davanstrien/model_cards_with_readmes_with_duplicates

davanstrien/dataset_mentions

davanstrien

mpnet

13

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

1

0

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

false

true

1,422

# davanstrien/dataset_mentions 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("davanstrien/dataset_mentions") # 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} } ```

ahmad1289/ppo-SnowballTarget

ahmad1289

null

30

2

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

856

# **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: ahmad1289/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

fathyshalab/massive_transport-roberta-large-v1-2-0.15

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,472

# fathyshalab/massive_transport-roberta-large-v1-2-0.15 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/massive_transport-roberta-large-v1-2-0.15") # 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} } ```

apatidar0/conversation-summ

apatidar0

bart

14

3

transformers

0

text2text-generation

true

false

mit

null

['samsum']

null

0

['generated_from_trainer']

true

1,711

# conversation-summ This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.4048 - Rouge1: 51.7796 - Rouge2: 26.1341 - Rougel: 41.4013 - Rougelsum: 41.4563 - Gen Len: 29.656 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.5781 | 1.0 | 500 | 0.3637 | 50.8871 | 26.6178 | 41.8757 | 41.9291 | 25.16 | | 0.2183 | 2.0 | 1000 | 0.3586 | 50.7919 | 25.4277 | 40.8428 | 40.8421 | 27.712 | | 0.1354 | 3.0 | 1500 | 0.4048 | 51.7796 | 26.1341 | 41.4013 | 41.4563 | 29.656 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

lucataco/startuplogos-lora-small

lucataco

null

15

0

diffusers

1

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

390

# LoRA text2image fine-tuning - https://huggingface.co/lucataco/startuplogos-lora-supersmall These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lucataco/startuplogo-captions dataset. 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)

Mustafa21/detr-resnet-50_finetuned_cppe5

Mustafa21

detr

15

2

transformers

0

object-detection

true

false

apache-2.0

null

['cppe-5']

null

0

['generated_from_trainer']

true

976

# detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

fathyshalab/massive_calendar-roberta-large-v1-2-0.89

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,470

# fathyshalab/massive_calendar-roberta-large-v1-2-0.89 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/massive_calendar-roberta-large-v1-2-0.89") # 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} } ```

Elytum/my_awesome_model

Elytum

distilbert

10

19

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,275

# my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3322 - Accuracy: 0.9279 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.3177 | 1.0 | 152679 | 0.3123 | 0.9248 | | 0.2212 | 2.0 | 305358 | 0.3322 | 0.9279 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

fathyshalab/massive_play-roberta-large-v1-2-0.64

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,462

# fathyshalab/massive_play-roberta-large-v1-2-0.64 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/massive_play-roberta-large-v1-2-0.64") # 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} } ```

dasaprakashk/Reinforce-Pixelcopter-PLE-v0

dasaprakashk

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

vvn0/a2c-AntBulletEnv-v0

vvn0

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 ... ```

ahmad1289/pyramids-RND-1

ahmad1289

null

16

2

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: ahmad1289/pyramids-RND-1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

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

victorivus

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="victorivus/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"]) ```

pabloac31/ppo-Pyramids

pabloac31

null

18

1

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: pabloac31/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

victorivus/Q-Learner-Taxi-v3

victorivus

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

374

# **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="victorivus/Q-Learner-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"]) ```

JosephSf7/en_pipeline

JosephSf7

null

25

2

spacy

0

text-classification

false

null

['en']

null

0

['spacy', 'token-classification', 'text-classification']

false

true

6,178

| Feature | Description | | --- | --- | | **Name** | `en_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.2.3,<3.3.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `ner`, `textcat` | | **Vectors** | 684830 keys, 684830 unique vectors (300 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (197 labels for 5 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` | | **`morphologizer`** | `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `POS=ADP`, `Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Quot`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `Aspect=Prog\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `POS=ADV`, `POS=VERB\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PART`, `POS=PUNCT\|PunctType=Comm`, `POS=PRON`, `POS=SCONJ`, `POS=VERB\|VerbForm=Inf`, `POS=PUNCT\|PunctType=Peri`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADJ`, `ConjType=Cmp\|POS=CCONJ`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SPACE`, `Definite=Ind\|POS=DET\|PronType=Art`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Degree=Sup\|POS=ADJ`, `POS=VERB\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `NumType=Card\|POS=NUM`, `Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Sup\|POS=ADV`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Quot`, `POS=DET`, `Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PUNCT\|PunctType=Dash`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Dem`, `POS=AUX\|VerbForm=Ger`, `POS=AUX`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=X`, `POS=ADV\|PronType=Dem`, `POS=PART\|Polarity=Neg`, `Number=Plur\|POS=PRON\|PronType=Dem`, `POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADV`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SYM`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PUNCT`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbType=Mod`, `POS=DET\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `NumType=Mult\|POS=ADV`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=AUX`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|POS=PRON\|PronType=Art`, `Aspect=Prog\|POS=AUX\|Tense=Pres\|VerbForm=Part`, `POS=X`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs` | | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `dep`, `det`, `dobj`, `expl`, `mark`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `pcomp`, `pobj`, `poss`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | | **`ner`** | `CARDINAL`, `DATE`, `FAC`, `GPE`, `LANGUAGE`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | | **`textcat`** | `Blaming_Geopolitics`, `Blaming_Government`, `Blaming_Migrants`, `No_Frustration`, `Uses_Infrastructure` | </details> ### Accuracy | Type | Score | | --- | --- | | `TAG_ACC` | 99.94 | | `POS_ACC` | 99.94 | | `MORPH_ACC` | 99.93 | | `DEP_UAS` | 99.80 | | `DEP_LAS` | 98.90 | | `SENTS_P` | 100.00 | | `SENTS_R` | 100.00 | | `SENTS_F` | 100.00 | | `ENTS_F` | 99.71 | | `ENTS_P` | 99.71 | | `ENTS_R` | 99.71 | | `CATS_SCORE` | 99.43 | | `CATS_MICRO_P` | 99.43 | | `CATS_MICRO_R` | 99.43 | | `CATS_MICRO_F` | 99.43 | | `CATS_MACRO_P` | 99.43 | | `CATS_MACRO_R` | 99.43 | | `CATS_MACRO_F` | 99.43 | | `CATS_MACRO_AUC` | 100.00 | | `CATS_MACRO_AUC_PER_TYPE` | 0.00 | | `TOK2VEC_LOSS` | 441813.62 | | `TAGGER_LOSS` | 3246.21 | | `MORPHOLOGIZER_LOSS` | 3554.80 | | `PARSER_LOSS` | 333496.66 | | `NER_LOSS` | 6933.11 | | `TEXTCAT_LOSS` | 1.50 |

Elifr/clasificador-sentimientos-pln-uned

Elifr

electra

10

4

transformers

0

text-classification

true

false

null

0

['classification', 'generated_from_trainer']

true

1,379

# clasificador-sentimientos-pln-uned This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3848 - Accuracy: 0.4297 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 388 | 1.3848 | 0.3806 | | 1.4224 | 2.0 | 776 | 1.2911 | 0.4090 | | 1.0722 | 3.0 | 1164 | 1.3848 | 0.4297 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

yonas/stt_rw_sw_lg_conformer_ctc_large

yonas

null

3

6

nemo

0

automatic-speech-recognition

true

false

cc-by-4.0

['rw']

['mozilla-foundation/common_voice_11_0']

null

0

['automatic-speech-recognition', 'speech', 'ASR', 'Kinyarwanda', 'Swahili', 'Luganda', 'Multilingual', 'audio', 'CTC', 'Conformer', 'Transformer', 'NeMo', 'pytorch']

true

2,167

## Model Overview <DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("yonas/stt_rw_sw_lg_conformer_ctc_large") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="yonas/stt_rw_sw_lg_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture <ADD SOME INFORMATION ABOUT THE ARCHITECTURE> ## Training <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC> ### Datasets <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)> ## Performance <LIST THE SCORES OF THE MODEL - OR USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS> ## Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## References <ADD ANY REFERENCES HERE AS NEEDED> [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

YoriV/Reinforce-CartPole-v1

YoriV

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

zmaro/zmaroavatar

zmaro

null

16

46

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

418

### zmaroavatar Dreambooth model trained by zmaro 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/massive_datetime-roberta-large-v1-2-0.82

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,470

# fathyshalab/massive_datetime-roberta-large-v1-2-0.82 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/massive_datetime-roberta-large-v1-2-0.82") # 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} } ```

Izc/stt_rw_sw_lg_conformer_ctc_large

Izc

null

3

1

nemo

0

automatic-speech-recognition

true

false

cc-by-4.0

['rw']

['mozilla-foundation/common_voice_11_0']

null

0

['automatic-speech-recognition', 'speech', 'ASR', 'Kinyarwanda', 'Swahili', 'Luganda', 'Multilingual', 'audio', 'CTC', 'Conformer', 'Transformer', 'NeMo', 'pytorch']

true

2,163

## Model Overview <DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("Izc/stt_rw_sw_lg_conformer_ctc_large") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="Izc/stt_rw_sw_lg_conformer_ctc_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture <ADD SOME INFORMATION ABOUT THE ARCHITECTURE> ## Training <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC> ### Datasets <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)> ## Performance <LIST THE SCORES OF THE MODEL - OR USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS> ## Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## References <ADD ANY REFERENCES HERE AS NEEDED> [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

vvn0/a2c-PandaReachDense-v2

vvn0

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 ... ```

frangiral/dqn-SpaceInvadersNoFrameskip-v4-2

frangiral

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<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga frangiral -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 frangiral -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 frangiral ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('buffer_size', 50000), ('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', 0.001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

pfunk/Pong-v4-DQPN_p50_e0.10-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

1,989

# (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_p50_e0.10.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p50_e0.10]" python -m cleanrl_utils.enjoy --exp-name DQPN_p50_e0.10 --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_p50_e0.10-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.10-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.10-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p50_e0.10 --start-policy-f 50000 --end-policy-f 1000 --evaluation-fraction 0.10 --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': 1000, 'env_id': 'Pong-v4', 'evaluation_fraction': 0.1, 'exp_name': 'DQPN_p50_e0.10', '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': 50000, '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'} ```

YoriV/Reinforce-PixelCopter

YoriV

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

apatidar0/pegasus_conversation-summ

apatidar0

pegasus

15

2

transformers

0

text2text-generation

true

false

null

['samsum']

null

0

['generated_from_trainer']

true

1,005

# pegasus_conversation-summ This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

tomasabril/bonusunit1

tomasabril

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: tomasabril/bonusunit1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ChemicalM/aaacups_LoRA

ChemicalM

null

3

0

null

1

null

false

creativeml-openrail-m

null

0

[]

false

true

431

LoRA created from: https://civitai.com/models/4462/aaacups by https://civitai.com/user/modelsforall Currently testing, on photorealistic models. Weights between 0.5 and 2.0 seem to give good results depending on the proportions of the starting model/img and desired amount of reduction. Try a higher value on something like URPMv1.2, lower for Realistic Vision V1.3. Going too high reduces skin texture and introduces artifacting.

OrK7/parler_hate_speech

OrK7

deberta-v2

9

transformers

1

text-classification

true

false

null

['en']

null

0

['hate', 'hate_speech']

false

true

3,508

<a href="https://colab.research.google.com/github/OrKatz7/parler-hate-speech/blob/main/colab_demo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> Social Network Hate Detection: Finding Social Media Posts Containing Hateful Information Using Ensemble Methods and Back-Translation Recent research efforts have been directed toward the development of automated systems for detecting hateful content to assist social media providers in identifying and removing such content before it can be viewed by the public. This paper introduces a unique ensemble approach that utilizes DeBERTa models, which benefits from pre-training on massive synthetic data and the integration of back-translation techniques during training and testing. Our findings reveal that this approach delivers state-of-the-art results in hate-speech detection. The results demonstrate that the combination of back-translation, ensemble, and test-time augmentation results in a considerable improvement across various metrics and models in both the Parler and GAB datasets. We show that our method reduces models’ bias in an effective and meaningful way, and also reduces the RMSE from 0.838 to around 0.766 and increases R-squared from 0.520 to 0.599. The biggest improvement was seen in small Deberate models, while for large models, there was either a minor improvement or no change. ## Results <img src="https://github.com/OrKatz7/parler-hate-speech/blob/main/docs/parler_results.jpeg?raw=true"> ``` !pip install huggingface_hub !pip install tokenizers transformers !pip install iterative-stratification !git clone https://github.com/OrKatz7/parler-hate-speech %cd parler-hate-speech/src ``` ``` from huggingface_hub import hf_hub_download import torch import sys from model import CustomModel,MeanPooling from transformers import AutoTokenizer, AutoModel, AutoConfig import numpy as np class CFG: model="microsoft/deberta-v3-base" target_cols=['label_mean'] ``` ``` name = "OrK7/parler_hate_speech" downloaded_model_path = hf_hub_download(repo_id=name, filename="pytorch_model.bin") model = torch.load(downloaded_model_path) tokenizer = AutoTokenizer.from_pretrained(name) ``` ``` def prepare_input(text): inputs = tokenizer.encode_plus( text, return_tensors=None, add_special_tokens=True, max_length=512, pad_to_max_length=True, truncation=True ) for k, v in inputs.items(): inputs[k] = torch.tensor(np.array(v).reshape(1,-1), dtype=torch.long) return inputs def collate(inputs): mask_len = int(inputs["attention_mask"].sum(axis=1).max()) for k, v in inputs.items(): inputs[k] = inputs[k][:,:mask_len] return inputs ``` ``` from transformers import Pipeline class HatePipeline(Pipeline): def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "maybe_arg" in kwargs: preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"] return preprocess_kwargs, {}, {} def preprocess(self, inputs): out = prepare_input(inputs) return collate(out) def _forward(self, model_inputs): outputs = self.model(model_inputs) return outputs def postprocess(self, model_outputs): return np.array(model_outputs[0,0].numpy()).clip(0,1)*4+1 ``` ``` pipe = HatePipeline(model=model) pipe("I Love you #") ``` results: 1.0 ``` pipe("I Hate #$%#$%Jewish%$#@%^^@#") ``` results: 4.155200004577637

kkarpou/autotrain-greek-sentiment-analysis-3351392404

kkarpou

deberta-v2

9

transformers

0

text-classification

true

false

null

['en']

['kkarpou/autotrain-data-greek-sentiment-analysis']

{'emissions': 4.129267471119826}

0

['autotrain', 'text-classification']

false

true

1,126

# Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 3351392404 - CO2 Emissions (in grams): 4.1293 ## Validation Metrics - Loss: 0.479 - Accuracy: 0.844 - Macro F1: 0.844 - Micro F1: 0.844 - Weighted F1: 0.843 - Macro Precision: 0.847 - Micro Precision: 0.844 - Weighted Precision: 0.849 - Macro Recall: 0.846 - Micro Recall: 0.844 - Weighted Recall: 0.844 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/kkarpou/autotrain-greek-sentiment-analysis-3351392404 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("kkarpou/autotrain-greek-sentiment-analysis-3351392404", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("kkarpou/autotrain-greek-sentiment-analysis-3351392404", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

Luisfrdz/PPO-RL-1-LunarLander-v3

Luisfrdz

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

368

# **PPO-RL-Agent** Agent playing **LunarLander-v2** This is a trained model of a **PPO-RL-Agent** 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 ... ```

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

fjaragones

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="fjaragones/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"]) ```

sgoodfriend/poca-SoccerTwos-v3

sgoodfriend

null

20

265

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

848

# **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: sgoodfriend/poca-SoccerTwos-v3 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

fjaragones/Taxi-v3

fjaragones

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

364

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

kasrahabib/FInall__XXX08_02_23-bucket-finetunned

kasrahabib

bert

10

2

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,745

# kasrahabib/FInall__XXX08_02_23-bucket-finetunned 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: 0.0954 - Validation Loss: 0.3458 - 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10940, '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} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.4270 | 0.3147 | 0 | | 0.2937 | 0.3138 | 1 | | 0.2277 | 0.3316 | 2 | | 0.1909 | 0.3294 | 3 | | 0.1589 | 0.3413 | 4 | | 0.1374 | 0.3520 | 5 | | 0.1223 | 0.3439 | 6 | | 0.1112 | 0.3468 | 7 | | 0.1034 | 0.3494 | 8 | | 0.0954 | 0.3458 | 9 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

ajders/ddisco_classifier

ajders

bert

86

142

transformers

0

text-classification

true

false

cc-by-4.0

['da']

['ajders/ddisco']

null

0

['generated_from_trainer']

true

1,996

# da-discourse-coherence-base This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the [DDisco](https://huggingface.co/datasets/ajders/ddisco) dataset. It achieves the following results on the evaluation set: - Loss: 0.7487 - Accuracy: 0.6915 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 703 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 6.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3422 | 0.4 | 5 | 1.0166 | 0.5721 | | 0.9645 | 0.8 | 10 | 0.8966 | 0.5721 | | 0.9854 | 1.24 | 15 | 0.8499 | 0.5721 | | 0.8628 | 1.64 | 20 | 0.8379 | 0.6517 | | 0.9046 | 2.08 | 25 | 0.8228 | 0.5721 | | 0.8361 | 2.48 | 30 | 0.7980 | 0.5821 | | 0.8158 | 2.88 | 35 | 0.8095 | 0.5821 | | 0.8689 | 3.32 | 40 | 0.7989 | 0.6169 | | 0.8125 | 3.72 | 45 | 0.7730 | 0.6965 | | 0.843 | 4.16 | 50 | 0.7566 | 0.6418 | | 0.7421 | 4.56 | 55 | 0.7840 | 0.6517 | | 0.7949 | 4.96 | 60 | 0.7531 | 0.6915 | | 0.828 | 5.4 | 65 | 0.7464 | 0.6816 | | 0.7438 | 5.8 | 70 | 0.7487 | 0.6915 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.0a0+d0d6b1f - Datasets 2.9.0 - Tokenizers 0.13.2 ### Contributor [ajders](https://github.com/AJDERS)

kitintouch/kit-the-bear

kitintouch

null

4

0

null

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

1,532

### kit the bear Dreambooth model trained by kitintouch 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: kitthebear (use that on your prompt) ![kitthebear 0](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%281%29.jpg)![kitthebear 1](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%282%29.jpg)![kitthebear 2](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%283%29.jpg)![kitthebear 3](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%284%29.jpg)![kitthebear 4](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%285%29.jpg)![kitthebear 5](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%286%29.jpg)![kitthebear 6](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%287%29.jpg)![kitthebear 7](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%288%29.jpg)![kitthebear 8](https://huggingface.co/kitintouch/kit-the-bear/resolve/main/concept_images/kitthebear_%289%29.jpg)

augustogeog/q-FrozenLake-v1-noslippery

augustogeog

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="augustogeog/q-FrozenLake-v1-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"]) ```

lllyasviel/ControlNet

lllyasviel

null

18

0

null

56

null

false

openrail

null

0

2

1

[]

false

true

2,706

This is the pretrained weights and some other detector weights of ControlNet. See also: https://github.com/lllyasviel/ControlNet # Description of Files ControlNet/models/control_sd15_canny.pth - The ControlNet+SD1.5 model to control SD using canny edge detection. ControlNet/models/control_sd15_depth.pth - The ControlNet+SD1.5 model to control SD using Midas depth estimation. ControlNet/models/control_sd15_hed.pth - The ControlNet+SD1.5 model to control SD using HED edge detection (soft edge). ControlNet/models/control_sd15_mlsd.pth - The ControlNet+SD1.5 model to control SD using M-LSD line detection (will also work with traditional Hough transform). ControlNet/models/control_sd15_normal.pth - The ControlNet+SD1.5 model to control SD using normal map. Best to use the normal map generated by that Gradio app. Other normal maps may also work as long as the direction is correct (left looks red, right looks blue, up looks green, down looks purple). ControlNet/models/control_sd15_openpose.pth - The ControlNet+SD1.5 model to control SD using OpenPose pose detection. Directly manipulating pose skeleton should also work. ControlNet/models/control_sd15_scribble.pth - The ControlNet+SD1.5 model to control SD using human scribbles. The model is trained with boundary edges with very strong data augmentation to simulate boundary lines similar to that drawn by human. ControlNet/models/control_sd15_seg.pth - The ControlNet+SD1.5 model to control SD using semantic segmentation. The protocol is ADE20k. ControlNet/annotator/ckpts/body_pose_model.pth - Third-party model: Openpose’s pose detection model. ControlNet/annotator/ckpts/hand_pose_model.pth - Third-party model: Openpose’s hand detection model. ControlNet/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt - Third-party model: Midas depth estimation model. ControlNet/annotator/ckpts/mlsd_large_512_fp32.pth - Third-party model: M-LSD detection model. ControlNet/annotator/ckpts/mlsd_tiny_512_fp32.pth - Third-party model: M-LSD’s another smaller detection model (we do not use this one). ControlNet/annotator/ckpts/network-bsds500.pth - Third-party model: HED boundary detection. ControlNet/annotator/ckpts/upernet_global_small.pth - Third-party model: Uniformer semantic segmentation. ControlNet/training/fill50k.zip - The data for our training tutorial. # Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

albertqueralto/ppo-SnowballTarget

albertqueralto

null

20

1

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

861

# **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: albertqueralto/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

sgoodfriend/PPO-sb3-LunarLander-v2

sgoodfriend

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 ... ```

augustogeog/q-Taxi-v3

augustogeog

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

367

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

pfunk/Pong-v4-DQPN_p50_e0.25-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

1,990

# (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_p50_e0.25.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p50_e0.25]" python -m cleanrl_utils.enjoy --exp-name DQPN_p50_e0.25 --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_p50_e0.25-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.25-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_e0.25-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p50_e0.25 --start-policy-f 50000 --end-policy-f 1000 --evaluation-fraction 0.25 --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': 1000, 'env_id': 'Pong-v4', 'evaluation_fraction': 0.25, 'exp_name': 'DQPN_p50_e0.25', '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': 50000, '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'} ```

Iggg0r/rl_course

Iggg0r

null

19

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 ... ```

pneubauer/basic-poca-SoccerTwos_1

pneubauer

null

7

259

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

851

# **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: pneubauer/basic-poca-SoccerTwos_1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Snim/dqn-SpaceInvadersNoFrameskip-v4

Snim

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,205

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Snim -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 Snim -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 Snim ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

kasrahabib/FInall_35__XXX08_02_23-bucket-finetunned

kasrahabib

bert

10

2

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,747

# kasrahabib/FInall_35__XXX08_02_23-bucket-finetunned 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: 0.0836 - Validation Loss: 0.2277 - 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', '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}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3305 | 0.2448 | 0 | | 0.2215 | 0.2168 | 1 | | 0.1728 | 0.2316 | 2 | | 0.1443 | 0.2283 | 3 | | 0.1225 | 0.2261 | 4 | | 0.1096 | 0.2232 | 5 | | 0.1027 | 0.2235 | 6 | | 0.0937 | 0.2273 | 7 | | 0.0876 | 0.2278 | 8 | | 0.0836 | 0.2277 | 9 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

johko/mcc_co3dv2_all_categories

johko

null

3

0

null

0

null

false

apache-2.0

null

['CO3Dv2']

null

0

['3D Reconstruction']

false

true

399

# Multiview Compressive Coding (MCC) ## Model Description These are model weights originally provided by the authors of the paper [Multiview Compressive Coding (MCC)](https://arxiv.org/abs/2301.08247). Their method aims to create a 3D multiview object from a single RGB-D image. ## Datasets The authors trained the model on [the CO3D v2 dataset](https://ai.facebook.com/datasets/CO3D-dataset/)

saurabhnaik/dqn-SpaceInvadersNoFrameskip-v4

saurabhnaik

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,227

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saurabhnaik -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 saurabhnaik -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 saurabhnaik ``` ## 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)]) ```

mfrayha/marcelo

mfrayha

null

19

35

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

435

### Marcelo Dreambooth model trained by mfrayha with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept:

DeepaKrish/roberta-base-finetuned-squad

DeepaKrish

roberta

13

5

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,192

# roberta-base-finetuned-squad 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: 0.0491 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 27 | 0.1224 | | No log | 2.0 | 54 | 0.0491 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.9.0 - Datasets 2.5.1 - Tokenizers 0.13.2

DarwinAnim8or/GPT-Grug-355m

DarwinAnim8or

gpt2

10

11

transformers

0

text-generation

true

false

mit

['en']

['DarwinAnim8or/grug']

null

0

['grug', 'caveman', 'fun']

false

true

1,690

# GPT-Grug-355m A finetuned version of [GPT2-Medium](https://huggingface.co/gpt2-medium) on the 'grug' dataset. A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/grug-chat) If you're interested, there's a smaller model available here: [GPT-Grug-125m](https://huggingface.co/DarwinAnim8or/gpt-grug-125m) Do note however that it is very limited by comparison. # Training Procedure This was trained on the 'grug' dataset, using the "HappyTransformers" library on Google Colab. This model was trained for 4 epochs with learning rate 1e-2. The notebook used to train has been included in this repo. # Biases & Limitations This likely contains the same biases and limitations as the original GPT2 that it is based on, and additionally heavy biases from the grug datasets. # Intended Use This model is meant for fun, please do not take anything this caveman says seriously. # Sample Use ```python #Import model: from happytransformer import HappyGeneration happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/gpt-grug-355m") #Set generation settings: from happytransformer import GENSettings args_top_k = GENSettings(no_repeat_ngram_size=2, do_sample=True,top_k=50, temperature=0.7, max_length=50, early_stopping=False) #Generate a response: result = happy_gen.generate_text("""Person: "Hello grug" Grug: "hello person" ### Person: "how are you grug" Grug: "grug doing ok. grug find many berry. good for tribe." ### Person: "what does grug think of new spear weapon?" Grug: "grug no like new spear weapon. grug stick bigger. spear too small, break easy" ### Person: "what does grug think of football?" Grug: \"""", args=args_top_k) print(result) print(result.text) ```

HueyNemud/icdar23-entrydetector_plaintext

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

2,488

# icdar23-entrydetector_plaintext This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0424 - Ebegin: {'precision': 0.9725125822686799, 'recall': 0.9447160586686725, 'f1': 0.9584128195345288, 'number': 2659} - Eend: {'precision': 0.9570211189329382, 'recall': 0.9652466367713004, 'f1': 0.9611162790697675, 'number': 2676} - Overall Precision: 0.9646 - Overall Recall: 0.9550 - Overall F1: 0.9598 - Overall Accuracy: 0.9923 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 7500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.0487 | 0.9874 | 0.9565 | 0.9717 | 0.9943 | | 0.1698 | 0.14 | 600 | 0.0310 | 0.9891 | 0.9709 | 0.9799 | 0.9959 | | 0.1698 | 0.21 | 900 | 0.0267 | 0.9746 | 0.9764 | 0.9755 | 0.9953 | | 0.0346 | 0.29 | 1200 | 0.0217 | 0.9885 | 0.9685 | 0.9784 | 0.9956 | | 0.0237 | 0.36 | 1500 | 0.0201 | 0.9866 | 0.9742 | 0.9804 | 0.9960 | | 0.0237 | 0.43 | 1800 | 0.0268 | 0.9883 | 0.9561 | 0.9719 | 0.9944 | | 0.0205 | 0.5 | 2100 | 0.0216 | 0.9823 | 0.9779 | 0.9801 | 0.9959 | | 0.0205 | 0.57 | 2400 | 0.0236 | 0.9874 | 0.9700 | 0.9787 | 0.9957 | | 0.0196 | 0.64 | 2700 | 0.0246 | 0.9877 | 0.9668 | 0.9772 | 0.9954 | | 0.0195 | 0.72 | 3000 | 0.0254 | 0.9789 | 0.9682 | 0.9735 | 0.9950 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

kasrahabib/Sentence_transformer_FInall_35__XXX08_02_23-bucket-finetunned

kasrahabib

bert

10

2

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,719

# kasrahabib/Sentence_transformer_FInall_35__XXX08_02_23-bucket-finetunned This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2971 - Validation Loss: 0.8087 - Epoch: 8 ## 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', '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}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1510 | 1.4873 | 0 | | 1.2438 | 1.0710 | 1 | | 0.8797 | 0.9329 | 2 | | 0.6640 | 0.8707 | 3 | | 0.5321 | 0.8403 | 4 | | 0.4386 | 0.8146 | 5 | | 0.3740 | 0.8131 | 6 | | 0.3240 | 0.8202 | 7 | | 0.2971 | 0.8087 | 8 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

Kaludi/food-category-classification-v2.0

Kaludi

swin

5

203

transformers

0

image-classification

true

false

null

['Kaludi/food-category-classification-v2.0']

{'emissions': 12.456278925446485}

0

['vision', 'image-classification']

false

true

1,592

# Food Category Classification v2.0 This is an updated Food Category Image Classifier model of the [old](https://huggingface.co/Kaludi/food-category-classification) model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize **12** different categories of foods, which includes **Bread**, **Dairy**, **Dessert**, **Egg**, **Fried Food**, **Fruit**, **Meat**, **Noodles**, **Rice**, **Seafood**, **Soup**, and **Vegetable**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience. ### Gradio This model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model: [![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Category-Classification_V2_App) ## Validation Metrics - Problem type: Multi-class Classification - Model ID: 3353292434 - CO2 Emissions (in grams): 12.4563 - Loss: 0.144 - Accuracy: 0.960 - Macro F1: 0.959 - Micro F1: 0.960 - Weighted F1: 0.959 - Macro Precision: 0.962 - Micro Precision: 0.960 - Weighted Precision: 0.962 - Macro Recall: 0.960 - Micro Recall: 0.960 - Weighted Recall: 0.960

Anjoe/poetry-gpt2-large-no-hoel_3

Anjoe

gpt2

14

25

transformers

1

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,246

# poetry-gpt2-large-no-hoel_3 This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7234 ## 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: 2 - eval_batch_size: 2 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 3.678 | 1.0 | 19917 | 3.7393 | | 3.3445 | 2.0 | 39834 | 3.7234 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Anjoe/poetry-gpt2-large-no_schiller_3

Anjoe

gpt2

14

35

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,250

# poetry-gpt2-large-no_schiller_3 This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7301 ## 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: 2 - eval_batch_size: 2 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 3.6925 | 1.0 | 20041 | 3.7494 | | 3.3496 | 2.0 | 40082 | 3.7301 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Anjoe/poetry-gpt2-large-complete_3

Anjoe

gpt2

14

25

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,247

# poetry-gpt2-large-complete_3 This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7483 ## 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: 2 - eval_batch_size: 2 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 3.6838 | 1.0 | 20371 | 3.7627 | | 3.3382 | 2.0 | 40742 | 3.7483 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

gupta99riya/mbert-fine-tune-ner_fr

gupta99riya

bert

13

9

transformers

0

token-classification

true

false

apache-2.0

null

['wikiann']

null

0

['generated_from_trainer']

true

1,456

# mbert-fine-tune-ner_fr This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1887 - Precision: 0.9000 - Recall: 0.9078 - F1: 0.9038 - Accuracy: 0.9491 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2929 | 1.0 | 1250 | 0.2028 | 0.8782 | 0.8938 | 0.8860 | 0.9417 | | 0.1355 | 2.0 | 2500 | 0.1887 | 0.9000 | 0.9078 | 0.9038 | 0.9491 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

SRobbins/a2c-PandaReachDense-v2

SRobbins

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 ... ```

aimarsg/pharmacoNER

aimarsg

roberta

13

14

transformers

0

token-classification

true

false

apache-2.0

null

['pharmaconer']

null

0

['generated_from_trainer']

true

1,539

# pharmacoNER This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the pharmaconer dataset. It achieves the following results on the evaluation set: - Loss: 0.0251 - Precision: 0.9058 - Recall: 0.9026 - F1: 0.9042 - Accuracy: 0.9948 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0272 | 1.0 | 1017 | 0.0288 | 0.8047 | 0.8503 | 0.8269 | 0.9914 | | 0.0114 | 2.0 | 2034 | 0.0240 | 0.8950 | 0.8998 | 0.8974 | 0.9945 | | 0.006 | 3.0 | 3051 | 0.0251 | 0.9058 | 0.9026 | 0.9042 | 0.9948 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

HueyNemud/icdar23-entrydetector_plaintext_breaks

HueyNemud

camembert

11

2

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

2,771

# icdar23-entrydetector_plaintext_breaks This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0120 - Ebegin: {'precision': 0.9901997738409348, 'recall': 0.9879654005265137, 'f1': 0.9890813253012049, 'number': 2659} - Eend: {'precision': 0.9916824196597354, 'recall': 0.9801943198804185, 'f1': 0.9859049050930276, 'number': 2676} - Overall Precision: 0.9909 - Overall Recall: 0.9841 - Overall F1: 0.9875 - Overall Accuracy: 0.9977 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 7500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.0318 | 0.9847 | 0.9761 | 0.9803 | 0.9966 | | 0.1683 | 0.14 | 600 | 0.0164 | 0.9878 | 0.9890 | 0.9884 | 0.9978 | | 0.1683 | 0.21 | 900 | 0.0146 | 0.9900 | 0.9853 | 0.9876 | 0.9976 | | 0.0203 | 0.29 | 1200 | 0.0112 | 0.9862 | 0.9902 | 0.9882 | 0.9978 | | 0.0123 | 0.36 | 1500 | 0.0089 | 0.9943 | 0.9878 | 0.9910 | 0.9983 | | 0.0123 | 0.43 | 1800 | 0.0139 | 0.9970 | 0.9814 | 0.9891 | 0.9979 | | 0.0109 | 0.5 | 2100 | 0.0101 | 0.9937 | 0.9882 | 0.9909 | 0.9982 | | 0.0109 | 0.57 | 2400 | 0.0087 | 0.9949 | 0.9896 | 0.9922 | 0.9985 | | 0.0092 | 0.64 | 2700 | 0.0081 | 0.9849 | 0.9919 | 0.9884 | 0.9978 | | 0.0084 | 0.72 | 3000 | 0.0087 | 0.9937 | 0.9867 | 0.9902 | 0.9981 | | 0.0084 | 0.79 | 3300 | 0.0089 | 0.9915 | 0.9889 | 0.9902 | 0.9981 | | 0.0069 | 0.86 | 3600 | 0.0092 | 0.9899 | 0.9901 | 0.9900 | 0.9981 | | 0.0069 | 0.93 | 3900 | 0.0097 | 0.9845 | 0.9915 | 0.9880 | 0.9977 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

huggingtweets/101dadjokes-dadsjokes

huggingtweets

gpt2

11

2

transformers

0

text-generation

true

false

null

['en']

null

0

['huggingtweets']

false

true

3,498

<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406653045757317121/YCS9YykL_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/641271414/dad_jokes_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Dad Jokes & Dad Jokes</div> <div style="text-align: center; font-size: 14px;">@101dadjokes-dadsjokes</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Dad Jokes & Dad Jokes. | Data | Dad Jokes | Dad Jokes | | --- | --- | --- | | Tweets downloaded | 184 | 2043 | | Retweets | 14 | 0 | | Short tweets | 10 | 123 | | Tweets kept | 160 | 1920 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/od2iwqt2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @101dadjokes-dadsjokes's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7ruisgab) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7ruisgab/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/101dadjokes-dadsjokes') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

sanchit-gandhi/ast-fleurs-langid-dropout-0.2-layers-6

sanchit-gandhi

audio-spectrogram-transformer

9

0

transformers

0

audio-classification

true

false

bsd-3-clause

null

['fleurs']

null

0

['generated_from_trainer']

true

1,522

# ast-fleurs-langid-dropout-0.2-layers-6 This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 7.4304 - Accuracy: 0.1802 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0405 | 1.0 | 16987 | 6.6986 | 0.1722 | | 0.0002 | 2.0 | 33974 | 7.1284 | 0.1811 | | 0.0 | 3.0 | 50961 | 7.4304 | 0.1802 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

WitchHuntTV/XiJinPing_Singing

WitchHuntTV

null

3

0

null

0

null

false

gpl-3.0

['zh']

null

0

['VITS', 'so-vits3']

false

true

288

用于so-vits3的习近平歌声模型。欢迎任何人使用此模型进行创作，模型不定期更新，直到100k step为止。训练模型不易，显卡烧的都是钱，请考虑赞助，门罗币(XMR)地址：87MTHJgrCRfC6S8hV1TNiV1SyYZ19ny7o8YHui462TYKiVLzUpdTHDCfErqbSSSe4GMriVEfM2xK6eG87sEwvQPj4LMBqdD so-vits-svc项目地址：https://github.com/innnky/so-vits-svc 下载模型后，无需处理数据集也无需训练，直接跳到推理部分。推理教程参考B站cv20533940

hulkster/sd-class-butterflies-32

hulkster

null

6

2

diffusers

0

unconditional-image-generation

true

false

mit

null

0

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

false

true

365

# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('hulkster/sd-class-butterflies-32') image = pipeline().images[0] image ```

HueyNemud/icdar23-entrydetector_plaintext_breaks_indents_left_ref

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

3,891

# icdar23-entrydetector_plaintext_breaks_indents_left_ref This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0062 - Ebegin: {'precision': 0.997709049255441, 'recall': 0.9827002632568634, 'f1': 0.9901477832512315, 'number': 2659} - Eend: {'precision': 0.9973363774733638, 'recall': 0.9794469357249627, 'f1': 0.9883107088989442, 'number': 2676} - Overall Precision: 0.9975 - Overall Recall: 0.9811 - Overall F1: 0.9892 - Overall Accuracy: 0.9982 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 7500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.0389 | 0.9556 | 0.9663 | 0.9609 | 0.9949 | | 0.1775 | 0.14 | 600 | 0.0162 | 0.9854 | 0.9893 | 0.9873 | 0.9977 | | 0.1775 | 0.21 | 900 | 0.0114 | 0.9928 | 0.9889 | 0.9909 | 0.9984 | | 0.0229 | 0.29 | 1200 | 0.0172 | 0.9793 | 0.9851 | 0.9822 | 0.9975 | | 0.016 | 0.36 | 1500 | 0.0087 | 0.9906 | 0.9907 | 0.9907 | 0.9984 | | 0.016 | 0.43 | 1800 | 0.0079 | 0.9955 | 0.9879 | 0.9917 | 0.9985 | | 0.0115 | 0.5 | 2100 | 0.0093 | 0.9910 | 0.9912 | 0.9911 | 0.9984 | | 0.0115 | 0.57 | 2400 | 0.0102 | 0.9816 | 0.9942 | 0.9878 | 0.9978 | | 0.0109 | 0.64 | 2700 | 0.0072 | 0.9895 | 0.9939 | 0.9917 | 0.9985 | | 0.0075 | 0.72 | 3000 | 0.0055 | 0.9919 | 0.9917 | 0.9918 | 0.9985 | | 0.0075 | 0.79 | 3300 | 0.0078 | 0.9948 | 0.9910 | 0.9929 | 0.9987 | | 0.007 | 0.86 | 3600 | 0.0057 | 0.9937 | 0.9933 | 0.9935 | 0.9989 | | 0.007 | 0.93 | 3900 | 0.0059 | 0.9830 | 0.9957 | 0.9893 | 0.9981 | | 0.0055 | 1.0 | 4200 | 0.0049 | 0.9972 | 0.9899 | 0.9935 | 0.9988 | | 0.0029 | 1.07 | 4500 | 0.0064 | 0.9944 | 0.9926 | 0.9935 | 0.9989 | | 0.0029 | 1.14 | 4800 | 0.0057 | 0.9927 | 0.9919 | 0.9923 | 0.9987 | | 0.0043 | 1.22 | 5100 | 0.0064 | 0.9890 | 0.9945 | 0.9917 | 0.9986 | | 0.0043 | 1.29 | 5400 | 0.0058 | 0.9857 | 0.9957 | 0.9907 | 0.9983 | | 0.0028 | 1.36 | 5700 | 0.0049 | 0.9961 | 0.9922 | 0.9941 | 0.9990 | | 0.0034 | 1.43 | 6000 | 0.0048 | 0.9952 | 0.9937 | 0.9945 | 0.9990 | | 0.0034 | 1.5 | 6300 | 0.0050 | 0.9936 | 0.9937 | 0.9937 | 0.9989 | | 0.0022 | 1.57 | 6600 | 0.0046 | 0.9937 | 0.9934 | 0.9936 | 0.9989 | | 0.0022 | 1.65 | 6900 | 0.0042 | 0.9954 | 0.9929 | 0.9941 | 0.9990 | | 0.0039 | 1.72 | 7200 | 0.0042 | 0.9959 | 0.9931 | 0.9945 | 0.9990 | | 0.003 | 1.79 | 7500 | 0.0039 | 0.9968 | 0.9927 | 0.9947 | 0.9991 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

HueyNemud/icdar23-entrydetector_plaintext_breaks_indents_left_diff

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

3,433

# icdar23-entrydetector_plaintext_breaks_indents_left_diff This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0072 - Ebegin: {'precision': 0.9935508345978755, 'recall': 0.9849567506581421, 'f1': 0.9892351274787535, 'number': 2659} - Eend: {'precision': 0.9980857580398163, 'recall': 0.9742152466367713, 'f1': 0.9860060514372163, 'number': 2676} - Overall Precision: 0.9958 - Overall Recall: 0.9796 - Overall F1: 0.9876 - Overall Accuracy: 0.9980 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 7500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.0309 | 0.9593 | 0.9879 | 0.9734 | 0.9955 | | 0.161 | 0.14 | 600 | 0.0126 | 0.9890 | 0.9911 | 0.9900 | 0.9982 | | 0.161 | 0.21 | 900 | 0.0116 | 0.9730 | 0.9894 | 0.9811 | 0.9971 | | 0.0165 | 0.29 | 1200 | 0.0087 | 0.9938 | 0.9918 | 0.9928 | 0.9987 | | 0.0119 | 0.36 | 1500 | 0.0093 | 0.9851 | 0.9937 | 0.9894 | 0.9981 | | 0.0119 | 0.43 | 1800 | 0.0055 | 0.9942 | 0.9913 | 0.9928 | 0.9987 | | 0.0091 | 0.5 | 2100 | 0.0057 | 0.9951 | 0.9904 | 0.9928 | 0.9987 | | 0.0091 | 0.57 | 2400 | 0.0058 | 0.9920 | 0.9936 | 0.9928 | 0.9987 | | 0.0083 | 0.64 | 2700 | 0.0059 | 0.9896 | 0.9918 | 0.9907 | 0.9983 | | 0.0065 | 0.72 | 3000 | 0.0045 | 0.9968 | 0.9917 | 0.9942 | 0.9990 | | 0.0065 | 0.79 | 3300 | 0.0047 | 0.9920 | 0.9937 | 0.9929 | 0.9987 | | 0.0054 | 0.86 | 3600 | 0.0050 | 0.9945 | 0.9909 | 0.9926 | 0.9987 | | 0.0054 | 0.93 | 3900 | 0.0064 | 0.9838 | 0.9968 | 0.9903 | 0.9983 | | 0.0056 | 1.0 | 4200 | 0.0046 | 0.9971 | 0.9920 | 0.9946 | 0.9990 | | 0.0034 | 1.07 | 4500 | 0.0037 | 0.9959 | 0.9936 | 0.9948 | 0.9990 | | 0.0034 | 1.14 | 4800 | 0.0047 | 0.9983 | 0.9900 | 0.9941 | 0.9989 | | 0.0035 | 1.22 | 5100 | 0.0043 | 0.9936 | 0.9951 | 0.9944 | 0.9990 | | 0.0035 | 1.29 | 5400 | 0.0061 | 0.9892 | 0.9957 | 0.9925 | 0.9986 | | 0.002 | 1.36 | 5700 | 0.0057 | 0.9898 | 0.9947 | 0.9923 | 0.9986 | | 0.0048 | 1.43 | 6000 | 0.0042 | 0.9954 | 0.9933 | 0.9944 | 0.9990 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

kmposkid1/q-FrozenLake-v1-4x4-noSlippery

kmposkid1

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

398

# **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="kmposkid1/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"]) ```

kmposkid1/q-Taxi-v3

kmposkid1

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="kmposkid1/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"]) ```

slopezay/ppo-LunarLander-v2

slopezay

null

12

1

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 ... ```

DeepaKrish/roberta-base-squad2-finetuned

DeepaKrish

roberta

13

6

transformers

0

question-answering

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

1,223

# roberta-base-squad2-finetuned This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0010 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 27 | 0.0023 | | No log | 2.0 | 54 | 0.0010 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.9.0 - Datasets 2.5.1 - Tokenizers 0.13.2

pfunk/Pong-v4-DQPN_p500_pt0.1-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

2,002

# (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_pt0.1.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p500_pt0.1]" python -m cleanrl_utils.enjoy --exp-name DQPN_p500_pt0.1 --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_pt0.1-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p500_pt0.1 --start-policy-f 500000 --end-policy-f 500000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 0.1 --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_pt0.1', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 0.1, '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'} ```

Nonin/ppo-Huggy

Nonin

null

32

8

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: Nonin/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

pryjuli/ppo-Huggy

pryjuli

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

818

# **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: pryjuli/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

HueyNemud/icdar23-entrydetector_plaintext_breaks_indents_left_ref_right_ref

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

3,335

# icdar23-entrydetector_plaintext_breaks_indents_left_ref_right_ref This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0068 - Ebegin: {'precision': 1.0, 'recall': 0.9793155321549455, 'f1': 0.9895496864905947, 'number': 2659} - Eend: {'precision': 0.9988562714449104, 'recall': 0.9790732436472347, 'f1': 0.9888658237403285, 'number': 2676} - Overall Precision: 0.9994 - Overall Recall: 0.9792 - Overall F1: 0.9892 - Overall Accuracy: 0.9983 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 7500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.0309 | 0.9637 | 0.9910 | 0.9771 | 0.9964 | | 0.181 | 0.14 | 600 | 0.0144 | 0.9777 | 0.9863 | 0.9819 | 0.9974 | | 0.181 | 0.21 | 900 | 0.0095 | 0.9969 | 0.9845 | 0.9906 | 0.9985 | | 0.0168 | 0.29 | 1200 | 0.0105 | 0.9869 | 0.9913 | 0.9891 | 0.9982 | | 0.011 | 0.36 | 1500 | 0.0063 | 0.9937 | 0.9915 | 0.9926 | 0.9988 | | 0.011 | 0.43 | 1800 | 0.0064 | 0.9883 | 0.9940 | 0.9911 | 0.9986 | | 0.01 | 0.5 | 2100 | 0.0203 | 0.9552 | 0.9507 | 0.9529 | 0.9922 | | 0.01 | 0.57 | 2400 | 0.0049 | 0.9946 | 0.9925 | 0.9935 | 0.9989 | | 0.0144 | 0.64 | 2700 | 0.0056 | 0.9871 | 0.9944 | 0.9907 | 0.9984 | | 0.0058 | 0.72 | 3000 | 0.0051 | 0.9928 | 0.9930 | 0.9929 | 0.9988 | | 0.0058 | 0.79 | 3300 | 0.0036 | 0.9969 | 0.9920 | 0.9945 | 0.9991 | | 0.0048 | 0.86 | 3600 | 0.0047 | 0.9930 | 0.9947 | 0.9938 | 0.9990 | | 0.0048 | 0.93 | 3900 | 0.0053 | 0.9863 | 0.9965 | 0.9914 | 0.9985 | | 0.0052 | 1.0 | 4200 | 0.0033 | 0.9985 | 0.9909 | 0.9947 | 0.9991 | | 0.0029 | 1.07 | 4500 | 0.0039 | 0.9938 | 0.9954 | 0.9946 | 0.9991 | | 0.0029 | 1.14 | 4800 | 0.0038 | 0.9981 | 0.9906 | 0.9943 | 0.9991 | | 0.0034 | 1.22 | 5100 | 0.0044 | 0.9937 | 0.9934 | 0.9936 | 0.9989 | | 0.0034 | 1.29 | 5400 | 0.0040 | 0.9884 | 0.9959 | 0.9921 | 0.9987 | | 0.0027 | 1.36 | 5700 | 0.0040 | 0.9975 | 0.9910 | 0.9942 | 0.9990 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Isaacp/Reinforce-cartpole

Isaacp

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

ramelol/ppo-LunarLander-v2

ramelol

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 ... ```

ivi137/q-FrozenLake-v1-4x4-noSlippery

ivi137

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

395

# **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="ivi137/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"]) ```

guydegnol/ppo-LunarLander-v2

guydegnol

null

12

6

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 ... ```

ivi137/Taxi-v3

ivi137

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

360

# **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="ivi137/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"]) ```

Jomppe2/Face

Jomppe2

null

2

0

null

0

null

false

null

0

[]

false

true

4,907

# 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  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses  ## Direct Use  [More Information Needed] ## Downstream Use [optional]  [More Information Needed] ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations  [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]

stelladk/Reinforce-PixelCopter-PLE-v0

stelladk

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

jha2ee/riffusion-model-db

jha2ee

null

19

8

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

426

### riffusion_model-db Dreambooth model trained by jha2ee 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:

ivi137/q-Taxi-v3

ivi137

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

362

# **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="ivi137/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"]) ```

popcornell/chime7_task1_asr1_baseline

popcornell

null

23

7

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['chime7_task1']

null

0

['espnet', 'audio', 'automatic-speech-recognition', 'speech separation']

false

true

10,211

## ESPnet2 ASR model ### `popcornell/chime7_task1_asr1_baseline` This model was trained by popcornell using chime7_task1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [CHiME-7 DASR installation instructions](https://github.com/espnet/espnet/blob/master/egs2/chime7_task1/asr1/README.md) if you haven't done that already. ```bash cd espnet git checkout 15646109f254de8b39bbe310827d617da5ac858d # follow installation instruction for CHiME-7 DASR recipe https://github.com/espnet/espnet/blob/master/egs2/chime7_task1/asr1/README.md ./run.sh --decode-only 1 --use-pretrained popcornell/chime7_task1_asr1_baseline --ngpu PUT YOURS ```  # RESULTS See [CHiME-7 DASR README.md](https://github.com/espnet/espnet/blob/master/egs2/chime7_task1/asr1/README.md) ## Environments - date: `Wed Feb 8 23:41:28 UTC 2023` - python version: `3.9.2 (default, Mar 3 2021, 20:02:32) [GCC 7.3.0]` - espnet version: `espnet 202301` - pytorch version: `pytorch 1.13.1+cu116` - Git hash: `` - Commit date: `` ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k_raw_en_bpe500_batch_size640_scheduler_confwarmup_steps8000_max_epoch8_optim_conflr0.000500000000_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 5 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 44341 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 8 patience: 4 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 640 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe500_sp/train/speech_shape - exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/kaldi/train_all_mdm_ihm_rvb_gss_sp/wav.scp - speech - sound - - dump/raw/kaldi/train_all_mdm_ihm_rvb_gss_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/kaldi/chime6/dev/gss/wav.scp - speech - sound - - dump/raw/kaldi/chime6/dev/gss/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.0005 scheduler: warmuplr scheduler_conf: warmup_steps: 8000 token_list: - <blank> - <unk> - s - '''' - ▁i - t - ▁it - ▁a - e - ▁you - ▁the - ▁like - ▁yeah - a - d - ▁and - m - ▁that - ▁to - n - i - y - ing - o - u - ▁so - p - ▁of - ▁in - re - ▁was - c - r - ▁just - er - ▁know - ▁oh - ed - ▁but - ▁ummm - ▁we - l - ▁no - ▁they - ▁have - ▁do - g - ▁he - k - ll - ▁uhhh - ▁don - ▁for - h - ▁what - ▁be - ar - ▁is - ▁there - '-' - ▁s - ▁this - in - b - ▁ - en - ▁on - ▁p - ▁can - al - ▁not - w - ▁my - ▁one - ic - f - ▁or - ▁really - ▁go - ▁right - ▁me - an - ▁w - or - le - ▁f - ▁think - ▁okay - ▁all - ▁then - ▁with - ▁are - ▁get - it - ▁t - ▁st - ve - ▁hmmm - ▁g - ▁if - ce - 'on' - ▁she - ▁good - ▁e - es - ▁well - v - ▁re - th - ter - ch - ▁out - ▁up - ly - ▁b - ▁ma - il - ▁would - ▁at - ▁want - ▁mean - ▁ch - ▁your - ▁people - ur - ▁how - ▁k - ▁co - ▁about - ▁tr - ▁ba - ▁kind - ▁when - ▁mi - ▁because - ro - ▁had - ▁ho - ▁gonna - ▁time - ▁more - ▁got - ▁some - ▁two - ▁did - ▁see - ▁now - ▁pa - ra - ▁de - ▁lot - ▁actually - ▁o - ▁too - ate - ▁here - ▁cuz - ▁sp - ▁where - ▁going - ▁j - ▁from - ▁bo - ▁them - ▁bu - ▁put - ▁thing - ng - ▁were - ▁n - ▁sh - ▁work - el - ▁something - ▁se - ▁say - ke - ow - ▁ca - ▁fa - ▁need - sh - ▁di - ▁po - ▁make - la - ▁br - ▁v - ▁an - ▁who - ion - ▁y - ▁look - ▁didn - ▁could - ▁little - ver - ▁c - ▁mo - ▁much - ▁very - ir - ▁sa - ▁play - ▁pretty - ▁been - ▁d - ▁other - ▁year - and - ▁mm - ▁stuff - ▁dr - ▁why - ▁con - ▁su - ▁back - ▁ex - ting - ▁take - ▁li - ▁even - ▁should - ▁her - ally - lo - ation - ▁way - ▁guess - ▁has - z - ▁three - ry - ▁ha - ies - is - x - ▁ro - ▁yes - ▁th - ▁use - ▁down - ous - ▁over - ▁probably - ▁guys - ▁maybe - ▁still - ▁cr - ▁which - ▁nice - und - ▁sure - ▁l - ▁off - ▁la - ▁cu - est - ▁any - ▁fi - ▁these - ▁ra - ▁went - ▁things - ment - ▁doing - ▁day - ▁un - ▁lo - ▁da - ▁only - igh - ▁come - ▁big - ▁those - ▁wanna - ▁bit - ▁never - ▁us - ol - ▁though - ▁first - ive - ▁their - ▁let - ▁start - ▁his - ▁four - ▁le - ▁eat - ist - ▁school - us - ▁into - ▁yep - uck - ▁than - ▁him - ▁hi - ▁also - ▁five - side - ▁new - ▁comp - ▁cool - ▁talk - ▁said - ▁pro - ▁r - ▁always - ▁ri - ▁cl - ▁long - able - ▁sc - ▁gra - ▁by - ▁friend - age - ▁different - ▁live - ▁doesn - ▁place - ▁sorry - ▁will - ▁feel - ▁does - ▁part - ▁wait - ▁six - ▁watch - ▁anything - ▁man - ▁our - ▁car - ▁huh - ▁whatever - ▁last - ▁give - ▁ten - ▁before - ▁thought - ▁after - ▁game - ▁card - ▁fl - ▁every - cause - ▁same - ▁around - ▁cook - ▁week - ▁hu - ▁everything - ▁fine - ▁many - ▁qu - ▁read - ▁tea - ough - ance - ▁turn - ▁wow - ▁fun - ▁hard - ▁great - ▁love - ▁remember - ▁twenty - ▁whole - ▁happen - ▁seven - ▁keep - ▁food - ▁most - j - ▁might - ▁thank - ▁move - ▁job - ▁eight - ▁mu - ▁sort - ▁better - port - ▁another - ful - ▁point - ▁show - ▁again - ▁high - ize - ▁house - ▁home - ▁person - ▁old - ▁end - ▁through - ▁pick - ▁else - ▁guy - ▁app - ▁find - ▁nine - ▁hand - ▁kid - ▁interesting - ▁city - ▁called - ▁tell - ▁half - ▁name - ▁definitely - ▁made - ▁exactly - ▁came - ▁wood - ▁funny - ▁basically - ▁count - ▁usually - ▁help - ▁someone - ▁already - ▁dunno - ▁enough - ction - ▁own - ▁weird - ▁next - ▁hundred - ▁small - ▁money - ▁couple - ▁while - ▁close - ▁movie - ▁sometimes - ▁everyone - ▁away - ▁true - ▁super - ▁cheese - ▁class - ▁night - ▁life - ▁leave - ▁plan - ▁water - ▁left - ▁thirty - ▁family - ▁phone - ▁build - ▁room - ▁month - ▁open - ▁idea - ▁second - ▁dude - ▁music - ▁each - ▁learn - ▁girl - ▁together - ▁under - ▁run - ▁chicken - ▁having - ▁either - ▁almost - ▁crazy - ▁book - ▁sauce - ▁supposed - ▁course - ▁speak - ▁awesome - ▁anyway - ▁throw - ▁finish - ▁world - ▁reason - ▁check - ▁least - ▁parents - ▁everybody - ▁change - '&' - ä - '#' - ñ - â - é - ü - ']' - q - î - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: s3prl frontend_conf: frontend_conf: upstream: wavlm_large download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: false time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: false freq_mask_width_range: - 0 - 150 num_freq_mask: 4 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.15 num_time_mask: 3 normalize: utterance_mvn normalize_conf: {} model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false preencoder: linear preencoder_conf: input_size: 1024 output_size: 128 dropout: 0.2 encoder: transformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d2 normalize_before: true postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: input_layer: embed attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.0 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202301' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

AndrewOgn/IntelasMatcher_homecase

AndrewOgn

bert

12

6

sentence-transformers

0

sentence-similarity

true

false

null

0

['sentence-transformers', 'feature-extraction', 'sentence-similarity']

false

true

2,138

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 8 with parameters: ``` {'batch_size': 512, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors

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

petergoldstein

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

403

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

deprem-ml/distilroberta-tweet-clustering-embeddings

deprem-ml

null

3

0

null

0

feature-extraction

false

apache-2.0

['tr']

null

0

[]

false

true

297

## Topic Modelling için Clustering Embedding'leri Bu repository'i güncelleyeceğiz. Notebook burada: https://github.com/metekemertas/deprem-intent-classification-tfidf/blob/main/unsupervised_analysis.py Tweet verisinde ihtiyaç belirlemek (topic modelling) için çıkarılmış embedding'ler bu repo'da.

petergoldstein/Taxi-v3-default

petergoldstein

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

376

# **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="petergoldstein/Taxi-v3-default", 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"]) ```

rjac/test1

rjac

whisper

21

4

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['stakwork/bitcoin-clips-V3']

null

0

['generated_from_trainer']

true

1,035

# Whisper Cryptocurrency This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the stakwork/bitcoin-clips-V3 en dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 64 - 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: 4 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

daripaez/poca-SoccerTwos-1

daripaez

null

30

238

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: daripaez/poca-SoccerTwos-1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

kellyxuanlin/bert-finetuned-squad

kellyxuanlin

bert

12

9

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

954

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

HueyNemud/icdar23-entrydetector_plaintext_breaks_indents_left_diff_right_ref

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

3,167

# icdar23-entrydetector_plaintext_breaks_indents_left_diff_right_ref This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0078 - Ebegin: {'precision': 0.9920303605313093, 'recall': 0.9830763444904099, 'f1': 0.9875330562901399, 'number': 2659} - Eend: {'precision': 0.9958443520967133, 'recall': 0.9850523168908819, 'f1': 0.9904189366898367, 'number': 2676} - Overall Precision: 0.9939 - Overall Recall: 0.9841 - Overall F1: 0.9890 - Overall Accuracy: 0.9982 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 7500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.0314 | 0.9572 | 0.9870 | 0.9719 | 0.9956 | | 0.1574 | 0.14 | 600 | 0.0145 | 0.9897 | 0.9834 | 0.9866 | 0.9979 | | 0.1574 | 0.21 | 900 | 0.0098 | 0.9896 | 0.9917 | 0.9907 | 0.9985 | | 0.0161 | 0.29 | 1200 | 0.0079 | 0.9919 | 0.9921 | 0.9920 | 0.9987 | | 0.0107 | 0.36 | 1500 | 0.0072 | 0.9895 | 0.9928 | 0.9911 | 0.9986 | | 0.0107 | 0.43 | 1800 | 0.0116 | 0.9900 | 0.9877 | 0.9888 | 0.9981 | | 0.0114 | 0.5 | 2100 | 0.0069 | 0.9965 | 0.9898 | 0.9931 | 0.9988 | | 0.0114 | 0.57 | 2400 | 0.0055 | 0.9955 | 0.9907 | 0.9931 | 0.9989 | | 0.0082 | 0.64 | 2700 | 0.0051 | 0.9870 | 0.9956 | 0.9913 | 0.9985 | | 0.0062 | 0.72 | 3000 | 0.0046 | 0.9903 | 0.9957 | 0.9930 | 0.9988 | | 0.0062 | 0.79 | 3300 | 0.0038 | 0.9957 | 0.9929 | 0.9943 | 0.9990 | | 0.0051 | 0.86 | 3600 | 0.0038 | 0.9956 | 0.9943 | 0.9949 | 0.9992 | | 0.0051 | 0.93 | 3900 | 0.0047 | 0.9902 | 0.9942 | 0.9921 | 0.9987 | | 0.0041 | 1.0 | 4200 | 0.0035 | 0.9979 | 0.9917 | 0.9948 | 0.9991 | | 0.0029 | 1.07 | 4500 | 0.0036 | 0.9973 | 0.9926 | 0.9949 | 0.9992 | | 0.0029 | 1.14 | 4800 | 0.0038 | 0.9969 | 0.9916 | 0.9942 | 0.9990 | | 0.0034 | 1.22 | 5100 | 0.0036 | 0.9953 | 0.9935 | 0.9944 | 0.9991 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

petergoldstein/Taxi-v3-1M

petergoldstein

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

371

# **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="petergoldstein/Taxi-v3-1M", 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"]) ```

thanat/marian-finetuned-kde4-en-to-fr

thanat

marian

10

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,497

# thanat/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the [kde4](https://huggingface.co/datasets/kde4) dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6857 - Validation Loss: 0.8034 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, '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 | |:----------:|:---------------:|:-----:| | 1.0622 | 0.8817 | 0 | | 0.7977 | 0.8203 | 1 | | 0.6857 | 0.8034 | 2 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

SuryaaSeran/bert-base-uncased-finetuned-swag

SuryaaSeran

bert

16

15

transformers

0

multiple-choice

true

false

apache-2.0

null

['dream']

null

0

['generated_from_trainer']

true

1,332

# bert-base-uncased-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the dream dataset. It achieves the following results on the evaluation set: - Loss: 1.0986 - Accuracy: 0.3642 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1039 | 1.0 | 3058 | 1.0983 | 0.3779 | | 1.0995 | 2.0 | 6116 | 1.0986 | 0.3544 | | 1.1029 | 3.0 | 9174 | 1.0986 | 0.3642 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

yizhangliu/poca-SoccerTwos-v4

yizhangliu

null

21

240

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

847

# **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: yizhangliu/poca-SoccerTwos-v4 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

coreml/coreml-3DKX

coreml

null

17

0

null

2

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

9,737

# Core ML Converted Model: - This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br> - Provide the model to an app such as Mochi Diffusion [Github](https://github.com/godly-devotion/MochiDiffusion) - [Discord](https://discord.gg/x2kartzxGv) to generate images.<br> - `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> - `original` version is only compatible with CPU & GPU option.<br> - `vae` tagged files have a vae embedded into the model. Most common are 1.5 or 2.1 others are named accordingly.<br> - Descriptions are posted as-is from original model source. Not all features and/or results may be available in CoreML format.<br> # Note: Some models do not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). # H&A 3DKX: Source(s): [Hugging Face](https://huggingface.co/HavoAloe/3DKX_1.0b) - [CivitAI](https://civitai.com/models/2504/handas-3dkx-11) <a href="https://discord.gg/havonaloe"> <img src="https://cdn.discordapp.com/attachments/1051410188592226364/1061335270194171944/havo_aloe_banner_copie.jpg" alt="image description" width="768"> </a> <a href="https://www.patreon.com/aloeNhavo"> <img src="https://cdn.discordapp.com/attachments/1051410188592226364/1061335270483566662/havo_aloe_banner_patreon.jpg" alt="image description" width="768"> </a> Model name: H&A 3DKX Model versions: 1.0b, 1.1(latest) ## Changelog: V1.1: Minor update based on feedback, containing the following fixes: -“nsfw”, “nudity” , and “erotica” have been trained into the model and work as Negatives to greatly reduce unintended NSFW content. - CFG can be pushed a bit higher before the images become burnt. As a result, the model can accommodate more complicated prompts now. - Oversaturated images will be encountered way less often ## Description: SFW model with limited nsfw capabilities (suggestive nsfw) that is highly versatile for 3D renders. The model has the particularity of splitting itself into two different well balanced styles. If you'd like to have your 3D characters have a more "Cartoony" face, you simply start your prompt with "3d cartoon of", and if you want the classic 3D render style, you write "a 3d render of". Please check the cheat sheet for prompting tips as the structure of the prompt and negatives used has a huge effect. Note: Model has an embedded VAE, so do not add one with this model. It will be the best in most cases, and configured for higher resolutions. ## Model has an embedded VAE, do not use an extra one! If you want to chat with us and join our community visit our discord: https://discord.gg/CPyqJgXdRG ## Dataset: - between 140 and 180 pictures of 3D render of all kind ## PromptGuide/Cheat Sheet [3DKX_1.0b/1.1 Guide](https://docs.google.com/document/d/15pJ3TkbmX3LRoSTNsMYsbetvO7A46L60wVOxIL2ZZ6E/) ## Has a high success rate at: - sfw portraits, full body poses, close ups, etc - high versatility in terms of outputs, it isn't locked to perform well on portraits - Landscapes, cyberpunk, steampunk, natural, scifi, etc - 2B Nier Automata (Don't ask us why) - different body types - different ethnicity - nsfw portraits, full body poses, close ups, etc ## What it "In theory" shouldn't exceed at: - anything outside the scope of portraits, people, landscapes, game artworks, 3D sculptures, 3D fantasy, 3D film stills, etc - celebrities - highly specific animated cartoon characters - multiple subjects - highly specific video-game characters - pornography, genitalia and highly explicit materials <img width="768px" src="https://cdn.discordapp.com/attachments/1056287982363086930/1056331346177425438/00011-3928902726-A203d20render20of2020epic20portrait20close20shot20of20beautiful20turkish20woman20wearing20with20angelic20feathered20wings20gold20armour20neckline.png"> <img width="768px" src="https://cdn.discordapp.com/attachments/323893037379878912/1056823178846031882/00494-2262985444-3d_render_of_a_sharp_focused_detailed_photo_of_a_super_car_with_iridescent_metallic_color_driving_on_a_midnight_road_multicolo.png"> <img width="768px" src="https://media.discordapp.net/attachments/1056287982363086930/1056387208900268062/00102-971809704-movie_still_of_a_alien_from_mass_effect_wearing_scifi_armor_disney_pixar_animation_3d_render_4k_resolution_very_detail.png"> <img width="768px" src="https://cdn.discordapp.com/attachments/1056287982363086930/1056399456695767151/00143-556286556-3d_render_of_a_cute_simba_from_the_lion_king_disney_pixar_animation_RDR_2_game_render_lion_king_movie_still_very_detailed_4.png"> <img width="768px" src="https://media.discordapp.net/attachments/1056287982363086930/1056385527907110963/00097-3269033961-picture_of_a_handsome_viking_chief_in_his_village_disney_pixar_animation_3d_render_4k_resolution_very_detailed_movie_stil.png?width=645&height=806"> <img width="768px" src="https://cdn.discordapp.com/attachments/1056287982363086930/1056340815011659917/02082-2709311262-A_3d_render_of_a_cute_tiny_little_fluffy_monster_with_googly_eyes_running_in_a_huge_bedroom_antview_bokeh_closeup_highly_det.png"> <img width="768px" src="https://media.discordapp.net/attachments/1051410188592226364/1056636097330942022/02045-172340656-A_3d_render_of_A_mature_woman_with_short_styled_hair_and_wearing_a_colorful_printed_blouse_seated_in_a_cozy_armchair_with_a_wa.png"> <img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1056636096412389508/02029-172340656-A_3d_cartoon_of_A_mature_woman_with_short_styled_hair_and_wearing_a_colorful_printed_blouse_seated_in_a_cozy_armchair_with_a.png"> <img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1056636286347247616/01925-2050823061-A_woman_with_short_bobbed_hair_styled_in_a_choppy_textured_look_wearing_a_cyberpunk-inspired_outfit_with_neon_accents_and_boo.png"> <img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1056636296103198740/01858-1327022461-A_slender_woman_with_pale_skin_short_blonde_hair_and_bright_blue_eyes._She_is_standing_in_a_bright_white_studio_surrounded_by.png"> <img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1056636414059618406/02107-599009770-A_3d_cartoon_of_a_a_beautiful_spanish_woman_wearing_Kimono_neckline_fine_-_art_photography_cinematic_portrait_shot_8_k_mid.png"> ## Use Restrictions You agree not to use the Model or Derivatives of the Model: - In any way that violates any applicable national, federal, state, local or international law or regulation; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate personal identifiable information that can be used to harm an individual; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories; - To provide medical advice and medical results interpretation; - To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use). ## Important notes: - This model’s datasets do NOT contain any character that could be remotely described as a child, or underage. - Our datasets contains no mentions of the artist's name, nor specific styles from any artist whatsoever. - The creators (Havo and Aloe Vera) will not be held accountable for the way this model is being used or the outputs that any person may generate. - The purpose of this model isn't to replicate a style, but to provide a useful tool to creators of all kinds to generate 3D related contents - Be advised that this model can generate explicit material and therefore shouldn't be used in any way to cause harm or produce non-consensual sexual content. ## Conclusion: We do have limited resources, so our weeks worth of testing cannot realistically encapsulate the full potential of the model. Which is why we're very excited to discover that YOU, the awesome creators will make out of this tool. And for anyone that feels like we're worth a shot, we invite you to please have a look at our Patreon in which you can choose to chip in and support our work. We have many plans and we'd like to have some more resources that will allow us to work more efficiently and to eventually be able to create models of professional standards. It's our goal ! https://www.patreon.com/aloeNhavo