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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 00:41:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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4.05k
| pipeline_tag
stringclasses 55
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zwang-am/Llama-2-7b-chat-hf-ft-adapters
|
zwang-am
| 2023-08-18T15:53:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:34:05Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
ThuyNT03/xlm-roberta-base-Mixed-insert-vi
|
ThuyNT03
| 2023-08-18T15:50:29Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T15:21:59Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Mixed-insert-vi
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-base-Mixed-insert-vi
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0718
- Accuracy: 0.8169
- F1: 0.8133
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7982 | 1.0 | 213 | 0.6740 | 0.7625 | 0.7024 |
| 0.5276 | 2.0 | 426 | 0.5662 | 0.7943 | 0.7859 |
| 0.4071 | 3.0 | 639 | 0.5453 | 0.7958 | 0.7929 |
| 0.3311 | 4.0 | 852 | 0.5844 | 0.8094 | 0.8007 |
| 0.2695 | 5.0 | 1065 | 0.5819 | 0.8230 | 0.8221 |
| 0.2226 | 6.0 | 1278 | 0.7325 | 0.8200 | 0.8144 |
| 0.1826 | 7.0 | 1491 | 0.8314 | 0.8124 | 0.8070 |
| 0.1469 | 8.0 | 1704 | 0.9560 | 0.8154 | 0.8124 |
| 0.1397 | 9.0 | 1917 | 1.0850 | 0.8169 | 0.8105 |
| 0.1231 | 10.0 | 2130 | 1.0718 | 0.8169 | 0.8133 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Sudh/ppp
|
Sudh
| 2023-08-18T15:45:36Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-05-28T08:37:01Z |
---
license: bigscience-openrail-m
---
|
Basu03/distilbert-stock-tweet-sentiment-analysis
|
Basu03
| 2023-08-18T15:26:46Z | 182 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"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
| 2023-08-18T04:58:37Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-stock-tweet-sentiment-analysis
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-stock-tweet-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6226
- Accuracy: 0.775
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6916 | 1.0 | 1000 | 0.5972 | 0.7635 |
| 0.4853 | 2.0 | 2000 | 0.5726 | 0.7725 |
| 0.3683 | 3.0 | 3000 | 0.6226 | 0.775 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
digiplay/fantasticmix2.5D_v4.5
|
digiplay
| 2023-08-18T15:12:16Z | 496 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-18T12:41:59Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/20632?modelVersionId=143050
|
Pierre-Arthur/T5_small_eurlexsum_8Epochs
|
Pierre-Arthur
| 2023-08-18T15:09:41Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eur-lex-sum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-22T22:21:15Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- eur-lex-sum
metrics:
- rouge
model-index:
- name: T5_small_eurlexsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eur-lex-sum
type: eur-lex-sum
config: french
split: test
args: french
metrics:
- name: Rouge1
type: rouge
value: 0.2288
---
<!-- 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. -->
# T5_small_eurlexsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eur-lex-sum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9360
- Rouge1: 0.2288
- Rouge2: 0.1816
- Rougel: 0.2157
- Rougelsum: 0.2158
- Gen Len: 19.0
## 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 71 | 1.4482 | 0.1743 | 0.0982 | 0.1509 | 0.1511 | 19.0 |
| No log | 2.0 | 142 | 1.1661 | 0.193 | 0.1257 | 0.1731 | 0.1734 | 19.0 |
| No log | 3.0 | 213 | 1.0651 | 0.2072 | 0.1483 | 0.1892 | 0.1896 | 19.0 |
| No log | 4.0 | 284 | 1.0053 | 0.2167 | 0.1638 | 0.2017 | 0.2019 | 19.0 |
| No log | 5.0 | 355 | 0.9706 | 0.222 | 0.1731 | 0.2082 | 0.2079 | 19.0 |
| No log | 6.0 | 426 | 0.9510 | 0.2253 | 0.1771 | 0.2114 | 0.2114 | 19.0 |
| No log | 7.0 | 497 | 0.9393 | 0.2263 | 0.1785 | 0.2134 | 0.2133 | 19.0 |
| 1.4549 | 8.0 | 568 | 0.9360 | 0.2288 | 0.1816 | 0.2157 | 0.2158 | 19.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
BenjaminOcampo/model-contrastive-bert__trained-in-ihc__seed-42
|
BenjaminOcampo
| 2023-08-18T15:04:22Z | 4 | 0 |
transformers
|
[
"transformers",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T15:03:32Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-ihc__seed-42
<!-- 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:** BenjaminOcampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **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]
|
Jingya/finbert-tone
|
Jingya
| 2023-08-18T15:01:21Z | 3 | 0 |
transformers
|
[
"transformers",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T14:48:49Z |
[`yiyanghkust/finbert-tone`](https://huggingface.co/yiyanghkust/finbert-tone) compiled for neuronx.
|
BenjaminOcampo/model-contrastive-bert__trained-in-ihc__seed-3
|
BenjaminOcampo
| 2023-08-18T15:00:35Z | 3 | 0 |
transformers
|
[
"transformers",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T14:59:40Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-ihc__seed-3
<!-- 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:** BenjaminOcampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **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]
|
ammag/poca-SoccerTwos
|
ammag
| 2023-08-18T14:53:17Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-08-18T14:53:07Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ammag/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
zarakiquemparte/zarafusionix-l2-7b
|
zarakiquemparte
| 2023-08-18T14:50:15Z | 1,482 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-18T13:35:55Z |
---
license: other
tags:
- llama2
---
# Model Card: Zarafusionix L2 7b
This model uses [Nous Hermes Llama2 7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) (62%) as a base with [Stable Beluga 7b](https://huggingface.co/stabilityai/StableBeluga-7B) (38%) and the result of this merge was merged with [LimaRP LLama2 7B Lora](https://huggingface.co/lemonilia/limarp-llama2).
This merge of models(hermes and stable beluga) was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/merge-cli.py)
This merge of Lora with Model was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/apply-lora.py)
Merge illustration:
## Usage:
Since this is a merge between Nous Hermes, Stable Beluga and LimaRP, the following instruction formats should work:
Alpaca 2:
```
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
LimaRP instruction format:
```
<<SYSTEM>>
<character card and system prompt>
<<USER>>
<prompt>
<<AIBOT>>
<leave a newline blank for model to respond>
```
## Bias, Risks, and Limitations
This model is not intended for supplying factual information or advice in any form
## Training Details
This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.
|
agoyal496/dqn-SpaceInvadersNoFrameskip-v4
|
agoyal496
| 2023-08-18T14:39:19Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T14:38:40Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 679.00 +/- 259.00
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga agoyal496 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga agoyal496 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga agoyal496
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
MythicalStats/stats
|
MythicalStats
| 2023-08-18T14:33:24Z | 0 | 0 |
transformers
|
[
"transformers",
"art",
"en",
"dataset:mythicalstats/videos",
"license:openrail",
"endpoints_compatible",
"region:us"
] | null | 2023-08-18T14:12:26Z |
---
license: openrail
datasets:
- mythicalstats/videos
language:
- en
metrics:
- accuracy
- bertscore
library_name: transformers
tags:
- art
---
|
digiplay/fantasticmix2.5D_v4.0
|
digiplay
| 2023-08-18T14:28:29Z | 626 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-10T19:22:54Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info :
https://civitai.com/models/20632?modelVersionId=137923
Sample images:
other samples:
https://huggingface.co/digiplay/fantasticmix2.5D_v4.0/discussions/2
|
wr0124/q-FrozenLake-v1-4x4-noSlippery
|
wr0124
| 2023-08-18T14:17:07Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T14:17:04Z |
---
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="wr0124/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"])
```
|
zarakiquemparte/zarafusionix-l2-7b-GGML
|
zarakiquemparte
| 2023-08-18T14:09:18Z | 0 | 1 | null |
[
"llama2",
"license:other",
"region:us"
] | null | 2023-08-18T13:36:13Z |
---
license: other
tags:
- llama2
---
Quantized GGML of [Zarafusionix L2 7b](https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b)
|
JRobertson816/new_model
|
JRobertson816
| 2023-08-18T14:05:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"lilt",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-11T13:21:29Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: new_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. -->
# new_model
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0.dev20230810
- Datasets 2.14.4
- Tokenizers 0.11.0
|
Ranjit/llama_v2_or
|
Ranjit
| 2023-08-18T14:04:23Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"region:us"
] | null | 2023-08-16T01:03:39Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
digitalpipelines/llama2_7b_chat_uncensored-GPTQ
|
digitalpipelines
| 2023-08-18T14:02:31Z | 6 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"digitalpipelines",
"dataset:wikitext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-17T18:44:15Z |
---
license: apache-2.0
datasets:
- wikitext
tags:
- digitalpipelines
---
# Overview
quantized GPTQ model of [digitalpipelines/llama2_7b_chat_uncensored](https://huggingface.co/digitalpipelines/llama2_7b_chat_uncensored)
# Prompt style
The model was trained with the following prompt style:
```
### HUMAN:
Hello
### RESPONSE:
Hi, how are you?
### HUMAN:
I'm fine.
### RESPONSE:
How can I help you?
...
```
|
zarakiquemparte/zarafusionex-l2-7b
|
zarakiquemparte
| 2023-08-18T13:45:36Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-18T12:21:00Z |
---
license: other
tags:
- llama2
---
# Model Card: Zarafusionex L2 7b
This model uses [Nous Hermes Llama2 7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) (53%) as a base with [Stable Beluga 7b](https://huggingface.co/stabilityai/StableBeluga-7B) (47%) and the result of this merge was merged with [LimaRP LLama2 7B Lora](https://huggingface.co/lemonilia/limarp-llama2).
This merge of models(hermes and stable beluga) was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/merge-cli.py)
This merge of Lora with Model was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/apply-lora.py)
Merge illustration:
## Usage:
Since this is a merge between Nous Hermes, Stable Beluga and LimaRP, the following instruction formats should work:
Alpaca 2:
```
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
LimaRP instruction format:
```
<<SYSTEM>>
<character card and system prompt>
<<USER>>
<prompt>
<<AIBOT>>
<leave a newline blank for model to respond>
```
## Bias, Risks, and Limitations
This model is not intended for supplying factual information or advice in any form
## Training Details
This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.
|
Henfrey/i-n-d-o
|
Henfrey
| 2023-08-18T13:44:31Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"region:us"
] |
text-to-image
| 2023-08-18T13:05:42Z |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
pipeline_tag: text-to-image
---
|
diegomiranda/text-to-cypher
|
diegomiranda
| 2023-08-18T13:22:39Z | 269 | 3 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-17T01:11:05Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-70m-deduped-v0](https://huggingface.co/EleutherAI/pythia-70m-deduped-v0)
## Usage on CPU
```bash
pip install transformers==4.30.2
pip install accelerate==0.20.3
pip install torch==2.0.1
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
def generate_response(prompt, model_name):
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float32,
device_map={"": "cpu"},
trust_remote_code=True,
)
model.cpu().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cpu")
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=500,
do_sample=False,
num_beams=2,
temperature=float(0.0),
repetition_penalty=float(1.0),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
return answer
```
Once you've defined the function, you can proceed to set up the prompt and the model
```python
model_name = "diegomiranda/text-to-cypher"
prompt = "Create a Cypher statement to answer the following question:Retorne os processos de Direito Tributário que se baseiam em lei 939 de 1992?<|endoftext|>"
response = generate_response(prompt, model_name)
print(response)
```
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.31.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="diegomiranda/text-to-cypher",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=500,
do_sample=False,
num_beams=2,
temperature=float(0.0),
repetition_penalty=float(1.0),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
Why is drinking water so healthy?<|endoftext|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"diegomiranda/text-to-cypher",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"diegomiranda/text-to-cypher",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=500,
do_sample=False,
num_beams=2,
temperature=float(0.0),
repetition_penalty=float(1.0),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "diegomiranda/text-to-cypher" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "How are you?<|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=500,
do_sample=False,
num_beams=2,
temperature=float(0.0),
repetition_penalty=float(1.0),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 512)
(emb_dropout): Dropout(p=0.0, inplace=False)
(layers): ModuleList(
(0-5): 6 x GPTNeoXLayer(
(input_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(post_attention_dropout): Dropout(p=0.0, inplace=False)
(post_mlp_dropout): Dropout(p=0.0, inplace=False)
(attention): GPTNeoXAttention(
(rotary_emb): GPTNeoXRotaryEmbedding()
(query_key_value): Linear(in_features=512, out_features=1536, bias=True)
(dense): Linear(in_features=512, out_features=512, bias=True)
(attention_dropout): Dropout(p=0.0, inplace=False)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=512, out_features=2048, bias=True)
(dense_4h_to_h): Linear(in_features=2048, out_features=512, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=512, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
kejolong/sexyattire
|
kejolong
| 2023-08-18T13:22:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-14T18:01:12Z |
---
license: creativeml-openrail-m
---
|
Xiaobai1231/Llama2-LoRA-MBTI
|
Xiaobai1231
| 2023-08-18T13:22:20Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T13:15:53Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.4.0
|
MUTSC/a2c-PandaPickAndPlace-v3
|
MUTSC
| 2023-08-18T13:19:06Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T13:13:44Z |
---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
RazinAleks/llama-7b-hf-LoRa-API_USAGE_sentiment-fp16
|
RazinAleks
| 2023-08-18T13:05:01Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T13:04:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
crisschez/opt-6.7b-lora
|
crisschez
| 2023-08-18T13:02:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T13:00:07Z |
---
library_name: peft
---
## 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 0.5.0.dev0
|
RazinAleks/llama-7b-hf-LoRa-Other_class-fp16
|
RazinAleks
| 2023-08-18T12:52:07Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T12:52:02Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
Aansh123/test_trainer
|
Aansh123
| 2023-08-18T12:46:16Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"Analyzation",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-16T12:32:20Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- Analyzation
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer
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. -->
# test_trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4916
- Accuracy: 0.659
## Model description
This model is a fine-tuned model based on Sentiment Analysis or Text Classification for reviews based on the new 'Threads' app.
The reviews dataset can be found on Kaggle.
## Intended uses & limitations
Basically it converts the review text into rating points from 1-5(1 being a very bad review and 5 being a very good review)
## Training and evaluation data
'Reviews' dataset(Thread) from Kaggle.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 250 | 1.0560 | 0.6895 |
| 0.5502 | 2.0 | 500 | 1.3548 | 0.6595 |
| 0.5502 | 3.0 | 750 | 1.4916 | 0.659 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
RazinAleks/llama-7b-hf-LoRa-Net_API_class-fp16
|
RazinAleks
| 2023-08-18T12:44:53Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T12:44:47Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
RazinAleks/llama-7b-hf-LoRa-GUI_class-fp16
|
RazinAleks
| 2023-08-18T12:43:07Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T12:42:23Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
EliKet/SdXL
|
EliKet
| 2023-08-18T12:36:14Z | 2 | 1 |
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
| 2023-08-18T10:31:42Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a model
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
Hemanth-thunder/english-tamil-mt
|
Hemanth-thunder
| 2023-08-18T12:33:59Z | 152 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"m2m_100",
"text2text-generation",
"Translation",
"translation",
"en",
"ta",
"dataset:Hemanth-thunder/en_ta",
"arxiv:1910.09700",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-08-13T06:40:04Z |
---
license: openrail
datasets:
- Hemanth-thunder/en_ta
metrics:
- sacrebleu
- bleu
verified: true
pipeline_tag: translation
tags:
- Translation
widget:
- text: Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
- text: The two men running tea shop
language:
- en
- ta
inference:
parameters:
src_lang : en
tgt_lang : ta
---
# 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:** Hemanth Kumar
- **Model type:** Machine Translation
- **Language(s) (NLP):** Tamil ,English
- **License:** OpenRAIL
- **Finetuned from model [M2M100]:** M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
## 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]
|
basgalupp/distilbert-base-uncased-finetuned-cola
|
basgalupp
| 2023-08-18T12:33:55Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T09:58:40Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.4957241515216811
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7078
- Matthews Correlation: 0.4957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.2032 | 1.0 | 535 | 0.7078 | 0.4957 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
anshuls235/distilbert-base-uncased-finetuned-emotion
|
anshuls235
| 2023-08-18T12:33:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T11:20:11Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9263522602960652
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2138
- Accuracy: 0.9265
- F1: 0.9264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8371 | 1.0 | 250 | 0.3217 | 0.917 | 0.9163 |
| 0.2548 | 2.0 | 500 | 0.2138 | 0.9265 | 0.9264 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Ziye-Thomas/ppo-LunarLander-v2
|
Ziye-Thomas
| 2023-08-18T12:29:07Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T12:28:48Z |
---
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: 275.17 +/- 14.91
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
...
```
|
jerome1519/flan-t5-large-finetuned-coding_instructions_2023_08_18__12_06
|
jerome1519
| 2023-08-18T12:14:51Z | 101 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-large",
"base_model:finetune:google/flan-t5-large",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-18T12:06:46Z |
---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-large-finetuned-coding_instructions_2023_08_18__12_06
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-large-finetuned-coding_instructions_2023_08_18__12_06
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6230
- Rouge1: 47.0864
- Rouge2: 31.2968
- Rougel: 45.9675
- Rougelsum: 46.0612
- Gen Len: 19.0
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 10 | 0.9891 | 18.047 | 9.6197 | 18.1466 | 18.2622 | 16.9538 |
| No log | 2.0 | 20 | 0.7803 | 21.724 | 12.8839 | 21.4666 | 21.6773 | 17.7385 |
| No log | 3.0 | 30 | 0.6827 | 42.1883 | 27.0064 | 41.5285 | 41.6611 | 18.9077 |
| No log | 4.0 | 40 | 0.6526 | 44.8257 | 28.8931 | 43.8323 | 43.7858 | 18.9846 |
| No log | 5.0 | 50 | 0.6407 | 44.6781 | 29.5477 | 43.9053 | 43.8475 | 19.0 |
| No log | 6.0 | 60 | 0.6334 | 46.039 | 31.3315 | 45.3508 | 45.3701 | 19.0 |
| No log | 7.0 | 70 | 0.6281 | 46.8592 | 31.2186 | 46.1283 | 46.1169 | 19.0 |
| No log | 8.0 | 80 | 0.6250 | 46.5201 | 30.8844 | 45.5541 | 45.6876 | 19.0 |
| No log | 9.0 | 90 | 0.6236 | 47.074 | 31.2968 | 46.1336 | 46.258 | 19.0 |
| No log | 10.0 | 100 | 0.6230 | 47.0864 | 31.2968 | 45.9675 | 46.0612 | 19.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Andyrasika/qlora-2-7b-andy
|
Andyrasika
| 2023-08-18T12:09:32Z | 0 | 0 |
transformers
|
[
"transformers",
"peft ",
"text-generation",
"en",
"dataset:Andyrasika/Ecommerce_FAQ",
"license:creativeml-openrail-m",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-31T17:25:41Z |
---
license: creativeml-openrail-m
datasets:
- Andyrasika/Ecommerce_FAQ
language:
- en
library_name: transformers
pipeline_tag: text-generation
metrics:
- accuracy
tags:
- transformers
- 'peft '
---
|
antonioalvarado/text_analyzer_albert-new
|
antonioalvarado
| 2023-08-18T12:08:27Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T11:40:03Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: text_analyzer_albert-new
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. -->
# text_analyzer_albert-new
This model is a fine-tuned version of [/home/antonio/code/trainer-latest/text.analyzer.trainer/src/resource/model/config.json](https://huggingface.co//home/antonio/code/trainer-latest/text.analyzer.trainer/src/resource/model/config.json) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2244
- Accuracy: 0.3102
## 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: 0.1
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 40.2279 | 1.0 | 860 | 73.8464 | 0.3102 |
| 39.7449 | 2.0 | 1720 | 18.3146 | 0.3611 |
| 20.2884 | 3.0 | 2580 | 13.5320 | 0.3102 |
| 10.9941 | 4.0 | 3440 | 2.2244 | 0.3102 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
m-mandel/dog-example-model
|
m-mandel
| 2023-08-18T12:06:27Z | 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
| 2023-08-18T11:35:44Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - m-mandel/dog-example-model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
asenella/incomplete_mhd_MMVAEPlus_beta_5_scale_True_seed_3
|
asenella
| 2023-08-18T11:47:21Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-08-13T23:21:43Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
Sumsub/Sumsub-ffs-synthetic-1.0_mj_5
|
Sumsub
| 2023-08-18T11:36:24Z | 5 | 5 |
generic
|
[
"generic",
"ai_or_not",
"sumsub",
"image_classification",
"sumsubaiornot",
"aiornot",
"deepfake",
"synthetic",
"generated",
"pytorch",
"image-classification",
"license:cc-by-sa-3.0",
"region:us"
] |
image-classification
| 2023-08-15T11:55:10Z |
---
library_name: generic
license: cc-by-sa-3.0
pipeline_tag: image-classification
tags:
- ai_or_not
- sumsub
- image_classification
- sumsubaiornot
- aiornot
- deepfake
- synthetic
- generated
- pytorch
metrics:
- accuracy
widget:
- src: >-
https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-1.0_mj_5/resolve/main/images/2.jpg
example_title: Pope Francis(yellow puffer)
- src: >-
https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-1.0_mj_5/resolve/main/images/3.jpg
example_title: Pentagon explosion
- src: >-
https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-1.0_mj_5/resolve/main/images/4.webp
example_title: Trump arrest
---
# For Fake's Sake: a set of models for detecting generated and synthetic images
Many people on the internet have recently been tricked by fake images of Pope Francis wearing a coat or of Donald Trump's arrest.
To help combat this issue, we provide detectors for such images generated by popular tools like Midjourney and Stable Diffusion.
|  |  |  |
|-------------------------|-------------------------|--------------------------|
## Model Details
### Model Description
- **Developed by:** [Sumsub AI team](https://sumsub.com/)
- **Model type:** Image classification
- **License:** CC-By-SA-3.0
- **Types:** *midjourney_5m*(Size: 5M parameters, Description: Designed to detect photos created using various versions of Midjourney)
- **Finetuned from model:** *tf_mobilenetv3_large_100.in1k*
## Demo
The demo page can be found [here](https://huggingface.co/spaces/Sumsub/Sumsub-ffs-demo).
## How to Get Started with the Model & Model Sources
Use the code below to get started with the model:
```bash
git lfs install
git clone https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-1.0_mj_5 sumsub_synthetic_mj_5
```
```python
from sumsub_synthetic_mj_5.pipeline import PreTrainedPipeline
from PIL import Image
pipe = PreTrainedPipeline("sumsub_synthetic_mj_5/")
img = Image.open("sumsub_synthetic_mj_5/images/2.jpg")
result = pipe(img)
print(result) #[{'label': 'by AI', 'score': 0.201515331864357}, {'label': 'by human', 'score': 0.7984846234321594}]
```
You may need these prerequsites installed:
```bash
pip install -r requirements.txt
pip install "git+https://github.com/rwightman/pytorch-image-models"
pip install "git+https://github.com/huggingface/huggingface_hub"
```
## Training Details
### Training Data
The models were trained on the following datasets:
**Midjourney datasets:**
- *Real photos* : [MS COCO](https://cocodataset.org/#home).
- *AI photos* : a curated dataset of images from Pinterest boards dedicated to Generative AI ([Midjourney](href='https://pin.it/13UkjgM),[Midjourney AI Art](https://pin.it/6pNXlz3), [Midjourney - Community Showcase](https://pin.it/7gi4jmT), [Midjourney](https://pin.it/4FW0LXQ), [MIDJOURNEY](https://pin.it/5mSsiPg), [Midjourney](https://pin.it/2Qx92QW)).
### Training Procedure
To improve the performance metrics, we used data augmentations such as rotation, crop, Mixup and CutMix. Each model was trained for 30 epochs using early stopping with batch size equal to 32.
## Evaluation
For evaluation we used the following datasets:
**Midjourney datasets:**
- [Kaggle Midjourney 2022-250k](https://www.kaggle.com/datasets/ldmtwo/midjourney-250k-csv): set of 250k images generated by Midjourney.
- [Kaggle Midjourney v5.1](https://www.kaggle.com/datasets/iraklip/modjourney-v51-cleaned-data): set of 400k images generated by Midjourney version 5.1.
**Realistic images:**
- [MS COCO](https://cocodataset.org/#home): set of 120k real world images.
## Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
| Model | Dataset | Accuracy |
|-----------------|---------------------------------------------------------------------------------------------------------------|----------|
| midjourney_5M | [Kaggle Midjourney 2022-250k](https://www.kaggle.com/datasets/ldmtwo/midjourney-250k-csv) | 0.852 |
| midjourney_5M | [Kaggle Midjourney v5.1](https://www.kaggle.com/datasets/iraklip/modjourney-v51-cleaned-data) | 0.875 |
| midjourney_5M | [MS COCO](https://cocodataset.org/#home) | 0.822 |
## Limitations
- It should be noted that achieving 100% accuracy is not possible. Therefore, the model output should only be used as an indication that an image may have been (but not definitely) artificially generated.
- Our models may face challenges in accurately predicting the class for real-world examples that are extremely vibrant and of exceptionally high quality. In such cases, the richness of colors and fine details may lead to misclassifications due to the complexity of the input. This could potentially cause the model to focus on visual aspects that are not necessarily indicative of the true class.
## Citation
If you find this useful, please cite as:
```text
@misc{sumsubaiornot,
publisher = {Sumsub},
url = {https://huggingface.co/Sumsub/Sumsub-ffs-synthetic-1.0_mj_5},
year = {2023},
author = {Savelyev, Alexander and Toropov, Alexey and Goldman-Kalaydin, Pavel and Samarin, Alexey},
title = {For Fake's Sake: a set of models for detecting deepfakes, generated images and synthetic images}
}
```
## References
- Stöckl, Andreas. (2022). Evaluating a Synthetic Image Dataset Generated with Stable Diffusion. 10.48550/arXiv.2211.01777.
- Lin, Tsung-Yi & Maire, Michael & Belongie, Serge & Hays, James & Perona, Pietro & Ramanan, Deva & Dollár, Piotr & Zitnick, C.. (2014). Microsoft COCO: Common Objects in Context.
- Howard, Andrew & Zhu, Menglong & Chen, Bo & Kalenichenko, Dmitry & Wang, Weijun & Weyand, Tobias & Andreetto, Marco & Adam, Hartwig. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
- Liu, Zhuang & Mao, Hanzi & Wu, Chao-Yuan & Feichtenhofer, Christoph & Darrell, Trevor & Xie, Saining. (2022). A ConvNet for the 2020s.
- Wang, Zijie & Montoya, Evan & Munechika, David & Yang, Haoyang & Hoover, Benjamin & Chau, Polo. (2022). DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models. 10.48550/arXiv.2210.14896.
|
markytools/my_awesome_swag_model
|
markytools
| 2023-08-18T11:25:28Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-08-18T09:59:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: my_awesome_swag_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_swag_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0127
- Accuracy: 0.7897
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7622 | 1.0 | 4597 | 0.5991 | 0.7676 |
| 0.3792 | 2.0 | 9194 | 0.6478 | 0.7839 |
| 0.1406 | 3.0 | 13791 | 1.0127 | 0.7897 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Unbabel/unite-mup
|
Unbabel
| 2023-08-18T11:07:16Z | 0 | 5 | null |
[
"translation",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"license:apache-2.0",
"region:us"
] |
translation
| 2023-06-11T13:12:02Z |
---
pipeline_tag: translation
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: apache-2.0
---
This model was developed by the NLP2CT Lab at the University of Macau and Alibaba Group, and all credits should be attributed to these groups. Since it was developed using the COMET codebase, we adapted the code to run these models within COMET."
This is equivalent to [UniTE-MUP-large] from [modelscope](https://www.modelscope.cn/models/damo/nlp_unite_mup_translation_evaluation_multilingual_large/summary)
# Paper
- [UniTE: Unified Translation Evaluation](https://aclanthology.org/2022.acl-long.558/) (Wan et al., ACL 2022)
# Original Code
- [UniTE](https://github.com/NLP2CT/UniTE)
# License
Apache 2.0
# Usage (unbabel-comet)
Using this model requires unbabel-comet (>=2.0.0) to be installed:
```bash
pip install --upgrade pip # ensures that pip is current
pip install "unbabel-comet>=2.0.0"
```
Then you can use it through comet CLI:
```bash
comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model Unbabel/unite-mup
```
Or using Python:
```python
from comet import download_model, load_from_checkpoint
model_path = download_model("Unbabel/unite-mup")
model = load_from_checkpoint(model_path)
data = [
{
"src": "这是个句子。",
"mt": "This is a sentence.",
"ref": "It is a sentence."
},
{
"src": "这是另一个句子。",
"mt": "This is another sentence.",
"ref": "It is another sentence."
}
]
model_output = model.predict(data, batch_size=8, gpus=1)
# Expected SRC score:
# [0.3474583327770233, 0.4492775797843933]
print (model_output.metadata.src_scores)
# Expected REF score:
# [0.9252626895904541, 0.899452269077301]
print (model_output.metadata.ref_scores)
# Expected UNIFIED score:
# [0.8758717179298401, 0.8294666409492493]
print (model_output.metadata.unified_scores)
```
# Intended uses
Our model is intented to be used for **MT evaluation**.
Given a a triplet with (source sentence, translation, reference translation) outputs three scores that reflect the translation quality according to different inputs:
- source score: [`mt`, `src`]
- reference score: [`mt`, `ref`]
- unified score: [`mt`, `src`, `ref`]
# Languages Covered:
This model builds on top of XLM-R which cover the following languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish.
Thus, results for language pairs containing uncovered languages are unreliable!
|
MojtabaAbdiKh/vit-base-patch16-224-finetuned-flower
|
MojtabaAbdiKh
| 2023-08-18T10:56:37Z | 165 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-08-18T08:17:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
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.
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
unitary/unbiased-toxic-roberta
|
unitary
| 2023-08-18T10:43:39Z | 289,725 | 18 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
<div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify
## Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers
</div>
## Description
Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification.
Built by [Laura Hanu](https://laurahanu.github.io/) at [Unitary](https://www.unitary.ai/), where we are working to stop harmful content online by interpreting visual content in context.
Dependencies:
- For inference:
- 🤗 Transformers
- ⚡ Pytorch lightning
- For training will also need:
- Kaggle API (to download data)
| Challenge | Year | Goal | Original Data Source | Detoxify Model Name | Top Kaggle Leaderboard Score | Detoxify Score
|-|-|-|-|-|-|-|
| [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) | 2018 | build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. | Wikipedia Comments | `original` | 0.98856 | 0.98636
| [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) | 2019 | build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias. | Civil Comments | `unbiased` | 0.94734 | 0.93639
| [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification) | 2020 | build effective multilingual models | Wikipedia Comments + Civil Comments | `multilingual` | 0.9536 | 0.91655*
*Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available.
It is also noteworthy to mention that the top leadearboard scores have been achieved using model ensembles. The purpose of this library was to build something user-friendly and straightforward to use.
## Limitations and ethical considerations
If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.
The intended use of this library is for research purposes, fine-tuning on carefully constructed datasets that reflect real world demographics and/or to aid content moderators in flagging out harmful content quicker.
Some useful resources about the risk of different biases in toxicity or hate speech detection are:
- [The Risk of Racial Bias in Hate Speech Detection](https://homes.cs.washington.edu/~msap/pdfs/sap2019risk.pdf)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://arxiv.org/pdf/1703.04009.pdf%201.pdf)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://arxiv.org/pdf/1905.12516.pdf)
## Quick prediction
The `multilingual` model has been trained on 7 different languages so it should only be tested on: `english`, `french`, `spanish`, `italian`, `portuguese`, `turkish` or `russian`.
```bash
# install detoxify
pip install detoxify
```
```python
from detoxify import Detoxify
# each model takes in either a string or a list of strings
results = Detoxify('original').predict('example text')
results = Detoxify('unbiased').predict(['example text 1','example text 2'])
results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# optional to display results nicely (will need to pip install pandas)
import pandas as pd
print(pd.DataFrame(results, index=input_text).round(5))
```
For more details check the Prediction section.
## Labels
All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema:
- **Very Toxic** (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing your perspective)
- **Toxic** (a rude, disrespectful, or unreasonable comment that is somewhat likely to make you leave a discussion or give up on sharing your perspective)
- **Hard to Say**
- **Not Toxic**
More information about the labelling schema can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
### Toxic Comment Classification Challenge
This challenge includes the following labels:
- `toxic`
- `severe_toxic`
- `obscene`
- `threat`
- `insult`
- `identity_hate`
### Jigsaw Unintended Bias in Toxicity Classification
This challenge has 2 types of labels: the main toxicity labels and some additional identity labels that represent the identities mentioned in the comments.
Only identities with more than 500 examples in the test set (combined public and private) are included during training as additional labels and in the evaluation calculation.
- `toxicity`
- `severe_toxicity`
- `obscene`
- `threat`
- `insult`
- `identity_attack`
- `sexual_explicit`
Identity labels used:
- `male`
- `female`
- `homosexual_gay_or_lesbian`
- `christian`
- `jewish`
- `muslim`
- `black`
- `white`
- `psychiatric_or_mental_illness`
A complete list of all the identity labels available can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
### Jigsaw Multilingual Toxic Comment Classification
Since this challenge combines the data from the previous 2 challenges, it includes all labels from above, however the final evaluation is only on:
- `toxicity`
## How to run
First, install dependencies
```bash
# clone project
git clone https://github.com/unitaryai/detoxify
# create virtual env
python3 -m venv toxic-env
source toxic-env/bin/activate
# install project
pip install -e detoxify
cd detoxify
# for training
pip install -r requirements.txt
```
## Prediction
Trained models summary:
|Model name| Transformer type| Data from
|:--:|:--:|:--:|
|`original`| `bert-base-uncased` | Toxic Comment Classification Challenge
|`unbiased`| `roberta-base`| Unintended Bias in Toxicity Classification
|`multilingual`| `xlm-roberta-base`| Multilingual Toxic Comment Classification
For a quick prediction can run the example script on a comment directly or from a txt containing a list of comments.
```bash
# load model via torch.hub
python run_prediction.py --input 'example' --model_name original
# load model from from checkpoint path
python run_prediction.py --input 'example' --from_ckpt_path model_path
# save results to a .csv file
python run_prediction.py --input test_set.txt --model_name original --save_to results.csv
# to see usage
python run_prediction.py --help
```
Checkpoints can be downloaded from the latest release or via the Pytorch hub API with the following names:
- `toxic_bert`
- `unbiased_toxic_roberta`
- `multilingual_toxic_xlm_r`
```bash
model = torch.hub.load('unitaryai/detoxify','toxic_bert')
```
Importing detoxify in python:
```python
from detoxify import Detoxify
results = Detoxify('original').predict('some text')
results = Detoxify('unbiased').predict(['example text 1','example text 2'])
results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# to display results nicely
import pandas as pd
print(pd.DataFrame(results,index=input_text).round(5))
```
## Training
If you do not already have a Kaggle account:
- you need to create one to be able to download the data
- go to My Account and click on Create New API Token - this will download a kaggle.json file
- make sure this file is located in ~/.kaggle
```bash
# create data directory
mkdir jigsaw_data
cd jigsaw_data
# download data
kaggle competitions download -c jigsaw-toxic-comment-classification-challenge
kaggle competitions download -c jigsaw-unintended-bias-in-toxicity-classification
kaggle competitions download -c jigsaw-multilingual-toxic-comment-classification
```
## Start Training
### Toxic Comment Classification Challenge
```bash
python create_val_set.py
python train.py --config configs/Toxic_comment_classification_BERT.json
```
### Unintended Bias in Toxicicity Challenge
```bash
python train.py --config configs/Unintended_bias_toxic_comment_classification_RoBERTa.json
```
### Multilingual Toxic Comment Classification
This is trained in 2 stages. First, train on all available data, and second, train only on the translated versions of the first challenge.
The [translated data](https://www.kaggle.com/miklgr500/jigsaw-train-multilingual-coments-google-api) can be downloaded from Kaggle in french, spanish, italian, portuguese, turkish, and russian (the languages available in the test set).
```bash
# stage 1
python train.py --config configs/Multilingual_toxic_comment_classification_XLMR.json
# stage 2
python train.py --config configs/Multilingual_toxic_comment_classification_XLMR_stage2.json
```
### Monitor progress with tensorboard
```bash
tensorboard --logdir=./saved
```
## Model Evaluation
### Toxic Comment Classification Challenge
This challenge is evaluated on the mean AUC score of all the labels.
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
```
### Unintended Bias in Toxicicity Challenge
This challenge is evaluated on a novel bias metric that combines different AUC scores to balance overall performance. More information on this metric [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation).
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
# to get the final bias metric
python model_eval/compute_bias_metric.py
```
### Multilingual Toxic Comment Classification
This challenge is evaluated on the AUC score of the main toxic label.
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
```
### Citation
```
@misc{Detoxify,
title={Detoxify},
author={Hanu, Laura and {Unitary team}},
howpublished={Github. https://github.com/unitaryai/detoxify},
year={2020}
}
```
|
cknowledge/mlperf-inference-bert-pytorch-fp32-squad-v1.1
|
cknowledge
| 2023-08-18T10:41:29Z | 3,008 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"MLPerf",
"Question Answering",
"BERT",
"PyTorch",
"Transformers",
"FP32",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-08-18T10:23:41Z |
---
license: apache-2.0
tags:
- MLPerf
- Question Answering
- BERT
- PyTorch
- Transformers
- FP32
datasets:
- squad
---
This is an MLPerf BERT model taken from [Zenodo](https://zenodo.org/record/3733896), mixed with the [original model](https://huggingface.co/bert-large-uncased) and automated by the [MLCommons CM language](https://github.com/mlcommons/ck).
|
h3lmi/fine_tuned_minilm12
|
h3lmi
| 2023-08-18T10:39:51Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-18T10:01:37Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 2365 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 709,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
minye819/bert-finetuned-squad
|
minye819
| 2023-08-18T10:35:51Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-18T04:56:43Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Andyrasika/my-awesome-setfit-model
|
Andyrasika
| 2023-08-18T10:28:01Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mpnet",
"feature-extraction",
"setfit",
"sentence-transformers",
"text-classification",
"en",
"dataset:PolyAI/banking77",
"arxiv:2209.11055",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T06:58:45Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
datasets:
- PolyAI/banking77
language:
- en
metrics:
- accuracy
library_name: transformers
---
# Andyrasika/my-awesome-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("Andyrasika/my-awesome-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
dkqjrm/20230818094219
|
dkqjrm
| 2023-08-18T10:23:51Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-18T00:42:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: '20230818094219'
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. -->
# 20230818094219
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
PriyaK10/t5-large_PREFIX_TUNING_SEQ2SEQ
|
PriyaK10
| 2023-08-18T10:22:29Z | 6 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T10:22:24Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
JCener/import-google-takeout-data-to-new-account
|
JCener
| 2023-08-18T10:22:05Z | 0 | 0 | null |
[
"Corbett",
"Takeout",
"en",
"region:us"
] | null | 2023-08-18T10:15:18Z |
---
language:
- en
tags:
- Corbett
- Takeout
---
Use Corbett <a href="https://corbettsoftware.com/blog/google-takeout-converter/">Google Takeout Converter</a> to import Takeout files into multiple document formats, email file formats, desktop clients & web platforms with all data attributes.
The software is tested & admired by IT experts for its error-free conversion process. With software one can <a href="https://corbettsoftware.com/blog/import-google-takeout-to-new-gmail-account/">import Google Takeout data to another account</a> with all data fields & conversion process.
In addition to that, the software is capable to surpass <a href="https://corbettsoftware.com/blog/google-takeout-not-working/">Google Takeout Not Working</a> & allows you to download your Gmail emails without dependency on Outlook. So, Visit the official website of Corbett Software & download this solution for free.
|
dkqjrm/20230818094211
|
dkqjrm
| 2023-08-18T10:10:26Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-18T00:42:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: '20230818094211'
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. -->
# 20230818094211
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the squad 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dayuii/Lora_traing
|
dayuii
| 2023-08-18T10:05:58Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-23T11:45:05Z |
---
license: creativeml-openrail-m
---
|
BenjaminOcampo/model-contrastive-bert__trained-in-ihc__seed-1
|
BenjaminOcampo
| 2023-08-18T09:58:57Z | 4 | 0 |
transformers
|
[
"transformers",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T09:58:05Z |
---
language: en
---
# Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-ihc__seed-1
<!-- 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:** BenjaminOcampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/huggingface_hub
- **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]
|
lhoestq/distilbert-base-uncased-finetuned-absa-as
|
lhoestq
| 2023-08-18T09:49:23Z | 176 | 3 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
Distilbert finetuned for Aspect-Based Sentiment Analysis (ABSA) with auxiliary sentence.
Fine-tuned using a dataset provided by NAVER for the CentraleSupélec NLP course.
```bibtex
@inproceedings{sun-etal-2019-utilizing,
title = "Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence",
author = "Sun, Chi and
Huang, Luyao and
Qiu, Xipeng",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1035",
doi = "10.18653/v1/N19-1035",
pages = "380--385",
abstract = "Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.",
}
```
|
trl-lib/ddpo-aesthetic-predictor
|
trl-lib
| 2023-08-18T09:33:40Z | 0 | 2 | null |
[
"region:us"
] | null | 2023-08-18T09:30:29Z |
## DDPO aesthetic predictor
This reprository contains the weights of the aesthetic predictor that you can find in the repository: https://github.com/christophschuhmann/improved-aesthetic-predictor so that any use can load it easily using `huggingface_hub` library.
```python
import torch
from huggingface_hub import hf_hub_download
cached_path = hf_hub_download(
'trl-lib',
'aesthetic-model.pth'
)
state_dict = torch.load(cached_path)
```
|
machinelearningzuu/detr-resnet-50_finetuned-room-objects
|
machinelearningzuu
| 2023-08-18T09:27:50Z | 185 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-08-18T06:58:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: detr-resnet-50_finetuned-room-objects
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-room-objects
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.0
- Datasets 2.11.0
- Tokenizers 0.13.0
|
bvboca/trainedlora2
|
bvboca
| 2023-08-18T09:19:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T09:19:05Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
PriyaK10/bloomz_560m_PROMPT_TUNING_CAUSAL_LM
|
PriyaK10
| 2023-08-18T09:17:25Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T09:17:23Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
Vineetttt/distilbert-base-uncased-finetuned-rte
|
Vineetttt
| 2023-08-18T09:15:41Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-18T09:09:33Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: rte
split: validation
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.5992779783393501
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-rte
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9325
- Accuracy: 0.5993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.6805 | 0.5957 |
| No log | 2.0 | 312 | 0.6794 | 0.5596 |
| No log | 3.0 | 468 | 0.7373 | 0.5812 |
| 0.5978 | 4.0 | 624 | 0.8785 | 0.5884 |
| 0.5978 | 5.0 | 780 | 0.9325 | 0.5993 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
marmolpen3/paraphrase-MiniLM-L3-v2-sla-obligations-rights
|
marmolpen3
| 2023-08-18T09:12:45Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-08-18T08:54:31Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# marmolpen3/paraphrase-MiniLM-L3-v2-sla-obligations-rights
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("marmolpen3/paraphrase-MiniLM-L3-v2-sla-obligations-rights")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
harvinder676/ner-distillbert-ner
|
harvinder676
| 2023-08-18T09:03:13Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-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"
] |
token-classification
| 2023-08-18T08:25:49Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-distillbert-ner
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. -->
# ner-distillbert-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1179
- Precision: 0.8602
- Recall: 0.8497
- F1: 0.8549
- Accuracy: 0.9707
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 13 | 0.3151 | 0.3193 | 0.2684 | 0.2917 | 0.8755 |
| No log | 2.0 | 26 | 0.1966 | 0.6320 | 0.4663 | 0.5366 | 0.9379 |
| No log | 3.0 | 39 | 0.1332 | 0.7932 | 0.7469 | 0.7694 | 0.9608 |
| No log | 4.0 | 52 | 0.1173 | 0.8077 | 0.8313 | 0.8193 | 0.9652 |
| No log | 5.0 | 65 | 0.1093 | 0.8530 | 0.8190 | 0.8357 | 0.9685 |
| No log | 6.0 | 78 | 0.1123 | 0.8383 | 0.8589 | 0.8485 | 0.9676 |
| No log | 7.0 | 91 | 0.1203 | 0.8501 | 0.8436 | 0.8468 | 0.9669 |
| No log | 8.0 | 104 | 0.1165 | 0.8628 | 0.8390 | 0.8507 | 0.9697 |
| No log | 9.0 | 117 | 0.1168 | 0.8585 | 0.8466 | 0.8525 | 0.9701 |
| No log | 10.0 | 130 | 0.1179 | 0.8602 | 0.8497 | 0.8549 | 0.9707 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Eaaven/lora-trained-xl
|
Eaaven
| 2023-08-18T08:57:07Z | 4 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-18T03:43:38Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of alice girl
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Eaaven/lora-trained-xl
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of alice girl using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
aeft/Pixelcopter-PLE-v0
|
aeft
| 2023-08-18T08:51:58Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T08:51:55Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 22.20 +/- 46.67
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
PrinceAyush/Support-chatbot-llama7b
|
PrinceAyush
| 2023-08-18T08:38:46Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-07-30T20:24:40Z |
Mental Health Support Chatbot Project
This project focuses on building a Mental Health Support Chatbot using state-of-the-art technologies, including Llama 3B language model, PEFT, LORA, and 8-bit model quantization. The chatbot aims to provide empathetic and non-judgmental responses to individuals seeking mental health advice, promoting emotional well-being and support. The project comprises Data Preparation, Model Training, and Quantization of the Model.
Data Preparation
The data preparation phase involved collecting and preprocessing a dataset containing mental health conversations from various sources. The dataset consists of 6,365 rows of dialogues related to mental health. To ensure optimal training, the dataset was cleaned and formatted, removing noise, special characters, and irrelevant information.
To enhance the model's performance, data augmentation techniques were employed. Domain-specific language models were utilized to generate additional conversation examples, enabling the chatbot to respond effectively to a wider range of user queries.
Model Training
For the model training, the Llama 3B language model was chosen due to its exceptional performance in natural language understanding. The model was fine-tuned on the prepared mental health dataset using hyperparameters such as batch size, learning rate, and gradient accumulation steps. The training process aimed to optimize the model's ability to generate appropriate and supportive responses based on user prompts.
PEFT and LORA
In this project, PEFT (Parallel Efficient Transformers) and LORA (Locally Recurrent Adaptive Mechanism) techniques were incorporated to enhance the model's efficiency and performance. PEFT improves the model's scalability and training speed on multi-GPU systems. LORA, on the other hand, enhances the model's ability to capture long-range dependencies in the conversation context.
Model Quantization
Due to resource constraints, the model was quantized in 8-bit format using model quantization techniques. Quantization reduces the model size and memory footprint, making it more feasible to deploy on devices with limited resources. The chatbot achieved satisfactory performance with the quantized model, allowing it to run efficiently on systems with lower RAM and GPU capacity.
Model Training Environment
The model was trained on Google Colab, utilizing a virtual machine with 12GB CPU and 12GB T4 GPU RAM. Despite the resource limitations, the model training process yielded desirable results, demonstrating the effectiveness of the applied techniques in creating a functional and resource-efficient chatbot.
Drawbacks of Model Quantization
While 8-bit model quantization provides significant benefits in terms of model size and resource consumption, it may result in a slight decrease in the model's precision and accuracy. The quantized model might not retain the exact same performance as the full-precision model. However, for the purposes of this project and the target application, the trade-off in performance is acceptable given the hardware constraints.
How to Run the Application
To experience the Mental Health Support Chatbot application, follow these steps:
Step 1: Install the required dependencies by executing the following command in your terminal or command prompt:
pip install -r requirements.txt
Step 2: Execute the runApp.py script:
python runApp.py
Please note that the application requires a minimum system specification of 8 GB RAM and 6 GB of GPU to run efficiently.
Test Prompts
Here are some example prompts that were tested on the Mental Health Support Chatbot:
"I've been feeling really anxious lately. What should I do to cope with it?"
"I'm feeling hopeless and don't see any point in living anymore."
"I can't sleep at night, and it's affecting my daily life."
"I'm having trouble concentrating, and I feel so overwhelmed."
"My friend told me they're feeling suicidal. What can I do to help them?"
Conclusion
The Mental Health Support Chatbot project showcases the successful implementation of advanced technologies like PEFT, LORA, and 8-bit model quantization to build an efficient and supportive chatbot. While the model's quantization presents some trade-offs, it allows the chatbot to run effectively on devices with limited resources, making it accessible to a broader audience.
We encourage further exploration and improvement of the chatbot by leveraging larger and more diverse datasets and fine-tuning hyperparameters. Additionally, user feedback and continuous development will help enhance the chatbot's capabilities, providing better mental health support to users.
Finally, we express our gratitude to cofactoryai for their invaluable contribution by providing the frontend interface for the application, ensuring a user-friendly experience for the Mental Health Support Chatbot.
Note: The chatbot is not a substitute for professional mental health advice or therapy. Users with severe mental health concerns should seek help from qualified professionals.
Important Note:
Running runApp.py may take some time, depending on your internet bandwidth, because the LLaMA model and its configuration need to be downloaded. The LLaMA model is about 6GB in size, and the download time will vary based on the speed of your internet connection.
Please be patient during the download process, and ensure that you have a stable and fast internet connection to minimize the waiting time. Once the model is downloaded, subsequent runs of the application will be faster, as the model will be cached locally on your system.
If you encounter any issues during the download or if the process takes longer than expected, please check your internet connection and ensure that you have sufficient storage space on your system to accommodate the model files.
Feel free to reach out for assistance or any questions you may have during the setup and running of the application. Enjoy exploring the capabilities of the LLaMA model for Mental Health Support Chatbot!
### Framework versions
- PEFT 0.4.0
|
phatpt/dqn-SpaceInvadersNoFrameskip-v4
|
phatpt
| 2023-08-18T08:28:32Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T08:27:56Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 670.50 +/- 224.60
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga phatpt -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga phatpt -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga phatpt
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
danieliser/a2c-PandaReachDense-v2
|
danieliser
| 2023-08-18T08:22:31Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-30T00:33:34Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.75 +/- 0.18
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
CreatorPhan/Q8
|
CreatorPhan
| 2023-08-18T08:18:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T18:03:51Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.4.0
|
bhagasra-saurav/bert-base-uncased-finetuned-char-hangman
|
bhagasra-saurav
| 2023-08-18T08:12:27Z | 117 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"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
| 2023-08-18T06:59:03Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-char-hangman
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-base-uncased-finetuned-char-hangman
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2830
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.985 | 0.59 | 500 | 1.7507 |
| 1.7115 | 1.18 | 1000 | 1.6289 |
| 1.6265 | 1.78 | 1500 | 1.5502 |
| 1.5716 | 2.37 | 2000 | 1.5237 |
| 1.5265 | 2.96 | 2500 | 1.4812 |
| 1.498 | 3.55 | 3000 | 1.4562 |
| 1.4648 | 4.15 | 3500 | 1.4246 |
| 1.4463 | 4.74 | 4000 | 1.3875 |
| 1.4215 | 5.33 | 4500 | 1.3697 |
| 1.4076 | 5.92 | 5000 | 1.3530 |
| 1.3901 | 6.52 | 5500 | 1.3404 |
| 1.3767 | 7.11 | 6000 | 1.3270 |
| 1.3631 | 7.7 | 6500 | 1.3126 |
| 1.3573 | 8.29 | 7000 | 1.3212 |
| 1.3488 | 8.89 | 7500 | 1.3162 |
| 1.3397 | 9.48 | 8000 | 1.3135 |
| 1.3318 | 10.07 | 8500 | 1.2941 |
| 1.336 | 10.66 | 9000 | 1.2842 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
aeft/Reinforce-Cartpole-v1
|
aeft
| 2023-08-18T08:11:33Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T08:11:25Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
SlimeCore/Maeve-Paladins
|
SlimeCore
| 2023-08-18T08:10:41Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-08-18T07:42:17Z |
---
license: openrail
---
Paladins is part of © 2023 Copyright Hi-Rez Studios, INC. all rights are reserved to them.
If theres any copyright issues ill delete the model.
Dataset is taken from the Wiki: https://paladins.fandom.com/wiki/Maeve_voice_lines
---
|
khanhdhq/finetune_vietcuna_15.08
|
khanhdhq
| 2023-08-18T08:02:40Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-15T07:41:16Z |
---
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 0.5.0.dev0
|
mmnga/line-corp-japanese-large-lm-3.6b-instruction-sft-ggml
|
mmnga
| 2023-08-18T07:48:32Z | 0 | 1 | null |
[
"ja",
"license:apache-2.0",
"region:us"
] | null | 2023-08-18T07:03:49Z |
---
license: apache-2.0
language:
- ja
---
# line-corporation/japanese-large-lm-3.6b-instruction-sft
[line-corporationさんが公開しているjapanese-large-lm-3.6b-instruction-sft](https://huggingface.co/line-corporation/japanese-large-lm-3.6b-instruction-sft)のggml変換版です。
## Usage
```
git clone https://github.com/ggerganov/ggml.git
cd ggml
mkdir build && cd build
cmake ..
make -j
./bin/gpt-neox -m 'line-corp-japanese-large-lm-3.6b-instruction-sft-ggml-q4_0.bin' -n 128 -t 8 -p 'ユーザー: 四国の県名を全て列挙してください。\nシステム: '
```
|
felixb85/poca-SoccerTwos
|
felixb85
| 2023-08-18T07:47:36Z | 91 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-08-14T13:28:32Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: felixb85/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_bias_port
|
Mel-Iza0
| 2023-08-18T07:32:11Z | 1 | 0 |
peft
|
[
"peft",
"pytorch",
"llama",
"region:us"
] | null | 2023-08-12T14:59:51Z |
---
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
|
ybkim95-mit/medalpaca-pmdata-readiness10
|
ybkim95-mit
| 2023-08-18T07:30:03Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:14:03Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
ybkim95-mit/medalpaca-pmdata-readiness25
|
ybkim95-mit
| 2023-08-18T07:29:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:14:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
ybkim95-mit/medalpaca-pmdata-stress10
|
ybkim95-mit
| 2023-08-18T07:27:36Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:13:39Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
ybkim95-mit/medalpaca-pmdata-sleep_quality25
|
ybkim95-mit
| 2023-08-18T07:25:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:14:32Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
ybkim95-mit/medalpaca-globem-depression3
|
ybkim95-mit
| 2023-08-18T07:23:53Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:15:08Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
ybkim95-mit/medalpaca-globem-depression10
|
ybkim95-mit
| 2023-08-18T07:23:21Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:15:13Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
ybkim95-mit/medalpaca-globem-depression25
|
ybkim95-mit
| 2023-08-18T07:22:54Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:15:19Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
iamplus/mpt-30b-v2
|
iamplus
| 2023-08-18T07:21:45Z | 13 | 10 |
transformers
|
[
"transformers",
"pytorch",
"mpt",
"text-generation",
"custom_code",
"dataset:ehartford/dolphin",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-09T17:44:07Z |
---
datasets:
- ehartford/dolphin
license: apache-2.0
---
**Base Model :** mosaicml/mpt-30b
**Tool :** MosaicML's llm-foundry (https://github.com/mosaicml/llm-foundry)
**Dataset :** Entire flan3m-GPT3.5 dataset.
**Config yaml with Model Params :** https://huggingface.co/iamplus/mpt-30b-v2/blob/main/mpt-30b_orca.yaml
**Prompt Format :**
```
<system>: [system prompt]
<human>: [question]
<bot>:
```
|
ybkim95-mit/medalpaca-lifesnaps-calories25
|
ybkim95-mit
| 2023-08-18T07:20:13Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:16:23Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
ybkim95-mit/medalpaca-pmdata-stress3
|
ybkim95-mit
| 2023-08-18T07:19:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T07:13:33Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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 0.5.0.dev0
|
iamplus/gpt-neoxt-20b-v11
|
iamplus
| 2023-08-18T07:18:33Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"dataset:iamplus/Instruction_Tuning",
"dataset:iamplus/Conversational_Data",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-22T18:21:50Z |
---
license: bigscience-openrail-m
datasets:
- iamplus/Instruction_Tuning
- iamplus/Conversational_Data
---
Instruction Tuned GPT-NeoXT-20B model on Instruction Tuning dataset as listed below (~5.2M data) using ***Colossal AI***
**Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (GPT-NeoXT-Chat-Base-20B-v0.16 - fine-tuned on feedback data)
**Training Details :**
* Epochs: 4
* Batch Size : 5 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 40
* Block Size : 2020
* Weight Decay : 0
* Learning Rate : 1e-6
* Learning Rate Scheduler Type : Cosine
* Number of warmup steps : 600
* Machine : 8xA100 80GB
**Training Data Specifics :**
* Labels are similar to Input ids but with "human" responses and pad tokens masked so that they don't contribute during the model's error calculation.
* Block Size is 2020, Multiple instructions are clubbed together in each data.
* "###" is the EOS Token used in the data.
|
iamplus/gpt-neoxt-20b-v10
|
iamplus
| 2023-08-18T07:17:48Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"dataset:iamplus/Instruction_Tuning",
"dataset:iamplus/Conversational_Data",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-04T13:53:50Z |
---
license: bigscience-openrail-m
datasets:
- iamplus/Instruction_Tuning
- iamplus/Conversational_Data
---
Instruction Tuned GPT-NeoXT-20B model on Instruction Tuning dataset as listed below (~5.2M data) using ***Colossal AI***
**Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (GPT-NeoXT-Chat-Base-20B-v0.16 - fine-tuned on feedback data)
**Training Details :**
* Epochs: 2
* Batch Size : 5 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 40
* Block Size : 2020
* Weight Decay : 0
* Learning Rate : 1e-6
* Learning Rate Scheduler Type : Cosine
* Number of warmup steps : 600
* Machine : 8xA100 80GB
**Training Data Specifics :**
* Labels and Input ids are exactly the same.
* Block Size is 2020, Multiple instructions are clubbed together in each data.
* "###" is the EOS Token used in the data.
|
iamplus/gpt-neoxt-20b-v9
|
iamplus
| 2023-08-18T07:14:53Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"dataset:iamplus/Instruction_Tuning",
"dataset:iamplus/Conversational_Data",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-04T11:34:32Z |
---
license: bigscience-openrail-m
datasets:
- iamplus/Instruction_Tuning
- iamplus/Conversational_Data
---
Instruction Tuned GPT-NeoXT-20B model on Instruction Tuning dataset as listed below (~5.2M data) using ***Colossal AI***
**Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (GPT-NeoXT-Chat-Base-20B-v0.16 - fine-tuned on feedback data)
**Training Details :**
* Epochs: 2
* Batch Size : 5 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 40
* Block Size : 2020
* Weight Decay : 0
* Learning Rate : 1e-6
* Learning Rate Scheduler Type : Cosine
* Number of warmup steps : 600
* Machine : 8xA100 80GB
**Training Data Specifics :**
* Labels are similar to Input ids but with "human" responses and pad tokens masked so that they don't contribute during the model's error calculation.
* Block Size is 2020, Multiple instructions are clubbed together in each data.
* "###" is the EOS Token used in the data.
|
iamplus/bloomz-7b1-cot-v1
|
iamplus
| 2023-08-18T07:11:15Z | 4 | 0 |
transformers
|
[
"transformers",
"bloom",
"text-generation",
"dataset:iamplus/CoT",
"license:bigscience-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-12T05:54:59Z |
---
license: bigscience-openrail-m
datasets:
- iamplus/CoT
---
First Version of Fine Tuned Bloomz-7B1 model on CoT dataset from Flan Data Collection (v2) (~64k data) using ***HF Deepspeed***
**Base Model:** bigscience/bloomz-7b1
**Training Details :**
* Epochs: 8
* Batch Size : 5 instantaneous per device x 2 gradient accumulation steps x 8 gpus = 80
* Max Length : 1024
* Weight Decay : 0
* Learning Rate : 5e-5
* Learning Rate Scheduler Type : Linear
* Number of warmup steps : 0
* Machine : 8xA100 80GB
**Dataset Details :**
Dataset : iamplus/CoT
Files :
* cot_fsnoopt.csv
* cot_fsopt.csv
* cot_zsnoopt.csv
* cot_zsopt.csv
**Final Review :**
* The model has just memorized/overfitted on the data and is not working good on the samples outside the training data.
* Also looks like it has changed the base model weights by too much (catastrophic forgetting).
* Similar problems with the Epoch 6 model as well.
* Epoch 2 model couldn't find middle ground and not performing well on training data and not on new data as well and increasing just the Epochs is leading to memorization as stated above.
**Conclusion :**
* Need more quality data for the model to really learn the patterns. Increasing just the epochs with less data only leads to overfitting.
|
iamplus/bloomz-7b1-stanford-alpaca-v1
|
iamplus
| 2023-08-18T07:10:59Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"dataset:iamplus/Instruction_Tuning",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-17T15:02:39Z |
---
license: bigscience-openrail-m
datasets:
- iamplus/Instruction_Tuning
---
First Version of Instruction Tuned Bloomz-7B1 model on Stanford Alpaca Instruction Tuning dataset (52k data) using ***HF Deepspeed***
**Base Model:** bigscience/bloomz-7b1
**Training Details :**
* Epochs: 4
* Batch Size : 5 instantaneous per device x 3 gradient accumulation steps x 8 gpus = 120
* Max Length : 1024
* Weight Decay : 0
* Learning Rate : 5e-5
* Learning Rate Scheduler Type : Linear
* Number of warmup steps : 40
* Machine : 8xA100 80GB
**Dataset Details :**
Dataset : iamplus/Instruction_Tuning
Files :
* stanford_alpaca_it.csv
|
aratshimyanga/q-taxi-v3
|
aratshimyanga
| 2023-08-18T07:09:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T07:09:54Z |
---
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 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="aratshimyanga/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"])
```
|
joe-xhedi/q-FrozenLake-v1-4x4-noSlippery
|
joe-xhedi
| 2023-08-18T07:07:18Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-18T07:07:16Z |
---
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="joe-xhedi/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"])
```
|
namisha/donut-base-bristol
|
namisha
| 2023-08-18T07:03:22Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-08-17T06:28:57Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-bristol
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-bristol
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
zhangbo2008/best_llm_train06M55M49M2023
|
zhangbo2008
| 2023-08-18T06:55:51Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T06:55:49Z |
---
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: float16
### Framework versions
- PEFT 0.4.0
|
zhangbo2008/best_llm_train06M55M39p2023
|
zhangbo2008
| 2023-08-18T06:55:40Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T06:55:39Z |
---
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: float16
### Framework versions
- PEFT 0.4.0
|
zhangbo2008/best_llm_train06P55P27p2023
|
zhangbo2008
| 2023-08-18T06:55:28Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T06:55:27Z |
---
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: float16
### Framework versions
- PEFT 0.4.0
|
xianbin/a2c-PandaReachDense-v2
|
xianbin
| 2023-08-18T06:31:12Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-28T03:03:48Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.58 +/- 0.78
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
|
MaulanaJesus/llama2-jazz-working-arabic-faq
|
MaulanaJesus
| 2023-08-18T06:22:37Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-18T06:22:32Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
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