davanstrien/model_cards_with_readmes_with_duplicates

tamitani/xlm-roberta-base-finetuned-panx-de

tamitani

xlm-roberta

11

0

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,319

# xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1358 - F1: 0.8638 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 | | 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 | | 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

fathyshalab/domain_transfer_clinic_credit_cards-massive_email-roberta-large-v1-2-40

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,532

# fathyshalab/domain_transfer_clinic_credit_cards-massive_email-roberta-large-v1-2-40 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_email-roberta-large-v1-2-40") # 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} } ```

fathyshalab/domain_transfer_clinic_credit_cards-massive_iot-roberta-large-v1-2-6

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,526

# fathyshalab/domain_transfer_clinic_credit_cards-massive_iot-roberta-large-v1-2-6 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_iot-roberta-large-v1-2-6") # 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} } ```

fathyshalab/domain_transfer_clinic_credit_cards-massive_general-roberta-large-v1-2-96

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,536

# fathyshalab/domain_transfer_clinic_credit_cards-massive_general-roberta-large-v1-2-96 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_general-roberta-large-v1-2-96") # 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} } ```

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

mingdinghan

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

400

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="mingdinghan/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"]) ```

jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR100-40

jojoUla

bert

15

0

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,305

# bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR100-40 This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1928 | 1.0 | 1 | 5.0343 | | 3.8865 | 2.0 | 2 | 4.7751 | | 4.0526 | 3.0 | 3 | 2.2212 | | 2.3444 | 4.0 | 4 | 1.6810 | | 1.596 | 5.0 | 5 | 1.3135 | | 1.6805 | 6.0 | 6 | 1.2568 | | 1.1736 | 7.0 | 7 | 1.5288 | | 1.2663 | 8.0 | 8 | 1.4556 | | 1.3703 | 9.0 | 9 | 1.1139 | | 0.9768 | 10.0 | 10 | 1.0658 | | 1.0132 | 11.0 | 11 | 1.2556 | | 0.9896 | 12.0 | 12 | 1.1046 | | 1.1184 | 13.0 | 13 | 1.0522 | | 0.8142 | 14.0 | 14 | 1.3122 | | 0.706 | 15.0 | 15 | 1.0713 | | 0.7227 | 16.0 | 16 | 1.4111 | | 0.7169 | 17.0 | 17 | 0.5603 | | 0.7922 | 18.0 | 18 | 1.0911 | | 0.7763 | 19.0 | 19 | 0.6882 | | 0.5832 | 20.0 | 20 | 1.4459 | | 0.7265 | 21.0 | 21 | 1.5459 | | 0.7249 | 22.0 | 22 | 0.9200 | | 0.5397 | 23.0 | 23 | 1.0976 | | 0.5063 | 24.0 | 24 | 1.1201 | | 0.6569 | 25.0 | 25 | 1.0701 | | 0.472 | 26.0 | 26 | 1.7735 | | 0.6124 | 27.0 | 27 | 1.3597 | | 0.6042 | 28.0 | 28 | 0.9292 | | 0.5232 | 29.0 | 29 | 1.4994 | | 0.4961 | 30.0 | 30 | 1.2059 | | 0.371 | 31.0 | 31 | 1.2648 | | 0.4746 | 32.0 | 32 | 1.0907 | | 0.4901 | 33.0 | 33 | 1.2564 | | 0.5066 | 34.0 | 34 | 1.9231 | | 0.6352 | 35.0 | 35 | 1.0160 | | 0.5672 | 36.0 | 36 | 1.2958 | | 0.5139 | 37.0 | 37 | 0.9384 | | 0.5583 | 38.0 | 38 | 1.9518 | | 0.5443 | 39.0 | 39 | 1.4243 | | 0.5935 | 40.0 | 40 | 1.3882 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

pjrodriguez/unit_1_lunar_lander

pjrodriguez

null

40

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 ... ```

flogau/a2c_cartpole

flogau

null

3

0

null

0

null

false

null

0

[]

false

true

268

# Modèle entraîné pour l'environnement Cartpole-v1 Pour utiliser le modèle, télécharger le fichier zip "a2c_cartpole.zip" puis dans votre script python charger votre modèle de la manière suivante : model = A2C.load("a2c_cartpole") Le modèle est prêt à être utilisé. Même chose pour le fichier a2c_panda_reach.zip

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

calvincbzhang

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

402

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

calvincbzhang/q-Taxi-v3

calvincbzhang

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

369

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

fathyshalab/domain_transfer_clinic_credit_cards-massive_audio-roberta-large-v1-2-0

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,530

# fathyshalab/domain_transfer_clinic_credit_cards-massive_audio-roberta-large-v1-2-0 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_audio-roberta-large-v1-2-0") # 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} } ```

fathyshalab/domain_transfer_clinic_credit_cards-massive_lists-roberta-large-v1-2-93

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,532

# fathyshalab/domain_transfer_clinic_credit_cards-massive_lists-roberta-large-v1-2-93 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_lists-roberta-large-v1-2-93") # 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} } ```

YoriV/Pyramids-training-unity

YoriV

null

18

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

839

# **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: YoriV/Pyramids-training-unity 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

nhiro3303/Reinforce-Pixelcopter-PLE-v0

nhiro3303

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

anthol/bert-finetuned-ner

anthol

bert

12

0

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,518

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - Precision: 0.9310 - Recall: 0.9497 - F1: 0.9403 - Accuracy: 0.9864 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0884 | 1.0 | 1756 | 0.0697 | 0.9128 | 0.9283 | 0.9205 | 0.9819 | | 0.0322 | 2.0 | 3512 | 0.0660 | 0.9267 | 0.9473 | 0.9369 | 0.9859 | | 0.0175 | 3.0 | 5268 | 0.0616 | 0.9310 | 0.9497 | 0.9403 | 0.9864 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

kibrq/greedy-intersection

kibrq

null

9

0

transformers

0

null

true

false

null

0

[]

false

true

1,460

To load this model, use the following code: ```py from transformers import PreTrainedTokenizerFast, AutoModelForCausalLM, AutoConfig tokenizer = PreTrainedTokenizerFast.from_pretrained('kibrq/greedy-intersection') config = AutoConfig.from_pretrained('kibrq/greedy-intersection', trust_remote_code = True) config._from_tokenizer(freegroup_dimension, tokenizer) model = AutoModelForCausalLM.from_config(config, trust_remote_code = True) ``` To generate words from the intersection, use this code: ```py from freegroup.sampling import free_group_bounded from freegroup.tools import is_from_singleton_normal_closure from freegroup.commutators import to_tokenizer, from_tokenizer from itertools import islice batch_size = 20 prefix_length = 15 generation_config = dict( max_new_tokens = 200, ) num_runs = 10 for _ in range(num_runs): inputs = islice(free_group_bounded(3, max_length = prefix_length, random_length_method="constant"), batch_size) inputs = list(map(to_tokenizer, input)) inputs = tokenizer(input, return_tensors='pt').input_ids outputs = model.generate( inputs = input, **generation_config ) outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) outputs = map(from_tokenizer, outputs) condition = lambda x: all(map(lambda gen: is_from_singleton_normal_closure(gen, x), [[1], [2], [3], [1, 2, 3]])) outputs = filter(condition, outputs) print(list(outputs)) ```

fathyshalab/domain_transfer_clinic_credit_cards-massive_qa-roberta-large-v1-2-71

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,526

# fathyshalab/domain_transfer_clinic_credit_cards-massive_qa-roberta-large-v1-2-71 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_qa-roberta-large-v1-2-71") # 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} } ```

Beegbrain/Reinforce-PixelCopter2

Beegbrain

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

fathyshalab/domain_transfer_clinic_credit_cards-massive_cooking-roberta-large-v1-2-4

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,534

# fathyshalab/domain_transfer_clinic_credit_cards-massive_cooking-roberta-large-v1-2-4 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_cooking-roberta-large-v1-2-4") # 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} } ```

fathyshalab/domain_transfer_clinic_credit_cards-massive_takeaway-roberta-large-v1-2-86

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,538

# fathyshalab/domain_transfer_clinic_credit_cards-massive_takeaway-roberta-large-v1-2-86 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_takeaway-roberta-large-v1-2-86") # 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} } ```

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

wptmdoorn

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="wptmdoorn/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"]) ```

mwissing/Reinforce-Pixelcopter-PLE-v0

mwissing

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

SergejSchweizer/poca-SoccerTwos

SergejSchweizer

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

849

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

numan966/poca-SoccerTwos

numan966

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

842

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: numan966/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

fathyshalab/domain_transfer_clinic_credit_cards-massive_music-roberta-large-v1-2-7

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,530

# fathyshalab/domain_transfer_clinic_credit_cards-massive_music-roberta-large-v1-2-7 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_music-roberta-large-v1-2-7") # 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} } ```

wptmdoorn/Taxi-v3

wptmdoorn

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

363

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

krystv/hestyle-diffusion

krystv

null

30

0

diffusers

1

text-to-image

false

creativeml-openrail-m

['en']

null

0

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

false

true

1,708

### heStyle-Diffusion Is a Dreamboothed model of `runway_1-5v` stable diffusion model. Trained on some beautiful anime images online speciallly for Room interior and Wallpapers. ### Trigger Word(Otional): `hestyle` may be useful in cases where model behave something else. ### 💞 Send me Query at : [![Instagram](https://img.shields.io/badge/Instagram-%23E4405F.svg?style=for-the-badge&logo=Instagram&logoColor=white)](https://www.instagram.com/iamhemantindia) You can 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: ![0](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00020-915239277.png) ![1](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00043-209812484.png) ![2](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00048-2044894744.png) ![3](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00049-2044894745.png) ![4](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00056-4225074758.png) ![5](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00118-1283733305.png) ![6](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00125-2850239766.png) ![7](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00132-2553663004.png) ![8](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00136-3956442682.png) ![9](https://huggingface.co/krystv/hestyle-diffusion/resolve/main/sample_images/00060-2995570975.png)

fathyshalab/domain_transfer_clinic_credit_cards-massive_alarm-roberta-large-v1-2-48

fathyshalab

roberta

14

0

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,532

# fathyshalab/domain_transfer_clinic_credit_cards-massive_alarm-roberta-large-v1-2-48 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_clinic_credit_cards-massive_alarm-roberta-large-v1-2-48") # 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} } ```

IDMCrackDownload/idmcrack

IDMCrackDownload

null

3

0

null

1

null

false

unknown

null

0

[]

false

true

716

# Download IDM Crack with Internet Download Manager [Latest Version]  Download the Latest Version of IDM Crack or Patch ▶ [Click Here](https://www.idmlover.com). # IDM Crack Details  - **Developed by:** Unknown - **Shared by:** IDMLover.com - **Setup type:** Offline Setup - **Language(s):** Mulitlanguage - **Last Updated:** 1 Day ago # IDM FULL Crack Uses  - Use IDM for FREE - No Serial Key Needed - Video Download Panel - IDM Trial Extend

pszemraj/pythia-6.9b-HC3

pszemraj

gpt_neox

19

0

transformers

0

text-generation

true

false

apache-2.0

null

['pszemraj/HC3-textgen-qa']

null

0

['generated_from_trainer', 'HC3', 'chatGPT', 'assistant']

false

true

2,912

# pythia-6.9b-deduped for general QA <a href="https://colab.research.google.com/gist/pszemraj/351f04fc2afb6346c763885f127284ef/pythia-6-9b-deduped-for-general-qa.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> This model is a fine-tuned version of [EleutherAI/pythia-6.9b-deduped](https://huggingface.co/EleutherAI/pythia-6.9b-deduped) on the pszemraj/HC3-textgen-qa dataset. It achieves the following results on the evaluation set: - Loss: 1.2372 - Accuracy: 0.6769 - perplexity: 3.446 ## Model description Text generation model trained on the HC3 text data of human questions + chatGPT answers. ![example](https://i.imgur.com/iMqPDXU.png) ### Usage Install necessary packages for inference (_unless you have a big boi GPU_) ```bash pip install -U -q transformers bitsandbytes accelerate ``` Basic inference example: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-6.9b-HC3") model = AutoModelForCausalLM.from_pretrained( "pszemraj/pythia-6.9b-HC3", load_in_8bit=True, device_map="auto" ) # shards are ~4GB each, there are eight total prompt = "I was wondering how much wood a woodchuck could chuck? <answer>" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=300 ) # default generation config (+ 300 tokens) result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] result = result.split("<end_answer>")[0].strip() import pprint as pp pp.pprint(result) ``` The defautl `GenerationConfig` uses contrastive search with `top_k=4` and `penalty_alpha=0.6`. For more information on inference and parameters to use, see [the transformers docs](https://huggingface.co/docs/transformers/generation_strategies#decoding-strategies). ## Intended uses & limitations - **Intended use:** research/exploration into comparing RLHF tuning vs. "guided"/specific tuning on "quality" datasets/responses of _"what the human would want as answer anyway"_ - This is **not** trained/fine-tuned with RLHF and therefore will not be as helpful/generalizable/safe as chatGPT (_outside of the fact that this model is ~30x smaller_) ## Training and evaluation data ```yaml model-index: - name: pythia-6.9b-hc3-qa-assistant results: - task: name: Causal Language Modeling type: text-generation dataset: name: pszemraj/HC3-textgen-qa metrics: - name: Accuracy type: accuracy value: 0.6768941789814655 ``` ## Training procedure Two epochs on the `pszemraj/HC3-textgen-qa` dataset. ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2598 | 0.99 | 79 | 1.3291 | 0.6496 | | 0.7446 | 1.99 | 158 | 1.2372 | 0.6769 |

amnahhebrahim/bert-base-arabertv2-finetuned-arcd-squad

amnahhebrahim

bert

10

0

transformers

0

question-answering

true

false

null

['arcd']

null

0

['generated_from_trainer']

true

944

# bert-base-arabertv2-finetuned-arcd-squad This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the arcd 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: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

DJJ42/sd-class-butterflies-32

DJJ42

null

6

0

diffusers

0

unconditional-image-generation

true

false

mit

null

0

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

false

true

362

# 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('DJJ42/sd-class-butterflies-32') image = pipeline().images[0] image ```

Hemorphage/Reinforce-CartPole_1

Hemorphage

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

augustogeog/q-Taxi-v3-iterative

augustogeog

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

377

# **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-iterative", 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"]) ```

NathanS-HuggingFace/LunarLander

NathanS-HuggingFace

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 ... ```

khaled5321/poca-SoccerTwos

khaled5321

null

12

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

844

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: khaled5321/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

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

ammr

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

393

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

Jaehaerys-I/ppo_LunarLander

Jaehaerys-I

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 ... ```

boira/clasificador-muchocine

boira

electra

10

0

transformers

1

text-classification

true

false

null

0

['classification', 'generated_from_trainer']

true

1,367

# clasificador-muchocine 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: 1.4200 - Accuracy: 0.4335 ## 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 | 1.3759 | 0.4 | | 1.4142 | 2.0 | 776 | 1.2896 | 0.4271 | | 1.0464 | 3.0 | 1164 | 1.4200 | 0.4335 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

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

Akriel

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="Akriel/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"]) ```

cfisicaro/Reinforce-CartPole-v1

cfisicaro

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

desh2608/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-small

desh2608

null

30

0

null

0

null

false

apache-2.0

null

0

[]

false

true

654

# LibriSpeech pruned_transducer_stateless7_streaming This model is based on the icefall `pruned_transducer_stateless7_streaming` recipe, but it the model parameters are modified to be smaller in size. It can be considered a streaming version of [this model](https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-20M-2023-01-28) and follows the same parameter configuration. ## Performance Record | Decoding method | test-clean | test-other | |---------------------------|------------|------------| | greedy search | 3.94 | 9.79 | | modified beam search | 3.88 | 9.53 |

Akriel/q-Taxi-v3

Akriel

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="Akriel/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"]) ```

GBaker/nystromformer-4096-medqa-usmle-MiniLM-IR-cs

GBaker

nystromformer

20

0

transformers

0

multiple-choice

true

false

null

0

['generated_from_trainer']

true

1,907

# nystromformer-4096-medqa-usmle-MiniLM-IR-cs This model is a fine-tuned version of [GBaker/nystromformer-4096-medqa-usmle-MiniLM-IR-cs](https://huggingface.co/GBaker/nystromformer-4096-medqa-usmle-MiniLM-IR-cs) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8436 - Accuracy: 0.2812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | No log | 0.99 | 79 | 0.2372 | 1.3863 | | No log | 1.99 | 158 | 0.2655 | 1.3861 | | No log | 2.99 | 237 | 0.2545 | 1.3859 | | No log | 3.99 | 316 | 0.2765 | 1.3837 | | No log | 4.99 | 395 | 0.2820 | 1.3876 | | No log | 5.99 | 474 | 1.3819 | 0.2639 | | 1.3342 | 6.99 | 553 | 1.4875 | 0.2694 | | 1.3342 | 7.99 | 632 | 1.6126 | 0.2718 | | 1.3342 | 8.99 | 711 | 1.7637 | 0.2804 | | 1.3342 | 9.99 | 790 | 1.8436 | 0.2812 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

pridaj/distilbert-base-uncased-emotion-nlp-with-transformers

pridaj

distilbert

12

0

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,295

# distilbert-base-uncased-emotion-nlp-with-transformers This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1360 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5072 | 1.0 | 250 | 0.2032 | | 0.1464 | 2.0 | 500 | 0.1409 | | 0.094 | 3.0 | 750 | 0.1360 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Schoolar/dqn-SpaceInvadersNoFrameskip-v4

Schoolar

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,217

# **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 Schoolar -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 Schoolar -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 Schoolar ``` ## 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)]) ```

mshibatatt/Reinforce-Pixelcopter-PLE-v0

mshibatatt

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

NoNameFound/pocadeci-SoccerTwos

NoNameFound

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

849

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

kapilkd13/ppo-LunarLander-v2

kapilkd13

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 ... ```

mitra-mir/setfit-model-Feb12-Misinformation-on-Convoy

mitra-mir

mpnet

13

0

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 768 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 201 with parameters: ``` {'batch_size': 64, '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": 201, "warmup_steps": 21, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, '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

augustogeog/q-Taxi-v3-iterative-10mil

augustogeog

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

383

# **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-iterative-10mil", 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"]) ```

ammr/q-FrozenLake-v1-4x4

ammr

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

382

# **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="ammr/q-FrozenLake-v1-4x4", 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"]) ```

pabagcha/rah_toki_pona

pabagcha

wav2vec2

15

0

transformers

0

automatic-speech-recognition

true

false

null

['common_voice_11_0']

null

0

['generated_from_trainer']

true

1,442

# rah_toki_pona This model was finetuned from facebook/wav2vec2-xls-r-300m on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1053 - Wer: 0.0640 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0516 | 3.22 | 400 | 0.1301 | 0.0996 | | 0.0817 | 6.45 | 800 | 0.1319 | 0.0899 | | 0.0567 | 9.67 | 1200 | 0.1009 | 0.0682 | | 0.0376 | 12.9 | 1600 | 0.1053 | 0.0640 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Whitemelody/CreamLike

Whitemelody

null

6

0

null

1

null

false

null

0

[]

false

true

1,170

Both models has soft colors. A model for soft colors and fantasy style clothes. B model for soft colors and modern style clothes. RECIPE CreamLike_A -0.5[0.7(0.5AbyssOrangeMix2_sfw + 0.5counterfeit-v2.5fp16) + 0.3pastelmix-better-vae-fp32] + 0.5powercolorV1 CreamLike_B 0.75(0.75counterfeit-v2.5fp16 + 0.25pastelmix-better-vae-fp32) + 0.25AbyssOrangeMix2_sfw Sampling method and Hires.fix DPM++ SDE Karras: 28~30 steps / R-ESRGAN 4x+ Anime6B: 2x, 10 steps / Denoising strength:0.5 ~ 0.7 CreamLike_A ![업스9.png](https://s3.amazonaws.com/moonup/production/uploads/1676227296081-63e9281cccae1fe5c61dedec.png) ![업스12.png](https://s3.amazonaws.com/moonup/production/uploads/1676227315123-63e9281cccae1fe5c61dedec.png) ![업스17.png](https://s3.amazonaws.com/moonup/production/uploads/1676227331141-63e9281cccae1fe5c61dedec.png) CreamLike_B ![업스14.png](https://s3.amazonaws.com/moonup/production/uploads/1676227361895-63e9281cccae1fe5c61dedec.png) ![업스15.png](https://s3.amazonaws.com/moonup/production/uploads/1676227369045-63e9281cccae1fe5c61dedec.png) ![업스16.png](https://s3.amazonaws.com/moonup/production/uploads/1676227379313-63e9281cccae1fe5c61dedec.png)

gordondavidf/ppo-LunarLander-v2

gordondavidf

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 ... ```

dotunadegbite/Reinforce-Pixelcopter-PLE-v0

dotunadegbite

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

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

Azher

null

17

0

diffusers

0

null

false

null

0

[]

false

true

347

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

nikogarro/PPO-SnowballTarget

nikogarro

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

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

unit0113/ppo-Huggy

unit0113

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

819

# **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: unit0113/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

DataIntelligenceTeam/marazzi2.0

DataIntelligenceTeam

layoutlmv3

12

0

transformers

0

token-classification

true

false

cc-by-nc-sa-4.0

null

['sroie']

null

0

['generated_from_trainer']

true

3,562

# marazzi2.0 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset. It achieves the following results on the evaluation set: - Loss: 0.0477 - Precision: 0.8218 - Recall: 0.7386 - F1: 0.7780 - Accuracy: 0.9937 ## 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: 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: 2500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.24 | 100 | 0.1429 | 0.5870 | 0.0882 | 0.1534 | 0.9809 | | No log | 0.47 | 200 | 0.1163 | 0.5870 | 0.0882 | 0.1534 | 0.9809 | | No log | 0.71 | 300 | 0.0919 | 0.5690 | 0.1078 | 0.1813 | 0.9815 | | No log | 0.95 | 400 | 0.0787 | 0.6304 | 0.2843 | 0.3919 | 0.9858 | | 0.0844 | 1.18 | 500 | 0.0755 | 0.6522 | 0.4412 | 0.5263 | 0.9873 | | 0.0844 | 1.42 | 600 | 0.0621 | 0.6533 | 0.4804 | 0.5537 | 0.9872 | | 0.0844 | 1.66 | 700 | 0.0631 | 0.7415 | 0.4967 | 0.5949 | 0.9895 | | 0.0844 | 1.9 | 800 | 0.0463 | 0.7764 | 0.6013 | 0.6777 | 0.9912 | | 0.0844 | 2.13 | 900 | 0.0429 | 0.7821 | 0.6569 | 0.7140 | 0.9922 | | 0.0265 | 2.37 | 1000 | 0.0421 | 0.7881 | 0.6928 | 0.7374 | 0.9929 | | 0.0265 | 2.61 | 1100 | 0.0516 | 0.8050 | 0.6340 | 0.7093 | 0.9919 | | 0.0265 | 2.84 | 1200 | 0.0474 | 0.7854 | 0.6340 | 0.7016 | 0.9917 | | 0.0265 | 3.08 | 1300 | 0.0378 | 0.8134 | 0.7549 | 0.7831 | 0.9942 | | 0.0265 | 3.32 | 1400 | 0.0374 | 0.8143 | 0.7451 | 0.7782 | 0.9938 | | 0.0116 | 3.55 | 1500 | 0.0466 | 0.8213 | 0.7059 | 0.7592 | 0.9933 | | 0.0116 | 3.79 | 1600 | 0.0444 | 0.8172 | 0.7157 | 0.7631 | 0.9933 | | 0.0116 | 4.03 | 1700 | 0.0442 | 0.8218 | 0.7386 | 0.7780 | 0.9937 | | 0.0116 | 4.27 | 1800 | 0.0473 | 0.8118 | 0.7190 | 0.7626 | 0.9933 | | 0.0116 | 4.5 | 1900 | 0.0520 | 0.8030 | 0.7059 | 0.7513 | 0.9932 | | 0.0074 | 4.74 | 2000 | 0.0476 | 0.8155 | 0.7222 | 0.7660 | 0.9936 | | 0.0074 | 4.98 | 2100 | 0.0504 | 0.8038 | 0.6830 | 0.7385 | 0.9931 | | 0.0074 | 5.21 | 2200 | 0.0475 | 0.8267 | 0.7484 | 0.7856 | 0.9937 | | 0.0074 | 5.45 | 2300 | 0.0506 | 0.8081 | 0.7157 | 0.7591 | 0.9933 | | 0.0074 | 5.69 | 2400 | 0.0508 | 0.8168 | 0.7288 | 0.7703 | 0.9936 | | 0.005 | 5.92 | 2500 | 0.0477 | 0.8218 | 0.7386 | 0.7780 | 0.9937 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.2.2 - Tokenizers 0.13.2

dor88/Reinforce-cartpole

dor88

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

lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER-5e-05-3

lixiqi

beit

14

0

transformers

0

image-classification

true

false

apache-2.0

null

['image_folder']

null

0

['generated_from_trainer']

true

1,506

# beit-base-patch16-224-pt22k-ft22k-finetuned-FER-5e-05-3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.8598 - Accuracy: 0.6860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1566 | 1.0 | 224 | 0.9830 | 0.6311 | | 1.0301 | 2.0 | 448 | 0.8939 | 0.6730 | | 0.991 | 3.0 | 672 | 0.8598 | 0.6860 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

thiagoms7/poca-SoccerTwos2

thiagoms7

null

11

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

844

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: thiagoms7/poca-SoccerTwos2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ammr/q-FrozenLake-v1-8x8-noSlippery

ammr

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-8x8-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

393

# **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="ammr/q-FrozenLake-v1-8x8-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"]) ```

ammr/q-FrozenLake-v1-8x8

ammr

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-8x8', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

382

# **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="ammr/q-FrozenLake-v1-8x8", 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"]) ```

nikogarro/PPO-PyramidsRND

nikogarro

null

16

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

835

# **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: nikogarro/PPO-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Achitha/ta-eng-data

Achitha

whisper

14

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['tamil_eng_data']

null

0

['generated_from_trainer']

true

1,265

# ta-eng-data This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

atorre/poca-SoccerTwos-70M

atorre

null

28

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

844

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: atorre/poca-SoccerTwos-70M 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

osanpo/khanon_lora-training

osanpo

null

158

0

null

0

null

false

mit

null

0

[]

false

true

3,564

# Gachashit LoRA repository Here you will find the various LoRAs I've trained, typically of Blue Archive characters. ## Blue Archive ブルーアーカイブ / 블루 아카이브 / 碧蓝档案 ### Arona [Arona / アロナ / 아로나 / 阿罗娜](https://huggingface.co/khanon/lora-training/blob/main/arona/README.md) [![Arona](arona/chara-arona-v1.png)](https://huggingface.co/khanon/lora-training/blob/main/arona/README.md) ### Atsuko [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Chise [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Hibiki [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Hina [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Iroha [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Izuna [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Koharu [Shimoe Koharu / 下江コハル / 시모에 코하루 / 下江小春](https://huggingface.co/khanon/lora-training/blob/main/koharu/README.md) [![Koharu](koharu/chara-koharu-v3.png)](https://huggingface.co/khanon/lora-training/blob/main/koharu/README.md) ### Kokona [Sunohara Kokona / 春原ココナ / 스노하라 코코나 / 春原心奈](https://huggingface.co/khanon/lora-training/blob/main/kokona/README.md) [![Kokona](kokona/chara-kokona.png)](https://huggingface.co/khanon/lora-training/blob/main/kokona/README.md) ### Mari [Iochi Mari / 伊落マリー / 이오치 마리 / 伊落玛丽](https://huggingface.co/khanon/lora-training/blob/main/mari/README.md) [![Mari](mari/chara-mari-v5b.png)](https://huggingface.co/khanon/lora-training/blob/main/mari/README.md) ### Michiru [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Miyako (WIP) [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Mutsuki [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Natsu (WIP) [Available on old Mega.co.nz repository.](https://mega.nz/folder/SqYwQTRI#GN2SmGTBsV6S4q-L-V4VeA) ### Reisa [Uzawa Reisa / 宇沢レイサ / 우자와 레이사](https://huggingface.co/khanon/lora-training/blob/main/reisa/README.md) [![Reisa](reisa/chara-reisa-v3.png)](https://huggingface.co/khanon/lora-training/blob/main/reisa/README.md) ### Seia [Yurizono Seia / 百合園セイア / 유리조노 세이아 / 百合園圣娅](https://huggingface.co/khanon/lora-training/blob/main/seia/README.md) [![Seia](seia/chara-seia-v1.png)](https://huggingface.co/khanon/lora-training/blob/main/seia/README.md) ### Shizuko [Kawawa Shizuko / 河和シズコ / 카와와 시즈코 / 河和静子](https://huggingface.co/khanon/lora-training/blob/main/shizuko/README.md) [![Shizuko](shizuko/chara-shizuko.png)](https://huggingface.co/khanon/lora-training/blob/main/shizuko/README.md) ### Sora [Sora / ソラ / 소라 / 空](https://huggingface.co/khanon/lora-training/blob/main/sora/README.md) [![Sora](sora/chara-sora-v3.png)](https://huggingface.co/khanon/lora-training/blob/main/sora/README.md) ## Useful links ### Negative embedding I frequently use these negative embeddings in my prompts to improve the output quality. I recommend lowering the attention to ~0.75. - `bad-artist`, `bad-artist-anime` - https://huggingface.co/nick-x-hacker/bad-artist - `badpromptv2` - https://huggingface.co/datasets/Nerfgun3/bad_prompt - `bad-image-v2` (not sure of the original author) - [bad-image-v2.pt](https://huggingface.co/khanon/lora-training/blob/main/bad-image-v2.pt)

jaese/t5-small-finetuned-amazon-en-fr

jaese

t5

15

0

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,169

# t5-small-finetuned-amazon-en-fr This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 2.9766 - eval_rouge1: 0.1418 - eval_rouge2: 0.0716 - eval_rougeL: 0.1369 - eval_rougeLsum: 0.1383 - eval_runtime: 5.36 - eval_samples_per_second: 52.426 - eval_steps_per_second: 6.716 - epoch: 2.0 - step: 2798 ## 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: 5.6e-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 ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu102 - Datasets 2.9.0 - Tokenizers 0.12.1

RamonAnkersmit/poca-SoccerTwos-Gpu-50M

RamonAnkersmit

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

856

# **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: RamonAnkersmit/poca-SoccerTwos-Gpu-50M 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Porridge9243/SoccerTwos

Porridge9243

null

20

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

841

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: Porridge9243/SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

pete88b/PPO-MLP-LunarLander-v2-0.0.1

pete88b

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 ... ```

johnslegers/instruct-pix2pix

johnslegers

null

23

0

diffusers

0

null

false

mit

null

0

[]

false

true

1,687

# ORIGINAL MODEL BY timbrooks This is just a clone of https://huggingface.co/timbrooks/instruct-pix2pix. However, I replaced the 7.7 GB ckpt & satetensor files with more convenient 2.13 GB pruned fp16 versions for convenience sake. Conversion from diffusers format to ckpt & satetensor formats was done with https://github.com/ShivamShrirao/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py. ------------- # InstructPix2Pix: Learning to Follow Image Editing Instructions GitHub: https://github.com/timothybrooks/instruct-pix2pix <img src='https://instruct-pix2pix.timothybrooks.com/teaser.jpg'/> ## Example To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release ```bash pip install diffusers accelerate safetensors transformers ``` ```python import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler model_id = "timbrooks/instruct-pix2pix" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg" def download_image(url): image = PIL.Image.open(requests.get(url, stream=True).raw) image = PIL.ImageOps.exif_transpose(image) image = image.convert("RGB") return image image = download_image(URL) prompt = "turn him into cyborg" images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images images[0] ```

postbot/bert_uncased_L-2_H-256_A-4-mlm-multi-emails-hq

postbot

bert

15

0

transformers

0

fill-mask

true

false

apache-2.0

['en']

['postbot/multi-emails-hq']

null

0

['generated_from_trainer', 'BERT']

true

1,762

# bert_uncased_L-2_H-256_A-4-mlm-multi-emails-hq This model is a fine-tuned version of [google/bert_uncased_L-2_H-256_A-4](https://huggingface.co/google/bert_uncased_L-2_H-256_A-4) on the `postbot/multi-emails-hq` dataset. It achieves the following results on the evaluation set: - Loss: 2.4596 - Accuracy: 0.5642 ## Model description This is a ~40MB version of BERT finetuned on an MLM task on email data. ## Intended uses & limitations - this is mostly a test/example ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 8.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.097 | 0.99 | 141 | 2.8195 | 0.5180 | | 2.9097 | 1.99 | 282 | 2.6704 | 0.5367 | | 2.8335 | 2.99 | 423 | 2.5764 | 0.5485 | | 2.7433 | 3.99 | 564 | 2.5213 | 0.5563 | | 2.6828 | 4.99 | 705 | 2.4667 | 0.5641 | | 2.666 | 5.99 | 846 | 2.4688 | 0.5642 | | 2.6517 | 6.99 | 987 | 2.4452 | 0.5679 | | 2.6309 | 7.99 | 1128 | 2.4596 | 0.5642 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 2.0.0.dev20230129+cu118 - Datasets 2.8.0 - Tokenizers 0.13.1

rudzinskimaciej/crystalpunk

rudzinskimaciej

null

16

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

428

### crystalpunk Dreambooth model trained by rudzinskimaciej 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:

ammr/q-Taxi-v3

ammr

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="ammr/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"]) ```

postbot/bert_uncased_tiny-multi-emails-hq

postbot

bert

15

0

transformers

0

fill-mask

true

false

apache-2.0

['en']

['postbot/multi-emails-hq']

null

0

['generated_from_trainer']

true

1,728

# bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq (BERT-tiny) This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0981 - Accuracy: 0.4728 ## Model description BERT-tiny fine-tuned on email data for eight epochs. ## Intended uses & limitations - this is mostly a test ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 8.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.8974 | 0.99 | 141 | 3.5129 | 0.4218 | | 3.7009 | 1.99 | 282 | 3.3295 | 0.4452 | | 3.5845 | 2.99 | 423 | 3.2219 | 0.4589 | | 3.4976 | 3.99 | 564 | 3.1618 | 0.4666 | | 3.4356 | 4.99 | 705 | 3.1002 | 0.4739 | | 3.4493 | 5.99 | 846 | 3.1028 | 0.4746 | | 3.4199 | 6.99 | 987 | 3.0857 | 0.4766 | | 3.4086 | 7.99 | 1128 | 3.0981 | 0.4728 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 2.0.0.dev20230129+cu118 - Datasets 2.8.0 - Tokenizers 0.13.1

nsecord/ppo-LunarLander-v2

nsecord

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 ... ```

cfisicaro/Reinforce-Pixelcopter-PLE-v0

cfisicaro

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

amoselberg/a2c-AntBulletEnv-v0

amoselberg

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 ... ```

postbot/bert_uncased_tiny_2xthicc-multi-emails-hq

postbot

bert

15

0

transformers

0

fill-mask

true

false

apache-2.0

['en']

['postbot/multi-emails-hq']

null

0

['generated_from_trainer']

true

1,749

# bert_uncased_L-4_H-128_A-2-mlm-multi-emails-hq This model is a fine-tuned version of [google/bert_uncased_L-4_H-128_A-2](https://huggingface.co/google/bert_uncased_L-4_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8524 - Accuracy: 0.5077 ## Model description Double the layers of BERT-tiny, fine-tuned on email data for eight epochs. ## Intended uses & limitations - This is primarily an example/test ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 8.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.5477 | 0.99 | 141 | 3.2637 | 0.4551 | | 3.3307 | 1.99 | 282 | 3.0873 | 0.4785 | | 3.252 | 2.99 | 423 | 2.9842 | 0.4911 | | 3.1415 | 3.99 | 564 | 2.9230 | 0.4995 | | 3.0903 | 4.99 | 705 | 2.8625 | 0.5070 | | 3.0996 | 5.99 | 846 | 2.8615 | 0.5087 | | 3.0641 | 6.99 | 987 | 2.8407 | 0.5120 | | 3.0514 | 7.99 | 1128 | 2.8524 | 0.5077 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 2.0.0.dev20230129+cu118 - Datasets 2.8.0 - Tokenizers 0.13.1

apatole/PPOlunar

apatole

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 ... ```

saamdilmaghani/bert-finetuned-ner

saamdilmaghani

bert

12

0

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,518

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0590 - Precision: 0.9357 - Recall: 0.9507 - F1: 0.9432 - Accuracy: 0.9867 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0872 | 1.0 | 1756 | 0.0709 | 0.9194 | 0.9334 | 0.9263 | 0.9822 | | 0.033 | 2.0 | 3512 | 0.0622 | 0.9298 | 0.9497 | 0.9396 | 0.9861 | | 0.0183 | 3.0 | 5268 | 0.0590 | 0.9357 | 0.9507 | 0.9432 | 0.9867 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Isaacp/ppo-SnowballTarget1

Isaacp

null

30

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

854

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

lmqg/flan-t5-base-squad-qg

lmqg

t5

14

0

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qg_squad']

null

0

['question generation']

true

3,880

# Model Card of `lmqg/flan-t5-base-squad-qg` This model is fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/flan-t5-base-squad-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/flan-t5-base-squad-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-base-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.53 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 58.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 42.68 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 32.99 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 26.1 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 26.99 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 64.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 53.2 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/flan-t5-base-squad-ae`](https://huggingface.co/lmqg/flan-t5-base-squad-ae). [raw metric file](https://huggingface.co/lmqg/flan-t5-base-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_flan-t5-base-squad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 92.69 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 64.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 92.51 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 64.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 92.88 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 64.37 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: paragraph_answer - output_types: question - prefix_types: ['qg'] - model: google/flan-t5-base - max_length: 512 - max_length_output: 32 - epoch: 7 - batch: 16 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-base-squad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

jingchan/sd-class-butterflies-32

jingchan

null

6

0

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('jingchan/sd-class-butterflies-32') image = pipeline().images[0] image ```

pfunk/CartPole-v1-DQPN_min_mean_std-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

1,997

# (CleanRL) **DQN** Agent Playing **CartPole-v1** This is a trained model of a DQN agent playing CartPole-v1. 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_min_mean_std.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_min_mean_std]" python -m cleanrl_utils.enjoy --exp-name DQPN_min_mean_std --env-id CartPole-v1 ``` 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/CartPole-v1-DQPN_min_mean_std-seed1/raw/main/dqpn_duncan.py curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_min_mean_std-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_min_mean_std-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_duncan.py --exp-name DQPN_min_mean_std --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 CartPole-v1 --seed 1 --total-timesteps 100000 --min-mean-std True ``` # Hyperparameters ```python {'batch_size': 128, 'buffer_size': 10000, 'capture_video': False, 'cuda': True, 'end_e': 0.05, 'env_id': 'CartPole-v1', 'exp_name': 'DQPN_min_mean_std', 'exploration_fraction': 0.5, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.00025, 'learning_starts': 10000, 'min_mean_std': True, 'policy_network_frequency': 500, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 100, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 100000, 'track': True, 'train_frequency': 10, 'update_scalar': False, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

pfunk/CartPole-v1-DQPN_scalar_update-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

2,006

# (CleanRL) **DQN** Agent Playing **CartPole-v1** This is a trained model of a DQN agent playing CartPole-v1. 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_scalar_update.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_scalar_update]" python -m cleanrl_utils.enjoy --exp-name DQPN_scalar_update --env-id CartPole-v1 ``` 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/CartPole-v1-DQPN_scalar_update-seed1/raw/main/dqpn_duncan.py curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_scalar_update-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_scalar_update-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_duncan.py --exp-name DQPN_scalar_update --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 CartPole-v1 --seed 1 --total-timesteps 100000 --update-scalar True ``` # Hyperparameters ```python {'batch_size': 128, 'buffer_size': 10000, 'capture_video': False, 'cuda': True, 'end_e': 0.05, 'env_id': 'CartPole-v1', 'exp_name': 'DQPN_scalar_update', 'exploration_fraction': 0.5, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.00025, 'learning_starts': 10000, 'min_mean_std': False, 'policy_network_frequency': 500, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 100, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 100000, 'track': True, 'train_frequency': 10, 'update_scalar': True, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

sabby/distilbert-base-uncased-finetuned-imdb

sabby

distilbert

16

0

transformers

0

fill-mask

true

false

null

['imdb']

null

0

['generated_from_trainer']

true

1,342

# distilbert-base-uncased-finetuned-imdb This model was trained from scratch on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.3679 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7072 | 1.0 | 157 | 2.4852 | | 2.573 | 2.0 | 314 | 2.4151 | | 2.5113 | 3.0 | 471 | 2.4185 | | 2.4979 | 4.0 | 628 | 2.3714 | | 2.4843 | 5.0 | 785 | 2.4271 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

EdenYav/Reinforce-PixelCopter

EdenYav

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

cartesinus/fedcsis-intent_baseline-xlm_r-leyzer_en

cartesinus

xlm-roberta

11

0

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,894

# fedcsis-intent_baseline-xlm_r-leyzer_en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1065 - Accuracy: 0.9799 - F1: 0.9799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 3.4638 | 1.0 | 814 | 1.7267 | 0.6100 | 0.6100 | | 1.327 | 2.0 | 1628 | 0.9051 | 0.8208 | 0.8208 | | 0.9046 | 3.0 | 2442 | 0.5209 | 0.9006 | 0.9006 | | 0.4666 | 4.0 | 3256 | 0.3450 | 0.9372 | 0.9372 | | 0.2699 | 5.0 | 4070 | 0.2331 | 0.9546 | 0.9546 | | 0.206 | 6.0 | 4884 | 0.1716 | 0.9705 | 0.9705 | | 0.1253 | 7.0 | 5698 | 0.1398 | 0.9747 | 0.9747 | | 0.0862 | 8.0 | 6512 | 0.1183 | 0.9794 | 0.9794 | | 0.0739 | 9.0 | 7326 | 0.1094 | 0.9794 | 0.9794 | | 0.0645 | 10.0 | 8140 | 0.1065 | 0.9799 | 0.9799 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

cartesinus/fedcsis-slot_baseline-xlm_r-leyzer_en

cartesinus

xlm-roberta

11

0

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,181

# fedcsis-slot_baseline-xlm_r-leyzer_en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1017 - Precision: 0.9735 - Recall: 0.9722 - F1: 0.9729 - Accuracy: 0.9852 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.2565 | 1.0 | 814 | 0.2709 | 0.8433 | 0.8717 | 0.8573 | 0.9313 | | 0.183 | 2.0 | 1628 | 0.1226 | 0.9413 | 0.9568 | 0.9490 | 0.9758 | | 0.0961 | 3.0 | 2442 | 0.1082 | 0.9561 | 0.9612 | 0.9586 | 0.9798 | | 0.0528 | 4.0 | 3256 | 0.0795 | 0.9678 | 0.9690 | 0.9684 | 0.9855 | | 0.0334 | 5.0 | 4070 | 0.0742 | 0.9720 | 0.9709 | 0.9715 | 0.9855 | | 0.027 | 6.0 | 4884 | 0.0960 | 0.9705 | 0.9714 | 0.9710 | 0.9838 | | 0.0234 | 7.0 | 5698 | 0.0910 | 0.9730 | 0.9736 | 0.9733 | 0.9861 | | 0.0111 | 8.0 | 6512 | 0.0871 | 0.9732 | 0.9728 | 0.9730 | 0.9871 | | 0.0067 | 9.0 | 7326 | 0.1016 | 0.9714 | 0.9716 | 0.9715 | 0.9861 | | 0.0067 | 10.0 | 8140 | 0.1017 | 0.9735 | 0.9722 | 0.9729 | 0.9852 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

amoselberg/a2c-PandaReachDense-v2

amoselberg

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 ... ```

miatia/nre_lora

miatia

null

18

0

null

1

null

false

other

null

0

[]

false

true

2,263

# nre_lora NRE-styleの試験場ベースモデルは7th_anime_3.1_A.ckpt[6e350084a6] ## サンプル **V1 LoRAなし** ![v1_cherrypick_before](https://huggingface.co/miatia/nre_lora/resolve/main/samples/v1_cherrypick_before.png) ``` 1 girl loli kawaii nre style Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 1440655960, Size: 512x512, Model hash: 6e350084a6, Clip skip: 2, ENSD: 31337, Eta: 0.67 ``` **V1 LoRAあり** ![v1_cherrypick_after](https://huggingface.co/miatia/nre_lora/resolve/main/samples/v1_cherrypick_after.png) ``` 1 girl loli kawaii nre style <lora:nre_v1:1> Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 1440655960, Size: 512x512, Model hash: 6e350084a6, Clip skip: 2, ENSD: 31337, Eta: 0.67 ``` **V2 LoRAなし** ![v2_cherrypick_before](https://huggingface.co/miatia/nre_lora/resolve/main/samples/v2_cherrypick_before.png) ``` 1 girl loli kawaii cute child waifu solo, long hair,expressionless, in an atelier clothing, dark souls, dark, dark, dim background, in a ruined messy dark room, from under, dutch angle, simple dark background, detailed, high quality, detailed, high quality, detailed, high quality, Negative prompt: 3d, grayscale, monochrome,light color bright color, eye lights, highlights, smile, energetic, cheerful, symmetry, greyscale Steps: 28, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 735105893, Size: 704x384, Model hash: 6e350084a6, Denoising strength: 0.65, Clip skip: 2, ENSD: 31337, Hires resize: 1024x576, Hires upscaler: Latent ``` **V2 LoRAあり** ![v2_cherrypick_after](https://huggingface.co/miatia/nre_lora/resolve/main/samples/v2_cherrypick_after.png) ``` 1 girl loli kawaii cute child waifu solo, long hair,expressionless, in an atelier clothing, dark souls, dark, dark, dim background, in a ruined messy dark room, from under, dutch angle, simple dark background, detailed, high quality, detailed, high quality, detailed, high quality, nre style <lora:nre_v2_i20_e4_dim4:1> Negative prompt: 3d, grayscale, monochrome,light color bright color, eye lights, highlights, smile, energetic, cheerful, symmetry, greyscale Steps: 28, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 735105893, Size: 704x384, Model hash: 6e350084a6, Denoising strength: 0.65, Clip skip: 2, ENSD: 31337, Hires resize: 1024x576, Hires upscaler: Latent ```

Isaacp/ppo-Pyramids_Training

Isaacp

null

16

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

838

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

relbert/flan-t5-small-analogy

relbert

t5

10

0

transformers

0

text2text-generation

true

false

null

0

[]

false

true

693

# relbert/flan-t5-small-analogy This is [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) for analogy generation, which is to generate a word pair (eg. `bird is to crow`) given a query (eg. `mammal is to whale`) so that the query and the generated word pair form an analogy statement. ### Usage ```python from transformers import pipeline pipe = pipeline('text2text-generation', model="relbert/flan-t5-small-analogy") output = pipe("generate analogy: mammal is to whale") print(output) >>> [{'generated_text': 'bird is to crow'}] ```

pfunk/CartPole-v1-DQPN_baseline-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

1,941

# (CleanRL) **DQN** Agent Playing **CartPole-v1** This is a trained model of a DQN agent playing CartPole-v1. 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_baseline.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_baseline]" python -m cleanrl_utils.enjoy --exp-name DQPN_baseline --env-id CartPole-v1 ``` 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/CartPole-v1-DQPN_baseline-seed1/raw/main/dqpn_duncan.py curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_baseline-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/CartPole-v1-DQPN_baseline-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_duncan.py --exp-name DQPN_baseline --target-tau 1 --policy-tau 1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id CartPole-v1 --seed 1 --total-timesteps 100000 ``` # Hyperparameters ```python {'batch_size': 128, 'buffer_size': 10000, 'capture_video': False, 'cuda': True, 'end_e': 0.05, 'env_id': 'CartPole-v1', 'exp_name': 'DQPN_baseline', 'exploration_fraction': 0.5, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.00025, 'learning_starts': 10000, 'min_mean_std': False, 'policy_network_frequency': 500, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 100, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 100000, 'track': True, 'train_frequency': 10, 'update_scalar': False, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

mycringefactory/vintagerecipecards

mycringefactory

null

3

0

null

0

text-to-image

false

mit

null

0

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

false

true

654

This is a hypernetwork that generates pictures of food with the vintage recipe card look. Ingredients can be specified to give more specific results. Dataset is images and text from https://linkin.bio/vintagerecipecards #### vintagerecipecards_v1 * Trained with Stable Diffusion 2 model. (768-v-ema.safetensors) * Hypernetwork learning rate = 0.00001 * 512x512 images #### Example <img src="https://i.imgur.com/ceDdW1M.png" width="300px"> ``` <hypernet:vintagerecipecards-50000:1.2> dorito casserole, doritos, blue paint Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 8, Seed: 3180085176, Size: 512x512, Model hash: 703d49a1d8, Model: 768-v-ema ```

MBARKI/layoutlm-funsd

MBARKI

layoutlm

7

0

transformers

0

token-classification

true

false

null

['funsd']

null

0

['generated_from_trainer']

true

9,210

# layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 0.6924 - Answer: {'precision': 0.6991525423728814, 'recall': 0.8158220024721878, 'f1': 0.7529948659440959, 'number': 809} - Header: {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119} - Question: {'precision': 0.7807250221043325, 'recall': 0.8291079812206573, 'f1': 0.8041894353369764, 'number': 1065} - Overall Precision: 0.7197 - Overall Recall: 0.7948 - Overall F1: 0.7554 - Overall Accuracy: 0.8040 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.8027 | 1.0 | 10 | 1.6152 | {'precision': 0.0103359173126615, 'recall': 0.004944375772558714, 'f1': 0.006688963210702341, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18232044198895028, 'recall': 0.061971830985915494, 'f1': 0.09250175192711983, 'number': 1065} | 0.0935 | 0.0351 | 0.0511 | 0.3249 | | 1.4899 | 2.0 | 20 | 1.2751 | {'precision': 0.18495297805642633, 'recall': 0.21878862793572312, 'f1': 0.20045300113250283, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4306969459671104, 'recall': 0.5164319248826291, 'f1': 0.46968403074295473, 'number': 1065} | 0.3254 | 0.3648 | 0.3440 | 0.5899 | | 1.1133 | 3.0 | 30 | 0.9514 | {'precision': 0.4911937377690802, 'recall': 0.6205191594561187, 'f1': 0.5483342435827417, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5651144435674822, 'recall': 0.672300469483568, 'f1': 0.614065180102916, 'number': 1065} | 0.5314 | 0.6111 | 0.5685 | 0.6982 | | 0.8513 | 4.0 | 40 | 0.8326 | {'precision': 0.5850746268656717, 'recall': 0.7268232385661311, 'f1': 0.6482910694597575, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6844003606853021, 'recall': 0.7126760563380282, 'f1': 0.6982520699172033, 'number': 1065} | 0.6248 | 0.6759 | 0.6493 | 0.7384 | | 0.7153 | 5.0 | 50 | 0.7422 | {'precision': 0.6159274193548387, 'recall': 0.7552533992583437, 'f1': 0.6785119378123265, 'number': 809} | {'precision': 0.08695652173913043, 'recall': 0.05042016806722689, 'f1': 0.06382978723404256, 'number': 119} | {'precision': 0.6826051112943117, 'recall': 0.7774647887323943, 'f1': 0.726953467954346, 'number': 1065} | 0.6354 | 0.7250 | 0.6773 | 0.7734 | | 0.5972 | 6.0 | 60 | 0.7031 | {'precision': 0.6330645161290323, 'recall': 0.7762669962917181, 'f1': 0.6973903387007219, 'number': 809} | {'precision': 0.15492957746478872, 'recall': 0.09243697478991597, 'f1': 0.11578947368421053, 'number': 119} | {'precision': 0.6802189210320563, 'recall': 0.8169014084507042, 'f1': 0.742320819112628, 'number': 1065} | 0.6443 | 0.7572 | 0.6962 | 0.7836 | | 0.5209 | 7.0 | 70 | 0.6902 | {'precision': 0.6597510373443983, 'recall': 0.7861557478368356, 'f1': 0.7174280879864636, 'number': 809} | {'precision': 0.2755102040816326, 'recall': 0.226890756302521, 'f1': 0.2488479262672811, 'number': 119} | {'precision': 0.7128463476070529, 'recall': 0.7971830985915493, 'f1': 0.7526595744680851, 'number': 1065} | 0.6711 | 0.7587 | 0.7122 | 0.7902 | | 0.4673 | 8.0 | 80 | 0.6693 | {'precision': 0.6649642492339122, 'recall': 0.8046971569839307, 'f1': 0.7281879194630874, 'number': 809} | {'precision': 0.28, 'recall': 0.23529411764705882, 'f1': 0.2557077625570776, 'number': 119} | {'precision': 0.7322314049586777, 'recall': 0.831924882629108, 'f1': 0.7789010989010988, 'number': 1065} | 0.6837 | 0.7852 | 0.7310 | 0.7965 | | 0.4151 | 9.0 | 90 | 0.6684 | {'precision': 0.6839323467230444, 'recall': 0.799752781211372, 'f1': 0.7373219373219373, 'number': 809} | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119} | {'precision': 0.7363560033585222, 'recall': 0.8234741784037559, 'f1': 0.7774822695035462, 'number': 1065} | 0.6910 | 0.7832 | 0.7342 | 0.8017 | | 0.3689 | 10.0 | 100 | 0.6742 | {'precision': 0.6954643628509719, 'recall': 0.796044499381953, 'f1': 0.7423631123919308, 'number': 809} | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119} | {'precision': 0.7483221476510067, 'recall': 0.8375586854460094, 'f1': 0.7904297740363314, 'number': 1065} | 0.7043 | 0.7888 | 0.7441 | 0.8000 | | 0.3327 | 11.0 | 110 | 0.6861 | {'precision': 0.6843198338525441, 'recall': 0.8145859085290482, 'f1': 0.7437923250564334, 'number': 809} | {'precision': 0.32456140350877194, 'recall': 0.31092436974789917, 'f1': 0.31759656652360513, 'number': 119} | {'precision': 0.7709790209790209, 'recall': 0.828169014084507, 'f1': 0.7985513807152557, 'number': 1065} | 0.7105 | 0.7918 | 0.7489 | 0.8031 | | 0.3167 | 12.0 | 120 | 0.6912 | {'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809} | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119} | {'precision': 0.7727272727272727, 'recall': 0.8300469483568075, 'f1': 0.8003621548211861, 'number': 1065} | 0.7138 | 0.7908 | 0.7503 | 0.8013 | | 0.3012 | 13.0 | 130 | 0.6878 | {'precision': 0.7015086206896551, 'recall': 0.8046971569839307, 'f1': 0.7495682210708117, 'number': 809} | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119} | {'precision': 0.7694974003466204, 'recall': 0.8338028169014085, 'f1': 0.8003605227579991, 'number': 1065} | 0.7162 | 0.7928 | 0.7526 | 0.8073 | | 0.2882 | 14.0 | 140 | 0.6930 | {'precision': 0.6997885835095138, 'recall': 0.8182941903584673, 'f1': 0.7544159544159544, 'number': 809} | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119} | {'precision': 0.7793468667255075, 'recall': 0.8291079812206573, 'f1': 0.8034576888080072, 'number': 1065} | 0.7199 | 0.7958 | 0.7560 | 0.8024 | | 0.2811 | 15.0 | 150 | 0.6924 | {'precision': 0.6991525423728814, 'recall': 0.8158220024721878, 'f1': 0.7529948659440959, 'number': 809} | {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119} | {'precision': 0.7807250221043325, 'recall': 0.8291079812206573, 'f1': 0.8041894353369764, 'number': 1065} | 0.7197 | 0.7948 | 0.7554 | 0.8040 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2