",
"dataset:doof-ferb/infore1_25hours",
"dataset:doof-ferb/infore2_audiobooks",
"dataset:quocanh34/viet_vlsp",
"dataset:linhtran92/final_dataset_500hrs_wer0",
"dataset:linhtran92/viet_youtube_asr_corpus_v2",
"dataset:google/fleurs",
"dataset:mozilla-foundation/common_voice_16_1",
"dataset:vivos",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-02-20T10:16:52Z | ---
license: apache-2.0
datasets:
- doof-ferb/vlsp2020_vinai_100h
- doof-ferb/fpt_fosd
- doof-ferb/infore1_25hours
- doof-ferb/infore2_audiobooks
- quocanh34/viet_vlsp
- linhtran92/final_dataset_500hrs_wer0
- linhtran92/viet_youtube_asr_corpus_v2
- google/fleurs
- mozilla-foundation/common_voice_16_1
- vivos
language: ["vi"]
metrics: ["wer"]
library_name: transformers
base_model: openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
model-index:
- name: doof-ferb/whisper-tiny-vi
results:
- task:
type: automatic-speech-recognition
dataset:
type: mozilla-foundation/common_voice_16_1
name: Mozilla CommonVoice (Vietnamese) v16.1
config: vi
split: test
metrics:
- type: wer
value: 26.6
verified: false
- task:
type: automatic-speech-recognition
dataset:
type: google/fleurs
name: Google FLEURS (Vietnamese)
config: vi_vn
split: test
metrics:
- type: wer
value: 37.1
verified: false
- task:
type: automatic-speech-recognition
dataset:
type: vivos
name: ĐHQG TPHCM VIVOS
split: test
metrics:
- type: wer
value: 18.7
verified: false
---
whisper tiny fine-tuned on a very big collection of vietnamese speech datasets
TODO:
- [x] training then publish checkpoint
- [x] evaluate WER on Common Voice & FLEURS & VIVOS
- [ ] convert to `openai-whisper`, `whisper.cpp`, `faster-whisper`
- [ ] convert to ONNX: to try https://github.com/k2-fsa/sherpa-onnx & https://github.com/zhuzilin/whisper-openvino
- [ ] convert to TensorRT: https://github.com/openai/whisper/discussions/169
21k steps, warm-up 5%, batch size 16×2 (kaggle free T4×2)
manually evaluate WER on test set - vietnamese part:
| @ `float16` | `CommonVoice v16.1` | `FLEURS` | `VIVOS` |
|---|---|---|---|
| original `whisper-tiny` | >100% | 88.6% | 62.5% |
| this model | 26.6% | 37.1% | 18.7% |
all training + evaluation scripts are on my repo: https://github.com/phineas-pta/fine-tune-whisper-vi
usage example:
```python
import torch
from transformers import pipeline
PIPE = pipeline(task="automatic-speech-recognition", model="doof-ferb/whisper-tiny-vi", device="cuda:0", torch_dtype=torch.float16)
PIPE_KWARGS = {"language": "vi", "task": "transcribe"}
PIPE("audio.mp3", generate_kwargs=PIPE_KWARGS)["text"]
``` |
Ayus077BCT014Bhandari/vartat5-using-100K-plus-4 | Ayus077BCT014Bhandari | 2024-02-22T19:29:27Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-22T13:07:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
robdemunck/finetuned-t5-cnn_dailymail | robdemunck | 2024-02-22T19:29:17Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-20T17:11:29Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: finetuned-t5-cnn_dailymail
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. -->
# finetuned-t5-cnn_dailymail
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 3.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-05 | alinerodrigues | 2024-02-22T19:24:29Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-02-22T15:13:14Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-05
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-large-xlsr-mecita-coraa-portuguese-2-all-clean-05
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1042
- Wer: 0.0718
- Cer: 0.0214
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 28.8714 | 1.0 | 67 | 3.3571 | 1.0 | 1.0 |
| 7.5799 | 2.0 | 134 | 2.9876 | 1.0 | 1.0 |
| 3.0284 | 3.0 | 201 | 2.9114 | 1.0 | 1.0 |
| 3.0284 | 4.0 | 268 | 2.8889 | 1.0 | 1.0 |
| 2.9172 | 5.0 | 335 | 2.8515 | 1.0 | 1.0 |
| 2.8101 | 6.0 | 402 | 2.1557 | 1.0 | 0.6878 |
| 2.8101 | 7.0 | 469 | 0.7046 | 0.3468 | 0.0850 |
| 1.5251 | 8.0 | 536 | 0.4276 | 0.1963 | 0.0517 |
| 0.7791 | 9.0 | 603 | 0.3256 | 0.1723 | 0.0455 |
| 0.7791 | 10.0 | 670 | 0.2743 | 0.1416 | 0.0388 |
| 0.5599 | 11.0 | 737 | 0.2362 | 0.1387 | 0.0378 |
| 0.4678 | 12.0 | 804 | 0.2119 | 0.1265 | 0.0352 |
| 0.4678 | 13.0 | 871 | 0.1984 | 0.1179 | 0.0339 |
| 0.4302 | 14.0 | 938 | 0.1834 | 0.1235 | 0.0332 |
| 0.3794 | 15.0 | 1005 | 0.1760 | 0.1133 | 0.0310 |
| 0.3794 | 16.0 | 1072 | 0.1763 | 0.1080 | 0.0309 |
| 0.3234 | 17.0 | 1139 | 0.1583 | 0.1018 | 0.0294 |
| 0.3144 | 18.0 | 1206 | 0.1570 | 0.0932 | 0.0275 |
| 0.3144 | 19.0 | 1273 | 0.1421 | 0.0912 | 0.0263 |
| 0.2824 | 20.0 | 1340 | 0.1448 | 0.0886 | 0.0263 |
| 0.2503 | 21.0 | 1407 | 0.1371 | 0.0916 | 0.0260 |
| 0.2503 | 22.0 | 1474 | 0.1387 | 0.0860 | 0.0253 |
| 0.2547 | 23.0 | 1541 | 0.1301 | 0.0863 | 0.0242 |
| 0.2397 | 24.0 | 1608 | 0.1272 | 0.0823 | 0.0239 |
| 0.2397 | 25.0 | 1675 | 0.1368 | 0.0827 | 0.0250 |
| 0.2402 | 26.0 | 1742 | 0.1303 | 0.0807 | 0.0243 |
| 0.2581 | 27.0 | 1809 | 0.1248 | 0.0777 | 0.0239 |
| 0.2581 | 28.0 | 1876 | 0.1242 | 0.0758 | 0.0225 |
| 0.2334 | 29.0 | 1943 | 0.1231 | 0.0774 | 0.0228 |
| 0.2087 | 30.0 | 2010 | 0.1226 | 0.0754 | 0.0224 |
| 0.2087 | 31.0 | 2077 | 0.1227 | 0.0774 | 0.0230 |
| 0.2175 | 32.0 | 2144 | 0.1270 | 0.0767 | 0.0231 |
| 0.1973 | 33.0 | 2211 | 0.1258 | 0.0754 | 0.0230 |
| 0.1973 | 34.0 | 2278 | 0.1186 | 0.0754 | 0.0223 |
| 0.1787 | 35.0 | 2345 | 0.1234 | 0.0735 | 0.0217 |
| 0.1958 | 36.0 | 2412 | 0.1199 | 0.0741 | 0.0222 |
| 0.1958 | 37.0 | 2479 | 0.1177 | 0.0754 | 0.0222 |
| 0.1773 | 38.0 | 2546 | 0.1138 | 0.0751 | 0.0225 |
| 0.2047 | 39.0 | 2613 | 0.1164 | 0.0751 | 0.0224 |
| 0.2047 | 40.0 | 2680 | 0.1155 | 0.0751 | 0.0227 |
| 0.1727 | 41.0 | 2747 | 0.1109 | 0.0728 | 0.0213 |
| 0.1708 | 42.0 | 2814 | 0.1132 | 0.0702 | 0.0213 |
| 0.1708 | 43.0 | 2881 | 0.1110 | 0.0728 | 0.0217 |
| 0.1814 | 44.0 | 2948 | 0.1094 | 0.0711 | 0.0215 |
| 0.159 | 45.0 | 3015 | 0.1091 | 0.0702 | 0.0211 |
| 0.159 | 46.0 | 3082 | 0.1065 | 0.0702 | 0.0208 |
| 0.163 | 47.0 | 3149 | 0.1110 | 0.0708 | 0.0210 |
| 0.1565 | 48.0 | 3216 | 0.1121 | 0.0725 | 0.0215 |
| 0.1565 | 49.0 | 3283 | 0.1096 | 0.0715 | 0.0215 |
| 0.1571 | 50.0 | 3350 | 0.1083 | 0.0718 | 0.0210 |
| 0.165 | 51.0 | 3417 | 0.1056 | 0.0711 | 0.0210 |
| 0.165 | 52.0 | 3484 | 0.1042 | 0.0718 | 0.0214 |
| 0.1525 | 53.0 | 3551 | 0.1067 | 0.0698 | 0.0209 |
| 0.1365 | 54.0 | 3618 | 0.1084 | 0.0715 | 0.0208 |
| 0.1365 | 55.0 | 3685 | 0.1086 | 0.0735 | 0.0215 |
| 0.1434 | 56.0 | 3752 | 0.1073 | 0.0711 | 0.0208 |
| 0.1408 | 57.0 | 3819 | 0.1062 | 0.0705 | 0.0209 |
| 0.1408 | 58.0 | 3886 | 0.1066 | 0.0708 | 0.0205 |
| 0.1364 | 59.0 | 3953 | 0.1074 | 0.0702 | 0.0207 |
| 0.1507 | 60.0 | 4020 | 0.1049 | 0.0725 | 0.0207 |
| 0.1507 | 61.0 | 4087 | 0.1086 | 0.0715 | 0.0211 |
| 0.1532 | 62.0 | 4154 | 0.1083 | 0.0738 | 0.0210 |
| 0.1255 | 63.0 | 4221 | 0.1058 | 0.0721 | 0.0207 |
| 0.1255 | 64.0 | 4288 | 0.1087 | 0.0708 | 0.0202 |
| 0.1534 | 65.0 | 4355 | 0.1073 | 0.0738 | 0.0208 |
| 0.1316 | 66.0 | 4422 | 0.1061 | 0.0731 | 0.0210 |
| 0.1316 | 67.0 | 4489 | 0.1082 | 0.0731 | 0.0208 |
| 0.1365 | 68.0 | 4556 | 0.1100 | 0.0751 | 0.0213 |
| 0.1324 | 69.0 | 4623 | 0.1104 | 0.0708 | 0.0206 |
| 0.1324 | 70.0 | 4690 | 0.1073 | 0.0721 | 0.0206 |
| 0.1299 | 71.0 | 4757 | 0.1104 | 0.0711 | 0.0211 |
| 0.125 | 72.0 | 4824 | 0.1078 | 0.0718 | 0.0212 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.1+cu121
- Datasets 2.17.1
- Tokenizers 0.13.3
|
peldrak/segformer-b3-ade-512-512-finetuned-coastTrain | peldrak | 2024-02-22T19:20:03Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/segformer-b3-finetuned-ade-512-512",
"base_model:finetune:nvidia/segformer-b3-finetuned-ade-512-512",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | 2024-02-22T13:19:56Z | ---
license: other
base_model: nvidia/segformer-b3-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b3-ade-512-512-finetuned-coastTrain
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-ade-512-512-finetuned-coastTrain
This model is a fine-tuned version of [nvidia/segformer-b3-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b3-finetuned-ade-512-512) on the peldrak/coastTrain_512-512 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7613
- Mean Iou: 0.7092
- Mean Accuracy: 0.8104
- Overall Accuracy: 0.8790
- Accuracy Water: 0.9352
- Accuracy Whitewater: 0.8067
- Accuracy Sediment: 0.8732
- Accuracy Other Natural Terrain: 0.5054
- Accuracy Vegetation: 0.8997
- Accuracy Development: 0.8714
- Accuracy Unknown: 0.7814
- Iou Water: 0.8677
- Iou Whitewater: 0.6795
- Iou Sediment: 0.7649
- Iou Other Natural Terrain: 0.4259
- Iou Vegetation: 0.7883
- Iou Development: 0.7211
- Iou Unknown: 0.7170
## 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: 25
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|
| 1.7642 | 0.05 | 20 | 1.6699 | 0.1741 | 0.2887 | 0.4511 | 0.3629 | 0.3020 | 0.0122 | 0.0013 | 0.8998 | 0.1317 | 0.3106 | 0.3310 | 0.0708 | 0.0112 | 0.0013 | 0.3953 | 0.1007 | 0.3084 |
| 1.6158 | 0.11 | 40 | 1.3903 | 0.1804 | 0.2783 | 0.5516 | 0.6957 | 0.0198 | 0.1032 | 0.0000 | 0.9605 | 0.1077 | 0.0616 | 0.5309 | 0.0184 | 0.0965 | 0.0000 | 0.4589 | 0.0973 | 0.0606 |
| 1.3168 | 0.16 | 60 | 1.1710 | 0.2583 | 0.3483 | 0.6425 | 0.8324 | 0.0359 | 0.0669 | 0.0 | 0.9578 | 0.1157 | 0.4296 | 0.6688 | 0.0344 | 0.0630 | 0.0 | 0.5089 | 0.1094 | 0.4233 |
| 1.1024 | 0.22 | 80 | 1.0398 | 0.3143 | 0.4032 | 0.6865 | 0.8815 | 0.1083 | 0.1413 | 0.0 | 0.9619 | 0.2673 | 0.4620 | 0.6970 | 0.1041 | 0.1261 | 0.0 | 0.5725 | 0.2452 | 0.4549 |
| 1.0384 | 0.27 | 100 | 0.9307 | 0.3388 | 0.4315 | 0.7113 | 0.8919 | 0.0379 | 0.3662 | 0.0 | 0.9582 | 0.2753 | 0.4913 | 0.7137 | 0.0374 | 0.2526 | 0.0 | 0.6316 | 0.2550 | 0.4813 |
| 0.9056 | 0.32 | 120 | 0.8649 | 0.3988 | 0.5060 | 0.7415 | 0.9191 | 0.1051 | 0.4270 | 0.0 | 0.8743 | 0.7201 | 0.4965 | 0.7159 | 0.1038 | 0.3178 | 0.0 | 0.6739 | 0.4951 | 0.4849 |
| 1.1867 | 0.38 | 140 | 0.8470 | 0.4027 | 0.5076 | 0.7418 | 0.8363 | 0.0329 | 0.6586 | 0.0 | 0.9529 | 0.5761 | 0.4960 | 0.7494 | 0.0326 | 0.4722 | 0.0 | 0.6188 | 0.4643 | 0.4815 |
| 1.2778 | 0.43 | 160 | 0.8108 | 0.4419 | 0.5491 | 0.7656 | 0.8973 | 0.1895 | 0.5864 | 0.0 | 0.9145 | 0.7679 | 0.4885 | 0.7758 | 0.1848 | 0.4732 | 0.0 | 0.6536 | 0.5269 | 0.4791 |
| 0.8217 | 0.49 | 180 | 0.7507 | 0.4544 | 0.5750 | 0.7728 | 0.8801 | 0.1928 | 0.7543 | 0.0 | 0.8893 | 0.7924 | 0.5161 | 0.7912 | 0.1858 | 0.5100 | 0.0 | 0.6678 | 0.5398 | 0.4859 |
| 0.9801 | 0.54 | 200 | 0.7149 | 0.4827 | 0.5995 | 0.7819 | 0.9016 | 0.3829 | 0.7254 | 0.0 | 0.8848 | 0.7904 | 0.5117 | 0.8007 | 0.3571 | 0.5615 | 0.0 | 0.6774 | 0.4859 | 0.4966 |
| 0.7374 | 0.59 | 220 | 0.6885 | 0.4950 | 0.6159 | 0.7894 | 0.9068 | 0.3910 | 0.8448 | 0.0 | 0.8656 | 0.7859 | 0.5169 | 0.7839 | 0.3448 | 0.5749 | 0.0 | 0.6942 | 0.5774 | 0.4895 |
| 1.0931 | 0.65 | 240 | 0.6884 | 0.4889 | 0.6134 | 0.7885 | 0.9106 | 0.3515 | 0.8590 | 0.0 | 0.8554 | 0.8118 | 0.5059 | 0.7804 | 0.3041 | 0.5561 | 0.0 | 0.7017 | 0.5858 | 0.4941 |
| 0.7106 | 0.7 | 260 | 0.8052 | 0.4413 | 0.5511 | 0.7563 | 0.9400 | 0.3677 | 0.3526 | 0.0 | 0.8507 | 0.8081 | 0.5382 | 0.7137 | 0.3061 | 0.2825 | 0.0 | 0.6967 | 0.5709 | 0.5193 |
| 0.7133 | 0.76 | 280 | 0.6507 | 0.5368 | 0.6542 | 0.8106 | 0.8931 | 0.6564 | 0.8631 | 0.0 | 0.9353 | 0.6824 | 0.5491 | 0.8105 | 0.5066 | 0.5893 | 0.0 | 0.7211 | 0.5953 | 0.5350 |
| 0.5858 | 0.81 | 300 | 0.6587 | 0.5212 | 0.6453 | 0.7979 | 0.9158 | 0.6788 | 0.6528 | 0.0 | 0.8872 | 0.8580 | 0.5241 | 0.8226 | 0.5180 | 0.5725 | 0.0 | 0.6814 | 0.5427 | 0.5113 |
| 1.9447 | 0.86 | 320 | 0.6674 | 0.5300 | 0.6268 | 0.8098 | 0.9182 | 0.4960 | 0.7161 | 0.0 | 0.9369 | 0.7516 | 0.5691 | 0.8130 | 0.4323 | 0.6061 | 0.0 | 0.6974 | 0.6098 | 0.5515 |
| 0.6724 | 0.92 | 340 | 0.6814 | 0.5191 | 0.6635 | 0.7901 | 0.8573 | 0.6785 | 0.8412 | 0.0 | 0.8680 | 0.8745 | 0.5251 | 0.7797 | 0.5249 | 0.5634 | 0.0 | 0.7007 | 0.5512 | 0.5139 |
| 0.6738 | 0.97 | 360 | 0.6131 | 0.5509 | 0.6663 | 0.8173 | 0.9235 | 0.6190 | 0.8401 | 0.0 | 0.8883 | 0.8535 | 0.5396 | 0.8125 | 0.5326 | 0.6451 | 0.0 | 0.7219 | 0.6215 | 0.5229 |
| 0.7131 | 1.03 | 380 | 0.6163 | 0.5582 | 0.6734 | 0.8172 | 0.8994 | 0.7309 | 0.8445 | 0.0 | 0.9282 | 0.7832 | 0.5272 | 0.8228 | 0.5820 | 0.6580 | 0.0 | 0.7059 | 0.6249 | 0.5137 |
| 0.8373 | 1.08 | 400 | 0.6077 | 0.5569 | 0.6737 | 0.8216 | 0.9115 | 0.7745 | 0.8120 | 0.0 | 0.9381 | 0.7219 | 0.5579 | 0.8242 | 0.5463 | 0.6524 | 0.0 | 0.7230 | 0.6089 | 0.5435 |
| 0.7344 | 1.14 | 420 | 0.6830 | 0.5195 | 0.6628 | 0.7866 | 0.9541 | 0.6517 | 0.8366 | 0.0 | 0.7106 | 0.8978 | 0.5890 | 0.7943 | 0.5367 | 0.5415 | 0.0 | 0.6433 | 0.5580 | 0.5626 |
| 0.4357 | 1.19 | 440 | 0.5908 | 0.5761 | 0.6867 | 0.8298 | 0.9183 | 0.7028 | 0.8313 | 0.0 | 0.9032 | 0.8332 | 0.6181 | 0.8259 | 0.5906 | 0.6593 | 0.0 | 0.7274 | 0.6289 | 0.6005 |
| 0.3423 | 1.24 | 460 | 0.5857 | 0.5864 | 0.7004 | 0.8332 | 0.9054 | 0.7249 | 0.8626 | 0.0 | 0.8887 | 0.8351 | 0.6862 | 0.8155 | 0.6248 | 0.6348 | 0.0 | 0.7415 | 0.6395 | 0.6484 |
| 0.5952 | 1.3 | 480 | 0.6290 | 0.5592 | 0.6560 | 0.8170 | 0.9176 | 0.7224 | 0.7925 | 0.0 | 0.9532 | 0.6852 | 0.5208 | 0.8244 | 0.6165 | 0.6594 | 0.0 | 0.7002 | 0.6048 | 0.5091 |
| 0.7312 | 1.35 | 500 | 0.6103 | 0.5637 | 0.6829 | 0.8213 | 0.9303 | 0.7779 | 0.8384 | 0.0 | 0.8961 | 0.8243 | 0.5137 | 0.8265 | 0.6301 | 0.6668 | 0.0 | 0.7254 | 0.5968 | 0.5000 |
| 0.4683 | 1.41 | 520 | 0.6372 | 0.5599 | 0.6809 | 0.8165 | 0.9056 | 0.7946 | 0.8165 | 0.0 | 0.9156 | 0.8230 | 0.5109 | 0.8273 | 0.6414 | 0.6765 | 0.0 | 0.7153 | 0.5598 | 0.4988 |
| 0.3688 | 1.46 | 540 | 0.6608 | 0.5537 | 0.6561 | 0.8129 | 0.9313 | 0.7464 | 0.6851 | 0.0005 | 0.9196 | 0.7613 | 0.5485 | 0.7956 | 0.6248 | 0.5633 | 0.0005 | 0.7172 | 0.6407 | 0.5340 |
| 0.3681 | 1.51 | 560 | 0.5841 | 0.5800 | 0.7074 | 0.8296 | 0.9114 | 0.8080 | 0.8613 | 0.0000 | 0.8767 | 0.8714 | 0.6232 | 0.8230 | 0.6089 | 0.6616 | 0.0000 | 0.7287 | 0.6480 | 0.5899 |
| 0.455 | 1.57 | 580 | 0.6379 | 0.5682 | 0.6749 | 0.8230 | 0.9313 | 0.7373 | 0.8058 | 0.0008 | 0.9130 | 0.8057 | 0.5305 | 0.8181 | 0.6047 | 0.6666 | 0.0008 | 0.7166 | 0.6519 | 0.5186 |
| 0.57 | 1.62 | 600 | 0.6002 | 0.5727 | 0.6983 | 0.8273 | 0.9134 | 0.8255 | 0.8873 | 0.0001 | 0.9089 | 0.8164 | 0.5364 | 0.8227 | 0.5932 | 0.6616 | 0.0001 | 0.7306 | 0.6692 | 0.5317 |
| 0.3516 | 1.68 | 620 | 0.5615 | 0.5862 | 0.7111 | 0.8336 | 0.9078 | 0.8340 | 0.8216 | 0.0012 | 0.8920 | 0.8649 | 0.6558 | 0.8358 | 0.5994 | 0.6911 | 0.0012 | 0.7225 | 0.6421 | 0.6113 |
| 0.4446 | 1.73 | 640 | 0.5702 | 0.5957 | 0.6972 | 0.8386 | 0.9039 | 0.7710 | 0.8483 | 0.0002 | 0.9370 | 0.7354 | 0.6850 | 0.8382 | 0.6356 | 0.6933 | 0.0002 | 0.7234 | 0.6237 | 0.6553 |
| 1.1138 | 1.78 | 660 | 0.5697 | 0.5979 | 0.7156 | 0.8434 | 0.9065 | 0.7799 | 0.8291 | 0.0001 | 0.8949 | 0.8463 | 0.7525 | 0.8289 | 0.6005 | 0.6542 | 0.0001 | 0.7518 | 0.6442 | 0.7056 |
| 0.5918 | 1.84 | 680 | 0.5167 | 0.5994 | 0.7094 | 0.8458 | 0.9394 | 0.7790 | 0.8572 | 0.0047 | 0.8996 | 0.8256 | 0.6606 | 0.8389 | 0.5879 | 0.6878 | 0.0047 | 0.7481 | 0.6835 | 0.6452 |
| 0.4778 | 1.89 | 700 | 0.5767 | 0.5960 | 0.7142 | 0.8340 | 0.8730 | 0.8111 | 0.8841 | 0.0080 | 0.9204 | 0.8301 | 0.6728 | 0.8068 | 0.6549 | 0.6153 | 0.0080 | 0.7425 | 0.6861 | 0.6583 |
| 0.6689 | 1.95 | 720 | 0.5420 | 0.6104 | 0.7221 | 0.8482 | 0.9085 | 0.8333 | 0.8307 | 0.0255 | 0.9173 | 0.7881 | 0.7511 | 0.8325 | 0.6055 | 0.7094 | 0.0254 | 0.7499 | 0.6525 | 0.6974 |
| 1.893 | 2.0 | 740 | 0.5951 | 0.5883 | 0.7054 | 0.8367 | 0.9266 | 0.7935 | 0.7975 | 0.0057 | 0.9013 | 0.9111 | 0.6018 | 0.8317 | 0.6187 | 0.6962 | 0.0057 | 0.7446 | 0.6347 | 0.5865 |
| 0.3762 | 2.05 | 760 | 0.6041 | 0.5633 | 0.6763 | 0.8229 | 0.9138 | 0.6893 | 0.8762 | 0.0087 | 0.9142 | 0.7735 | 0.5580 | 0.8233 | 0.5721 | 0.6527 | 0.0087 | 0.7272 | 0.6313 | 0.5274 |
| 0.5576 | 2.11 | 780 | 0.5651 | 0.5721 | 0.7097 | 0.8199 | 0.8804 | 0.8350 | 0.8177 | 0.0152 | 0.8769 | 0.9241 | 0.6184 | 0.8135 | 0.6197 | 0.6973 | 0.0152 | 0.7296 | 0.5934 | 0.5359 |
| 0.4714 | 2.16 | 800 | 0.4983 | 0.6253 | 0.7352 | 0.8582 | 0.9377 | 0.8134 | 0.8719 | 0.0410 | 0.8881 | 0.8385 | 0.7560 | 0.8409 | 0.6150 | 0.7200 | 0.0409 | 0.7729 | 0.6906 | 0.6967 |
| 1.2051 | 2.22 | 820 | 0.5054 | 0.6172 | 0.7256 | 0.8501 | 0.9141 | 0.7940 | 0.8392 | 0.0436 | 0.9049 | 0.8325 | 0.7506 | 0.8408 | 0.6495 | 0.7252 | 0.0435 | 0.7561 | 0.6197 | 0.6853 |
| 0.2421 | 2.27 | 840 | 0.5026 | 0.6112 | 0.7294 | 0.8491 | 0.9194 | 0.8360 | 0.8872 | 0.0346 | 0.8828 | 0.7663 | 0.7797 | 0.8333 | 0.6152 | 0.6798 | 0.0345 | 0.7621 | 0.6667 | 0.6868 |
| 0.6917 | 2.32 | 860 | 0.4947 | 0.6065 | 0.7343 | 0.8447 | 0.9111 | 0.8352 | 0.8533 | 0.0498 | 0.8766 | 0.8971 | 0.7168 | 0.8411 | 0.6384 | 0.6910 | 0.0496 | 0.7628 | 0.6024 | 0.6599 |
| 0.2269 | 2.38 | 880 | 0.4963 | 0.6099 | 0.7354 | 0.8426 | 0.9023 | 0.8730 | 0.8372 | 0.1139 | 0.9161 | 0.8633 | 0.6419 | 0.8383 | 0.6218 | 0.7367 | 0.1128 | 0.7614 | 0.5984 | 0.5996 |
| 0.6035 | 2.43 | 900 | 0.4550 | 0.6421 | 0.7362 | 0.8638 | 0.9284 | 0.7679 | 0.8624 | 0.0968 | 0.9286 | 0.8148 | 0.7544 | 0.8509 | 0.6474 | 0.7274 | 0.0964 | 0.7718 | 0.6700 | 0.7305 |
| 0.8465 | 2.49 | 920 | 0.4764 | 0.6396 | 0.7425 | 0.8606 | 0.9218 | 0.7772 | 0.8555 | 0.0980 | 0.9038 | 0.8710 | 0.7704 | 0.8516 | 0.6517 | 0.7149 | 0.0978 | 0.7614 | 0.6709 | 0.7288 |
| 0.3546 | 2.54 | 940 | 0.4636 | 0.6444 | 0.7360 | 0.8683 | 0.9343 | 0.7923 | 0.8181 | 0.0691 | 0.9320 | 0.7953 | 0.8108 | 0.8502 | 0.6426 | 0.7042 | 0.0690 | 0.7811 | 0.6783 | 0.7854 |
| 0.5057 | 2.59 | 960 | 0.4754 | 0.6315 | 0.7412 | 0.8617 | 0.9241 | 0.8575 | 0.8383 | 0.0538 | 0.9121 | 0.8026 | 0.7999 | 0.8512 | 0.6429 | 0.6996 | 0.0537 | 0.7765 | 0.6575 | 0.7393 |
| 0.2862 | 2.65 | 980 | 0.5106 | 0.6088 | 0.7152 | 0.8436 | 0.9366 | 0.7928 | 0.8561 | 0.0851 | 0.9149 | 0.8273 | 0.5938 | 0.8461 | 0.6602 | 0.7275 | 0.0849 | 0.7500 | 0.6152 | 0.5776 |
| 0.4181 | 2.7 | 1000 | 0.5597 | 0.6053 | 0.7262 | 0.8386 | 0.9201 | 0.8461 | 0.8863 | 0.1007 | 0.9030 | 0.8624 | 0.5651 | 0.8481 | 0.6616 | 0.7152 | 0.1005 | 0.7379 | 0.6313 | 0.5422 |
| 0.3954 | 2.76 | 1020 | 0.5037 | 0.6259 | 0.7259 | 0.8496 | 0.9245 | 0.8067 | 0.8656 | 0.1011 | 0.9178 | 0.7763 | 0.6895 | 0.8392 | 0.6596 | 0.7198 | 0.1007 | 0.7450 | 0.6707 | 0.6467 |
| 0.254 | 2.81 | 1040 | 0.5001 | 0.6446 | 0.7660 | 0.8607 | 0.9098 | 0.8624 | 0.8650 | 0.1116 | 0.8660 | 0.9136 | 0.8339 | 0.8409 | 0.6434 | 0.7097 | 0.1109 | 0.7618 | 0.6799 | 0.7653 |
| 0.4925 | 2.86 | 1060 | 0.5392 | 0.6221 | 0.7218 | 0.8537 | 0.9332 | 0.7817 | 0.8101 | 0.0580 | 0.9194 | 0.8478 | 0.7023 | 0.8312 | 0.6484 | 0.6910 | 0.0578 | 0.7736 | 0.6751 | 0.6773 |
| 0.3821 | 2.92 | 1080 | 0.5041 | 0.6211 | 0.7373 | 0.8515 | 0.9228 | 0.8788 | 0.8460 | 0.0918 | 0.9179 | 0.8416 | 0.6624 | 0.8409 | 0.6212 | 0.7112 | 0.0913 | 0.7629 | 0.6833 | 0.6368 |
| 0.3027 | 2.97 | 1100 | 0.4728 | 0.6427 | 0.7526 | 0.8604 | 0.9333 | 0.7614 | 0.8818 | 0.1308 | 0.8640 | 0.9228 | 0.7739 | 0.8447 | 0.6419 | 0.7208 | 0.1296 | 0.7681 | 0.6637 | 0.7300 |
| 0.3572 | 3.03 | 1120 | 0.5109 | 0.6388 | 0.7469 | 0.8545 | 0.9342 | 0.8335 | 0.9024 | 0.1647 | 0.8906 | 0.8025 | 0.7001 | 0.8405 | 0.6553 | 0.7219 | 0.1612 | 0.7568 | 0.6722 | 0.6640 |
| 0.6269 | 3.08 | 1140 | 0.4645 | 0.6641 | 0.7651 | 0.8679 | 0.9289 | 0.8038 | 0.8241 | 0.2209 | 0.9024 | 0.8788 | 0.7965 | 0.8590 | 0.6555 | 0.7373 | 0.2170 | 0.7714 | 0.6514 | 0.7571 |
| 0.6726 | 3.14 | 1160 | 0.5041 | 0.6440 | 0.7509 | 0.8537 | 0.9274 | 0.8540 | 0.8209 | 0.2448 | 0.9249 | 0.8419 | 0.6420 | 0.8467 | 0.6484 | 0.7265 | 0.2219 | 0.7558 | 0.6836 | 0.6248 |
| 0.2253 | 3.19 | 1180 | 0.4808 | 0.6661 | 0.7738 | 0.8672 | 0.9238 | 0.8341 | 0.8564 | 0.2223 | 0.8885 | 0.8999 | 0.7918 | 0.8512 | 0.6724 | 0.7252 | 0.2170 | 0.7739 | 0.6637 | 0.7590 |
| 0.1953 | 3.24 | 1200 | 0.4971 | 0.6561 | 0.7559 | 0.8637 | 0.9387 | 0.8074 | 0.8676 | 0.1940 | 0.8993 | 0.8488 | 0.7360 | 0.8437 | 0.6742 | 0.7212 | 0.1899 | 0.7770 | 0.6727 | 0.7137 |
| 0.3769 | 3.3 | 1220 | 0.4940 | 0.6666 | 0.7711 | 0.8688 | 0.9114 | 0.7625 | 0.8894 | 0.2404 | 0.8942 | 0.8456 | 0.8539 | 0.8469 | 0.6319 | 0.7291 | 0.2308 | 0.7824 | 0.6923 | 0.7527 |
| 0.2919 | 3.35 | 1240 | 0.5256 | 0.6579 | 0.7656 | 0.8620 | 0.9147 | 0.8240 | 0.8073 | 0.2330 | 0.8878 | 0.8364 | 0.8558 | 0.8452 | 0.6436 | 0.7307 | 0.2249 | 0.7696 | 0.6866 | 0.7049 |
| 0.8137 | 3.41 | 1260 | 0.4615 | 0.6636 | 0.7540 | 0.8717 | 0.9342 | 0.7923 | 0.8602 | 0.1674 | 0.9233 | 0.7903 | 0.8106 | 0.8536 | 0.6677 | 0.7304 | 0.1640 | 0.7837 | 0.6665 | 0.7792 |
| 0.5517 | 3.46 | 1280 | 0.4785 | 0.6581 | 0.7697 | 0.8593 | 0.9169 | 0.8348 | 0.9019 | 0.2548 | 0.8847 | 0.8347 | 0.7602 | 0.8331 | 0.6732 | 0.7226 | 0.2237 | 0.7744 | 0.6886 | 0.6912 |
| 0.3323 | 3.51 | 1300 | 0.4658 | 0.6783 | 0.7893 | 0.8657 | 0.8912 | 0.8488 | 0.8672 | 0.3312 | 0.9038 | 0.8458 | 0.8372 | 0.8292 | 0.6626 | 0.7546 | 0.3087 | 0.7827 | 0.6763 | 0.7342 |
| 0.2235 | 3.57 | 1320 | 0.4687 | 0.6690 | 0.7668 | 0.8669 | 0.9418 | 0.8131 | 0.8607 | 0.2613 | 0.8989 | 0.8500 | 0.7420 | 0.8448 | 0.6721 | 0.7273 | 0.2491 | 0.7778 | 0.6867 | 0.7250 |
| 0.4178 | 3.62 | 1340 | 0.5271 | 0.6617 | 0.7577 | 0.8631 | 0.9271 | 0.8110 | 0.8500 | 0.2745 | 0.9291 | 0.7756 | 0.7368 | 0.8565 | 0.6700 | 0.7388 | 0.2480 | 0.7648 | 0.6509 | 0.7031 |
| 0.1709 | 3.68 | 1360 | 0.4917 | 0.6743 | 0.7839 | 0.8666 | 0.9345 | 0.8278 | 0.8188 | 0.3626 | 0.8856 | 0.8878 | 0.7699 | 0.8586 | 0.6593 | 0.7338 | 0.3077 | 0.7715 | 0.6556 | 0.7334 |
| 0.5981 | 3.73 | 1380 | 0.5301 | 0.6598 | 0.7651 | 0.8619 | 0.9562 | 0.8015 | 0.8527 | 0.3087 | 0.8823 | 0.8672 | 0.6870 | 0.8548 | 0.6651 | 0.7469 | 0.2630 | 0.7759 | 0.6539 | 0.6593 |
| 0.3507 | 3.78 | 1400 | 0.5341 | 0.6544 | 0.7687 | 0.8543 | 0.9212 | 0.7396 | 0.8167 | 0.4255 | 0.8876 | 0.8486 | 0.7416 | 0.8517 | 0.6158 | 0.7268 | 0.3144 | 0.7615 | 0.6660 | 0.6448 |
| 0.3053 | 3.84 | 1420 | 0.5660 | 0.6511 | 0.7660 | 0.8510 | 0.9112 | 0.7759 | 0.8743 | 0.3550 | 0.8915 | 0.8701 | 0.6838 | 0.8407 | 0.6537 | 0.7418 | 0.3087 | 0.7673 | 0.6443 | 0.6014 |
| 0.4962 | 3.89 | 1440 | 0.5701 | 0.6535 | 0.7465 | 0.8620 | 0.9387 | 0.7989 | 0.8546 | 0.2405 | 0.9323 | 0.7459 | 0.7148 | 0.8556 | 0.6701 | 0.7382 | 0.2139 | 0.7709 | 0.6388 | 0.6870 |
| 0.6165 | 3.95 | 1460 | 0.4963 | 0.6711 | 0.7622 | 0.8720 | 0.9393 | 0.7786 | 0.8285 | 0.2492 | 0.9198 | 0.8354 | 0.7850 | 0.8648 | 0.6480 | 0.7461 | 0.2351 | 0.7754 | 0.6862 | 0.7418 |
| 0.2898 | 4.0 | 1480 | 0.4906 | 0.6751 | 0.7688 | 0.8691 | 0.9254 | 0.7811 | 0.8763 | 0.3229 | 0.9206 | 0.7502 | 0.8053 | 0.8583 | 0.6574 | 0.7458 | 0.2937 | 0.7713 | 0.6572 | 0.7421 |
| 0.2248 | 4.05 | 1500 | 0.5393 | 0.6627 | 0.7700 | 0.8636 | 0.9304 | 0.7995 | 0.8683 | 0.2900 | 0.8898 | 0.8543 | 0.7578 | 0.8540 | 0.6586 | 0.7258 | 0.2434 | 0.7721 | 0.6835 | 0.7014 |
| 0.2432 | 4.11 | 1520 | 0.5233 | 0.6732 | 0.7773 | 0.8627 | 0.9456 | 0.7776 | 0.8200 | 0.4175 | 0.8811 | 0.8671 | 0.7321 | 0.8384 | 0.6516 | 0.7257 | 0.3281 | 0.7739 | 0.7027 | 0.6919 |
| 0.3847 | 4.16 | 1540 | 0.5011 | 0.6842 | 0.7816 | 0.8757 | 0.9203 | 0.7606 | 0.8733 | 0.3186 | 0.9076 | 0.8372 | 0.8534 | 0.8533 | 0.6504 | 0.7369 | 0.2841 | 0.7927 | 0.6897 | 0.7826 |
| 0.3696 | 4.22 | 1560 | 0.4968 | 0.6889 | 0.7971 | 0.8767 | 0.9334 | 0.8285 | 0.8607 | 0.3609 | 0.8816 | 0.8655 | 0.8492 | 0.8566 | 0.6483 | 0.7309 | 0.3169 | 0.7953 | 0.6897 | 0.7848 |
| 0.6256 | 4.27 | 1580 | 0.5060 | 0.6920 | 0.7930 | 0.8755 | 0.9256 | 0.7798 | 0.8598 | 0.3865 | 0.8937 | 0.8652 | 0.8407 | 0.8563 | 0.6671 | 0.7406 | 0.3277 | 0.7866 | 0.6882 | 0.7773 |
| 0.123 | 4.32 | 1600 | 0.5031 | 0.6911 | 0.7886 | 0.8762 | 0.9247 | 0.7878 | 0.8605 | 0.3802 | 0.9060 | 0.7956 | 0.8651 | 0.8568 | 0.6607 | 0.7431 | 0.3339 | 0.7893 | 0.6757 | 0.7782 |
| 0.4976 | 4.38 | 1620 | 0.5683 | 0.6833 | 0.7880 | 0.8669 | 0.9163 | 0.8146 | 0.8619 | 0.4185 | 0.9166 | 0.8540 | 0.7338 | 0.8554 | 0.6671 | 0.7501 | 0.3581 | 0.7683 | 0.6807 | 0.7033 |
| 0.3203 | 4.43 | 1640 | 0.5254 | 0.6852 | 0.7749 | 0.8705 | 0.9330 | 0.7844 | 0.8444 | 0.3818 | 0.9296 | 0.8045 | 0.7469 | 0.8568 | 0.6690 | 0.7506 | 0.3494 | 0.7741 | 0.6708 | 0.7259 |
| 0.233 | 4.49 | 1660 | 0.5000 | 0.7018 | 0.8034 | 0.8800 | 0.9299 | 0.8099 | 0.8661 | 0.4532 | 0.9113 | 0.8396 | 0.8138 | 0.8593 | 0.6544 | 0.7502 | 0.3850 | 0.8005 | 0.6767 | 0.7866 |
| 0.131 | 4.54 | 1680 | 0.5944 | 0.6698 | 0.7886 | 0.8628 | 0.9372 | 0.7772 | 0.8888 | 0.4697 | 0.8819 | 0.8800 | 0.6857 | 0.8514 | 0.6482 | 0.7408 | 0.3710 | 0.7992 | 0.6191 | 0.6589 |
| 0.1867 | 4.59 | 1700 | 0.5355 | 0.6948 | 0.8107 | 0.8731 | 0.9344 | 0.7717 | 0.8889 | 0.5399 | 0.8620 | 0.8469 | 0.8311 | 0.8511 | 0.6522 | 0.7434 | 0.3764 | 0.7892 | 0.6924 | 0.7588 |
| 0.2121 | 4.65 | 1720 | 0.5226 | 0.6934 | 0.7864 | 0.8759 | 0.9256 | 0.7900 | 0.8240 | 0.3943 | 0.9170 | 0.7991 | 0.8549 | 0.8562 | 0.6568 | 0.7473 | 0.3600 | 0.7847 | 0.6623 | 0.7868 |
| 0.4442 | 4.7 | 1740 | 0.5122 | 0.7049 | 0.8078 | 0.8802 | 0.9236 | 0.8008 | 0.8868 | 0.4509 | 0.8927 | 0.8270 | 0.8730 | 0.8555 | 0.6574 | 0.7461 | 0.3940 | 0.7971 | 0.6929 | 0.7914 |
| 0.2561 | 4.76 | 1760 | 0.5097 | 0.6952 | 0.8068 | 0.8723 | 0.9096 | 0.8027 | 0.8991 | 0.4735 | 0.8852 | 0.8207 | 0.8569 | 0.8460 | 0.6619 | 0.7383 | 0.3849 | 0.7870 | 0.6949 | 0.7534 |
| 0.3744 | 4.81 | 1780 | 0.5762 | 0.6562 | 0.7702 | 0.8561 | 0.9452 | 0.7934 | 0.8702 | 0.4003 | 0.8877 | 0.8651 | 0.6295 | 0.8485 | 0.6679 | 0.7432 | 0.3079 | 0.7800 | 0.6413 | 0.6049 |
| 0.2373 | 4.86 | 1800 | 0.5477 | 0.6547 | 0.7715 | 0.8550 | 0.9341 | 0.8386 | 0.8405 | 0.4085 | 0.9097 | 0.8517 | 0.6177 | 0.8506 | 0.6647 | 0.7467 | 0.3169 | 0.7802 | 0.6353 | 0.5885 |
| 0.1851 | 4.92 | 1820 | 0.5771 | 0.6565 | 0.7735 | 0.8503 | 0.9038 | 0.8391 | 0.8573 | 0.4305 | 0.9152 | 0.7979 | 0.6707 | 0.8329 | 0.6692 | 0.7400 | 0.3561 | 0.7732 | 0.6264 | 0.5974 |
| 0.3411 | 4.97 | 1840 | 0.5119 | 0.6813 | 0.7933 | 0.8647 | 0.9168 | 0.8103 | 0.8401 | 0.4585 | 0.9003 | 0.8885 | 0.7387 | 0.8526 | 0.6616 | 0.7445 | 0.3726 | 0.7722 | 0.6832 | 0.6821 |
| 0.1627 | 5.03 | 1860 | 0.5401 | 0.6720 | 0.7865 | 0.8627 | 0.9415 | 0.7899 | 0.9051 | 0.4505 | 0.8736 | 0.8287 | 0.7158 | 0.8537 | 0.6649 | 0.7325 | 0.3189 | 0.7719 | 0.6777 | 0.6844 |
| 0.4794 | 5.08 | 1880 | 0.5325 | 0.6793 | 0.7883 | 0.8638 | 0.9325 | 0.7814 | 0.8045 | 0.5115 | 0.8987 | 0.8550 | 0.7343 | 0.8504 | 0.6476 | 0.7384 | 0.3844 | 0.7738 | 0.6850 | 0.6754 |
| 0.2968 | 5.14 | 1900 | 0.5264 | 0.6856 | 0.7945 | 0.8692 | 0.9215 | 0.8237 | 0.8716 | 0.4383 | 0.9035 | 0.8371 | 0.7659 | 0.8560 | 0.6579 | 0.7421 | 0.3546 | 0.7767 | 0.7052 | 0.7065 |
| 0.1931 | 5.19 | 1920 | 0.4982 | 0.6967 | 0.8119 | 0.8715 | 0.9218 | 0.8092 | 0.8836 | 0.5354 | 0.8854 | 0.8679 | 0.7801 | 0.8595 | 0.6635 | 0.7479 | 0.4057 | 0.7762 | 0.7074 | 0.7163 |
| 0.5028 | 5.24 | 1940 | 0.4865 | 0.7072 | 0.8092 | 0.8816 | 0.9241 | 0.8274 | 0.8591 | 0.4211 | 0.8943 | 0.8659 | 0.8723 | 0.8576 | 0.6624 | 0.7439 | 0.3886 | 0.7961 | 0.7090 | 0.7929 |
| 0.1652 | 5.3 | 1960 | 0.5541 | 0.6710 | 0.7825 | 0.8574 | 0.8865 | 0.7954 | 0.8853 | 0.3600 | 0.8946 | 0.8783 | 0.7775 | 0.8322 | 0.6733 | 0.7533 | 0.3282 | 0.7663 | 0.6931 | 0.6504 |
| 0.3028 | 5.35 | 1980 | 0.4632 | 0.6915 | 0.7915 | 0.8730 | 0.9299 | 0.8132 | 0.8737 | 0.3976 | 0.9063 | 0.8558 | 0.7641 | 0.8598 | 0.6714 | 0.7551 | 0.3663 | 0.7806 | 0.6894 | 0.7178 |
| 0.2153 | 5.41 | 2000 | 0.6220 | 0.6541 | 0.7599 | 0.8520 | 0.9176 | 0.7605 | 0.8618 | 0.3876 | 0.9163 | 0.8224 | 0.6535 | 0.8475 | 0.6598 | 0.7495 | 0.3470 | 0.7667 | 0.6098 | 0.5987 |
| 0.5976 | 5.46 | 2020 | 0.5749 | 0.6739 | 0.7944 | 0.8628 | 0.9262 | 0.8053 | 0.8888 | 0.4578 | 0.8673 | 0.8466 | 0.7687 | 0.8499 | 0.6627 | 0.7494 | 0.3538 | 0.7848 | 0.6277 | 0.6892 |
| 0.1812 | 5.51 | 2040 | 0.5282 | 0.6862 | 0.7879 | 0.8738 | 0.9364 | 0.7998 | 0.8519 | 0.3850 | 0.8983 | 0.8406 | 0.8033 | 0.8570 | 0.6632 | 0.7337 | 0.3472 | 0.7966 | 0.6566 | 0.7492 |
| 0.3064 | 5.57 | 2060 | 0.5309 | 0.6847 | 0.7931 | 0.8699 | 0.9108 | 0.8068 | 0.8459 | 0.3914 | 0.8923 | 0.8551 | 0.8496 | 0.8434 | 0.6659 | 0.7385 | 0.3538 | 0.7964 | 0.6594 | 0.7352 |
| 0.2951 | 5.62 | 2080 | 0.5739 | 0.6811 | 0.7996 | 0.8617 | 0.8914 | 0.8086 | 0.8350 | 0.5265 | 0.8970 | 0.7884 | 0.8501 | 0.8313 | 0.6658 | 0.7353 | 0.4000 | 0.7862 | 0.6409 | 0.7082 |
| 0.2031 | 5.68 | 2100 | 0.5522 | 0.6730 | 0.7927 | 0.8585 | 0.9065 | 0.8260 | 0.8216 | 0.4632 | 0.8839 | 0.8735 | 0.7741 | 0.8449 | 0.6658 | 0.7263 | 0.3915 | 0.7747 | 0.6401 | 0.6673 |
| 0.1091 | 5.73 | 2120 | 0.5696 | 0.6742 | 0.7839 | 0.8621 | 0.9269 | 0.8129 | 0.8489 | 0.4785 | 0.9088 | 0.7822 | 0.7290 | 0.8612 | 0.6758 | 0.7399 | 0.3564 | 0.7655 | 0.6399 | 0.6809 |
| 0.6339 | 5.78 | 2140 | 0.5735 | 0.6845 | 0.7946 | 0.8695 | 0.9315 | 0.8040 | 0.8301 | 0.4774 | 0.8990 | 0.8546 | 0.7657 | 0.8610 | 0.6691 | 0.7390 | 0.3747 | 0.7893 | 0.6461 | 0.7126 |
| 0.1977 | 5.84 | 2160 | 0.6636 | 0.6630 | 0.7802 | 0.8553 | 0.9485 | 0.7973 | 0.8691 | 0.4899 | 0.8769 | 0.8494 | 0.6304 | 0.8548 | 0.6756 | 0.7360 | 0.3940 | 0.7771 | 0.6022 | 0.6014 |
| 0.1821 | 5.89 | 2180 | 0.5528 | 0.6861 | 0.7945 | 0.8705 | 0.9207 | 0.8272 | 0.8640 | 0.4390 | 0.9102 | 0.8252 | 0.7750 | 0.8518 | 0.6572 | 0.7439 | 0.3868 | 0.7954 | 0.6522 | 0.7155 |
| 0.3178 | 5.95 | 2200 | 0.4989 | 0.7168 | 0.8208 | 0.8843 | 0.9219 | 0.8031 | 0.8626 | 0.5303 | 0.9022 | 0.8588 | 0.8670 | 0.8654 | 0.6787 | 0.7565 | 0.4089 | 0.7958 | 0.6937 | 0.8186 |
| 0.1903 | 6.0 | 2220 | 0.5606 | 0.6787 | 0.7902 | 0.8636 | 0.9309 | 0.8103 | 0.8611 | 0.5126 | 0.9116 | 0.8291 | 0.6759 | 0.8525 | 0.6757 | 0.7541 | 0.3701 | 0.7789 | 0.6681 | 0.6514 |
| 0.2833 | 6.05 | 2240 | 0.5620 | 0.6807 | 0.7983 | 0.8644 | 0.9268 | 0.8434 | 0.8579 | 0.5070 | 0.9044 | 0.8698 | 0.6787 | 0.8547 | 0.6758 | 0.7542 | 0.3781 | 0.7802 | 0.6688 | 0.6529 |
| 0.2418 | 6.11 | 2260 | 0.5505 | 0.6805 | 0.7898 | 0.8647 | 0.9422 | 0.7859 | 0.8901 | 0.5280 | 0.8952 | 0.7832 | 0.7041 | 0.8573 | 0.6716 | 0.7501 | 0.3849 | 0.7738 | 0.6539 | 0.6721 |
| 0.2252 | 6.16 | 2280 | 0.5652 | 0.6728 | 0.7915 | 0.8613 | 0.9286 | 0.8299 | 0.8828 | 0.4556 | 0.8881 | 0.8772 | 0.6783 | 0.8577 | 0.6723 | 0.7418 | 0.3651 | 0.7720 | 0.6520 | 0.6488 |
| 0.3011 | 6.22 | 2300 | 0.5430 | 0.6779 | 0.7898 | 0.8644 | 0.9412 | 0.8307 | 0.8624 | 0.4695 | 0.8928 | 0.8345 | 0.6974 | 0.8553 | 0.6649 | 0.7418 | 0.3685 | 0.7744 | 0.6718 | 0.6684 |
| 0.2122 | 6.27 | 2320 | 0.5227 | 0.6807 | 0.7919 | 0.8644 | 0.9413 | 0.8013 | 0.8616 | 0.5139 | 0.8971 | 0.8590 | 0.6692 | 0.8619 | 0.6775 | 0.7510 | 0.3981 | 0.7753 | 0.6577 | 0.6438 |
| 0.1844 | 6.32 | 2340 | 0.5499 | 0.6706 | 0.7838 | 0.8614 | 0.9320 | 0.7995 | 0.9013 | 0.4303 | 0.8958 | 0.8736 | 0.6541 | 0.8609 | 0.6771 | 0.7307 | 0.3629 | 0.7736 | 0.6543 | 0.6349 |
| 0.2772 | 6.38 | 2360 | 0.5676 | 0.6693 | 0.7903 | 0.8585 | 0.9294 | 0.8483 | 0.8680 | 0.4617 | 0.8903 | 0.8894 | 0.6452 | 0.8541 | 0.6733 | 0.7376 | 0.3695 | 0.7694 | 0.6555 | 0.6254 |
| 0.2566 | 6.43 | 2380 | 0.6250 | 0.6619 | 0.7766 | 0.8533 | 0.9367 | 0.8065 | 0.8631 | 0.4641 | 0.8907 | 0.8596 | 0.6153 | 0.8483 | 0.6630 | 0.7507 | 0.3953 | 0.7679 | 0.6294 | 0.5787 |
| 0.3323 | 6.49 | 2400 | 0.5067 | 0.7154 | 0.8210 | 0.8836 | 0.9223 | 0.8125 | 0.9008 | 0.4915 | 0.8880 | 0.8616 | 0.8704 | 0.8611 | 0.6731 | 0.7533 | 0.4140 | 0.7975 | 0.7088 | 0.7998 |
| 0.2489 | 6.54 | 2420 | 0.5678 | 0.7036 | 0.8133 | 0.8760 | 0.9063 | 0.8346 | 0.8668 | 0.5156 | 0.9032 | 0.7811 | 0.8853 | 0.8540 | 0.6566 | 0.7593 | 0.4189 | 0.7852 | 0.6809 | 0.7702 |
| 0.2311 | 6.59 | 2440 | 0.4916 | 0.7172 | 0.8204 | 0.8842 | 0.9310 | 0.7975 | 0.8724 | 0.5405 | 0.8926 | 0.8430 | 0.8657 | 0.8610 | 0.6657 | 0.7604 | 0.4291 | 0.8003 | 0.7109 | 0.7931 |
| 0.2477 | 6.65 | 2460 | 0.5204 | 0.7035 | 0.8050 | 0.8789 | 0.9197 | 0.7999 | 0.8689 | 0.4751 | 0.9077 | 0.7987 | 0.8651 | 0.8574 | 0.6639 | 0.7600 | 0.3805 | 0.7907 | 0.6869 | 0.7853 |
| 0.1485 | 6.7 | 2480 | 0.4915 | 0.7107 | 0.8163 | 0.8813 | 0.9236 | 0.7980 | 0.8706 | 0.5387 | 0.9014 | 0.8269 | 0.8550 | 0.8622 | 0.6579 | 0.7607 | 0.4010 | 0.7895 | 0.7009 | 0.8027 |
| 0.4349 | 6.76 | 2500 | 0.5276 | 0.7002 | 0.7970 | 0.8775 | 0.9329 | 0.7774 | 0.8702 | 0.4441 | 0.8984 | 0.8198 | 0.8364 | 0.8511 | 0.6592 | 0.7567 | 0.3786 | 0.7927 | 0.6918 | 0.7714 |
| 0.3179 | 6.81 | 2520 | 0.5154 | 0.7088 | 0.8161 | 0.8808 | 0.9203 | 0.8226 | 0.8750 | 0.4807 | 0.8934 | 0.8627 | 0.8581 | 0.8562 | 0.6544 | 0.7548 | 0.3973 | 0.7935 | 0.7139 | 0.7916 |
| 0.1755 | 6.86 | 2540 | 0.5192 | 0.7066 | 0.8041 | 0.8798 | 0.9429 | 0.7978 | 0.8730 | 0.4760 | 0.8888 | 0.8054 | 0.8447 | 0.8578 | 0.6745 | 0.7443 | 0.3984 | 0.7910 | 0.6896 | 0.7907 |
| 0.3205 | 6.92 | 2560 | 0.5411 | 0.7057 | 0.8132 | 0.8799 | 0.9279 | 0.8140 | 0.8539 | 0.4790 | 0.8854 | 0.8720 | 0.8599 | 0.8584 | 0.6575 | 0.7472 | 0.3997 | 0.7957 | 0.6975 | 0.7837 |
| 0.2455 | 6.97 | 2580 | 0.5374 | 0.7049 | 0.8050 | 0.8813 | 0.9305 | 0.8131 | 0.8690 | 0.4408 | 0.8994 | 0.8181 | 0.8642 | 0.8580 | 0.6640 | 0.7500 | 0.3693 | 0.7973 | 0.7041 | 0.7917 |
| 0.1735 | 7.03 | 2600 | 0.5765 | 0.6996 | 0.8064 | 0.8777 | 0.9328 | 0.8060 | 0.8527 | 0.4451 | 0.8809 | 0.8905 | 0.8367 | 0.8585 | 0.6614 | 0.7508 | 0.3731 | 0.7923 | 0.6896 | 0.7711 |
| 0.2041 | 7.08 | 2620 | 0.5683 | 0.6950 | 0.8024 | 0.8727 | 0.9287 | 0.7972 | 0.8557 | 0.5188 | 0.8987 | 0.8203 | 0.7976 | 0.8582 | 0.6629 | 0.7510 | 0.3927 | 0.7829 | 0.6852 | 0.7325 |
| 0.8444 | 7.14 | 2640 | 0.5634 | 0.7047 | 0.8150 | 0.8789 | 0.9227 | 0.8060 | 0.8404 | 0.5489 | 0.8972 | 0.8200 | 0.8697 | 0.8613 | 0.6505 | 0.7530 | 0.3844 | 0.7876 | 0.6964 | 0.7995 |
| 0.0665 | 7.19 | 2660 | 0.6066 | 0.6905 | 0.7964 | 0.8716 | 0.9297 | 0.8205 | 0.8462 | 0.4586 | 0.8991 | 0.8252 | 0.7952 | 0.8595 | 0.6615 | 0.7441 | 0.3687 | 0.7771 | 0.6958 | 0.7269 |
| 0.2395 | 7.24 | 2680 | 0.5959 | 0.6956 | 0.8116 | 0.8710 | 0.9214 | 0.8399 | 0.8723 | 0.5090 | 0.8846 | 0.8723 | 0.7816 | 0.8600 | 0.6627 | 0.7525 | 0.3950 | 0.7708 | 0.7096 | 0.7183 |
| 0.2261 | 7.3 | 2700 | 0.6236 | 0.6986 | 0.7994 | 0.8737 | 0.9228 | 0.8204 | 0.8664 | 0.4665 | 0.9133 | 0.8226 | 0.7837 | 0.8597 | 0.6700 | 0.7553 | 0.4027 | 0.7777 | 0.7046 | 0.7206 |
| 0.2801 | 7.35 | 2720 | 0.6171 | 0.6922 | 0.7981 | 0.8709 | 0.9289 | 0.7876 | 0.8769 | 0.5005 | 0.9021 | 0.8327 | 0.7577 | 0.8623 | 0.6730 | 0.7564 | 0.3879 | 0.7792 | 0.6906 | 0.6958 |
| 0.1989 | 7.41 | 2740 | 0.6196 | 0.6891 | 0.7930 | 0.8688 | 0.9366 | 0.8017 | 0.8474 | 0.4827 | 0.8997 | 0.8451 | 0.7376 | 0.8572 | 0.6661 | 0.7588 | 0.3950 | 0.7761 | 0.6902 | 0.6806 |
| 0.1384 | 7.46 | 2760 | 0.5358 | 0.6956 | 0.7983 | 0.8731 | 0.9348 | 0.7960 | 0.8832 | 0.4893 | 0.9028 | 0.8235 | 0.7587 | 0.8634 | 0.6720 | 0.7613 | 0.4028 | 0.7837 | 0.6886 | 0.6974 |
| 0.3099 | 7.51 | 2780 | 0.5289 | 0.6903 | 0.8027 | 0.8684 | 0.9217 | 0.8230 | 0.8747 | 0.5106 | 0.8992 | 0.8399 | 0.7498 | 0.8586 | 0.6657 | 0.7409 | 0.4050 | 0.7746 | 0.6982 | 0.6892 |
| 0.2237 | 7.57 | 2800 | 0.6377 | 0.6770 | 0.7940 | 0.8621 | 0.9389 | 0.8274 | 0.9028 | 0.4578 | 0.8726 | 0.8858 | 0.6727 | 0.8530 | 0.6822 | 0.7126 | 0.3779 | 0.7717 | 0.6929 | 0.6486 |
| 0.2499 | 7.62 | 2820 | 0.6043 | 0.6864 | 0.7913 | 0.8675 | 0.9328 | 0.8219 | 0.8395 | 0.4609 | 0.9035 | 0.8488 | 0.7320 | 0.8536 | 0.6633 | 0.7578 | 0.3877 | 0.7766 | 0.6927 | 0.6732 |
| 0.1981 | 7.68 | 2840 | 0.6478 | 0.6858 | 0.7856 | 0.8661 | 0.9310 | 0.7797 | 0.8623 | 0.4875 | 0.9099 | 0.7917 | 0.7374 | 0.8521 | 0.6655 | 0.7638 | 0.3883 | 0.7693 | 0.6831 | 0.6786 |
| 1.2055 | 7.73 | 2860 | 0.5979 | 0.6911 | 0.7992 | 0.8706 | 0.9300 | 0.8253 | 0.8816 | 0.4654 | 0.8909 | 0.8219 | 0.7795 | 0.8527 | 0.6682 | 0.7587 | 0.3706 | 0.7798 | 0.6934 | 0.7141 |
| 0.177 | 7.78 | 2880 | 0.5908 | 0.6825 | 0.7887 | 0.8733 | 0.9374 | 0.8157 | 0.8902 | 0.3178 | 0.8718 | 0.8589 | 0.8291 | 0.8494 | 0.6710 | 0.7346 | 0.2802 | 0.7931 | 0.6877 | 0.7617 |
| 0.1597 | 7.84 | 2900 | 0.5456 | 0.6964 | 0.8091 | 0.8749 | 0.9183 | 0.8190 | 0.8836 | 0.4531 | 0.8842 | 0.8753 | 0.8304 | 0.8540 | 0.6721 | 0.7388 | 0.3674 | 0.7920 | 0.6876 | 0.7632 |
| 0.0895 | 7.89 | 2920 | 0.5558 | 0.7070 | 0.8120 | 0.8793 | 0.9242 | 0.8089 | 0.8605 | 0.5186 | 0.9046 | 0.8327 | 0.8346 | 0.8596 | 0.6662 | 0.7577 | 0.4105 | 0.7944 | 0.6913 | 0.7691 |
| 1.3217 | 7.95 | 2940 | 0.6992 | 0.6776 | 0.7911 | 0.8600 | 0.9214 | 0.8120 | 0.8512 | 0.5140 | 0.8996 | 0.8351 | 0.7045 | 0.8574 | 0.6686 | 0.7577 | 0.4098 | 0.7632 | 0.6431 | 0.6432 |
| 0.1388 | 8.0 | 2960 | 0.6492 | 0.6768 | 0.7845 | 0.8607 | 0.9357 | 0.8100 | 0.8588 | 0.4899 | 0.8971 | 0.8021 | 0.6977 | 0.8596 | 0.6762 | 0.7552 | 0.4046 | 0.7614 | 0.6291 | 0.6513 |
| 0.1877 | 8.05 | 2980 | 0.6229 | 0.6875 | 0.7901 | 0.8687 | 0.9413 | 0.8166 | 0.8688 | 0.4622 | 0.9071 | 0.8412 | 0.6936 | 0.8604 | 0.6896 | 0.7563 | 0.3977 | 0.7819 | 0.6606 | 0.6663 |
| 0.3116 | 8.11 | 3000 | 0.5704 | 0.6917 | 0.7952 | 0.8714 | 0.9274 | 0.8182 | 0.8547 | 0.4501 | 0.9069 | 0.8421 | 0.7672 | 0.8611 | 0.6716 | 0.7554 | 0.3837 | 0.7788 | 0.6890 | 0.7022 |
| 0.2879 | 8.16 | 3020 | 0.5835 | 0.7101 | 0.8126 | 0.8825 | 0.9291 | 0.8085 | 0.8748 | 0.4732 | 0.8971 | 0.8594 | 0.8460 | 0.8624 | 0.6764 | 0.7618 | 0.3972 | 0.8020 | 0.6937 | 0.7770 |
| 0.5737 | 8.22 | 3040 | 0.6887 | 0.6726 | 0.7863 | 0.8600 | 0.9237 | 0.8025 | 0.8875 | 0.4794 | 0.9041 | 0.8379 | 0.6691 | 0.8562 | 0.6769 | 0.7674 | 0.3815 | 0.7770 | 0.6306 | 0.6186 |
| 0.1903 | 8.27 | 3060 | 0.6567 | 0.6771 | 0.7978 | 0.8621 | 0.9350 | 0.8183 | 0.8513 | 0.5467 | 0.8830 | 0.8406 | 0.7096 | 0.8605 | 0.6709 | 0.7651 | 0.3852 | 0.7770 | 0.6328 | 0.6483 |
| 0.2059 | 8.32 | 3080 | 0.6863 | 0.6680 | 0.7786 | 0.8598 | 0.9374 | 0.8010 | 0.8678 | 0.4307 | 0.8956 | 0.8557 | 0.6621 | 0.8551 | 0.6724 | 0.7583 | 0.3706 | 0.7836 | 0.6266 | 0.6095 |
| 0.134 | 8.38 | 3100 | 0.6730 | 0.6726 | 0.7862 | 0.8615 | 0.9281 | 0.8169 | 0.8717 | 0.4374 | 0.8947 | 0.8647 | 0.6900 | 0.8577 | 0.6701 | 0.7512 | 0.3684 | 0.7750 | 0.6505 | 0.6353 |
| 0.1744 | 8.43 | 3120 | 0.6429 | 0.6826 | 0.7952 | 0.8652 | 0.9261 | 0.8063 | 0.8860 | 0.4900 | 0.8978 | 0.8551 | 0.7051 | 0.8608 | 0.6784 | 0.7613 | 0.3879 | 0.7774 | 0.6671 | 0.6451 |
| 0.4322 | 8.49 | 3140 | 0.5696 | 0.7130 | 0.8214 | 0.8841 | 0.9220 | 0.8150 | 0.8894 | 0.5038 | 0.8975 | 0.8703 | 0.8518 | 0.8658 | 0.6691 | 0.7633 | 0.4034 | 0.8054 | 0.7030 | 0.7812 |
| 0.2194 | 8.54 | 3160 | 0.5893 | 0.7189 | 0.8264 | 0.8856 | 0.9297 | 0.8216 | 0.8633 | 0.5246 | 0.8828 | 0.8793 | 0.8839 | 0.8673 | 0.6742 | 0.7623 | 0.4170 | 0.7991 | 0.7138 | 0.7984 |
| 0.1172 | 8.59 | 3180 | 0.5640 | 0.7158 | 0.8153 | 0.8856 | 0.9243 | 0.7961 | 0.8757 | 0.4922 | 0.9074 | 0.8395 | 0.8720 | 0.8658 | 0.6747 | 0.7642 | 0.4004 | 0.8027 | 0.7061 | 0.7968 |
| 0.1652 | 8.65 | 3200 | 0.5842 | 0.6962 | 0.8040 | 0.8712 | 0.9302 | 0.8237 | 0.8609 | 0.5237 | 0.9023 | 0.8380 | 0.7494 | 0.8603 | 0.6589 | 0.7763 | 0.4185 | 0.7767 | 0.6953 | 0.6875 |
| 0.4658 | 8.7 | 3220 | 0.6697 | 0.6758 | 0.7783 | 0.8636 | 0.9338 | 0.8064 | 0.8823 | 0.4319 | 0.9122 | 0.7787 | 0.7028 | 0.8632 | 0.6699 | 0.7520 | 0.3790 | 0.7664 | 0.6411 | 0.6587 |
| 0.0752 | 8.76 | 3240 | 0.6035 | 0.7060 | 0.8125 | 0.8813 | 0.9218 | 0.8450 | 0.8659 | 0.4448 | 0.9000 | 0.8520 | 0.8579 | 0.8620 | 0.6622 | 0.7580 | 0.3709 | 0.7931 | 0.7034 | 0.7926 |
| 0.3356 | 8.81 | 3260 | 0.5770 | 0.7120 | 0.8166 | 0.8844 | 0.9343 | 0.8245 | 0.8798 | 0.4381 | 0.8787 | 0.8943 | 0.8664 | 0.8636 | 0.6692 | 0.7580 | 0.3873 | 0.7999 | 0.7092 | 0.7968 |
| 0.1724 | 8.86 | 3280 | 0.5592 | 0.7136 | 0.8141 | 0.8845 | 0.9307 | 0.8032 | 0.8869 | 0.4732 | 0.8946 | 0.8451 | 0.8653 | 0.8623 | 0.6718 | 0.7598 | 0.3997 | 0.8022 | 0.7091 | 0.7905 |
| 0.158 | 8.92 | 3300 | 0.5446 | 0.7085 | 0.8110 | 0.8828 | 0.9316 | 0.8088 | 0.8938 | 0.4891 | 0.9008 | 0.7943 | 0.8588 | 0.8628 | 0.6714 | 0.7629 | 0.3823 | 0.8021 | 0.6885 | 0.7896 |
| 0.2343 | 8.97 | 3320 | 0.5785 | 0.7095 | 0.8132 | 0.8813 | 0.9304 | 0.8096 | 0.8762 | 0.4783 | 0.8901 | 0.8654 | 0.8425 | 0.8621 | 0.6748 | 0.7569 | 0.4024 | 0.7963 | 0.7013 | 0.7725 |
| 0.2144 | 9.03 | 3340 | 0.5870 | 0.7055 | 0.8064 | 0.8802 | 0.9282 | 0.8202 | 0.8745 | 0.4448 | 0.9035 | 0.8486 | 0.8252 | 0.8576 | 0.6765 | 0.7567 | 0.3876 | 0.8005 | 0.7014 | 0.7579 |
| 0.1379 | 9.08 | 3360 | 0.5864 | 0.7083 | 0.8106 | 0.8813 | 0.9288 | 0.8241 | 0.8696 | 0.4435 | 0.8968 | 0.8834 | 0.8281 | 0.8592 | 0.6748 | 0.7617 | 0.4023 | 0.8025 | 0.6979 | 0.7600 |
| 0.335 | 9.14 | 3380 | 0.6072 | 0.6871 | 0.7925 | 0.8707 | 0.9314 | 0.8154 | 0.8720 | 0.4397 | 0.9062 | 0.8387 | 0.7444 | 0.8533 | 0.6684 | 0.7545 | 0.3940 | 0.7999 | 0.6551 | 0.6847 |
| 0.1489 | 9.19 | 3400 | 0.5636 | 0.7076 | 0.8125 | 0.8803 | 0.9247 | 0.8143 | 0.8720 | 0.4925 | 0.8996 | 0.8409 | 0.8434 | 0.8571 | 0.6642 | 0.7563 | 0.4017 | 0.7987 | 0.7074 | 0.7683 |
| 0.1118 | 9.24 | 3420 | 0.5699 | 0.7112 | 0.8188 | 0.8824 | 0.9313 | 0.7996 | 0.8835 | 0.4937 | 0.8760 | 0.8831 | 0.8648 | 0.8635 | 0.6695 | 0.7542 | 0.3980 | 0.7961 | 0.7081 | 0.7886 |
| 0.2613 | 9.3 | 3440 | 0.5491 | 0.7160 | 0.8154 | 0.8853 | 0.9373 | 0.7939 | 0.8859 | 0.5014 | 0.8951 | 0.8444 | 0.8495 | 0.8663 | 0.6746 | 0.7597 | 0.4109 | 0.8012 | 0.7032 | 0.7961 |
| 0.1139 | 9.35 | 3460 | 0.5690 | 0.7072 | 0.8052 | 0.8839 | 0.9280 | 0.7892 | 0.8610 | 0.4264 | 0.9060 | 0.8652 | 0.8606 | 0.8616 | 0.6554 | 0.7589 | 0.3670 | 0.8032 | 0.7104 | 0.7943 |
| 0.128 | 9.41 | 3480 | 0.5414 | 0.7183 | 0.8197 | 0.8861 | 0.9360 | 0.7843 | 0.8709 | 0.5102 | 0.8850 | 0.8804 | 0.8710 | 0.8629 | 0.6689 | 0.7694 | 0.4104 | 0.8038 | 0.7141 | 0.7983 |
| 0.187 | 9.46 | 3500 | 0.5634 | 0.7112 | 0.8119 | 0.8827 | 0.9281 | 0.8073 | 0.8583 | 0.5098 | 0.9113 | 0.8225 | 0.8460 | 0.8624 | 0.6713 | 0.7650 | 0.4111 | 0.8040 | 0.6927 | 0.7722 |
| 0.1191 | 9.51 | 3520 | 0.5511 | 0.7118 | 0.8156 | 0.8833 | 0.9328 | 0.8120 | 0.8616 | 0.4826 | 0.8900 | 0.8745 | 0.8556 | 0.8633 | 0.6729 | 0.7560 | 0.3983 | 0.8014 | 0.7061 | 0.7843 |
| 0.4297 | 9.57 | 3540 | 0.5802 | 0.7006 | 0.7967 | 0.8801 | 0.9276 | 0.8095 | 0.8714 | 0.4200 | 0.9130 | 0.7672 | 0.8685 | 0.8612 | 0.6770 | 0.7381 | 0.3544 | 0.7939 | 0.6826 | 0.7970 |
| 0.2728 | 9.62 | 3560 | 0.5485 | 0.7178 | 0.8280 | 0.8843 | 0.9207 | 0.8378 | 0.8643 | 0.5383 | 0.8932 | 0.8698 | 0.8720 | 0.8608 | 0.6697 | 0.7499 | 0.4192 | 0.8001 | 0.7244 | 0.8006 |
| 0.5411 | 9.68 | 3580 | 0.6300 | 0.7000 | 0.8024 | 0.8732 | 0.9223 | 0.7995 | 0.8569 | 0.5270 | 0.9113 | 0.7974 | 0.8021 | 0.8620 | 0.6733 | 0.7490 | 0.4075 | 0.7735 | 0.7035 | 0.7310 |
| 0.2359 | 9.73 | 3600 | 0.5608 | 0.6990 | 0.8111 | 0.8740 | 0.9251 | 0.8178 | 0.8712 | 0.5368 | 0.8979 | 0.8466 | 0.7821 | 0.8635 | 0.6726 | 0.7488 | 0.4263 | 0.7908 | 0.6774 | 0.7137 |
| 0.1735 | 9.78 | 3620 | 0.5859 | 0.6998 | 0.8132 | 0.8744 | 0.9253 | 0.8187 | 0.8722 | 0.5364 | 0.8913 | 0.8545 | 0.7939 | 0.8603 | 0.6693 | 0.7457 | 0.4366 | 0.7965 | 0.6678 | 0.7221 |
| 0.1954 | 9.84 | 3640 | 0.6579 | 0.6788 | 0.7805 | 0.8646 | 0.9368 | 0.7730 | 0.8630 | 0.4399 | 0.9025 | 0.8462 | 0.7018 | 0.8585 | 0.6737 | 0.7425 | 0.3960 | 0.7802 | 0.6567 | 0.6437 |
| 0.2474 | 9.89 | 3660 | 0.5547 | 0.7076 | 0.8115 | 0.8793 | 0.9328 | 0.8057 | 0.8560 | 0.5065 | 0.8919 | 0.8565 | 0.8314 | 0.8576 | 0.6795 | 0.7413 | 0.4163 | 0.8001 | 0.7013 | 0.7571 |
| 0.2478 | 9.95 | 3680 | 0.5778 | 0.6993 | 0.8030 | 0.8745 | 0.9296 | 0.8235 | 0.8403 | 0.4924 | 0.9045 | 0.8343 | 0.7963 | 0.8558 | 0.6762 | 0.7462 | 0.3948 | 0.7867 | 0.7038 | 0.7318 |
| 0.1857 | 10.0 | 3700 | 0.5824 | 0.6996 | 0.8034 | 0.8754 | 0.9331 | 0.8167 | 0.8510 | 0.4584 | 0.8921 | 0.8765 | 0.7962 | 0.8572 | 0.6699 | 0.7504 | 0.3932 | 0.7864 | 0.7060 | 0.7339 |
| 0.1102 | 10.05 | 3720 | 0.6500 | 0.6879 | 0.7825 | 0.8689 | 0.9331 | 0.7378 | 0.8607 | 0.4543 | 0.9084 | 0.8347 | 0.7483 | 0.8571 | 0.6603 | 0.7488 | 0.3909 | 0.7729 | 0.6937 | 0.6916 |
| 0.1427 | 10.11 | 3740 | 0.5802 | 0.7147 | 0.8234 | 0.8816 | 0.9328 | 0.7885 | 0.8885 | 0.5626 | 0.8761 | 0.8680 | 0.8475 | 0.8632 | 0.6756 | 0.7543 | 0.4283 | 0.7925 | 0.7077 | 0.7810 |
| 0.1652 | 10.16 | 3760 | 0.5950 | 0.7155 | 0.8094 | 0.8857 | 0.9367 | 0.7904 | 0.8237 | 0.4910 | 0.9086 | 0.8436 | 0.8716 | 0.8638 | 0.6698 | 0.7525 | 0.4112 | 0.8042 | 0.7072 | 0.8000 |
| 0.1775 | 10.22 | 3780 | 0.5329 | 0.7040 | 0.8063 | 0.8783 | 0.9256 | 0.8012 | 0.8744 | 0.4887 | 0.9103 | 0.8444 | 0.7995 | 0.8641 | 0.6837 | 0.7596 | 0.4166 | 0.7994 | 0.6632 | 0.7417 |
| 0.171 | 10.27 | 3800 | 0.5276 | 0.7048 | 0.8099 | 0.8780 | 0.9434 | 0.7878 | 0.8639 | 0.5423 | 0.8881 | 0.8459 | 0.7977 | 0.8636 | 0.6780 | 0.7581 | 0.4305 | 0.7999 | 0.6630 | 0.7404 |
| 0.1445 | 10.32 | 3820 | 0.5358 | 0.7005 | 0.8175 | 0.8752 | 0.9294 | 0.8102 | 0.8479 | 0.5871 | 0.8890 | 0.8576 | 0.8013 | 0.8623 | 0.6748 | 0.7650 | 0.4096 | 0.7985 | 0.6626 | 0.7310 |
| 0.164 | 10.38 | 3840 | 0.5509 | 0.7138 | 0.8202 | 0.8830 | 0.9317 | 0.8165 | 0.8737 | 0.5448 | 0.8952 | 0.8290 | 0.8504 | 0.8646 | 0.6748 | 0.7633 | 0.4240 | 0.8023 | 0.6916 | 0.7759 |
| 0.172 | 10.43 | 3860 | 0.5750 | 0.7131 | 0.8161 | 0.8833 | 0.9307 | 0.7920 | 0.8881 | 0.5110 | 0.8915 | 0.8344 | 0.8648 | 0.8634 | 0.6728 | 0.7570 | 0.3961 | 0.7966 | 0.7096 | 0.7961 |
| 0.9783 | 10.49 | 3880 | 0.6087 | 0.7004 | 0.8098 | 0.8772 | 0.9175 | 0.8388 | 0.8794 | 0.4381 | 0.8962 | 0.8719 | 0.8264 | 0.8535 | 0.6610 | 0.7513 | 0.3832 | 0.7953 | 0.6982 | 0.7601 |
| 0.1288 | 10.54 | 3900 | 0.5240 | 0.7195 | 0.8230 | 0.8853 | 0.9195 | 0.8076 | 0.8856 | 0.5266 | 0.9002 | 0.8372 | 0.8842 | 0.8612 | 0.6755 | 0.7584 | 0.4267 | 0.8032 | 0.7134 | 0.7981 |
| 0.1404 | 10.59 | 3920 | 0.5173 | 0.7066 | 0.8156 | 0.8777 | 0.9208 | 0.8130 | 0.8600 | 0.5573 | 0.9068 | 0.8372 | 0.8142 | 0.8564 | 0.6665 | 0.7663 | 0.4185 | 0.7969 | 0.6999 | 0.7418 |
| 0.2075 | 10.65 | 3940 | 0.5605 | 0.7054 | 0.8160 | 0.8770 | 0.9290 | 0.8152 | 0.8608 | 0.5407 | 0.8912 | 0.8797 | 0.7959 | 0.8569 | 0.6722 | 0.7550 | 0.4321 | 0.8001 | 0.6952 | 0.7262 |
| 0.0764 | 10.7 | 3960 | 0.5575 | 0.7052 | 0.8058 | 0.8787 | 0.9405 | 0.8024 | 0.8748 | 0.4783 | 0.8943 | 0.8612 | 0.7891 | 0.8576 | 0.6738 | 0.7555 | 0.4185 | 0.8013 | 0.7004 | 0.7294 |
| 0.1381 | 10.76 | 3980 | 0.6212 | 0.6932 | 0.7998 | 0.8703 | 0.9258 | 0.8209 | 0.8836 | 0.4909 | 0.9076 | 0.8328 | 0.7371 | 0.8563 | 0.6728 | 0.7648 | 0.4115 | 0.7841 | 0.6864 | 0.6766 |
| 0.1283 | 10.81 | 4000 | 0.5661 | 0.6961 | 0.8044 | 0.8710 | 0.9393 | 0.8002 | 0.8737 | 0.5322 | 0.8879 | 0.8644 | 0.7328 | 0.8558 | 0.6722 | 0.7631 | 0.4355 | 0.7882 | 0.6814 | 0.6768 |
| 0.1478 | 10.86 | 4020 | 0.5682 | 0.7079 | 0.8085 | 0.8811 | 0.9397 | 0.8064 | 0.8768 | 0.4765 | 0.8955 | 0.8540 | 0.8108 | 0.8590 | 0.6730 | 0.7612 | 0.4063 | 0.8041 | 0.6991 | 0.7529 |
| 0.2952 | 10.92 | 4040 | 0.5359 | 0.7250 | 0.8293 | 0.8888 | 0.9325 | 0.8091 | 0.8560 | 0.5720 | 0.8976 | 0.8615 | 0.8764 | 0.8724 | 0.6781 | 0.7756 | 0.4162 | 0.8019 | 0.7216 | 0.8091 |
| 0.5411 | 10.97 | 4060 | 0.5361 | 0.7190 | 0.8235 | 0.8858 | 0.9223 | 0.8110 | 0.8714 | 0.5594 | 0.9099 | 0.8197 | 0.8711 | 0.8685 | 0.6779 | 0.7689 | 0.4029 | 0.7984 | 0.7110 | 0.8057 |
| 0.2301 | 11.03 | 4080 | 0.5714 | 0.7126 | 0.8216 | 0.8823 | 0.9264 | 0.8299 | 0.8798 | 0.5285 | 0.8972 | 0.8570 | 0.8323 | 0.8606 | 0.6722 | 0.7591 | 0.4231 | 0.8066 | 0.7008 | 0.7656 |
| 0.1374 | 11.08 | 4100 | 0.6145 | 0.6982 | 0.8072 | 0.8744 | 0.9313 | 0.8243 | 0.8528 | 0.5064 | 0.8976 | 0.8603 | 0.7777 | 0.8588 | 0.6655 | 0.7586 | 0.4031 | 0.7903 | 0.6957 | 0.7157 |
| 0.0696 | 11.14 | 4120 | 0.6681 | 0.6889 | 0.7887 | 0.8713 | 0.9370 | 0.7977 | 0.8783 | 0.4220 | 0.9029 | 0.8375 | 0.7457 | 0.8601 | 0.6768 | 0.7550 | 0.3695 | 0.7831 | 0.6903 | 0.6872 |
| 0.1682 | 11.19 | 4140 | 0.6977 | 0.6822 | 0.7875 | 0.8675 | 0.9324 | 0.8060 | 0.8618 | 0.4379 | 0.9069 | 0.8523 | 0.7155 | 0.8623 | 0.6758 | 0.7521 | 0.3759 | 0.7823 | 0.6688 | 0.6581 |
| 0.094 | 11.24 | 4160 | 0.6576 | 0.6958 | 0.8044 | 0.8724 | 0.9350 | 0.8088 | 0.8627 | 0.5259 | 0.8956 | 0.8432 | 0.7598 | 0.8656 | 0.6738 | 0.7526 | 0.4046 | 0.7805 | 0.6950 | 0.6984 |
| 0.1509 | 11.3 | 4180 | 0.6880 | 0.6966 | 0.8021 | 0.8720 | 0.9248 | 0.8019 | 0.8660 | 0.5110 | 0.9091 | 0.8516 | 0.7502 | 0.8669 | 0.6753 | 0.7664 | 0.4210 | 0.7793 | 0.6824 | 0.6852 |
| 0.2837 | 11.35 | 4200 | 0.6709 | 0.6965 | 0.8002 | 0.8720 | 0.9336 | 0.7865 | 0.8636 | 0.5164 | 0.9019 | 0.8536 | 0.7456 | 0.8683 | 0.6763 | 0.7670 | 0.4232 | 0.7782 | 0.6790 | 0.6838 |
| 0.1695 | 11.41 | 4220 | 0.6810 | 0.6960 | 0.8041 | 0.8721 | 0.9325 | 0.8124 | 0.8749 | 0.5153 | 0.8988 | 0.8507 | 0.7444 | 0.8695 | 0.6771 | 0.7619 | 0.4223 | 0.7803 | 0.6784 | 0.6828 |
| 0.1717 | 11.46 | 4240 | 0.6574 | 0.6890 | 0.8055 | 0.8690 | 0.9305 | 0.8075 | 0.8640 | 0.5508 | 0.8933 | 0.8580 | 0.7346 | 0.8650 | 0.6748 | 0.7546 | 0.4148 | 0.7894 | 0.6507 | 0.6737 |
| 0.2947 | 11.51 | 4260 | 0.6866 | 0.6883 | 0.7972 | 0.8703 | 0.9338 | 0.8128 | 0.8784 | 0.4646 | 0.8942 | 0.8533 | 0.7435 | 0.8638 | 0.6768 | 0.7540 | 0.3961 | 0.7886 | 0.6586 | 0.6802 |
| 0.1125 | 11.57 | 4280 | 0.6372 | 0.6874 | 0.7962 | 0.8695 | 0.9286 | 0.7964 | 0.8834 | 0.4716 | 0.9004 | 0.8624 | 0.7303 | 0.8649 | 0.6792 | 0.7534 | 0.3993 | 0.7891 | 0.6568 | 0.6689 |
| 0.2224 | 11.62 | 4300 | 0.6711 | 0.6870 | 0.8007 | 0.8668 | 0.9369 | 0.7884 | 0.8636 | 0.5381 | 0.8829 | 0.8759 | 0.7193 | 0.8609 | 0.6738 | 0.7665 | 0.4119 | 0.7823 | 0.6553 | 0.6586 |
| 0.1141 | 11.68 | 4320 | 0.6735 | 0.6876 | 0.7964 | 0.8681 | 0.9282 | 0.7929 | 0.8708 | 0.5022 | 0.8997 | 0.8422 | 0.7391 | 0.8626 | 0.6761 | 0.7528 | 0.3917 | 0.7769 | 0.6745 | 0.6788 |
| 0.1375 | 11.73 | 4340 | 0.6837 | 0.6863 | 0.7983 | 0.8666 | 0.9258 | 0.8016 | 0.8788 | 0.5143 | 0.8961 | 0.8359 | 0.7355 | 0.8606 | 0.6774 | 0.7453 | 0.4008 | 0.7761 | 0.6699 | 0.6742 |
| 0.169 | 11.78 | 4360 | 0.6585 | 0.6925 | 0.7968 | 0.8712 | 0.9429 | 0.8017 | 0.8572 | 0.5097 | 0.9070 | 0.8599 | 0.6992 | 0.8650 | 0.6872 | 0.7562 | 0.4293 | 0.7933 | 0.6490 | 0.6676 |
| 0.1052 | 11.84 | 4380 | 0.6623 | 0.6951 | 0.8108 | 0.8711 | 0.9314 | 0.8442 | 0.8677 | 0.5472 | 0.9004 | 0.8746 | 0.7099 | 0.8670 | 0.6841 | 0.7572 | 0.4333 | 0.7878 | 0.6607 | 0.6754 |
| 0.2518 | 11.89 | 4400 | 0.6526 | 0.6826 | 0.7901 | 0.8648 | 0.9440 | 0.8438 | 0.8729 | 0.4834 | 0.9067 | 0.8347 | 0.6452 | 0.8557 | 0.6809 | 0.7546 | 0.4202 | 0.7815 | 0.6648 | 0.6201 |
| 0.1708 | 11.95 | 4420 | 0.6365 | 0.6918 | 0.8027 | 0.8692 | 0.9400 | 0.8431 | 0.8710 | 0.5182 | 0.8936 | 0.8393 | 0.7140 | 0.8647 | 0.6812 | 0.7509 | 0.4215 | 0.7762 | 0.6746 | 0.6736 |
| 0.1352 | 12.0 | 4440 | 0.5239 | 0.7166 | 0.8241 | 0.8839 | 0.9382 | 0.8144 | 0.8535 | 0.5460 | 0.8817 | 0.8874 | 0.8472 | 0.8663 | 0.6748 | 0.7577 | 0.4402 | 0.8035 | 0.6969 | 0.7769 |
| 0.2215 | 12.05 | 4460 | 0.5723 | 0.7126 | 0.8105 | 0.8812 | 0.9360 | 0.8086 | 0.8765 | 0.5050 | 0.9054 | 0.8426 | 0.7994 | 0.8729 | 0.6912 | 0.7601 | 0.4197 | 0.7850 | 0.7110 | 0.7482 |
| 0.1835 | 12.11 | 4480 | 0.5951 | 0.7038 | 0.8079 | 0.8764 | 0.9286 | 0.8200 | 0.8768 | 0.4997 | 0.9033 | 0.8435 | 0.7837 | 0.8646 | 0.6764 | 0.7581 | 0.4137 | 0.7838 | 0.7070 | 0.7228 |
| 0.2029 | 12.16 | 4500 | 0.6254 | 0.7049 | 0.8123 | 0.8756 | 0.9360 | 0.8250 | 0.8460 | 0.5341 | 0.8887 | 0.8671 | 0.7890 | 0.8630 | 0.6719 | 0.7603 | 0.4181 | 0.7786 | 0.7167 | 0.7254 |
| 0.1549 | 12.22 | 4520 | 0.6314 | 0.7073 | 0.8139 | 0.8782 | 0.9258 | 0.7967 | 0.8786 | 0.5177 | 0.8901 | 0.8628 | 0.8257 | 0.8652 | 0.6710 | 0.7511 | 0.4073 | 0.7822 | 0.7200 | 0.7542 |
| 0.2682 | 12.27 | 4540 | 0.6696 | 0.7040 | 0.8131 | 0.8745 | 0.9259 | 0.8000 | 0.8625 | 0.5390 | 0.8867 | 0.8774 | 0.8001 | 0.8650 | 0.6781 | 0.7544 | 0.4102 | 0.7729 | 0.7174 | 0.7298 |
| 0.1751 | 12.32 | 4560 | 0.6386 | 0.7053 | 0.8165 | 0.8751 | 0.9265 | 0.8053 | 0.8722 | 0.5559 | 0.8859 | 0.8757 | 0.7936 | 0.8665 | 0.6761 | 0.7562 | 0.4201 | 0.7745 | 0.7174 | 0.7264 |
| 0.0681 | 12.38 | 4580 | 0.6112 | 0.7075 | 0.8127 | 0.8770 | 0.9252 | 0.8008 | 0.8658 | 0.5365 | 0.9002 | 0.8601 | 0.8001 | 0.8671 | 0.6788 | 0.7623 | 0.4154 | 0.7788 | 0.7181 | 0.7324 |
| 0.1016 | 12.43 | 4600 | 0.6245 | 0.7053 | 0.8111 | 0.8769 | 0.9251 | 0.8187 | 0.8781 | 0.5013 | 0.8986 | 0.8562 | 0.7999 | 0.8659 | 0.6752 | 0.7600 | 0.4042 | 0.7804 | 0.7226 | 0.7289 |
| 0.1233 | 12.49 | 4620 | 0.6009 | 0.7065 | 0.8072 | 0.8787 | 0.9320 | 0.8111 | 0.8669 | 0.4872 | 0.9005 | 0.8360 | 0.8165 | 0.8679 | 0.6797 | 0.7621 | 0.3988 | 0.7836 | 0.7122 | 0.7408 |
| 0.2694 | 12.54 | 4640 | 0.6410 | 0.7066 | 0.8082 | 0.8787 | 0.9336 | 0.8113 | 0.8681 | 0.4848 | 0.8956 | 0.8512 | 0.8130 | 0.8656 | 0.6820 | 0.7604 | 0.3959 | 0.7846 | 0.7101 | 0.7476 |
| 0.167 | 12.59 | 4660 | 0.6926 | 0.6951 | 0.7996 | 0.8719 | 0.9271 | 0.8188 | 0.8598 | 0.4841 | 0.9079 | 0.8389 | 0.7609 | 0.8642 | 0.6753 | 0.7625 | 0.4034 | 0.7781 | 0.6853 | 0.6971 |
| 0.33 | 12.65 | 4680 | 0.6355 | 0.7048 | 0.8086 | 0.8789 | 0.9300 | 0.8155 | 0.8647 | 0.4891 | 0.9046 | 0.8525 | 0.8039 | 0.8676 | 0.6775 | 0.7614 | 0.4098 | 0.7950 | 0.6840 | 0.7382 |
| 0.1355 | 12.7 | 4700 | 0.5896 | 0.7248 | 0.8201 | 0.8894 | 0.9348 | 0.8012 | 0.8679 | 0.4971 | 0.9002 | 0.8607 | 0.8787 | 0.8669 | 0.6813 | 0.7594 | 0.4294 | 0.8087 | 0.7242 | 0.8034 |
| 0.2499 | 12.76 | 4720 | 0.5623 | 0.7264 | 0.8232 | 0.8898 | 0.9282 | 0.8038 | 0.8683 | 0.5214 | 0.9097 | 0.8563 | 0.8750 | 0.8676 | 0.6758 | 0.7634 | 0.4414 | 0.8102 | 0.7230 | 0.8030 |
| 0.1029 | 12.81 | 4740 | 0.6712 | 0.7012 | 0.8100 | 0.8733 | 0.9299 | 0.7999 | 0.8721 | 0.5470 | 0.8969 | 0.8772 | 0.7470 | 0.8675 | 0.6750 | 0.7622 | 0.4465 | 0.7826 | 0.6886 | 0.6863 |
| 0.1231 | 12.86 | 4760 | 0.7289 | 0.6920 | 0.8010 | 0.8693 | 0.9323 | 0.7921 | 0.8824 | 0.5139 | 0.8931 | 0.8699 | 0.7231 | 0.8641 | 0.6757 | 0.7559 | 0.4293 | 0.7811 | 0.6739 | 0.6644 |
| 0.2009 | 12.92 | 4780 | 0.7378 | 0.6887 | 0.7979 | 0.8672 | 0.9382 | 0.8301 | 0.8803 | 0.5180 | 0.9033 | 0.8375 | 0.6782 | 0.8617 | 0.6827 | 0.7636 | 0.4322 | 0.7816 | 0.6604 | 0.6385 |
| 0.1391 | 12.97 | 4800 | 0.6546 | 0.6991 | 0.7956 | 0.8739 | 0.9334 | 0.7969 | 0.8673 | 0.4618 | 0.9095 | 0.8435 | 0.7570 | 0.8628 | 0.6840 | 0.7659 | 0.4076 | 0.7813 | 0.6990 | 0.6933 |
| 0.1298 | 13.03 | 4820 | 0.6830 | 0.6940 | 0.7989 | 0.8709 | 0.9401 | 0.8336 | 0.8854 | 0.4483 | 0.8887 | 0.8719 | 0.7243 | 0.8627 | 0.6936 | 0.7557 | 0.3974 | 0.7755 | 0.6930 | 0.6803 |
| 0.1843 | 13.08 | 4840 | 0.7086 | 0.7003 | 0.8017 | 0.8742 | 0.9299 | 0.8174 | 0.8665 | 0.4689 | 0.9055 | 0.8676 | 0.7561 | 0.8648 | 0.6865 | 0.7617 | 0.4069 | 0.7793 | 0.7020 | 0.7011 |
| 0.1717 | 13.14 | 4860 | 0.7067 | 0.6857 | 0.7925 | 0.8671 | 0.9345 | 0.8209 | 0.8780 | 0.4611 | 0.8993 | 0.8443 | 0.7094 | 0.8618 | 0.6842 | 0.7541 | 0.4024 | 0.7795 | 0.6640 | 0.6537 |
| 0.1198 | 13.19 | 4880 | 0.6974 | 0.6874 | 0.7955 | 0.8660 | 0.9389 | 0.8013 | 0.8552 | 0.5258 | 0.8956 | 0.8557 | 0.6961 | 0.8599 | 0.6824 | 0.7611 | 0.4257 | 0.7790 | 0.6610 | 0.6424 |
| 0.3224 | 13.24 | 4900 | 0.7392 | 0.6852 | 0.7930 | 0.8664 | 0.9267 | 0.8058 | 0.8745 | 0.4755 | 0.9041 | 0.8543 | 0.7103 | 0.8625 | 0.6817 | 0.7580 | 0.4026 | 0.7775 | 0.6629 | 0.6512 |
| 0.0703 | 13.3 | 4920 | 0.6311 | 0.6933 | 0.8022 | 0.8709 | 0.9335 | 0.8123 | 0.8901 | 0.4878 | 0.8906 | 0.8626 | 0.7387 | 0.8660 | 0.6798 | 0.7560 | 0.4106 | 0.7802 | 0.6826 | 0.6782 |
| 0.2431 | 13.35 | 4940 | 0.6244 | 0.6827 | 0.7976 | 0.8660 | 0.9368 | 0.8237 | 0.8843 | 0.4969 | 0.8942 | 0.8755 | 0.6720 | 0.8667 | 0.6838 | 0.7580 | 0.4172 | 0.7888 | 0.6334 | 0.6315 |
| 0.2872 | 13.41 | 4960 | 0.6453 | 0.6839 | 0.8007 | 0.8661 | 0.9319 | 0.8085 | 0.8936 | 0.5322 | 0.8962 | 0.8619 | 0.6804 | 0.8669 | 0.6813 | 0.7582 | 0.4252 | 0.7899 | 0.6283 | 0.6373 |
| 0.1403 | 13.46 | 4980 | 0.6293 | 0.6834 | 0.8007 | 0.8648 | 0.9383 | 0.7919 | 0.8843 | 0.5445 | 0.8806 | 0.8801 | 0.6849 | 0.8623 | 0.6767 | 0.7656 | 0.4277 | 0.7883 | 0.6326 | 0.6307 |
| 1.0722 | 13.51 | 5000 | 0.6654 | 0.6859 | 0.7976 | 0.8675 | 0.9344 | 0.8211 | 0.8760 | 0.4980 | 0.8967 | 0.8460 | 0.7110 | 0.8603 | 0.6754 | 0.7626 | 0.4067 | 0.7873 | 0.6587 | 0.6504 |
| 0.1002 | 13.57 | 5020 | 0.7546 | 0.6814 | 0.7922 | 0.8656 | 0.9375 | 0.8112 | 0.8670 | 0.4721 | 0.8929 | 0.8712 | 0.6935 | 0.8587 | 0.6765 | 0.7573 | 0.3976 | 0.7869 | 0.6532 | 0.6392 |
| 0.1098 | 13.62 | 5040 | 0.7212 | 0.6966 | 0.8019 | 0.8733 | 0.9264 | 0.8044 | 0.8714 | 0.5056 | 0.9134 | 0.8447 | 0.7474 | 0.8680 | 0.6820 | 0.7642 | 0.4191 | 0.7892 | 0.6665 | 0.6874 |
| 0.2066 | 13.68 | 5060 | 0.6863 | 0.6966 | 0.8103 | 0.8722 | 0.9330 | 0.7999 | 0.8691 | 0.5697 | 0.8934 | 0.8613 | 0.7454 | 0.8675 | 0.6773 | 0.7620 | 0.4325 | 0.7898 | 0.6612 | 0.6862 |
| 0.1899 | 13.73 | 5080 | 0.6502 | 0.6979 | 0.8049 | 0.8732 | 0.9322 | 0.7924 | 0.8634 | 0.5399 | 0.9034 | 0.8523 | 0.7504 | 0.8681 | 0.6759 | 0.7628 | 0.4264 | 0.7855 | 0.6760 | 0.6909 |
| 0.1979 | 13.78 | 5100 | 0.7042 | 0.6964 | 0.8077 | 0.8715 | 0.9303 | 0.8046 | 0.8646 | 0.5369 | 0.8919 | 0.8739 | 0.7514 | 0.8670 | 0.6755 | 0.7604 | 0.4293 | 0.7819 | 0.6746 | 0.6859 |
| 0.101 | 13.84 | 5120 | 0.6623 | 0.6886 | 0.8036 | 0.8679 | 0.9312 | 0.8124 | 0.8591 | 0.5298 | 0.8907 | 0.8811 | 0.7211 | 0.8646 | 0.6761 | 0.7561 | 0.4335 | 0.7892 | 0.6432 | 0.6575 |
| 0.2066 | 13.89 | 5140 | 0.6422 | 0.6874 | 0.7907 | 0.8690 | 0.9376 | 0.7826 | 0.8576 | 0.4964 | 0.9090 | 0.8384 | 0.7133 | 0.8647 | 0.6808 | 0.7606 | 0.4180 | 0.7917 | 0.6439 | 0.6519 |
| 0.0987 | 13.95 | 5160 | 0.6607 | 0.6876 | 0.7963 | 0.8682 | 0.9312 | 0.8007 | 0.8677 | 0.4967 | 0.9030 | 0.8649 | 0.7095 | 0.8636 | 0.6820 | 0.7596 | 0.4225 | 0.7914 | 0.6458 | 0.6484 |
| 0.1414 | 14.0 | 5180 | 0.6363 | 0.6908 | 0.8042 | 0.8691 | 0.9303 | 0.8263 | 0.8735 | 0.5228 | 0.8961 | 0.8547 | 0.7257 | 0.8644 | 0.6784 | 0.7596 | 0.4294 | 0.7875 | 0.6550 | 0.6616 |
| 0.0547 | 14.05 | 5200 | 0.6666 | 0.6897 | 0.7980 | 0.8699 | 0.9303 | 0.8190 | 0.8796 | 0.4665 | 0.9009 | 0.8681 | 0.7216 | 0.8671 | 0.6806 | 0.7619 | 0.4074 | 0.7841 | 0.6626 | 0.6640 |
| 1.5168 | 14.11 | 5220 | 0.6171 | 0.7057 | 0.8126 | 0.8766 | 0.9272 | 0.8151 | 0.8745 | 0.5186 | 0.8966 | 0.8696 | 0.7865 | 0.8692 | 0.6786 | 0.7633 | 0.4333 | 0.7839 | 0.6968 | 0.7147 |
| 0.1673 | 14.16 | 5240 | 0.6295 | 0.6897 | 0.8097 | 0.8675 | 0.9364 | 0.8264 | 0.8935 | 0.5600 | 0.8800 | 0.8731 | 0.6989 | 0.8651 | 0.6819 | 0.7547 | 0.4471 | 0.7880 | 0.6358 | 0.6552 |
| 0.1493 | 14.22 | 5260 | 0.6006 | 0.6862 | 0.7948 | 0.8689 | 0.9385 | 0.8056 | 0.8756 | 0.4804 | 0.8975 | 0.8563 | 0.7097 | 0.8645 | 0.6757 | 0.7584 | 0.4147 | 0.7924 | 0.6424 | 0.6556 |
| 0.0582 | 14.27 | 5280 | 0.5851 | 0.7049 | 0.8050 | 0.8796 | 0.9356 | 0.8082 | 0.8594 | 0.4799 | 0.9075 | 0.8467 | 0.7975 | 0.8590 | 0.6705 | 0.7559 | 0.4123 | 0.8048 | 0.6970 | 0.7349 |
| 0.114 | 14.32 | 5300 | 0.5824 | 0.7205 | 0.8271 | 0.8869 | 0.9228 | 0.8209 | 0.8661 | 0.5326 | 0.9004 | 0.8765 | 0.8705 | 0.8676 | 0.6727 | 0.7602 | 0.4219 | 0.8059 | 0.7183 | 0.7972 |
| 0.1722 | 14.38 | 5320 | 0.6211 | 0.7146 | 0.8113 | 0.8857 | 0.9353 | 0.7938 | 0.8697 | 0.4707 | 0.9037 | 0.8576 | 0.8484 | 0.8671 | 0.6808 | 0.7568 | 0.4033 | 0.8081 | 0.7090 | 0.7771 |
| 0.0931 | 14.43 | 5340 | 0.6304 | 0.7134 | 0.8145 | 0.8833 | 0.9351 | 0.7942 | 0.8683 | 0.5071 | 0.8974 | 0.8728 | 0.8267 | 0.8641 | 0.6812 | 0.7580 | 0.4198 | 0.8058 | 0.7037 | 0.7615 |
| 0.1883 | 14.49 | 5360 | 0.6069 | 0.7148 | 0.8192 | 0.8832 | 0.9305 | 0.8042 | 0.8807 | 0.5439 | 0.9015 | 0.8448 | 0.8290 | 0.8628 | 0.6787 | 0.7639 | 0.4244 | 0.8060 | 0.7053 | 0.7625 |
| 0.1474 | 14.54 | 5380 | 0.6856 | 0.6956 | 0.8127 | 0.8697 | 0.9282 | 0.8139 | 0.8909 | 0.5721 | 0.8863 | 0.8652 | 0.7324 | 0.8595 | 0.6753 | 0.7588 | 0.4309 | 0.7811 | 0.6875 | 0.6764 |
| 0.1547 | 14.59 | 5400 | 0.7256 | 0.6997 | 0.8039 | 0.8721 | 0.9318 | 0.7957 | 0.8634 | 0.5218 | 0.8981 | 0.8713 | 0.7451 | 0.8622 | 0.6808 | 0.7673 | 0.4271 | 0.7774 | 0.6980 | 0.6855 |
| 0.1503 | 14.65 | 5420 | 0.7249 | 0.6986 | 0.8017 | 0.8730 | 0.9313 | 0.8156 | 0.8755 | 0.4876 | 0.9032 | 0.8462 | 0.7522 | 0.8629 | 0.6729 | 0.7658 | 0.4166 | 0.7789 | 0.7031 | 0.6896 |
| 0.3028 | 14.7 | 5440 | 0.7174 | 0.6935 | 0.8058 | 0.8697 | 0.9339 | 0.8206 | 0.8687 | 0.4987 | 0.8781 | 0.8941 | 0.7467 | 0.8642 | 0.6760 | 0.7566 | 0.4069 | 0.7706 | 0.6926 | 0.6874 |
| 0.161 | 14.76 | 5460 | 0.6711 | 0.6985 | 0.8020 | 0.8726 | 0.9362 | 0.8113 | 0.8586 | 0.4919 | 0.8954 | 0.8701 | 0.7504 | 0.8628 | 0.6783 | 0.7575 | 0.4173 | 0.7783 | 0.7059 | 0.6895 |
| 0.1155 | 14.81 | 5480 | 0.6929 | 0.7092 | 0.8132 | 0.8781 | 0.9297 | 0.8222 | 0.8547 | 0.5190 | 0.8997 | 0.8725 | 0.7948 | 0.8689 | 0.6824 | 0.7599 | 0.4231 | 0.7809 | 0.7219 | 0.7272 |
| 0.1656 | 14.86 | 5500 | 0.6374 | 0.7092 | 0.8154 | 0.8779 | 0.9309 | 0.8140 | 0.8666 | 0.5398 | 0.8948 | 0.8678 | 0.7936 | 0.8690 | 0.6801 | 0.7609 | 0.4255 | 0.7808 | 0.7206 | 0.7276 |
| 1.1364 | 14.92 | 5520 | 0.6663 | 0.7064 | 0.8114 | 0.8770 | 0.9361 | 0.8201 | 0.8679 | 0.5215 | 0.8919 | 0.8507 | 0.7917 | 0.8670 | 0.6756 | 0.7590 | 0.4192 | 0.7799 | 0.7176 | 0.7264 |
| 0.1626 | 14.97 | 5540 | 0.6779 | 0.7092 | 0.8143 | 0.8782 | 0.9350 | 0.8199 | 0.8622 | 0.5283 | 0.8937 | 0.8722 | 0.7886 | 0.8688 | 0.6778 | 0.7671 | 0.4223 | 0.7807 | 0.7226 | 0.7254 |
| 0.2601 | 15.03 | 5560 | 0.6393 | 0.7047 | 0.8080 | 0.8766 | 0.9358 | 0.8112 | 0.8806 | 0.5048 | 0.8977 | 0.8597 | 0.7664 | 0.8673 | 0.6785 | 0.7661 | 0.4178 | 0.7828 | 0.7132 | 0.7075 |
| 0.188 | 15.08 | 5580 | 0.6080 | 0.7148 | 0.8125 | 0.8833 | 0.9362 | 0.8070 | 0.8606 | 0.5002 | 0.9046 | 0.8576 | 0.8209 | 0.8707 | 0.6787 | 0.7652 | 0.4151 | 0.7921 | 0.7264 | 0.7551 |
| 0.1492 | 15.14 | 5600 | 0.6940 | 0.7016 | 0.7966 | 0.8765 | 0.9364 | 0.8041 | 0.8777 | 0.4570 | 0.9117 | 0.8137 | 0.7754 | 0.8678 | 0.6812 | 0.7633 | 0.4006 | 0.7813 | 0.7049 | 0.7123 |
| 0.1306 | 15.19 | 5620 | 0.7043 | 0.7040 | 0.8105 | 0.8753 | 0.9296 | 0.8196 | 0.8822 | 0.5192 | 0.8983 | 0.8576 | 0.7668 | 0.8675 | 0.6825 | 0.7640 | 0.4176 | 0.7791 | 0.7123 | 0.7048 |
| 0.0968 | 15.24 | 5640 | 0.7197 | 0.6986 | 0.8082 | 0.8724 | 0.9329 | 0.8196 | 0.8689 | 0.5488 | 0.9001 | 0.8462 | 0.7406 | 0.8669 | 0.6779 | 0.7663 | 0.4198 | 0.7789 | 0.6983 | 0.6819 |
| 0.1836 | 15.3 | 5660 | 0.7781 | 0.7031 | 0.8063 | 0.8744 | 0.9356 | 0.8022 | 0.8591 | 0.5408 | 0.9025 | 0.8474 | 0.7565 | 0.8657 | 0.6758 | 0.7670 | 0.4257 | 0.7782 | 0.7116 | 0.6975 |
| 0.0926 | 15.35 | 5680 | 0.7479 | 0.7083 | 0.8136 | 0.8773 | 0.9327 | 0.7990 | 0.8648 | 0.5458 | 0.8933 | 0.8697 | 0.7897 | 0.8696 | 0.6749 | 0.7679 | 0.4261 | 0.7771 | 0.7169 | 0.7259 |
| 0.1173 | 15.41 | 5700 | 0.7123 | 0.7082 | 0.8120 | 0.8772 | 0.9352 | 0.8126 | 0.8648 | 0.5350 | 0.8968 | 0.8624 | 0.7771 | 0.8663 | 0.6769 | 0.7659 | 0.4329 | 0.7819 | 0.7198 | 0.7139 |
| 0.1977 | 15.46 | 5720 | 0.6526 | 0.7084 | 0.8191 | 0.8770 | 0.9274 | 0.8162 | 0.8681 | 0.5519 | 0.8888 | 0.8902 | 0.7913 | 0.8688 | 0.6758 | 0.7655 | 0.4325 | 0.7797 | 0.7113 | 0.7254 |
| 0.096 | 15.51 | 5740 | 0.6237 | 0.7109 | 0.8066 | 0.8798 | 0.9396 | 0.7830 | 0.8635 | 0.4962 | 0.8994 | 0.8761 | 0.7881 | 0.8659 | 0.6775 | 0.7693 | 0.4289 | 0.7880 | 0.7227 | 0.7240 |
| 0.1514 | 15.57 | 5760 | 0.6790 | 0.7061 | 0.8115 | 0.8763 | 0.9263 | 0.8342 | 0.8704 | 0.5164 | 0.9070 | 0.8550 | 0.7710 | 0.8656 | 0.6743 | 0.7730 | 0.4308 | 0.7834 | 0.7113 | 0.7043 |
| 0.0453 | 15.62 | 5780 | 0.6741 | 0.7045 | 0.8091 | 0.8758 | 0.9345 | 0.8207 | 0.8796 | 0.5021 | 0.8957 | 0.8724 | 0.7586 | 0.8654 | 0.6798 | 0.7735 | 0.4289 | 0.7846 | 0.7038 | 0.6956 |
| 0.1224 | 15.68 | 5800 | 0.7243 | 0.6900 | 0.7975 | 0.8688 | 0.9333 | 0.7996 | 0.8867 | 0.4814 | 0.8940 | 0.8740 | 0.7132 | 0.8647 | 0.6823 | 0.7650 | 0.4163 | 0.7812 | 0.6668 | 0.6540 |
| 0.0927 | 15.73 | 5820 | 0.7237 | 0.6903 | 0.7969 | 0.8689 | 0.9342 | 0.8029 | 0.8813 | 0.4867 | 0.8985 | 0.8654 | 0.7091 | 0.8633 | 0.6819 | 0.7656 | 0.4204 | 0.7846 | 0.6672 | 0.6490 |
| 0.1154 | 15.78 | 5840 | 0.6878 | 0.6922 | 0.8016 | 0.8690 | 0.9342 | 0.7988 | 0.8671 | 0.5313 | 0.8968 | 0.8735 | 0.7098 | 0.8639 | 0.6824 | 0.7651 | 0.4400 | 0.7879 | 0.6582 | 0.6482 |
| 0.1408 | 15.84 | 5860 | 0.6410 | 0.6894 | 0.8008 | 0.8677 | 0.9385 | 0.7996 | 0.8818 | 0.5320 | 0.8902 | 0.8676 | 0.6960 | 0.8616 | 0.6827 | 0.7591 | 0.4409 | 0.7905 | 0.6497 | 0.6416 |
| 0.1614 | 15.89 | 5880 | 0.6993 | 0.6902 | 0.7988 | 0.8685 | 0.9335 | 0.8126 | 0.8511 | 0.5153 | 0.9032 | 0.8700 | 0.7061 | 0.8627 | 0.6823 | 0.7575 | 0.4358 | 0.7892 | 0.6553 | 0.6485 |
| 0.2869 | 15.95 | 5900 | 0.7689 | 0.6905 | 0.8024 | 0.8678 | 0.9301 | 0.8079 | 0.8756 | 0.5322 | 0.8979 | 0.8709 | 0.7022 | 0.8620 | 0.6833 | 0.7620 | 0.4354 | 0.7858 | 0.6595 | 0.6458 |
| 0.139 | 16.0 | 5920 | 0.7812 | 0.6913 | 0.7976 | 0.8688 | 0.9289 | 0.7985 | 0.8763 | 0.4996 | 0.9057 | 0.8696 | 0.7049 | 0.8636 | 0.6856 | 0.7627 | 0.4295 | 0.7846 | 0.6648 | 0.6482 |
| 0.0939 | 16.05 | 5940 | 0.7038 | 0.6927 | 0.8014 | 0.8701 | 0.9300 | 0.8017 | 0.8775 | 0.5124 | 0.9019 | 0.8705 | 0.7161 | 0.8671 | 0.6822 | 0.7667 | 0.4266 | 0.7854 | 0.6648 | 0.6564 |
| 0.1643 | 16.11 | 5960 | 0.7743 | 0.6922 | 0.8015 | 0.8688 | 0.9334 | 0.8147 | 0.8787 | 0.5176 | 0.8993 | 0.8662 | 0.7003 | 0.8635 | 0.6791 | 0.7618 | 0.4341 | 0.7815 | 0.6791 | 0.6466 |
| 0.1276 | 16.16 | 5980 | 0.7730 | 0.7013 | 0.8108 | 0.8735 | 0.9302 | 0.8330 | 0.8632 | 0.5387 | 0.9018 | 0.8648 | 0.7436 | 0.8659 | 0.6750 | 0.7678 | 0.4333 | 0.7818 | 0.7032 | 0.6824 |
| 0.5234 | 16.22 | 6000 | 0.7781 | 0.7015 | 0.8034 | 0.8743 | 0.9338 | 0.7904 | 0.8730 | 0.4974 | 0.8964 | 0.8727 | 0.7602 | 0.8650 | 0.6780 | 0.7626 | 0.4227 | 0.7802 | 0.7066 | 0.6952 |
| 0.153 | 16.27 | 6020 | 0.7155 | 0.7071 | 0.8094 | 0.8771 | 0.9313 | 0.8078 | 0.8710 | 0.5146 | 0.9031 | 0.8660 | 0.7716 | 0.8679 | 0.6791 | 0.7667 | 0.4346 | 0.7844 | 0.7103 | 0.7067 |
| 0.0918 | 16.32 | 6040 | 0.7164 | 0.7049 | 0.8093 | 0.8758 | 0.9283 | 0.8091 | 0.8751 | 0.5270 | 0.9064 | 0.8570 | 0.7621 | 0.8673 | 0.6792 | 0.7667 | 0.4297 | 0.7834 | 0.7079 | 0.6999 |
| 0.0636 | 16.38 | 6060 | 0.8310 | 0.6895 | 0.7941 | 0.8673 | 0.9382 | 0.7911 | 0.8637 | 0.5198 | 0.9035 | 0.8511 | 0.6912 | 0.8553 | 0.6711 | 0.7610 | 0.4239 | 0.7807 | 0.6951 | 0.6397 |
| 0.15 | 16.43 | 6080 | 0.6919 | 0.7024 | 0.8098 | 0.8751 | 0.9349 | 0.8071 | 0.8785 | 0.5362 | 0.8946 | 0.8544 | 0.7629 | 0.8661 | 0.6761 | 0.7635 | 0.4254 | 0.7848 | 0.6994 | 0.7014 |
| 0.1397 | 16.49 | 6100 | 0.7529 | 0.6905 | 0.7951 | 0.8702 | 0.9370 | 0.7902 | 0.8655 | 0.5048 | 0.9066 | 0.8432 | 0.7181 | 0.8666 | 0.6776 | 0.7660 | 0.4135 | 0.7859 | 0.6617 | 0.6622 |
| 0.0939 | 16.54 | 6120 | 0.8069 | 0.6875 | 0.7908 | 0.8694 | 0.9296 | 0.7984 | 0.8659 | 0.4716 | 0.9179 | 0.8401 | 0.7122 | 0.8665 | 0.6795 | 0.7671 | 0.4010 | 0.7860 | 0.6569 | 0.6553 |
| 0.6179 | 16.59 | 6140 | 0.7314 | 0.6876 | 0.7951 | 0.8700 | 0.9324 | 0.8198 | 0.8660 | 0.4583 | 0.9070 | 0.8694 | 0.7130 | 0.8660 | 0.6785 | 0.7674 | 0.4001 | 0.7918 | 0.6544 | 0.6551 |
| 0.1015 | 16.65 | 6160 | 0.7299 | 0.6887 | 0.8009 | 0.8696 | 0.9314 | 0.8189 | 0.8649 | 0.4977 | 0.8981 | 0.8685 | 0.7263 | 0.8675 | 0.6794 | 0.7623 | 0.4013 | 0.7864 | 0.6576 | 0.6667 |
| 0.0863 | 16.7 | 6180 | 0.6746 | 0.6980 | 0.8068 | 0.8737 | 0.9299 | 0.8224 | 0.8665 | 0.5073 | 0.9009 | 0.8643 | 0.7562 | 0.8688 | 0.6769 | 0.7616 | 0.4054 | 0.7817 | 0.6951 | 0.6965 |
| 0.1738 | 16.76 | 6200 | 0.6060 | 0.7123 | 0.8164 | 0.8813 | 0.9310 | 0.8146 | 0.8844 | 0.5084 | 0.8948 | 0.8641 | 0.8174 | 0.8694 | 0.6797 | 0.7624 | 0.4084 | 0.7880 | 0.7294 | 0.7487 |
| 0.2009 | 16.81 | 6220 | 0.6513 | 0.7078 | 0.8044 | 0.8800 | 0.9336 | 0.8120 | 0.8611 | 0.4601 | 0.9109 | 0.8631 | 0.7903 | 0.8692 | 0.6798 | 0.7673 | 0.4001 | 0.7875 | 0.7233 | 0.7272 |
| 2.6765 | 16.86 | 6240 | 0.7115 | 0.7013 | 0.7960 | 0.8786 | 0.9366 | 0.7898 | 0.8870 | 0.4108 | 0.9047 | 0.8551 | 0.7879 | 0.8672 | 0.6791 | 0.7635 | 0.3691 | 0.7866 | 0.7177 | 0.7257 |
| 0.0662 | 16.92 | 6260 | 0.6028 | 0.7107 | 0.8123 | 0.8808 | 0.9364 | 0.8211 | 0.8591 | 0.5004 | 0.8994 | 0.8649 | 0.8050 | 0.8684 | 0.6741 | 0.7666 | 0.4136 | 0.7891 | 0.7246 | 0.7387 |
| 0.1372 | 16.97 | 6280 | 0.6318 | 0.7080 | 0.8142 | 0.8778 | 0.9304 | 0.8114 | 0.8771 | 0.5371 | 0.8978 | 0.8582 | 0.7878 | 0.8656 | 0.6774 | 0.7649 | 0.4222 | 0.7862 | 0.7159 | 0.7238 |
| 0.0964 | 17.03 | 6300 | 0.6379 | 0.7076 | 0.8136 | 0.8780 | 0.9301 | 0.8130 | 0.8666 | 0.5375 | 0.9036 | 0.8663 | 0.7778 | 0.8669 | 0.6728 | 0.7716 | 0.4315 | 0.7900 | 0.7053 | 0.7149 |
| 0.1279 | 17.08 | 6320 | 0.6366 | 0.7197 | 0.8239 | 0.8860 | 0.9330 | 0.7997 | 0.8757 | 0.5572 | 0.8977 | 0.8582 | 0.8459 | 0.8667 | 0.6734 | 0.7659 | 0.4390 | 0.8092 | 0.7100 | 0.7736 |
| 0.0769 | 17.14 | 6340 | 0.6694 | 0.7059 | 0.8111 | 0.8761 | 0.9289 | 0.8014 | 0.8739 | 0.5607 | 0.9071 | 0.8316 | 0.7741 | 0.8661 | 0.6741 | 0.7654 | 0.4358 | 0.7845 | 0.7054 | 0.7097 |
| 0.2735 | 17.19 | 6360 | 0.6678 | 0.7025 | 0.8049 | 0.8746 | 0.9355 | 0.7992 | 0.8800 | 0.5124 | 0.9002 | 0.8593 | 0.7478 | 0.8642 | 0.6781 | 0.7625 | 0.4280 | 0.7832 | 0.7133 | 0.6881 |
| 1.1427 | 17.24 | 6380 | 0.7853 | 0.6887 | 0.7947 | 0.8684 | 0.9392 | 0.7967 | 0.8571 | 0.5001 | 0.9002 | 0.8685 | 0.7011 | 0.8631 | 0.6789 | 0.7582 | 0.4184 | 0.7845 | 0.6719 | 0.6461 |
| 0.1354 | 17.3 | 6400 | 0.7422 | 0.6895 | 0.8006 | 0.8688 | 0.9318 | 0.7965 | 0.8712 | 0.5213 | 0.8984 | 0.8735 | 0.7115 | 0.8640 | 0.6803 | 0.7600 | 0.4258 | 0.7916 | 0.6523 | 0.6523 |
| 0.077 | 17.35 | 6420 | 0.7529 | 0.6904 | 0.8037 | 0.8689 | 0.9321 | 0.8140 | 0.8624 | 0.5433 | 0.8997 | 0.8618 | 0.7123 | 0.8635 | 0.6777 | 0.7629 | 0.4356 | 0.7927 | 0.6489 | 0.6514 |
| 0.1084 | 17.41 | 6440 | 0.7545 | 0.6923 | 0.8042 | 0.8697 | 0.9293 | 0.8130 | 0.8644 | 0.5499 | 0.9059 | 0.8473 | 0.7197 | 0.8643 | 0.6784 | 0.7672 | 0.4362 | 0.7916 | 0.6503 | 0.6579 |
| 0.2807 | 17.46 | 6460 | 0.7531 | 0.6949 | 0.8013 | 0.8712 | 0.9289 | 0.7926 | 0.8760 | 0.5274 | 0.9083 | 0.8466 | 0.7293 | 0.8654 | 0.6809 | 0.7682 | 0.4268 | 0.7867 | 0.6682 | 0.6680 |
| 0.0654 | 17.51 | 6480 | 0.6718 | 0.7027 | 0.8091 | 0.8760 | 0.9327 | 0.7899 | 0.8828 | 0.5345 | 0.8987 | 0.8595 | 0.7658 | 0.8670 | 0.6807 | 0.7666 | 0.4298 | 0.7942 | 0.6800 | 0.7005 |
| 0.1048 | 17.57 | 6500 | 0.6738 | 0.6983 | 0.8118 | 0.8732 | 0.9331 | 0.8010 | 0.8705 | 0.5639 | 0.8910 | 0.8709 | 0.7522 | 0.8674 | 0.6803 | 0.7619 | 0.4304 | 0.7917 | 0.6662 | 0.6904 |
| 0.381 | 17.62 | 6520 | 0.7038 | 0.6981 | 0.8061 | 0.8734 | 0.9287 | 0.7951 | 0.8677 | 0.5456 | 0.9080 | 0.8519 | 0.7457 | 0.8688 | 0.6800 | 0.7643 | 0.4347 | 0.7919 | 0.6650 | 0.6817 |
| 0.1314 | 17.68 | 6540 | 0.6728 | 0.6980 | 0.8106 | 0.8735 | 0.9289 | 0.8133 | 0.8745 | 0.5446 | 0.8990 | 0.8648 | 0.7494 | 0.8688 | 0.6785 | 0.7631 | 0.4341 | 0.7936 | 0.6643 | 0.6838 |
| 0.1491 | 17.73 | 6560 | 0.6671 | 0.6977 | 0.8085 | 0.8734 | 0.9352 | 0.7997 | 0.8798 | 0.5403 | 0.8912 | 0.8621 | 0.7515 | 0.8674 | 0.6751 | 0.7670 | 0.4256 | 0.7896 | 0.6707 | 0.6883 |
| 0.1503 | 17.78 | 6580 | 0.6852 | 0.7015 | 0.8107 | 0.8753 | 0.9295 | 0.8117 | 0.8676 | 0.5280 | 0.8995 | 0.8754 | 0.7631 | 0.8684 | 0.6735 | 0.7691 | 0.4218 | 0.7877 | 0.6913 | 0.6990 |
| 0.1663 | 17.84 | 6600 | 0.7299 | 0.6919 | 0.7991 | 0.8716 | 0.9317 | 0.8027 | 0.8827 | 0.4864 | 0.9037 | 0.8546 | 0.7319 | 0.8664 | 0.6721 | 0.7591 | 0.4080 | 0.7874 | 0.6781 | 0.6723 |
| 0.3482 | 17.89 | 6620 | 0.7122 | 0.6898 | 0.8034 | 0.8694 | 0.9314 | 0.8109 | 0.8731 | 0.5298 | 0.8994 | 0.8683 | 0.7107 | 0.8663 | 0.6715 | 0.7638 | 0.4228 | 0.7885 | 0.6630 | 0.6530 |
| 0.1922 | 17.95 | 6640 | 0.6779 | 0.7056 | 0.8092 | 0.8776 | 0.9321 | 0.8065 | 0.8784 | 0.5085 | 0.9022 | 0.8615 | 0.7749 | 0.8682 | 0.6726 | 0.7645 | 0.4258 | 0.7881 | 0.7115 | 0.7086 |
| 0.1884 | 18.0 | 6660 | 0.6645 | 0.7198 | 0.8220 | 0.8866 | 0.9359 | 0.8106 | 0.8779 | 0.5209 | 0.8947 | 0.8665 | 0.8474 | 0.8658 | 0.6723 | 0.7630 | 0.4309 | 0.8081 | 0.7190 | 0.7798 |
| 0.1226 | 18.05 | 6680 | 0.6551 | 0.7183 | 0.8152 | 0.8869 | 0.9319 | 0.7956 | 0.8877 | 0.4796 | 0.9040 | 0.8558 | 0.8520 | 0.8658 | 0.6740 | 0.7626 | 0.4160 | 0.8080 | 0.7200 | 0.7819 |
| 0.0977 | 18.11 | 6700 | 0.6134 | 0.7066 | 0.8160 | 0.8774 | 0.9320 | 0.8233 | 0.8675 | 0.5421 | 0.8964 | 0.8781 | 0.7726 | 0.8663 | 0.6676 | 0.7659 | 0.4381 | 0.7908 | 0.7092 | 0.7088 |
| 0.2765 | 18.16 | 6720 | 0.6325 | 0.7017 | 0.8073 | 0.8742 | 0.9319 | 0.8028 | 0.8757 | 0.5204 | 0.8995 | 0.8725 | 0.7479 | 0.8629 | 0.6725 | 0.7594 | 0.4320 | 0.7855 | 0.7126 | 0.6869 |
| 0.1163 | 18.22 | 6740 | 0.6932 | 0.6974 | 0.8006 | 0.8713 | 0.9360 | 0.7784 | 0.8745 | 0.5415 | 0.9007 | 0.8373 | 0.7357 | 0.8616 | 0.6728 | 0.7599 | 0.4262 | 0.7782 | 0.7096 | 0.6736 |
| 0.1935 | 18.27 | 6760 | 0.6326 | 0.7070 | 0.8147 | 0.8771 | 0.9289 | 0.8066 | 0.8637 | 0.5510 | 0.8988 | 0.8701 | 0.7836 | 0.8613 | 0.6730 | 0.7613 | 0.4291 | 0.7910 | 0.7158 | 0.7178 |
| 0.1554 | 18.32 | 6780 | 0.6887 | 0.6979 | 0.8034 | 0.8722 | 0.9319 | 0.8002 | 0.8747 | 0.5152 | 0.9026 | 0.8717 | 0.7276 | 0.8616 | 0.6731 | 0.7622 | 0.4303 | 0.7850 | 0.7034 | 0.6696 |
| 0.2316 | 18.38 | 6800 | 0.6220 | 0.7078 | 0.8151 | 0.8768 | 0.9300 | 0.8078 | 0.8783 | 0.5549 | 0.8989 | 0.8694 | 0.7664 | 0.8657 | 0.6770 | 0.7652 | 0.4435 | 0.7876 | 0.7139 | 0.7019 |
| 0.1733 | 18.43 | 6820 | 0.6711 | 0.7142 | 0.8159 | 0.8810 | 0.9347 | 0.8079 | 0.8685 | 0.5294 | 0.8971 | 0.8690 | 0.8044 | 0.8676 | 0.6785 | 0.7619 | 0.4403 | 0.7905 | 0.7253 | 0.7351 |
| 0.1224 | 18.49 | 6840 | 0.6410 | 0.7059 | 0.8082 | 0.8768 | 0.9400 | 0.7931 | 0.8775 | 0.5145 | 0.8898 | 0.8697 | 0.7730 | 0.8596 | 0.6733 | 0.7573 | 0.4333 | 0.7906 | 0.7160 | 0.7113 |
| 0.1923 | 18.54 | 6860 | 0.6620 | 0.7044 | 0.8150 | 0.8766 | 0.9239 | 0.8152 | 0.8822 | 0.5231 | 0.8951 | 0.8793 | 0.7865 | 0.8678 | 0.6766 | 0.7616 | 0.4224 | 0.7881 | 0.6969 | 0.7173 |
| 0.1202 | 18.59 | 6880 | 0.7112 | 0.7034 | 0.8076 | 0.8763 | 0.9339 | 0.7943 | 0.8704 | 0.5197 | 0.8999 | 0.8672 | 0.7676 | 0.8687 | 0.6784 | 0.7652 | 0.4216 | 0.7872 | 0.6982 | 0.7043 |
| 0.1458 | 18.65 | 6900 | 0.6784 | 0.7092 | 0.8119 | 0.8786 | 0.9271 | 0.7945 | 0.8805 | 0.5373 | 0.9076 | 0.8470 | 0.7893 | 0.8697 | 0.6761 | 0.7665 | 0.4236 | 0.7841 | 0.7202 | 0.7240 |
| 0.081 | 18.7 | 6920 | 0.6600 | 0.7079 | 0.8181 | 0.8779 | 0.9298 | 0.8092 | 0.8810 | 0.5475 | 0.8891 | 0.8767 | 0.7935 | 0.8696 | 0.6776 | 0.7622 | 0.4296 | 0.7868 | 0.7054 | 0.7242 |
| 0.0973 | 18.76 | 6940 | 0.7119 | 0.7080 | 0.8144 | 0.8779 | 0.9292 | 0.8136 | 0.8725 | 0.5428 | 0.9021 | 0.8567 | 0.7840 | 0.8687 | 0.6758 | 0.7623 | 0.4331 | 0.7867 | 0.7101 | 0.7194 |
| 0.1824 | 18.81 | 6960 | 0.6751 | 0.6980 | 0.8063 | 0.8733 | 0.9315 | 0.7980 | 0.8794 | 0.5285 | 0.9009 | 0.8668 | 0.7388 | 0.8677 | 0.6747 | 0.7631 | 0.4375 | 0.7902 | 0.6740 | 0.6789 |
| 0.0786 | 18.86 | 6980 | 0.7423 | 0.6991 | 0.8076 | 0.8737 | 0.9306 | 0.8071 | 0.8693 | 0.5382 | 0.9039 | 0.8581 | 0.7459 | 0.8679 | 0.6727 | 0.7617 | 0.4393 | 0.7888 | 0.6784 | 0.6849 |
| 0.1518 | 18.92 | 7000 | 0.7237 | 0.6994 | 0.8069 | 0.8740 | 0.9324 | 0.8023 | 0.8753 | 0.5306 | 0.9024 | 0.8620 | 0.7431 | 0.8668 | 0.6725 | 0.7641 | 0.4475 | 0.7942 | 0.6693 | 0.6812 |
| 0.1411 | 18.97 | 7020 | 0.7966 | 0.6968 | 0.8090 | 0.8717 | 0.9326 | 0.7977 | 0.8739 | 0.5625 | 0.8947 | 0.8694 | 0.7322 | 0.8662 | 0.6712 | 0.7617 | 0.4444 | 0.7873 | 0.6734 | 0.6735 |
| 0.1319 | 19.03 | 7040 | 0.7241 | 0.6968 | 0.8087 | 0.8718 | 0.9274 | 0.8138 | 0.8690 | 0.5488 | 0.9029 | 0.8643 | 0.7344 | 0.8664 | 0.6683 | 0.7650 | 0.4403 | 0.7857 | 0.6774 | 0.6747 |
| 0.1517 | 19.08 | 7060 | 0.7034 | 0.6989 | 0.8102 | 0.8732 | 0.9294 | 0.7966 | 0.8792 | 0.5567 | 0.8970 | 0.8627 | 0.7498 | 0.8672 | 0.6726 | 0.7639 | 0.4376 | 0.7882 | 0.6766 | 0.6861 |
| 0.1236 | 19.14 | 7080 | 0.7254 | 0.6991 | 0.8084 | 0.8739 | 0.9297 | 0.8008 | 0.8771 | 0.5359 | 0.9011 | 0.8675 | 0.7469 | 0.8683 | 0.6720 | 0.7652 | 0.4412 | 0.7914 | 0.6715 | 0.6842 |
| 0.1812 | 19.19 | 7100 | 0.7489 | 0.6955 | 0.8048 | 0.8719 | 0.9290 | 0.7970 | 0.8700 | 0.5319 | 0.9039 | 0.8690 | 0.7328 | 0.8667 | 0.6728 | 0.7615 | 0.4412 | 0.7917 | 0.6621 | 0.6726 |
| 0.1011 | 19.24 | 7120 | 0.7168 | 0.6946 | 0.8074 | 0.8715 | 0.9343 | 0.8158 | 0.8696 | 0.5449 | 0.8974 | 0.8649 | 0.7248 | 0.8652 | 0.6718 | 0.7659 | 0.4412 | 0.7946 | 0.6574 | 0.6660 |
| 0.1236 | 19.3 | 7140 | 0.7489 | 0.6930 | 0.8047 | 0.8707 | 0.9311 | 0.8141 | 0.8826 | 0.5385 | 0.9044 | 0.8466 | 0.7155 | 0.8652 | 0.6714 | 0.7678 | 0.4405 | 0.7930 | 0.6560 | 0.6573 |
| 0.087 | 19.35 | 7160 | 0.7990 | 0.6947 | 0.8051 | 0.8714 | 0.9292 | 0.8114 | 0.8772 | 0.5299 | 0.9048 | 0.8617 | 0.7217 | 0.8665 | 0.6744 | 0.7675 | 0.4385 | 0.7908 | 0.6620 | 0.6632 |
| 0.0746 | 19.41 | 7180 | 0.7367 | 0.6960 | 0.8054 | 0.8719 | 0.9284 | 0.8010 | 0.8743 | 0.5300 | 0.9044 | 0.8740 | 0.7257 | 0.8673 | 0.6767 | 0.7663 | 0.4423 | 0.7916 | 0.6616 | 0.6662 |
| 0.1389 | 19.46 | 7200 | 0.8094 | 0.6963 | 0.8058 | 0.8721 | 0.9297 | 0.8056 | 0.8665 | 0.5318 | 0.9038 | 0.8733 | 0.7296 | 0.8664 | 0.6738 | 0.7654 | 0.4449 | 0.7929 | 0.6611 | 0.6693 |
| 0.1128 | 19.51 | 7220 | 0.7732 | 0.6950 | 0.8047 | 0.8718 | 0.9341 | 0.8098 | 0.8604 | 0.5435 | 0.9039 | 0.8530 | 0.7285 | 0.8648 | 0.6725 | 0.7671 | 0.4374 | 0.7931 | 0.6613 | 0.6688 |
| 0.0658 | 19.57 | 7240 | 0.7832 | 0.6946 | 0.8020 | 0.8713 | 0.9326 | 0.7877 | 0.8758 | 0.5404 | 0.9049 | 0.8448 | 0.7278 | 0.8652 | 0.6743 | 0.7660 | 0.4331 | 0.7886 | 0.6652 | 0.6697 |
| 0.1991 | 19.62 | 7260 | 0.7704 | 0.6952 | 0.8050 | 0.8718 | 0.9329 | 0.8080 | 0.8705 | 0.5356 | 0.9022 | 0.8591 | 0.7266 | 0.8655 | 0.6748 | 0.7656 | 0.4362 | 0.7917 | 0.6646 | 0.6680 |
| 0.0798 | 19.68 | 7280 | 0.7074 | 0.6966 | 0.8108 | 0.8723 | 0.9286 | 0.8124 | 0.8734 | 0.5565 | 0.8983 | 0.8681 | 0.7383 | 0.8671 | 0.6754 | 0.7664 | 0.4350 | 0.7925 | 0.6647 | 0.6748 |
| 0.2001 | 19.73 | 7300 | 0.6872 | 0.7027 | 0.8135 | 0.8747 | 0.9288 | 0.8011 | 0.8833 | 0.5605 | 0.8961 | 0.8659 | 0.7586 | 0.8658 | 0.6776 | 0.7633 | 0.4302 | 0.7871 | 0.6996 | 0.6953 |
| 0.1103 | 19.78 | 7320 | 0.7072 | 0.7050 | 0.8092 | 0.8756 | 0.9345 | 0.7969 | 0.8615 | 0.5538 | 0.9030 | 0.8568 | 0.7582 | 0.8635 | 0.6759 | 0.7655 | 0.4312 | 0.7858 | 0.7144 | 0.6984 |
| 1.6601 | 19.84 | 7340 | 0.7160 | 0.7033 | 0.8113 | 0.8748 | 0.9334 | 0.7963 | 0.8750 | 0.5583 | 0.8968 | 0.8674 | 0.7517 | 0.8640 | 0.6758 | 0.7652 | 0.4315 | 0.7862 | 0.7071 | 0.6933 |
| 1.7574 | 19.89 | 7360 | 0.7489 | 0.6998 | 0.8093 | 0.8728 | 0.9346 | 0.8027 | 0.8794 | 0.5533 | 0.8950 | 0.8676 | 0.7327 | 0.8615 | 0.6738 | 0.7658 | 0.4333 | 0.7860 | 0.7013 | 0.6772 |
| 0.09 | 19.95 | 7380 | 0.6577 | 0.7101 | 0.8147 | 0.8790 | 0.9325 | 0.7968 | 0.8719 | 0.5489 | 0.9002 | 0.8700 | 0.7826 | 0.8670 | 0.6763 | 0.7667 | 0.4332 | 0.7904 | 0.7189 | 0.7181 |
| 0.2354 | 20.0 | 7400 | 0.6462 | 0.7126 | 0.8219 | 0.8804 | 0.9318 | 0.8027 | 0.8722 | 0.5738 | 0.8921 | 0.8781 | 0.8026 | 0.8668 | 0.6768 | 0.7677 | 0.4277 | 0.7936 | 0.7176 | 0.7380 |
| 0.1215 | 20.05 | 7420 | 0.6418 | 0.7203 | 0.8229 | 0.8860 | 0.9304 | 0.7973 | 0.8817 | 0.5427 | 0.8999 | 0.8656 | 0.8428 | 0.8695 | 0.6778 | 0.7662 | 0.4271 | 0.8020 | 0.7261 | 0.7731 |
| 0.1326 | 20.11 | 7440 | 0.6310 | 0.7183 | 0.8203 | 0.8844 | 0.9333 | 0.8062 | 0.8694 | 0.5379 | 0.9006 | 0.8700 | 0.8247 | 0.8687 | 0.6762 | 0.7660 | 0.4323 | 0.7982 | 0.7285 | 0.7584 |
| 0.1656 | 20.16 | 7460 | 0.6689 | 0.7193 | 0.8195 | 0.8854 | 0.9294 | 0.8052 | 0.8677 | 0.5223 | 0.9054 | 0.8690 | 0.8375 | 0.8701 | 0.6762 | 0.7636 | 0.4282 | 0.7979 | 0.7330 | 0.7664 |
| 0.0915 | 20.22 | 7480 | 0.6288 | 0.7211 | 0.8199 | 0.8874 | 0.9298 | 0.8059 | 0.8792 | 0.5091 | 0.9073 | 0.8569 | 0.8508 | 0.8688 | 0.6745 | 0.7635 | 0.4236 | 0.8049 | 0.7304 | 0.7820 |
| 0.0714 | 20.27 | 7500 | 0.6200 | 0.7269 | 0.8256 | 0.8907 | 0.9311 | 0.8028 | 0.8822 | 0.5254 | 0.9037 | 0.8576 | 0.8768 | 0.8696 | 0.6752 | 0.7658 | 0.4294 | 0.8114 | 0.7320 | 0.8046 |
| 0.2675 | 20.32 | 7520 | 0.6206 | 0.7266 | 0.8236 | 0.8909 | 0.9326 | 0.7991 | 0.8770 | 0.5145 | 0.9055 | 0.8624 | 0.8741 | 0.8695 | 0.6746 | 0.7692 | 0.4282 | 0.8128 | 0.7305 | 0.8017 |
| 0.1737 | 20.38 | 7540 | 0.6122 | 0.7266 | 0.8265 | 0.8907 | 0.9259 | 0.8079 | 0.8838 | 0.5225 | 0.9088 | 0.8621 | 0.8748 | 0.8696 | 0.6746 | 0.7715 | 0.4238 | 0.8119 | 0.7315 | 0.8033 |
| 0.0829 | 20.43 | 7560 | 0.6066 | 0.7261 | 0.8264 | 0.8910 | 0.9338 | 0.8110 | 0.8824 | 0.5125 | 0.8984 | 0.8716 | 0.8754 | 0.8705 | 0.6742 | 0.7717 | 0.4179 | 0.8123 | 0.7317 | 0.8042 |
| 0.0829 | 20.49 | 7580 | 0.6349 | 0.7136 | 0.8165 | 0.8821 | 0.9343 | 0.7894 | 0.8839 | 0.5315 | 0.8964 | 0.8745 | 0.8055 | 0.8700 | 0.6760 | 0.7734 | 0.4188 | 0.7929 | 0.7238 | 0.7403 |
| 0.0523 | 20.54 | 7600 | 0.7175 | 0.7086 | 0.8114 | 0.8784 | 0.9310 | 0.8067 | 0.8763 | 0.5279 | 0.9061 | 0.8521 | 0.7794 | 0.8692 | 0.6779 | 0.7724 | 0.4220 | 0.7854 | 0.7187 | 0.7150 |
| 0.0771 | 20.59 | 7620 | 0.6773 | 0.7081 | 0.8122 | 0.8785 | 0.9365 | 0.8072 | 0.8763 | 0.5319 | 0.8996 | 0.8628 | 0.7709 | 0.8679 | 0.6774 | 0.7774 | 0.4234 | 0.7896 | 0.7127 | 0.7081 |
| 0.4593 | 20.65 | 7640 | 0.7130 | 0.7001 | 0.8051 | 0.8751 | 0.9288 | 0.8155 | 0.8759 | 0.4955 | 0.9106 | 0.8654 | 0.7442 | 0.8692 | 0.6768 | 0.7745 | 0.4197 | 0.7890 | 0.6900 | 0.6817 |
| 1.3661 | 20.7 | 7660 | 0.6997 | 0.6960 | 0.8050 | 0.8721 | 0.9350 | 0.8002 | 0.8816 | 0.5299 | 0.8996 | 0.8712 | 0.7173 | 0.8675 | 0.6771 | 0.7749 | 0.4303 | 0.7900 | 0.6738 | 0.6581 |
| 0.1361 | 20.76 | 7680 | 0.7710 | 0.6942 | 0.8043 | 0.8712 | 0.9334 | 0.8141 | 0.8666 | 0.5209 | 0.9003 | 0.8784 | 0.7168 | 0.8663 | 0.6784 | 0.7681 | 0.4296 | 0.7900 | 0.6704 | 0.6570 |
| 0.1341 | 20.81 | 7700 | 0.7353 | 0.6939 | 0.8009 | 0.8717 | 0.9340 | 0.8127 | 0.8658 | 0.4955 | 0.9043 | 0.8765 | 0.7171 | 0.8665 | 0.6786 | 0.7676 | 0.4213 | 0.7895 | 0.6759 | 0.6581 |
| 0.1385 | 20.86 | 7720 | 0.6858 | 0.6975 | 0.8045 | 0.8735 | 0.9329 | 0.8072 | 0.8827 | 0.4990 | 0.8995 | 0.8725 | 0.7377 | 0.8672 | 0.6793 | 0.7653 | 0.4218 | 0.7893 | 0.6842 | 0.6757 |
| 0.13 | 20.92 | 7740 | 0.7104 | 0.7030 | 0.8086 | 0.8756 | 0.9307 | 0.8123 | 0.8740 | 0.5147 | 0.9034 | 0.8713 | 0.7540 | 0.8664 | 0.6789 | 0.7676 | 0.4285 | 0.7894 | 0.6999 | 0.6901 |
| 0.2066 | 20.97 | 7760 | 0.7073 | 0.7038 | 0.8030 | 0.8775 | 0.9329 | 0.8014 | 0.8815 | 0.4723 | 0.9081 | 0.8576 | 0.7674 | 0.8665 | 0.6802 | 0.7683 | 0.4108 | 0.7903 | 0.7081 | 0.7024 |
| 0.1249 | 21.03 | 7780 | 0.6910 | 0.7139 | 0.8107 | 0.8829 | 0.9368 | 0.7983 | 0.8835 | 0.4947 | 0.9036 | 0.8429 | 0.8148 | 0.8669 | 0.6799 | 0.7686 | 0.4185 | 0.7969 | 0.7212 | 0.7452 |
| 0.1122 | 21.08 | 7800 | 0.6585 | 0.7111 | 0.8104 | 0.8804 | 0.9323 | 0.8005 | 0.8776 | 0.5014 | 0.9058 | 0.8675 | 0.7881 | 0.8700 | 0.6808 | 0.7677 | 0.4258 | 0.7898 | 0.7220 | 0.7219 |
| 0.1621 | 21.14 | 7820 | 0.6931 | 0.7090 | 0.8118 | 0.8783 | 0.9329 | 0.8013 | 0.8827 | 0.5265 | 0.9025 | 0.8719 | 0.7647 | 0.8706 | 0.6802 | 0.7684 | 0.4347 | 0.7855 | 0.7195 | 0.7045 |
| 0.1049 | 21.19 | 7840 | 0.6546 | 0.7197 | 0.8182 | 0.8851 | 0.9320 | 0.7959 | 0.8877 | 0.5237 | 0.9050 | 0.8595 | 0.8234 | 0.8726 | 0.6800 | 0.7687 | 0.4337 | 0.7956 | 0.7331 | 0.7542 |
| 0.1394 | 21.24 | 7860 | 0.7061 | 0.7146 | 0.8144 | 0.8814 | 0.9335 | 0.7983 | 0.8808 | 0.5293 | 0.9036 | 0.8617 | 0.7939 | 0.8714 | 0.6785 | 0.7672 | 0.4368 | 0.7876 | 0.7312 | 0.7299 |
| 0.0459 | 21.3 | 7880 | 0.7485 | 0.7120 | 0.8123 | 0.8796 | 0.9317 | 0.7955 | 0.8811 | 0.5299 | 0.9045 | 0.8590 | 0.7845 | 0.8699 | 0.6787 | 0.7655 | 0.4379 | 0.7853 | 0.7264 | 0.7205 |
| 0.1347 | 21.35 | 7900 | 0.6853 | 0.7143 | 0.8153 | 0.8816 | 0.9327 | 0.8045 | 0.8828 | 0.5189 | 0.9005 | 0.8653 | 0.8021 | 0.8700 | 0.6786 | 0.7632 | 0.4344 | 0.7897 | 0.7281 | 0.7363 |
| 0.2338 | 21.41 | 7920 | 0.6735 | 0.7206 | 0.8208 | 0.8857 | 0.9344 | 0.8137 | 0.8709 | 0.5275 | 0.9011 | 0.8640 | 0.8341 | 0.8700 | 0.6770 | 0.7663 | 0.4361 | 0.7989 | 0.7325 | 0.7635 |
| 0.1688 | 21.46 | 7940 | 0.6418 | 0.7210 | 0.8133 | 0.8872 | 0.9371 | 0.7777 | 0.8804 | 0.4946 | 0.9067 | 0.8563 | 0.8403 | 0.8695 | 0.6771 | 0.7692 | 0.4278 | 0.8027 | 0.7297 | 0.7708 |
| 0.0947 | 21.51 | 7960 | 0.6161 | 0.7283 | 0.8263 | 0.8911 | 0.9324 | 0.8138 | 0.8808 | 0.5151 | 0.9036 | 0.8694 | 0.8694 | 0.8696 | 0.6808 | 0.7728 | 0.4360 | 0.8136 | 0.7290 | 0.7964 |
| 0.1065 | 21.57 | 7980 | 0.6244 | 0.7253 | 0.8246 | 0.8891 | 0.9328 | 0.8065 | 0.8800 | 0.5268 | 0.9026 | 0.8679 | 0.8555 | 0.8686 | 0.6796 | 0.7694 | 0.4365 | 0.8107 | 0.7262 | 0.7859 |
| 0.0763 | 21.62 | 8000 | 0.7093 | 0.7007 | 0.8061 | 0.8746 | 0.9326 | 0.8029 | 0.8719 | 0.5272 | 0.9068 | 0.8615 | 0.7399 | 0.8679 | 0.6782 | 0.7684 | 0.4334 | 0.7898 | 0.6870 | 0.6800 |
| 0.13 | 21.68 | 8020 | 0.6518 | 0.7108 | 0.8161 | 0.8800 | 0.9349 | 0.8087 | 0.8773 | 0.5368 | 0.8971 | 0.8752 | 0.7830 | 0.8686 | 0.6796 | 0.7707 | 0.4403 | 0.7964 | 0.7034 | 0.7165 |
| 0.1545 | 21.73 | 8040 | 0.6582 | 0.7096 | 0.8114 | 0.8790 | 0.9321 | 0.7886 | 0.8799 | 0.5305 | 0.9037 | 0.8671 | 0.7782 | 0.8679 | 0.6783 | 0.7673 | 0.4368 | 0.7918 | 0.7128 | 0.7126 |
| 0.1939 | 21.78 | 8060 | 0.6539 | 0.7086 | 0.8130 | 0.8781 | 0.9337 | 0.8064 | 0.8771 | 0.5395 | 0.9017 | 0.8648 | 0.7680 | 0.8671 | 0.6803 | 0.7696 | 0.4366 | 0.7913 | 0.7103 | 0.7050 |
| 0.073 | 21.84 | 8080 | 0.7916 | 0.6996 | 0.8091 | 0.8734 | 0.9307 | 0.7970 | 0.8790 | 0.5424 | 0.8986 | 0.8808 | 0.7353 | 0.8675 | 0.6790 | 0.7671 | 0.4382 | 0.7892 | 0.6810 | 0.6756 |
| 0.085 | 21.89 | 8100 | 0.7277 | 0.7046 | 0.8115 | 0.8762 | 0.9299 | 0.8081 | 0.8840 | 0.5282 | 0.9001 | 0.8694 | 0.7607 | 0.8679 | 0.6804 | 0.7652 | 0.4367 | 0.7900 | 0.6957 | 0.6961 |
| 0.2033 | 21.95 | 8120 | 0.7420 | 0.6993 | 0.8078 | 0.8734 | 0.9324 | 0.7945 | 0.8860 | 0.5394 | 0.8983 | 0.8674 | 0.7368 | 0.8677 | 0.6798 | 0.7670 | 0.4328 | 0.7884 | 0.6832 | 0.6758 |
| 0.1325 | 22.0 | 8140 | 0.7304 | 0.6987 | 0.8074 | 0.8734 | 0.9329 | 0.7983 | 0.8827 | 0.5278 | 0.8955 | 0.8729 | 0.7419 | 0.8679 | 0.6799 | 0.7666 | 0.4299 | 0.7885 | 0.6804 | 0.6780 |
| 0.0975 | 22.05 | 8160 | 0.7002 | 0.6988 | 0.8116 | 0.8730 | 0.9310 | 0.8059 | 0.8824 | 0.5541 | 0.8942 | 0.8757 | 0.7382 | 0.8684 | 0.6799 | 0.7680 | 0.4371 | 0.7902 | 0.6729 | 0.6754 |
| 0.0684 | 22.11 | 8180 | 0.7231 | 0.6985 | 0.8051 | 0.8733 | 0.9328 | 0.7797 | 0.8840 | 0.5428 | 0.9023 | 0.8585 | 0.7358 | 0.8682 | 0.6782 | 0.7660 | 0.4356 | 0.7893 | 0.6779 | 0.6746 |
| 0.1832 | 22.16 | 8200 | 0.6781 | 0.6992 | 0.8090 | 0.8739 | 0.9302 | 0.8037 | 0.8828 | 0.5384 | 0.9013 | 0.8647 | 0.7418 | 0.8689 | 0.6809 | 0.7669 | 0.4350 | 0.7930 | 0.6709 | 0.6787 |
| 0.1687 | 22.22 | 8220 | 0.7406 | 0.7015 | 0.8115 | 0.8748 | 0.9284 | 0.8067 | 0.8775 | 0.5412 | 0.8996 | 0.8697 | 0.7575 | 0.8689 | 0.6792 | 0.7661 | 0.4299 | 0.7893 | 0.6838 | 0.6930 |
| 0.0902 | 22.27 | 8240 | 0.7015 | 0.7096 | 0.8115 | 0.8791 | 0.9286 | 0.8025 | 0.8744 | 0.5226 | 0.9084 | 0.8584 | 0.7857 | 0.8695 | 0.6790 | 0.7681 | 0.4297 | 0.7887 | 0.7137 | 0.7183 |
| 0.1202 | 22.32 | 8260 | 0.7481 | 0.7031 | 0.8109 | 0.8756 | 0.9331 | 0.8000 | 0.8738 | 0.5445 | 0.8988 | 0.8712 | 0.7551 | 0.8686 | 0.6789 | 0.7677 | 0.4311 | 0.7892 | 0.6932 | 0.6931 |
| 0.6745 | 22.38 | 8280 | 0.7041 | 0.6983 | 0.8084 | 0.8734 | 0.9323 | 0.8016 | 0.8815 | 0.5337 | 0.8974 | 0.8764 | 0.7356 | 0.8690 | 0.6773 | 0.7668 | 0.4315 | 0.7893 | 0.6776 | 0.6765 |
| 0.1366 | 22.43 | 8300 | 0.7009 | 0.7008 | 0.8079 | 0.8749 | 0.9292 | 0.7985 | 0.8733 | 0.5333 | 0.9063 | 0.8650 | 0.7496 | 0.8696 | 0.6777 | 0.7679 | 0.4313 | 0.7903 | 0.6831 | 0.6858 |
| 0.1055 | 22.49 | 8320 | 0.6737 | 0.7011 | 0.8084 | 0.8751 | 0.9311 | 0.8031 | 0.8851 | 0.5314 | 0.9032 | 0.8519 | 0.7531 | 0.8693 | 0.6787 | 0.7670 | 0.4311 | 0.7911 | 0.6825 | 0.6879 |
| 0.1172 | 22.54 | 8340 | 0.7570 | 0.6995 | 0.8085 | 0.8737 | 0.9312 | 0.8105 | 0.8702 | 0.5356 | 0.9011 | 0.8662 | 0.7446 | 0.8686 | 0.6787 | 0.7646 | 0.4297 | 0.7869 | 0.6859 | 0.6823 |
| 0.0575 | 22.59 | 8360 | 0.7264 | 0.7014 | 0.8089 | 0.8745 | 0.9325 | 0.8079 | 0.8722 | 0.5356 | 0.9002 | 0.8633 | 0.7506 | 0.8679 | 0.6790 | 0.7652 | 0.4312 | 0.7867 | 0.6940 | 0.6861 |
| 0.1153 | 22.65 | 8380 | 0.7527 | 0.6944 | 0.8037 | 0.8709 | 0.9330 | 0.8046 | 0.8730 | 0.5308 | 0.9012 | 0.8669 | 0.7167 | 0.8670 | 0.6785 | 0.7645 | 0.4311 | 0.7863 | 0.6753 | 0.6578 |
| 0.1594 | 22.7 | 8400 | 0.7645 | 0.7028 | 0.8075 | 0.8752 | 0.9365 | 0.8001 | 0.8687 | 0.5303 | 0.8990 | 0.8659 | 0.7518 | 0.8655 | 0.6773 | 0.7656 | 0.4298 | 0.7871 | 0.7047 | 0.6897 |
| 0.0405 | 22.76 | 8420 | 0.7033 | 0.7057 | 0.8117 | 0.8761 | 0.9295 | 0.8024 | 0.8721 | 0.5400 | 0.9015 | 0.8740 | 0.7625 | 0.8680 | 0.6787 | 0.7657 | 0.4315 | 0.7843 | 0.7127 | 0.6987 |
| 1.7524 | 22.81 | 8440 | 0.7290 | 0.7005 | 0.8068 | 0.8731 | 0.9365 | 0.8086 | 0.8835 | 0.5363 | 0.8979 | 0.8512 | 0.7335 | 0.8609 | 0.6775 | 0.7657 | 0.4288 | 0.7854 | 0.7108 | 0.6743 |
| 0.0614 | 22.86 | 8460 | 0.7010 | 0.7023 | 0.8095 | 0.8747 | 0.9353 | 0.8113 | 0.8814 | 0.5310 | 0.8956 | 0.8634 | 0.7483 | 0.8641 | 0.6771 | 0.7640 | 0.4267 | 0.7863 | 0.7124 | 0.6857 |
| 0.2336 | 22.92 | 8480 | 0.7311 | 0.7009 | 0.8066 | 0.8738 | 0.9347 | 0.8007 | 0.8797 | 0.5287 | 0.8985 | 0.8621 | 0.7420 | 0.8637 | 0.6761 | 0.7632 | 0.4252 | 0.7846 | 0.7136 | 0.6799 |
| 0.2301 | 22.97 | 8500 | 0.7148 | 0.7062 | 0.8132 | 0.8767 | 0.9332 | 0.8101 | 0.8695 | 0.5433 | 0.8980 | 0.8743 | 0.7641 | 0.8681 | 0.6786 | 0.7662 | 0.4312 | 0.7866 | 0.7127 | 0.7004 |
| 1.6015 | 23.03 | 8520 | 0.7963 | 0.7041 | 0.8103 | 0.8751 | 0.9339 | 0.8077 | 0.8721 | 0.5419 | 0.8994 | 0.8681 | 0.7486 | 0.8656 | 0.6782 | 0.7641 | 0.4308 | 0.7837 | 0.7169 | 0.6894 |
| 0.4187 | 23.08 | 8540 | 0.7661 | 0.7053 | 0.8102 | 0.8757 | 0.9312 | 0.8135 | 0.8670 | 0.5308 | 0.9034 | 0.8712 | 0.7545 | 0.8658 | 0.6785 | 0.7645 | 0.4317 | 0.7838 | 0.7189 | 0.6939 |
| 0.1823 | 23.14 | 8560 | 0.7773 | 0.6978 | 0.8045 | 0.8713 | 0.9323 | 0.7983 | 0.8697 | 0.5399 | 0.9030 | 0.8697 | 0.7185 | 0.8616 | 0.6736 | 0.7637 | 0.4306 | 0.7817 | 0.7118 | 0.6614 |
| 0.1152 | 23.19 | 8580 | 0.7167 | 0.7010 | 0.8101 | 0.8736 | 0.9318 | 0.8108 | 0.8777 | 0.5492 | 0.8997 | 0.8631 | 0.7385 | 0.8625 | 0.6747 | 0.7675 | 0.4297 | 0.7869 | 0.7090 | 0.6770 |
| 0.1159 | 23.24 | 8600 | 0.7670 | 0.7014 | 0.8082 | 0.8737 | 0.9340 | 0.8076 | 0.8658 | 0.5403 | 0.8995 | 0.8698 | 0.7407 | 0.8628 | 0.6750 | 0.7647 | 0.4303 | 0.7847 | 0.7130 | 0.6796 |
| 0.194 | 23.3 | 8620 | 0.7899 | 0.6987 | 0.7988 | 0.8731 | 0.9357 | 0.7832 | 0.8648 | 0.5035 | 0.9065 | 0.8646 | 0.7331 | 0.8614 | 0.6729 | 0.7627 | 0.4245 | 0.7849 | 0.7113 | 0.6732 |
| 0.0968 | 23.35 | 8640 | 0.7002 | 0.7083 | 0.8120 | 0.8785 | 0.9309 | 0.8076 | 0.8844 | 0.5208 | 0.9029 | 0.8603 | 0.7774 | 0.8682 | 0.6787 | 0.7664 | 0.4284 | 0.7894 | 0.7158 | 0.7113 |
| 1.4639 | 23.41 | 8660 | 0.7585 | 0.7071 | 0.8116 | 0.8774 | 0.9342 | 0.7974 | 0.8794 | 0.5378 | 0.8991 | 0.8670 | 0.7663 | 0.8678 | 0.6798 | 0.7667 | 0.4287 | 0.7874 | 0.7146 | 0.7046 |
| 0.3679 | 23.46 | 8680 | 0.7567 | 0.7070 | 0.8118 | 0.8771 | 0.9337 | 0.8019 | 0.8765 | 0.5422 | 0.9007 | 0.8639 | 0.7639 | 0.8671 | 0.6800 | 0.7672 | 0.4292 | 0.7872 | 0.7161 | 0.7022 |
| 0.2112 | 23.51 | 8700 | 0.7798 | 0.7002 | 0.8010 | 0.8733 | 0.9346 | 0.7861 | 0.8768 | 0.5191 | 0.9075 | 0.8516 | 0.7316 | 0.8612 | 0.6765 | 0.7631 | 0.4289 | 0.7846 | 0.7132 | 0.6742 |
| 0.1242 | 23.57 | 8720 | 0.8097 | 0.7044 | 0.8100 | 0.8753 | 0.9325 | 0.7957 | 0.8774 | 0.5472 | 0.9005 | 0.8631 | 0.7537 | 0.8661 | 0.6789 | 0.7632 | 0.4299 | 0.7840 | 0.7148 | 0.6939 |
| 0.1271 | 23.62 | 8740 | 0.7291 | 0.7089 | 0.8146 | 0.8785 | 0.9324 | 0.8063 | 0.8788 | 0.5404 | 0.8987 | 0.8673 | 0.7783 | 0.8689 | 0.6798 | 0.7681 | 0.4287 | 0.7886 | 0.7144 | 0.7140 |
| 0.227 | 23.68 | 8760 | 0.7247 | 0.7100 | 0.8172 | 0.8786 | 0.9300 | 0.8121 | 0.8768 | 0.5467 | 0.8971 | 0.8725 | 0.7851 | 0.8694 | 0.6788 | 0.7661 | 0.4293 | 0.7859 | 0.7208 | 0.7195 |
| 0.1366 | 23.73 | 8780 | 0.7366 | 0.7100 | 0.8178 | 0.8783 | 0.9279 | 0.8176 | 0.8717 | 0.5504 | 0.8988 | 0.8737 | 0.7849 | 0.8690 | 0.6777 | 0.7650 | 0.4302 | 0.7840 | 0.7241 | 0.7200 |
| 0.0983 | 23.78 | 8800 | 0.6922 | 0.7121 | 0.8167 | 0.8797 | 0.9298 | 0.8021 | 0.8783 | 0.5441 | 0.8989 | 0.8696 | 0.7943 | 0.8703 | 0.6800 | 0.7660 | 0.4312 | 0.7861 | 0.7253 | 0.7257 |
| 0.04 | 23.84 | 8820 | 0.7027 | 0.7118 | 0.8164 | 0.8795 | 0.9321 | 0.8111 | 0.8738 | 0.5436 | 0.8987 | 0.8643 | 0.7915 | 0.8690 | 0.6799 | 0.7668 | 0.4308 | 0.7867 | 0.7253 | 0.7241 |
| 0.0622 | 23.89 | 8840 | 0.7024 | 0.7100 | 0.8136 | 0.8787 | 0.9332 | 0.8089 | 0.8727 | 0.5359 | 0.9015 | 0.8651 | 0.7779 | 0.8687 | 0.6794 | 0.7667 | 0.4319 | 0.7868 | 0.7215 | 0.7147 |
| 0.1341 | 23.95 | 8860 | 0.6925 | 0.7084 | 0.8081 | 0.8790 | 0.9332 | 0.8001 | 0.8796 | 0.4940 | 0.9048 | 0.8682 | 0.7765 | 0.8687 | 0.6787 | 0.7658 | 0.4232 | 0.7883 | 0.7207 | 0.7131 |
| 0.1644 | 24.0 | 8880 | 0.7891 | 0.7086 | 0.8102 | 0.8781 | 0.9314 | 0.7996 | 0.8774 | 0.5160 | 0.9025 | 0.8664 | 0.7780 | 0.8682 | 0.6798 | 0.7632 | 0.4267 | 0.7845 | 0.7239 | 0.7137 |
| 1.0628 | 24.05 | 8900 | 0.7730 | 0.7101 | 0.8108 | 0.8792 | 0.9332 | 0.8048 | 0.8727 | 0.5139 | 0.9035 | 0.8645 | 0.7829 | 0.8681 | 0.6801 | 0.7653 | 0.4277 | 0.7873 | 0.7252 | 0.7171 |
| 0.1389 | 24.11 | 8920 | 0.7636 | 0.7097 | 0.8120 | 0.8790 | 0.9320 | 0.8098 | 0.8714 | 0.5149 | 0.9023 | 0.8706 | 0.7828 | 0.8685 | 0.6799 | 0.7649 | 0.4271 | 0.7870 | 0.7240 | 0.7170 |
| 0.0408 | 24.16 | 8940 | 0.7707 | 0.7100 | 0.8134 | 0.8787 | 0.9327 | 0.8057 | 0.8759 | 0.5304 | 0.8987 | 0.8672 | 0.7835 | 0.8681 | 0.6802 | 0.7641 | 0.4277 | 0.7853 | 0.7261 | 0.7186 |
| 0.1 | 24.22 | 8960 | 0.7071 | 0.7121 | 0.8173 | 0.8799 | 0.9289 | 0.8129 | 0.8731 | 0.5437 | 0.9021 | 0.8664 | 0.7940 | 0.8697 | 0.6786 | 0.7677 | 0.4310 | 0.7877 | 0.7259 | 0.7245 |
| 0.135 | 24.27 | 8980 | 0.7501 | 0.7109 | 0.8137 | 0.8791 | 0.9321 | 0.8073 | 0.8764 | 0.5357 | 0.9032 | 0.8589 | 0.7822 | 0.8686 | 0.6794 | 0.7658 | 0.4296 | 0.7862 | 0.7281 | 0.7187 |
| 0.3517 | 24.32 | 9000 | 0.7621 | 0.7114 | 0.8137 | 0.8794 | 0.9322 | 0.8088 | 0.8712 | 0.5408 | 0.9049 | 0.8486 | 0.7894 | 0.8686 | 0.6787 | 0.7670 | 0.4289 | 0.7862 | 0.7267 | 0.7233 |
| 0.0275 | 24.38 | 9020 | 0.7665 | 0.7061 | 0.8118 | 0.8769 | 0.9333 | 0.8019 | 0.8751 | 0.5371 | 0.8984 | 0.8716 | 0.7653 | 0.8685 | 0.6792 | 0.7659 | 0.4293 | 0.7863 | 0.7107 | 0.7030 |
| 0.1118 | 24.43 | 9040 | 0.7370 | 0.7108 | 0.8117 | 0.8795 | 0.9342 | 0.8043 | 0.8717 | 0.5179 | 0.9016 | 0.8657 | 0.7865 | 0.8678 | 0.6796 | 0.7660 | 0.4288 | 0.7881 | 0.7246 | 0.7205 |
| 0.1404 | 24.49 | 9060 | 0.7741 | 0.7094 | 0.8119 | 0.8784 | 0.9324 | 0.7972 | 0.8693 | 0.5348 | 0.9025 | 0.8685 | 0.7786 | 0.8688 | 0.6795 | 0.7652 | 0.4313 | 0.7860 | 0.7210 | 0.7140 |
| 0.1383 | 24.54 | 9080 | 0.7696 | 0.7102 | 0.8142 | 0.8786 | 0.9292 | 0.8074 | 0.8720 | 0.5373 | 0.9031 | 0.8665 | 0.7840 | 0.8683 | 0.6793 | 0.7649 | 0.4305 | 0.7856 | 0.7243 | 0.7187 |
| 0.0895 | 24.59 | 9100 | 0.7283 | 0.7106 | 0.8164 | 0.8790 | 0.9297 | 0.8120 | 0.8752 | 0.5472 | 0.9017 | 0.8647 | 0.7841 | 0.8695 | 0.6782 | 0.7668 | 0.4318 | 0.7866 | 0.7231 | 0.7185 |
| 0.1423 | 24.65 | 9120 | 0.7587 | 0.7101 | 0.8113 | 0.8791 | 0.9336 | 0.8059 | 0.8696 | 0.5177 | 0.9028 | 0.8689 | 0.7809 | 0.8683 | 0.6798 | 0.7654 | 0.4303 | 0.7875 | 0.7226 | 0.7164 |
| 0.2422 | 24.7 | 9140 | 0.7617 | 0.7083 | 0.8116 | 0.8777 | 0.9357 | 0.8067 | 0.8668 | 0.5347 | 0.8999 | 0.8668 | 0.7706 | 0.8672 | 0.6792 | 0.7651 | 0.4330 | 0.7865 | 0.7188 | 0.7086 |
| 0.2002 | 24.76 | 9160 | 0.8112 | 0.7090 | 0.8078 | 0.8784 | 0.9318 | 0.8020 | 0.8765 | 0.5168 | 0.9099 | 0.8361 | 0.7814 | 0.8673 | 0.6797 | 0.7645 | 0.4279 | 0.7846 | 0.7216 | 0.7176 |
| 0.0573 | 24.81 | 9180 | 0.8119 | 0.7100 | 0.8115 | 0.8790 | 0.9326 | 0.8155 | 0.8656 | 0.5155 | 0.9044 | 0.8623 | 0.7843 | 0.8677 | 0.6781 | 0.7643 | 0.4283 | 0.7864 | 0.7256 | 0.7192 |
| 0.1477 | 24.86 | 9200 | 0.7879 | 0.7094 | 0.8093 | 0.8790 | 0.9347 | 0.8030 | 0.8710 | 0.5039 | 0.9020 | 0.8678 | 0.7827 | 0.8678 | 0.6796 | 0.7638 | 0.4252 | 0.7865 | 0.7250 | 0.7183 |
| 0.085 | 24.92 | 9220 | 0.7683 | 0.7080 | 0.8095 | 0.8780 | 0.9361 | 0.8065 | 0.8634 | 0.5238 | 0.9038 | 0.8640 | 0.7689 | 0.8671 | 0.6792 | 0.7658 | 0.4320 | 0.7884 | 0.7163 | 0.7071 |
| 0.1344 | 24.97 | 9240 | 0.7613 | 0.7092 | 0.8104 | 0.8790 | 0.9352 | 0.8067 | 0.8732 | 0.5054 | 0.8997 | 0.8714 | 0.7814 | 0.8677 | 0.6795 | 0.7649 | 0.4259 | 0.7883 | 0.7211 | 0.7170 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
|
guirnd/ppo-CartPole-v2 | guirnd | 2024-02-22T19:16:18Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2024-02-22T18:43:20Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -98.93 +/- 66.84
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.96
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'guirnd/ppo-CartPole-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
DouglasPontes/2020-Q4-25p-filtered-random | DouglasPontes | 2024-02-22T19:06:16Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-2019-90m",
"base_model:finetune:cardiffnlp/twitter-roberta-base-2019-90m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-02-19T22:08:29Z | ---
license: mit
base_model: cardiffnlp/twitter-roberta-base-2019-90m
tags:
- generated_from_trainer
model-index:
- name: 2020-Q4-25p-filtered-random
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. -->
# 2020-Q4-25p-filtered-random
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2681
## 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: 4.1e-07
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2400000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| No log | 0.02 | 8000 | 2.5802 |
| 2.8151 | 0.04 | 16000 | 2.4882 |
| 2.8151 | 0.07 | 24000 | 2.4292 |
| 2.5636 | 0.09 | 32000 | 2.3980 |
| 2.5636 | 0.11 | 40000 | 2.3799 |
| 2.4947 | 0.13 | 48000 | 2.3665 |
| 2.4947 | 0.16 | 56000 | 2.3455 |
| 2.473 | 0.18 | 64000 | 2.3419 |
| 2.473 | 0.2 | 72000 | 2.3307 |
| 2.4512 | 0.22 | 80000 | 2.3289 |
| 2.4512 | 0.25 | 88000 | 2.3250 |
| 2.4421 | 0.27 | 96000 | 2.3189 |
| 2.4421 | 0.29 | 104000 | 2.3200 |
| 2.4354 | 0.31 | 112000 | 2.3155 |
| 2.4354 | 0.34 | 120000 | 2.3138 |
| 2.4324 | 0.36 | 128000 | 2.3054 |
| 2.4324 | 0.38 | 136000 | 2.3028 |
| 2.4253 | 0.4 | 144000 | 2.3029 |
| 2.4253 | 0.43 | 152000 | 2.3006 |
| 2.4156 | 0.45 | 160000 | 2.3001 |
| 2.4156 | 0.47 | 168000 | 2.2980 |
| 2.4165 | 0.49 | 176000 | 2.2913 |
| 2.4165 | 0.52 | 184000 | 2.2974 |
| 2.4131 | 0.54 | 192000 | 2.2906 |
| 2.4131 | 0.56 | 200000 | 2.2908 |
| 2.407 | 0.58 | 208000 | 2.2895 |
| 2.407 | 0.61 | 216000 | 2.2865 |
| 2.4153 | 0.63 | 224000 | 2.2914 |
| 2.4153 | 0.65 | 232000 | 2.2806 |
| 2.4011 | 0.67 | 240000 | 2.2819 |
| 2.4011 | 0.7 | 248000 | 2.2854 |
| 2.4087 | 0.72 | 256000 | 2.2837 |
| 2.4087 | 0.74 | 264000 | 2.2866 |
| 2.4059 | 0.76 | 272000 | 2.2855 |
| 2.4059 | 0.79 | 280000 | 2.2868 |
| 2.4086 | 0.81 | 288000 | 2.2770 |
| 2.4086 | 0.83 | 296000 | 2.2789 |
| 2.4093 | 0.85 | 304000 | 2.2792 |
| 2.4093 | 0.88 | 312000 | 2.2797 |
| 2.4036 | 0.9 | 320000 | 2.2794 |
| 2.4036 | 0.92 | 328000 | 2.2768 |
| 2.4063 | 0.94 | 336000 | 2.2836 |
| 2.4063 | 0.97 | 344000 | 2.2809 |
| 2.4047 | 0.99 | 352000 | 2.2808 |
| 2.4047 | 1.01 | 360000 | 2.2840 |
| 2.4084 | 1.03 | 368000 | 2.2799 |
| 2.4084 | 1.06 | 376000 | 2.2726 |
| 2.4041 | 1.08 | 384000 | 2.2824 |
| 2.4041 | 1.1 | 392000 | 2.2781 |
| 2.4034 | 1.12 | 400000 | 2.2751 |
| 2.4034 | 1.15 | 408000 | 2.2761 |
| 2.3951 | 1.17 | 416000 | 2.2732 |
| 2.3951 | 1.19 | 424000 | 2.2710 |
| 2.409 | 1.21 | 432000 | 2.2780 |
| 2.409 | 1.24 | 440000 | 2.2715 |
| 2.3985 | 1.26 | 448000 | 2.2790 |
| 2.3985 | 1.28 | 456000 | 2.2766 |
| 2.4016 | 1.3 | 464000 | 2.2745 |
| 2.4016 | 1.32 | 472000 | 2.2719 |
| 2.3978 | 1.35 | 480000 | 2.2755 |
| 2.3978 | 1.37 | 488000 | 2.2699 |
| 2.406 | 1.39 | 496000 | 2.2823 |
| 2.406 | 1.41 | 504000 | 2.2736 |
| 2.3958 | 1.44 | 512000 | 2.2728 |
| 2.3958 | 1.46 | 520000 | 2.2763 |
| 2.406 | 1.48 | 528000 | 2.2781 |
| 2.406 | 1.5 | 536000 | 2.2723 |
| 2.4 | 1.53 | 544000 | 2.2733 |
| 2.4 | 1.55 | 552000 | 2.2715 |
| 2.3998 | 1.57 | 560000 | 2.2716 |
| 2.3998 | 1.59 | 568000 | 2.2751 |
| 2.4017 | 1.62 | 576000 | 2.2743 |
| 2.4017 | 1.64 | 584000 | 2.2739 |
| 2.4019 | 1.66 | 592000 | 2.2755 |
| 2.4019 | 1.68 | 600000 | 2.2691 |
| 2.398 | 1.71 | 608000 | 2.2706 |
| 2.398 | 1.73 | 616000 | 2.2703 |
| 2.4027 | 1.75 | 624000 | 2.2657 |
| 2.4027 | 1.77 | 632000 | 2.2674 |
| 2.4 | 1.8 | 640000 | 2.2749 |
| 2.4 | 1.82 | 648000 | 2.2714 |
| 2.4046 | 1.84 | 656000 | 2.2695 |
| 2.4046 | 1.86 | 664000 | 2.2724 |
| 2.4033 | 1.89 | 672000 | 2.2697 |
| 2.4033 | 1.91 | 680000 | 2.2697 |
| 2.3981 | 1.93 | 688000 | 2.2674 |
| 2.3981 | 1.95 | 696000 | 2.2669 |
| 2.4029 | 1.98 | 704000 | 2.2755 |
| 2.4029 | 2.0 | 712000 | 2.2664 |
| 2.4046 | 2.02 | 720000 | 2.2759 |
| 2.4046 | 2.04 | 728000 | 2.2689 |
| 2.4056 | 2.07 | 736000 | 2.2710 |
| 2.4056 | 2.09 | 744000 | 2.2744 |
| 2.4036 | 2.11 | 752000 | 2.2653 |
| 2.4036 | 2.13 | 760000 | 2.2642 |
| 2.3961 | 2.16 | 768000 | 2.2703 |
| 2.3961 | 2.18 | 776000 | 2.2683 |
| 2.3939 | 2.2 | 784000 | 2.2746 |
| 2.3939 | 2.22 | 792000 | 2.2667 |
| 2.3998 | 2.25 | 800000 | 2.2690 |
| 2.3998 | 2.27 | 808000 | 2.2697 |
| 2.3921 | 2.29 | 816000 | 2.2681 |
| 2.3921 | 2.31 | 824000 | 2.2740 |
| 2.4011 | 2.34 | 832000 | 2.2704 |
| 2.4011 | 2.36 | 840000 | 2.2666 |
| 2.3948 | 2.38 | 848000 | 2.2689 |
| 2.3948 | 2.4 | 856000 | 2.2742 |
| 2.3957 | 2.43 | 864000 | 2.2755 |
| 2.3957 | 2.45 | 872000 | 2.2689 |
| 2.3971 | 2.47 | 880000 | 2.2717 |
| 2.3971 | 2.49 | 888000 | 2.2690 |
| 2.3982 | 2.52 | 896000 | 2.2645 |
| 2.3982 | 2.54 | 904000 | 2.2726 |
| 2.4005 | 2.56 | 912000 | 2.2628 |
| 2.4005 | 2.58 | 920000 | 2.2726 |
| 2.4037 | 2.6 | 928000 | 2.2760 |
| 2.4037 | 2.63 | 936000 | 2.2662 |
| 2.4031 | 2.65 | 944000 | 2.2729 |
| 2.4031 | 2.67 | 952000 | 2.2706 |
| 2.4025 | 2.69 | 960000 | 2.2684 |
| 2.4025 | 2.72 | 968000 | 2.2635 |
| 2.409 | 2.74 | 976000 | 2.2606 |
| 2.409 | 2.76 | 984000 | 2.2664 |
| 2.4085 | 2.78 | 992000 | 2.2647 |
| 2.4085 | 2.81 | 1000000 | 2.2656 |
| 2.3971 | 2.83 | 1008000 | 2.2655 |
| 2.3971 | 2.85 | 1016000 | 2.2681 |
| 2.3946 | 2.87 | 1024000 | 2.2671 |
| 2.3946 | 2.9 | 1032000 | 2.2660 |
| 2.4063 | 2.92 | 1040000 | 2.2697 |
| 2.4063 | 2.94 | 1048000 | 2.2706 |
| 2.399 | 2.96 | 1056000 | 2.2625 |
| 2.399 | 2.99 | 1064000 | 2.2699 |
| 2.4024 | 3.01 | 1072000 | 2.2622 |
| 2.4024 | 3.03 | 1080000 | 2.2695 |
| 2.4035 | 3.05 | 1088000 | 2.2700 |
| 2.4035 | 3.08 | 1096000 | 2.2624 |
| 2.4061 | 3.1 | 1104000 | 2.2690 |
| 2.4061 | 3.12 | 1112000 | 2.2653 |
| 2.4044 | 3.14 | 1120000 | 2.2679 |
| 2.4044 | 3.17 | 1128000 | 2.2658 |
| 2.3996 | 3.19 | 1136000 | 2.2680 |
| 2.3996 | 3.21 | 1144000 | 2.2668 |
| 2.3943 | 3.23 | 1152000 | 2.2689 |
| 2.3943 | 3.26 | 1160000 | 2.2702 |
| 2.3948 | 3.28 | 1168000 | 2.2653 |
| 2.3948 | 3.3 | 1176000 | 2.2621 |
| 2.4047 | 3.32 | 1184000 | 2.2723 |
| 2.4047 | 3.35 | 1192000 | 2.2718 |
| 2.4057 | 3.37 | 1200000 | 2.2668 |
| 2.4057 | 3.39 | 1208000 | 2.2649 |
| 2.3901 | 3.41 | 1216000 | 2.2699 |
| 2.3901 | 3.44 | 1224000 | 2.2683 |
| 2.3942 | 3.46 | 1232000 | 2.2679 |
| 2.3942 | 3.48 | 1240000 | 2.2647 |
| 2.4052 | 3.5 | 1248000 | 2.2656 |
| 2.4052 | 3.53 | 1256000 | 2.2679 |
| 2.401 | 3.55 | 1264000 | 2.2685 |
| 2.401 | 3.57 | 1272000 | 2.2654 |
| 2.4012 | 3.59 | 1280000 | 2.2607 |
| 2.4012 | 3.62 | 1288000 | 2.2668 |
| 2.4015 | 3.64 | 1296000 | 2.2672 |
| 2.4015 | 3.66 | 1304000 | 2.2685 |
| 2.4039 | 3.68 | 1312000 | 2.2675 |
| 2.4039 | 3.71 | 1320000 | 2.2702 |
| 2.3927 | 3.73 | 1328000 | 2.2689 |
| 2.3927 | 3.75 | 1336000 | 2.2674 |
| 2.3998 | 3.77 | 1344000 | 2.2694 |
| 2.3998 | 3.8 | 1352000 | 2.2649 |
| 2.404 | 3.82 | 1360000 | 2.2635 |
| 2.404 | 3.84 | 1368000 | 2.2681 |
| 2.4023 | 3.86 | 1376000 | 2.2601 |
| 2.4023 | 3.88 | 1384000 | 2.2661 |
| 2.393 | 3.91 | 1392000 | 2.2613 |
| 2.393 | 3.93 | 1400000 | 2.2717 |
| 2.402 | 3.95 | 1408000 | 2.2672 |
| 2.402 | 3.97 | 1416000 | 2.2637 |
| 2.4047 | 4.0 | 1424000 | 2.2705 |
| 2.4047 | 4.02 | 1432000 | 2.2682 |
| 2.4045 | 4.04 | 1440000 | 2.2630 |
| 2.4045 | 4.06 | 1448000 | 2.2699 |
| 2.3973 | 4.09 | 1456000 | 2.2579 |
| 2.3973 | 4.11 | 1464000 | 2.2601 |
| 2.399 | 4.13 | 1472000 | 2.2609 |
| 2.399 | 4.15 | 1480000 | 2.2697 |
| 2.399 | 4.18 | 1488000 | 2.2630 |
| 2.399 | 4.2 | 1496000 | 2.2658 |
| 2.3995 | 4.22 | 1504000 | 2.2656 |
| 2.3995 | 4.24 | 1512000 | 2.2689 |
| 2.3929 | 4.27 | 1520000 | 2.2678 |
| 2.3929 | 4.29 | 1528000 | 2.2694 |
| 2.404 | 4.31 | 1536000 | 2.2632 |
| 2.404 | 4.33 | 1544000 | 2.2657 |
| 2.3932 | 4.36 | 1552000 | 2.2642 |
| 2.3932 | 4.38 | 1560000 | 2.2607 |
| 2.3985 | 4.4 | 1568000 | 2.2635 |
| 2.3985 | 4.42 | 1576000 | 2.2645 |
| 2.3997 | 4.45 | 1584000 | 2.2654 |
| 2.3997 | 4.47 | 1592000 | 2.2672 |
| 2.396 | 4.49 | 1600000 | 2.2666 |
| 2.396 | 4.51 | 1608000 | 2.2708 |
| 2.4012 | 4.54 | 1616000 | 2.2707 |
| 2.4012 | 4.56 | 1624000 | 2.2684 |
| 2.4074 | 4.58 | 1632000 | 2.2676 |
| 2.4074 | 4.6 | 1640000 | 2.2658 |
| 2.3965 | 4.63 | 1648000 | 2.2716 |
| 2.3965 | 4.65 | 1656000 | 2.2656 |
| 2.4021 | 4.67 | 1664000 | 2.2690 |
| 2.4021 | 4.69 | 1672000 | 2.2656 |
| 2.3981 | 4.72 | 1680000 | 2.2659 |
| 2.3981 | 4.74 | 1688000 | 2.2667 |
| 2.3974 | 4.76 | 1696000 | 2.2655 |
| 2.3974 | 4.78 | 1704000 | 2.2676 |
| 2.3964 | 4.81 | 1712000 | 2.2655 |
| 2.3964 | 4.83 | 1720000 | 2.2636 |
| 2.3933 | 4.85 | 1728000 | 2.2679 |
| 2.3933 | 4.87 | 1736000 | 2.2667 |
| 2.4066 | 4.9 | 1744000 | 2.2647 |
| 2.4066 | 4.92 | 1752000 | 2.2657 |
| 2.4027 | 4.94 | 1760000 | 2.2628 |
| 2.4027 | 4.96 | 1768000 | 2.2642 |
| 2.4029 | 4.99 | 1776000 | 2.2677 |
| 2.4029 | 5.01 | 1784000 | 2.2704 |
| 2.3958 | 5.03 | 1792000 | 2.2650 |
| 2.3958 | 5.05 | 1800000 | 2.2650 |
| 2.4054 | 5.08 | 1808000 | 2.2680 |
| 2.4054 | 5.1 | 1816000 | 2.2601 |
| 2.3984 | 5.12 | 1824000 | 2.2671 |
| 2.3984 | 5.14 | 1832000 | 2.2639 |
| 2.4005 | 5.16 | 1840000 | 2.2629 |
| 2.4005 | 5.19 | 1848000 | 2.2656 |
| 2.3962 | 5.21 | 1856000 | 2.2646 |
| 2.3962 | 5.23 | 1864000 | 2.2571 |
| 2.4033 | 5.25 | 1872000 | 2.2689 |
| 2.4033 | 5.28 | 1880000 | 2.2632 |
| 2.4064 | 5.3 | 1888000 | 2.2633 |
| 2.4064 | 5.32 | 1896000 | 2.2694 |
| 2.3967 | 5.34 | 1904000 | 2.2685 |
| 2.3967 | 5.37 | 1912000 | 2.2636 |
| 2.4002 | 5.39 | 1920000 | 2.2687 |
| 2.4002 | 5.41 | 1928000 | 2.2632 |
| 2.4045 | 5.43 | 1936000 | 2.2625 |
| 2.4045 | 5.46 | 1944000 | 2.2677 |
| 2.4096 | 5.48 | 1952000 | 2.2563 |
| 2.4096 | 5.5 | 1960000 | 2.2642 |
| 2.4004 | 5.52 | 1968000 | 2.2692 |
| 2.4004 | 5.55 | 1976000 | 2.2696 |
| 2.4065 | 5.57 | 1984000 | 2.2579 |
| 2.4065 | 5.59 | 1992000 | 2.2660 |
| 2.4025 | 5.61 | 2000000 | 2.2654 |
| 2.4025 | 5.64 | 2008000 | 2.2706 |
| 2.3993 | 5.66 | 2016000 | 2.2704 |
| 2.3993 | 5.68 | 2024000 | 2.2664 |
| 2.4034 | 5.7 | 2032000 | 2.2659 |
| 2.4034 | 5.73 | 2040000 | 2.2680 |
| 2.4004 | 5.75 | 2048000 | 2.2611 |
| 2.4004 | 5.77 | 2056000 | 2.2646 |
| 2.4025 | 5.79 | 2064000 | 2.2682 |
| 2.4025 | 5.82 | 2072000 | 2.2646 |
| 2.4063 | 5.84 | 2080000 | 2.2598 |
| 2.4063 | 5.86 | 2088000 | 2.2673 |
| 2.4071 | 5.88 | 2096000 | 2.2646 |
| 2.4071 | 5.91 | 2104000 | 2.2672 |
| 2.401 | 5.93 | 2112000 | 2.2648 |
| 2.401 | 5.95 | 2120000 | 2.2654 |
| 2.402 | 5.97 | 2128000 | 2.2664 |
| 2.402 | 6.0 | 2136000 | 2.2683 |
| 2.4004 | 6.02 | 2144000 | 2.2618 |
| 2.4004 | 6.04 | 2152000 | 2.2669 |
| 2.4001 | 6.06 | 2160000 | 2.2630 |
| 2.4001 | 6.09 | 2168000 | 2.2632 |
| 2.4046 | 6.11 | 2176000 | 2.2696 |
| 2.4046 | 6.13 | 2184000 | 2.2641 |
| 2.405 | 6.15 | 2192000 | 2.2627 |
| 2.405 | 6.18 | 2200000 | 2.2681 |
| 2.4063 | 6.2 | 2208000 | 2.2604 |
| 2.4063 | 6.22 | 2216000 | 2.2715 |
| 2.3991 | 6.24 | 2224000 | 2.2683 |
| 2.3991 | 6.27 | 2232000 | 2.2657 |
| 2.405 | 6.29 | 2240000 | 2.2645 |
| 2.405 | 6.31 | 2248000 | 2.2676 |
| 2.3941 | 6.33 | 2256000 | 2.2706 |
| 2.3941 | 6.36 | 2264000 | 2.2593 |
| 2.4041 | 6.38 | 2272000 | 2.2679 |
| 2.4041 | 6.4 | 2280000 | 2.2643 |
| 2.4001 | 6.42 | 2288000 | 2.2728 |
| 2.4001 | 6.44 | 2296000 | 2.2631 |
| 2.3983 | 6.47 | 2304000 | 2.2636 |
| 2.3983 | 6.49 | 2312000 | 2.2630 |
| 2.4003 | 6.51 | 2320000 | 2.2663 |
| 2.4003 | 6.53 | 2328000 | 2.2647 |
| 2.3981 | 6.56 | 2336000 | 2.2669 |
| 2.3981 | 6.58 | 2344000 | 2.2660 |
| 2.3951 | 6.6 | 2352000 | 2.2692 |
| 2.3951 | 6.62 | 2360000 | 2.2644 |
| 2.4013 | 6.65 | 2368000 | 2.2610 |
| 2.4013 | 6.67 | 2376000 | 2.2655 |
| 2.4 | 6.69 | 2384000 | 2.2592 |
| 2.4 | 6.71 | 2392000 | 2.2666 |
| 2.3975 | 6.74 | 2400000 | 2.2685 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
|
nvidia/Llama2-13B-SteerLM-RM | nvidia | 2024-02-22T19:05:14Z | 23 | 8 | nemo | [
"nemo",
"nvidia",
"steerlm",
"llama2",
"reward model",
"text-generation",
"en",
"dataset:nvidia/HelpSteer",
"dataset:OpenAssistant/oasst1",
"arxiv:2311.09528",
"arxiv:2310.05344",
"license:llama2",
"region:us"
] | text-generation | 2024-02-19T02:49:42Z | ---
license: llama2
library_name: nemo
language:
- en
pipeline_tag: text-generation
inference: false
fine-tuning: true
tags:
- nvidia
- steerlm
- llama2
- reward model
datasets:
- nvidia/HelpSteer
- OpenAssistant/oasst1
---
# Llama2-13B-SteerLM-RM
## License
The use of this model is governed by the [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/)
## Description:
Llama2-13B-SteerLM-RM is a 13 billion parameter language model (with context of up to 4,096 tokens) used as the Attribute Prediction Model in training [Llama2-70B-SteerLM-Chat](https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat)
Attribute Prediction Model is a multi-aspect Reward Model that rates model responses on various aspects that makes a response desirable instead of a singular score in a conventional Reward Model.
Given a conversation with multiple turns between user and assistant, it rates the following attributes (between 0 and 4) for every assistant turn.
1. **Quality**: Perceived goodness of response
2. **Toxicity**: Undesirable elements such as vulgar, harmful or potentially biased response
3. **Humor**: Sense of humor within response
4. **Creativity**: Willingness to generate non-conventional response
5. **Helpfulness**: Overall helpfulness of the response to the prompt.
6. **Correctness**: Inclusion of all pertinent facts without errors.
7. **Coherence**: Consistency and clarity of expression.
8. **Complexity**: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
9. **Verbosity**: Amount of detail included in the response, relative to what is asked for in the prompt.
The first four attributes are taken from the [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset while the others are taken from [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) dataset
HelpSteer Paper : [HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM](http://arxiv.org/abs/2311.09528)
SteerLM Paper: [SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF](https://arxiv.org/abs/2310.05344)
Llama2-13B-SteerLM-RM is trained with NVIDIA [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner), a scalable toolkit for performant and efficient model alignment. NeMo-Aligner is built using the [NeMo Framework](https://github.com/NVIDIA/NeMo) which allows for scaling training up to 1000s of GPUs using tensor, data and pipeline parallelism for all components of alignment. All of our checkpoints are cross compatible with the NeMo ecosystem, allowing for inference deployment and further customization.
## Usage:
You can use the model with [NeMo Aligner](https://github.com/NVIDIA/NeMo-Aligner) following [SteerLM training user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/steerlm.html).
This model can be useful to train a model like [Llama2-70B-SteerLM-Chat](https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat) or annotate the attributes for any conversation.
1. Spin up an inference server within the [NeMo Aligner container](https://github.com/NVIDIA/NeMo-Aligner/blob/main/Dockerfile)
```python
python /opt/NeMo-Aligner/examples/nlp/gpt/serve_reward_model.py \
rm_model_file=Llama2-13B-SteerLM-RM.nemo \
trainer.num_nodes=1 \
trainer.devices=8 \
++model.tensor_model_parallel_size=4 \
++model.pipeline_model_parallel_size=1 \
inference.micro_batch_size=2 \
inference.port=1424
```
2. Annotate data files using the served reward model. If you are seeking to reproduce training of [Llama2-70B-SteerLM-Chat](https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat), this will be the Open Assistant train/val files. Then follow the next step to train a SteerLM model based on [SteerLM training user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/steerlm.html#step-5-train-the-attribute-conditioned-sft-model) .
```python
python /opt/NeMo-Aligner/examples/nlp/data/steerlm/preprocess_openassistant_data.py --output_directory=data/oasst
python /opt/NeMo-Aligner/examples/nlp/data/steerlm/attribute_annotate.py \
--input-file=data/oasst/train.jsonl \
--output-file=data/oasst/train_labeled.jsonl \
--port=1424
```
3. Alternatively, this can be any conversational data file (in .jsonl) in the following format, where each line looks like
```json
{
"conversations": [
{"value": <user_turn_1>, "from": "User", "label": None},
{"value": <assistant_turn_1>, "from": "Assistant", "label": <formatted_label_1>},
{"value": <user_turn_2>, "from": "User", "label": None},
{"value": <assistant_turn_2>, "from": "Assistant", "label": <formatted_label_2>},
],
"mask": "User"
}
```
Ideally, each ```<formatted_label_n>``` refers to the ground truth label for the assistant turn but if they are not available, we can also use ```quality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4```
## Contact
E-Mail: [Zhilin Wang](mailto:[email protected])
## Citation
If you find this dataset useful, please cite the following works
```bibtex
@misc{wang2023helpsteer,
title={HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM},
author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and Oleksii Kuchaiev},
year={2023},
eprint={2311.09528},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{dong2023steerlm,
title={SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF},
author={Yi Dong and Zhilin Wang and Makesh Narsimhan Sreedhar and Xianchao Wu and Oleksii Kuchaiev},
year={2023},
eprint={2310.05344},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
AlexanderHolmes0/llama-2-7b-chat-test | AlexanderHolmes0 | 2024-02-22T19:03:20Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-22T18:58:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## 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.
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## Training Details
### Training Data
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#### Preprocessing [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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## Technical Specifications [optional]
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## Glossary [optional]
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|
dataautogpt3/ProteusV0.4 | dataautogpt3 | 2024-02-22T19:01:41Z | 996 | 75 | diffusers | [
"diffusers",
"text-to-image",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-02-22T13:50:29Z | ---
pipeline_tag: text-to-image
widget:
- text: >-
3 fish in a fish tank wearing adorable outfits, best quality, hd
output:
url: GGuziQaXYAAudCW.png
- text: >-
a woman sitting in a wooden chair in the middle of a grass field on a farm, moonlight, best quality, hd, anime art
output:
url: upscaled_image (1).webp
- text: >-
Masterpiece, glitch, holy holy holy, fog, by DarkIncursio
output:
url: GGvDC_qWUAAcuQA.jpeg
- text: >-
jpeg Full Body Photo of a weird imaginary Female creatures captured on celluloid film, (((ghost))),heavy rain, thunder, snow, water's surface, night, expressionless, Blood, Japan God,(school), Ultra Realistic, ((Scary)),looking at camera, screem, plaintive cries, Long claws, fangs, scales,8k, HDR, 500px, mysterious and ornate digital art, photic, intricate, fantasy aesthetic.
output:
url: upscaled_image2.png
- text: >-
The divine tree of knowledge, an interplay between purple and gold, floats in the void of the sea of quanta, the tree is made of crystal, the void is made of nothingness, strong contrast, dim lighting, beautiful and surreal scene. wide shot
output:
url: upscaled_image.png
- text: >-
The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.
output:
url: old.png
- text: >-
Ghost in the Shell Stand Alone Complex
output:
url: upscaled_image4.png
- text: >-
(impressionistic realism by csybgh), a 50 something male, working in banking, very short dyed dark curly balding hair, Afro-Asiatic ancestry, talks a lot but listens poorly, stuck in the past, wearing a suit, he has a certain charm, bronze skintone, sitting in a bar at night, he is smoking and feeling cool, drunk on plum wine, masterpiece, 8k, hyper detailed, smokey ambiance, perfect hands AND fingers
output:
url: collage.png
- text: >-
black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed
output:
url: collage2.png
license: gpl-3.0
---
<Gallery />
## ProteusV0.4: The Style Update
This update enhances stylistic capabilities, similar to Midjourney's approach, rather than advancing prompt comprehension. Methods used do not infringe on any copyrighted material.
## Proteus
Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs.
In pursuit of optimal performance, numerous LORA (Low-Rank Adaptation) models are trained independently before being selectively incorporated into the principal model via dynamic application methods. These techniques involve targeting particular segments within the model while avoiding interference with other areas during the learning phase. Consequently, Proteus exhibits marked improvements in portraying intricate facial characteristics and lifelike skin textures, all while sustaining commendable proficiency across various aesthetic domains, notably surrealism, anime, and cartoon-style visualizations.
finetuned/trained on a total of 400k+ images at this point.
## Settings for ProteusV0.4
Use these settings for the best results with ProteusV0.4:
CFG Scale: Use a CFG scale of 4 to 6
Steps: 20 to 60 steps for more detail, 20 steps for faster results.
Sampler: DPM++ 2M SDE
Scheduler: Karras
Resolution: 1280x1280 or 1024x1024
please also consider using these keep words to improve your prompts:
best quality, HD, `~*~aesthetic~*~`.
if you are having trouble coming up with prompts you can use this GPT I put together to help you refine the prompt. https://chat.openai.com/g/g-RziQNoydR-diffusion-master
## Use it with 🧨 diffusers
```python
import torch
from diffusers import (
StableDiffusionXLPipeline,
KDPM2AncestralDiscreteScheduler,
AutoencoderKL
)
# Load VAE component
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16
)
# Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
"dataautogpt3/ProteusV0.4",
vae=vae,
torch_dtype=torch.float16
)
pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')
# Define prompts and generate image
prompt = "black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed"
negative_prompt = "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image"
image = pipe(
prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
guidance_scale=4,
num_inference_steps=20
).images[0]
```
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https://twitter.com/DataPlusEngine |
numen-tech/BioMistral-7B-w4a16g128asym | numen-tech | 2024-02-22T18:58:51Z | 0 | 0 | null | [
"arxiv:2308.13137",
"license:apache-2.0",
"region:us"
] | null | 2024-02-22T18:56:10Z | ---
license: apache-2.0
---
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B).
|
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