modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-31 06:26:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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Ja-ck/llama-2-13b-instruct-Y24-v2
|
Ja-ck
| 2023-11-29T06:28:41Z | 2,309 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T06:18:43Z |
---
license: apache-2.0
language:
- ko
pipeline_tag: text-generation
---
## Prompt Template
```
### 질문: {instruction}
### 답변: {output}
```
|
cottyard/ppo-LunarLander-v2
|
cottyard
| 2023-11-29T06:27:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T06:27:20Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.66 +/- 17.29
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Realgon/roberta_sst2_padding0model
|
Realgon
| 2023-11-29T06:27:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-29T06:01:32Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding0model
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. -->
# roberta_sst2_padding0model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4539
- Accuracy: 0.9484
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.1891 | 0.9407 |
| 0.3324 | 2.0 | 866 | 0.3948 | 0.9176 |
| 0.1922 | 3.0 | 1299 | 0.2418 | 0.9379 |
| 0.126 | 4.0 | 1732 | 0.3080 | 0.9407 |
| 0.069 | 5.0 | 2165 | 0.4075 | 0.9396 |
| 0.0358 | 6.0 | 2598 | 0.3955 | 0.9418 |
| 0.0298 | 7.0 | 3031 | 0.4060 | 0.9429 |
| 0.0298 | 8.0 | 3464 | 0.4284 | 0.9379 |
| 0.0207 | 9.0 | 3897 | 0.4804 | 0.9401 |
| 0.0197 | 10.0 | 4330 | 0.5089 | 0.9347 |
| 0.0177 | 11.0 | 4763 | 0.5430 | 0.9336 |
| 0.0143 | 12.0 | 5196 | 0.4997 | 0.9385 |
| 0.0138 | 13.0 | 5629 | 0.4695 | 0.9429 |
| 0.0066 | 14.0 | 6062 | 0.5391 | 0.9363 |
| 0.0066 | 15.0 | 6495 | 0.5354 | 0.9412 |
| 0.0042 | 16.0 | 6928 | 0.4295 | 0.9473 |
| 0.0067 | 17.0 | 7361 | 0.4948 | 0.9429 |
| 0.0053 | 18.0 | 7794 | 0.4720 | 0.9473 |
| 0.0041 | 19.0 | 8227 | 0.4552 | 0.9451 |
| 0.0068 | 20.0 | 8660 | 0.4539 | 0.9484 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
lol-cod/captchasolving
|
lol-cod
| 2023-11-29T06:26:01Z | 0 | 0 |
keras
|
[
"keras",
"onnx",
"captcha",
"ocr",
"ai captcha solving",
"en",
"arxiv:1910.09700",
"license:unlicense",
"region:us"
] | null | 2023-11-29T06:02:19Z |
---
license: unlicense
language:
- en
library_name: keras
tags:
- captcha
- keras
- ocr
- ai captcha solving
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Ashish Chaudhary aka lolcod]
- **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:** [https://github.com/lol-cod/solvingcaptchakeras]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
Direct Use
The model is designed for solving 4-lettered captchas with an 80% accuracy rate. It can be directly employed for captcha-solving tasks without the need for fine-tuning or integration into a larger ecosystem or application.
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
The model is not intended for tasks beyond solving 4-lettered captchas. It may not perform well on captchas with a different format or on tasks unrelated to captcha-solving.
Bias, Risks, and Limitations
The model's performance may vary based on the complexity and variability of captchas. It may not generalize well to captchas with different characteristics or lengths. Additionally, there is a risk of misclassification, leading to incorrect solutions. The model might be sensitive to changes in background, font styles, or other captcha variations.
Recommendations
Users, both direct and downstream, should be aware of the model's limitations and potential biases. It is recommended to assess the performance on a diverse set of captchas to understand the model's capabilities and shortcomings.
How to Get Started with the Model
To use the model, you can leverage the following code:
python
Copy code
# Sample code for using the captcha-solving model
import keras
from keras.models import load_model
from captcha_solver import solve_captcha
# Load the pre-trained model
model = load_model('captcha_model.h5')
# Provide the captcha image as input
captcha_image = 'path/to/your/captcha.png'
solution = solve_captcha(model, captcha_image)
# Print the solution
print('Captcha Solution:', solution)
[More Information Needed]
Training Details
Training Data
The model was trained on a dataset of 4-lettered captchas. For more detailed information about the training data, refer to the accompanying Dataset Card.
[More Information Needed]
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 -->
[More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
[More Information Needed]
<!-- 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]
|
Noveled/test-500
|
Noveled
| 2023-11-29T06:19:04Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | null | 2023-11-29T06:19:01Z |
---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
|
Noveled/test
|
Noveled
| 2023-11-29T06:18:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | null | 2023-11-28T07:56:33Z |
---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
|
Realgon/distilbert_sst2_padding10model
|
Realgon
| 2023-11-29T06:16:58Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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
| 2023-11-28T18:24:14Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding10model
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_sst2_padding10model
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: 0.8972
- Accuracy: 0.9017
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2551 | 0.9023 |
| 0.3447 | 2.0 | 866 | 0.3196 | 0.8979 |
| 0.1734 | 3.0 | 1299 | 0.3882 | 0.9001 |
| 0.0877 | 4.0 | 1732 | 0.4801 | 0.9050 |
| 0.0444 | 5.0 | 2165 | 0.6567 | 0.8918 |
| 0.0206 | 6.0 | 2598 | 0.6090 | 0.9023 |
| 0.0145 | 7.0 | 3031 | 0.6415 | 0.9028 |
| 0.0145 | 8.0 | 3464 | 0.7532 | 0.9023 |
| 0.0083 | 9.0 | 3897 | 0.6840 | 0.9116 |
| 0.0073 | 10.0 | 4330 | 0.8115 | 0.9001 |
| 0.0131 | 11.0 | 4763 | 0.7755 | 0.9017 |
| 0.0083 | 12.0 | 5196 | 0.7370 | 0.9083 |
| 0.0045 | 13.0 | 5629 | 0.8288 | 0.9066 |
| 0.0072 | 14.0 | 6062 | 0.8585 | 0.9017 |
| 0.0072 | 15.0 | 6495 | 0.8054 | 0.9028 |
| 0.0064 | 16.0 | 6928 | 0.8080 | 0.9039 |
| 0.0059 | 17.0 | 7361 | 0.8245 | 0.9050 |
| 0.0019 | 18.0 | 7794 | 0.9924 | 0.8940 |
| 0.001 | 19.0 | 8227 | 0.9138 | 0.8984 |
| 0.0016 | 20.0 | 8660 | 0.8972 | 0.9017 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Bhandari007/openai-whisper-large-open-slr-0.0.1
|
Bhandari007
| 2023-11-29T06:12:52Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-large",
"base_model:adapter:openai/whisper-large",
"region:us"
] | null | 2023-11-29T06:12:44Z |
---
library_name: peft
base_model: openai/whisper-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
|
Ja-ck/llama-2-13b-instruct-Y24-v1
|
Ja-ck
| 2023-11-29T06:11:50Z | 2,296 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T06:02:43Z |
---
license: apache-2.0
language:
- ko
pipeline_tag: text-generation
---
## Prompt Template
```
### 질문: {instruction}
### 답변: {output}
```
|
saumyasinha0510/T5-Kaggle_resource_pipeline
|
saumyasinha0510
| 2023-11-29T06:10:39Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-28T09:42:48Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: saumyasinha0510/T5-Kaggle_resource_pipeline
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. -->
# saumyasinha0510/T5-Kaggle_resource_pipeline
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.0704
- Validation Loss: 1.8716
- Train Lr: 2e-05
- 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Lr | Epoch |
|:----------:|:---------------:|:--------:|:-----:|
| 2.2602 | 1.9319 | 2e-05 | 0 |
| 2.1136 | 1.8929 | 2e-05 | 1 |
| 2.0704 | 1.8716 | 2e-05 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
rika37/a2c-PandaReachDense-v3
|
rika37
| 2023-11-29T06:06:34Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T06:02:18Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -2.29 +/- 4.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
HarshaSingamshetty1/detr-resnet-50_finetuned_cppe5
|
HarshaSingamshetty1
| 2023-11-29T06:04:23Z | 220 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cppe-5",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-11-27T05:54:46Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Vishal24/Keyword_category_adapter_v1
|
Vishal24
| 2023-11-29T05:58:17Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-11-29T05:58:07Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
## 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
|
dvijay/mistral-alpaca-finetune
|
dvijay
| 2023-11-29T05:50:10Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T05:18:36Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: out
results: []
---
[<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)
# mistral-alpaca-finetune
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the mhenrichsen/alpaca_2k_test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9152 | 0.01 | 1 | 0.9037 |
| 0.9101 | 0.15 | 18 | 0.8461 |
| 0.7589 | 0.3 | 36 | 0.8437 |
| 0.8274 | 0.45 | 54 | 0.8441 |
| 0.7255 | 0.61 | 72 | 0.8435 |
| 0.85 | 0.76 | 90 | 0.8419 |
| 0.9083 | 0.91 | 108 | 0.8408 |
| 0.3208 | 1.06 | 126 | 0.9177 |
| 0.3738 | 1.21 | 144 | 0.8924 |
| 0.4034 | 1.36 | 162 | 0.8914 |
| 0.3936 | 1.51 | 180 | 0.9032 |
| 0.3188 | 1.66 | 198 | 0.9001 |
| 0.4331 | 1.82 | 216 | 0.8973 |
| 0.3946 | 1.97 | 234 | 0.8963 |
| 0.1531 | 2.12 | 252 | 0.9653 |
| 0.1741 | 2.27 | 270 | 0.9841 |
| 0.2371 | 2.42 | 288 | 0.9784 |
| 0.271 | 2.57 | 306 | 0.9801 |
| 0.2632 | 2.72 | 324 | 0.9808 |
| 0.1691 | 2.87 | 342 | 0.9808 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
LangChain12/my_awesome_wnut_model
|
LangChain12
| 2023-11-29T05:49:21Z | 107 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-29T05:20:14Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.4704918032786885
- name: Recall
type: recall
value: 0.2659870250231696
- name: F1
type: f1
value: 0.33984606275902896
- name: Accuracy
type: accuracy
value: 0.9393356419135565
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2839
- Precision: 0.4705
- Recall: 0.2660
- F1: 0.3398
- Accuracy: 0.9393
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2976 | 0.4098 | 0.1937 | 0.2631 | 0.9349 |
| No log | 2.0 | 426 | 0.2839 | 0.4705 | 0.2660 | 0.3398 | 0.9393 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
iamshnoo/yi-alpaca-2-6b-hindi
|
iamshnoo
| 2023-11-29T05:48:39Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-11-23T05:02:41Z |
---
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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
Jungwonchang/whisper-large-v2-LoRA-SPGIspeech-xs
|
Jungwonchang
| 2023-11-29T05:34:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-large-v2",
"base_model:adapter:openai/whisper-large-v2",
"model-index",
"region:us"
] | null | 2023-11-27T13:11:26Z |
---
library_name: peft
base_model: openai/whisper-large-v2
model-index:
- name: Jungwonchang/whisper-large-v2-LoRA-SPGIspeech-xs
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Test set for spgispeech
type: kensho/spgispeech
config: S
split: test
metrics:
- type: wer
value: 6.72
name: WER
- type: cer
value: 1.99
name: CER
---
# 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]
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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]
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## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
gianyrox/SeussDream
|
gianyrox
| 2023-11-29T05:29:03Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-16T19:49:43Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of Dr. Seuss's Lorax
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - gianyrox/SeussDream
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of Dr. Seuss's Lorax using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
K-kiron/distilbert-lr-linear
|
K-kiron
| 2023-11-29T05:26:39Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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
| 2023-11-29T05:23:43Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-lr-linear
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-lr-linear
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: 1.1209
- Accuracy: 0.8961
- F1: 0.8962
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.3
|
SharatChandra/whisper-fine-banking-dataset
|
SharatChandra
| 2023-11-29T05:19:21Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-28T08:00:18Z |
---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-fine-banking-dataset
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-fine-banking-dataset
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6014
- Wer: 96.7495
## 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0001 | 32.26 | 1000 | 0.5225 | 96.6399 |
| 0.0 | 64.52 | 2000 | 0.5617 | 96.7495 |
| 0.0 | 96.77 | 3000 | 0.5931 | 96.7495 |
| 0.0 | 129.03 | 4000 | 0.6014 | 96.7495 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
K-kiron/distilbert-batch-size-32
|
K-kiron
| 2023-11-29T05:15:44Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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
| 2023-11-29T05:13:19Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-batch-size-32
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-batch-size-32
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: 1.0549
- Accuracy: 0.8963
- F1: 0.8963
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hkivancoral/hushem_5x_beit_base_rms_001_fold4
|
hkivancoral
| 2023-11-29T05:09:38Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T04:37:08Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7619047619047619
---
<!-- 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. -->
# hushem_5x_beit_base_rms_001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5372
- Accuracy: 0.7619
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4749 | 1.0 | 28 | 1.3999 | 0.2381 |
| 1.39 | 2.0 | 56 | 1.4010 | 0.2619 |
| 1.4057 | 3.0 | 84 | 1.3886 | 0.2381 |
| 1.3953 | 4.0 | 112 | 1.3773 | 0.2381 |
| 1.3855 | 5.0 | 140 | 1.3607 | 0.2619 |
| 1.3721 | 6.0 | 168 | 1.1238 | 0.5 |
| 1.2199 | 7.0 | 196 | 1.2305 | 0.4762 |
| 1.1505 | 8.0 | 224 | 0.9832 | 0.4762 |
| 1.1076 | 9.0 | 252 | 0.9145 | 0.5476 |
| 1.04 | 10.0 | 280 | 0.9689 | 0.5476 |
| 0.9947 | 11.0 | 308 | 0.8866 | 0.6429 |
| 1.0266 | 12.0 | 336 | 0.8639 | 0.6905 |
| 0.9955 | 13.0 | 364 | 0.8959 | 0.6190 |
| 0.9564 | 14.0 | 392 | 0.8608 | 0.6667 |
| 0.9123 | 15.0 | 420 | 0.7711 | 0.6905 |
| 0.9391 | 16.0 | 448 | 0.7070 | 0.7619 |
| 0.9117 | 17.0 | 476 | 0.7366 | 0.7619 |
| 0.902 | 18.0 | 504 | 0.7650 | 0.7143 |
| 0.8479 | 19.0 | 532 | 0.7181 | 0.7381 |
| 0.8138 | 20.0 | 560 | 0.8337 | 0.6667 |
| 0.7593 | 21.0 | 588 | 0.8325 | 0.6905 |
| 0.8558 | 22.0 | 616 | 0.7211 | 0.8095 |
| 0.8609 | 23.0 | 644 | 0.7758 | 0.7619 |
| 0.7997 | 24.0 | 672 | 0.8535 | 0.7143 |
| 0.6915 | 25.0 | 700 | 0.8962 | 0.7381 |
| 0.7445 | 26.0 | 728 | 0.7116 | 0.7619 |
| 0.6818 | 27.0 | 756 | 0.9464 | 0.5714 |
| 0.6812 | 28.0 | 784 | 0.6802 | 0.7143 |
| 0.662 | 29.0 | 812 | 1.0464 | 0.5476 |
| 0.6161 | 30.0 | 840 | 0.7154 | 0.7857 |
| 0.5942 | 31.0 | 868 | 0.6122 | 0.7619 |
| 0.571 | 32.0 | 896 | 0.6263 | 0.7857 |
| 0.5357 | 33.0 | 924 | 0.8564 | 0.8095 |
| 0.4815 | 34.0 | 952 | 0.9986 | 0.7381 |
| 0.5261 | 35.0 | 980 | 0.9173 | 0.8095 |
| 0.3508 | 36.0 | 1008 | 1.0846 | 0.7619 |
| 0.3469 | 37.0 | 1036 | 0.9412 | 0.8333 |
| 0.3024 | 38.0 | 1064 | 0.9602 | 0.8333 |
| 0.2908 | 39.0 | 1092 | 1.1234 | 0.8333 |
| 0.2222 | 40.0 | 1120 | 1.1275 | 0.8095 |
| 0.2149 | 41.0 | 1148 | 1.4618 | 0.7381 |
| 0.2207 | 42.0 | 1176 | 1.3470 | 0.7857 |
| 0.094 | 43.0 | 1204 | 1.5389 | 0.7619 |
| 0.1227 | 44.0 | 1232 | 1.3819 | 0.7857 |
| 0.0713 | 45.0 | 1260 | 1.5287 | 0.7619 |
| 0.0383 | 46.0 | 1288 | 1.5676 | 0.8095 |
| 0.0259 | 47.0 | 1316 | 1.4966 | 0.7857 |
| 0.023 | 48.0 | 1344 | 1.5355 | 0.7619 |
| 0.0304 | 49.0 | 1372 | 1.5372 | 0.7619 |
| 0.0233 | 50.0 | 1400 | 1.5372 | 0.7619 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Aleksia/finetuning-distilBert_sentiment
|
Aleksia
| 2023-11-29T05:05:44Z | 120 | 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
| 2023-11-29T03:14:44Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-distilBert_sentiment
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. -->
# finetuning-distilBert_sentiment
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: 0.2217
- Accuracy: 0.9148
- F1: 0.9149
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- 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 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
BaoLocTown/sft-metamath-mistral-7b-vi-v1
|
BaoLocTown
| 2023-11-29T05:00:22Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:hllj/meta-math-mistral-vi-math",
"base_model:finetune:hllj/meta-math-mistral-vi-math",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T03:05:19Z |
---
base_model: hllj/meta-math-mistral-vi-math
tags:
- generated_from_trainer
model-index:
- name: sft-metamath-mistral-7b-vi-v1
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. -->
# sft-metamath-mistral-7b-vi-v1
This model is a fine-tuned version of [hllj/meta-math-mistral-vi-math](https://huggingface.co/hllj/meta-math-mistral-vi-math) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4947
## 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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3128 | 0.26 | 500 | 0.5093 |
| 0.2751 | 1.07 | 1000 | 0.4884 |
| 0.2585 | 1.33 | 1500 | 0.4943 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
justswim/lnmdlsktchfsh-512
|
justswim
| 2023-11-29T04:57:59Z | 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
| 2023-11-29T02:47:38Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: lnmdlsktchfsh-512
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
hkivancoral/hushem_5x_beit_base_rms_001_fold3
|
hkivancoral
| 2023-11-29T04:36:20Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T04:05:25Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.627906976744186
---
<!-- 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. -->
# hushem_5x_beit_base_rms_001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4768
- Accuracy: 0.6279
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.51 | 1.0 | 28 | 1.6351 | 0.2558 |
| 1.3869 | 2.0 | 56 | 1.4127 | 0.2558 |
| 1.3848 | 3.0 | 84 | 1.3895 | 0.2558 |
| 1.4113 | 4.0 | 112 | 1.3824 | 0.2558 |
| 1.3569 | 5.0 | 140 | 1.4121 | 0.2326 |
| 1.4625 | 6.0 | 168 | 1.3739 | 0.2326 |
| 1.3804 | 7.0 | 196 | 1.2185 | 0.5349 |
| 1.1352 | 8.0 | 224 | 1.1411 | 0.4884 |
| 1.0899 | 9.0 | 252 | 1.2426 | 0.3953 |
| 1.0945 | 10.0 | 280 | 1.1820 | 0.3488 |
| 1.1149 | 11.0 | 308 | 1.4574 | 0.3023 |
| 0.9942 | 12.0 | 336 | 1.4728 | 0.3256 |
| 1.0204 | 13.0 | 364 | 0.9801 | 0.5581 |
| 0.9987 | 14.0 | 392 | 1.0096 | 0.5349 |
| 1.0664 | 15.0 | 420 | 1.0007 | 0.5814 |
| 0.9463 | 16.0 | 448 | 1.2188 | 0.3953 |
| 0.9756 | 17.0 | 476 | 1.1284 | 0.5116 |
| 0.9698 | 18.0 | 504 | 1.4394 | 0.4419 |
| 1.061 | 19.0 | 532 | 1.1162 | 0.4884 |
| 0.8426 | 20.0 | 560 | 1.9296 | 0.3721 |
| 0.876 | 21.0 | 588 | 1.0070 | 0.5581 |
| 0.8908 | 22.0 | 616 | 1.2196 | 0.5349 |
| 0.8599 | 23.0 | 644 | 0.9502 | 0.6047 |
| 0.8338 | 24.0 | 672 | 0.8737 | 0.6279 |
| 0.785 | 25.0 | 700 | 1.1006 | 0.5814 |
| 0.82 | 26.0 | 728 | 1.0398 | 0.5814 |
| 0.8016 | 27.0 | 756 | 1.6671 | 0.3256 |
| 0.8574 | 28.0 | 784 | 1.1704 | 0.6279 |
| 0.8104 | 29.0 | 812 | 1.0502 | 0.6279 |
| 0.7421 | 30.0 | 840 | 0.9270 | 0.5814 |
| 0.7093 | 31.0 | 868 | 1.8057 | 0.4186 |
| 0.7469 | 32.0 | 896 | 0.9665 | 0.5814 |
| 0.7175 | 33.0 | 924 | 0.8190 | 0.6512 |
| 0.7129 | 34.0 | 952 | 1.0680 | 0.6279 |
| 0.7793 | 35.0 | 980 | 1.0966 | 0.5581 |
| 0.6879 | 36.0 | 1008 | 0.9990 | 0.5814 |
| 0.7016 | 37.0 | 1036 | 1.7556 | 0.4884 |
| 0.6238 | 38.0 | 1064 | 1.5792 | 0.4651 |
| 0.6025 | 39.0 | 1092 | 1.1502 | 0.6047 |
| 0.7264 | 40.0 | 1120 | 1.3317 | 0.5349 |
| 0.6063 | 41.0 | 1148 | 1.5492 | 0.5116 |
| 0.5816 | 42.0 | 1176 | 1.5787 | 0.5814 |
| 0.4627 | 43.0 | 1204 | 1.1301 | 0.6047 |
| 0.4652 | 44.0 | 1232 | 1.5008 | 0.6279 |
| 0.3885 | 45.0 | 1260 | 1.3167 | 0.6279 |
| 0.4003 | 46.0 | 1288 | 1.3851 | 0.6512 |
| 0.3882 | 47.0 | 1316 | 1.4601 | 0.6047 |
| 0.353 | 48.0 | 1344 | 1.4699 | 0.6279 |
| 0.3487 | 49.0 | 1372 | 1.4768 | 0.6279 |
| 0.2789 | 50.0 | 1400 | 1.4768 | 0.6279 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
AfterRain007/results
|
AfterRain007
| 2023-11-29T04:28:23Z | 183 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:ElKulako/cryptobert",
"base_model:finetune:ElKulako/cryptobert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-29T04:27:37Z |
---
base_model: ElKulako/cryptobert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [ElKulako/cryptobert](https://huggingface.co/ElKulako/cryptobert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8983
- Accuracy: 0.6433
- Precision: 0.6614
- Recall: 0.6433
- F1: 0.6461
## 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: 3.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69420
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9586 | 0.19 | 100 | 0.8746 | 0.6033 | 0.5990 | 0.6033 | 0.5944 |
| 0.7362 | 0.38 | 200 | 0.8187 | 0.63 | 0.6322 | 0.63 | 0.6232 |
| 0.577 | 0.57 | 300 | 0.8065 | 0.6767 | 0.6821 | 0.6767 | 0.6761 |
| 0.4632 | 0.76 | 400 | 0.8437 | 0.63 | 0.6411 | 0.63 | 0.6321 |
| 0.3243 | 0.95 | 500 | 0.8983 | 0.6433 | 0.6614 | 0.6433 | 0.6461 |
| 0.2257 | 1.14 | 600 | 1.3704 | 0.6033 | 0.6863 | 0.6033 | 0.6046 |
| 0.1333 | 1.33 | 700 | 1.2951 | 0.6033 | 0.6201 | 0.6033 | 0.6052 |
| 0.0574 | 1.52 | 800 | 1.5119 | 0.6333 | 0.6331 | 0.6333 | 0.6309 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0
|
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t3000_e5
|
FounderOfHuggingface
| 2023-11-29T04:24:55Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-11-29T03:49:05Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
## Training procedure
### Framework versions
- PEFT 0.6.2
|
phuong-tk-nguyen/vit-base-patch16-224-finetuned
|
phuong-tk-nguyen
| 2023-11-29T04:01:41Z | 193 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-28T09:15:03Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.967
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2073
- Accuracy: 0.967
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1833 | 0.14 | 10 | 1.6004 | 0.626 |
| 1.3976 | 0.28 | 20 | 0.8484 | 0.909 |
| 0.9003 | 0.43 | 30 | 0.4514 | 0.946 |
| 0.6423 | 0.57 | 40 | 0.3037 | 0.96 |
| 0.5084 | 0.71 | 50 | 0.2468 | 0.96 |
| 0.47 | 0.85 | 60 | 0.2161 | 0.965 |
| 0.4753 | 0.99 | 70 | 0.2073 | 0.967 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
niksss/xlm-roberta-large-finetuned-ebay
|
niksss
| 2023-11-29T03:57:05Z | 161 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-29T03:55:57Z |
---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large-finetuned-ebay
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-ebay
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
sh-zheng/vit-base-patch16-224-in21k-fintuned-SurfaceRoughness
|
sh-zheng
| 2023-11-29T03:53:34Z | 190 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"en",
"dataset:sh-zheng/SurfaceRoughness",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T03:34:16Z |
---
license: mit
datasets:
- sh-zheng/SurfaceRoughness
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
---
## Vision Transformer (Fine-Tuned model)
refer to https://huggingface.co/google/vit-base-patch16-224 for model detail and how to use
## Model Description
Predict surface roughness category using snips taken from google maps aerial view. There are 3 categories: surface roughness B, surface roughness C, surface roughness D as defined in ASCE 7-16 section 26.7.2.
|
cuongtk2002/my_awesome_qa_model
|
cuongtk2002
| 2023-11-29T03:43:45Z | 117 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-11-29T02:59:32Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.1527 |
| 2.6394 | 2.0 | 500 | 1.6314 |
| 2.6394 | 3.0 | 750 | 1.6024 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
hkivancoral/hushem_5x_beit_base_rms_001_fold1
|
hkivancoral
| 2023-11-29T03:32:00Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T03:00:12Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.4444444444444444
---
<!-- 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. -->
# hushem_5x_beit_base_rms_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2430
- Accuracy: 0.4444
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5782 | 1.0 | 27 | 1.4061 | 0.2444 |
| 1.4004 | 2.0 | 54 | 1.4559 | 0.2444 |
| 1.3873 | 3.0 | 81 | 1.4120 | 0.2444 |
| 1.3666 | 4.0 | 108 | 1.6275 | 0.2444 |
| 1.3597 | 5.0 | 135 | 1.4398 | 0.2444 |
| 1.2814 | 6.0 | 162 | 1.5328 | 0.2444 |
| 1.2056 | 7.0 | 189 | 1.5389 | 0.2 |
| 1.1635 | 8.0 | 216 | 1.5332 | 0.2444 |
| 1.1235 | 9.0 | 243 | 1.6681 | 0.2444 |
| 1.1484 | 10.0 | 270 | 1.6176 | 0.2667 |
| 1.1757 | 11.0 | 297 | 1.6312 | 0.2444 |
| 1.1297 | 12.0 | 324 | 1.5067 | 0.2444 |
| 1.1448 | 13.0 | 351 | 1.5657 | 0.2444 |
| 1.1725 | 14.0 | 378 | 1.5184 | 0.1556 |
| 1.1591 | 15.0 | 405 | 1.5790 | 0.2444 |
| 1.1549 | 16.0 | 432 | 1.5501 | 0.2444 |
| 1.0865 | 17.0 | 459 | 1.5776 | 0.2444 |
| 1.1351 | 18.0 | 486 | 1.6195 | 0.3111 |
| 1.0974 | 19.0 | 513 | 1.5360 | 0.2444 |
| 1.0992 | 20.0 | 540 | 1.5742 | 0.3111 |
| 1.0894 | 21.0 | 567 | 1.4918 | 0.3778 |
| 1.0557 | 22.0 | 594 | 1.5742 | 0.2444 |
| 1.0574 | 23.0 | 621 | 1.5043 | 0.4222 |
| 1.0148 | 24.0 | 648 | 1.3535 | 0.4222 |
| 1.1133 | 25.0 | 675 | 1.4897 | 0.4 |
| 1.02 | 26.0 | 702 | 1.4554 | 0.4222 |
| 1.0107 | 27.0 | 729 | 1.4238 | 0.4 |
| 0.9307 | 28.0 | 756 | 1.7644 | 0.3556 |
| 0.8335 | 29.0 | 783 | 2.0253 | 0.3556 |
| 0.8203 | 30.0 | 810 | 1.7990 | 0.3556 |
| 0.7263 | 31.0 | 837 | 1.6909 | 0.3778 |
| 0.8387 | 32.0 | 864 | 1.4758 | 0.4 |
| 0.6837 | 33.0 | 891 | 2.1584 | 0.3556 |
| 0.7155 | 34.0 | 918 | 1.7102 | 0.3778 |
| 0.6349 | 35.0 | 945 | 1.1875 | 0.4667 |
| 0.6331 | 36.0 | 972 | 1.9965 | 0.4222 |
| 0.5871 | 37.0 | 999 | 1.7881 | 0.4 |
| 0.595 | 38.0 | 1026 | 1.7629 | 0.4 |
| 0.5266 | 39.0 | 1053 | 1.6720 | 0.4222 |
| 0.4985 | 40.0 | 1080 | 2.3229 | 0.4222 |
| 0.4855 | 41.0 | 1107 | 1.6470 | 0.4444 |
| 0.503 | 42.0 | 1134 | 1.7515 | 0.4667 |
| 0.4432 | 43.0 | 1161 | 2.0538 | 0.4222 |
| 0.3668 | 44.0 | 1188 | 2.1471 | 0.4444 |
| 0.3654 | 45.0 | 1215 | 2.0004 | 0.4444 |
| 0.3317 | 46.0 | 1242 | 2.1973 | 0.4444 |
| 0.2413 | 47.0 | 1269 | 2.2882 | 0.4444 |
| 0.2395 | 48.0 | 1296 | 2.2389 | 0.4444 |
| 0.2502 | 49.0 | 1323 | 2.2430 | 0.4444 |
| 0.237 | 50.0 | 1350 | 2.2430 | 0.4444 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
phuong-tk-nguyen/resnet-50-finetuned
|
phuong-tk-nguyen
| 2023-11-29T03:24:53Z | 224 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/resnet-50",
"base_model:finetune:microsoft/resnet-50",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-28T09:12:06Z |
---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.199
---
<!-- 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. -->
# resnet-50-finetuned
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2724
- Accuracy: 0.199
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3021 | 0.14 | 10 | 2.2994 | 0.112 |
| 2.2929 | 0.28 | 20 | 2.2911 | 0.137 |
| 2.2875 | 0.43 | 30 | 2.2848 | 0.151 |
| 2.2824 | 0.57 | 40 | 2.2812 | 0.175 |
| 2.2792 | 0.71 | 50 | 2.2758 | 0.191 |
| 2.2766 | 0.85 | 60 | 2.2726 | 0.197 |
| 2.2765 | 0.99 | 70 | 2.2724 | 0.199 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.10.1+cu111
- Datasets 2.14.6
- Tokenizers 0.13.3
|
kaizerBox/gpt2-small-summarization
|
kaizerBox
| 2023-11-29T03:22:10Z | 113 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:xsum",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T03:22:06Z |
---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: gpt2-small-summarization
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-small-summarization
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.1568 | 1.0 | 5762 | 4.6058 |
| 4.5202 | 2.0 | 11525 | 4.4583 |
| 4.4225 | 3.0 | 17286 | 4.4258 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
evolevelyn/distilgpt2-finetuned-slangQA
|
evolevelyn
| 2023-11-29T03:15:44Z | 118 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-28T02:14:39Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-slangQA
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. -->
# distilgpt2-finetuned-slangQA
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2789
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5804 | 1.0 | 1022 | 6.4914 |
| 6.2955 | 2.0 | 2044 | 6.3266 |
| 6.2102 | 3.0 | 3066 | 6.2789 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
ramsenth/outputs
|
ramsenth
| 2023-11-29T03:05:01Z | 119 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-uncased",
"base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-29T03:04:18Z |
---
base_model: dccuchile/bert-base-spanish-wwm-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: langbot-gec
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. -->
# langbot-gec
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1379
- Precision: 0.7729
- Recall: 0.3969
- F1: 0.5244
- Accuracy: 0.9553
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1778 | 1.0 | 126 | 0.1379 | 0.7729 | 0.3969 | 0.5244 | 0.9553 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Starbourne/cogvlm-grounding-generalist-hf
|
Starbourne
| 2023-11-29T03:01:48Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation",
"custom_code",
"arxiv:2311.03079",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T02:41:27Z |
# CogVLM
**CogVLM** 是一个强大的开源视觉语言模型（VLM）。CogVLM-17B 拥有 100 亿视觉参数和 70 亿语言参数，在 10 个经典跨模态基准测试上取得了 SOTA 性能，包括 NoCaps、Flicker30k captioning、RefCOCO、RefCOCO+、RefCOCOg、Visual7W、GQA、ScienceQA、VizWiz VQA 和 TDIUC，而在 VQAv2、OKVQA、TextVQA、COCO captioning 等方面则排名第二，超越或与 PaLI-X 55B 持平。您可以通过线上 [demo](http://36.103.203.44:7861/) 体验 CogVLM 多模态对话。
**CogVLM** is a powerful **open-source visual language model** (**VLM**). CogVLM-17B has 10 billion vision parameters and 7 billion language parameters. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and rank the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., **surpassing or matching PaLI-X 55B**. CogVLM can also [chat with you](http://36.103.203.44:7861/) about images.
<div align="center">
<img src="https://github.com/THUDM/CogVLM/raw/main/assets/metrics-min.png" alt="img" style="zoom: 50%;" />
</div>
# 快速开始（Qiuckstart）
```python
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
model = AutoModelForCausalLM.from_pretrained(
'THUDM/cogvlm-grounding-generalist-hf',
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).to('cuda').eval()
query = 'Can you provide a description of the image and include the coordinates [[x0,y0,x1,y1]] for each mentioned object?'
image = Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/4.jpg?raw=true', stream=True).raw).convert('RGB')
inputs = model.build_conversation_input_ids(tokenizer, query=query, images=[image])
inputs = {
'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]],
}
gen_kwargs = {"max_length": 2048, "do_sample": False}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0]))
```
# 方法（Method）
CogVLM 模型包括四个基本组件：视觉变换器（ViT）编码器、MLP适配器、预训练的大型语言模型（GPT）和一个**视觉专家模块**。更多细节请参见[Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf)。
CogVLM model comprises four fundamental components: a vision transformer (ViT) encoder, an MLP adapter, a pretrained large language model (GPT), and a **visual expert module**. See [Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf) for more details.
<div align="center">
<img src="https://github.com/THUDM/CogVLM/raw/main/assets/method-min.png" style="zoom:50%;" />
</div>
# 许可（License）
此存储库中的代码是根据 [Apache-2.0 许可](https://github.com/THUDM/CogVLM/raw/main/LICENSE) 开放源码，而使用 CogVLM 模型权重必须遵循 [模型许可](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE)。
The code in this repository is open source under the [Apache-2.0 license](https://github.com/THUDM/CogVLM/raw/main/LICENSE), while the use of the CogVLM model weights must comply with the [Model License](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE).
# 引用（Citation）
If you find our work helpful, please consider citing the following papers
```
@article{wang2023cogvlm,
title={CogVLM: Visual Expert for Pretrained Language Models},
author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
year={2023},
eprint={2311.03079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
george-yeung/dogbooth
|
george-yeung
| 2023-11-29T02:53:17Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:37:41Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - george-yeung/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
shiya-orsted-com/dogbooth
|
shiya-orsted-com
| 2023-11-29T02:53:04Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:37:28Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - shiya-orsted-com/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
TejaMat/dogbooth
|
TejaMat
| 2023-11-29T02:48:58Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:33:20Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - TejaMat/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
jlbaker361/fine-tune_addition_subtraction_decimal_whole
|
jlbaker361
| 2023-11-29T02:44:20Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-11-18T23:49:04Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
speedmessage/dogbooth
|
speedmessage
| 2023-11-29T02:43:52Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:28:23Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - speedmessage/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
mhsong/dogbooth
|
mhsong
| 2023-11-29T02:41:56Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:06:33Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - mhsong/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
vlaurenzano/dogbooth
|
vlaurenzano
| 2023-11-29T02:38:43Z | 30 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:23:04Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - vlaurenzano/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
shashank7777/dogbooth
|
shashank7777
| 2023-11-29T02:37:02Z | 30 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:21:12Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - shashank7777/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
fsrv0/dogbooth
|
fsrv0
| 2023-11-29T02:35:34Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:19:50Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - fsrv0/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
nellaisenthil/dogbooth
|
nellaisenthil
| 2023-11-29T02:35:13Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:19:41Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - nellaisenthil/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
athirdpath/BigLlama-20b
|
athirdpath
| 2023-11-29T02:35:13Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T02:27:09Z |
---
license: llama2
---
I'm going to compare DARE merges using this (mostly vanilla, alpaca-tinted) 20b model vs using Harmonia.
slices:
- sources:
- model: athirdpath/alpaca-2-13b-english_full-model
-
layer_range: [0, 16]
- sources:
- model: TheBloke/Llama-2-13B-fp16
-
layer_range: [8, 24]
- sources:
- model: athirdpath/alpaca-2-13b-english_full-model
-
layer_range: [17, 32]
- sources:
- model: TheBloke/Llama-2-13B-fp16
-
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
|
jcbrz88/dogbooth
|
jcbrz88
| 2023-11-29T02:34:35Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:18:59Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - jcbrz88/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
smuthalib/dogbooth
|
smuthalib
| 2023-11-29T02:32:53Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:17:12Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - smuthalib/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
synrb/dogbooth
|
synrb
| 2023-11-29T02:29:25Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:13:36Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - synrb/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
hkivancoral/hushem_5x_beit_base_sgd_00001_fold4
|
hkivancoral
| 2023-11-29T02:28:36Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T02:00:04Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_sgd_00001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.30952380952380953
---
<!-- 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. -->
# hushem_5x_beit_base_sgd_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4856
- Accuracy: 0.3095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5648 | 1.0 | 28 | 1.5024 | 0.3095 |
| 1.5958 | 2.0 | 56 | 1.5016 | 0.3095 |
| 1.5478 | 3.0 | 84 | 1.5008 | 0.3095 |
| 1.6175 | 4.0 | 112 | 1.5001 | 0.3095 |
| 1.5019 | 5.0 | 140 | 1.4994 | 0.3095 |
| 1.5612 | 6.0 | 168 | 1.4987 | 0.3095 |
| 1.5556 | 7.0 | 196 | 1.4981 | 0.3095 |
| 1.5275 | 8.0 | 224 | 1.4974 | 0.3095 |
| 1.529 | 9.0 | 252 | 1.4968 | 0.3095 |
| 1.5306 | 10.0 | 280 | 1.4962 | 0.3095 |
| 1.5486 | 11.0 | 308 | 1.4956 | 0.3095 |
| 1.5567 | 12.0 | 336 | 1.4950 | 0.3095 |
| 1.5578 | 13.0 | 364 | 1.4945 | 0.3095 |
| 1.5601 | 14.0 | 392 | 1.4939 | 0.3095 |
| 1.5869 | 15.0 | 420 | 1.4934 | 0.3095 |
| 1.5292 | 16.0 | 448 | 1.4929 | 0.3095 |
| 1.584 | 17.0 | 476 | 1.4924 | 0.3095 |
| 1.5709 | 18.0 | 504 | 1.4919 | 0.3095 |
| 1.5246 | 19.0 | 532 | 1.4915 | 0.3095 |
| 1.508 | 20.0 | 560 | 1.4911 | 0.3095 |
| 1.5627 | 21.0 | 588 | 1.4907 | 0.3095 |
| 1.543 | 22.0 | 616 | 1.4904 | 0.3095 |
| 1.5306 | 23.0 | 644 | 1.4900 | 0.3095 |
| 1.5347 | 24.0 | 672 | 1.4896 | 0.3095 |
| 1.5296 | 25.0 | 700 | 1.4893 | 0.3095 |
| 1.5722 | 26.0 | 728 | 1.4889 | 0.3095 |
| 1.6103 | 27.0 | 756 | 1.4886 | 0.3095 |
| 1.5352 | 28.0 | 784 | 1.4883 | 0.3095 |
| 1.5133 | 29.0 | 812 | 1.4880 | 0.3095 |
| 1.4677 | 30.0 | 840 | 1.4878 | 0.3095 |
| 1.5424 | 31.0 | 868 | 1.4876 | 0.3095 |
| 1.5132 | 32.0 | 896 | 1.4873 | 0.3095 |
| 1.5611 | 33.0 | 924 | 1.4871 | 0.3095 |
| 1.5494 | 34.0 | 952 | 1.4869 | 0.3095 |
| 1.5087 | 35.0 | 980 | 1.4867 | 0.3095 |
| 1.5719 | 36.0 | 1008 | 1.4865 | 0.3095 |
| 1.5037 | 37.0 | 1036 | 1.4864 | 0.3095 |
| 1.5457 | 38.0 | 1064 | 1.4863 | 0.3095 |
| 1.5227 | 39.0 | 1092 | 1.4861 | 0.3095 |
| 1.5024 | 40.0 | 1120 | 1.4860 | 0.3095 |
| 1.5112 | 41.0 | 1148 | 1.4859 | 0.3095 |
| 1.4872 | 42.0 | 1176 | 1.4858 | 0.3095 |
| 1.5623 | 43.0 | 1204 | 1.4858 | 0.3095 |
| 1.5147 | 44.0 | 1232 | 1.4857 | 0.3095 |
| 1.5196 | 45.0 | 1260 | 1.4857 | 0.3095 |
| 1.5574 | 46.0 | 1288 | 1.4856 | 0.3095 |
| 1.5277 | 47.0 | 1316 | 1.4856 | 0.3095 |
| 1.602 | 48.0 | 1344 | 1.4856 | 0.3095 |
| 1.5259 | 49.0 | 1372 | 1.4856 | 0.3095 |
| 1.5075 | 50.0 | 1400 | 1.4856 | 0.3095 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
athirdpath/alpaca-2-13b-english_full-model
|
athirdpath
| 2023-11-29T02:17:53Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T01:41:21Z |
---
license: llama2
---
This is the LORA from iamshnoo/alpaca-2-13b-english applied to TheBloke/Llama-2-13B-fp16.
|
JLenScott/dogbooth
|
JLenScott
| 2023-11-29T02:17:10Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T02:01:32Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - JLenScott/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
jpolun/dogbooth
|
jpolun
| 2023-11-29T02:14:35Z | 30 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T01:58:54Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - jpolun/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
dobbali/dogbooth
|
dobbali
| 2023-11-29T02:13:06Z | 30 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T01:57:15Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - dobbali/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
bogdansorlea/dogbooth
|
bogdansorlea
| 2023-11-29T02:10:42Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T01:54:57Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - bogdansorlea/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
mateotrejo/luke-davidson
|
mateotrejo
| 2023-11-29T02:03:39Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-11-29T02:02:10Z |
---
license: other
license_name: luke-davidson
license_link: LICENSE
---
|
jsl28/q-Taxi-v3
|
jsl28
| 2023-11-29T02:03:17Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T02:03:14Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jsl28/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-01
|
alinerodrigues
| 2023-11-29T01:59:06Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-28T18:30:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-01
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1429
- Wer: 0.0862
- Cer: 0.0263
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 31.9255 | 1.0 | 86 | 3.2559 | 1.0 | 1.0 |
| 8.1132 | 2.0 | 172 | 3.0009 | 1.0 | 1.0 |
| 3.0533 | 3.0 | 258 | 2.9120 | 1.0 | 1.0 |
| 2.93 | 4.0 | 344 | 2.8877 | 1.0 | 1.0 |
| 2.8396 | 5.0 | 430 | 1.9900 | 1.0 | 0.7238 |
| 1.8884 | 6.0 | 516 | 0.6068 | 0.3305 | 0.0859 |
| 0.8835 | 7.0 | 602 | 0.3984 | 0.2086 | 0.0553 |
| 0.8835 | 8.0 | 688 | 0.3249 | 0.1780 | 0.0492 |
| 0.6254 | 9.0 | 774 | 0.2698 | 0.1662 | 0.0450 |
| 0.5176 | 10.0 | 860 | 0.2420 | 0.1408 | 0.0400 |
| 0.4379 | 11.0 | 946 | 0.2315 | 0.1322 | 0.0376 |
| 0.3823 | 12.0 | 1032 | 0.2185 | 0.1261 | 0.0356 |
| 0.3601 | 13.0 | 1118 | 0.2176 | 0.1148 | 0.0344 |
| 0.3457 | 14.0 | 1204 | 0.2050 | 0.1119 | 0.0330 |
| 0.3457 | 15.0 | 1290 | 0.1928 | 0.1048 | 0.0306 |
| 0.2942 | 16.0 | 1376 | 0.1864 | 0.0994 | 0.0305 |
| 0.3054 | 17.0 | 1462 | 0.1826 | 0.0987 | 0.0297 |
| 0.321 | 18.0 | 1548 | 0.1755 | 0.0974 | 0.0298 |
| 0.2667 | 19.0 | 1634 | 0.1743 | 0.0996 | 0.0297 |
| 0.2706 | 20.0 | 1720 | 0.1720 | 0.0965 | 0.0288 |
| 0.2355 | 21.0 | 1806 | 0.1646 | 0.0935 | 0.0283 |
| 0.2355 | 22.0 | 1892 | 0.1645 | 0.0935 | 0.0287 |
| 0.2355 | 23.0 | 1978 | 0.1583 | 0.0891 | 0.0271 |
| 0.2401 | 24.0 | 2064 | 0.1578 | 0.0923 | 0.0283 |
| 0.236 | 25.0 | 2150 | 0.1587 | 0.0894 | 0.0280 |
| 0.2314 | 26.0 | 2236 | 0.1547 | 0.0896 | 0.0274 |
| 0.2209 | 27.0 | 2322 | 0.1513 | 0.0891 | 0.0266 |
| 0.2269 | 28.0 | 2408 | 0.1550 | 0.0891 | 0.0270 |
| 0.2269 | 29.0 | 2494 | 0.1566 | 0.0898 | 0.0273 |
| 0.2123 | 30.0 | 2580 | 0.1572 | 0.0898 | 0.0273 |
| 0.1941 | 31.0 | 2666 | 0.1518 | 0.0867 | 0.0266 |
| 0.2108 | 32.0 | 2752 | 0.1492 | 0.0869 | 0.0266 |
| 0.187 | 33.0 | 2838 | 0.1479 | 0.0864 | 0.0268 |
| 0.1799 | 34.0 | 2924 | 0.1429 | 0.0862 | 0.0263 |
| 0.1804 | 35.0 | 3010 | 0.1472 | 0.0835 | 0.0257 |
| 0.1804 | 36.0 | 3096 | 0.1457 | 0.0857 | 0.0262 |
| 0.1756 | 37.0 | 3182 | 0.1456 | 0.0830 | 0.0254 |
| 0.1684 | 38.0 | 3268 | 0.1459 | 0.0857 | 0.0259 |
| 0.1692 | 39.0 | 3354 | 0.1461 | 0.0840 | 0.0263 |
| 0.1609 | 40.0 | 3440 | 0.1432 | 0.0837 | 0.0260 |
| 0.1877 | 41.0 | 3526 | 0.1475 | 0.0818 | 0.0253 |
| 0.1611 | 42.0 | 3612 | 0.1434 | 0.0830 | 0.0261 |
| 0.1611 | 43.0 | 3698 | 0.1438 | 0.0827 | 0.0247 |
| 0.1887 | 44.0 | 3784 | 0.1478 | 0.0832 | 0.0255 |
| 0.1712 | 45.0 | 3870 | 0.1493 | 0.0842 | 0.0257 |
| 0.1531 | 46.0 | 3956 | 0.1493 | 0.0803 | 0.0250 |
| 0.1605 | 47.0 | 4042 | 0.1479 | 0.0842 | 0.0257 |
| 0.1599 | 48.0 | 4128 | 0.1455 | 0.0818 | 0.0250 |
| 0.1623 | 49.0 | 4214 | 0.1470 | 0.0823 | 0.0252 |
| 0.1601 | 50.0 | 4300 | 0.1474 | 0.0793 | 0.0252 |
| 0.1601 | 51.0 | 4386 | 0.1491 | 0.0798 | 0.0250 |
| 0.1478 | 52.0 | 4472 | 0.1491 | 0.0796 | 0.0254 |
| 0.1497 | 53.0 | 4558 | 0.1481 | 0.0815 | 0.0249 |
| 0.1454 | 54.0 | 4644 | 0.1455 | 0.0796 | 0.0245 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
|
Realgon/bert_twitterfin_padding80model
|
Realgon
| 2023-11-29T01:58:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"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
| 2023-11-29T01:33:48Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_twitterfin_padding80model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_twitterfin_padding80model
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:
- Loss: 1.0344
- Accuracy: 0.8840
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6173 | 1.0 | 597 | 0.3644 | 0.8723 |
| 0.3317 | 2.0 | 1194 | 0.3204 | 0.8815 |
| 0.235 | 3.0 | 1791 | 0.4381 | 0.8882 |
| 0.1385 | 4.0 | 2388 | 0.5672 | 0.8777 |
| 0.108 | 5.0 | 2985 | 0.6875 | 0.8748 |
| 0.0466 | 6.0 | 3582 | 0.7728 | 0.8823 |
| 0.0414 | 7.0 | 4179 | 0.7724 | 0.8869 |
| 0.0306 | 8.0 | 4776 | 0.7541 | 0.8840 |
| 0.0336 | 9.0 | 5373 | 0.7872 | 0.8907 |
| 0.0221 | 10.0 | 5970 | 0.8676 | 0.8832 |
| 0.0195 | 11.0 | 6567 | 0.9031 | 0.8811 |
| 0.01 | 12.0 | 7164 | 0.8561 | 0.8823 |
| 0.0148 | 13.0 | 7761 | 0.9173 | 0.8890 |
| 0.0093 | 14.0 | 8358 | 0.9178 | 0.8874 |
| 0.0052 | 15.0 | 8955 | 0.9563 | 0.8865 |
| 0.0046 | 16.0 | 9552 | 0.9723 | 0.8857 |
| 0.0051 | 17.0 | 10149 | 0.9839 | 0.8882 |
| 0.0029 | 18.0 | 10746 | 1.0261 | 0.8844 |
| 0.0041 | 19.0 | 11343 | 1.0228 | 0.8869 |
| 0.0023 | 20.0 | 11940 | 1.0344 | 0.8840 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
jsl28/q-FrozenLake-v1-4x4-noSlippery
|
jsl28
| 2023-11-29T01:56:40Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T01:56:37Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jsl28/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mateotrejo/ember-lumen
|
mateotrejo
| 2023-11-29T01:54:49Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-11-29T01:48:23Z |
---
license: other
license_name: ember-lumen
license_link: LICENSE
---
|
mmenendezg/vit_pneumonia_classifier
|
mmenendezg
| 2023-11-29T01:51:04Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2023-11-29T01:50:27Z |
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Nadam |
| learning_rate | 1.374011446841905e-07 |
| decay | 0.004 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| training_precision | float32 |
|
btmccarthy15/SDLORA2
|
btmccarthy15
| 2023-11-29T01:43:24Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:57:55Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - btmccarthy15/SDLORA2
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
Serdarmuhammet/bert-base-banking77
|
Serdarmuhammet
| 2023-11-29T01:32:21Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T08:43:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9311318811051271
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-banking77
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2967
- F1: 0.9311
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0391 | 1.0 | 626 | 0.7670 | 0.8543 |
| 0.3676 | 2.0 | 1252 | 0.3623 | 0.9209 |
| 0.1715 | 3.0 | 1878 | 0.2967 | 0.9311 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.1.1+cu121
- Datasets 2.9.0
- Tokenizers 0.13.3
|
Robinsh2023/pegasus-samsum
|
Robinsh2023
| 2023-11-29T01:06:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:google/pegasus-cnn_dailymail",
"base_model:finetune:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-28T13:39:29Z |
---
base_model: google/pegasus-cnn_dailymail
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4861
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6724 | 0.54 | 500 | 1.4861 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.13.2
|
camfiander/bert-finetuned-prep
|
camfiander
| 2023-11-29T01:02:32Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"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"
] |
fill-mask
| 2023-11-29T01:00:08Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-prep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-prep
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:
- Loss: 0.0014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0012 | 1.0 | 258 | 0.0013 |
| 0.0002 | 2.0 | 516 | 0.0014 |
| 0.0003 | 3.0 | 774 | 0.0014 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.2
|
hkivancoral/hushem_5x_beit_base_sgd_00001_fold1
|
hkivancoral
| 2023-11-29T01:02:25Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T00:35:09Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_sgd_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.26666666666666666
---
<!-- 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. -->
# hushem_5x_beit_base_sgd_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5922
- Accuracy: 0.2667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4867 | 1.0 | 27 | 1.6071 | 0.2667 |
| 1.5392 | 2.0 | 54 | 1.6064 | 0.2667 |
| 1.5844 | 3.0 | 81 | 1.6056 | 0.2667 |
| 1.5797 | 4.0 | 108 | 1.6050 | 0.2667 |
| 1.5108 | 5.0 | 135 | 1.6044 | 0.2667 |
| 1.5236 | 6.0 | 162 | 1.6037 | 0.2667 |
| 1.5199 | 7.0 | 189 | 1.6031 | 0.2667 |
| 1.544 | 8.0 | 216 | 1.6026 | 0.2667 |
| 1.5317 | 9.0 | 243 | 1.6020 | 0.2667 |
| 1.537 | 10.0 | 270 | 1.6014 | 0.2667 |
| 1.5415 | 11.0 | 297 | 1.6010 | 0.2667 |
| 1.5478 | 12.0 | 324 | 1.6004 | 0.2667 |
| 1.4666 | 13.0 | 351 | 1.6000 | 0.2667 |
| 1.5352 | 14.0 | 378 | 1.5995 | 0.2667 |
| 1.478 | 15.0 | 405 | 1.5990 | 0.2667 |
| 1.5333 | 16.0 | 432 | 1.5986 | 0.2667 |
| 1.5245 | 17.0 | 459 | 1.5982 | 0.2667 |
| 1.5379 | 18.0 | 486 | 1.5978 | 0.2667 |
| 1.52 | 19.0 | 513 | 1.5975 | 0.2667 |
| 1.5508 | 20.0 | 540 | 1.5971 | 0.2667 |
| 1.5421 | 21.0 | 567 | 1.5967 | 0.2667 |
| 1.4919 | 22.0 | 594 | 1.5963 | 0.2667 |
| 1.483 | 23.0 | 621 | 1.5960 | 0.2667 |
| 1.5087 | 24.0 | 648 | 1.5957 | 0.2667 |
| 1.5236 | 25.0 | 675 | 1.5954 | 0.2667 |
| 1.5228 | 26.0 | 702 | 1.5951 | 0.2667 |
| 1.5439 | 27.0 | 729 | 1.5949 | 0.2667 |
| 1.5272 | 28.0 | 756 | 1.5946 | 0.2667 |
| 1.5029 | 29.0 | 783 | 1.5943 | 0.2667 |
| 1.5695 | 30.0 | 810 | 1.5941 | 0.2667 |
| 1.5057 | 31.0 | 837 | 1.5939 | 0.2667 |
| 1.5092 | 32.0 | 864 | 1.5937 | 0.2667 |
| 1.575 | 33.0 | 891 | 1.5935 | 0.2667 |
| 1.5175 | 34.0 | 918 | 1.5934 | 0.2667 |
| 1.4801 | 35.0 | 945 | 1.5932 | 0.2667 |
| 1.4771 | 36.0 | 972 | 1.5930 | 0.2667 |
| 1.5042 | 37.0 | 999 | 1.5929 | 0.2667 |
| 1.5372 | 38.0 | 1026 | 1.5928 | 0.2667 |
| 1.5158 | 39.0 | 1053 | 1.5927 | 0.2667 |
| 1.4902 | 40.0 | 1080 | 1.5926 | 0.2667 |
| 1.4904 | 41.0 | 1107 | 1.5925 | 0.2667 |
| 1.4817 | 42.0 | 1134 | 1.5924 | 0.2667 |
| 1.5064 | 43.0 | 1161 | 1.5923 | 0.2667 |
| 1.4625 | 44.0 | 1188 | 1.5923 | 0.2667 |
| 1.5064 | 45.0 | 1215 | 1.5923 | 0.2667 |
| 1.4956 | 46.0 | 1242 | 1.5922 | 0.2667 |
| 1.502 | 47.0 | 1269 | 1.5922 | 0.2667 |
| 1.495 | 48.0 | 1296 | 1.5922 | 0.2667 |
| 1.4896 | 49.0 | 1323 | 1.5922 | 0.2667 |
| 1.5118 | 50.0 | 1350 | 1.5922 | 0.2667 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
FranGMC/ppo-LunarLander-v2
|
FranGMC
| 2023-11-29T00:51:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T00:51:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 235.99 +/- 72.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
jlbaker361/fine-tune_addition_subtraction_decimal
|
jlbaker361
| 2023-11-29T00:49:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-11-18T14:18:08Z |
---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
rrw23/train8
|
rrw23
| 2023-11-29T00:44:11Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-28T20:06:35Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - rrw23/train8
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
Oufei123/third_try_v2
|
Oufei123
| 2023-11-29T00:43:17Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-large-v2",
"base_model:adapter:openai/whisper-large-v2",
"region:us"
] | null | 2023-11-29T00:43:07Z |
---
library_name: peft
base_model: openai/whisper-large-v2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
|
bradmin/reward-gpt-duplicate-answer-300
|
bradmin
| 2023-11-29T00:41:31Z | 17 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/polyglot-ko-1.3b",
"base_model:finetune:EleutherAI/polyglot-ko-1.3b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T23:58:23Z |
---
license: apache-2.0
base_model: EleutherAI/polyglot-ko-1.3b
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: reward-gpt-duplicate-answer-300
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reward-gpt-duplicate-answer-300
This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1708
- 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: 9e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 2023
- gradient_accumulation_steps: 10
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2699 | 0.17 | 100 | 0.2734 | 0.0 |
| 0.2827 | 0.34 | 200 | 0.2311 | 0.0 |
| 0.2001 | 0.51 | 300 | 0.1920 | 0.0 |
| 0.1955 | 0.69 | 400 | 0.1799 | 0.0 |
| 0.1334 | 0.86 | 500 | 0.1708 | 0.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
jsl28/ppo-Huggy
|
jsl28
| 2023-11-29T00:39:16Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-11-29T00:39:10Z |
---
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: jsl28/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hkivancoral/hushem_5x_beit_base_sgd_0001_fold5
|
hkivancoral
| 2023-11-29T00:33:23Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T00:04:52Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_sgd_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.3170731707317073
---
<!-- 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. -->
# hushem_5x_beit_base_sgd_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4856
- Accuracy: 0.3171
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5711 | 1.0 | 28 | 1.6258 | 0.2439 |
| 1.5362 | 2.0 | 56 | 1.6161 | 0.2439 |
| 1.5243 | 3.0 | 84 | 1.6077 | 0.2439 |
| 1.5675 | 4.0 | 112 | 1.5988 | 0.2439 |
| 1.5133 | 5.0 | 140 | 1.5920 | 0.2439 |
| 1.5639 | 6.0 | 168 | 1.5854 | 0.2439 |
| 1.555 | 7.0 | 196 | 1.5785 | 0.2439 |
| 1.5064 | 8.0 | 224 | 1.5727 | 0.2439 |
| 1.4878 | 9.0 | 252 | 1.5672 | 0.2439 |
| 1.5121 | 10.0 | 280 | 1.5615 | 0.2439 |
| 1.4492 | 11.0 | 308 | 1.5578 | 0.2439 |
| 1.5023 | 12.0 | 336 | 1.5529 | 0.2439 |
| 1.5035 | 13.0 | 364 | 1.5492 | 0.2439 |
| 1.4801 | 14.0 | 392 | 1.5454 | 0.2439 |
| 1.4838 | 15.0 | 420 | 1.5419 | 0.2683 |
| 1.4587 | 16.0 | 448 | 1.5385 | 0.2683 |
| 1.4655 | 17.0 | 476 | 1.5343 | 0.2683 |
| 1.4244 | 18.0 | 504 | 1.5315 | 0.2927 |
| 1.4339 | 19.0 | 532 | 1.5284 | 0.2927 |
| 1.4266 | 20.0 | 560 | 1.5249 | 0.2927 |
| 1.4474 | 21.0 | 588 | 1.5220 | 0.2927 |
| 1.4652 | 22.0 | 616 | 1.5188 | 0.3171 |
| 1.4621 | 23.0 | 644 | 1.5163 | 0.3171 |
| 1.4655 | 24.0 | 672 | 1.5146 | 0.3171 |
| 1.4192 | 25.0 | 700 | 1.5130 | 0.3171 |
| 1.4459 | 26.0 | 728 | 1.5105 | 0.3171 |
| 1.469 | 27.0 | 756 | 1.5090 | 0.3171 |
| 1.3585 | 28.0 | 784 | 1.5067 | 0.3171 |
| 1.4084 | 29.0 | 812 | 1.5049 | 0.3171 |
| 1.4047 | 30.0 | 840 | 1.5031 | 0.3171 |
| 1.4414 | 31.0 | 868 | 1.5013 | 0.3171 |
| 1.3836 | 32.0 | 896 | 1.4995 | 0.3171 |
| 1.3896 | 33.0 | 924 | 1.4979 | 0.3171 |
| 1.4222 | 34.0 | 952 | 1.4964 | 0.3171 |
| 1.4396 | 35.0 | 980 | 1.4952 | 0.3171 |
| 1.3891 | 36.0 | 1008 | 1.4939 | 0.3171 |
| 1.393 | 37.0 | 1036 | 1.4925 | 0.3171 |
| 1.3697 | 38.0 | 1064 | 1.4914 | 0.3171 |
| 1.4252 | 39.0 | 1092 | 1.4901 | 0.3171 |
| 1.365 | 40.0 | 1120 | 1.4892 | 0.3171 |
| 1.4164 | 41.0 | 1148 | 1.4883 | 0.3171 |
| 1.3854 | 42.0 | 1176 | 1.4876 | 0.3171 |
| 1.3744 | 43.0 | 1204 | 1.4870 | 0.3171 |
| 1.4041 | 44.0 | 1232 | 1.4865 | 0.3171 |
| 1.3952 | 45.0 | 1260 | 1.4861 | 0.3171 |
| 1.3758 | 46.0 | 1288 | 1.4858 | 0.3171 |
| 1.3986 | 47.0 | 1316 | 1.4857 | 0.3171 |
| 1.3628 | 48.0 | 1344 | 1.4856 | 0.3171 |
| 1.4108 | 49.0 | 1372 | 1.4856 | 0.3171 |
| 1.4199 | 50.0 | 1400 | 1.4856 | 0.3171 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
cyuzhang/rank4_lr6_batch4_10k
|
cyuzhang
| 2023-11-29T00:28:43Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:26:51Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr6_batch4_10k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
Realgon/bert_twitterfin_padding40model
|
Realgon
| 2023-11-29T00:28:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"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
| 2023-11-29T00:08:35Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_twitterfin_padding40model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_twitterfin_padding40model
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:
- Loss: 1.1063
- Accuracy: 0.8819
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7718 | 1.0 | 597 | 0.4637 | 0.8208 |
| 0.4046 | 2.0 | 1194 | 0.3366 | 0.8765 |
| 0.2833 | 3.0 | 1791 | 0.3967 | 0.8836 |
| 0.1856 | 4.0 | 2388 | 0.5273 | 0.8790 |
| 0.1388 | 5.0 | 2985 | 0.6270 | 0.8786 |
| 0.0609 | 6.0 | 3582 | 0.7756 | 0.8585 |
| 0.0517 | 7.0 | 4179 | 0.7692 | 0.8773 |
| 0.0324 | 8.0 | 4776 | 0.7837 | 0.8798 |
| 0.031 | 9.0 | 5373 | 0.8253 | 0.8773 |
| 0.0253 | 10.0 | 5970 | 0.8893 | 0.8823 |
| 0.0133 | 11.0 | 6567 | 0.9943 | 0.8802 |
| 0.0119 | 12.0 | 7164 | 0.9277 | 0.8786 |
| 0.0148 | 13.0 | 7761 | 1.0189 | 0.8836 |
| 0.0051 | 14.0 | 8358 | 1.0542 | 0.8790 |
| 0.005 | 15.0 | 8955 | 1.0600 | 0.8802 |
| 0.0055 | 16.0 | 9552 | 1.0521 | 0.8794 |
| 0.0052 | 17.0 | 10149 | 1.0653 | 0.8777 |
| 0.0021 | 18.0 | 10746 | 1.0891 | 0.8832 |
| 0.002 | 19.0 | 11343 | 1.1045 | 0.8811 |
| 0.0039 | 20.0 | 11940 | 1.1063 | 0.8819 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
cyuzhang/rank4_lr6_batch4_3k
|
cyuzhang
| 2023-11-29T00:23:00Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:22:11Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr6_batch4_3k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
KRayRay/layoutlm-funsd
|
KRayRay
| 2023-11-29T00:22:02Z | 78 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-28T23:52:12Z |
---
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.2750
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0 | 1.0 | 19 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 2.0 | 38 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 3.0 | 57 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 4.0 | 76 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 5.0 | 95 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 6.0 | 114 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 7.0 | 133 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 8.0 | 152 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 9.0 | 171 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 10.0 | 190 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 11.0 | 209 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 12.0 | 228 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 13.0 | 247 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 14.0 | 266 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
| 0.0 | 15.0 | 285 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065} | 0.0 | 0.0 | 0.0 | 0.2750 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231123
- Datasets 2.15.0
- Tokenizers 0.15.0
|
cyuzhang/rank4_lr4_batch16_3k
|
cyuzhang
| 2023-11-29T00:20:18Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:19:24Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr4_batch16_3k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
cyuzhang/rank4_lr4_batch16_6k
|
cyuzhang
| 2023-11-29T00:17:51Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:17:00Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr4_batch16_6k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
imi2/goliath-120b-f16-gguf
|
imi2
| 2023-11-29T00:15:47Z | 0 | 0 | null |
[
"conversational",
"en",
"license:llama2",
"region:us"
] |
text-generation
| 2023-11-28T13:27:50Z |
---
license: llama2
language:
- en
pipeline_tag: conversational
---
# 16-bit precision GGUF version of goliath-120b
- join these model parts with `cat goliath-120b-f16.gguf* > goliath-120b-f16.gguf`
---
# Goliath 120B
An auto-regressive causal LM created by combining 2x finetuned [Llama-2 70B](https://huggingface.co/meta-llama/llama-2-70b-hf) into one.
Please check out the quantized formats provided by [@TheBloke](https:///huggingface.co/TheBloke) and [@Panchovix](https://huggingface.co/Panchovix):
- [GGUF](https://huggingface.co/TheBloke/goliath-120b-GGUF) (llama.cpp)
- [GPTQ](https://huggingface.co/TheBloke/goliath-120b-GPTQ) (KoboldAI, TGW, Aphrodite)
- [AWQ](https://huggingface.co/TheBloke/goliath-120b-AWQ) (TGW, Aphrodite, vLLM)
- [Exllamav2](https://huggingface.co/Panchovix/goliath-120b-exl2) (TGW, KoboldAI)
# Prompting Format
Both Vicuna and Alpaca will work, but due the initial and final layers belonging primarily to Xwin, I expect Vicuna to work the best.
# Merge process
The models used in the merge are [Xwin](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) and [Euryale](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B).
The layer ranges used are as follows:
```yaml
- range 0, 16
Xwin
- range 8, 24
Euryale
- range 17, 32
Xwin
- range 25, 40
Euryale
- range 33, 48
Xwin
- range 41, 56
Euryale
- range 49, 64
Xwin
- range 57, 72
Euryale
- range 65, 80
Xwin
```
# Screenshots
# Benchmarks
Coming soon.
# Acknowledgements
Credits goes to [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).
Special thanks to [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios.
|
cyuzhang/rank4_lr4_batch16_10k
|
cyuzhang
| 2023-11-29T00:14:54Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:14:00Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr4_batch16_10k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
elvis92/pets_rank_8_val
|
elvis92
| 2023-11-29T00:14:37Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-28T20:34:36Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - elvis92/pets_rank_8_val
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
cyuzhang/rank4_lr4_batch1_9.5k
|
cyuzhang
| 2023-11-29T00:11:16Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:10:25Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr4_batch1_9.5k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
stevendee5/base-model
|
stevendee5
| 2023-11-29T00:08:35Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-27T23:31:08Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: base-model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.8791946308724832
---
<!-- 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. -->
# base-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3648
- Accuracy: 0.88
- F1: 0.8792
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Realgon/bert_twitterfin_padding30model
|
Realgon
| 2023-11-29T00:08:32Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"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
| 2023-11-28T23:50:21Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_twitterfin_padding30model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_twitterfin_padding30model
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:
- Loss: 1.0811
- Accuracy: 0.8857
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5721 | 1.0 | 597 | 0.3568 | 0.8735 |
| 0.3173 | 2.0 | 1194 | 0.3192 | 0.8848 |
| 0.2143 | 3.0 | 1791 | 0.4767 | 0.8760 |
| 0.1357 | 4.0 | 2388 | 0.6198 | 0.8832 |
| 0.1059 | 5.0 | 2985 | 0.6062 | 0.8811 |
| 0.05 | 6.0 | 3582 | 0.7149 | 0.8819 |
| 0.0381 | 7.0 | 4179 | 0.8061 | 0.8848 |
| 0.0309 | 8.0 | 4776 | 0.7961 | 0.8815 |
| 0.0315 | 9.0 | 5373 | 0.8086 | 0.8802 |
| 0.0214 | 10.0 | 5970 | 0.8231 | 0.8924 |
| 0.0178 | 11.0 | 6567 | 0.8589 | 0.8861 |
| 0.0127 | 12.0 | 7164 | 0.9441 | 0.8853 |
| 0.0129 | 13.0 | 7761 | 0.9523 | 0.8899 |
| 0.0102 | 14.0 | 8358 | 1.0047 | 0.8848 |
| 0.009 | 15.0 | 8955 | 1.0004 | 0.8882 |
| 0.0047 | 16.0 | 9552 | 1.0421 | 0.8848 |
| 0.0049 | 17.0 | 10149 | 1.0416 | 0.8865 |
| 0.0035 | 18.0 | 10746 | 1.0695 | 0.8869 |
| 0.0021 | 19.0 | 11343 | 1.0858 | 0.8844 |
| 0.003 | 20.0 | 11940 | 1.0811 | 0.8857 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
rrw23/train9
|
rrw23
| 2023-11-29T00:07:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-28T21:12:43Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - rrw23/train9
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
cyuzhang/rank4_lr4_batch1_6k
|
cyuzhang
| 2023-11-29T00:05:37Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T00:04:47Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr4_batch1_6k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
gmazur591/falcon-7b-instruct-ft-adapters
|
gmazur591
| 2023-11-29T00:02:17Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"falcon",
"custom_code",
"arxiv:1910.09700",
"base_model:tiiuae/falcon-7b-instruct",
"base_model:adapter:tiiuae/falcon-7b-instruct",
"region:us"
] | null | 2023-11-26T22:25:31Z |
---
library_name: peft
base_model: tiiuae/falcon-7b-instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
|
StringCheese/distilbert-base-uncased-lora-text-classification
|
StringCheese
| 2023-11-29T00:02:01Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T22:28:01Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-lora-text-classification
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: 0.8839
- Accuracy: {'accuracy': 0.901}
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.3334 | {'accuracy': 0.892} |
| 0.3999 | 2.0 | 500 | 0.3850 | {'accuracy': 0.892} |
| 0.3999 | 3.0 | 750 | 0.4382 | {'accuracy': 0.895} |
| 0.2004 | 4.0 | 1000 | 0.5518 | {'accuracy': 0.895} |
| 0.2004 | 5.0 | 1250 | 0.6261 | {'accuracy': 0.899} |
| 0.0674 | 6.0 | 1500 | 0.8357 | {'accuracy': 0.892} |
| 0.0674 | 7.0 | 1750 | 0.8303 | {'accuracy': 0.901} |
| 0.0301 | 8.0 | 2000 | 0.8756 | {'accuracy': 0.894} |
| 0.0301 | 9.0 | 2250 | 0.8779 | {'accuracy': 0.897} |
| 0.0028 | 10.0 | 2500 | 0.8839 | {'accuracy': 0.901} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.15.0
|
elvis92/pets_rank_2_val
|
elvis92
| 2023-11-28T23:54:59Z | 0 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-28T06:02:21Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - elvis92/pets_rank_2_val
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
cyuzhang/rank4_lr4_batch4_3k
|
cyuzhang
| 2023-11-28T23:53:43Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-28T23:51:17Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr4_batch4_3k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
vicfeuga/ppo-SnowballTarget
|
vicfeuga
| 2023-11-28T23:52:38Z | 17 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-11-28T23:52:33Z |
---
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: vicfeuga/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Realgon/bert_twitterfin_padding20model
|
Realgon
| 2023-11-28T23:50:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"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
| 2023-11-28T23:32:42Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_twitterfin_padding20model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_twitterfin_padding20model
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:
- Loss: 1.0153
- Accuracy: 0.8865
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6193 | 1.0 | 597 | 0.3863 | 0.8597 |
| 0.3289 | 2.0 | 1194 | 0.3259 | 0.8765 |
| 0.2266 | 3.0 | 1791 | 0.4277 | 0.8790 |
| 0.1408 | 4.0 | 2388 | 0.5860 | 0.8827 |
| 0.0999 | 5.0 | 2985 | 0.6335 | 0.8823 |
| 0.0371 | 6.0 | 3582 | 0.7146 | 0.8882 |
| 0.0368 | 7.0 | 4179 | 0.7644 | 0.8794 |
| 0.0326 | 8.0 | 4776 | 0.7843 | 0.8840 |
| 0.0211 | 9.0 | 5373 | 0.8496 | 0.8794 |
| 0.0246 | 10.0 | 5970 | 0.8321 | 0.8865 |
| 0.0146 | 11.0 | 6567 | 0.8637 | 0.8786 |
| 0.0094 | 12.0 | 7164 | 0.9359 | 0.8844 |
| 0.0149 | 13.0 | 7761 | 0.8658 | 0.8857 |
| 0.0077 | 14.0 | 8358 | 0.9680 | 0.8840 |
| 0.0065 | 15.0 | 8955 | 0.9877 | 0.8903 |
| 0.0038 | 16.0 | 9552 | 0.9742 | 0.8827 |
| 0.0031 | 17.0 | 10149 | 0.9920 | 0.8861 |
| 0.0017 | 18.0 | 10746 | 1.0075 | 0.8903 |
| 0.0037 | 19.0 | 11343 | 1.0174 | 0.8857 |
| 0.0008 | 20.0 | 11940 | 1.0153 | 0.8865 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
vladmandic/temporaldiff
|
vladmandic
| 2023-11-28T23:49:33Z | 9 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"license:openrail",
"region:us"
] | null | 2023-11-28T23:40:08Z |
---
license: openrail
---
Copy of <https://huggingface.co/CiaraRowles/TemporalDiff> in Huggingface Diffusers format so it can be loaded directly using `MotionAdapter.from_pretrained`
|
cyuzhang/rank4_lr4_batch4_9.5k
|
cyuzhang
| 2023-11-28T23:48:11Z | 0 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-28T23:47:21Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - cyuzhang/rank4_lr4_batch4_9.5k
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the pcuenq/oxford-pets dataset. You can find some example images in the following.
|
devvanshhh/flan-xl-gen6
|
devvanshhh
| 2023-11-28T23:43:07Z | 79 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ybelkada/flan-t5-xl-sharded-bf16",
"base_model:quantized:ybelkada/flan-t5-xl-sharded-bf16",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text2text-generation
| 2023-11-28T18:17:25Z |
---
base_model: ybelkada/flan-t5-xl-sharded-bf16
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-xl-gen6
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-xl-gen6
This model is a fine-tuned version of [ybelkada/flan-t5-xl-sharded-bf16](https://huggingface.co/ybelkada/flan-t5-xl-sharded-bf16) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4978
- Rouge1: 29.5362
- Rouge2: 20.6621
- Rougel: 25.7689
- Rougelsum: 26.2351
- Gen Len: 12.7388
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 800
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 328 | 0.6921 | 34.9112 | 26.7503 | 31.4124 | 31.7295 | 10.0172 |
| 6.8746 | 2.0 | 656 | 0.6025 | 33.9134 | 25.3236 | 30.1968 | 30.472 | 10.8454 |
| 6.8746 | 3.0 | 984 | 0.5687 | 31.6178 | 22.9463 | 27.8758 | 28.3572 | 11.8729 |
| 0.6462 | 4.0 | 1312 | 0.5355 | 30.8157 | 22.1783 | 27.1641 | 27.569 | 12.1306 |
| 0.5618 | 5.0 | 1640 | 0.5160 | 29.9183 | 21.0842 | 26.1671 | 26.5965 | 12.5017 |
| 0.5618 | 6.0 | 1968 | 0.5025 | 29.7823 | 21.1443 | 26.0286 | 26.5215 | 12.5086 |
| 0.498 | 7.0 | 2296 | 0.4978 | 29.1043 | 20.2391 | 25.3347 | 25.804 | 12.8969 |
| 0.4551 | 8.0 | 2624 | 0.4978 | 29.5362 | 20.6621 | 25.7689 | 26.2351 | 12.7388 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.