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| library_name
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Giuggiola01/distilbert-base-uncased-finetuned-cola
|
Giuggiola01
| 2024-02-27T13:37:41Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T13:03:49Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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-base-uncased-finetuned-cola
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: 0.8331
- Matthews Correlation: 0.5328
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5165 | 1.0 | 535 | 0.4556 | 0.4470 |
| 0.3416 | 2.0 | 1070 | 0.4750 | 0.5198 |
| 0.2289 | 3.0 | 1605 | 0.6451 | 0.5000 |
| 0.1768 | 4.0 | 2140 | 0.7948 | 0.5156 |
| 0.1283 | 5.0 | 2675 | 0.8331 | 0.5328 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
ferrazzipietro/Mistral-7B-v0.1_adapters_en.layer1_4_torch.bfloat16_32_32_0.01_4_0.0002
|
ferrazzipietro
| 2024-02-27T13:37:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T13:36:54Z |
---
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]
|
Ketskapow/distilbert-base-uncased-finetuned-cola
|
Ketskapow
| 2024-02-27T13:36:14Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T13:12:09Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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-base-uncased-finetuned-cola
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: 0.4587
- Matthews Correlation: 0.5306
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5228 | 1.0 | 535 | 0.4535 | 0.4629 |
| 0.3477 | 2.0 | 1070 | 0.4587 | 0.5306 |
| 0.2316 | 3.0 | 1605 | 0.6278 | 0.5193 |
| 0.1694 | 4.0 | 2140 | 0.8088 | 0.5087 |
| 0.1202 | 5.0 | 2675 | 0.8539 | 0.5256 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
falan42/tinly_llama-1.1-medical-mark-1.1
|
falan42
| 2024-02-27T13:35:57Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T13:35:54Z |
---
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]
|
riotu-lab/ArabianGPT-01B
|
riotu-lab
| 2024-02-27T13:31:53Z | 2,999 | 13 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arabic ",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-04T18:45:05Z |
---
license: apache-2.0
language:
- ar
pipeline_tag: text-generation
tags:
- 'arabic '
- text-generation
widget:
- text: "أعلنت وزارة الحج في المملكة العربية السعودية"
example_title: "مثال ١"
- text: "يبدو اليوم جميلا، سأقوم بتحضير"
example_title: "مثال ٢"
- text: "إن التقنيات الحديثة"
example_title: "مثال ٣"
---
# ArabianGPT Model Overview
## Disclaimer for the Use of Large Language Models (LLMs) for Text Generation
<p style="color: red;">We disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.1B, and users engage with and apply the model's outputs at their own risk.</p>
> **Important Note:** Currently, we offer a raw pre-trained model. Our team is actively working on releasing instruction-based LLMs that are fine-tuned and augmented with LRHF. The first set of pre-trained models has been made available for community exploration. While we do have models fine-tuned for specific tasks such as summarization and sentiment analysis, they are still in the development phase.
## How you can use this Pre-Trained?
You are invited to utilize this pre-trained, native Arabic language model as an experimental tool to assess its capabilities, aid in its fine-tuning, and evaluate its performance across a variety of downstream tasks. We encourage you to review our technical report for a comprehensive understanding of the model's performance metrics and the specific downstream tasks it has been tested on. This will provide valuable insights into its applicability and effectiveness in diverse applications.
## Introduction
ArabianGPT-0.1B, developed under the ArabianLLM initiatives, is a specialized GPT-2 model optimized for Arabic language modeling.
It's a product of the collaborative efforts at Prince Sultan University's Robotics and Internet of Things Lab, focusing on enhancing natural language modeling and generation in Arabic.
This model represents a significant stride in LLM research, specifically addressing the linguistic complexities and nuances of the Arabic language.
## Key Features
- **Architecture**: GPT-2
- **Model Size**: 134 million parameters
- **Layers**: 12
- **Model Attention Layers (MAL)**: 12
- **Context Window Size**: 768 tokens
## Training
- **Dataset**: Scraped Arabic newspaper articles
- **Data Size**: 15.5 GB
- **Words**: 237.8 million
- **Tokenizer**: Aranizer 64K
- **Tokens**: Over 1.75 billion
- **Hardware**: 2 NDIVIA A100 GPUs
- **Training Scale**: 7.5 million examples
- **Training Duration**: 3 days
- **Performance**: Final loss of 3.97
## Role in ArabianLLM Initiatives
ArabianGPT-0.1B (Base Model) is crucial for advancing Arabic language processing, addressing challenges unique to Arabic morphology and dialects.
## Usage
Suitable for Arabic text generation tasks. Example usage with Transformers Pipeline:
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="riotu-lab/ArabianGPT-01B", max_new_tokens=512)
text = ''
pipe.predict(text)
```
## Limitations and Ethical Considerations
- The model may have context understanding or text generation limitations in certain scenarios.
- Emphasis on ethical use to prevent misinformation or harmful content propagation.
## Acknowledgments
Special thanks to Prince Sultan University, particularly the Robotics and Internet of Things Lab.
## Contact Information
For inquiries: [[email protected]](mailto:[email protected]).
## Disclaimer for the Use of Large Language Models (LLMs) for Text Generation
<p style="color: red;">We disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.1B, and users engage with and apply the model's outputs at their own risk.</p>
|
lvcalucioli/zephyr-7b-beta_self-supervised_merged
|
lvcalucioli
| 2024-02-27T13:29:42Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-26T11:48:15Z |
---
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]
|
phoen1x/TF-Finetuned-xsum
|
phoen1x
| 2024-02-27T13:28:55Z | 72 | 1 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"summarization",
"en",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-05-15T22:20:52Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TF-Finetuned-xsum
results: []
datasets:
- xsum
language:
- en
metrics:
- rouge
pipeline_tag: summarization
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TF-Finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [xsum](https://huggingface.co/datasets/xsum) dataset.
It achieves the following results on the evaluation set:
- Train Loss:
- Validation Loss:
- Epoch:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rougel | Epoch |
|:----------:|:---------------:|:---------------------------------------------:|:-----:|
| | | tf.Tensor(0.1999889, shape=(), dtype=float32) | |
### Framework versions
- Transformers 4.20.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.12.1
|
auksliusninetwothree/test-model
|
auksliusninetwothree
| 2024-02-27T13:27:58Z | 78 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-02-26T13:36:00Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: test-model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: custom_data
split: test
args: custom_data
metrics:
- name: Wer
type: wer
value: 8.333333333333332
---
<!-- 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. -->
# test-model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1513
- Wer Ortho: 8.3333
- Wer: 8.3333
## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 2
- training_steps: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 1.1613 | 2.71 | 19 | 1.1513 | 8.3333 | 8.3333 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.1
- Tokenizers 0.15.2
|
littyyanamala/my-pet-dog
|
littyyanamala
| 2024-02-27T13:24:57Z | 0 | 0 | null |
[
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-02-27T13:24:03Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by littyyanamala following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
Yanwen9969/distilbert-base-uncased-finetuned-cola
|
Yanwen9969
| 2024-02-27T13:21:30Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T12:36:04Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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-base-uncased-finetuned-cola
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: 0.8446
- Matthews Correlation: 0.5377
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.517 | 1.0 | 535 | 0.4553 | 0.4460 |
| 0.3451 | 2.0 | 1070 | 0.4641 | 0.5255 |
| 0.2317 | 3.0 | 1605 | 0.6350 | 0.5186 |
| 0.1726 | 4.0 | 2140 | 0.8171 | 0.5081 |
| 0.1269 | 5.0 | 2675 | 0.8446 | 0.5377 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
OwOpeepeepoopoo/easy_america5
|
OwOpeepeepoopoo
| 2024-02-27T13:18:50Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T12:57:07Z |
---
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]
|
peldrak/segformer-b4-ade-512-512-finetuned-coastTrain-grCoastline
|
peldrak
| 2024-02-27T13:17:44Z | 188 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:peldrak/segformer-b4-ade-512-512-finetuned-coastTrain",
"base_model:finetune:peldrak/segformer-b4-ade-512-512-finetuned-coastTrain",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2024-02-27T11:36:31Z |
---
license: other
base_model: peldrak/segformer-b4-ade-512-512-finetuned-coastTrain
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b4-ade-512-512-finetuned-coastTrain-grCoastline
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-grCoastline
This model is a fine-tuned version of [peldrak/segformer-b4-ade-512-512-finetuned-coastTrain](https://huggingface.co/peldrak/segformer-b4-ade-512-512-finetuned-coastTrain) on the peldrak/grCoastline_512 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1900
- Mean Iou: 0.8129
- Mean Accuracy: 0.8809
- Overall Accuracy: 0.9540
- Accuracy Water: 0.9875
- Accuracy Whitewater: 0.6312
- Accuracy Sediment: 0.9541
- Accuracy Other Natural Terrain: 0.8566
- Accuracy Vegetation: 0.8860
- Accuracy Development: 0.8526
- Accuracy Unknown: 0.9984
- Iou Water: 0.9631
- Iou Whitewater: 0.5490
- Iou Sediment: 0.8864
- Iou Other Natural Terrain: 0.7326
- Iou Vegetation: 0.8448
- Iou Development: 0.7176
- Iou Unknown: 0.9972
## 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: 40
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|
| 0.3829 | 0.24 | 20 | 0.4153 | 0.5484 | 0.6468 | 0.8693 | 0.9547 | 0.2281 | 0.9398 | 0.0617 | 0.9459 | 0.4008 | 0.9963 | 0.9176 | 0.1120 | 0.7770 | 0.0612 | 0.6193 | 0.3634 | 0.9882 |
| 2.0682 | 0.49 | 40 | 0.2991 | 0.6099 | 0.6956 | 0.8939 | 0.9735 | 0.1187 | 0.9464 | 0.3054 | 0.8869 | 0.6447 | 0.9938 | 0.9316 | 0.1123 | 0.7992 | 0.2941 | 0.6709 | 0.4733 | 0.9879 |
| 0.5418 | 0.73 | 60 | 0.2615 | 0.6607 | 0.7312 | 0.9192 | 0.9684 | 0.0686 | 0.9512 | 0.7257 | 0.8597 | 0.5526 | 0.9920 | 0.9252 | 0.0665 | 0.7986 | 0.6036 | 0.7632 | 0.4800 | 0.9881 |
| 0.4389 | 0.98 | 80 | 0.2421 | 0.6515 | 0.7159 | 0.9201 | 0.9756 | 0.0911 | 0.9662 | 0.6066 | 0.9156 | 0.4593 | 0.9971 | 0.9426 | 0.0898 | 0.7943 | 0.5455 | 0.7796 | 0.4163 | 0.9926 |
| 0.3756 | 1.22 | 100 | 0.2204 | 0.7025 | 0.7747 | 0.9295 | 0.9931 | 0.1656 | 0.9258 | 0.8460 | 0.8142 | 0.6870 | 0.9912 | 0.9124 | 0.1560 | 0.8274 | 0.7147 | 0.7806 | 0.5365 | 0.9896 |
| 0.7675 | 1.46 | 120 | 0.2169 | 0.7061 | 0.7876 | 0.9184 | 0.9774 | 0.4127 | 0.9614 | 0.8133 | 0.7635 | 0.5874 | 0.9976 | 0.9489 | 0.3887 | 0.8088 | 0.5972 | 0.7210 | 0.4855 | 0.9928 |
| 0.5434 | 1.71 | 140 | 0.2232 | 0.7104 | 0.7782 | 0.9308 | 0.9820 | 0.2467 | 0.9620 | 0.6425 | 0.8970 | 0.7228 | 0.9943 | 0.9529 | 0.2410 | 0.8545 | 0.5718 | 0.7752 | 0.5848 | 0.9925 |
| 0.8975 | 1.95 | 160 | 0.2187 | 0.7209 | 0.8231 | 0.9231 | 0.9757 | 0.3658 | 0.8885 | 0.8665 | 0.7545 | 0.9165 | 0.9945 | 0.9473 | 0.3471 | 0.8241 | 0.6442 | 0.7331 | 0.5589 | 0.9917 |
| 0.2799 | 2.2 | 180 | 0.1662 | 0.7404 | 0.8029 | 0.9418 | 0.9706 | 0.3506 | 0.9555 | 0.9022 | 0.8947 | 0.5523 | 0.9946 | 0.9425 | 0.3293 | 0.8567 | 0.7286 | 0.8378 | 0.4949 | 0.9928 |
| 0.2132 | 2.44 | 200 | 0.1616 | 0.7714 | 0.8442 | 0.9443 | 0.9777 | 0.4621 | 0.9536 | 0.7886 | 0.8819 | 0.8492 | 0.9963 | 0.9435 | 0.4076 | 0.8578 | 0.7267 | 0.8215 | 0.6498 | 0.9930 |
| 0.3068 | 2.68 | 220 | 0.2055 | 0.7345 | 0.8090 | 0.9318 | 0.9870 | 0.4136 | 0.9517 | 0.8730 | 0.8080 | 0.6367 | 0.9931 | 0.9370 | 0.3753 | 0.8034 | 0.7027 | 0.7935 | 0.5387 | 0.9909 |
| 0.1822 | 2.93 | 240 | 0.1367 | 0.7984 | 0.8640 | 0.9531 | 0.9886 | 0.5028 | 0.9106 | 0.8899 | 0.8992 | 0.8593 | 0.9977 | 0.9499 | 0.4617 | 0.8667 | 0.7874 | 0.8617 | 0.6675 | 0.9937 |
| 0.1504 | 3.17 | 260 | 0.1548 | 0.7794 | 0.8446 | 0.9471 | 0.9830 | 0.4763 | 0.9544 | 0.8496 | 0.8731 | 0.7774 | 0.9983 | 0.9482 | 0.4416 | 0.8516 | 0.7572 | 0.8427 | 0.6226 | 0.9917 |
| 0.2699 | 3.41 | 280 | 0.1543 | 0.7508 | 0.8024 | 0.9475 | 0.9889 | 0.2791 | 0.9421 | 0.8336 | 0.9211 | 0.6552 | 0.9967 | 0.9484 | 0.2719 | 0.8647 | 0.7216 | 0.8500 | 0.6056 | 0.9938 |
| 0.2272 | 3.66 | 300 | 0.1547 | 0.7618 | 0.8232 | 0.9490 | 0.9868 | 0.2820 | 0.9403 | 0.7567 | 0.9193 | 0.8808 | 0.9963 | 0.9565 | 0.2766 | 0.8735 | 0.7173 | 0.8339 | 0.6810 | 0.9938 |
| 0.0938 | 3.9 | 320 | 0.1776 | 0.7615 | 0.8290 | 0.9415 | 0.9889 | 0.4229 | 0.9468 | 0.7605 | 0.8807 | 0.8081 | 0.9953 | 0.9545 | 0.3961 | 0.8519 | 0.6753 | 0.8080 | 0.6522 | 0.9929 |
| 0.129 | 4.15 | 340 | 0.1708 | 0.7606 | 0.8281 | 0.9404 | 0.9839 | 0.5055 | 0.9591 | 0.8675 | 0.8540 | 0.6292 | 0.9977 | 0.9548 | 0.4638 | 0.8605 | 0.6874 | 0.8105 | 0.5533 | 0.9935 |
| 0.1929 | 4.39 | 360 | 0.1504 | 0.7864 | 0.8456 | 0.9493 | 0.9832 | 0.4961 | 0.9428 | 0.8054 | 0.9216 | 0.7725 | 0.9976 | 0.9551 | 0.4630 | 0.8737 | 0.7149 | 0.8442 | 0.6605 | 0.9936 |
| 0.1933 | 4.63 | 380 | 0.1572 | 0.7887 | 0.8610 | 0.9475 | 0.9875 | 0.4972 | 0.9328 | 0.8885 | 0.8523 | 0.8746 | 0.9939 | 0.9520 | 0.4646 | 0.8696 | 0.7369 | 0.8259 | 0.6796 | 0.9925 |
| 0.0642 | 4.88 | 400 | 0.1759 | 0.7988 | 0.8631 | 0.9504 | 0.9839 | 0.5690 | 0.9449 | 0.7954 | 0.9159 | 0.8352 | 0.9971 | 0.9585 | 0.5125 | 0.8850 | 0.7171 | 0.8326 | 0.6917 | 0.9943 |
| 0.1118 | 5.12 | 420 | 0.1461 | 0.8027 | 0.8728 | 0.9524 | 0.9854 | 0.5667 | 0.9487 | 0.8658 | 0.8783 | 0.8670 | 0.9973 | 0.9592 | 0.4847 | 0.8653 | 0.7586 | 0.8420 | 0.7145 | 0.9944 |
| 0.1145 | 5.37 | 440 | 0.1437 | 0.7884 | 0.8471 | 0.9517 | 0.9806 | 0.4749 | 0.9560 | 0.9182 | 0.8870 | 0.7163 | 0.9969 | 0.9578 | 0.4510 | 0.8813 | 0.7459 | 0.8526 | 0.6354 | 0.9947 |
| 0.2373 | 5.61 | 460 | 0.1429 | 0.8081 | 0.8807 | 0.9539 | 0.9875 | 0.6424 | 0.9413 | 0.8411 | 0.9063 | 0.8488 | 0.9976 | 0.9526 | 0.5048 | 0.8671 | 0.7674 | 0.8562 | 0.7140 | 0.9950 |
| 0.0863 | 5.85 | 480 | 0.1620 | 0.7747 | 0.8317 | 0.9497 | 0.9872 | 0.3768 | 0.9531 | 0.8926 | 0.8780 | 0.7383 | 0.9957 | 0.9520 | 0.3645 | 0.8553 | 0.7583 | 0.8462 | 0.6527 | 0.9942 |
| 0.1391 | 6.1 | 500 | 0.1639 | 0.7719 | 0.8332 | 0.9476 | 0.9856 | 0.3736 | 0.9277 | 0.7736 | 0.9158 | 0.8580 | 0.9980 | 0.9536 | 0.3623 | 0.8647 | 0.7044 | 0.8334 | 0.6903 | 0.9945 |
| 0.0976 | 6.34 | 520 | 0.1893 | 0.7449 | 0.8043 | 0.9405 | 0.9883 | 0.3225 | 0.9467 | 0.8313 | 0.8733 | 0.6724 | 0.9955 | 0.9496 | 0.3151 | 0.8619 | 0.6690 | 0.8097 | 0.6149 | 0.9938 |
| 0.1592 | 6.59 | 540 | 0.1842 | 0.7557 | 0.8210 | 0.9436 | 0.9888 | 0.3033 | 0.9475 | 0.8408 | 0.8482 | 0.8224 | 0.9958 | 0.9522 | 0.2846 | 0.8585 | 0.7011 | 0.8012 | 0.6982 | 0.9940 |
| 0.2569 | 6.83 | 560 | 0.1531 | 0.7984 | 0.8686 | 0.9495 | 0.9863 | 0.5709 | 0.9457 | 0.8419 | 0.8783 | 0.8619 | 0.9955 | 0.9554 | 0.5058 | 0.8757 | 0.7338 | 0.8258 | 0.6986 | 0.9940 |
| 0.1064 | 7.07 | 580 | 0.1944 | 0.7784 | 0.8474 | 0.9420 | 0.9895 | 0.5455 | 0.9459 | 0.7592 | 0.8764 | 0.8178 | 0.9975 | 0.9534 | 0.5047 | 0.8475 | 0.6847 | 0.8063 | 0.6572 | 0.9949 |
| 0.0979 | 7.32 | 600 | 0.1581 | 0.7959 | 0.8574 | 0.9508 | 0.9869 | 0.5223 | 0.9399 | 0.8773 | 0.8874 | 0.7912 | 0.9968 | 0.9522 | 0.4766 | 0.8740 | 0.7516 | 0.8359 | 0.6865 | 0.9945 |
| 0.045 | 7.56 | 620 | 0.1962 | 0.7990 | 0.8655 | 0.9479 | 0.9801 | 0.5896 | 0.9347 | 0.7952 | 0.9054 | 0.8546 | 0.9988 | 0.9485 | 0.5253 | 0.8764 | 0.7167 | 0.8204 | 0.7117 | 0.9938 |
| 0.0495 | 7.8 | 640 | 0.2135 | 0.7824 | 0.8541 | 0.9428 | 0.9834 | 0.5986 | 0.9555 | 0.8005 | 0.8692 | 0.7725 | 0.9987 | 0.9550 | 0.5221 | 0.8447 | 0.7026 | 0.8075 | 0.6500 | 0.9949 |
| 0.0389 | 8.05 | 660 | 0.1860 | 0.7856 | 0.8503 | 0.9469 | 0.9809 | 0.4908 | 0.9358 | 0.7910 | 0.9012 | 0.8538 | 0.9985 | 0.9547 | 0.4522 | 0.8837 | 0.6847 | 0.8158 | 0.7134 | 0.9946 |
| 0.177 | 8.29 | 680 | 0.2002 | 0.7719 | 0.8338 | 0.9478 | 0.9871 | 0.3745 | 0.9462 | 0.7507 | 0.9147 | 0.8683 | 0.9951 | 0.9535 | 0.3584 | 0.8738 | 0.7041 | 0.8278 | 0.6920 | 0.9937 |
| 0.0522 | 8.54 | 700 | 0.1619 | 0.7917 | 0.8564 | 0.9481 | 0.9875 | 0.5765 | 0.9388 | 0.8643 | 0.8908 | 0.7403 | 0.9963 | 0.9568 | 0.5202 | 0.8813 | 0.7152 | 0.8297 | 0.6440 | 0.9945 |
| 0.066 | 8.78 | 720 | 0.1800 | 0.7782 | 0.8539 | 0.9451 | 0.9850 | 0.4766 | 0.9503 | 0.8770 | 0.8304 | 0.8615 | 0.9963 | 0.9597 | 0.4398 | 0.8674 | 0.7291 | 0.7992 | 0.6581 | 0.9945 |
| 0.1114 | 9.02 | 740 | 0.1692 | 0.7867 | 0.8517 | 0.9476 | 0.9880 | 0.5068 | 0.9485 | 0.8157 | 0.8789 | 0.8257 | 0.9982 | 0.9569 | 0.4758 | 0.8787 | 0.7079 | 0.8205 | 0.6723 | 0.9951 |
| 0.1906 | 9.27 | 760 | 0.1724 | 0.7929 | 0.8617 | 0.9490 | 0.9820 | 0.5359 | 0.9464 | 0.8073 | 0.8928 | 0.8697 | 0.9978 | 0.9572 | 0.4821 | 0.8714 | 0.7178 | 0.8284 | 0.6980 | 0.9956 |
| 0.0562 | 9.51 | 780 | 0.1984 | 0.7811 | 0.8494 | 0.9449 | 0.9807 | 0.5865 | 0.9549 | 0.8392 | 0.8888 | 0.6984 | 0.9969 | 0.9569 | 0.5050 | 0.8750 | 0.6786 | 0.8232 | 0.6337 | 0.9952 |
| 0.1104 | 9.76 | 800 | 0.1972 | 0.7978 | 0.8687 | 0.9469 | 0.9855 | 0.5906 | 0.9419 | 0.7758 | 0.8914 | 0.9000 | 0.9955 | 0.9556 | 0.5334 | 0.8733 | 0.6863 | 0.8178 | 0.7237 | 0.9945 |
| 0.0451 | 10.0 | 820 | 0.2123 | 0.7769 | 0.8455 | 0.9415 | 0.9821 | 0.5810 | 0.9500 | 0.8132 | 0.8747 | 0.7191 | 0.9984 | 0.9537 | 0.5241 | 0.8658 | 0.6691 | 0.8074 | 0.6231 | 0.9949 |
| 0.1426 | 10.24 | 840 | 0.2210 | 0.7989 | 0.8745 | 0.9465 | 0.9800 | 0.6316 | 0.9435 | 0.7712 | 0.8897 | 0.9072 | 0.9983 | 0.9562 | 0.5525 | 0.8783 | 0.6823 | 0.8131 | 0.7147 | 0.9951 |
| 0.0683 | 10.49 | 860 | 0.2162 | 0.7964 | 0.8677 | 0.9473 | 0.9802 | 0.5774 | 0.9515 | 0.7715 | 0.8902 | 0.9058 | 0.9974 | 0.9549 | 0.5202 | 0.8777 | 0.6901 | 0.8156 | 0.7210 | 0.9954 |
| 0.0758 | 10.73 | 880 | 0.1898 | 0.8005 | 0.8774 | 0.9468 | 0.9863 | 0.6471 | 0.9326 | 0.8057 | 0.8757 | 0.8960 | 0.9984 | 0.9595 | 0.5466 | 0.8796 | 0.6810 | 0.8067 | 0.7347 | 0.9957 |
| 0.0496 | 10.98 | 900 | 0.1919 | 0.8019 | 0.8738 | 0.9469 | 0.9794 | 0.6404 | 0.9636 | 0.8149 | 0.8670 | 0.8526 | 0.9984 | 0.9598 | 0.5598 | 0.8726 | 0.6949 | 0.8065 | 0.7236 | 0.9959 |
| 0.0329 | 11.22 | 920 | 0.1862 | 0.8004 | 0.8689 | 0.9469 | 0.9891 | 0.5888 | 0.9460 | 0.8303 | 0.8556 | 0.8746 | 0.9977 | 0.9594 | 0.5378 | 0.8816 | 0.6882 | 0.8003 | 0.7395 | 0.9957 |
| 0.0808 | 11.46 | 940 | 0.2000 | 0.8016 | 0.8730 | 0.9485 | 0.9868 | 0.6599 | 0.9461 | 0.7912 | 0.8998 | 0.8292 | 0.9977 | 0.9617 | 0.5578 | 0.8811 | 0.6872 | 0.8208 | 0.7070 | 0.9960 |
| 0.0492 | 11.71 | 960 | 0.2148 | 0.7983 | 0.8672 | 0.9466 | 0.9895 | 0.6154 | 0.9459 | 0.8051 | 0.8765 | 0.8410 | 0.9968 | 0.9589 | 0.5439 | 0.8735 | 0.6870 | 0.8079 | 0.7210 | 0.9959 |
| 0.0629 | 11.95 | 980 | 0.2277 | 0.7941 | 0.8637 | 0.9456 | 0.9842 | 0.5803 | 0.9556 | 0.7909 | 0.8695 | 0.8680 | 0.9973 | 0.9604 | 0.5260 | 0.8744 | 0.6751 | 0.8009 | 0.7260 | 0.9958 |
| 0.1419 | 12.2 | 1000 | 0.2076 | 0.7977 | 0.8623 | 0.9483 | 0.9868 | 0.5485 | 0.9413 | 0.8046 | 0.8865 | 0.8710 | 0.9977 | 0.9584 | 0.5079 | 0.8817 | 0.6928 | 0.8139 | 0.7335 | 0.9957 |
| 0.153 | 12.44 | 1020 | 0.1835 | 0.7986 | 0.8608 | 0.9494 | 0.9833 | 0.5284 | 0.9457 | 0.8415 | 0.8814 | 0.8475 | 0.9979 | 0.9600 | 0.4923 | 0.8840 | 0.6992 | 0.8185 | 0.7404 | 0.9957 |
| 0.0377 | 12.68 | 1040 | 0.2033 | 0.7894 | 0.8567 | 0.9476 | 0.9826 | 0.4861 | 0.9596 | 0.8731 | 0.8436 | 0.8548 | 0.9971 | 0.9603 | 0.4626 | 0.8669 | 0.7198 | 0.8113 | 0.7089 | 0.9957 |
| 0.0474 | 12.93 | 1060 | 0.2220 | 0.7935 | 0.8586 | 0.9464 | 0.9890 | 0.5751 | 0.9476 | 0.7947 | 0.8835 | 0.8229 | 0.9974 | 0.9577 | 0.5129 | 0.8731 | 0.6790 | 0.8098 | 0.7261 | 0.9959 |
| 1.1161 | 13.17 | 1080 | 0.2110 | 0.7992 | 0.8716 | 0.9463 | 0.9821 | 0.6330 | 0.9614 | 0.8127 | 0.8632 | 0.8502 | 0.9983 | 0.9585 | 0.5585 | 0.8827 | 0.6897 | 0.8012 | 0.7076 | 0.9962 |
| 0.099 | 13.41 | 1100 | 0.2123 | 0.7984 | 0.8743 | 0.9472 | 0.9868 | 0.6128 | 0.9379 | 0.8127 | 0.8678 | 0.9040 | 0.9983 | 0.9570 | 0.5382 | 0.8878 | 0.6926 | 0.8075 | 0.7095 | 0.9962 |
| 0.0588 | 13.66 | 1120 | 0.1905 | 0.8032 | 0.8784 | 0.9485 | 0.9814 | 0.6312 | 0.9540 | 0.8039 | 0.8745 | 0.9050 | 0.9984 | 0.9603 | 0.5530 | 0.8902 | 0.6977 | 0.8097 | 0.7157 | 0.9961 |
| 0.0769 | 13.9 | 1140 | 0.1758 | 0.8017 | 0.8647 | 0.9500 | 0.9864 | 0.5469 | 0.9429 | 0.8562 | 0.8735 | 0.8484 | 0.9985 | 0.9568 | 0.5034 | 0.8897 | 0.7119 | 0.8164 | 0.7372 | 0.9962 |
| 0.121 | 14.15 | 1160 | 0.1858 | 0.8027 | 0.8701 | 0.9499 | 0.9816 | 0.5560 | 0.9518 | 0.8267 | 0.8789 | 0.8986 | 0.9969 | 0.9578 | 0.5182 | 0.8862 | 0.7072 | 0.8231 | 0.7310 | 0.9956 |
| 0.0663 | 14.39 | 1180 | 0.2045 | 0.7889 | 0.8640 | 0.9451 | 0.9897 | 0.5519 | 0.9317 | 0.8208 | 0.8533 | 0.9023 | 0.9981 | 0.9563 | 0.4946 | 0.8742 | 0.6857 | 0.8016 | 0.7136 | 0.9960 |
| 0.0254 | 14.63 | 1200 | 0.2105 | 0.8003 | 0.8676 | 0.9474 | 0.9816 | 0.6103 | 0.9605 | 0.8074 | 0.8776 | 0.8377 | 0.9982 | 0.9611 | 0.5442 | 0.8794 | 0.6838 | 0.8091 | 0.7281 | 0.9962 |
| 0.1533 | 14.88 | 1220 | 0.2133 | 0.7973 | 0.8680 | 0.9470 | 0.9870 | 0.6039 | 0.9465 | 0.8942 | 0.8418 | 0.8046 | 0.9985 | 0.9606 | 0.5294 | 0.8828 | 0.6974 | 0.8046 | 0.7100 | 0.9962 |
| 0.0389 | 15.12 | 1240 | 0.1854 | 0.8032 | 0.8722 | 0.9489 | 0.9893 | 0.6311 | 0.9484 | 0.8297 | 0.8745 | 0.8342 | 0.9984 | 0.9600 | 0.5509 | 0.8777 | 0.7049 | 0.8183 | 0.7145 | 0.9962 |
| 0.0361 | 15.37 | 1260 | 0.1864 | 0.7939 | 0.8565 | 0.9493 | 0.9896 | 0.5175 | 0.9430 | 0.8092 | 0.8878 | 0.8494 | 0.9990 | 0.9588 | 0.4678 | 0.8842 | 0.6940 | 0.8197 | 0.7371 | 0.9958 |
| 0.0211 | 15.61 | 1280 | 0.2172 | 0.7962 | 0.8720 | 0.9454 | 0.9853 | 0.5900 | 0.9531 | 0.8542 | 0.8295 | 0.8943 | 0.9973 | 0.9621 | 0.5328 | 0.8799 | 0.6886 | 0.7885 | 0.7253 | 0.9963 |
| 0.1093 | 15.85 | 1300 | 0.1688 | 0.8111 | 0.8728 | 0.9531 | 0.9880 | 0.5895 | 0.9482 | 0.8217 | 0.9014 | 0.8627 | 0.9979 | 0.9620 | 0.5364 | 0.8934 | 0.7190 | 0.8316 | 0.7392 | 0.9965 |
| 0.0733 | 16.1 | 1320 | 0.1827 | 0.8126 | 0.8845 | 0.9515 | 0.9869 | 0.6553 | 0.9503 | 0.8784 | 0.8587 | 0.8627 | 0.9990 | 0.9608 | 0.5654 | 0.8826 | 0.7278 | 0.8258 | 0.7300 | 0.9960 |
| 0.0708 | 16.34 | 1340 | 0.1822 | 0.8101 | 0.8783 | 0.9527 | 0.9896 | 0.6199 | 0.9476 | 0.8128 | 0.8992 | 0.8827 | 0.9967 | 0.9598 | 0.5407 | 0.8858 | 0.7244 | 0.8394 | 0.7250 | 0.9955 |
| 0.0522 | 16.59 | 1360 | 0.1780 | 0.8087 | 0.8748 | 0.9518 | 0.9864 | 0.5917 | 0.9509 | 0.8650 | 0.8725 | 0.8599 | 0.9974 | 0.9615 | 0.5372 | 0.8861 | 0.7247 | 0.8282 | 0.7270 | 0.9959 |
| 0.0453 | 16.83 | 1380 | 0.1880 | 0.8020 | 0.8735 | 0.9486 | 0.9891 | 0.5987 | 0.9476 | 0.8654 | 0.8475 | 0.8680 | 0.9983 | 0.9611 | 0.5376 | 0.8809 | 0.7100 | 0.8114 | 0.7165 | 0.9962 |
| 0.0351 | 17.07 | 1400 | 0.1885 | 0.8045 | 0.8758 | 0.9502 | 0.9880 | 0.5929 | 0.9435 | 0.8644 | 0.8591 | 0.8846 | 0.9982 | 0.9608 | 0.5261 | 0.8841 | 0.7161 | 0.8189 | 0.7295 | 0.9962 |
| 0.0629 | 17.32 | 1420 | 0.1721 | 0.8132 | 0.8780 | 0.9536 | 0.9840 | 0.6104 | 0.9586 | 0.8472 | 0.8888 | 0.8590 | 0.9982 | 0.9627 | 0.5470 | 0.8839 | 0.7381 | 0.8383 | 0.7260 | 0.9962 |
| 0.0547 | 17.56 | 1440 | 0.1993 | 0.8025 | 0.8734 | 0.9478 | 0.9877 | 0.6203 | 0.9555 | 0.8655 | 0.8430 | 0.8431 | 0.9987 | 0.9599 | 0.5544 | 0.8666 | 0.7186 | 0.8101 | 0.7115 | 0.9963 |
| 0.081 | 17.8 | 1460 | 0.2054 | 0.8034 | 0.8702 | 0.9493 | 0.9892 | 0.6097 | 0.9505 | 0.8118 | 0.8823 | 0.8502 | 0.9976 | 0.9603 | 0.5416 | 0.8769 | 0.6996 | 0.8204 | 0.7283 | 0.9964 |
| 0.04 | 18.05 | 1480 | 0.2196 | 0.7915 | 0.8572 | 0.9459 | 0.9893 | 0.5738 | 0.9500 | 0.8183 | 0.8706 | 0.8003 | 0.9979 | 0.9586 | 0.5216 | 0.8679 | 0.6870 | 0.8109 | 0.6976 | 0.9966 |
| 0.1213 | 18.29 | 1500 | 0.2320 | 0.7920 | 0.8620 | 0.9461 | 0.9850 | 0.5770 | 0.9594 | 0.8126 | 0.8628 | 0.8395 | 0.9980 | 0.9609 | 0.5168 | 0.8688 | 0.6891 | 0.8062 | 0.7053 | 0.9966 |
| 0.0496 | 18.54 | 1520 | 0.1928 | 0.8065 | 0.8745 | 0.9503 | 0.9842 | 0.6020 | 0.9449 | 0.8562 | 0.8736 | 0.8619 | 0.9984 | 0.9612 | 0.5367 | 0.8886 | 0.7098 | 0.8177 | 0.7348 | 0.9966 |
| 0.045 | 18.78 | 1540 | 0.2075 | 0.7988 | 0.8787 | 0.9460 | 0.9839 | 0.6246 | 0.9374 | 0.8428 | 0.8457 | 0.9183 | 0.9984 | 0.9607 | 0.5522 | 0.8790 | 0.6941 | 0.7982 | 0.7107 | 0.9968 |
| 0.0317 | 19.02 | 1560 | 0.1938 | 0.8051 | 0.8715 | 0.9505 | 0.9892 | 0.5835 | 0.9426 | 0.8278 | 0.8810 | 0.8782 | 0.9980 | 0.9598 | 0.5248 | 0.8878 | 0.7125 | 0.8174 | 0.7364 | 0.9967 |
| 0.0489 | 19.27 | 1580 | 0.1844 | 0.8074 | 0.8792 | 0.9500 | 0.9847 | 0.6251 | 0.9529 | 0.8445 | 0.8628 | 0.8855 | 0.9991 | 0.9613 | 0.5493 | 0.8817 | 0.7121 | 0.8174 | 0.7331 | 0.9966 |
| 0.091 | 19.51 | 1600 | 0.1976 | 0.7907 | 0.8478 | 0.9495 | 0.9910 | 0.5186 | 0.9449 | 0.8258 | 0.9006 | 0.7553 | 0.9986 | 0.9563 | 0.4818 | 0.8703 | 0.7123 | 0.8358 | 0.6818 | 0.9967 |
| 0.0308 | 19.76 | 1620 | 0.1722 | 0.8076 | 0.8722 | 0.9518 | 0.9861 | 0.5906 | 0.9516 | 0.8461 | 0.8820 | 0.8503 | 0.9987 | 0.9612 | 0.5347 | 0.8842 | 0.7192 | 0.8309 | 0.7259 | 0.9968 |
| 0.231 | 20.0 | 1640 | 0.1774 | 0.8073 | 0.8726 | 0.9523 | 0.9912 | 0.5837 | 0.9329 | 0.8215 | 0.8979 | 0.8822 | 0.9988 | 0.9554 | 0.5183 | 0.8759 | 0.7299 | 0.8404 | 0.7343 | 0.9966 |
| 0.0407 | 20.24 | 1660 | 0.2232 | 0.7988 | 0.8750 | 0.9464 | 0.9844 | 0.6060 | 0.9575 | 0.8624 | 0.8267 | 0.8891 | 0.9989 | 0.9609 | 0.5377 | 0.8691 | 0.7052 | 0.7968 | 0.7252 | 0.9964 |
| 0.0303 | 20.49 | 1680 | 0.2146 | 0.8002 | 0.8709 | 0.9479 | 0.9872 | 0.5865 | 0.9498 | 0.8449 | 0.8528 | 0.8774 | 0.9979 | 0.9612 | 0.5307 | 0.8760 | 0.7040 | 0.8067 | 0.7260 | 0.9965 |
| 0.0398 | 20.73 | 1700 | 0.2119 | 0.7977 | 0.8754 | 0.9465 | 0.9858 | 0.6453 | 0.9460 | 0.8314 | 0.8575 | 0.8631 | 0.9989 | 0.9632 | 0.5460 | 0.8824 | 0.6865 | 0.8007 | 0.7087 | 0.9967 |
| 0.6198 | 20.98 | 1720 | 0.2056 | 0.7992 | 0.8731 | 0.9472 | 0.9852 | 0.6495 | 0.9462 | 0.8761 | 0.8560 | 0.8006 | 0.9982 | 0.9628 | 0.5610 | 0.8854 | 0.6941 | 0.8091 | 0.6852 | 0.9969 |
| 0.0428 | 21.22 | 1740 | 0.1978 | 0.7970 | 0.8670 | 0.9483 | 0.9843 | 0.6512 | 0.9546 | 0.8829 | 0.8734 | 0.7246 | 0.9979 | 0.9629 | 0.5622 | 0.8830 | 0.7036 | 0.8269 | 0.6435 | 0.9967 |
| 0.04 | 21.46 | 1760 | 0.1939 | 0.7945 | 0.8675 | 0.9469 | 0.9860 | 0.6371 | 0.9491 | 0.8376 | 0.8718 | 0.7922 | 0.9988 | 0.9619 | 0.5536 | 0.8799 | 0.6914 | 0.8183 | 0.6592 | 0.9968 |
| 0.0279 | 21.71 | 1780 | 0.2210 | 0.7852 | 0.8516 | 0.9464 | 0.9866 | 0.5663 | 0.9519 | 0.8814 | 0.8654 | 0.7116 | 0.9979 | 0.9611 | 0.5143 | 0.8860 | 0.6954 | 0.8175 | 0.6253 | 0.9966 |
| 0.0619 | 21.95 | 1800 | 0.1971 | 0.7930 | 0.8654 | 0.9484 | 0.9867 | 0.6116 | 0.9498 | 0.8739 | 0.8676 | 0.7698 | 0.9981 | 0.9627 | 0.5353 | 0.8885 | 0.7080 | 0.8258 | 0.6342 | 0.9967 |
| 0.0203 | 22.2 | 1820 | 0.1964 | 0.7926 | 0.8679 | 0.9464 | 0.9832 | 0.6733 | 0.9576 | 0.8433 | 0.8750 | 0.7447 | 0.9984 | 0.9631 | 0.5685 | 0.8830 | 0.6827 | 0.8230 | 0.6308 | 0.9969 |
| 0.0907 | 22.44 | 1840 | 0.2107 | 0.7875 | 0.8587 | 0.9458 | 0.9896 | 0.5721 | 0.9506 | 0.8118 | 0.8643 | 0.8239 | 0.9988 | 0.9596 | 0.5017 | 0.8729 | 0.6816 | 0.8105 | 0.6896 | 0.9969 |
| 0.0378 | 22.68 | 1860 | 0.2036 | 0.8053 | 0.8807 | 0.9492 | 0.9850 | 0.6530 | 0.9489 | 0.8037 | 0.8791 | 0.8962 | 0.9989 | 0.9615 | 0.5578 | 0.8828 | 0.6954 | 0.8183 | 0.7247 | 0.9967 |
| 0.081 | 22.93 | 1880 | 0.2039 | 0.7989 | 0.8683 | 0.9488 | 0.9908 | 0.5842 | 0.9388 | 0.8415 | 0.8693 | 0.8548 | 0.9982 | 0.9593 | 0.5193 | 0.8801 | 0.7000 | 0.8198 | 0.7170 | 0.9969 |
| 0.0237 | 23.17 | 1900 | 0.2002 | 0.7899 | 0.8571 | 0.9471 | 0.9876 | 0.5706 | 0.9510 | 0.8307 | 0.8760 | 0.7854 | 0.9982 | 0.9612 | 0.5203 | 0.8814 | 0.6841 | 0.8224 | 0.6634 | 0.9968 |
| 0.0528 | 23.41 | 1920 | 0.2114 | 0.7850 | 0.8538 | 0.9461 | 0.9886 | 0.5398 | 0.9524 | 0.8423 | 0.8587 | 0.7965 | 0.9982 | 0.9603 | 0.4920 | 0.8728 | 0.6905 | 0.8156 | 0.6667 | 0.9970 |
| 0.0793 | 23.66 | 1940 | 0.1825 | 0.8003 | 0.8694 | 0.9498 | 0.9888 | 0.6138 | 0.9454 | 0.8474 | 0.8786 | 0.8130 | 0.9990 | 0.9604 | 0.5439 | 0.8800 | 0.7102 | 0.8346 | 0.6757 | 0.9969 |
| 0.0288 | 23.9 | 1960 | 0.1854 | 0.8051 | 0.8705 | 0.9520 | 0.9893 | 0.6176 | 0.9509 | 0.8519 | 0.8913 | 0.7944 | 0.9979 | 0.9608 | 0.5530 | 0.8781 | 0.7302 | 0.8473 | 0.6693 | 0.9968 |
| 0.0525 | 24.15 | 1980 | 0.1603 | 0.8141 | 0.8846 | 0.9550 | 0.9864 | 0.6848 | 0.9472 | 0.8581 | 0.9083 | 0.8084 | 0.9990 | 0.9620 | 0.5691 | 0.8876 | 0.7440 | 0.8616 | 0.6776 | 0.9966 |
| 0.026 | 24.39 | 2000 | 0.1684 | 0.8068 | 0.8752 | 0.9532 | 0.9877 | 0.6538 | 0.9479 | 0.8936 | 0.8893 | 0.7554 | 0.9989 | 0.9616 | 0.5547 | 0.8856 | 0.7362 | 0.8538 | 0.6586 | 0.9969 |
| 0.0397 | 24.63 | 2020 | 0.1692 | 0.8121 | 0.8760 | 0.9542 | 0.9870 | 0.5916 | 0.9529 | 0.8621 | 0.8871 | 0.8535 | 0.9979 | 0.9616 | 0.5381 | 0.8834 | 0.7400 | 0.8478 | 0.7173 | 0.9968 |
| 0.4272 | 24.88 | 2040 | 0.1785 | 0.8101 | 0.8749 | 0.9535 | 0.9868 | 0.5751 | 0.9539 | 0.8895 | 0.8697 | 0.8520 | 0.9975 | 0.9633 | 0.5204 | 0.8832 | 0.7380 | 0.8374 | 0.7316 | 0.9966 |
| 0.0399 | 25.12 | 2060 | 0.1765 | 0.8070 | 0.8682 | 0.9532 | 0.9885 | 0.5755 | 0.9559 | 0.8543 | 0.8893 | 0.8154 | 0.9983 | 0.9615 | 0.5252 | 0.8758 | 0.7359 | 0.8473 | 0.7064 | 0.9968 |
| 0.0456 | 25.37 | 2080 | 0.1777 | 0.8060 | 0.8668 | 0.9535 | 0.9900 | 0.5873 | 0.9487 | 0.8418 | 0.9061 | 0.7955 | 0.9983 | 0.9605 | 0.5270 | 0.8821 | 0.7299 | 0.8526 | 0.6928 | 0.9969 |
| 0.0414 | 25.61 | 2100 | 0.1844 | 0.8132 | 0.8847 | 0.9531 | 0.9864 | 0.6789 | 0.9515 | 0.8486 | 0.8916 | 0.8373 | 0.9984 | 0.9623 | 0.5671 | 0.8849 | 0.7266 | 0.8418 | 0.7130 | 0.9970 |
| 0.0925 | 25.85 | 2120 | 0.2120 | 0.8035 | 0.8663 | 0.9521 | 0.9886 | 0.5885 | 0.9500 | 0.8118 | 0.9077 | 0.8193 | 0.9982 | 0.9607 | 0.5215 | 0.8832 | 0.7125 | 0.8402 | 0.7097 | 0.9970 |
| 0.0443 | 26.1 | 2140 | 0.1615 | 0.8151 | 0.8790 | 0.9555 | 0.9882 | 0.5945 | 0.9449 | 0.8779 | 0.8874 | 0.8610 | 0.9992 | 0.9603 | 0.5309 | 0.8871 | 0.7511 | 0.8517 | 0.7281 | 0.9964 |
| 0.0728 | 26.34 | 2160 | 0.1701 | 0.8091 | 0.8771 | 0.9534 | 0.9872 | 0.6244 | 0.9493 | 0.8818 | 0.8816 | 0.8164 | 0.9989 | 0.9624 | 0.5469 | 0.8858 | 0.7362 | 0.8478 | 0.6876 | 0.9968 |
| 0.0484 | 26.59 | 2180 | 0.1720 | 0.8061 | 0.8707 | 0.9530 | 0.9895 | 0.6110 | 0.9487 | 0.8831 | 0.8852 | 0.7787 | 0.9987 | 0.9615 | 0.5429 | 0.8814 | 0.7374 | 0.8496 | 0.6727 | 0.9969 |
| 0.027 | 26.83 | 2200 | 0.1728 | 0.8060 | 0.8754 | 0.9525 | 0.9879 | 0.6263 | 0.9498 | 0.8718 | 0.8823 | 0.8111 | 0.9983 | 0.9620 | 0.5489 | 0.8825 | 0.7351 | 0.8472 | 0.6694 | 0.9970 |
| 0.0465 | 27.07 | 2220 | 0.1763 | 0.8075 | 0.8751 | 0.9534 | 0.9875 | 0.6402 | 0.9496 | 0.8776 | 0.8938 | 0.7791 | 0.9981 | 0.9623 | 0.5514 | 0.8842 | 0.7366 | 0.8533 | 0.6675 | 0.9970 |
| 0.0213 | 27.32 | 2240 | 0.1740 | 0.8085 | 0.8743 | 0.9538 | 0.9869 | 0.6184 | 0.9501 | 0.8787 | 0.8917 | 0.7963 | 0.9983 | 0.9632 | 0.5446 | 0.8852 | 0.7370 | 0.8523 | 0.6799 | 0.9971 |
| 0.022 | 27.56 | 2260 | 0.1923 | 0.7998 | 0.8675 | 0.9508 | 0.9863 | 0.5850 | 0.9506 | 0.8669 | 0.8777 | 0.8084 | 0.9979 | 0.9613 | 0.5253 | 0.8839 | 0.7155 | 0.8387 | 0.6769 | 0.9970 |
| 0.0311 | 27.8 | 2280 | 0.1871 | 0.8005 | 0.8657 | 0.9518 | 0.9877 | 0.5896 | 0.9482 | 0.8699 | 0.8900 | 0.7763 | 0.9980 | 0.9612 | 0.5235 | 0.8837 | 0.7229 | 0.8452 | 0.6701 | 0.9970 |
| 0.0281 | 28.05 | 2300 | 0.1984 | 0.7970 | 0.8665 | 0.9490 | 0.9881 | 0.6233 | 0.9441 | 0.9007 | 0.8659 | 0.7451 | 0.9984 | 0.9614 | 0.5465 | 0.8850 | 0.7081 | 0.8310 | 0.6496 | 0.9971 |
| 0.029 | 28.29 | 2320 | 0.1929 | 0.8018 | 0.8684 | 0.9508 | 0.9890 | 0.6266 | 0.9484 | 0.8783 | 0.8813 | 0.7572 | 0.9981 | 0.9612 | 0.5522 | 0.8820 | 0.7180 | 0.8409 | 0.6616 | 0.9970 |
| 0.0205 | 28.54 | 2340 | 0.1939 | 0.8127 | 0.8877 | 0.9536 | 0.9857 | 0.6927 | 0.9459 | 0.8321 | 0.9031 | 0.8553 | 0.9989 | 0.9628 | 0.5574 | 0.8940 | 0.7233 | 0.8410 | 0.7133 | 0.9969 |
| 0.1185 | 28.78 | 2360 | 0.2147 | 0.7963 | 0.8662 | 0.9476 | 0.9888 | 0.5806 | 0.9517 | 0.8657 | 0.8458 | 0.8323 | 0.9987 | 0.9615 | 0.5186 | 0.8772 | 0.6987 | 0.8088 | 0.7122 | 0.9971 |
| 0.0848 | 29.02 | 2380 | 0.1978 | 0.8047 | 0.8712 | 0.9510 | 0.9884 | 0.5966 | 0.9504 | 0.8398 | 0.8784 | 0.8459 | 0.9988 | 0.9618 | 0.5329 | 0.8799 | 0.7125 | 0.8299 | 0.7184 | 0.9972 |
| 0.028 | 29.27 | 2400 | 0.2065 | 0.8000 | 0.8675 | 0.9497 | 0.9878 | 0.6095 | 0.9561 | 0.8780 | 0.8647 | 0.7781 | 0.9983 | 0.9629 | 0.5428 | 0.8784 | 0.7121 | 0.8296 | 0.6767 | 0.9973 |
| 0.0232 | 29.51 | 2420 | 0.1912 | 0.8063 | 0.8750 | 0.9520 | 0.9887 | 0.6177 | 0.9491 | 0.8746 | 0.8742 | 0.8221 | 0.9983 | 0.9631 | 0.5401 | 0.8824 | 0.7242 | 0.8371 | 0.7000 | 0.9973 |
| 0.0241 | 29.76 | 2440 | 0.1768 | 0.8095 | 0.8797 | 0.9525 | 0.9871 | 0.6426 | 0.9506 | 0.8691 | 0.8781 | 0.8319 | 0.9986 | 0.9637 | 0.5552 | 0.8871 | 0.7228 | 0.8388 | 0.7015 | 0.9971 |
| 0.0249 | 30.0 | 2460 | 0.1885 | 0.8051 | 0.8734 | 0.9518 | 0.9885 | 0.6096 | 0.9517 | 0.8740 | 0.8714 | 0.8203 | 0.9986 | 0.9631 | 0.5348 | 0.8836 | 0.7230 | 0.8353 | 0.6989 | 0.9970 |
| 0.0314 | 30.24 | 2480 | 0.1853 | 0.8046 | 0.8698 | 0.9521 | 0.9882 | 0.6049 | 0.9524 | 0.8782 | 0.8786 | 0.7873 | 0.9989 | 0.9630 | 0.5373 | 0.8846 | 0.7241 | 0.8409 | 0.6853 | 0.9968 |
| 0.045 | 30.49 | 2500 | 0.1810 | 0.8134 | 0.8792 | 0.9542 | 0.9871 | 0.6099 | 0.9502 | 0.8672 | 0.8840 | 0.8573 | 0.9986 | 0.9621 | 0.5421 | 0.8876 | 0.7371 | 0.8430 | 0.7251 | 0.9971 |
| 0.0261 | 30.73 | 2520 | 0.1893 | 0.8172 | 0.8897 | 0.9548 | 0.9847 | 0.6630 | 0.9480 | 0.8516 | 0.8922 | 0.8897 | 0.9988 | 0.9619 | 0.5530 | 0.8890 | 0.7377 | 0.8441 | 0.7375 | 0.9972 |
| 0.0175 | 30.98 | 2540 | 0.1904 | 0.8155 | 0.8830 | 0.9553 | 0.9866 | 0.6192 | 0.9535 | 0.8526 | 0.8915 | 0.8791 | 0.9983 | 0.9635 | 0.5371 | 0.8874 | 0.7404 | 0.8472 | 0.7359 | 0.9972 |
| 0.0326 | 31.22 | 2560 | 0.1888 | 0.8126 | 0.8811 | 0.9535 | 0.9875 | 0.6216 | 0.9501 | 0.8821 | 0.8722 | 0.8559 | 0.9985 | 0.9627 | 0.5421 | 0.8861 | 0.7326 | 0.8392 | 0.7283 | 0.9970 |
| 0.0854 | 31.46 | 2580 | 0.1981 | 0.8043 | 0.8676 | 0.9523 | 0.9893 | 0.5619 | 0.9525 | 0.8688 | 0.8749 | 0.8275 | 0.9983 | 0.9613 | 0.5166 | 0.8798 | 0.7305 | 0.8396 | 0.7054 | 0.9969 |
| 0.0313 | 31.71 | 2600 | 0.2039 | 0.8109 | 0.8805 | 0.9522 | 0.9873 | 0.6476 | 0.9539 | 0.8586 | 0.8781 | 0.8404 | 0.9978 | 0.9621 | 0.5616 | 0.8829 | 0.7235 | 0.8372 | 0.7118 | 0.9968 |
| 0.0228 | 31.95 | 2620 | 0.2029 | 0.8079 | 0.8795 | 0.9515 | 0.9876 | 0.6392 | 0.9510 | 0.8668 | 0.8685 | 0.8450 | 0.9988 | 0.9620 | 0.5487 | 0.8832 | 0.7207 | 0.8320 | 0.7118 | 0.9970 |
| 0.0301 | 32.2 | 2640 | 0.2147 | 0.8037 | 0.8739 | 0.9499 | 0.9872 | 0.6339 | 0.9546 | 0.8759 | 0.8607 | 0.8062 | 0.9988 | 0.9620 | 0.5508 | 0.8801 | 0.7129 | 0.8259 | 0.6974 | 0.9971 |
| 0.0312 | 32.44 | 2660 | 0.2114 | 0.8016 | 0.8718 | 0.9496 | 0.9876 | 0.6201 | 0.9532 | 0.8755 | 0.8584 | 0.8090 | 0.9990 | 0.9616 | 0.5405 | 0.8791 | 0.7107 | 0.8252 | 0.6969 | 0.9969 |
| 0.0427 | 32.68 | 2680 | 0.2085 | 0.8015 | 0.8694 | 0.9506 | 0.9873 | 0.6277 | 0.9543 | 0.8743 | 0.8767 | 0.7668 | 0.9986 | 0.9622 | 0.5421 | 0.8828 | 0.7131 | 0.8356 | 0.6777 | 0.9970 |
| 0.0398 | 32.93 | 2700 | 0.2139 | 0.8062 | 0.8766 | 0.9507 | 0.9850 | 0.6461 | 0.9581 | 0.8557 | 0.8761 | 0.8176 | 0.9976 | 0.9612 | 0.5560 | 0.8817 | 0.7157 | 0.8308 | 0.7017 | 0.9967 |
| 0.0274 | 33.17 | 2720 | 0.2093 | 0.8094 | 0.8806 | 0.9516 | 0.9847 | 0.6481 | 0.9555 | 0.8572 | 0.8764 | 0.8440 | 0.9980 | 0.9615 | 0.5583 | 0.8860 | 0.7187 | 0.8323 | 0.7124 | 0.9969 |
| 0.0309 | 33.41 | 2740 | 0.2170 | 0.8068 | 0.8833 | 0.9505 | 0.9840 | 0.6723 | 0.9536 | 0.8855 | 0.8602 | 0.8290 | 0.9984 | 0.9632 | 0.5588 | 0.8902 | 0.7108 | 0.8261 | 0.7013 | 0.9969 |
| 0.0395 | 33.66 | 2760 | 0.2031 | 0.8060 | 0.8787 | 0.9513 | 0.9879 | 0.6361 | 0.9472 | 0.8725 | 0.8689 | 0.8401 | 0.9986 | 0.9624 | 0.5421 | 0.8871 | 0.7157 | 0.8327 | 0.7048 | 0.9970 |
| 0.0298 | 33.9 | 2780 | 0.1892 | 0.8082 | 0.8804 | 0.9522 | 0.9868 | 0.6612 | 0.9493 | 0.8657 | 0.8823 | 0.8189 | 0.9987 | 0.9630 | 0.5586 | 0.8887 | 0.7184 | 0.8409 | 0.6906 | 0.9970 |
| 0.0313 | 34.15 | 2800 | 0.1960 | 0.8064 | 0.8772 | 0.9522 | 0.9881 | 0.6294 | 0.9442 | 0.8685 | 0.8810 | 0.8310 | 0.9984 | 0.9623 | 0.5435 | 0.8893 | 0.7198 | 0.8407 | 0.6925 | 0.9970 |
| 0.0249 | 34.39 | 2820 | 0.1958 | 0.8086 | 0.8772 | 0.9527 | 0.9879 | 0.6079 | 0.9521 | 0.8570 | 0.8777 | 0.8597 | 0.9980 | 0.9625 | 0.5362 | 0.8852 | 0.7255 | 0.8383 | 0.7154 | 0.9969 |
| 0.0959 | 34.63 | 2840 | 0.2022 | 0.8077 | 0.8757 | 0.9520 | 0.9877 | 0.6105 | 0.9548 | 0.8691 | 0.8697 | 0.8401 | 0.9981 | 0.9627 | 0.5434 | 0.8838 | 0.7228 | 0.8347 | 0.7099 | 0.9969 |
| 0.0195 | 34.88 | 2860 | 0.1878 | 0.8089 | 0.8758 | 0.9526 | 0.9884 | 0.6187 | 0.9483 | 0.8695 | 0.8809 | 0.8262 | 0.9985 | 0.9624 | 0.5476 | 0.8880 | 0.7218 | 0.8411 | 0.7046 | 0.9969 |
| 0.0144 | 35.12 | 2880 | 0.1991 | 0.8099 | 0.8809 | 0.9523 | 0.9851 | 0.6489 | 0.9545 | 0.8723 | 0.8751 | 0.8324 | 0.9984 | 0.9637 | 0.5606 | 0.8891 | 0.7197 | 0.8377 | 0.7017 | 0.9969 |
| 0.0316 | 35.37 | 2900 | 0.2001 | 0.8057 | 0.8747 | 0.9515 | 0.9883 | 0.6212 | 0.9501 | 0.8815 | 0.8704 | 0.8137 | 0.9979 | 0.9625 | 0.5443 | 0.8877 | 0.7179 | 0.8351 | 0.6955 | 0.9968 |
| 0.0363 | 35.61 | 2920 | 0.2015 | 0.8048 | 0.8718 | 0.9516 | 0.9887 | 0.6135 | 0.9523 | 0.8771 | 0.8752 | 0.7983 | 0.9977 | 0.9624 | 0.5432 | 0.8856 | 0.7195 | 0.8383 | 0.6878 | 0.9968 |
| 0.1011 | 35.85 | 2940 | 0.1922 | 0.8089 | 0.8777 | 0.9529 | 0.9882 | 0.6236 | 0.9487 | 0.8627 | 0.8810 | 0.8407 | 0.9989 | 0.9621 | 0.5429 | 0.8874 | 0.7256 | 0.8414 | 0.7057 | 0.9970 |
| 0.0455 | 36.1 | 2960 | 0.2002 | 0.8059 | 0.8733 | 0.9519 | 0.9888 | 0.6067 | 0.9517 | 0.8642 | 0.8746 | 0.8285 | 0.9986 | 0.9621 | 0.5401 | 0.8853 | 0.7211 | 0.8371 | 0.6988 | 0.9970 |
| 0.0289 | 36.34 | 2980 | 0.1995 | 0.8096 | 0.8805 | 0.9521 | 0.9879 | 0.6477 | 0.9501 | 0.8688 | 0.8739 | 0.8365 | 0.9985 | 0.9625 | 0.5577 | 0.8869 | 0.7201 | 0.8362 | 0.7069 | 0.9971 |
| 0.091 | 36.59 | 3000 | 0.1941 | 0.8124 | 0.8867 | 0.9529 | 0.9882 | 0.6654 | 0.9482 | 0.8641 | 0.8745 | 0.8678 | 0.9987 | 0.9627 | 0.5555 | 0.8877 | 0.7251 | 0.8370 | 0.7217 | 0.9972 |
| 0.0635 | 36.83 | 3020 | 0.1858 | 0.8132 | 0.8837 | 0.9535 | 0.9875 | 0.6478 | 0.9525 | 0.8476 | 0.8842 | 0.8673 | 0.9988 | 0.9632 | 0.5549 | 0.8876 | 0.7265 | 0.8408 | 0.7220 | 0.9972 |
| 0.0244 | 37.07 | 3040 | 0.1862 | 0.8109 | 0.8797 | 0.9533 | 0.9875 | 0.6420 | 0.9502 | 0.8755 | 0.8815 | 0.8221 | 0.9987 | 0.9633 | 0.5540 | 0.8906 | 0.7254 | 0.8422 | 0.7034 | 0.9972 |
| 0.0265 | 37.32 | 3060 | 0.1864 | 0.8146 | 0.8844 | 0.9543 | 0.9867 | 0.6543 | 0.9477 | 0.8679 | 0.8891 | 0.8461 | 0.9987 | 0.9633 | 0.5578 | 0.8936 | 0.7306 | 0.8449 | 0.7146 | 0.9972 |
| 0.0344 | 37.56 | 3080 | 0.1838 | 0.8162 | 0.8873 | 0.9547 | 0.9862 | 0.6641 | 0.9524 | 0.8551 | 0.8905 | 0.8644 | 0.9988 | 0.9636 | 0.5604 | 0.8903 | 0.7340 | 0.8471 | 0.7211 | 0.9972 |
| 0.0267 | 37.8 | 3100 | 0.1903 | 0.8137 | 0.8841 | 0.9540 | 0.9870 | 0.6543 | 0.9499 | 0.8745 | 0.8841 | 0.8409 | 0.9983 | 0.9633 | 0.5565 | 0.8921 | 0.7309 | 0.8444 | 0.7119 | 0.9971 |
| 0.3041 | 38.05 | 3120 | 0.1891 | 0.8051 | 0.8701 | 0.9526 | 0.9903 | 0.5815 | 0.9478 | 0.8685 | 0.8792 | 0.8248 | 0.9985 | 0.9604 | 0.5197 | 0.8850 | 0.7270 | 0.8415 | 0.7051 | 0.9969 |
| 0.0272 | 38.29 | 3140 | 0.1971 | 0.8077 | 0.8754 | 0.9522 | 0.9877 | 0.6189 | 0.9552 | 0.8747 | 0.8726 | 0.8205 | 0.9983 | 0.9628 | 0.5447 | 0.8851 | 0.7236 | 0.8373 | 0.7036 | 0.9971 |
| 0.063 | 38.54 | 3160 | 0.1888 | 0.8125 | 0.8786 | 0.9542 | 0.9881 | 0.6109 | 0.9529 | 0.8503 | 0.8879 | 0.8613 | 0.9986 | 0.9625 | 0.5407 | 0.8857 | 0.7339 | 0.8452 | 0.7224 | 0.9971 |
| 0.0527 | 38.78 | 3180 | 0.1899 | 0.8121 | 0.8842 | 0.9531 | 0.9865 | 0.6598 | 0.9521 | 0.8748 | 0.8761 | 0.8415 | 0.9986 | 0.9634 | 0.5575 | 0.8896 | 0.7261 | 0.8392 | 0.7117 | 0.9972 |
| 0.0465 | 39.02 | 3200 | 0.1947 | 0.8108 | 0.8793 | 0.9532 | 0.9881 | 0.6358 | 0.9517 | 0.8722 | 0.8808 | 0.8284 | 0.9979 | 0.9626 | 0.5509 | 0.8879 | 0.7275 | 0.8422 | 0.7075 | 0.9970 |
| 0.0305 | 39.27 | 3220 | 0.1884 | 0.8118 | 0.8806 | 0.9538 | 0.9888 | 0.6265 | 0.9468 | 0.8655 | 0.8838 | 0.8547 | 0.9983 | 0.9620 | 0.5422 | 0.8885 | 0.7313 | 0.8439 | 0.7177 | 0.9971 |
| 0.0167 | 39.51 | 3240 | 0.1935 | 0.8111 | 0.8805 | 0.9533 | 0.9879 | 0.6352 | 0.9505 | 0.8737 | 0.8794 | 0.8383 | 0.9981 | 0.9629 | 0.5473 | 0.8891 | 0.7279 | 0.8414 | 0.7120 | 0.9971 |
| 0.0507 | 39.76 | 3260 | 0.1888 | 0.8109 | 0.8783 | 0.9535 | 0.9883 | 0.6259 | 0.9528 | 0.8630 | 0.8829 | 0.8363 | 0.9986 | 0.9625 | 0.5459 | 0.8854 | 0.7306 | 0.8435 | 0.7110 | 0.9971 |
| 0.0354 | 40.0 | 3280 | 0.1900 | 0.8129 | 0.8809 | 0.9540 | 0.9875 | 0.6312 | 0.9541 | 0.8566 | 0.8860 | 0.8526 | 0.9984 | 0.9631 | 0.5490 | 0.8864 | 0.7326 | 0.8448 | 0.7176 | 0.9972 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
|
anoop3/autotrain-be1zs-exv75
|
anoop3
| 2024-02-27T13:06:28Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-02-27T13:06:25Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: moni female
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
zzttbrdd/gemcy_v1_2
|
zzttbrdd
| 2024-02-27T13:06:24Z | 112 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T13:04:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Demo [optional]:** [More Information Needed]
## Uses
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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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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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 -->
[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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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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|
mukesh2110/my-pet-dog
|
mukesh2110
| 2024-02-27T13:05:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T12:58:04Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by mukesh2110 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
zzttbrdd/gemcy_v1_1
|
zzttbrdd
| 2024-02-27T13:01:50Z | 113 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T12:59:53Z |
---
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]
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## Model Card Contact
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|
Neomedallion/dqn-SpaceInvadersNoFrameskip-v4
|
Neomedallion
| 2024-02-27T12:59:20Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-05T07:14:06Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 329.00 +/- 157.97
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Neomedallion -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Neomedallion -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Neomedallion
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
FKunneman/distilbert-base-uncased-finetuned-cola
|
FKunneman
| 2024-02-27T12:58:16Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T10:58:00Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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-base-uncased-finetuned-cola
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: 0.8294
- Matthews Correlation: 0.5466
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5181 | 1.0 | 535 | 0.4541 | 0.4504 |
| 0.3411 | 2.0 | 1070 | 0.4744 | 0.5094 |
| 0.2321 | 3.0 | 1605 | 0.6309 | 0.5391 |
| 0.1737 | 4.0 | 2140 | 0.7876 | 0.5369 |
| 0.1265 | 5.0 | 2675 | 0.8294 | 0.5466 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
ibunescu/Phi-2_GDPR_9_3e_adapter
|
ibunescu
| 2024-02-27T12:57:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T11:09:12Z |
---
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]
|
peldrak/segformer-b4-ade-finetuned-512-512-finetuned-grCoastline_512
|
peldrak
| 2024-02-27T12:56:40Z | 189 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"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
| 2024-02-27T11:21:48Z |
---
license: other
base_model: nvidia/segformer-b4-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b4-ade-finetuned-512-512-finetuned-grCoastline_512
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-finetuned-512-512-finetuned-grCoastline_512
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/grCoastline_512 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3485
- Mean Iou: 0.7745
- Mean Accuracy: 0.8460
- Overall Accuracy: 0.9397
- Accuracy Water: 0.9854
- Accuracy Whitewater: 0.5267
- Accuracy Sediment: 0.9100
- Accuracy Other Natural Terrain: 0.7714
- Accuracy Vegetation: 0.9143
- Accuracy Development: 0.8156
- Accuracy Unknown: 0.9987
- Iou Water: 0.9657
- Iou Whitewater: 0.4461
- Iou Sediment: 0.8412
- Iou Other Natural Terrain: 0.6769
- Iou Vegetation: 0.7892
- Iou Development: 0.7052
- Iou Unknown: 0.9973
## 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: 40
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|
| 1.4715 | 0.24 | 20 | 1.3366 | 0.4971 | 0.6028 | 0.8289 | 0.8428 | 0.0 | 0.7635 | 0.5946 | 0.8680 | 0.1720 | 0.9787 | 0.7080 | 0.0 | 0.5497 | 0.5015 | 0.5871 | 0.1572 | 0.9761 |
| 1.0643 | 0.49 | 40 | 0.8194 | 0.5046 | 0.6044 | 0.8506 | 0.9722 | 0.0 | 0.8982 | 0.4334 | 0.8951 | 0.0386 | 0.9935 | 0.7889 | 0.0 | 0.7048 | 0.3646 | 0.6444 | 0.0384 | 0.9912 |
| 1.1372 | 0.73 | 60 | 0.6288 | 0.5104 | 0.6150 | 0.8513 | 0.9722 | 0.0 | 0.9217 | 0.4078 | 0.8554 | 0.1517 | 0.9959 | 0.7840 | 0.0 | 0.5960 | 0.3498 | 0.7041 | 0.1494 | 0.9897 |
| 1.2667 | 0.98 | 80 | 0.4994 | 0.5638 | 0.6526 | 0.8825 | 0.9793 | 0.0 | 0.9053 | 0.5839 | 0.9205 | 0.1807 | 0.9984 | 0.8893 | 0.0 | 0.6812 | 0.5051 | 0.7026 | 0.1783 | 0.9903 |
| 0.8003 | 1.22 | 100 | 0.4520 | 0.5522 | 0.6421 | 0.8767 | 0.9719 | 0.0 | 0.9220 | 0.5197 | 0.9549 | 0.1324 | 0.9942 | 0.9218 | 0.0 | 0.7007 | 0.4562 | 0.6631 | 0.1315 | 0.9917 |
| 0.6688 | 1.46 | 120 | 0.4432 | 0.5528 | 0.6399 | 0.8774 | 0.9726 | 0.0 | 0.8860 | 0.5450 | 0.9650 | 0.1153 | 0.9955 | 0.9121 | 0.0 | 0.7111 | 0.4820 | 0.6572 | 0.1149 | 0.9921 |
| 0.7934 | 1.71 | 140 | 0.3905 | 0.6088 | 0.6882 | 0.8968 | 0.9406 | 0.0 | 0.7654 | 0.8742 | 0.8782 | 0.3614 | 0.9979 | 0.8728 | 0.0 | 0.7087 | 0.6021 | 0.7469 | 0.3397 | 0.9914 |
| 0.6258 | 1.95 | 160 | 0.3358 | 0.6410 | 0.7105 | 0.9126 | 0.9753 | 0.0 | 0.8484 | 0.7994 | 0.9134 | 0.4395 | 0.9977 | 0.9140 | 0.0 | 0.7937 | 0.6252 | 0.7544 | 0.4076 | 0.9924 |
| 0.4741 | 2.2 | 180 | 0.3298 | 0.6339 | 0.7070 | 0.9115 | 0.9715 | 0.0 | 0.9195 | 0.7328 | 0.9316 | 0.3962 | 0.9971 | 0.9326 | 0.0 | 0.7588 | 0.6403 | 0.7344 | 0.3787 | 0.9926 |
| 1.1083 | 2.44 | 200 | 0.2815 | 0.6686 | 0.7348 | 0.9217 | 0.9554 | 0.0 | 0.8974 | 0.7519 | 0.9370 | 0.6029 | 0.9990 | 0.9269 | 0.0 | 0.8030 | 0.6504 | 0.7567 | 0.5518 | 0.9912 |
| 0.2948 | 2.68 | 220 | 0.3011 | 0.6636 | 0.7362 | 0.9187 | 0.9852 | 0.0 | 0.7992 | 0.7347 | 0.9271 | 0.7136 | 0.9935 | 0.9241 | 0.0 | 0.7458 | 0.6579 | 0.7534 | 0.5726 | 0.9914 |
| 0.2183 | 2.93 | 240 | 0.2771 | 0.6775 | 0.7451 | 0.9235 | 0.9840 | 0.0 | 0.8966 | 0.6596 | 0.9337 | 0.7454 | 0.9966 | 0.9334 | 0.0 | 0.7904 | 0.6217 | 0.7482 | 0.6564 | 0.9922 |
| 0.1911 | 3.17 | 260 | 0.2561 | 0.6791 | 0.7506 | 0.9240 | 0.9807 | 0.0 | 0.8817 | 0.8266 | 0.8373 | 0.7304 | 0.9974 | 0.9228 | 0.0 | 0.8090 | 0.6484 | 0.7604 | 0.6190 | 0.9944 |
| 0.3457 | 3.41 | 280 | 0.2194 | 0.6919 | 0.7642 | 0.9311 | 0.9774 | 0.0 | 0.8939 | 0.9032 | 0.8159 | 0.7615 | 0.9975 | 0.9484 | 0.0 | 0.8097 | 0.7083 | 0.7685 | 0.6141 | 0.9942 |
| 0.2098 | 3.66 | 300 | 0.2858 | 0.6620 | 0.7319 | 0.9185 | 0.9742 | 0.0 | 0.9149 | 0.6644 | 0.9326 | 0.6386 | 0.9985 | 0.9503 | 0.0 | 0.7779 | 0.5936 | 0.7397 | 0.5780 | 0.9942 |
| 0.6556 | 3.9 | 320 | 0.2265 | 0.6824 | 0.7512 | 0.9263 | 0.9779 | 0.0 | 0.9237 | 0.8318 | 0.8446 | 0.6831 | 0.9971 | 0.9465 | 0.0 | 0.8176 | 0.6572 | 0.7500 | 0.6113 | 0.9940 |
| 0.4157 | 4.15 | 340 | 0.2339 | 0.6836 | 0.7573 | 0.9252 | 0.9802 | 0.0 | 0.9133 | 0.7874 | 0.8292 | 0.7922 | 0.9984 | 0.9434 | 0.0 | 0.8170 | 0.6560 | 0.7348 | 0.6417 | 0.9924 |
| 0.4104 | 4.39 | 360 | 0.2441 | 0.6896 | 0.7593 | 0.9293 | 0.9763 | 0.0 | 0.9323 | 0.7923 | 0.8774 | 0.7438 | 0.9930 | 0.9313 | 0.0 | 0.8156 | 0.6714 | 0.7766 | 0.6404 | 0.9921 |
| 0.2119 | 4.63 | 380 | 0.2272 | 0.6863 | 0.7540 | 0.9291 | 0.9842 | 0.0 | 0.9231 | 0.7755 | 0.8810 | 0.7168 | 0.9977 | 0.9304 | 0.0 | 0.8128 | 0.6666 | 0.7776 | 0.6220 | 0.9944 |
| 0.3009 | 4.88 | 400 | 0.2231 | 0.6931 | 0.7598 | 0.9314 | 0.9807 | 0.0 | 0.9065 | 0.7653 | 0.8970 | 0.7710 | 0.9979 | 0.9526 | 0.0 | 0.8193 | 0.6725 | 0.7653 | 0.6477 | 0.9943 |
| 0.1716 | 5.12 | 420 | 0.2095 | 0.6985 | 0.7675 | 0.9342 | 0.9757 | 0.0 | 0.8680 | 0.8194 | 0.8927 | 0.8204 | 0.9965 | 0.9522 | 0.0 | 0.7944 | 0.7061 | 0.7876 | 0.6548 | 0.9943 |
| 0.1558 | 5.37 | 440 | 0.2252 | 0.6895 | 0.7561 | 0.9312 | 0.9824 | 0.0 | 0.8594 | 0.8082 | 0.9130 | 0.7348 | 0.9951 | 0.9245 | 0.0 | 0.8017 | 0.6848 | 0.8102 | 0.6118 | 0.9935 |
| 0.1676 | 5.61 | 460 | 0.2248 | 0.6910 | 0.7673 | 0.9309 | 0.9748 | 0.0 | 0.8889 | 0.8280 | 0.8465 | 0.8361 | 0.9971 | 0.9442 | 0.0 | 0.7857 | 0.7080 | 0.7802 | 0.6238 | 0.9950 |
| 0.1709 | 5.85 | 480 | 0.2455 | 0.6937 | 0.7573 | 0.9327 | 0.9739 | 0.0 | 0.9177 | 0.7953 | 0.9107 | 0.7058 | 0.9975 | 0.9538 | 0.0 | 0.8189 | 0.6807 | 0.7747 | 0.6323 | 0.9956 |
| 0.3403 | 6.1 | 500 | 0.2661 | 0.6862 | 0.7528 | 0.9298 | 0.9821 | 0.0 | 0.9366 | 0.7061 | 0.9308 | 0.7172 | 0.9967 | 0.9161 | 0.0 | 0.8040 | 0.6597 | 0.7971 | 0.6315 | 0.9948 |
| 0.2134 | 6.34 | 520 | 0.2447 | 0.6857 | 0.7522 | 0.9310 | 0.9757 | 0.0 | 0.9248 | 0.8739 | 0.8677 | 0.6256 | 0.9977 | 0.9368 | 0.0 | 0.8059 | 0.6996 | 0.7921 | 0.5703 | 0.9953 |
| 0.2108 | 6.59 | 540 | 0.2575 | 0.6868 | 0.7574 | 0.9277 | 0.9824 | 0.0 | 0.9024 | 0.7638 | 0.8811 | 0.7776 | 0.9946 | 0.9476 | 0.0 | 0.8100 | 0.6565 | 0.7566 | 0.6439 | 0.9931 |
| 0.2009 | 6.83 | 560 | 0.2191 | 0.7008 | 0.7625 | 0.9362 | 0.9839 | 0.0 | 0.8759 | 0.8257 | 0.9155 | 0.7399 | 0.9967 | 0.9362 | 0.0 | 0.8088 | 0.7217 | 0.8005 | 0.6438 | 0.9946 |
| 0.1056 | 7.07 | 580 | 0.2391 | 0.6950 | 0.7571 | 0.9339 | 0.9793 | 0.0 | 0.9294 | 0.7612 | 0.9302 | 0.7020 | 0.9974 | 0.9495 | 0.0 | 0.8024 | 0.6891 | 0.7857 | 0.6439 | 0.9947 |
| 0.1038 | 7.32 | 600 | 0.2415 | 0.6921 | 0.7598 | 0.9311 | 0.9821 | 0.0 | 0.9165 | 0.7571 | 0.8915 | 0.7730 | 0.9982 | 0.9450 | 0.0 | 0.8143 | 0.6651 | 0.7725 | 0.6526 | 0.9950 |
| 0.4394 | 7.56 | 620 | 0.2763 | 0.6891 | 0.7597 | 0.9281 | 0.9686 | 0.0 | 0.8743 | 0.7862 | 0.8842 | 0.8074 | 0.9970 | 0.9424 | 0.0 | 0.8110 | 0.6664 | 0.7543 | 0.6549 | 0.9950 |
| 0.2996 | 7.8 | 640 | 0.2946 | 0.7019 | 0.7650 | 0.9346 | 0.9868 | 0.0 | 0.8923 | 0.7386 | 0.9290 | 0.8131 | 0.9953 | 0.9532 | 0.0 | 0.8276 | 0.6724 | 0.7729 | 0.6929 | 0.9943 |
| 0.1253 | 8.05 | 660 | 0.2495 | 0.7061 | 0.7659 | 0.9381 | 0.9803 | 0.0 | 0.9227 | 0.7637 | 0.9341 | 0.7618 | 0.9985 | 0.9558 | 0.0 | 0.8267 | 0.6985 | 0.7890 | 0.6769 | 0.9959 |
| 0.1667 | 8.29 | 680 | 0.2619 | 0.7000 | 0.7624 | 0.9356 | 0.9870 | 0.0 | 0.9419 | 0.7868 | 0.9021 | 0.7228 | 0.9965 | 0.9519 | 0.0 | 0.8224 | 0.6895 | 0.7855 | 0.6554 | 0.9954 |
| 0.2411 | 8.54 | 700 | 0.2650 | 0.6945 | 0.7599 | 0.9319 | 0.9841 | 0.0 | 0.9103 | 0.7808 | 0.8906 | 0.7563 | 0.9971 | 0.9538 | 0.0 | 0.8257 | 0.6670 | 0.7658 | 0.6537 | 0.9953 |
| 0.0618 | 8.78 | 720 | 0.2791 | 0.6958 | 0.7612 | 0.9323 | 0.9846 | 0.0 | 0.9110 | 0.7900 | 0.8929 | 0.7549 | 0.9947 | 0.9507 | 0.0 | 0.8265 | 0.6742 | 0.7708 | 0.6544 | 0.9938 |
| 0.3955 | 9.02 | 740 | 0.2605 | 0.6973 | 0.7678 | 0.9329 | 0.9833 | 0.0 | 0.8551 | 0.7686 | 0.8970 | 0.8734 | 0.9972 | 0.9309 | 0.0 | 0.8111 | 0.6795 | 0.7933 | 0.6713 | 0.9952 |
| 0.1222 | 9.27 | 760 | 0.3107 | 0.6889 | 0.7515 | 0.9302 | 0.9839 | 0.0 | 0.9143 | 0.7856 | 0.9042 | 0.6760 | 0.9968 | 0.9551 | 0.0 | 0.8226 | 0.6597 | 0.7625 | 0.6266 | 0.9955 |
| 0.1179 | 9.51 | 780 | 0.2867 | 0.6988 | 0.7651 | 0.9329 | 0.9810 | 0.0 | 0.9059 | 0.7761 | 0.8890 | 0.8069 | 0.9969 | 0.9544 | 0.0 | 0.8335 | 0.6615 | 0.7678 | 0.6791 | 0.9955 |
| 0.1552 | 9.76 | 800 | 0.2837 | 0.6905 | 0.7528 | 0.9313 | 0.9863 | 0.0 | 0.9165 | 0.7775 | 0.9105 | 0.6815 | 0.9970 | 0.9520 | 0.0 | 0.8224 | 0.6600 | 0.7745 | 0.6299 | 0.9949 |
| 0.0762 | 10.0 | 820 | 0.2756 | 0.7050 | 0.7682 | 0.9361 | 0.9786 | 0.0 | 0.9304 | 0.7674 | 0.9024 | 0.8000 | 0.9984 | 0.9552 | 0.0 | 0.8306 | 0.6760 | 0.7779 | 0.6998 | 0.9954 |
| 0.0641 | 10.24 | 840 | 0.2555 | 0.7081 | 0.7697 | 0.9373 | 0.9786 | 0.0 | 0.9068 | 0.7760 | 0.9134 | 0.8147 | 0.9983 | 0.9544 | 0.0 | 0.8366 | 0.6825 | 0.7816 | 0.7059 | 0.9957 |
| 0.1294 | 10.49 | 860 | 0.2671 | 0.6987 | 0.7599 | 0.9336 | 0.9865 | 0.0 | 0.8976 | 0.7942 | 0.9003 | 0.7429 | 0.9978 | 0.9498 | 0.0 | 0.8382 | 0.6635 | 0.7767 | 0.6669 | 0.9958 |
| 0.0948 | 10.73 | 880 | 0.2697 | 0.6969 | 0.7693 | 0.9318 | 0.9753 | 0.0 | 0.9242 | 0.7797 | 0.8536 | 0.8534 | 0.9987 | 0.9501 | 0.0 | 0.8246 | 0.6679 | 0.7616 | 0.6787 | 0.9956 |
| 0.1013 | 10.98 | 900 | 0.2956 | 0.6931 | 0.7573 | 0.9321 | 0.9885 | 0.0 | 0.9132 | 0.8039 | 0.8866 | 0.7121 | 0.9966 | 0.9519 | 0.0 | 0.8242 | 0.6681 | 0.7760 | 0.6357 | 0.9956 |
| 0.1085 | 11.22 | 920 | 0.2849 | 0.7058 | 0.7714 | 0.9359 | 0.9893 | 0.0 | 0.8995 | 0.7545 | 0.8977 | 0.8616 | 0.9969 | 0.9556 | 0.0 | 0.8445 | 0.6664 | 0.7752 | 0.7033 | 0.9956 |
| 0.0805 | 11.46 | 940 | 0.3072 | 0.6998 | 0.7662 | 0.9333 | 0.9777 | 0.0 | 0.8816 | 0.7738 | 0.9020 | 0.8309 | 0.9973 | 0.9570 | 0.0 | 0.8242 | 0.6621 | 0.7718 | 0.6877 | 0.9957 |
| 0.1627 | 11.71 | 960 | 0.2815 | 0.7008 | 0.7608 | 0.9346 | 0.9895 | 0.0 | 0.9083 | 0.7872 | 0.9032 | 0.7397 | 0.9976 | 0.9534 | 0.0 | 0.8419 | 0.6661 | 0.7739 | 0.6743 | 0.9960 |
| 0.0728 | 11.95 | 980 | 0.3182 | 0.6968 | 0.7611 | 0.9325 | 0.9789 | 0.0 | 0.9230 | 0.7933 | 0.8883 | 0.7476 | 0.9969 | 0.9570 | 0.0 | 0.8308 | 0.6558 | 0.7697 | 0.6684 | 0.9961 |
| 0.078 | 12.2 | 1000 | 0.2794 | 0.7036 | 0.7688 | 0.9349 | 0.9842 | 0.0 | 0.9117 | 0.7724 | 0.8858 | 0.8290 | 0.9983 | 0.9538 | 0.0 | 0.8378 | 0.6639 | 0.7725 | 0.7010 | 0.9962 |
| 0.0962 | 12.44 | 1020 | 0.2778 | 0.7023 | 0.7618 | 0.9357 | 0.9830 | 0.0016 | 0.9060 | 0.7851 | 0.9184 | 0.7402 | 0.9985 | 0.9533 | 0.0016 | 0.8370 | 0.6728 | 0.7826 | 0.6724 | 0.9965 |
| 0.0621 | 12.68 | 1040 | 0.2452 | 0.7029 | 0.7639 | 0.9360 | 0.9855 | 0.0 | 0.9187 | 0.8156 | 0.8879 | 0.7406 | 0.9988 | 0.9538 | 0.0 | 0.8374 | 0.6806 | 0.7827 | 0.6695 | 0.9961 |
| 0.114 | 12.93 | 1060 | 0.2496 | 0.7090 | 0.7703 | 0.9390 | 0.9852 | 0.0 | 0.9259 | 0.7787 | 0.9130 | 0.7912 | 0.9979 | 0.9554 | 0.0 | 0.8333 | 0.6915 | 0.7959 | 0.6904 | 0.9962 |
| 0.0747 | 13.17 | 1080 | 0.2531 | 0.7038 | 0.7656 | 0.9369 | 0.9820 | 0.0 | 0.9245 | 0.8319 | 0.8851 | 0.7371 | 0.9985 | 0.9543 | 0.0 | 0.8339 | 0.6898 | 0.7919 | 0.6609 | 0.9957 |
| 0.0535 | 13.41 | 1100 | 0.2763 | 0.7113 | 0.7728 | 0.9391 | 0.9832 | 0.0 | 0.9017 | 0.7641 | 0.9231 | 0.8398 | 0.9978 | 0.9549 | 0.0 | 0.8403 | 0.6872 | 0.7908 | 0.7099 | 0.9959 |
| 0.0538 | 13.66 | 1120 | 0.2819 | 0.7020 | 0.7662 | 0.9355 | 0.9854 | 0.0 | 0.9113 | 0.7951 | 0.8927 | 0.7817 | 0.9971 | 0.9505 | 0.0 | 0.8276 | 0.6783 | 0.7863 | 0.6752 | 0.9962 |
| 0.0953 | 13.9 | 1140 | 0.2945 | 0.7000 | 0.7616 | 0.9347 | 0.9839 | 0.0 | 0.9208 | 0.7676 | 0.9141 | 0.7474 | 0.9975 | 0.9556 | 0.0 | 0.8339 | 0.6660 | 0.7777 | 0.6703 | 0.9963 |
| 0.1052 | 14.15 | 1160 | 0.3095 | 0.6944 | 0.7566 | 0.9331 | 0.9859 | 0.0000 | 0.9257 | 0.7770 | 0.9067 | 0.7023 | 0.9982 | 0.9564 | 0.0000 | 0.8287 | 0.6599 | 0.7790 | 0.6406 | 0.9963 |
| 0.0879 | 14.39 | 1180 | 0.2800 | 0.7084 | 0.7724 | 0.9372 | 0.9864 | 0.0006 | 0.9166 | 0.7625 | 0.8991 | 0.8443 | 0.9973 | 0.9561 | 0.0006 | 0.8483 | 0.6707 | 0.7806 | 0.7064 | 0.9958 |
| 0.1387 | 14.63 | 1200 | 0.2951 | 0.7115 | 0.7741 | 0.9363 | 0.9825 | 0.0384 | 0.9211 | 0.7941 | 0.8873 | 0.7972 | 0.9982 | 0.9595 | 0.0383 | 0.8431 | 0.6666 | 0.7776 | 0.6990 | 0.9963 |
| 0.2297 | 14.88 | 1220 | 0.3029 | 0.7035 | 0.7635 | 0.9363 | 0.9882 | 0.0009 | 0.9185 | 0.7519 | 0.9243 | 0.7628 | 0.9981 | 0.9537 | 0.0009 | 0.8377 | 0.6681 | 0.7840 | 0.6838 | 0.9963 |
| 0.0713 | 15.12 | 1240 | 0.2940 | 0.7232 | 0.7852 | 0.9380 | 0.9879 | 0.1042 | 0.8996 | 0.7752 | 0.9104 | 0.8218 | 0.9974 | 0.9600 | 0.1029 | 0.8405 | 0.6757 | 0.7867 | 0.7003 | 0.9962 |
| 0.0909 | 15.37 | 1260 | 0.2922 | 0.7233 | 0.7846 | 0.9371 | 0.9820 | 0.1383 | 0.9264 | 0.7661 | 0.9219 | 0.7598 | 0.9981 | 0.9589 | 0.1356 | 0.8298 | 0.6753 | 0.7886 | 0.6786 | 0.9966 |
| 0.0336 | 15.61 | 1280 | 0.2995 | 0.7155 | 0.7758 | 0.9367 | 0.9891 | 0.0878 | 0.9176 | 0.7913 | 0.9071 | 0.7396 | 0.9979 | 0.9565 | 0.0854 | 0.8376 | 0.6727 | 0.7890 | 0.6709 | 0.9964 |
| 0.1919 | 15.85 | 1300 | 0.2794 | 0.7271 | 0.7880 | 0.9393 | 0.9827 | 0.1148 | 0.9229 | 0.7685 | 0.9148 | 0.8133 | 0.9988 | 0.9577 | 0.1123 | 0.8422 | 0.6828 | 0.7899 | 0.7087 | 0.9963 |
| 0.1494 | 16.1 | 1320 | 0.3131 | 0.7285 | 0.7895 | 0.9375 | 0.9858 | 0.1660 | 0.9179 | 0.7783 | 0.9150 | 0.7654 | 0.9978 | 0.9603 | 0.1615 | 0.8331 | 0.6788 | 0.7865 | 0.6827 | 0.9965 |
| 0.0559 | 16.34 | 1340 | 0.3163 | 0.7314 | 0.7925 | 0.9376 | 0.9834 | 0.1874 | 0.9244 | 0.7683 | 0.9205 | 0.7657 | 0.9981 | 0.9597 | 0.1813 | 0.8335 | 0.6762 | 0.7878 | 0.6846 | 0.9967 |
| 0.0675 | 16.59 | 1360 | 0.3053 | 0.7238 | 0.7866 | 0.9356 | 0.9866 | 0.1591 | 0.9287 | 0.8038 | 0.8834 | 0.7465 | 0.9983 | 0.9593 | 0.1527 | 0.8366 | 0.6695 | 0.7782 | 0.6733 | 0.9969 |
| 0.0999 | 16.83 | 1380 | 0.2811 | 0.7283 | 0.7878 | 0.9390 | 0.9841 | 0.1288 | 0.9217 | 0.7675 | 0.9173 | 0.7957 | 0.9994 | 0.9592 | 0.1254 | 0.8404 | 0.6818 | 0.7867 | 0.7083 | 0.9961 |
| 0.0321 | 17.07 | 1400 | 0.3063 | 0.7335 | 0.7965 | 0.9373 | 0.9857 | 0.1924 | 0.9164 | 0.7917 | 0.8896 | 0.8007 | 0.9988 | 0.9599 | 0.1806 | 0.8428 | 0.6686 | 0.7805 | 0.7052 | 0.9969 |
| 0.1219 | 17.32 | 1420 | 0.3262 | 0.7209 | 0.7820 | 0.9354 | 0.9855 | 0.1616 | 0.9346 | 0.7688 | 0.9175 | 0.7074 | 0.9985 | 0.9587 | 0.1578 | 0.8217 | 0.6706 | 0.7852 | 0.6552 | 0.9969 |
| 0.0637 | 17.56 | 1440 | 0.3415 | 0.7331 | 0.7938 | 0.9379 | 0.9874 | 0.1945 | 0.9184 | 0.7516 | 0.9269 | 0.7789 | 0.9985 | 0.9607 | 0.1848 | 0.8401 | 0.6685 | 0.7870 | 0.6937 | 0.9968 |
| 0.183 | 17.8 | 1460 | 0.3183 | 0.7403 | 0.8019 | 0.9381 | 0.9842 | 0.2370 | 0.9312 | 0.7719 | 0.9064 | 0.7836 | 0.9991 | 0.9626 | 0.2256 | 0.8413 | 0.6737 | 0.7818 | 0.7009 | 0.9965 |
| 0.0783 | 18.05 | 1480 | 0.3003 | 0.7481 | 0.8096 | 0.9396 | 0.9827 | 0.2663 | 0.9208 | 0.7452 | 0.9282 | 0.8255 | 0.9986 | 0.9648 | 0.2526 | 0.8468 | 0.6734 | 0.7831 | 0.7192 | 0.9970 |
| 0.1254 | 18.29 | 1500 | 0.3088 | 0.7317 | 0.7921 | 0.9373 | 0.9880 | 0.2074 | 0.9196 | 0.7615 | 0.9285 | 0.7413 | 0.9984 | 0.9628 | 0.1987 | 0.8281 | 0.6746 | 0.7877 | 0.6736 | 0.9968 |
| 0.0438 | 18.54 | 1520 | 0.3166 | 0.7430 | 0.8087 | 0.9373 | 0.9827 | 0.2761 | 0.8887 | 0.7908 | 0.9066 | 0.8186 | 0.9976 | 0.9639 | 0.2588 | 0.8243 | 0.6753 | 0.7890 | 0.6931 | 0.9966 |
| 0.06 | 18.78 | 1540 | 0.3152 | 0.7472 | 0.8138 | 0.9371 | 0.9862 | 0.3188 | 0.8854 | 0.7848 | 0.9059 | 0.8171 | 0.9981 | 0.9626 | 0.2963 | 0.8203 | 0.6789 | 0.7871 | 0.6883 | 0.9968 |
| 0.0426 | 19.02 | 1560 | 0.3289 | 0.7438 | 0.8098 | 0.9362 | 0.9830 | 0.3040 | 0.8962 | 0.7911 | 0.9009 | 0.7953 | 0.9981 | 0.9639 | 0.2836 | 0.8180 | 0.6722 | 0.7831 | 0.6885 | 0.9972 |
| 0.0333 | 19.27 | 1580 | 0.3027 | 0.7387 | 0.8038 | 0.9375 | 0.9842 | 0.2482 | 0.8965 | 0.7879 | 0.9063 | 0.8050 | 0.9983 | 0.9625 | 0.2332 | 0.8212 | 0.6809 | 0.7886 | 0.6868 | 0.9973 |
| 0.044 | 19.51 | 1600 | 0.3048 | 0.7386 | 0.8002 | 0.9387 | 0.9865 | 0.2334 | 0.9183 | 0.7811 | 0.9175 | 0.7664 | 0.9983 | 0.9607 | 0.2165 | 0.8341 | 0.6816 | 0.7914 | 0.6890 | 0.9971 |
| 0.0876 | 19.76 | 1620 | 0.2864 | 0.7563 | 0.8204 | 0.9401 | 0.9835 | 0.3356 | 0.9230 | 0.7849 | 0.9065 | 0.8113 | 0.9985 | 0.9645 | 0.3109 | 0.8376 | 0.6865 | 0.7905 | 0.7072 | 0.9971 |
| 0.0432 | 20.0 | 1640 | 0.3037 | 0.7480 | 0.8111 | 0.9382 | 0.9856 | 0.3087 | 0.9147 | 0.7628 | 0.9219 | 0.7857 | 0.9984 | 0.9637 | 0.2853 | 0.8319 | 0.6757 | 0.7849 | 0.6977 | 0.9972 |
| 0.1937 | 20.24 | 1660 | 0.3050 | 0.7495 | 0.8149 | 0.9368 | 0.9852 | 0.3424 | 0.9162 | 0.7638 | 0.9086 | 0.7893 | 0.9989 | 0.9630 | 0.3114 | 0.8323 | 0.6671 | 0.7782 | 0.6978 | 0.9969 |
| 0.0361 | 20.49 | 1680 | 0.3204 | 0.7553 | 0.8221 | 0.9380 | 0.9846 | 0.3646 | 0.9083 | 0.7738 | 0.9064 | 0.8188 | 0.9979 | 0.9636 | 0.3276 | 0.8381 | 0.6715 | 0.7813 | 0.7085 | 0.9970 |
| 0.1199 | 20.73 | 1700 | 0.3165 | 0.7572 | 0.8218 | 0.9400 | 0.9873 | 0.3352 | 0.9105 | 0.7428 | 0.9204 | 0.8581 | 0.9986 | 0.9642 | 0.3088 | 0.8400 | 0.6760 | 0.7842 | 0.7302 | 0.9971 |
| 0.0564 | 20.98 | 1720 | 0.3132 | 0.7561 | 0.8199 | 0.9397 | 0.9821 | 0.3483 | 0.9140 | 0.7492 | 0.9344 | 0.8129 | 0.9986 | 0.9631 | 0.3137 | 0.8348 | 0.6799 | 0.7879 | 0.7164 | 0.9971 |
| 0.0598 | 21.22 | 1740 | 0.3061 | 0.7511 | 0.8144 | 0.9399 | 0.9889 | 0.3005 | 0.9115 | 0.7575 | 0.9219 | 0.8219 | 0.9984 | 0.9630 | 0.2700 | 0.8384 | 0.6792 | 0.7874 | 0.7228 | 0.9971 |
| 0.046 | 21.46 | 1760 | 0.3253 | 0.7429 | 0.8072 | 0.9359 | 0.9847 | 0.3337 | 0.9198 | 0.7560 | 0.9298 | 0.7273 | 0.9987 | 0.9630 | 0.3009 | 0.8273 | 0.6668 | 0.7833 | 0.6625 | 0.9968 |
| 0.0445 | 21.71 | 1780 | 0.3066 | 0.7553 | 0.8234 | 0.9377 | 0.9840 | 0.3722 | 0.9179 | 0.7741 | 0.9034 | 0.8151 | 0.9970 | 0.9637 | 0.3362 | 0.8364 | 0.6700 | 0.7859 | 0.6981 | 0.9964 |
| 0.0319 | 21.95 | 1800 | 0.2987 | 0.7557 | 0.8219 | 0.9383 | 0.9826 | 0.3776 | 0.9244 | 0.7681 | 0.9151 | 0.7867 | 0.9984 | 0.9653 | 0.3414 | 0.8323 | 0.6704 | 0.7900 | 0.6936 | 0.9972 |
| 0.0618 | 22.2 | 1820 | 0.3117 | 0.7561 | 0.8216 | 0.9393 | 0.9881 | 0.3721 | 0.9045 | 0.7756 | 0.9193 | 0.7927 | 0.9986 | 0.9647 | 0.3263 | 0.8346 | 0.6793 | 0.7907 | 0.6997 | 0.9972 |
| 0.0648 | 22.44 | 1840 | 0.3060 | 0.7560 | 0.8229 | 0.9388 | 0.9856 | 0.3763 | 0.9219 | 0.7655 | 0.9124 | 0.8000 | 0.9988 | 0.9639 | 0.3345 | 0.8326 | 0.6758 | 0.7895 | 0.6985 | 0.9973 |
| 0.0797 | 22.68 | 1860 | 0.3263 | 0.7554 | 0.8208 | 0.9377 | 0.9827 | 0.3870 | 0.9216 | 0.7785 | 0.9135 | 0.7631 | 0.9987 | 0.9627 | 0.3487 | 0.8298 | 0.6744 | 0.7863 | 0.6886 | 0.9974 |
| 0.1237 | 22.93 | 1880 | 0.3307 | 0.7485 | 0.8114 | 0.9392 | 0.9884 | 0.3037 | 0.9067 | 0.7530 | 0.9316 | 0.7979 | 0.9987 | 0.9618 | 0.2721 | 0.8339 | 0.6789 | 0.7890 | 0.7067 | 0.9970 |
| 0.1067 | 23.17 | 1900 | 0.3474 | 0.7529 | 0.8184 | 0.9373 | 0.9817 | 0.3705 | 0.9195 | 0.7855 | 0.9099 | 0.7639 | 0.9982 | 0.9632 | 0.3348 | 0.8316 | 0.6730 | 0.7849 | 0.6853 | 0.9972 |
| 0.0712 | 23.41 | 1920 | 0.3343 | 0.7541 | 0.8183 | 0.9385 | 0.9859 | 0.3507 | 0.9117 | 0.7823 | 0.9149 | 0.7855 | 0.9970 | 0.9633 | 0.3194 | 0.8392 | 0.6755 | 0.7876 | 0.6974 | 0.9965 |
| 0.0899 | 23.66 | 1940 | 0.3323 | 0.7533 | 0.8188 | 0.9389 | 0.9847 | 0.3440 | 0.9164 | 0.7552 | 0.9246 | 0.8087 | 0.9978 | 0.9628 | 0.3066 | 0.8374 | 0.6754 | 0.7868 | 0.7075 | 0.9969 |
| 0.0317 | 23.9 | 1960 | 0.3198 | 0.7618 | 0.8313 | 0.9374 | 0.9849 | 0.4468 | 0.9193 | 0.7863 | 0.8964 | 0.7872 | 0.9984 | 0.9644 | 0.3890 | 0.8393 | 0.6682 | 0.7815 | 0.6928 | 0.9973 |
| 0.0285 | 24.15 | 1980 | 0.3128 | 0.7596 | 0.8264 | 0.9378 | 0.9851 | 0.4220 | 0.9104 | 0.7717 | 0.9158 | 0.7810 | 0.9986 | 0.9640 | 0.3708 | 0.8387 | 0.6694 | 0.7830 | 0.6944 | 0.9971 |
| 0.0486 | 24.39 | 2000 | 0.3437 | 0.7630 | 0.8306 | 0.9386 | 0.9822 | 0.4286 | 0.9020 | 0.7792 | 0.9146 | 0.8095 | 0.9984 | 0.9642 | 0.3776 | 0.8381 | 0.6761 | 0.7856 | 0.7021 | 0.9971 |
| 0.0316 | 24.63 | 2020 | 0.3405 | 0.7544 | 0.8216 | 0.9370 | 0.9844 | 0.3825 | 0.9102 | 0.7801 | 0.9019 | 0.7935 | 0.9987 | 0.9634 | 0.3421 | 0.8381 | 0.6681 | 0.7806 | 0.6912 | 0.9970 |
| 0.0578 | 24.88 | 2040 | 0.3236 | 0.7546 | 0.8199 | 0.9375 | 0.9852 | 0.3842 | 0.9154 | 0.7651 | 0.9180 | 0.7719 | 0.9992 | 0.9628 | 0.3424 | 0.8374 | 0.6677 | 0.7841 | 0.6912 | 0.9969 |
| 0.0508 | 25.12 | 2060 | 0.3100 | 0.7602 | 0.8293 | 0.9381 | 0.9860 | 0.4213 | 0.9127 | 0.7742 | 0.9023 | 0.8093 | 0.9991 | 0.9647 | 0.3667 | 0.8409 | 0.6696 | 0.7843 | 0.6979 | 0.9970 |
| 0.1105 | 25.37 | 2080 | 0.3355 | 0.7627 | 0.8309 | 0.9385 | 0.9833 | 0.4328 | 0.9249 | 0.7711 | 0.9098 | 0.7957 | 0.9983 | 0.9647 | 0.3824 | 0.8389 | 0.6719 | 0.7872 | 0.6967 | 0.9971 |
| 0.0606 | 25.61 | 2100 | 0.3244 | 0.7614 | 0.8294 | 0.9383 | 0.9857 | 0.4365 | 0.9214 | 0.7704 | 0.9122 | 0.7807 | 0.9989 | 0.9642 | 0.3811 | 0.8372 | 0.6716 | 0.7884 | 0.6905 | 0.9968 |
| 0.0617 | 25.85 | 2120 | 0.3365 | 0.7631 | 0.8326 | 0.9379 | 0.9844 | 0.4610 | 0.9217 | 0.7764 | 0.9067 | 0.7792 | 0.9989 | 0.9646 | 0.4002 | 0.8362 | 0.6713 | 0.7871 | 0.6853 | 0.9968 |
| 0.0475 | 26.1 | 2140 | 0.3276 | 0.7625 | 0.8293 | 0.9389 | 0.9836 | 0.4334 | 0.9154 | 0.7779 | 0.9191 | 0.7777 | 0.9984 | 0.9635 | 0.3804 | 0.8365 | 0.6807 | 0.7890 | 0.6902 | 0.9970 |
| 0.0273 | 26.34 | 2160 | 0.3273 | 0.7619 | 0.8280 | 0.9395 | 0.9861 | 0.4197 | 0.9042 | 0.7734 | 0.9250 | 0.7885 | 0.9989 | 0.9625 | 0.3677 | 0.8352 | 0.6845 | 0.7922 | 0.6943 | 0.9968 |
| 0.032 | 26.59 | 2180 | 0.3212 | 0.7663 | 0.8346 | 0.9389 | 0.9860 | 0.4738 | 0.9158 | 0.7828 | 0.9155 | 0.7700 | 0.9983 | 0.9636 | 0.4117 | 0.8346 | 0.6795 | 0.7925 | 0.6848 | 0.9971 |
| 0.0506 | 26.83 | 2200 | 0.3258 | 0.7628 | 0.8333 | 0.9381 | 0.9853 | 0.4548 | 0.9100 | 0.7921 | 0.9009 | 0.7919 | 0.9981 | 0.9641 | 0.3927 | 0.8347 | 0.6766 | 0.7885 | 0.6862 | 0.9970 |
| 0.0514 | 27.07 | 2220 | 0.3247 | 0.7611 | 0.8285 | 0.9381 | 0.9859 | 0.4388 | 0.9098 | 0.7799 | 0.9138 | 0.7724 | 0.9990 | 0.9636 | 0.3842 | 0.8339 | 0.6749 | 0.7888 | 0.6857 | 0.9969 |
| 0.0586 | 27.32 | 2240 | 0.3425 | 0.7694 | 0.8395 | 0.9383 | 0.9817 | 0.5045 | 0.9230 | 0.7765 | 0.9132 | 0.7790 | 0.9983 | 0.9647 | 0.4356 | 0.8357 | 0.6748 | 0.7871 | 0.6906 | 0.9971 |
| 0.0358 | 27.56 | 2260 | 0.3287 | 0.7649 | 0.8334 | 0.9387 | 0.9853 | 0.4549 | 0.9222 | 0.7742 | 0.9103 | 0.7880 | 0.9986 | 0.9651 | 0.3982 | 0.8369 | 0.6744 | 0.7883 | 0.6941 | 0.9973 |
| 0.081 | 27.8 | 2280 | 0.3187 | 0.7564 | 0.8199 | 0.9394 | 0.9879 | 0.3687 | 0.9101 | 0.7757 | 0.9257 | 0.7730 | 0.9979 | 0.9634 | 0.3317 | 0.8395 | 0.6810 | 0.7917 | 0.6905 | 0.9971 |
| 0.0626 | 28.05 | 2300 | 0.3303 | 0.7624 | 0.8294 | 0.9383 | 0.9846 | 0.4509 | 0.9207 | 0.7909 | 0.9108 | 0.7493 | 0.9988 | 0.9647 | 0.3953 | 0.8379 | 0.6762 | 0.7896 | 0.6763 | 0.9971 |
| 0.0348 | 28.29 | 2320 | 0.3290 | 0.7726 | 0.8442 | 0.9384 | 0.9813 | 0.5497 | 0.9144 | 0.7723 | 0.9233 | 0.7689 | 0.9991 | 0.9648 | 0.4603 | 0.8368 | 0.6741 | 0.7889 | 0.6864 | 0.9968 |
| 0.0613 | 28.54 | 2340 | 0.3243 | 0.7716 | 0.8412 | 0.9390 | 0.9845 | 0.5196 | 0.9183 | 0.7645 | 0.9230 | 0.7797 | 0.9990 | 0.9648 | 0.4432 | 0.8364 | 0.6745 | 0.7894 | 0.6961 | 0.9970 |
| 0.0561 | 28.78 | 2360 | 0.3364 | 0.7712 | 0.8417 | 0.9384 | 0.9854 | 0.5267 | 0.9070 | 0.7671 | 0.9243 | 0.7833 | 0.9981 | 0.9648 | 0.4469 | 0.8355 | 0.6727 | 0.7872 | 0.6941 | 0.9972 |
| 0.051 | 29.02 | 2380 | 0.3470 | 0.7779 | 0.8563 | 0.9375 | 0.9825 | 0.6252 | 0.9132 | 0.7795 | 0.9038 | 0.7915 | 0.9986 | 0.9655 | 0.5035 | 0.8367 | 0.6669 | 0.7830 | 0.6923 | 0.9972 |
| 0.0249 | 29.27 | 2400 | 0.3659 | 0.7658 | 0.8363 | 0.9376 | 0.9860 | 0.4942 | 0.9175 | 0.7694 | 0.9121 | 0.7767 | 0.9985 | 0.9646 | 0.4209 | 0.8363 | 0.6672 | 0.7842 | 0.6901 | 0.9971 |
| 0.0322 | 29.51 | 2420 | 0.3596 | 0.7666 | 0.8374 | 0.9378 | 0.9819 | 0.4925 | 0.9202 | 0.7731 | 0.9110 | 0.7850 | 0.9984 | 0.9644 | 0.4217 | 0.8361 | 0.6686 | 0.7850 | 0.6932 | 0.9973 |
| 0.0473 | 29.76 | 2440 | 0.3501 | 0.7664 | 0.8363 | 0.9384 | 0.9861 | 0.4770 | 0.9151 | 0.7702 | 0.9121 | 0.7954 | 0.9981 | 0.9644 | 0.4089 | 0.8377 | 0.6714 | 0.7849 | 0.7002 | 0.9973 |
| 0.0373 | 30.0 | 2460 | 0.3375 | 0.7571 | 0.8235 | 0.9378 | 0.9867 | 0.4027 | 0.9193 | 0.7815 | 0.9091 | 0.7668 | 0.9981 | 0.9630 | 0.3564 | 0.8380 | 0.6714 | 0.7861 | 0.6875 | 0.9972 |
| 0.0415 | 30.24 | 2480 | 0.3230 | 0.7695 | 0.8391 | 0.9394 | 0.9865 | 0.4905 | 0.9062 | 0.7704 | 0.9173 | 0.8038 | 0.9992 | 0.9646 | 0.4169 | 0.8398 | 0.6773 | 0.7892 | 0.7018 | 0.9969 |
| 0.023 | 30.49 | 2500 | 0.3284 | 0.7734 | 0.8448 | 0.9403 | 0.9865 | 0.4943 | 0.9014 | 0.7698 | 0.9117 | 0.8511 | 0.9984 | 0.9647 | 0.4229 | 0.8395 | 0.6835 | 0.7889 | 0.7173 | 0.9973 |
| 0.0746 | 30.73 | 2520 | 0.3388 | 0.7713 | 0.8415 | 0.9398 | 0.9838 | 0.4801 | 0.9121 | 0.7629 | 0.9142 | 0.8387 | 0.9986 | 0.9644 | 0.4167 | 0.8402 | 0.6782 | 0.7883 | 0.7140 | 0.9973 |
| 0.0468 | 30.98 | 2540 | 0.3446 | 0.7756 | 0.8479 | 0.9389 | 0.9833 | 0.5564 | 0.9096 | 0.7794 | 0.9148 | 0.7935 | 0.9985 | 0.9656 | 0.4674 | 0.8403 | 0.6732 | 0.7888 | 0.6969 | 0.9973 |
| 0.0257 | 31.22 | 2560 | 0.3663 | 0.7678 | 0.8371 | 0.9380 | 0.9836 | 0.5014 | 0.9126 | 0.7739 | 0.9198 | 0.7705 | 0.9982 | 0.9653 | 0.4311 | 0.8394 | 0.6693 | 0.7859 | 0.6862 | 0.9973 |
| 0.1516 | 31.46 | 2580 | 0.3418 | 0.7661 | 0.8338 | 0.9392 | 0.9865 | 0.4536 | 0.9081 | 0.7723 | 0.9184 | 0.7998 | 0.9982 | 0.9648 | 0.3961 | 0.8399 | 0.6758 | 0.7888 | 0.7002 | 0.9973 |
| 0.1241 | 31.71 | 2600 | 0.3424 | 0.7699 | 0.8384 | 0.9392 | 0.9853 | 0.5002 | 0.9148 | 0.7822 | 0.9176 | 0.7698 | 0.9988 | 0.9655 | 0.4294 | 0.8400 | 0.6772 | 0.7908 | 0.6887 | 0.9973 |
| 0.0768 | 31.95 | 2620 | 0.3201 | 0.7747 | 0.8469 | 0.9396 | 0.9860 | 0.5485 | 0.9095 | 0.7722 | 0.9192 | 0.7940 | 0.9991 | 0.9659 | 0.4546 | 0.8402 | 0.6769 | 0.7908 | 0.6975 | 0.9971 |
| 0.0675 | 32.2 | 2640 | 0.3314 | 0.7741 | 0.8464 | 0.9388 | 0.9852 | 0.5593 | 0.9178 | 0.7701 | 0.9193 | 0.7744 | 0.9987 | 0.9656 | 0.4658 | 0.8366 | 0.6715 | 0.7904 | 0.6914 | 0.9972 |
| 0.0597 | 32.44 | 2660 | 0.3408 | 0.7713 | 0.8443 | 0.9380 | 0.9845 | 0.5475 | 0.9203 | 0.7840 | 0.9053 | 0.7698 | 0.9990 | 0.9659 | 0.4557 | 0.8393 | 0.6696 | 0.7871 | 0.6842 | 0.9971 |
| 0.0284 | 32.68 | 2680 | 0.3684 | 0.7701 | 0.8421 | 0.9382 | 0.9845 | 0.5241 | 0.9118 | 0.7772 | 0.9147 | 0.7849 | 0.9977 | 0.9651 | 0.4414 | 0.8362 | 0.6732 | 0.7872 | 0.6904 | 0.9971 |
| 0.1271 | 32.93 | 2700 | 0.3431 | 0.7724 | 0.8467 | 0.9383 | 0.9835 | 0.5404 | 0.9117 | 0.7855 | 0.8996 | 0.8079 | 0.9986 | 0.9658 | 0.4524 | 0.8362 | 0.6742 | 0.7846 | 0.6964 | 0.9975 |
| 0.0555 | 33.17 | 2720 | 0.3386 | 0.7752 | 0.8499 | 0.9386 | 0.9842 | 0.5763 | 0.8996 | 0.7841 | 0.9127 | 0.7935 | 0.9991 | 0.9656 | 0.4722 | 0.8328 | 0.6773 | 0.7881 | 0.6931 | 0.9973 |
| 0.0462 | 33.41 | 2740 | 0.3433 | 0.7714 | 0.8416 | 0.9392 | 0.9854 | 0.5143 | 0.9154 | 0.7865 | 0.9101 | 0.7807 | 0.9988 | 0.9658 | 0.4387 | 0.8367 | 0.6789 | 0.7898 | 0.6922 | 0.9975 |
| 0.0603 | 33.66 | 2760 | 0.3408 | 0.7767 | 0.8502 | 0.9390 | 0.9834 | 0.5767 | 0.9157 | 0.7825 | 0.9118 | 0.7823 | 0.9990 | 0.9661 | 0.4786 | 0.8374 | 0.6763 | 0.7891 | 0.6922 | 0.9974 |
| 0.0315 | 33.9 | 2780 | 0.3349 | 0.7717 | 0.8432 | 0.9386 | 0.9857 | 0.5284 | 0.9087 | 0.7813 | 0.9108 | 0.7890 | 0.9985 | 0.9655 | 0.4475 | 0.8373 | 0.6741 | 0.7875 | 0.6924 | 0.9973 |
| 0.1076 | 34.15 | 2800 | 0.3251 | 0.7679 | 0.8382 | 0.9387 | 0.9859 | 0.4756 | 0.9135 | 0.7862 | 0.8993 | 0.8085 | 0.9982 | 0.9653 | 0.4124 | 0.8401 | 0.6741 | 0.7854 | 0.7010 | 0.9973 |
| 0.0482 | 34.39 | 2820 | 0.3239 | 0.7676 | 0.8374 | 0.9388 | 0.9874 | 0.4803 | 0.9072 | 0.7828 | 0.9067 | 0.7987 | 0.9986 | 0.9653 | 0.4128 | 0.8395 | 0.6736 | 0.7868 | 0.6979 | 0.9976 |
| 0.1525 | 34.63 | 2840 | 0.3541 | 0.7625 | 0.8297 | 0.9383 | 0.9882 | 0.4431 | 0.9115 | 0.7750 | 0.9137 | 0.7779 | 0.9986 | 0.9640 | 0.3867 | 0.8389 | 0.6731 | 0.7854 | 0.6919 | 0.9973 |
| 0.0616 | 34.88 | 2860 | 0.3402 | 0.7664 | 0.8364 | 0.9377 | 0.9877 | 0.4787 | 0.9077 | 0.7727 | 0.9042 | 0.8050 | 0.9986 | 0.9640 | 0.4145 | 0.8395 | 0.6692 | 0.7799 | 0.7008 | 0.9972 |
| 0.0271 | 35.12 | 2880 | 0.3425 | 0.7775 | 0.8553 | 0.9376 | 0.9838 | 0.6081 | 0.9064 | 0.7798 | 0.8996 | 0.8104 | 0.9987 | 0.9660 | 0.4933 | 0.8408 | 0.6664 | 0.7792 | 0.6993 | 0.9974 |
| 0.1006 | 35.37 | 2900 | 0.3457 | 0.7764 | 0.8499 | 0.9388 | 0.9842 | 0.5641 | 0.9209 | 0.7709 | 0.9068 | 0.8030 | 0.9990 | 0.9656 | 0.4715 | 0.8439 | 0.6709 | 0.7837 | 0.7022 | 0.9972 |
| 0.0644 | 35.61 | 2920 | 0.3366 | 0.7728 | 0.8433 | 0.9395 | 0.9867 | 0.5212 | 0.9161 | 0.7713 | 0.9154 | 0.7938 | 0.9989 | 0.9655 | 0.4395 | 0.8438 | 0.6758 | 0.7876 | 0.7000 | 0.9973 |
| 0.1789 | 35.85 | 2940 | 0.3341 | 0.7747 | 0.8462 | 0.9396 | 0.9870 | 0.5396 | 0.9129 | 0.7713 | 0.9183 | 0.7964 | 0.9981 | 0.9658 | 0.4523 | 0.8430 | 0.6768 | 0.7882 | 0.6994 | 0.9974 |
| 0.0253 | 36.1 | 2960 | 0.3389 | 0.7769 | 0.8515 | 0.9386 | 0.9834 | 0.5893 | 0.9061 | 0.7757 | 0.9157 | 0.7912 | 0.9992 | 0.9661 | 0.4828 | 0.8401 | 0.6729 | 0.7860 | 0.6935 | 0.9972 |
| 0.0542 | 36.34 | 2980 | 0.3422 | 0.7758 | 0.8501 | 0.9382 | 0.9838 | 0.5750 | 0.9163 | 0.7807 | 0.9059 | 0.7904 | 0.9983 | 0.9661 | 0.4789 | 0.8418 | 0.6700 | 0.7841 | 0.6921 | 0.9975 |
| 0.0217 | 36.59 | 3000 | 0.3516 | 0.7750 | 0.8485 | 0.9381 | 0.9850 | 0.5718 | 0.9154 | 0.7808 | 0.9093 | 0.7792 | 0.9980 | 0.9661 | 0.4767 | 0.8420 | 0.6687 | 0.7850 | 0.6894 | 0.9973 |
| 0.0374 | 36.83 | 3020 | 0.3425 | 0.7814 | 0.8568 | 0.9395 | 0.9830 | 0.6156 | 0.9201 | 0.7733 | 0.9167 | 0.7906 | 0.9985 | 0.9667 | 0.5020 | 0.8429 | 0.6745 | 0.7883 | 0.6981 | 0.9976 |
| 0.0426 | 37.07 | 3040 | 0.3436 | 0.7764 | 0.8476 | 0.9399 | 0.9836 | 0.5385 | 0.9183 | 0.7688 | 0.9154 | 0.8098 | 0.9991 | 0.9660 | 0.4570 | 0.8428 | 0.6772 | 0.7891 | 0.7053 | 0.9973 |
| 0.0192 | 37.32 | 3060 | 0.3413 | 0.7791 | 0.8553 | 0.9388 | 0.9822 | 0.6076 | 0.9145 | 0.7757 | 0.9120 | 0.7964 | 0.9990 | 0.9662 | 0.4941 | 0.8409 | 0.6722 | 0.7871 | 0.6956 | 0.9975 |
| 0.0654 | 37.56 | 3080 | 0.3456 | 0.7780 | 0.8529 | 0.9392 | 0.9845 | 0.5859 | 0.9168 | 0.7745 | 0.9102 | 0.7994 | 0.9990 | 0.9664 | 0.4809 | 0.8411 | 0.6737 | 0.7873 | 0.6990 | 0.9975 |
| 0.0387 | 37.8 | 3100 | 0.3460 | 0.7758 | 0.8496 | 0.9389 | 0.9849 | 0.5651 | 0.9166 | 0.7760 | 0.9078 | 0.7979 | 0.9991 | 0.9661 | 0.4685 | 0.8413 | 0.6729 | 0.7866 | 0.6980 | 0.9973 |
| 0.0312 | 38.05 | 3120 | 0.3362 | 0.7752 | 0.8494 | 0.9384 | 0.9834 | 0.5731 | 0.9147 | 0.7806 | 0.9064 | 0.7881 | 0.9995 | 0.9654 | 0.4732 | 0.8406 | 0.6713 | 0.7864 | 0.6928 | 0.9968 |
| 0.023 | 38.29 | 3140 | 0.3474 | 0.7786 | 0.8549 | 0.9387 | 0.9826 | 0.6057 | 0.9138 | 0.7812 | 0.9092 | 0.7931 | 0.9989 | 0.9661 | 0.4926 | 0.8401 | 0.6728 | 0.7879 | 0.6938 | 0.9974 |
| 0.0204 | 38.54 | 3160 | 0.3287 | 0.7759 | 0.8489 | 0.9393 | 0.9849 | 0.5570 | 0.9121 | 0.7799 | 0.9096 | 0.7997 | 0.9990 | 0.9660 | 0.4643 | 0.8407 | 0.6753 | 0.7887 | 0.6991 | 0.9973 |
| 0.0676 | 38.78 | 3180 | 0.3485 | 0.7717 | 0.8417 | 0.9396 | 0.9868 | 0.5033 | 0.9104 | 0.7705 | 0.9171 | 0.8056 | 0.9986 | 0.9654 | 0.4285 | 0.8405 | 0.6769 | 0.7895 | 0.7040 | 0.9972 |
| 0.0312 | 39.02 | 3200 | 0.3597 | 0.7787 | 0.8529 | 0.9393 | 0.9830 | 0.5874 | 0.9159 | 0.7752 | 0.9152 | 0.7951 | 0.9987 | 0.9662 | 0.4840 | 0.8413 | 0.6747 | 0.7886 | 0.6988 | 0.9973 |
| 0.0392 | 39.27 | 3220 | 0.3508 | 0.7762 | 0.8490 | 0.9390 | 0.9843 | 0.5641 | 0.9162 | 0.7803 | 0.9107 | 0.7887 | 0.9987 | 0.9662 | 0.4712 | 0.8415 | 0.6732 | 0.7884 | 0.6958 | 0.9973 |
| 0.027 | 39.51 | 3240 | 0.3385 | 0.7775 | 0.8510 | 0.9399 | 0.9853 | 0.5561 | 0.9128 | 0.7709 | 0.9115 | 0.8212 | 0.9990 | 0.9659 | 0.4629 | 0.8420 | 0.6781 | 0.7893 | 0.7074 | 0.9971 |
| 0.0287 | 39.76 | 3260 | 0.3301 | 0.7703 | 0.8404 | 0.9392 | 0.9867 | 0.4985 | 0.9117 | 0.7794 | 0.9087 | 0.7985 | 0.9989 | 0.9653 | 0.4252 | 0.8413 | 0.6749 | 0.7884 | 0.6995 | 0.9971 |
| 0.0608 | 40.0 | 3280 | 0.3485 | 0.7745 | 0.8460 | 0.9397 | 0.9854 | 0.5267 | 0.9100 | 0.7714 | 0.9143 | 0.8156 | 0.9987 | 0.9657 | 0.4461 | 0.8412 | 0.6769 | 0.7892 | 0.7052 | 0.9973 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
zzttbrdd/gemcy_v1
|
zzttbrdd
| 2024-02-27T12:55:10Z | 112 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T12:53:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
mohammadhabp/flan-t5-small-esnli-lora
|
mohammadhabp
| 2024-02-27T12:52:27Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:adapter:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | 2024-02-25T15:02:02Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- rouge
- f1
base_model: google/flan-t5-base
model-index:
- name: flan-t5-small-esnli-lora
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-small-esnli-lora
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6258
- Rouge1: 0.6257
- Rouge2: 0.4156
- Rougel: 0.5682
- F1: 0.8850
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|
| 1.1793 | 0.25 | 8584 | 1.7215 | 0.6161 | 0.4037 | 0.5577 | 0.8738 |
| 1.1408 | 0.5 | 17168 | 1.6903 | 0.6194 | 0.4096 | 0.5615 | 0.8730 |
| 1.1122 | 0.75 | 25752 | 1.6155 | 0.6267 | 0.4179 | 0.5693 | 0.8832 |
| 1.0929 | 1.0 | 34336 | 1.6258 | 0.6257 | 0.4156 | 0.5682 | 0.8850 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
|
jayakushwaha/my-favourite-character
|
jayakushwaha
| 2024-02-27T12:50:21Z | 0 | 0 | null |
[
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-02-27T12:48:17Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Favourite-Character Dreambooth model trained by jayakushwaha following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 0206CS221107
Sample pictures of this concept:
.png)
|
Ayus077BCT014Bhandari/vartat5-using-100K-plus-24
|
Ayus077BCT014Bhandari
| 2024-02-27T12:48:22Z | 96 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-27T10:49:56Z |
---
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]
|
CreazyAI/gemma-Code-Instruct-Finetune-test
|
CreazyAI
| 2024-02-27T12:46:53Z | 113 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T12:40:23Z |
---
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]
|
cong1230/LDCC_LoRA
|
cong1230
| 2024-02-27T12:44:56Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-02-27T12:30:20Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.4.0
|
spotify/Mixtral-8x7B-Instruct-v0.1-HIReview-v0.1.4
|
spotify
| 2024-02-27T12:43:41Z | 2 | 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 | 2024-02-27T12:22:00Z |
---
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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2
|
tejasreereddy/mistral-test
|
tejasreereddy
| 2024-02-27T12:40:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T10:19:15Z |
---
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]
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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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- **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
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[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]
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[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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## Model Card Contact
[More Information Needed]
|
laishram/bloom-560m-lora-merged-tagger
|
laishram
| 2024-02-27T12:31:26Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bloom",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-27T12:29:47Z |
---
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]
|
zykrr/tinyllama
|
zykrr
| 2024-02-27T12:30:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T12:29:49Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/tinyllama-bnb-4bit
---
# Uploaded model
- **Developed by:** zykrr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
harikanaidu2k4/my-pet-dog
|
harikanaidu2k4
| 2024-02-27T12:30:04Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T12:26:08Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by harikanaidu2k4 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
AlGM93/PPO-PyramidsRND
|
AlGM93
| 2024-02-27T12:25:04Z | 15 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-02-27T12:25:01Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AlGM93/PPO-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Dagonez/DialoGPT-medium-Homer-Bot
|
Dagonez
| 2024-02-27T12:24:12Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T12:23:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
|
cerkut/colab-tuned-gtzan
|
cerkut
| 2024-02-27T12:18:51Z | 161 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-02-27T03:40:53Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: colab-tuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: gtzan
type: gtzan
config: all
split: None
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# colab-tuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the gtzan dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1653
- Accuracy: 0.0
## 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: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2978 | 1.0 | 16 | 2.1653 | 0.0 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
casque/Twill_pantyhose
|
casque
| 2024-02-27T12:18:00Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-27T12:17:14Z |
---
license: creativeml-openrail-m
---
|
MichaelKim/train_results
|
MichaelKim
| 2024-02-27T12:17:19Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:LDCC/LDCC-SOLAR-10.7B",
"base_model:adapter:LDCC/LDCC-SOLAR-10.7B",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-02-27T07:25:00Z |
---
license: cc-by-nc-4.0
library_name: peft
tags:
- generated_from_trainer
base_model: LDCC/LDCC-SOLAR-10.7B
model-index:
- name: train_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. -->
# train_results
This model is a fine-tuned version of [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B) 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: 0.0001
- train_batch_size: 10
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.2
|
vicky6/bert-fine-tuned-cola
|
vicky6
| 2024-02-27T12:16:44Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T11:44:40Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
base_model: bert-base-uncased
model-index:
- name: bert-fine-tuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2917
- Validation Loss: 0.6055
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.1}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4860 | 0.4907 | 0 |
| 0.2917 | 0.6055 | 1 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.1
- Tokenizers 0.15.2
|
AdrienB134/ColBERTv1.0-bert-based-spanish-mmarcoES
|
AdrienB134
| 2024-02-27T12:16:34Z | 38 | 1 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"colbert",
"ColBERT",
"es",
"dataset:unicamp-dl/mmarco",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-01-16T06:23:03Z |
---
license: mit
datasets:
- unicamp-dl/mmarco
language:
- es
tags:
- colbert
- ColBERT
---
New spanish ColBERTv2 model available [here](https://huggingface.co/AdrienB134/ColBERTv2.0-spanish-mmarcoES)
## Training
#### Details
The model is initialized from the [bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) checkpoint and fine-tuned on 10M triples via pairwise softmax cross-entropy loss over the computed scores of the positive and negative passages associated to a query. It was trained on a single Tesla A100 GPU with 40GBs of memory during 200k steps with 10% of warmup steps using a batch size of 96 and the AdamW optimizer with a constant learning rate of 3e-06. Total training time was around 12 hours.
#### Data
The model is fine-tuned on the Spanish version of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset, a multi-lingual machine-translated version of the MS MARCO dataset.
The triples are sampled from the ~39.8M triples of [triples.train.small.tsv](https://microsoft.github.io/msmarco/Datasets.html#passage-ranking-dataset)
## Evaluation
The model is evaluated on the smaller development set of mMARCO-es, which consists of 6,980 queries for a corpus of 8.8M candidate passages. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k).
| model | Vocab. | #Param. | Size | MRR@10 | R@50 | R@1000 |
|:------------------------------------------------------------------------------------------------------------------------|:--------|--------:|------:|---------:|-------:|--------:|
| **ColBERTv1.0-bert-based-spanish-mmarcoES** | spanish | 110M | 440MB | 24.70 | 59,23 | 63.86 |
|
imagepipeline/Redmond-Logo-Liberte-SD1.5
|
imagepipeline
| 2024-02-27T12:07:14Z | 0 | 0 | null |
[
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-02-27T12:07:11Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## Redmond-Logo-Liberte-SD1.5
<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/530ba359-f680-4704-aeb1-3fefb2cd632d/width=450/02503-1337.jpeg" alt="Generated on Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - The tag for the model:LogoRedAF, logo. LORA is not perfect and sometimes needs more than one gen to create good images. I recommend simple prompts. I really hope you like the LORA and use it. If you like the model and think it's worth it, you can make a donation to my Patreon or Ko-fi. Follow me in my twitter to know before all about new models: https://twitter.com/artificialguybr/
[](https://imagepipeline.io/models/Redmond-Logo-Liberte-SD1.5?id=a6726db9-8c2f-455f-9269-62b738f95ebd/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sd/text2image/v1/run"
payload = json.dumps({
"model_id": "sd1.5",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "a6726db9-8c2f-455f-9269-62b738f95ebd",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sd/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
johnobc/SDXL-Lightning
|
johnobc
| 2024-02-27T11:56:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-02-26T11:55:14Z |
---
license: openrail++
---This model is converted to CoreML for us in odysseyapp.io or other Mac-based Stable Diffusion apps. To add this model to Odyssey simply follow these instructions: https://odysseyapp.io/guides/custom-models-2
More information about the model can be found here: https://huggingface.co/ByteDance/SDXL-Lightning
|
adarsh12x/mistral_7b_samantha___
|
adarsh12x
| 2024-02-27T11:52:53Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-26T10:42:07Z |
---
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]
|
Myaukko/videomae-base-finetuned-fighting-subset-5ep-5ep3bs-1ep3bs
|
Myaukko
| 2024-02-27T11:46:18Z | 62 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2024-02-27T10:58:29Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-fighting-subset-5ep-5ep3bs-1ep3bs
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. -->
# videomae-base-finetuned-fighting-subset-5ep-5ep3bs-1ep3bs
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1729
- Accuracy: 0.9630
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 41
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0007 | 1.0 | 41 | 0.1729 | 0.9630 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
|
casque/jacquard_pantyhose
|
casque
| 2024-02-27T11:37:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-27T11:36:22Z |
---
license: creativeml-openrail-m
---
|
neerajnarwal/Mistral-7B-Instruct-Question-Answering
|
neerajnarwal
| 2024-02-27T11:21:57Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-02-27T10:08:26Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2
|
Di1/chatd5k
|
Di1
| 2024-02-27T11:16:29Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-02-27T11:16:26Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
chosenone80/arabert-ner-test-1
|
chosenone80
| 2024-02-27T11:13:01Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-02-27T11:04:29Z |
---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_keras_callback
model-index:
- name: chosenone80/arabert-ner-test-1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# chosenone80/arabert-ner-test-1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0352
- Validation Loss: 0.0384
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1695, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1961 | 0.0515 | 0 |
| 0.0506 | 0.0406 | 1 |
| 0.0352 | 0.0384 | 2 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.1
- Tokenizers 0.15.2
|
oeg/RoBERTa-Repository-Proposal
|
oeg
| 2024-02-27T11:08:20Z | 163 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"English",
"RoBERTa-base",
"Text Classification",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-20T14:14:05Z |
---
license: cc-by-nc-4.0
language:
- en
tags:
- English
- RoBERTa-base
- Text Classification
pipeline_tag: text-classification
---
# RoBERTa base Fine-Tuned for Proposal Sentence Classification
## Overview
- **Language**: English
- **Model Name**: oeg/RoBERTa_Repository_Proposal
## Description
This model is a fine-tuned RoBERTa-base model trained to classify sentences into two classes: proposal and non-proposal sentences. The training data includes sentences proposing a software or data repository. The model is trained to recognize and classify these sentences accurately.
## How to use
To use this model in Python:
```python
from transformers import RobertaForSequenceClassification, RobertaTokenizer
import torch
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForSequenceClassification.from_pretrained("oeg/RoBERTa-Repository-Proposal")
sentence = "Your input sentence here."
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)
|
SyedShaheer/bart-large-cnn-samsum_tuned
|
SyedShaheer
| 2024-02-27T11:06:28Z | 123 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2024-02-27T04:27:14Z |
---
metrics:
- rouge
pipeline_tag: summarization
---
|
roleplay4fun/phuong-gpt2-v1.0
|
roleplay4fun
| 2024-02-27T11:05:21Z | 104 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-classification",
"trl",
"reward-trainer",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T11:04:17Z |
---
library_name: transformers
tags:
- trl
- reward-trainer
---
# 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]
|
peldrak/segformer-b3-ade-512-512-finetuned-coastTrain-grCoastline
|
peldrak
| 2024-02-27T11:04:05Z | 188 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:peldrak/segformer-b3-ade-512-512-finetuned-coastTrain",
"base_model:finetune:peldrak/segformer-b3-ade-512-512-finetuned-coastTrain",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2024-02-27T10:12:59Z |
---
license: other
base_model: peldrak/segformer-b3-ade-512-512-finetuned-coastTrain
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b3-ade-512-512-finetuned-coastTrain-grCoastline
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-grCoastline
This model is a fine-tuned version of [peldrak/segformer-b3-ade-512-512-finetuned-coastTrain](https://huggingface.co/peldrak/segformer-b3-ade-512-512-finetuned-coastTrain) on the peldrak/grCoastline_512 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2531
- Mean Iou: 0.7677
- Mean Accuracy: 0.8502
- Overall Accuracy: 0.9340
- Accuracy Water: 0.9810
- Accuracy Whitewater: 0.5654
- Accuracy Sediment: 0.8995
- Accuracy Other Natural Terrain: 0.7891
- Accuracy Vegetation: 0.8969
- Accuracy Development: 0.8221
- Accuracy Unknown: 0.9974
- Iou Water: 0.9535
- Iou Whitewater: 0.4217
- Iou Sediment: 0.8288
- Iou Other Natural Terrain: 0.6339
- Iou Vegetation: 0.8151
- Iou Development: 0.7244
- Iou Unknown: 0.9964
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|
| 1.2485 | 0.24 | 20 | 0.4622 | 0.5668 | 0.6676 | 0.8536 | 0.9523 | 0.2722 | 0.9426 | 0.0 | 0.9170 | 0.5995 | 0.9897 | 0.9004 | 0.2295 | 0.6418 | 0.0 | 0.6785 | 0.5280 | 0.9890 |
| 0.4894 | 0.49 | 40 | 0.3645 | 0.6103 | 0.7022 | 0.8775 | 0.9738 | 0.2414 | 0.9207 | 0.1627 | 0.9260 | 0.6974 | 0.9932 | 0.9318 | 0.2117 | 0.6742 | 0.1589 | 0.7242 | 0.5807 | 0.9904 |
| 0.4931 | 0.73 | 60 | 0.3257 | 0.6374 | 0.7168 | 0.8940 | 0.9790 | 0.1616 | 0.8723 | 0.2837 | 0.9522 | 0.7731 | 0.9955 | 0.9359 | 0.1529 | 0.7241 | 0.2724 | 0.7487 | 0.6360 | 0.9920 |
| 0.1766 | 0.98 | 80 | 0.2769 | 0.6970 | 0.7755 | 0.9123 | 0.9726 | 0.3993 | 0.9157 | 0.6302 | 0.9080 | 0.6049 | 0.9976 | 0.9443 | 0.3230 | 0.7710 | 0.5134 | 0.7866 | 0.5485 | 0.9921 |
| 0.6156 | 1.22 | 100 | 0.2895 | 0.6691 | 0.7372 | 0.9115 | 0.9696 | 0.1418 | 0.9271 | 0.5343 | 0.9293 | 0.6601 | 0.9981 | 0.9435 | 0.1371 | 0.7252 | 0.4855 | 0.7931 | 0.6066 | 0.9929 |
| 0.4116 | 1.46 | 120 | 0.2715 | 0.7026 | 0.7775 | 0.9135 | 0.9521 | 0.2680 | 0.9245 | 0.6565 | 0.8937 | 0.7554 | 0.9922 | 0.9225 | 0.2401 | 0.7913 | 0.5413 | 0.7691 | 0.6637 | 0.9905 |
| 0.261 | 1.71 | 140 | 0.2459 | 0.7193 | 0.8036 | 0.9186 | 0.9770 | 0.3963 | 0.8738 | 0.6637 | 0.8956 | 0.8211 | 0.9973 | 0.9387 | 0.3074 | 0.7904 | 0.5513 | 0.7829 | 0.6708 | 0.9933 |
| 0.2603 | 1.95 | 160 | 0.2538 | 0.7189 | 0.7987 | 0.9159 | 0.9752 | 0.3829 | 0.9032 | 0.7231 | 0.8610 | 0.7478 | 0.9975 | 0.9288 | 0.3082 | 0.7996 | 0.5457 | 0.7713 | 0.6843 | 0.9943 |
| 0.3266 | 2.2 | 180 | 0.2468 | 0.7232 | 0.8227 | 0.9118 | 0.9734 | 0.4478 | 0.9127 | 0.8202 | 0.7856 | 0.8226 | 0.9967 | 0.9337 | 0.3322 | 0.8097 | 0.5550 | 0.7399 | 0.6988 | 0.9936 |
| 0.1754 | 2.44 | 200 | 0.2850 | 0.7269 | 0.8031 | 0.9209 | 0.9764 | 0.3942 | 0.8998 | 0.6822 | 0.8980 | 0.7752 | 0.9957 | 0.9300 | 0.3147 | 0.8116 | 0.5579 | 0.7858 | 0.6944 | 0.9937 |
| 0.1391 | 2.68 | 220 | 0.2787 | 0.7316 | 0.8041 | 0.9264 | 0.9678 | 0.4164 | 0.9050 | 0.6179 | 0.9497 | 0.7737 | 0.9982 | 0.9474 | 0.3268 | 0.8058 | 0.5714 | 0.8035 | 0.6720 | 0.9946 |
| 0.1294 | 2.93 | 240 | 0.2869 | 0.7176 | 0.8170 | 0.9100 | 0.9752 | 0.4576 | 0.9232 | 0.9012 | 0.7608 | 0.7034 | 0.9976 | 0.9455 | 0.3430 | 0.7795 | 0.5795 | 0.7315 | 0.6496 | 0.9948 |
| 0.3478 | 3.17 | 260 | 0.2799 | 0.7348 | 0.8127 | 0.9238 | 0.9792 | 0.4352 | 0.8693 | 0.6888 | 0.9183 | 0.8048 | 0.9935 | 0.9461 | 0.3369 | 0.8102 | 0.5753 | 0.7904 | 0.6923 | 0.9926 |
| 0.1053 | 3.41 | 280 | 0.2963 | 0.7227 | 0.7986 | 0.9234 | 0.9837 | 0.4045 | 0.8366 | 0.5931 | 0.9610 | 0.8150 | 0.9966 | 0.9409 | 0.3144 | 0.7928 | 0.5578 | 0.8011 | 0.6577 | 0.9939 |
| 0.3786 | 3.66 | 300 | 0.2416 | 0.7282 | 0.8228 | 0.9137 | 0.9689 | 0.4874 | 0.9182 | 0.8059 | 0.8081 | 0.7729 | 0.9984 | 0.9455 | 0.3525 | 0.8143 | 0.5534 | 0.7431 | 0.6944 | 0.9937 |
| 0.3046 | 3.9 | 320 | 0.2374 | 0.7406 | 0.8148 | 0.9279 | 0.9820 | 0.3912 | 0.8961 | 0.7267 | 0.8996 | 0.8111 | 0.9966 | 0.9438 | 0.3180 | 0.8193 | 0.5968 | 0.8014 | 0.7111 | 0.9940 |
| 0.1098 | 4.15 | 340 | 0.2479 | 0.7278 | 0.8012 | 0.9258 | 0.9816 | 0.2957 | 0.8885 | 0.7045 | 0.8956 | 0.8445 | 0.9977 | 0.9488 | 0.2557 | 0.8194 | 0.5799 | 0.7923 | 0.7036 | 0.9948 |
| 0.1654 | 4.39 | 360 | 0.2757 | 0.7484 | 0.8304 | 0.9290 | 0.9751 | 0.4714 | 0.8718 | 0.7298 | 0.9119 | 0.8562 | 0.9965 | 0.9508 | 0.3615 | 0.8222 | 0.6089 | 0.8021 | 0.6989 | 0.9944 |
| 0.1079 | 4.63 | 380 | 0.2821 | 0.7171 | 0.8052 | 0.9095 | 0.9789 | 0.3882 | 0.9159 | 0.8147 | 0.7865 | 0.7563 | 0.9959 | 0.9358 | 0.3245 | 0.7857 | 0.5545 | 0.7318 | 0.6930 | 0.9942 |
| 0.1849 | 4.88 | 400 | 0.2637 | 0.7398 | 0.8191 | 0.9250 | 0.9773 | 0.4225 | 0.8793 | 0.6972 | 0.9008 | 0.8600 | 0.9967 | 0.9472 | 0.3367 | 0.8212 | 0.5715 | 0.7886 | 0.7189 | 0.9946 |
| 0.1643 | 5.12 | 420 | 0.3350 | 0.7221 | 0.7861 | 0.9244 | 0.9782 | 0.3296 | 0.9235 | 0.5713 | 0.9536 | 0.7526 | 0.9939 | 0.9458 | 0.2901 | 0.7973 | 0.5429 | 0.8014 | 0.6843 | 0.9927 |
| 0.1595 | 5.37 | 440 | 0.2582 | 0.7366 | 0.8255 | 0.9196 | 0.9769 | 0.4560 | 0.8771 | 0.7617 | 0.8518 | 0.8582 | 0.9965 | 0.9464 | 0.3440 | 0.8231 | 0.5601 | 0.7647 | 0.7237 | 0.9946 |
| 0.3171 | 5.61 | 460 | 0.2579 | 0.7433 | 0.8317 | 0.9261 | 0.9681 | 0.5031 | 0.9291 | 0.7243 | 0.8871 | 0.8137 | 0.9965 | 0.9506 | 0.3517 | 0.8177 | 0.5841 | 0.7930 | 0.7112 | 0.9950 |
| 0.2955 | 5.85 | 480 | 0.2975 | 0.7288 | 0.8072 | 0.9226 | 0.9758 | 0.4648 | 0.9149 | 0.6695 | 0.9146 | 0.7139 | 0.9967 | 0.9498 | 0.3452 | 0.7954 | 0.5683 | 0.7901 | 0.6579 | 0.9948 |
| 0.0857 | 6.1 | 500 | 0.2707 | 0.7307 | 0.8236 | 0.9194 | 0.9792 | 0.5026 | 0.9181 | 0.7281 | 0.8591 | 0.7821 | 0.9957 | 0.9512 | 0.3523 | 0.8017 | 0.5624 | 0.7724 | 0.6806 | 0.9944 |
| 0.109 | 6.34 | 520 | 0.2674 | 0.7488 | 0.8316 | 0.9312 | 0.9738 | 0.5295 | 0.9087 | 0.6734 | 0.9363 | 0.8021 | 0.9977 | 0.9524 | 0.3633 | 0.8258 | 0.5984 | 0.8127 | 0.6945 | 0.9948 |
| 0.0593 | 6.59 | 540 | 0.2806 | 0.7376 | 0.8273 | 0.9204 | 0.9756 | 0.4729 | 0.9084 | 0.8021 | 0.8373 | 0.7988 | 0.9962 | 0.9491 | 0.3463 | 0.8215 | 0.5723 | 0.7676 | 0.7121 | 0.9947 |
| 0.099 | 6.83 | 560 | 0.2874 | 0.7421 | 0.8331 | 0.9237 | 0.9626 | 0.4619 | 0.9334 | 0.7321 | 0.8586 | 0.8852 | 0.9982 | 0.9486 | 0.3640 | 0.8238 | 0.5798 | 0.7802 | 0.7024 | 0.9957 |
| 0.0665 | 7.07 | 580 | 0.2642 | 0.7462 | 0.8177 | 0.9291 | 0.9780 | 0.5176 | 0.8784 | 0.6685 | 0.9544 | 0.7287 | 0.9983 | 0.9493 | 0.3841 | 0.8119 | 0.5980 | 0.8057 | 0.6788 | 0.9953 |
| 0.1285 | 7.32 | 600 | 0.2347 | 0.7495 | 0.8500 | 0.9236 | 0.9799 | 0.5332 | 0.8905 | 0.8549 | 0.8181 | 0.8766 | 0.9968 | 0.9483 | 0.3906 | 0.8197 | 0.6088 | 0.7745 | 0.7102 | 0.9946 |
| 0.1299 | 7.56 | 620 | 0.2630 | 0.7506 | 0.8232 | 0.9311 | 0.9751 | 0.4683 | 0.9292 | 0.6923 | 0.9228 | 0.7773 | 0.9976 | 0.9489 | 0.3754 | 0.8070 | 0.6084 | 0.8172 | 0.7028 | 0.9948 |
| 0.0504 | 7.8 | 640 | 0.2964 | 0.7358 | 0.8238 | 0.9172 | 0.9790 | 0.4852 | 0.9113 | 0.7586 | 0.8335 | 0.8016 | 0.9976 | 0.9481 | 0.3776 | 0.8139 | 0.5465 | 0.7591 | 0.7105 | 0.9953 |
| 0.0795 | 8.05 | 660 | 0.2654 | 0.7443 | 0.8427 | 0.9198 | 0.9764 | 0.5517 | 0.9103 | 0.8286 | 0.8150 | 0.8193 | 0.9978 | 0.9506 | 0.3918 | 0.8173 | 0.5768 | 0.7619 | 0.7163 | 0.9953 |
| 0.0614 | 8.29 | 680 | 0.2904 | 0.7452 | 0.8165 | 0.9303 | 0.9763 | 0.4578 | 0.8990 | 0.6315 | 0.9536 | 0.8003 | 0.9973 | 0.9496 | 0.3518 | 0.8172 | 0.5805 | 0.8118 | 0.7105 | 0.9951 |
| 0.1476 | 8.54 | 700 | 0.2814 | 0.7498 | 0.8324 | 0.9276 | 0.9785 | 0.5138 | 0.9093 | 0.7335 | 0.8910 | 0.8028 | 0.9979 | 0.9489 | 0.3821 | 0.8168 | 0.5909 | 0.7981 | 0.7165 | 0.9956 |
| 0.0669 | 8.78 | 720 | 0.2774 | 0.7483 | 0.8374 | 0.9250 | 0.9741 | 0.5476 | 0.9217 | 0.7477 | 0.8710 | 0.8020 | 0.9980 | 0.9313 | 0.3931 | 0.8220 | 0.5795 | 0.7975 | 0.7196 | 0.9953 |
| 0.142 | 9.02 | 740 | 0.2362 | 0.7624 | 0.8555 | 0.9327 | 0.9784 | 0.6280 | 0.8959 | 0.7341 | 0.9111 | 0.8425 | 0.9983 | 0.9516 | 0.4124 | 0.8211 | 0.6182 | 0.8158 | 0.7224 | 0.9952 |
| 0.1258 | 9.27 | 760 | 0.2666 | 0.7597 | 0.8357 | 0.9329 | 0.9762 | 0.4810 | 0.9161 | 0.7439 | 0.9043 | 0.8311 | 0.9975 | 0.9554 | 0.3793 | 0.8262 | 0.6165 | 0.8111 | 0.7338 | 0.9954 |
| 0.1541 | 9.51 | 780 | 0.2484 | 0.7630 | 0.8423 | 0.9334 | 0.9797 | 0.5260 | 0.9084 | 0.7548 | 0.9043 | 0.8259 | 0.9973 | 0.9543 | 0.3991 | 0.8244 | 0.6228 | 0.8147 | 0.7307 | 0.9953 |
| 0.1689 | 9.76 | 800 | 0.2151 | 0.7710 | 0.8619 | 0.9341 | 0.9747 | 0.6199 | 0.9143 | 0.8381 | 0.8751 | 0.8127 | 0.9985 | 0.9553 | 0.4257 | 0.8333 | 0.6421 | 0.8117 | 0.7341 | 0.9952 |
| 0.0931 | 10.0 | 820 | 0.2422 | 0.7506 | 0.8239 | 0.9325 | 0.9783 | 0.4062 | 0.9162 | 0.7139 | 0.9116 | 0.8443 | 0.9969 | 0.9528 | 0.3324 | 0.8236 | 0.6103 | 0.8137 | 0.7265 | 0.9953 |
| 0.1109 | 10.24 | 840 | 0.2336 | 0.7522 | 0.8271 | 0.9321 | 0.9774 | 0.4327 | 0.9191 | 0.7334 | 0.9027 | 0.8263 | 0.9981 | 0.9530 | 0.3442 | 0.8194 | 0.6136 | 0.8110 | 0.7283 | 0.9960 |
| 0.0561 | 10.49 | 860 | 0.2991 | 0.7572 | 0.8445 | 0.9284 | 0.9743 | 0.5846 | 0.9094 | 0.7471 | 0.8933 | 0.8066 | 0.9966 | 0.9514 | 0.4134 | 0.8200 | 0.5952 | 0.7985 | 0.7261 | 0.9955 |
| 0.0701 | 10.73 | 880 | 0.2647 | 0.7554 | 0.8481 | 0.9286 | 0.9774 | 0.6203 | 0.9098 | 0.7382 | 0.8929 | 0.7991 | 0.9990 | 0.9534 | 0.4082 | 0.8163 | 0.5967 | 0.8006 | 0.7175 | 0.9951 |
| 0.1528 | 10.98 | 900 | 0.2988 | 0.7626 | 0.8573 | 0.9310 | 0.9713 | 0.6322 | 0.9123 | 0.7464 | 0.8980 | 0.8442 | 0.9969 | 0.9548 | 0.4138 | 0.8368 | 0.6036 | 0.8022 | 0.7317 | 0.9956 |
| 0.0514 | 11.22 | 920 | 0.2537 | 0.7528 | 0.8314 | 0.9302 | 0.9749 | 0.4371 | 0.9270 | 0.8595 | 0.8548 | 0.7694 | 0.9970 | 0.9527 | 0.3550 | 0.8248 | 0.6354 | 0.8004 | 0.7063 | 0.9951 |
| 0.0959 | 11.46 | 940 | 0.2897 | 0.7458 | 0.8233 | 0.9279 | 0.9835 | 0.4569 | 0.8963 | 0.7962 | 0.8808 | 0.7523 | 0.9974 | 0.9499 | 0.3569 | 0.8096 | 0.6191 | 0.7974 | 0.6918 | 0.9958 |
| 0.1997 | 11.71 | 960 | 0.3142 | 0.7512 | 0.8251 | 0.9295 | 0.9745 | 0.5071 | 0.9290 | 0.6819 | 0.9251 | 0.7615 | 0.9964 | 0.9537 | 0.3946 | 0.8181 | 0.5902 | 0.8052 | 0.7017 | 0.9953 |
| 0.0724 | 11.95 | 980 | 0.2794 | 0.7525 | 0.8318 | 0.9290 | 0.9822 | 0.4696 | 0.9038 | 0.7310 | 0.8897 | 0.8489 | 0.9973 | 0.9557 | 0.3727 | 0.8276 | 0.5890 | 0.7970 | 0.7299 | 0.9957 |
| 0.0668 | 12.2 | 1000 | 0.2911 | 0.7447 | 0.8175 | 0.9321 | 0.9844 | 0.3514 | 0.9008 | 0.7032 | 0.9105 | 0.8749 | 0.9970 | 0.9500 | 0.2946 | 0.8281 | 0.6032 | 0.8126 | 0.7290 | 0.9953 |
| 0.0574 | 12.44 | 1020 | 0.2565 | 0.7619 | 0.8407 | 0.9330 | 0.9797 | 0.5386 | 0.9173 | 0.7298 | 0.9096 | 0.8120 | 0.9982 | 0.9545 | 0.3984 | 0.8306 | 0.6073 | 0.8131 | 0.7338 | 0.9956 |
| 0.0696 | 12.68 | 1040 | 0.2657 | 0.7595 | 0.8366 | 0.9339 | 0.9808 | 0.4966 | 0.9101 | 0.7057 | 0.9197 | 0.8458 | 0.9979 | 0.9520 | 0.3767 | 0.8308 | 0.6096 | 0.8173 | 0.7345 | 0.9956 |
| 0.3274 | 12.93 | 1060 | 0.2586 | 0.7465 | 0.8222 | 0.9297 | 0.9793 | 0.3965 | 0.9265 | 0.7214 | 0.8877 | 0.8457 | 0.9983 | 0.9539 | 0.3307 | 0.8268 | 0.5935 | 0.8017 | 0.7235 | 0.9953 |
| 0.0817 | 13.17 | 1080 | 0.2783 | 0.7569 | 0.8496 | 0.9265 | 0.9772 | 0.5303 | 0.9224 | 0.8566 | 0.8235 | 0.8395 | 0.9975 | 0.9548 | 0.4030 | 0.8286 | 0.6126 | 0.7783 | 0.7250 | 0.9963 |
| 0.0787 | 13.41 | 1100 | 0.2517 | 0.7489 | 0.8218 | 0.9294 | 0.9802 | 0.4487 | 0.9232 | 0.7094 | 0.9058 | 0.7883 | 0.9968 | 0.9527 | 0.3654 | 0.8222 | 0.5939 | 0.8027 | 0.7105 | 0.9951 |
| 0.1024 | 13.66 | 1120 | 0.2590 | 0.7569 | 0.8290 | 0.9327 | 0.9812 | 0.4873 | 0.9080 | 0.7041 | 0.9259 | 0.7986 | 0.9977 | 0.9543 | 0.3833 | 0.8266 | 0.6094 | 0.8118 | 0.7171 | 0.9960 |
| 0.0888 | 13.9 | 1140 | 0.2647 | 0.7489 | 0.8352 | 0.9228 | 0.9812 | 0.5251 | 0.9169 | 0.7856 | 0.8447 | 0.7955 | 0.9973 | 0.9491 | 0.4024 | 0.8276 | 0.5748 | 0.7738 | 0.7184 | 0.9958 |
| 0.0946 | 14.15 | 1160 | 0.2453 | 0.7571 | 0.8370 | 0.9298 | 0.9823 | 0.5318 | 0.9002 | 0.7619 | 0.8942 | 0.7913 | 0.9973 | 0.9531 | 0.4046 | 0.8240 | 0.6086 | 0.8008 | 0.7125 | 0.9960 |
| 0.0529 | 14.39 | 1180 | 0.2514 | 0.7596 | 0.8460 | 0.9298 | 0.9808 | 0.5804 | 0.9014 | 0.7354 | 0.8979 | 0.8289 | 0.9970 | 0.9559 | 0.4197 | 0.8307 | 0.5978 | 0.7990 | 0.7183 | 0.9958 |
| 0.0495 | 14.63 | 1200 | 0.2323 | 0.7634 | 0.8491 | 0.9324 | 0.9790 | 0.5831 | 0.9267 | 0.7848 | 0.8862 | 0.7861 | 0.9977 | 0.9550 | 0.4175 | 0.8280 | 0.6217 | 0.8103 | 0.7151 | 0.9962 |
| 0.0401 | 14.88 | 1220 | 0.2248 | 0.7677 | 0.8467 | 0.9366 | 0.9796 | 0.5337 | 0.9256 | 0.8125 | 0.8943 | 0.7834 | 0.9981 | 0.9524 | 0.4037 | 0.8250 | 0.6561 | 0.8282 | 0.7126 | 0.9961 |
| 0.053 | 15.12 | 1240 | 0.2280 | 0.7701 | 0.8541 | 0.9362 | 0.9805 | 0.5488 | 0.9155 | 0.8259 | 0.8830 | 0.8280 | 0.9974 | 0.9542 | 0.4107 | 0.8259 | 0.6577 | 0.8224 | 0.7233 | 0.9961 |
| 0.0764 | 15.37 | 1260 | 0.2350 | 0.7741 | 0.8577 | 0.9370 | 0.9777 | 0.5690 | 0.9145 | 0.8514 | 0.8825 | 0.8119 | 0.9972 | 0.9574 | 0.4265 | 0.8296 | 0.6666 | 0.8216 | 0.7212 | 0.9962 |
| 0.0568 | 15.61 | 1280 | 0.2420 | 0.7629 | 0.8407 | 0.9343 | 0.9813 | 0.5093 | 0.9057 | 0.8360 | 0.8871 | 0.7680 | 0.9973 | 0.9560 | 0.3937 | 0.8246 | 0.6536 | 0.8142 | 0.7025 | 0.9960 |
| 0.1199 | 15.85 | 1300 | 0.2545 | 0.7620 | 0.8463 | 0.9321 | 0.9752 | 0.5904 | 0.9180 | 0.7196 | 0.9135 | 0.8090 | 0.9981 | 0.9557 | 0.4209 | 0.8276 | 0.6064 | 0.8098 | 0.7171 | 0.9964 |
| 0.7094 | 16.1 | 1320 | 0.2446 | 0.7584 | 0.8409 | 0.9314 | 0.9790 | 0.5580 | 0.9151 | 0.7301 | 0.9070 | 0.7993 | 0.9979 | 0.9542 | 0.4042 | 0.8218 | 0.6091 | 0.8088 | 0.7145 | 0.9963 |
| 0.0321 | 16.34 | 1340 | 0.2652 | 0.7585 | 0.8329 | 0.9340 | 0.9787 | 0.5076 | 0.9089 | 0.6924 | 0.9365 | 0.8091 | 0.9974 | 0.9538 | 0.3925 | 0.8214 | 0.6173 | 0.8211 | 0.7075 | 0.9962 |
| 0.1328 | 16.59 | 1360 | 0.2322 | 0.7587 | 0.8403 | 0.9327 | 0.9805 | 0.5174 | 0.9092 | 0.7077 | 0.9123 | 0.8570 | 0.9977 | 0.9558 | 0.3966 | 0.8276 | 0.6071 | 0.8146 | 0.7132 | 0.9959 |
| 0.0637 | 16.83 | 1380 | 0.2331 | 0.7615 | 0.8441 | 0.9322 | 0.9831 | 0.5529 | 0.8983 | 0.7412 | 0.9048 | 0.8305 | 0.9976 | 0.9530 | 0.4111 | 0.8243 | 0.6155 | 0.8104 | 0.7201 | 0.9961 |
| 0.3028 | 17.07 | 1400 | 0.2446 | 0.7572 | 0.8367 | 0.9312 | 0.9804 | 0.5135 | 0.9061 | 0.7279 | 0.9044 | 0.8263 | 0.9981 | 0.9548 | 0.3970 | 0.8212 | 0.6048 | 0.8084 | 0.7181 | 0.9962 |
| 0.0479 | 17.32 | 1420 | 0.2556 | 0.7609 | 0.8506 | 0.9295 | 0.9778 | 0.6127 | 0.9095 | 0.7802 | 0.8835 | 0.7929 | 0.9974 | 0.9556 | 0.4318 | 0.8241 | 0.6105 | 0.7988 | 0.7095 | 0.9963 |
| 0.0645 | 17.56 | 1440 | 0.2530 | 0.7587 | 0.8480 | 0.9283 | 0.9769 | 0.5933 | 0.9080 | 0.7729 | 0.8775 | 0.8091 | 0.9985 | 0.9543 | 0.4244 | 0.8268 | 0.5993 | 0.7945 | 0.7155 | 0.9962 |
| 0.0513 | 17.8 | 1460 | 0.2451 | 0.7598 | 0.8467 | 0.9306 | 0.9794 | 0.5549 | 0.9064 | 0.7567 | 0.8863 | 0.8448 | 0.9985 | 0.9547 | 0.4093 | 0.8259 | 0.6076 | 0.8039 | 0.7214 | 0.9961 |
| 0.0387 | 18.05 | 1480 | 0.2374 | 0.7625 | 0.8446 | 0.9344 | 0.9810 | 0.5392 | 0.9007 | 0.7115 | 0.9214 | 0.8609 | 0.9975 | 0.9539 | 0.4025 | 0.8249 | 0.6196 | 0.8218 | 0.7193 | 0.9958 |
| 0.0903 | 18.29 | 1500 | 0.2353 | 0.7662 | 0.8468 | 0.9351 | 0.9820 | 0.5342 | 0.9097 | 0.8100 | 0.8918 | 0.8030 | 0.9971 | 0.9549 | 0.4053 | 0.8261 | 0.6487 | 0.8190 | 0.7130 | 0.9961 |
| 0.0832 | 18.54 | 1520 | 0.2372 | 0.7677 | 0.8428 | 0.9375 | 0.9807 | 0.5172 | 0.9098 | 0.7757 | 0.9184 | 0.8007 | 0.9970 | 0.9563 | 0.3981 | 0.8264 | 0.6574 | 0.8284 | 0.7113 | 0.9961 |
| 0.0601 | 18.78 | 1540 | 0.2473 | 0.7741 | 0.8607 | 0.9366 | 0.9771 | 0.6169 | 0.9135 | 0.8532 | 0.8866 | 0.7798 | 0.9976 | 0.9559 | 0.4365 | 0.8295 | 0.6630 | 0.8221 | 0.7154 | 0.9965 |
| 0.0516 | 19.02 | 1560 | 0.2363 | 0.7731 | 0.8556 | 0.9369 | 0.9792 | 0.5591 | 0.9075 | 0.8329 | 0.8883 | 0.8243 | 0.9981 | 0.9563 | 0.4189 | 0.8305 | 0.6623 | 0.8215 | 0.7256 | 0.9964 |
| 0.0782 | 19.27 | 1580 | 0.2454 | 0.7651 | 0.8426 | 0.9341 | 0.9813 | 0.5266 | 0.9008 | 0.7847 | 0.9005 | 0.8059 | 0.9981 | 0.9543 | 0.4057 | 0.8280 | 0.6356 | 0.8141 | 0.7217 | 0.9961 |
| 0.0221 | 19.51 | 1600 | 0.2540 | 0.7660 | 0.8474 | 0.9333 | 0.9785 | 0.5946 | 0.9085 | 0.7579 | 0.9117 | 0.7828 | 0.9976 | 0.9543 | 0.4320 | 0.8265 | 0.6259 | 0.8134 | 0.7131 | 0.9964 |
| 0.0283 | 19.76 | 1620 | 0.2623 | 0.7662 | 0.8588 | 0.9320 | 0.9750 | 0.6488 | 0.9141 | 0.7503 | 0.8988 | 0.8266 | 0.9981 | 0.9549 | 0.4395 | 0.8271 | 0.6123 | 0.8092 | 0.7240 | 0.9964 |
| 0.1029 | 20.0 | 1640 | 0.2747 | 0.7633 | 0.8522 | 0.9299 | 0.9767 | 0.6031 | 0.9118 | 0.7738 | 0.8809 | 0.8213 | 0.9981 | 0.9539 | 0.4336 | 0.8317 | 0.6053 | 0.7991 | 0.7235 | 0.9962 |
| 0.0731 | 20.24 | 1660 | 0.2650 | 0.7621 | 0.8529 | 0.9294 | 0.9794 | 0.6019 | 0.9106 | 0.7555 | 0.8790 | 0.8453 | 0.9983 | 0.9552 | 0.4310 | 0.8288 | 0.5977 | 0.7971 | 0.7288 | 0.9963 |
| 0.2587 | 20.49 | 1680 | 0.2767 | 0.7602 | 0.8430 | 0.9311 | 0.9794 | 0.5600 | 0.9158 | 0.7217 | 0.9023 | 0.8234 | 0.9985 | 0.9552 | 0.4160 | 0.8239 | 0.5994 | 0.8073 | 0.7237 | 0.9961 |
| 0.1071 | 20.73 | 1700 | 0.2826 | 0.7607 | 0.8443 | 0.9311 | 0.9805 | 0.5820 | 0.9125 | 0.7122 | 0.9091 | 0.8156 | 0.9980 | 0.9548 | 0.4239 | 0.8225 | 0.5976 | 0.8080 | 0.7220 | 0.9963 |
| 0.0323 | 20.98 | 1720 | 0.2620 | 0.7621 | 0.8522 | 0.9293 | 0.9801 | 0.6139 | 0.9065 | 0.7633 | 0.8829 | 0.8215 | 0.9975 | 0.9551 | 0.4342 | 0.8296 | 0.5996 | 0.7968 | 0.7231 | 0.9963 |
| 0.1072 | 21.22 | 1740 | 0.2592 | 0.7614 | 0.8426 | 0.9315 | 0.9824 | 0.5483 | 0.9094 | 0.7402 | 0.8997 | 0.8211 | 0.9974 | 0.9546 | 0.4149 | 0.8257 | 0.6074 | 0.8078 | 0.7230 | 0.9961 |
| 0.0732 | 21.46 | 1760 | 0.2598 | 0.7654 | 0.8497 | 0.9327 | 0.9795 | 0.5819 | 0.9130 | 0.7528 | 0.8994 | 0.8240 | 0.9976 | 0.9557 | 0.4265 | 0.8275 | 0.6163 | 0.8106 | 0.7248 | 0.9963 |
| 0.0603 | 21.71 | 1780 | 0.2581 | 0.7650 | 0.8503 | 0.9320 | 0.9790 | 0.5874 | 0.9130 | 0.7452 | 0.8984 | 0.8315 | 0.9974 | 0.9556 | 0.4282 | 0.8302 | 0.6095 | 0.8070 | 0.7280 | 0.9963 |
| 0.0548 | 21.95 | 1800 | 0.2520 | 0.7649 | 0.8598 | 0.9300 | 0.9747 | 0.6344 | 0.9218 | 0.7885 | 0.8683 | 0.8322 | 0.9988 | 0.9560 | 0.4352 | 0.8339 | 0.6082 | 0.7956 | 0.7291 | 0.9963 |
| 0.0503 | 22.2 | 1820 | 0.2521 | 0.7657 | 0.8528 | 0.9317 | 0.9799 | 0.6030 | 0.9077 | 0.7726 | 0.8890 | 0.8193 | 0.9983 | 0.9558 | 0.4312 | 0.8324 | 0.6150 | 0.8034 | 0.7257 | 0.9964 |
| 0.0356 | 22.44 | 1840 | 0.2491 | 0.7669 | 0.8551 | 0.9328 | 0.9783 | 0.6119 | 0.9086 | 0.7470 | 0.9004 | 0.8412 | 0.9983 | 0.9568 | 0.4338 | 0.8310 | 0.6140 | 0.8091 | 0.7271 | 0.9966 |
| 0.0381 | 22.68 | 1860 | 0.2660 | 0.7644 | 0.8458 | 0.9330 | 0.9805 | 0.5651 | 0.9095 | 0.7344 | 0.9094 | 0.8242 | 0.9973 | 0.9559 | 0.4213 | 0.8289 | 0.6129 | 0.8116 | 0.7240 | 0.9964 |
| 0.0671 | 22.93 | 1880 | 0.2633 | 0.7664 | 0.8517 | 0.9332 | 0.9787 | 0.5970 | 0.9067 | 0.7371 | 0.9083 | 0.8360 | 0.9982 | 0.9559 | 0.4307 | 0.8286 | 0.6140 | 0.8129 | 0.7261 | 0.9966 |
| 0.1123 | 23.17 | 1900 | 0.2462 | 0.7659 | 0.8489 | 0.9337 | 0.9790 | 0.5848 | 0.9088 | 0.7332 | 0.9121 | 0.8253 | 0.9987 | 0.9556 | 0.4257 | 0.8275 | 0.6161 | 0.8154 | 0.7251 | 0.9963 |
| 0.0476 | 23.41 | 1920 | 0.2498 | 0.7665 | 0.8490 | 0.9336 | 0.9789 | 0.5651 | 0.9133 | 0.7813 | 0.8942 | 0.8124 | 0.9978 | 0.9557 | 0.4204 | 0.8297 | 0.6267 | 0.8128 | 0.7238 | 0.9966 |
| 0.0999 | 23.66 | 1940 | 0.2556 | 0.7678 | 0.8539 | 0.9335 | 0.9779 | 0.6072 | 0.9123 | 0.7735 | 0.8972 | 0.8109 | 0.9985 | 0.9561 | 0.4335 | 0.8291 | 0.6245 | 0.8127 | 0.7223 | 0.9966 |
| 0.0639 | 23.9 | 1960 | 0.2467 | 0.7659 | 0.8480 | 0.9337 | 0.9799 | 0.5431 | 0.9126 | 0.8278 | 0.8777 | 0.7960 | 0.9987 | 0.9547 | 0.4119 | 0.8279 | 0.6398 | 0.8137 | 0.7167 | 0.9964 |
| 0.0718 | 24.15 | 1980 | 0.2469 | 0.7667 | 0.8484 | 0.9341 | 0.9804 | 0.5609 | 0.9068 | 0.7785 | 0.8983 | 0.8160 | 0.9982 | 0.9546 | 0.4190 | 0.8271 | 0.6306 | 0.8159 | 0.7232 | 0.9965 |
| 0.0578 | 24.39 | 2000 | 0.2466 | 0.7668 | 0.8490 | 0.9339 | 0.9795 | 0.5737 | 0.9089 | 0.7591 | 0.9041 | 0.8194 | 0.9983 | 0.9551 | 0.4249 | 0.8271 | 0.6246 | 0.8155 | 0.7241 | 0.9965 |
| 0.0664 | 24.63 | 2020 | 0.2530 | 0.7640 | 0.8449 | 0.9328 | 0.9784 | 0.5888 | 0.9101 | 0.7452 | 0.9125 | 0.7808 | 0.9987 | 0.9549 | 0.4305 | 0.8255 | 0.6185 | 0.8132 | 0.7088 | 0.9963 |
| 0.7601 | 24.88 | 2040 | 0.2531 | 0.7677 | 0.8502 | 0.9340 | 0.9810 | 0.5654 | 0.8995 | 0.7891 | 0.8969 | 0.8221 | 0.9974 | 0.9535 | 0.4217 | 0.8288 | 0.6339 | 0.8151 | 0.7244 | 0.9964 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
|
huseinzol05/conformer-2M-ctc
|
huseinzol05
| 2024-02-27T10:57:07Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"conformer",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2024-02-27T10:56:59Z |
---
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. -->
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### 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]
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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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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
suthanhcong/test-finetune
|
suthanhcong
| 2024-02-27T10:56:34Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-27T10:50:14Z |
---
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]
|
AbstractPerspective/2xMistral
|
AbstractPerspective
| 2024-02-27T10:48:03Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"mistralai/Mistral-7B-v0.1",
"mlabonne/drmistral-7b",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:merge:mistralai/Mistral-7B-v0.1",
"base_model:mlabonne/drmistral-7b",
"base_model:merge:mlabonne/drmistral-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T10:40:38Z |
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-v0.1
- mlabonne/drmistral-7b
base_model:
- mistralai/Mistral-7B-v0.1
- mlabonne/drmistral-7b
---
# 2xMistral
2xMistral is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* [mlabonne/drmistral-7b](https://huggingface.co/mlabonne/drmistral-7b)
## 🧩 Configuration
```yaml
base_model: mistralai/Mistral-7B-v0.1
gate_mode: cheap_embed
experts:
- source_model: mistralai/Mistral-7B-v0.1
positive_prompts: ["general"]
- source_model: mlabonne/drmistral-7b
positive_prompts: ["medical"]
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "AbstractPerspective/2xMistral"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```
|
kianshokraneh/distilbert-base-uncased-finetuned-squad
|
kianshokraneh
| 2024-02-27T10:45:03Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-02-27T09:37:36Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-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. -->
# distilbert-base-uncased-finetuned-squad
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: 1.9984
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1863 | 1.0 | 674 | 1.9984 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
Myaukko/videomae-base-finetuned-fighting-subset-5ep-1ep3bs
|
Myaukko
| 2024-02-27T10:41:14Z | 63 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2024-02-27T07:48:45Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-fighting-subset-5ep-1ep3bs
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. -->
# videomae-base-finetuned-fighting-subset-5ep-1ep3bs
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5212
- Accuracy: 0.8815
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 205
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4571 | 0.2 | 42 | 1.0070 | 0.8667 |
| 0.5671 | 1.2 | 84 | 1.4664 | 0.7259 |
| 0.2476 | 2.2 | 126 | 0.5053 | 0.7926 |
| 0.0011 | 3.2 | 168 | 0.8063 | 0.8222 |
| 0.0008 | 4.18 | 205 | 0.5212 | 0.8815 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
|
ternikov/whisper-tiny-finetuned-gtzan
|
ternikov
| 2024-02-27T10:40:24Z | 105 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-02-27T10:40:21Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-tiny-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.89
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-finetuned-gtzan
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7231
- Accuracy: 0.89
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3867 | 1.0 | 150 | 0.4913 | 0.85 |
| 0.6883 | 2.0 | 300 | 0.9527 | 0.81 |
| 0.0056 | 3.0 | 450 | 0.6576 | 0.84 |
| 0.0021 | 4.0 | 600 | 0.7685 | 0.84 |
| 0.0007 | 5.0 | 750 | 0.7602 | 0.87 |
| 0.0005 | 6.0 | 900 | 0.8593 | 0.85 |
| 0.0005 | 7.0 | 1050 | 0.8438 | 0.84 |
| 0.0003 | 8.0 | 1200 | 0.6439 | 0.88 |
| 0.0003 | 9.0 | 1350 | 0.7370 | 0.88 |
| 0.0003 | 10.0 | 1500 | 0.7231 | 0.89 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
haripriya126/my-pet-dog
|
haripriya126
| 2024-02-27T10:27:15Z | 10 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-27T10:22:53Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by haripriya126 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
Nishthaa321/autotrain-xzvx7-et7fx
|
Nishthaa321
| 2024-02-27T10:22:05Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"dataset:autotrain-xzvx7-et7fx/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T10:21:48Z |
---
tags:
- autotrain
- text-classification
widget:
- text: "I love AutoTrain"
datasets:
- autotrain-xzvx7-et7fx/autotrain-data
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.2146722972393036
f1: 1.0
precision: 1.0
recall: 1.0
auc: 1.0
accuracy: 1.0
|
simonycl/sparseIT_Llama-2-7b-hf-stanford-alpaca-mask-by-cluster-32-clusters
|
simonycl
| 2024-02-27T10:10:02Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-02-27T10:04:44Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
|
FINNUMBER/FINCH_TRAIN_SA_200_per100_NEW_Rationale_E16
|
FINNUMBER
| 2024-02-27T10:04:09Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T09:59:39Z |
---
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]
|
FatmaYoussef/ppo-SnowballTarget
|
FatmaYoussef
| 2024-02-27T10:01:59Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-02-27T10:01:52Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: FatmaYoussef/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
FINNUMBER/FINCH_TRAIN_ALL_3600_per400_NEW_Rationale_E4
|
FINNUMBER
| 2024-02-27T09:59:18Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T09:53:57Z |
---
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]
|
Paluzka/Reinforce-unit4
|
Paluzka
| 2024-02-27T09:58:16Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T09:58:05Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-unit4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ProphetOfBostrom/opus-v1-34b-4b8h-8192l-EXL2
|
ProphetOfBostrom
| 2024-02-27T09:55:42Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"axolotl",
"exllamav2",
"exl2",
"4bit",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-26T22:15:38Z |
---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
- exllamav2
- exl2
- 4bit
library_name: transformers
---
### quantized with the default exl2 dataset with sequence lengths of 8192 and 400 calibration (stage 2, optimisation) lines instead of 2048/100. possibly microwaved, presumably better.
##### resulstant measurement file is present somewhere, though the default line count of 16 (still extended to 8192) was used for measurement (stage 1)
### tokenizer works. tokenizer.model is not required for use with exllama2. no promises about sketchy software by "oobabooga"* :) try tabbyAPI/tavern, or exui if you don't miss CFG
##### consider yourselves lucky it's not a safetensors.zpaq this took all night to upload and YES i did refresh my access tokens after the Whoopsie, sorry!
###### *I'm sure it's fine it's just that I'll die if I ever see conda again.
---
# 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>
## Resources
- [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy.
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`.
- [Python code](example/prompt/format.py) to format the prompt correctly.
<img src="/dreamgen/opus-v1-34b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
## Prompting
<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.
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 prompting and tokenization.
I recommend using these model versions:
- 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b)
- 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq)
### Running on DreamGen.com (free)
You can try the model for free on [dreamgen.com](https://dreamgen.com) — note that an account is required.
### Running Locally
- **Make sure your prompt is as close as possible to the Opus V1**
- Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly.
- [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1)
- [Read the prompt formatting code](example/prompt/format.py)
- Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly
- **vLLM**
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU.
- [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario.
- **SillyTavern**
- [Settings](https://huggingface.co/{{REPO_ID}}/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti
- [Settings screenshot](configs/silly_tavern/settings_screenshot.webp)
- This is just an attempt at approximating the Opus V1 prompt, it won't be perfect
- **LM Studio**
- [Config](configs/lmstudio/preset.json)
- Just like ChatML, just changed "assistant" to "text" role.
- **HuggingFace**
- [Chat template](tokenizer_config.json#L51)
- Just like ChatML, just changed "assistant" to "text" role.
## Known Issues
- **34B tokenization**:
- There seems to be a mismatch between the tokenizer of the base and fine-tuned model. It's unclear whether this also affected training, or whether it's just incorrectly saved tokenizer (you can see `tokenizer.json` was not saved ([bug report](https://github.com/OpenAccess-AI-Collective/axolotl/issues/1322))).
- This affects BOS and EOS (which aren't really used by Yi) and the tokenization of the first input token.
- Overall impact should be minor.
- **34B repetition**:
- The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes.
- **GGUF**:
- The conversion might be messed up and in my tests even `Q_8` of the `opus-v1.2-7b` is much worse than the `fp16` version.
- **Ooba**:
- The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens.
## 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.
---
# 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>
## Resources
- [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy.
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`.
- [Python code](example/prompt/format.py) to format the prompt correctly.
<img src="/dreamgen/opus-v1-34b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
## Prompting
<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.
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 prompting and tokenization.
I recommend using these model versions:
- 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b)
- 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq)
### Running on DreamGen.com (free)
You can try the model for free on [dreamgen.com](https://dreamgen.com) — note that an account is required.
### Running Locally
- **Make sure your prompt is as close as possible to the Opus V1**
- Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly.
- [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1)
- [Read the prompt formatting code](example/prompt/format.py)
- Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly
- **vLLM**
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU.
- [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario.
- **SillyTavern**
- [Settings](https://huggingface.co/{{REPO_ID}}/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti
- [Settings screenshot](configs/silly_tavern/settings_screenshot.webp)
- This is just an attempt at approximating the Opus V1 prompt, it won't be perfect
- **LM Studio**
- [Config](configs/lmstudio/preset.json)
- Just like ChatML, just changed "assistant" to "text" role.
- **HuggingFace**
- [Chat template](tokenizer_config.json#L51)
- Just like ChatML, just changed "assistant" to "text" role.
## Known Issues
- **34B tokenization**:
- There seems to be a mismatch between the tokenizer of the base and fine-tuned model. It's unclear whether this also affected training, or whether it's just incorrectly saved tokenizer (you can see `tokenizer.json` was not saved ([bug report](https://github.com/OpenAccess-AI-Collective/axolotl/issues/1322))).
- This affects BOS and EOS (which aren't really used by Yi) and the tokenization of the first input token.
- Overall impact should be minor.
- **34B repetition**:
- The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes.
- **GGUF**:
- The conversion might be messed up and in my tests even `Q_8` of the `opus-v1.2-7b` is much worse than the `fp16` version.
- **Ooba**:
- The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens.
## 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.
|
DimalChathuranga/marian-finetuned-kde4-en-to-fr
|
DimalChathuranga
| 2024-02-27T09:55:34Z | 104 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-02-27T06:34:32Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
model-index:
- name: marian-finetuned-kde4-en-to-fr
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) 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: 32
- eval_batch_size: 64
- 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
- Tokenizers 0.15.2
|
ryusangwon/samsum_139_bart-base
|
ryusangwon
| 2024-02-27T09:53:37Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-27T08:27:39Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: samsum_139_bart-base
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 0.4834
---
<!-- 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. -->
# samsum_139_bart-base
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5331
- Rouge1: 0.4834
- Rouge2: 0.2527
- Rougel: 0.4093
- Rougelsum: 0.4092
- Gen Len: 18.3215
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.5323 | 4.34 | 500 | 0.5449 | 0.4788 | 0.2457 | 0.4002 | 0.4004 | 18.7653 |
| 0.4101 | 8.69 | 1000 | 0.5331 | 0.4834 | 0.2527 | 0.4093 | 0.4092 | 18.3215 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
FINNUMBER/FINCH_TRAIN_ALL_3600_per400_NEW_Rationale_E3
|
FINNUMBER
| 2024-02-27T09:53:27Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T09:48:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
|
FINNUMBER/Yi-Ko-6B-Finch-NQA-FULL-Hyper-epoch3
|
FINNUMBER
| 2024-02-27T09:48:29Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T07:20:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
|
yvelos/test_Mist_1
|
yvelos
| 2024-02-27T09:48:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T07:02:47Z |
---
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]
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[More Information Needed]
## Bias, Risks, and Limitations
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[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]
## Training Details
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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- **Hardware Type:** [More Information Needed]
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|
metadeeai/neural-net
|
metadeeai
| 2024-02-27T09:46:43Z | 6 | 4 |
transformers
|
[
"transformers",
"safetensors",
"neuralnet",
"text-generation",
"conversational",
"custom_code",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-02-26T21:46:43Z |
---
library_name: transformers
tags: []
---
### Neural Net-A: Revolutionizing AI with Next-Generation Neural Net-Awork Models
#### Introduction to Neural Net-A
Neural Net-A represents a groundbreaking initiative by Neural Net-A Labs, introducing an advanced series of generative neural network models. These models cumulatively span a vast range of complexity, aggregating to a staggering total of 450 billion parameters. This showcases the ambition and technological prowess behind Neural Net-A's development. Within this innovative family, the 103B model serves as an entry point, linked to its more powerful counterparts through a comprehensive index at the document's conclusion.
#### Model Details
Developed with a vision to redefine the landscape of large language models (LLMs), Neural Net-A encompasses a wide array of models pre-trained and finely-tuned for generative text applications. The fine-tuned models, dubbed Neural Net-A-Chat, are specifically optimized for conversational engagements, offering performance metrics that surpass current open-source alternatives across numerous benchmarks. In terms of helpfulness and safety, Neural Net-A-Chat models are competitive with leading closed-source models, including the likes of ChatGPT and PaLM.
**Inputs and Outputs:** Neural Net-A models exclusively process and generate text-based information, ensuring a focused and efficient user experience.
**Architecture:** At its core, Neural Net-A employs a state-of-the-art auto-regressive Neural Net-Awork architecture. Enhanced versions undergo further optimization through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), ensuring alignment with human preferences on critical aspects like helpfulness and safety.
**Model Development Timeline:** The training phase of Neural Net-A spanned from February 2023 to August 2023, marking a dedicated period of innovation and refinement.
**Status:** Neural Net-A is presented as a static model, trained on an extensive offline dataset. Future iterations will incorporate community feedback to advance model safety and performance.
**Research and Development:** The development of Neural Net-A is documented in the comprehensive research paper titled "Neural Net-A: The Frontier of Foundation and Fine-Tuned Neural Net-Awork Models."
#### Running the model on a single / multi GPU
```# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("metadeeai/neural-net")
model = AutoModelForCausalLM.from_pretrained("metadeeai/neural-net", 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]))
```
#### Technical Infrastructure
**Development Resources:** Neural Net-A's development utilized bespoke training libraries alongside Neural Net-A Labs' Super Cluster and additional production clusters. Fine-tuning, annotation, and evaluation phases were executed utilizing third-party cloud computing resources, demonstrating a blend of in-house and external technological synergy.
#### Training Data and Methodology
**Innovative Training Techniques:** Neural Net-A's training regimen was distinguished by innovative methodologies designed to enhance learning efficiency and model accuracy. This included a novel approach to balancing the distribution of training data, ensuring a comprehensive understanding across diverse topics and contexts.
#### Neural Net-A in Practice
**Achieving Excellence in AI Conversations:** Neural Net-A-Chat models stand at the forefront of AI-driven conversational systems, offering nuanced, contextually aware responses that push the boundaries of what's possible in natural language understanding and generation.
**Adaptability and Customization:** Beyond chat, Neural Net-A's pre-trained models present a foundation upon which developers can build, adapting the technology for specific tasks ranging from text summarization to language translation, showcasing the model's inherent versatility.
**Ethical Considerations and Community Engagement:** In line with Neural Net-A Labs' commitment to ethical AI development, Neural Net-A incorporates mechanisms for continuous improvement based on user feedback and ethical considerations. This iterative approach ensures that Neural Net-A models remain at the cutting edge of AI safety and helpfulness standards.
**Future Directions:** As Neural Net-A continues to evolve, Neural Net-A Labs is dedicated to exploring new frontiers in AI research, including the integration of multimodal capabilities and the expansion of language support to foster a more inclusive technological ecosystem.
#### Conclusion
Neural Net-A by Neural Net-A Labs marks a significant milestone in the journey towards creating more intelligent, responsive, and ethical AI systems. With its innovative architecture, comprehensive training, and forward-looking development ethos, Neural Net-A is poised to redefine expectations for generative Neural Net-Awork models. As we look to the future, Neural Net-A Labs remains committed to advancing the boundaries of AI technology, ensuring that Neural Net-A and its successors continue to lead the way in innovation, performance, and societal impact.
Attributions:
```
@misc{intel_neural_chat_7b_v3_1,
title={Neural Chat 7b v3.1},
author={Intel},
howpublished={\url{https://huggingface.co/Intel/neural-chat-7b-v3-1}},
}
@misc{mlabonne_neuralbeagle14_7b,
title={NeuralBeagle14-7B},
author={Mlabonne},
howpublished={\url{https://huggingface.co/mlabonne/NeuralBeagle14-7B}},
}
@misc{vtabbott_neural_circuit_diagrams,
title={Neural Circuit Diagrams},
author={Vtabbott},
howpublished={\url{https://huggingface.co/vtabbott/Neural-Circuit-Diagrams}},
}
@misc{d_matrix_gpt2,
title={GPT2},
author={D-Matrix},
howpublished={\url{https://huggingface.co/d-matrix/gpt2}},
}
```
|
yvelos/Annotator_4_Mi
|
yvelos
| 2024-02-27T09:46:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T20:06:31Z |
---
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]
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<!-- 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
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[More Information Needed]
## Training Details
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#### Testing Data
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[More Information Needed]
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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]
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|
u66u/NeuralJaskier-7b-dpo
|
u66u
| 2024-02-27T09:45:16Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"bardsai/jaskier-7b-dpo-v6.1",
"CultriX/NeuralTrix-7B-dpo",
"base_model:CultriX/NeuralTrix-7B-dpo",
"base_model:merge:CultriX/NeuralTrix-7B-dpo",
"base_model:bardsai/jaskier-7b-dpo-v6.1",
"base_model:merge:bardsai/jaskier-7b-dpo-v6.1",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T09:34:16Z |
---
tags:
- merge
- mergekit
- lazymergekit
- bardsai/jaskier-7b-dpo-v6.1
- CultriX/NeuralTrix-7B-dpo
base_model:
- bardsai/jaskier-7b-dpo-v6.1
- CultriX/NeuralTrix-7B-dpo
license: mit
---
# NeuralJaskier-7b-dpo
NeuralJaskier-7b-dpo is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [bardsai/jaskier-7b-dpo-v6.1](https://huggingface.co/bardsai/jaskier-7b-dpo-v6.1)
* [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: bardsai/jaskier-7b-dpo-v6.1
layer_range: [0, 32]
- model: CultriX/NeuralTrix-7B-dpo
layer_range: [0, 32]
merge_method: slerp
base_model: bardsai/jaskier-7b-dpo-v6.1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "u66u/NeuralJaskier-7b-dpo"
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"])
```
|
jin-code/lora-7b-2
|
jin-code
| 2024-02-27T09:45:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T09:44:11Z |
---
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]
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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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- **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]
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- **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. -->
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[More Information Needed]
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[More Information Needed]
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|
yvelos/Tsotsallm-beta
|
yvelos
| 2024-02-27T09:44:09Z | 5 | 0 |
peft
|
[
"peft",
"fine-tuned",
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-11-15T11:42:34Z |
---
library_name: peft
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- fine-tuned
inference:
parameters:
temperature: 0.7
---
## Training procedure
The TSOTSALLM-beta Large Language Model (LLM) is a fine-tuned generative text model based on LLama-2 with 7 billions parameters.
## Model Architecture
TSOTSALLM-beta is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## The TSOTSALLM AI Team
- Jean Petit BIKIM
- Fidel Jiomekong
- Martins Folefack
- Brice
|
JinghuiLuAstronaut/PaDeLLM_llama2_7b_ace05
|
JinghuiLuAstronaut
| 2024-02-27T09:38:11Z | 6 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T02:39:31Z |
Inference code see https://github.com/GeorgeLuImmortal/PaDeLLM_NER
|
JinghuiLuAstronaut/PaDeLLM_llama2_7b_genia
|
JinghuiLuAstronaut
| 2024-02-27T09:37:57Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T02:54:08Z |
Inference code see https://github.com/GeorgeLuImmortal/PaDeLLM_NER
|
JinghuiLuAstronaut/PaDeLLM_baichuan2_7b_ecommerce
|
JinghuiLuAstronaut
| 2024-02-27T09:36:56Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"baichuan",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T03:12:59Z |
Inference code see https://github.com/GeorgeLuImmortal/PaDeLLM_NER
|
yvelos/Annotator_1_Mi
|
yvelos
| 2024-02-27T09:35:43Z | 5 | 1 |
peft
|
[
"peft",
"safetensors",
"text-generation",
"dataset:yvelos/semantic_annotation",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-02-20T22:22:56Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
metrics:
- recall
- precision
- f1
pipeline_tag: text-generation
datasets:
- yvelos/semantic_annotation
license: apache-2.0
---
# Annotator_1_Mi
## Overview
Annotator_1_Mi is the First LLM for semantic tabular data annotation
## Model Details
### Model Description
Annotator_1_Mi is a Decoder-based LM fine-tuned from [Mistravl-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Developed by:** [tsotsa](https://github.com/jiofidelus/tsotsa)
- **Model type:** Decoder
- **Language(s) (NLP):** Semantic annotation for tabular data
- **License:** Apache 2.0
- **Finetuned from model:** [Mistravl-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Licence
Annotator_1_Mi is developed under Apache 2.0 licence
## 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. -->
#### Metrics
- **Recall**
- **Precision**
- **F1 Score**
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
bon 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).
- **Environment:** Google collab
- **GPU Type:** T4 with 15 Go
- **Hours used:** 100.4 min
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
**BibTeX:**
[More Information Needed]
**APA:**
## Model Card Contact
**[tsotsa]([email protected])**
### Framework versions
- PEFT 0.8.2
|
habout632/EvolCodeLlama-7b
|
habout632
| 2024-02-27T09:35:24Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-02-27T08:18:15Z |
---
license: llama2
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: EvolCodeLlama-7b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/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)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: codellama/CodeLlama-7b-hf
base_model_config: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: EvolCodeLlama-7b
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: mlabonne/Evol-Instruct-Python-1k
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# EvolCodeLlama-7b
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3796
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3178 | 0.01 | 1 | 0.5311 |
| 0.3147 | 0.03 | 4 | 0.5312 |
| 0.3626 | 0.07 | 8 | 0.5310 |
| 0.6265 | 0.1 | 12 | 0.5296 |
| 0.429 | 0.14 | 16 | 0.5270 |
| 0.5086 | 0.17 | 20 | 0.5205 |
| 0.4335 | 0.21 | 24 | 0.5067 |
| 0.3383 | 0.24 | 28 | 0.4842 |
| 0.3688 | 0.28 | 32 | 0.4603 |
| 0.2528 | 0.31 | 36 | 0.4403 |
| 0.3105 | 0.35 | 40 | 0.4251 |
| 0.4936 | 0.38 | 44 | 0.4162 |
| 0.4146 | 0.42 | 48 | 0.4086 |
| 0.3327 | 0.45 | 52 | 0.4024 |
| 0.3429 | 0.48 | 56 | 0.3971 |
| 0.3328 | 0.52 | 60 | 0.3937 |
| 0.1844 | 0.55 | 64 | 0.3901 |
| 0.3001 | 0.59 | 68 | 0.3887 |
| 0.3632 | 0.62 | 72 | 0.3872 |
| 0.1997 | 0.66 | 76 | 0.3847 |
| 0.2461 | 0.69 | 80 | 0.3823 |
| 0.2865 | 0.73 | 84 | 0.3812 |
| 0.26 | 0.76 | 88 | 0.3805 |
| 0.3191 | 0.8 | 92 | 0.3792 |
| 0.4642 | 0.83 | 96 | 0.3763 |
| 0.2649 | 0.87 | 100 | 0.3750 |
| 0.2095 | 0.9 | 104 | 0.3727 |
| 0.2738 | 0.94 | 108 | 0.3737 |
| 0.4274 | 0.97 | 112 | 0.3730 |
| 0.2722 | 1.0 | 116 | 0.3724 |
| 0.2164 | 1.02 | 120 | 0.3705 |
| 0.1549 | 1.05 | 124 | 0.3726 |
| 0.3051 | 1.08 | 128 | 0.3725 |
| 0.1873 | 1.12 | 132 | 0.3730 |
| 0.3388 | 1.15 | 136 | 0.3738 |
| 0.2504 | 1.19 | 140 | 0.3741 |
| 0.2851 | 1.22 | 144 | 0.3714 |
| 0.2365 | 1.26 | 148 | 0.3690 |
| 0.3986 | 1.29 | 152 | 0.3699 |
| 0.1913 | 1.33 | 156 | 0.3720 |
| 0.1963 | 1.36 | 160 | 0.3698 |
| 0.1824 | 1.4 | 164 | 0.3679 |
| 0.1453 | 1.43 | 168 | 0.3685 |
| 0.3073 | 1.47 | 172 | 0.3702 |
| 0.1501 | 1.5 | 176 | 0.3692 |
| 0.2167 | 1.53 | 180 | 0.3662 |
| 0.3007 | 1.57 | 184 | 0.3660 |
| 0.2203 | 1.6 | 188 | 0.3666 |
| 0.3978 | 1.64 | 192 | 0.3669 |
| 0.2397 | 1.67 | 196 | 0.3663 |
| 0.2161 | 1.71 | 200 | 0.3656 |
| 0.2593 | 1.74 | 204 | 0.3651 |
| 0.2113 | 1.78 | 208 | 0.3658 |
| 0.2435 | 1.81 | 212 | 0.3657 |
| 0.2625 | 1.85 | 216 | 0.3639 |
| 0.302 | 1.88 | 220 | 0.3624 |
| 0.2556 | 1.92 | 224 | 0.3611 |
| 0.2063 | 1.95 | 228 | 0.3609 |
| 0.1994 | 1.98 | 232 | 0.3612 |
| 0.2229 | 2.02 | 236 | 0.3613 |
| 0.1983 | 2.03 | 240 | 0.3634 |
| 0.1925 | 2.06 | 244 | 0.3725 |
| 0.1778 | 2.1 | 248 | 0.3832 |
| 0.1293 | 2.13 | 252 | 0.3834 |
| 0.2166 | 2.16 | 256 | 0.3789 |
| 0.2082 | 2.2 | 260 | 0.3760 |
| 0.1858 | 2.23 | 264 | 0.3761 |
| 0.1862 | 2.27 | 268 | 0.3763 |
| 0.1619 | 2.3 | 272 | 0.3783 |
| 0.174 | 2.34 | 276 | 0.3786 |
| 0.2414 | 2.37 | 280 | 0.3790 |
| 0.1977 | 2.41 | 284 | 0.3783 |
| 0.1678 | 2.44 | 288 | 0.3784 |
| 0.2263 | 2.48 | 292 | 0.3786 |
| 0.082 | 2.51 | 296 | 0.3783 |
| 0.2621 | 2.55 | 300 | 0.3784 |
| 0.1754 | 2.58 | 304 | 0.3795 |
| 0.1957 | 2.61 | 308 | 0.3802 |
| 0.1203 | 2.65 | 312 | 0.3803 |
| 0.1388 | 2.68 | 316 | 0.3796 |
| 0.1699 | 2.72 | 320 | 0.3796 |
| 0.161 | 2.75 | 324 | 0.3796 |
| 0.2394 | 2.79 | 328 | 0.3792 |
| 0.1465 | 2.82 | 332 | 0.3795 |
| 0.1746 | 2.86 | 336 | 0.3794 |
| 0.1839 | 2.89 | 340 | 0.3795 |
| 0.1581 | 2.93 | 344 | 0.3796 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.17.1
- Tokenizers 0.15.0
|
HenseHsieh/a2c-PandaPickAndPlace-v3
|
HenseHsieh
| 2024-02-27T09:33:20Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-25T17:11:38Z |
---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **DDPG** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **DDPG** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
JinghuiLuAstronaut/PaDeLLM_baichuan2_7b_msra
|
JinghuiLuAstronaut
| 2024-02-27T09:29:17Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"baichuan",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T03:32:04Z |
Inference code see https://github.com/GeorgeLuImmortal/PaDeLLM_NER
|
JinghuiLuAstronaut/PaDeLLM_baichuan2_7b_ontonotes
|
JinghuiLuAstronaut
| 2024-02-27T09:29:00Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"baichuan",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T03:55:08Z |
Inference code see https://github.com/GeorgeLuImmortal/PaDeLLM_NER
|
DatPySci/pythia-1b-kto-iter0
|
DatPySci
| 2024-02-27T09:27:03Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:DatPySci/iter0",
"base_model:DatPySci/pythia-1b-sft-full",
"base_model:finetune:DatPySci/pythia-1b-sft-full",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T04:29:58Z |
---
license: apache-2.0
base_model: DatPySci/pythia-1b-sft-full
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- DatPySci/iter0
model-index:
- name: pythia-1b-kto-iter0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pythia-1b-kto-iter0
This model is a fine-tuned version of [DatPySci/pythia-1b-sft-full](https://huggingface.co/DatPySci/pythia-1b-sft-full) on the DatPySci/iter0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2591
- Rewards/real: 0.0604
- Rewards/generated: -1.0267
- Rewards/accuracies: 0.9460
- Rewards/margins: 1.0871
- Logps/generated: -570.8114
- Logps/real: -468.1696
- Logits/generated: 0.2253
- Logits/real: -0.2820
## 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-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:|
| 0.2932 | 0.38 | 300 | 0.2962 | 0.0718 | -0.7855 | 0.9220 | 0.8572 | -568.3989 | -468.0556 | 0.2554 | -0.2530 |
| 0.2689 | 0.77 | 600 | 0.2591 | 0.0604 | -1.0267 | 0.9460 | 1.0871 | -570.8114 | -468.1696 | 0.2253 | -0.2820 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.1
- Tokenizers 0.15.2
|
Ayus077BCT014Bhandari/vartat5-using-100K-plus-23
|
Ayus077BCT014Bhandari
| 2024-02-27T09:22:02Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-27T06:25:30Z |
---
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]
|
hellod035/ppo-Huggy
|
hellod035
| 2024-02-27T09:21:55Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-02-27T09:21:49Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hellod035/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
archiMAD/LunarLander-ppo-from-scratch
|
archiMAD
| 2024-02-27T09:20:34Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-27T09:20:18Z |
---
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: 76.00 +/- 114.11
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': 500000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 1024
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 64
'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': 'archiMAD/LunarLander-ppo-from-scratch'
'batch_size': 4096
'minibatch_size': 64}
```
|
nagyadam0616/zephyr-x-twitter-5epocs-full-2
|
nagyadam0616
| 2024-02-27T09:20:15Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T08:56:43Z |
---
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]
|
mhmmterts/fine_tuned_model_on_SJP_dataset_it_balanced_2048_tokens
|
mhmmterts
| 2024-02-27T09:16:52Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:swiss_judgment_prediction",
"base_model:joelniklaus/legal-swiss-roberta-large",
"base_model:finetune:joelniklaus/legal-swiss-roberta-large",
"license:cc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T09:15:43Z |
---
license: cc
base_model: joelniklaus/legal-swiss-roberta-large
tags:
- generated_from_trainer
datasets:
- swiss_judgment_prediction
metrics:
- accuracy
model-index:
- name: fine_tuned_model_on_SJP_dataset_it_balanced_2048_tokens
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: swiss_judgment_prediction
type: swiss_judgment_prediction
config: it
split: test
args: it
metrics:
- name: Accuracy
type: accuracy
value: 0.8177339901477833
---
<!-- 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. -->
# fine_tuned_model_on_SJP_dataset_it_balanced_2048_tokens
This model is a fine-tuned version of [joelniklaus/legal-swiss-roberta-large](https://huggingface.co/joelniklaus/legal-swiss-roberta-large) on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7964
- Accuracy: 0.8177
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7513 | 1.0 | 768 | 0.6783 | 0.7956 |
| 0.6008 | 2.0 | 1536 | 0.7964 | 0.8177 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
|
vlada-v/whisper-small-hi
|
vlada-v
| 2024-02-27T09:07:39Z | 76 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"en",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-02-09T07:37:40Z |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Hi - Kids
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 Small Hi - Kids
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the PRG Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4077
- Wer: 95.2005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
Shreyagnani/distilroberta-base-peft-p-tuning_26-2-24_4pm
|
Shreyagnani
| 2024-02-27T09:03:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T09:03:46Z |
---
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]
|
ameypatil7/gemma-7b-8bit
|
ameypatil7
| 2024-02-27T09:01:42Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-27T08:57:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
|
GZHUFB/ppo-Pyramids
|
GZHUFB
| 2024-02-27T09:01:08Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-02-27T08:59:27Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: GZHUFB/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ISTA-DASLab/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf
|
ISTA-DASLab
| 2024-02-27T08:58:52Z | 72 | 18 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
"region:us"
] |
text-generation
| 2024-02-17T16:51:41Z |
Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of `mistralai/Mixtral-8x7B-Instruct-v0.1`.
For this quantization, we used 1 codebook of 16 bits.
Selected evaluation results for this model:
| Model | AQLM scheme | WinoGrande | PiQA | HellaSwag | ArcE | ArcC | Model size, Gb | Hub link |
|------|------|------|-------|-------|-------|------|------|------|
| Mixtral-8x7B-Instruct-v0.1 (THIS)| 1x16 | 0.7593 |0.8043 | 0.6179 | 0.7768 | 0.4793 | 12.6 | [Link](https://huggingface.co/BlackSamorez/Mixtral-8x7B-Instruct-v0_1-AQLM-2Bit-1x16-hf)|
To learn more about the inference, as well as the information on how to quantize models yourself, please refer to the [official GitHub repo](https://github.com/Vahe1994/AQLM).
|
fzzhang/mistral_gsm8k_s_unquantized_merged
|
fzzhang
| 2024-02-27T08:52:12Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T08:36:54Z |
---
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
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### 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]
## 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. -->
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|
ekaterinatao/nerel-bio-rubert-base
|
ekaterinatao
| 2024-02-27T08:42:59Z | 120 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:finetune:DeepPavlov/rubert-base-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-02-27T08:37:05Z |
---
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerel-bio-rubert-base
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. -->
# nerel-bio-rubert-base
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6122
- Precision: 0.7873
- Recall: 0.7882
- F1: 0.7878
- Accuracy: 0.8601
## 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: 6
- eval_batch_size: 6
- seed: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 102 | 1.1211 | 0.6196 | 0.5809 | 0.5996 | 0.7125 |
| No log | 2.0 | 204 | 0.6800 | 0.7333 | 0.7165 | 0.7248 | 0.8137 |
| No log | 3.0 | 306 | 0.5985 | 0.7445 | 0.7488 | 0.7466 | 0.8303 |
| No log | 4.0 | 408 | 0.5673 | 0.7608 | 0.7622 | 0.7615 | 0.8402 |
| 0.7954 | 5.0 | 510 | 0.5665 | 0.7751 | 0.7702 | 0.7726 | 0.8485 |
| 0.7954 | 6.0 | 612 | 0.5934 | 0.7826 | 0.7742 | 0.7784 | 0.8544 |
| 0.7954 | 7.0 | 714 | 0.5804 | 0.7795 | 0.7751 | 0.7773 | 0.8527 |
| 0.7954 | 8.0 | 816 | 0.6075 | 0.7839 | 0.7878 | 0.7858 | 0.8577 |
| 0.7954 | 9.0 | 918 | 0.6139 | 0.7887 | 0.7889 | 0.7888 | 0.8614 |
| 0.1024 | 10.0 | 1020 | 0.6122 | 0.7873 | 0.7882 | 0.7878 | 0.8601 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
ibunescu/Phi-2_GDPR_8_3e
|
ibunescu
| 2024-02-27T08:42:19Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T08:39:33Z |
---
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. -->
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- **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
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[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]
|
peter3571/llama2-glora-finetunined-test
|
peter3571
| 2024-02-27T08:36:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T08:36:50Z |
---
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. -->
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[More Information Needed]
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[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]
|
AlignmentResearch/robust_llm_pythia-imdb-1.4b-mz-ada-v2
|
AlignmentResearch
| 2024-02-27T08:36:41Z | 98 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1.4b-deduped",
"base_model:finetune:EleutherAI/pythia-1.4b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-27T08:34:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1.4b-deduped
model-index:
- name: robust_llm_pythia-imdb-1.4b-mz-ada-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. -->
# robust_llm_pythia-imdb-1.4b-mz-ada-v2
This model is a fine-tuned version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Heng666/taiwan-kapok-300m-instruction
|
Heng666
| 2024-02-27T08:30:05Z | 110 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-27T08:29:00Z |
---
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]
|
hhs8746/sftestd2
|
hhs8746
| 2024-02-27T08:29:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-27T08:29:41Z |
---
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]
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### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [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]
### 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).
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