davanstrien/model_cards_with_readmes_with_duplicates

edraper88/distilbert-base-uncased-finetuned-imdb

edraper88

distilbert

16

5

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,318

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Zekunli/flan-t5-large-extraction-cnndm_8000-all

Zekunli

t5

10

0

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,552

# flan-t5-large-extraction-cnndm_8000-all 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: 1.6960 - Rouge1: 35.1425 - Rouge2: 15.3877 - Rougel: 30.0992 - Rougelsum: 30.1879 - 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: 24 - seed: 1799 - 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1837 | 0.2 | 200 | 1.8342 | 33.7673 | 14.4744 | 28.8398 | 28.8415 | 19.0 | | 1.9557 | 0.4 | 400 | 1.7798 | 34.3577 | 14.8613 | 29.769 | 29.766 | 18.986 | | 1.9219 | 0.6 | 600 | 1.7428 | 34.8589 | 15.4488 | 30.1084 | 30.1336 | 18.99 | | 1.871 | 0.8 | 800 | 1.7408 | 35.001 | 15.597 | 30.3374 | 30.37 | 18.99 | | 1.8729 | 1.0 | 1000 | 1.7502 | 34.9305 | 15.5718 | 30.1495 | 30.1513 | 19.0 | | 1.7803 | 1.2 | 1200 | 1.7261 | 35.7504 | 15.4172 | 30.6898 | 30.7362 | 19.0 | | 1.7674 | 1.4 | 1400 | 1.7214 | 35.9564 | 15.6508 | 30.3541 | 30.4292 | 19.0 | | 1.7704 | 1.6 | 1600 | 1.7253 | 35.2706 | 15.7274 | 30.118 | 30.1324 | 19.0 | | 1.7656 | 1.8 | 1800 | 1.6960 | 35.1425 | 15.3877 | 30.0992 | 30.1879 | 19.0 | | 1.7545 | 2.0 | 2000 | 1.7186 | 34.6436 | 15.2712 | 29.9781 | 29.9698 | 19.0 | | 1.6739 | 2.2 | 2200 | 1.7245 | 35.4083 | 15.8808 | 30.6222 | 30.6752 | 19.0 | | 1.6836 | 2.4 | 2400 | 1.7212 | 35.1829 | 15.5181 | 30.2438 | 30.262 | 19.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1

mingdinghan/ppo-Huggy

mingdinghan

null

32

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

822

# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: mingdinghan/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

HusseinHE/saad

HusseinHE

null

99

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

1,366

### Saad Dreambooth model trained by HusseinHE with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: sksaad (use that on your prompt) ![sksaad 0](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%281%29.jpg)![sksaad 1](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%282%29.jpg)![sksaad 2](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%283%29.jpg)![sksaad 3](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%284%29.jpg)![sksaad 4](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%285%29.jpg)![sksaad 5](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%286%29.jpg)![sksaad 6](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%287%29.jpg)![sksaad 7](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%288%29.jpg)![sksaad 8](https://huggingface.co/HusseinHE/saad/resolve/main/concept_images/sksaad_%289%29.jpg)

css919/poca-SoccerTwos

css919

null

20

3

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

840

# **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: css919/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

amoselberg/ppo-SnowballTarget1

amoselberg

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

858

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: amoselberg/ppo-SnowballTarget1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

taqwa92/whisper-small-ArabicT12

taqwa92

whisper

16

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['taqwa92/tm_data']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,288

# Whisper Small Arabic- Taqwa This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the tm_data dataset. It achieves the following results on the evaluation set: - Loss: 0.5530 - Wer: 45.6372 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1812 | 5.0 | 500 | 0.5530 | 45.6372 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

amoselberg/pyramidsRND

amoselberg

null

12

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

832

# **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: amoselberg/pyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

TitanDiffuse108/EpiCentre

TitanDiffuse108

null

3

0

null

0

null

false

creativeml-openrail-m

null

0

[]

false

true

5,303

# Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). # Model Details ## Model Description hello everyone this is my first model ,its free so feel free to use it . EpiCentre was made by merging two models ,this model produces contrasting colours with amazing details with beautiful faces you can use this model anywhere you want  - **Developed by:*BETTER THAN NOTHING(CAPTIANTITAN)* - **Shared by [optional]:** [More Information Needed] - **Model type:*text to image* [More Information Needed] - **Language(s) (NLP):*English* [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:*deleberate, realastic vision* [More Information Needed] ## Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses this model is to be used to only generate images which are safe to the community  ## Direct Use  [More Information Needed] ## Downstream Use [optional]  [More Information Needed] ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations this model can be used without any filter ,please create the image humanly  [More Information Needed] ## Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [More Information Needed] # Evaluation  ## Testing Data, Factors & Metrics ### Testing Data  [More Information Needed] ### Factors  [More Information Needed] ### Metrics  [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional]  [More Information Needed] # Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] # Glossary [optional]  [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed]

EdenYav/Reinforce-1

EdenYav

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

dotunadegbite/Reinforce-CartPole-v1

dotunadegbite

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

quaizarv/Reinforce-PixelCopter

quaizarv

null

6

0

null

0

reinforcement-learning

false

null

0

['Pixelcopter-PLE-v0', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

300

# **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

mili7522/ppo-SnowballTarget

mili7522

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

855

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: mili7522/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Alsebay/Chilloutmix-Ni-fix

Alsebay

null

4

0

null

0

null

false

creativeml-openrail-m

['en']

null

0

[]

false

true

252

It a fix version of Chilloutmix-Ni because missing some Clip). - WARNING!!!!!!!!!! Fix version may make worse than the not fix one. Here is the main link: - https://civitai.com/models/6424/chilloutmix - https://huggingface.co/TASUKU2023/Chilloutmix

Azher/Anything-v4.5-vae-fp16-ckpt

Azher

null

3

0

null

0

null

false

null

0

[]

false

true

343

# Model: Anything v4.5 Has the following properties that are bundled right out of the box: - Included: vae - Half-precision floating point format: fp16 # Model Sample Outputs <img src="https://huggingface.co/Azher/Anything-v4.5-vae-fp16-diffuser/resolve/main/Image%201.png" alt="Vampire" width="300" height="300" style="display:inline-block;"> <img src="https://huggingface.co/Azher/Anything-v4.5-vae-fp16-diffuser/resolve/main/Image%202.png" alt="Vampire" width="300" height="300" style="display:inline-block;"> <img src="https://huggingface.co/Azher/Anything-v4.5-vae-fp16-diffuser/resolve/main/Image%203.png" alt="Vampire" width="300" height="300" style="display:inline-block;"> <img src="https://huggingface.co/Azher/Anything-v4.5-vae-fp16-diffuser/resolve/main/Image%204.png" alt="Vampire" width="300" height="300" style="display:inline-block;"> Output Information: - Prompt: ``` beautiful, masterpiece, black dress, black hair, red eyes, pale, 1girl, stunning, black collar choker, jeweled earrings ``` - Negative Prompt: ``` lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, nsfw ``` - Setup: ``` Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 11, Size: 512x512 ``` # Model Sources - **Original FP16 Model:** [https://huggingface.co/andite/anything-v4.0/blob/main/anything-v4.5-pruned-fp16.ckpt](https://huggingface.co/andite/anything-v4.0/blob/main/anything-v4.5-pruned-fp16.ckpt) - **vae swap:** [https://huggingface.co/andite/anything-v4.0/blob/main/anything-v4.0.vae.pt](https://huggingface.co/andite/anything-v4.0/blob/main/anything-v4.0.vae.pt)

pfunk/Pong-v4-DQPN_p500-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

1,955

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p500.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p500]" python -m cleanrl_utils.enjoy --exp-name DQPN_p500 --env-id Pong-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p500 --start-policy-f 500000 --end-policy-f 500000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000 ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'end_policy_f': 500000, 'env_id': 'Pong-v4', 'evaluation_fraction': 1.0, 'exp_name': 'DQPN_p500', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'start_policy_f': 500000, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

pittawat/poca-SoccerTwos

pittawat

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

842

# **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: pittawat/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

tomaccer/flan-t5-base-juraqanda

tomaccer

t5

13

0

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,792

# flan-t5-base-juraqanda This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0784 - Rouge1: 9.5491 - Rouge2: 1.4927 - Rougel: 8.828 - Rougelsum: 9.2708 - Gen Len: 18.5260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 4.0303 | 1.0 | 712 | 3.3466 | 9.4455 | 1.2684 | 8.8558 | 9.1832 | 18.7577 | | 3.6049 | 2.0 | 1424 | 3.1931 | 10.0714 | 1.4116 | 9.4163 | 9.8024 | 18.6461 | | 3.3464 | 3.0 | 2136 | 3.1246 | 9.6542 | 1.4317 | 8.9441 | 9.36 | 18.5485 | | 3.2831 | 4.0 | 2848 | 3.0910 | 9.6676 | 1.4584 | 8.9533 | 9.3876 | 18.6706 | | 3.2176 | 5.0 | 3560 | 3.0784 | 9.5491 | 1.4927 | 8.828 | 9.2708 | 18.5260 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

lora-library/margret-stalizburg-lora-test2

lora-library

null

71

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

502

# LoRA DreamBooth - margret-stalizburg-v1-lora These are LoRA adaption weights for [andite/anything-v4.0](https://huggingface.co/andite/anything-v4.0). The weights were trained on the instance prompt "margret stalizburg" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: margret stalizburg ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)

mili7522/ppo-Pyramids

mili7522

null

16

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

831

# **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: mili7522/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

mqy/mt5-small-finetuned-12feb-1

mqy

mt5

17

0

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

1,904

# mt5-small-finetuned-12feb-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4285 - Rouge1: 18.23 - Rouge2: 5.42 - Rougel: 18.09 ## 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: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 3.0346 | 1.0 | 311 | 2.4880 | 17.19 | 5.28 | 17.06 | | 2.8943 | 2.0 | 622 | 2.4751 | 17.77 | 5.18 | 17.59 | | 2.8397 | 3.0 | 933 | 2.4719 | 17.65 | 5.38 | 17.55 | | 2.806 | 4.0 | 1244 | 2.4614 | 18.26 | 5.23 | 18.03 | | 2.7842 | 5.0 | 1555 | 2.4464 | 18.08 | 5.51 | 17.96 | | 2.7855 | 6.0 | 1866 | 2.4437 | 17.9 | 5.37 | 17.8 | | 2.7796 | 7.0 | 2177 | 2.4270 | 18.07 | 5.38 | 17.95 | | 2.7951 | 8.0 | 2488 | 2.4267 | 17.96 | 5.36 | 17.85 | | 2.7864 | 9.0 | 2799 | 2.4285 | 18.23 | 5.42 | 18.09 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

antonellaavad/mistermango24-margret-stalizburg-zp92-dreambooth-v1-0

antonellaavad

null

71

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

502

# LoRA DreamBooth - margret-stalizburg-v1-lora These are LoRA adaption weights for [andite/anything-v4.0](https://huggingface.co/andite/anything-v4.0). The weights were trained on the instance prompt "margret stalizburg" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: margret stalizburg ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)

antonellaavad/https-huggingface-co-mistermango24-margret-stalizburg-zp92-dreambooth-v1-0

antonellaavad

null

71

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

502

# LoRA DreamBooth - margret-stalizburg-v1-lora These are LoRA adaption weights for [andite/anything-v4.0](https://huggingface.co/andite/anything-v4.0). The weights were trained on the instance prompt "margret stalizburg" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: margret stalizburg ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)

antonellaavad/mistermango-has-a-test

antonellaavad

null

161

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

510

# LoRA DreamBooth - margret-stalizburg-lora-test-3 These are LoRA adaption weights for [Linaqruf/anything-v3.0](https://huggingface.co/Linaqruf/anything-v3.0). The weights were trained on the instance prompt "margret stalizburg" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: margret stalizburg ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)

mili7522/ppo-PyramidsRND

mili7522

null

16

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

834

# **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: mili7522/ppo-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

xiazeng/poca-SoccerTwos

xiazeng

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

841

# **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: xiazeng/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

zxc12138/pegasus-samsum

zxc12138

pegasus

13

0

transformers

0

text2text-generation

true

false

null

0

['generated_from_trainer']

true

1,240

# pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6928 | 0.54 | 500 | 1.4812 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2

harshadbhatia/LunarLander-v2-ppo

harshadbhatia

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

darkvibes/lizzyflex

darkvibes

null

19

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

420

### lizzyflex Dreambooth model trained by darkvibes with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

vumichien/AnimeGANv2_Hayao

vumichien

null

3

0

null

0

null

false

apache-2.0

null

0

['AnimeGanv2']

false

true

678

## Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv2_Haya Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) ## License This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGAN given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.

vumichien/AnimeGANv3_PortraitSketch

vumichien

null

3

0

null

0

null

false

apache-2.0

null

0

['AnimeGanv3']

false

true

687

## Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv3_PortraitSketch Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) ## License This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGAN given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.

vumichien/AnimeGANv2_Paprika

vumichien

null

3

0

null

0

null

false

apache-2.0

null

0

['AnimeGanv2']

false

true

680

## Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv2_Paprika Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) ## License This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGAN given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.

Ransaka/ppo-SnowballTarget

Ransaka

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

854

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: Ransaka/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ahng79/ppo-Huggy

ahng79

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

817

# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: ahng79/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

vumichien/AnimeGANv2_Shinkai

vumichien

null

3

0

null

0

null

false

apache-2.0

null

0

['AnimeGanv2']

false

true

682

## Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv2_Shinkai Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) ## License This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGAN given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.

vumichien/AnimeGANv3_JP_face

vumichien

null

3

0

null

0

null

false

apache-2.0

null

0

['AnimeGanv3']

false

true

681

## Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv3_JP_face Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) ## License This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGAN given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.

acesanddiamonds/ppo-Huggy

acesanddiamonds

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

826

# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: acesanddiamonds/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Deysi/mt5-small-sumarizacion-textos-bilingual

Deysi

mt5

9

0

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,654

# Deysi/mt5-small-sumarizacion-textos-bilingual This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1454 - Validation Loss: 3.3754 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2282 | 4.6664 | 0 | | 6.0978 | 3.8777 | 1 | | 5.2791 | 3.6299 | 2 | | 4.8386 | 3.5296 | 3 | | 4.5569 | 3.4565 | 4 | | 4.3616 | 3.4055 | 5 | | 4.2154 | 3.3870 | 6 | | 4.1454 | 3.3754 | 7 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2

yl131/ppo-Huggy

yl131

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

816

# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: yl131/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

gbarcik/ppo-LundarLander-v2

gbarcik

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

girinlp-i2i/generic_ner_model

girinlp-i2i

bert

16

0

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,518

# generic_ner_model 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: 0.0999 - Precision: 0.8727 - Recall: 0.8953 - F1: 0.8838 - Accuracy: 0.9740 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1083 | 1.0 | 1958 | 0.1007 | 0.8684 | 0.8836 | 0.8759 | 0.9723 | | 0.0679 | 2.0 | 3916 | 0.0977 | 0.8672 | 0.8960 | 0.8813 | 0.9738 | | 0.0475 | 3.0 | 5874 | 0.0999 | 0.8727 | 0.8953 | 0.8838 | 0.9740 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

fathyshalab/domain_transfer_clinic_credit_cards-massive_social-roberta-large-v1-1-5

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,532

# fathyshalab/domain_transfer_clinic_credit_cards-massive_social-roberta-large-v1-1-5 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_social-roberta-large-v1-1-5") # 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} } ```

Martha-987/whisper-small-ArabicMartha

Martha-987

whisper

16

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['Martha-987/MyOwnData']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,288

# Whisper Small Ar- Martha This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the MyOwnData dataset. It achieves the following results on the evaluation set: - Loss: 0.4467 - Wer: 47.4812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.276 | 2.54 | 1000 | 0.4467 | 47.4812 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

fathyshalab/domain_transfer_clinic_credit_cards-massive_transport-roberta-large-v1-1-5

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,538

# fathyshalab/domain_transfer_clinic_credit_cards-massive_transport-roberta-large-v1-1-5 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_transport-roberta-large-v1-1-5") # 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} } ```

gbarcik/q-FrozenLake-v1-4x4-noSlippery

gbarcik

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

396

# **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="gbarcik/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"]) ```

Seyfelislem/arabic_whisper_small_version_2

Seyfelislem

whisper

14

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['mozilla-foundation/common_voice_11_0']

null

0

['generated_from_trainer']

true

1,413

# arabic_whisper_small_version_2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3355 - Wer: 44.5616 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0835 | 0.42 | 1000 | 0.3859 | 49.2094 | | 0.1607 | 0.83 | 2000 | 0.3481 | 46.8938 | | 0.08 | 1.25 | 3000 | 0.3355 | 44.5616 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

fathyshalab/domain_transfer_clinic_credit_cards-massive_social-roberta-large-v1-2-5

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,532

# fathyshalab/domain_transfer_clinic_credit_cards-massive_social-roberta-large-v1-2-5 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_social-roberta-large-v1-2-5") # 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} } ```

nhiro3303/Reinforce-CartPole-v1

nhiro3303

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

Duskfallcrew/10-minute-grumpy-hour

Duskfallcrew

null

21

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

1

0

['text-to-image']

false

true

1,036

[![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Duskfallcrew/10-minute-grumpy-hour) ### 10 Minute Grumpy Hour Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! This model is horrifying. I'm not responsible if it gives you rabies . XD prilosecotc1 (use that on your prompt) burgie (use that on your prompt)

yjoon/xlm-roberta-base-finetuned-panx-de-fr

yjoon

xlm-roberta

9

0

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,318

# xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.6706 - F1: 0.6245 ## 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: 192 - eval_batch_size: 192 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 11 | 1.1624 | 0.1327 | | No log | 2.0 | 22 | 0.7871 | 0.4940 | | No log | 3.0 | 33 | 0.6706 | 0.6245 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.10.0 - Datasets 2.8.0 - Tokenizers 0.13.2

lilouuch/Goodreads_Books_Reviews_Roberta_52

lilouuch

roberta

6

0

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,343

# Goodreads_Books_Reviews_Roberta_52 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8592 - F1: 0.5986 - Accuracy: 0.6349 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:| | 0.8824 | 1.0 | 25313 | 0.8754 | 0.5792 | 0.6254 | | 0.8127 | 2.0 | 50626 | 0.8592 | 0.5986 | 0.6349 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

pnparam/PNP_dys_asr_960h

pnparam

wav2vec2

16

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,357

# PNP_dys_asr_960h This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7169 - Wer: 1.4123 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.4668 | 1.63 | 500 | 2.6987 | 1.0226 | | 2.0533 | 3.26 | 1000 | 1.0528 | 2.4236 | | 0.4828 | 4.89 | 1500 | 0.7560 | 1.3358 | | 0.1604 | 6.51 | 2000 | 0.7169 | 1.4123 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.13.1+cu116 - Datasets 1.18.3 - Tokenizers 0.13.2

helpingstar/q-FrozenLake-v1-4x4-noSlippery

helpingstar

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

400

# **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="helpingstar/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"]) ```

mRoszak/PandaReach

mRoszak

null

11

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachJointsDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

370

# **A2C** Agent playing **PandaReachJointsDense-v2** This is a trained model of a **A2C** agent playing **PandaReachJointsDense-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 ... ```

helpingstar/q-Taxi-v3-v1

helpingstar

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

370

# **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="helpingstar/q-Taxi-v3-v1", 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"]) ```

alexcasq/OUTPUT

alexcasq

null

33

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

363

# LoRA DreamBooth - alexcasq/OUTPUT These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks alexcasq using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

KnutJaegersberg/topic-classification-IPTC-subject-labels

KnutJaegersberg

xlm-roberta

13

0

sentence-transformers

4

text-classification

true

false

null

0

['sentence-transformers', 'transformers', 'SetFit', 'News']

false

true

1,779

# IPTC topic classifier (multilingual) A SetFit model fit on 166 downlsampled multilingual IPTC Subject labels (concatenated for the lowest hierarchy level into artificial sentences of keywords) to predict the mid level news categories. The purpose of this classifier is to support exploring corpora as weak labeler, since the representations of these descriptions are only approximations of real documents from those topics. The dataset I used to train the model is based on this file: https://huggingface.co/datasets/KnutJaegersberg/News_topics_IPTC_codes_long Accuracy on highest level labels in eval: 0.9779412 Accuracy/F1/mcc on mid level labels in eval: 0.6992481/0.6666667/0.6992617 More interestingly, I used the kaggle dataset with headlines from huffington post and manually selected 15 overlapping high level categories to evaluate the performance. https://www.kaggle.com/datasets/rmisra/news-category-dataset While mcc 0.1968043 on this dataset does not sound as good as before, the mistakes usually could also be seen as a re-interpretation. I.e. news on arrests where categorized as entertainment in the huffington post dataset, the classifier put it into the crime category. My current impression is this system is useful for the aimed for purpose. The numeric categories can be joined with the labels by using this table: https://huggingface.co/datasets/KnutJaegersberg/IPTC-topic-classifier-labels Looks like try out api box to the right by huggingface does not yet handle setfit models, can't do anything about that. Use like any other SetFit model from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("KnutJaegersberg/IPTC-classifier-ml") # Run inference preds = model(["Rachel Dolezal Faces Felony Charges For Welfare Fraud", "Elon Musk just got lucky", "The hype on AI is different from the hype on other tech topics"])

helpingstar/q-Taxi-v3-v2

helpingstar

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

370

# **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="helpingstar/q-Taxi-v3-v2", 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"]) ```

mwissing/Reinforce-cartpole-v1

mwissing

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

Gatozu35/tortoise-tts

Gatozu35

null

8

0

null

1

text-to-speech

false

apache-2.0

['en']

null

0

['text-to-speech', 'audio']

false

true

6,206

# Model Card for TorToiSe  Tortoise is a text-to-speech program built with the following priorities: 1. Strong multi-voice capabilities. 2. Highly realistic prosody and intonation. # Table of Contents - [Model Card for TorToiSe](#model-card-for--model_id-) - [Table of Contents](#table-of-contents) - [Table of Contents](#table-of-contents-1) - [Model Details](#model-details) - [Model Description](#model-description) - [Uses](#uses) - [Direct Use](#direct-use) - [Out-of-Scope Use](#out-of-scope-use) - [Bias, Risks, and Limitations](#bias-risks-and-limitations) - [Recommendations](#recommendations) - [Training Details](#training-details) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Preprocessing](#preprocessing) - [Speeds, Sizes, Times](#speeds-sizes-times) - [Evaluation](#evaluation) - [Testing Data, Factors & Metrics](#testing-data-factors--metrics) - [Testing Data](#testing-data) - [Factors](#factors) - [Metrics](#metrics) - [Results](#results) - [Model Examination](#model-examination) - [Environmental Impact](#environmental-impact) - [Model Architecture and Objective](#model-architecture-and-objective) - [Compute Infrastructure](#compute-infrastructure) - [Hardware](#hardware) - [Software](#software) - [Citation](#citation) - [Model Card Contact](#model-card-contact) - [How to Get Started with the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  Tortoise is a text-to-speech program built with the following priorities: 1. Strong multi-voice capabilities. 2. Highly realistic prosody and intonation. - **Developed by:** James Betker - **Model type:** Language model - **Language(s) (NLP):** en - **License:** apache-2.0 - **Resources for more information:** - [GitHub Repo](https://github.com/152334H/tortoise-tts-fast) # Uses  ## Direct Use   ## Out-of-Scope Use   # Bias, Risks, and Limitations  ## Recommendations  # Training Details ## Training Data  More information on training data needed ## Training Procedure  ### Preprocessing More information needed ### Speeds, Sizes, Times  More information needed # Evaluation  ## Testing Data, Factors & Metrics ### Testing Data  More information needed ### Factors  More information needed ### Metrics  More information needed ## Results More information needed # Model Examination More information needed # Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed # Citation  **BibTeX:** Betker, J. (2022). TorToiSe text-to-speech (Version 2.0) [Computer software]. https://github.com/neonbjb/tortoise-tts **APA:** @software{Betker_TorToiSe_text-to-speech_2022, author = {Betker, James}, month = {4}, title = {{TorToiSe text-to-speech}}, url = {https://github.com/neonbjb/tortoise-tts}, version = {2.0}, year = {2022} } # Glossary [optional]  More information needed # More Information [optional] More information needed # Model Card Authors [optional]  Gatozu35 # Model Card Contact Use the discussion tab # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> More information needed </details>

chavicoski/poca-SoccerTwos

chavicoski

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

844

# **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: chavicoski/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Rowehn/poca-SoccerTwos-final

Rowehn

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

846

# **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: Rowehn/poca-SoccerTwos-final 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

AnnihilationOperator/ofa-huge-caption

AnnihilationOperator

ofa

6

0

transformers

0

null

true

false

apache-2.0

null

0

[]

false

true

3,728

# OFA-huge-caption This is the **huge** version of OFA pretrained model finetuned on COCO captioning task, forked & converted from the [original fairseq version](https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_huge_best.pt) and compressed into float16. The conversion script is custom, but the procedure described [Issue #171](https://github.com/OFA-Sys/OFA/issues/171) should also apply (quantization is not performed, but that's trivial). You will need a [OFA modified version of transformers](https://github.com/OFA-Sys/OFA/tree/feature/add_transformers) to use this model. No idea why it is still not in master. Tips: You can just copy-paste the `transformers` folder into your project and rename it, then monkey-patch the `transformers` module to point to your local copy to avoid having to install it. ## Original README below ## Introduction This is the **huge** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) to a simple sequence-to-sequence learning framework. The directory includes 4 files, namely `config.json` which consists of model configuration, `vocab.json` and `merge.txt` for our OFA tokenizer, and lastly `pytorch_model.bin` which consists of model weights. There is no need to worry about the mismatch between Fairseq and transformers, since we have addressed the issue yet. ## How to use To use it in transformers, please refer to <https://github.com/OFA-Sys/OFA/tree/feature/add_transformers>. Install the transformers and download the models as shown below. ```bash git clone --single-branch --branch feature/add_transformers https://github.com/OFA-Sys/OFA.git pip install OFA/transformers/ git clone https://huggingface.co/OFA-Sys/OFA-huge ``` After, refer the path to OFA-huge to `ckpt_dir`, and prepare an image for the testing example below. Also, ensure that you have pillow and torchvision in your environment. ```python >>> from PIL import Image >>> from torchvision import transforms >>> from transformers import OFATokenizer, OFAModel >>> from generate import sequence_generator >>> mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] >>> resolution = 480 >>> patch_resize_transform = transforms.Compose([ lambda image: image.convert("RGB"), transforms.Resize((resolution, resolution), interpolation=Image.BICUBIC), transforms.ToTensor(), transforms.Normalize(mean=mean, std=std) ]) >>> tokenizer = OFATokenizer.from_pretrained(ckpt_dir) >>> txt = " what does the image describe?" >>> inputs = tokenizer([txt], return_tensors="pt").input_ids >>> img = Image.open(path_to_image) >>> patch_img = patch_resize_transform(img).unsqueeze(0) # using the generator of fairseq version >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=True) >>> generator = sequence_generator.SequenceGenerator( tokenizer=tokenizer, beam_size=5, max_len_b=16, min_len=0, no_repeat_ngram_size=3, ) >>> data = {} >>> data["net_input"] = {"input_ids": inputs, 'patch_images': patch_img, 'patch_masks':torch.tensor([True])} >>> gen_output = generator.generate([model], data) >>> gen = [gen_output[i][0]["tokens"] for i in range(len(gen_output))] # using the generator of huggingface version >>> model = OFAModel.from_pretrained(ckpt_dir, use_cache=False) >>> gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3) >>> print(tokenizer.batch_decode(gen, skip_special_tokens=True)) ```

MPSTME/swin-tiny-patch4-window7-224-finetuned-skin-cancer

MPSTME

swin

10

0

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,071

# swin-tiny-patch4-window7-224-finetuned-skin-cancer This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

PeterBanning71/t5-small-finetuned-eLife

PeterBanning71

t5

14

0

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

1,576

# t5-small-finetuned-eLife This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8960 - Rouge1: 14.7239 - Rouge2: 2.8698 - Rougel: 11.0202 - Rougelsum: 13.3642 - 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.3558 | 1.0 | 544 | 2.9587 | 13.7915 | 2.6556 | 10.3265 | 12.5097 | 19.0 | | 3.1299 | 2.0 | 1088 | 2.9079 | 14.7136 | 2.7492 | 10.836 | 13.3664 | 19.0 | | 3.0917 | 3.0 | 1632 | 2.8960 | 14.7239 | 2.8698 | 11.0202 | 13.3642 | 19.0 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Lucetepolis/OctaFuzz

Lucetepolis

null

12

0

diffusers

3

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers']

false

true

4,192

# HXDC Counterfeit-V2.5 - <a href="https://huggingface.co/gsdf/Counterfeit-V2.5">Download</a> Treebark - <a href="https://huggingface.co/HIZ/aichan_pick">Download</a> HyperBomb, FaceBomb - <a href="https://huggingface.co/mocker/KaBoom">Download</a> qwerty - <a href="https://huggingface.co/1q2W3e/qwerty">Download</a> ultracolor.v4 - <a href="https://huggingface.co/xdive/ultracolor.v4">Download</a> donko-mix-hard - <a href="https://civitai.com/models/7037/donko-mix-nsfw-hard">Download</a> OrangePastelV2 - ~~Download~~ Currently not available. smix 1.12121 - <a href="https://civitai.com/models/8019/smix-1-series">Download</a> viewer-mix - <a href="https://civitai.com/models/7813/viewer-mix">Download</a> 0012-half - <a href="https://huggingface.co/1q2W3e/Attached-model_collection">Download</a> Null v2.2 - <a href="https://civitai.com/models/8173/null-v22">Download</a> school anime - <a href="https://civitai.com/models/7189/school-anime">Download</a> tlqkfniji7 - <a href="https://huggingface.co/uiouiouio/The_lovely_quality_kahlua_flavour">Download</a> 7th_anime_v3_B - <a href="https://huggingface.co/syaimu/7th_Layer">Download</a> Crowbox-Vol.1 - <a href="https://huggingface.co/kf1022/Crowbox-Vol.1">Download</a> EasyNegative and pastelmix-lora seem to work well with the models. EasyNegative - <a href="https://huggingface.co/datasets/gsdf/EasyNegative">Download</a> pastelmix-lora - <a href="https://huggingface.co/andite/pastel-mix">Download</a> # Formula ``` Counterfeit-V2.5 + Treebark = ct base_alpha = 0.009901 Weight values = 0.259221, 0.094699, 0.186355, 0.344377, 0.54691, 0.535689, 0.526122, 0.420305, 0.312004, 0.40172, 0.452608, 0.481439, 0.029126, 0.492655, 0.478894, 0.443794, 0.284518, 0.24424, 0.284451, 0.382469, 0.282082, 0.18387, 0.126064, 0.113941, 0.103878 ct + HyperBomb = cth base_alpha = 0.09009 Weight values = 0.208912, 0.290962, 0.44034, 0.426141, 0.294959, 0.258193, 0.279347, 0.219226, 0.100589, 0.076065, 0.061552, 0.053125, 0.225564, 0.013679, 0.029582, 0.067917, 0.209599, 0.238881, 0.209736, 0.097528, 0.143293, 0.18856, 0.227611, 0.336235, 0.40562 cth + qwerty = cthq base_alpha = 0.008929 Weight values = 0.298931, 0.286255, 0.185812, 0.136147, 0.100038, 0.09741, 0.069466, 0.065465, 0.099956, 0.218813, 0.27544, 0.304705, 0.184049, 0.021782, 0.051109, 0.115061, 0.291535, 0.319518, 0.291441, 0.197459, 0.295056, 0.359111, 0.375537, 0.264379, 0.170006 cthq + ultracolor.v4 = cthqu base_alpha = 0.081967 Weight values = 0.044348, 0.051224, 0.092643, 0.0896, 0.047055, 0.03864, 0.032217, 0.034381, 0.032329, 0.017, 0.009525, 0.005618, 0.380228, 0.060561, 0.083015, 0.128444, 0.233262, 0.247876, 0.234218, 0.103302, 0.082694, 0.111921, 0.235504, 0.634374, 0.746614 cthqu + FaceBomb = cthquf base_alpha = 0.45045 Weight values = 0.304652, 0.108189, 0.113682, 0.116402, 0.118828, 0.11284, 0.095841, 0.065612, 0.035945, 0.033428, 0.032195, 0.03155, 0.03663, 0.006005, 0.008193, 0.012592, 0.022593, 0.023941, 0.02257, 0.019395, 0.027618, 0.032024, 0.029911, 0.015144, 0.010908 cthquf + donko-mix-hard = cthqufd base_alpha = 0.310559 Weight values = 0.041071, 0.033818, 0.035788, 0.036933, 0.038236, 0.037834, 0.040386, 0.045727, 0.049152, 0.025509, 0.0135, 0.007091, 0.035336, 0.009262, 0.016837, 0.031714, 0.063923, 0.068124, 0.063941, 0.051919, 0.076044, 0.091518, 0.094579, 0.081523, 0.077707 cthqufd + OrangePastelV2 = OctaFuzz base_alpha = 0.03012 Weight values = 0.045454, 0.044635, 0.071192, 0.078145, 0.074833, 0.072486, 0.069609, 0.08331, 0.082494, 0.043373, 0.022197, 0.010507, 0.03413, 0.009176, 0.016555, 0.030733, 0.06007, 0.063741, 0.059989, 0.049022, 0.069114, 0.078421, 0.07162, 0.029375, 0.016293 smix 1.12121 + viewer-mix = sv base_alpha = 0.230769 Weight values = 0.395271, 0.35297, 0.359395, 0.382984, 0.448508, 0.468333, 0.478042, 0.475167, 0.419157, 0.446681, 0.469808, 0.48688, 0.230769, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5 sv + 0012-half = sv0 base_alpha = 0.434783 Weight values = 0.096641, 0.097719, 0.100011, 0.105301, 0.118931, 0.122252, 0.120899, 0.11391, 0.15397, 0.407393, 0.526559, 0.587752, 0.071429, 0.326817, 0.315594, 0.291682, 0.229445, 0.220024, 0.229364, 0.30164, 0.31157, 0.309196, 0.281226, 0.145209, 0.089865 sv0 + Null v2.2 = sv0n base_alpha = 0.115385 Weight values = 0.132862, 0.1371, 0.108727, 0.104247, 0.117468, 0.122796, 0.131157, 0.14836, 0.213205, 0.184383, 0.170088, 0.16255, 0.176471, 0.013049, 0.029363, 0.062385, 0.138653, 0.149139, 0.138776, 0.119286, 0.183455, 0.228237, 0.255516, 0.296091, 0.311362 sv0n + school anime = sv0ns base_alpha = 0.103448 Weight values = 0.087455, 0.088646, 0.114848, 0.110151, 0.070954, 0.064852, 0.054146, 0.06643, 0.083591, 0.111871, 0.125259, 0.132157, 0.055556, 0.014513, 0.032747, 0.067662, 0.139412, 0.148332, 0.139177, 0.054834, 0.040531, 0.031203, 0.02771, 0.029855, 0.03066 sv0ns + tlqkfniji7 = sv0nst base_alpha = 0.25641 Weight values = 0.366264, 0.082457, 0.061703, 0.0743, 0.128699, 0.132356, 0.090334, 0.073644, 0.120288, 0.066093, 0.038035, 0.022911, 0.016393, 0.010271, 0.010979, 0.012331, 0.015099, 0.015235, 0.014313, 0.006851, 0.005245, 0.005269, 0.008194, 0.021708, 0.026685 sv0nst + 7th_anime_v3_B = sv0nst7 base_alpha = 0.025 Weight values = 0.270768, 0.082819, 0.089464, 0.099695, 0.122101, 0.11876, 0.079592, 0.057662, 0.096981, 0.056373, 0.033881, 0.021306, 0.016129, 0.004163, 0.005616, 0.008379, 0.013987, 0.01468, 0.013977, 0.00666, 0.004674, 0.003356, 0.002823, 0.002944, 0.002989 sv0nst7 + Crowbox-Vol.1 = OctaBlend base_alpha = 0.007444 Weight values = 0.036592, 0.028764, 0.033246, 0.051828, 0.096045, 0.099435, 0.054162, 0.020355, 0.01281, 0.027376, 0.035261, 0.039613, 0.005348, 0.029654, 0.026405, 0.020164, 0.00725, 0.005724, 0.007621, 0.016328, 0.014867, 0.025298, 0.058555, 0.172774, 0.208144 OctaFuzz + OctaBlend = HXDC base_alpha = 0.5 Weight values = 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5 ``` # Converted weights ![G1](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Graphs/1.png) ![G2](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Graphs/2.png) ![G3](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Graphs/3.png) # Samples All of the images use following negatives/settings. EXIF preserved. ``` Negative prompt: (worst quality, low quality:1.4), EasyNegative, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits Steps: 28, Sampler: DPM++ 2M Karras, CFG scale: 7, Size: 768x512, Denoising strength: 0.6, Clip skip: 2, ENSD: 31337, Hires upscale: 1.5, Hires steps: 14, Hires upscaler: Latent (nearest-exact) ``` # OctaFuzz ![A1](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A1.png) ![A2](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A2.png) ![A3](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A3.png) ![A4](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A4.png) ![A5](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A5.png) ![A6](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A6.png) ![A7](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A7.png) ![A8](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/A8.png) # OctaBlend ![B1](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B1.png) ![B2](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B2.png) ![B3](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B3.png) ![B4](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B4.png) ![B5](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B5.png) ![B6](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B6.png) ![B7](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B7.png) ![B8](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/B8.png) # HXDC ![C1](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C1.png) ![C2](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C2.png) ![C3](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C3.png) ![C4](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C4.png) ![C5](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C5.png) ![C6](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C6.png) ![C7](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C7.png) ![C8](https://huggingface.co/Lucetepolis/OctaFuzz/resolve/main/Samples/C8.png)

Beegbrain/a2c-AntBulletEnv-v0-2

Beegbrain

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

352

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```

frankenstyle/q-FrozenLake-v1-4x4-noSlippery

frankenstyle

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

401

# **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="frankenstyle/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"]) ```

lmqg/flan-t5-base-squad-qag

lmqg

t5

13

0

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qag_squad']

null

0

['questions and answers generation']

true

3,881

# Model Card of `lmqg/flan-t5-base-squad-qag` This model is fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) - **Language:** en - **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/flan-t5-base-squad-qag") # model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/flan-t5-base-squad-qag") output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-base-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 93.04 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedF1Score (MoverScore) | 65.24 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (BERTScore) | 93.1 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (MoverScore) | 65.91 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (BERTScore) | 92.99 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (MoverScore) | 64.7 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_squad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: ['qag'] - model: google/flan-t5-base - max_length: 512 - max_length_output: 256 - epoch: 14 - batch: 8 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-base-squad-qag/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

johannes5117/kadoa-page-extraction

johannes5117

t5

12

0

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,354

# kadoa-page-extraction This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 0.8235 | | No log | 2.0 | 2 | 0.8235 | | No log | 3.0 | 3 | 0.8235 | | No log | 4.0 | 4 | 0.8235 | | No log | 5.0 | 5 | 0.8235 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

frankenstyle/q-taxi-v3

frankenstyle

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

368

# **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="frankenstyle/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"]) ```

Brainergy/ppiittuuffoo

Brainergy

null

16

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

423

### ppiittuuffoo Dreambooth model trained by Brainergy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

sunilSabnis/vit-mae-large-ai-or-not

sunilSabnis

vit

11

0

transformers

0

image-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,427

# vit-mae-large-ai-or-not This model is a fine-tuned version of [facebook/vit-mae-large](https://huggingface.co/facebook/vit-mae-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1883 - Accuracy: 0.9683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3623 | 0.19 | 200 | 0.2099 | 0.9243 | | 0.2465 | 0.38 | 400 | 0.4055 | 0.8545 | | 0.2164 | 0.57 | 600 | 0.1808 | 0.9259 | | 0.1943 | 0.76 | 800 | 0.1765 | 0.9329 | | 0.1723 | 0.95 | 1000 | 0.2083 | 0.9313 | | 0.118 | 1.15 | 1200 | 0.2295 | 0.9168 | | 0.0812 | 1.34 | 1400 | 0.1600 | 0.9511 | | 0.082 | 1.53 | 1600 | 0.1331 | 0.9624 | | 0.0863 | 1.72 | 1800 | 0.1352 | 0.9511 | | 0.0858 | 1.91 | 2000 | 0.1643 | 0.9506 | | 0.056 | 2.1 | 2200 | 0.1930 | 0.9586 | | 0.0319 | 2.29 | 2400 | 0.1595 | 0.9624 | | 0.0206 | 2.48 | 2600 | 0.2937 | 0.9447 | | 0.0299 | 2.67 | 2800 | 0.1680 | 0.9603 | | 0.0213 | 2.86 | 3000 | 0.1746 | 0.9586 | | 0.0164 | 3.05 | 3200 | 0.1579 | 0.9624 | | 0.0019 | 3.24 | 3400 | 0.1787 | 0.9646 | | 0.0022 | 3.44 | 3600 | 0.1976 | 0.9640 | | 0.0023 | 3.63 | 3800 | 0.2017 | 0.9651 | | 0.0045 | 3.82 | 4000 | 0.1883 | 0.9683 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

TieIncred/ddpm-celebahq-finetuned-butterflies-2epochs

TieIncred

null

6

0

diffusers

0

unconditional-image-generation

true

false

mit

null

0

['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class']

false

true

347

# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('TieIncred/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```

pmgautam/ppo-LunarLander-v2

pmgautam

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

spacemanidol/flan-t5-small-xsum

spacemanidol

t5

14

0

transformers

0

text2text-generation

true

false

apache-2.0

null

['xsum']

null

0

['generated_from_trainer']

true

1,148

# small This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.0998 - Rouge1: 33.2675 - Rouge2: 11.0862 - Rougel: 26.1709 - Rougelsum: 26.1668 - Gen Len: 28.0123 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.12.1

dmitry-np/a2c-AntBulletEnv-v0

dmitry-np

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['AntBulletEnv-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

352

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```

nikogarro/DQN-SpaceInvadersNoFrameskip-v4

nikogarro

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

2,219

# **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 SB3: https://github.com/DLR-RM/stable-baselines3 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 nikogarro -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 nikogarro -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 nikogarro ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 160000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.05), ('exploration_fraction', 0.2), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0005), ('learning_starts', 50000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 2000), ('train_freq', 4), ('normalize', False)]) ```

mshibatatt/Reinforce-CartPole-v1

mshibatatt

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

JessicaHsu/q-FrozenLake-v1-4x4-noSlippery

JessicaHsu

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

399

# **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="JessicaHsu/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"]) ```

JessicaHsu/qTaxi-v3

JessicaHsu

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

365

# **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="JessicaHsu/qTaxi-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"]) ```

augustocsc/gpt-m0

augustocsc

gpt2

7

0

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

2,103

# gpt-m0 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0036 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7384 | 0.61 | 500 | 1.6251 | | 0.0325 | 1.22 | 1000 | 0.0146 | | 0.0104 | 1.83 | 1500 | 0.0094 | | 0.008 | 2.44 | 2000 | 0.0074 | | 0.0061 | 3.05 | 2500 | 0.0058 | | 0.0057 | 3.66 | 3000 | 0.0050 | | 0.0059 | 4.27 | 3500 | 0.0050 | | 0.0047 | 4.88 | 4000 | 0.0050 | | 0.0043 | 5.49 | 4500 | 0.0045 | | 0.0043 | 6.11 | 5000 | 0.0045 | | 0.0036 | 6.72 | 5500 | 0.0043 | | 0.0038 | 7.33 | 6000 | 0.0041 | | 0.0034 | 7.94 | 6500 | 0.0044 | | 0.0036 | 8.55 | 7000 | 0.0040 | | 0.0032 | 9.16 | 7500 | 0.0039 | | 0.0033 | 9.77 | 8000 | 0.0037 | | 0.0032 | 10.38 | 8500 | 0.0036 | | 0.0029 | 10.99 | 9000 | 0.0035 | | 0.003 | 11.6 | 9500 | 0.0035 | | 0.0027 | 12.21 | 10000 | 0.0036 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

akghxhs55/poca-SoccerTwos-2

akghxhs55

null

30

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

845

# **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: akghxhs55/poca-SoccerTwos-2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

franfram/distillbert-base-spanish-uncased-finetuned-spanish-corpus

franfram

distilbert

15

0

transformers

0

fill-mask

true

false

null

0

['generated_from_trainer']

true

1,369

# distillbert-base-spanish-uncased-finetuned-spanish-corpus This model is a fine-tuned version of [CenIA/distillbert-base-spanish-uncased](https://huggingface.co/CenIA/distillbert-base-spanish-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7946 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.3776 | 1.0 | 56 | 3.7218 | | 3.3814 | 2.0 | 112 | 3.7694 | | 3.3348 | 3.0 | 168 | 3.7389 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

fathyshalab/domain_transfer_clinic_credit_cards-massive_transport-roberta-large-v1-2-5

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,538

# fathyshalab/domain_transfer_clinic_credit_cards-massive_transport-roberta-large-v1-2-5 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_transport-roberta-large-v1-2-5") # 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} } ```

kasrahabib/20_propogated

kasrahabib

bert

10

0

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,915

# kasrahabib/20_propogated This model is a fine-tuned version of [kasrahabib/XXX08_02_23__-bucket-finetunned](https://huggingface.co/kasrahabib/XXX08_02_23__-bucket-finetunned) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0504 - Validation Loss: 0.1528 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7660, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2492 | 0.1740 | 0 | | 0.1527 | 0.1501 | 1 | | 0.1092 | 0.1582 | 2 | | 0.0879 | 0.1568 | 3 | | 0.0774 | 0.1577 | 4 | | 0.0689 | 0.1513 | 5 | | 0.0597 | 0.1598 | 6 | | 0.0600 | 0.1536 | 7 | | 0.0526 | 0.1519 | 8 | | 0.0504 | 0.1528 | 9 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2

AntiSquid/DQN-SpaceInvadersNoFrameskip-v4

AntiSquid

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

2,221

# **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 SB3: https://github.com/DLR-RM/stable-baselines3 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 AntiSquid -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 AntiSquid -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 AntiSquid ``` ## 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', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

fathyshalab/domain_transfer_clinic_credit_cards-massive_calendar-roberta-large-v1-2-88

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,538

# fathyshalab/domain_transfer_clinic_credit_cards-massive_calendar-roberta-large-v1-2-88 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_calendar-roberta-large-v1-2-88") # 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} } ```

JessicaHsu/q-Taxi-v3-1

JessicaHsu

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

368

# **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="JessicaHsu/q-Taxi-v3-1", 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"]) ```

fathyshalab/domain_transfer_clinic_credit_cards-massive_play-roberta-large-v1-2-65

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,530

# fathyshalab/domain_transfer_clinic_credit_cards-massive_play-roberta-large-v1-2-65 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_play-roberta-large-v1-2-65") # 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} } ```

Cortes48/beachdreamwongkarwai

Cortes48

null

19

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

430

### BeachDreamWongKarWai Dreambooth model trained by Cortes48 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

fathyshalab/domain_transfer_clinic_credit_cards-massive_datetime-roberta-large-v1-2-95

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,538

# fathyshalab/domain_transfer_clinic_credit_cards-massive_datetime-roberta-large-v1-2-95 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_datetime-roberta-large-v1-2-95") # 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} } ```

jinhu2659/Taxi-v3

jinhu2659

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

379

# **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="jinhu2659/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"]) ```

mibalaguer/ppo-Huggy

mibalaguer

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

821

# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: mibalaguer/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Beegbrain/a2c-PandaReachDense-v2

Beegbrain

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **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 ... ```

YoriV/ppo-SnowballTarget

YoriV

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

852

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: YoriV/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

xiaofxiong/ppo-Huggy

xiaofxiong

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Huggy']

false

true

821

# **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: xiaofxiong/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

fathyshalab/domain_transfer_clinic_credit_cards-massive_recommendation-roberta-large-v1-2-15

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

['setfit', 'sentence-transformers', 'text-classification']

false

true

1,550

# fathyshalab/domain_transfer_clinic_credit_cards-massive_recommendation-roberta-large-v1-2-15 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("fathyshalab/domain_transfer_clinic_credit_cards-massive_recommendation-roberta-large-v1-2-15") # 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} } ```

Achitha/small_data_test

Achitha

whisper

14

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,047

# small_data_test This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

waifuwishes/WW_LoRAs

waifuwishes

null

7

0

null

0

text-to-image

false

null

0

['stable-diffusion', 'text-to-image', 'lora', 'anime']

false

true

3,508

# Table of Contents - [Overview](#overview) - [Installation](#installation) - [Usage](#usage) - [LoRAs](#loras) - [SocialMedia](#socialmedia) # Overview Inspired by amazing work done by [Trauter](https://huggingface.co/YoungMasterFromSect/Trauter_LoRAs) I decided to make a contribution to society by extending his work and developing new LoRAs. I'm going to train and test models on anime checkpoints like [WarriorMama777](https://huggingface.co/WarriorMama777/OrangeMixs), [Andite](https://huggingface.co/andite/anything-v4.0), [Gsdf](https://huggingface.co/gsdf/Counterfeit-V2.5), for that reason alone, I don't know how they will perform on your specific model. You can find comparision grid in **[model_name]/Previews** folder. Previews have metadata containing the prompt and settings used to create them, you can access this via "PNG Info" tab in [Automatic1111/WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) Every model is trained with [danbooru](https://danbooru.donmai.us/tags?commit=Search&search%5Bhide_empty%5D=yes&search%5Border%5D=count) tag, using [wd14-tagger](https://github.com/toriato/stable-diffusion-webui-wd14-tagger) with minor tweaks. Additionally, every character folder contains a json file with information about [training](https://github.com/bmaltais/kohya_ss) settings used for a specific model. As far as I can tell, there is no reason for training a model for more than 2 epochs (4000+ steps). # Installation Paste desired model (if you want thumbnail you can also paste preview image) into **\stable-diffusion-webui\models\Lora** Since LoRAs are now available directly in WebUI, you can use them as presented in the following [guide](https://rentry.org/2chAI_LoRA_Dreambooth_guide_english#usage). # Usage I make models with **ww** prefix: ``` ww_[source_name]_[character_name] ww_ov_widowmaker ``` The suffix in the model's name indicates the number of steps taken to train them. More steps means more training, so they are more likely to produce images that are close to the original source, but I find the differences to be very subtle in most cases. I wanted to somehow create flexible models. You can experiment with fewer tags by setting the LoRA weight to 1, or you may want to customize specific parts like hair type or length, clothes, breasts size, accessories with lesser weight: 0.6 - 0.7 # LoRAs - [Overwatch](#overwatch) - [Widowmaker](#widowmaker) - [Ashe](#ashe) # Overwatch - # Widowmaker [<img src="https://huggingface.co/waifuwishes/WW_LoRAs/resolve/main/Overwatch/Widowmaker/Previews/ww_ov_widowmaker_v1_1700.png" width="512" height="768">](https://huggingface.co/waifuwishes/WW_LoRAs/resolve/main/Overwatch/Widowmaker/Previews/ww_ov_widowmaker_v1_1700.png) <details> <summary>Prompt</summary> <pre> ww_ov_widowmaker, (masterpiece:1.2), (best quality), (extremely detailed), highres, illustration, depth of field, dark intense shadows, sharp focus, soft light, (good composition), standing, 1girl, solo, small breasts, pink bodysuit, arm tattoo, center opening, headgear, colored skin, earrings, gloves, pauldrons, lips, long hair, makeup, nose, ponytail, purple hair, purple lips, purple skin, short sleeves, yellow eyes, looking at viewer, <lora:ww_ov_widowmaker_v1_1700:0.7>, outdoors, night, detailed background Negative prompt: EasyNegative, extra fingers,fewer fingers, username, artist name, signature, disembodied limb, extra legs, extra arms, extra fingers, bad anatomy, username, signature Steps: 50, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 357744401, Size: 512x768, Model hash: 038ba203d8, Denoising strength: 0.5, Clip skip: 2, Hires upscale: 1.3, Hires upscaler: Latent, aesthetic_score: 6.7 </pre> </details> - # Ashe [<img src="https://huggingface.co/waifuwishes/WW_LoRAs/resolve/main/Overwatch/Ashe/Previews/ww_ov_ashe_v1_1600.png" width="512" height="768">](https://huggingface.co/waifuwishes/WW_LoRAs/resolve/main/Overwatch/Ashe/Previews/ww_ov_ashe_v1_1600.png) <details> <summary>Prompt</summary> <pre> ww_ov_ashe, (masterpiece:1.2), (best quality), (extremely detailed), highres, illustration, depth of field, dark intense shadows, sharp focus, soft light, (good composition), standing, 1girl, solo, asymmetrical hair, bob cut, white hair, medium hair, cowboy hat, earrings, shoulder armor, eyeshadow, (red eyes:1.2), cowboy hat, jewelry, lipstick, makeup, mole above mouth, necktie, nose, red lips, red necktie, white shirt, vest, looking at viewer, <lora:ww_ov_ashe_v1_1600:0.7>, outdoors, sunset, detailed background Negative prompt: EasyNegative, extra fingers,fewer fingers, username, artist name, signature, disembodied limb, extra legs, extra arms, extra fingers, bad anatomy, username, signature Steps: 50, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 4022578532, Size: 512x768, Model hash: 0873291ac5, Denoising strength: 0.5, Clip skip: 2, Hires upscale: 1.3, Hires upscaler: Latent, aesthetic_score: 6.8 </pre> </details> # SocialMedia [Twitter](https://twitter.com/Waifu_Wishes) [Reddit](https://www.reddit.com/user/waifu_wishes) [Instagram](https://www.instagram.com/waifuwishes/)