librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-15 12:29:39	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 12:28:52	card stringlengths 11 1.01M
MikkelGodsk/q-FrozenLake-v1-4x4-noSlippery	MikkelGodsk	2022-12-23T11:47:36Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T11:46:00Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="MikkelGodsk/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
research-backup/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0	research-backup	2022-12-23T11:11:20Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T05:34:46Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6765873015873016 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39037433155080214 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39762611275964393 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5108393551973318 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.678 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32456140350877194 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.881271658882025 - name: F1 (macro) type: f1_macro value: 0.8761005729675923 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7938967136150236 - name: F1 (macro) type: f1_macro value: 0.5852558618542903 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6397616468039004 - name: F1 (macro) type: f1_macro value: 0.6268814527849179 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9546497878556027 - name: F1 (macro) type: f1_macro value: 0.8651843170780902 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8708868693199624 - name: F1 (macro) type: f1_macro value: 0.8734929892090338 --- # relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0 RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39037433155080214 - Accuracy on SAT: 0.39762611275964393 - Accuracy on BATS: 0.5108393551973318 - Accuracy on U2: 0.32456140350877194 - Accuracy on U4: 0.375 - Accuracy on Google: 0.678 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/classification.json)): - Micro F1 score on BLESS: 0.881271658882025 - Micro F1 score on CogALexV: 0.7938967136150236 - Micro F1 score on EVALution: 0.6397616468039004 - Micro F1 score on K&H+N: 0.9546497878556027 - Micro F1 score on ROOT09: 0.8708868693199624 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6765873015873016 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: nce_logout - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 6 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 640 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
ShadoWxShinigamI/SD2-StatuesFigurines	ShadoWxShinigamI	2022-12-23T10:46:24Z	0	13	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-12-23T10:29:23Z	--- license: creativeml-openrail-m --- ##Textual Inversion Embed By ShadoWxShinigamI Use this embed to create statues of objects or people. It mixes well with [MJART](https://huggingface.co/ShadoWxShinigamI/SD-2-MJart) Embed + Hypernetwork if you want to create figurines. Examples:- Just This Embed (Default Nai Negatives, DPM++ 2S a Sampler, 20 Steps) - ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791723454-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791770707-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791861120-633a520aecbd8b19357b4806.png) With [MJART](https://huggingface.co/ShadoWxShinigamI/SD-2-MJart) Embed + Hypernetwork 0.5 strength (Default Nai Negatives, DPM++ 2S a Sampler, 20 Steps) - ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791996917-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671792022270-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671792063432-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671792086320-633a520aecbd8b19357b4806.png)
mrsteyk/openchatgpt-neo-125m	mrsteyk	2022-12-23T10:39:07Z	23	1	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neo", "text-generation", "generated_from_trainer", "text generation", "casual-lm", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-12-21T00:27:14Z	--- license: mit language: - en tags: - generated_from_trainer - text generation - pytorch - casual-lm metrics: - accuracy model-index: - name: openchatgpt-neo-r1 results: [] --- # --- Disclaimer --- # "Neo is an incredibly cursed codebase, it should not be used by anyone" (C) co-founder of EleutherAI - Connor Leahy # !!! USE [openchatgpt-neox-125m](https://huggingface.co/mrsteyk/openchatgpt-neox-125m) INSTEAD !!! # --- Archived --- # openchatgpt-neo-r1 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the openchatgpt safe-r1 dataset. It achieves the following results on the evaluation set: - Loss: 3.2156 - Accuracy: 0.8338 ## Model description Finetune based on the inner workings of ChatGPT. I won't elaborate on that. You must have a faint idea of how prompt is made for it to spit anything that's not garbled mess. This is effectively a schizophrenic idea that met the light of day. Practically a collab of 3 students in a virtual shed. ## Intended uses & limitations Intended uses & limitations fall in line with OpenAI's. Dataset used consists of safe texts (i.e. not highly sexual/erotica type stuff). NSFW version of the dataset is not planned to exist at the moment. Keep in mind that this is a 125m version of GPT-Neo. My 1050Ti Mobile couldn't even handle that without gradient thingmabobs. If anyone knows how to effectively finetune larger models on free colabs - feel free to let me know. Pile tokenizer also has one downside compared to native GPT-2/3 - `Assistant`. ## Training and evaluation data Data was split in ratio of 95%/5%. Preproccess included removing mentions of OpenAI wherever it was not deemed appropriete (GPT-2 has one of the appropriete mentions). Whole dataset consists of just shy off 3k input-output pairs. One input has multiple outputs (read as: one message has multiple variants of an answer). <<<1% (3 total) are curated lines (i.e. a huge mistake was spotted that needed corrections). Heavy bias on IT. ## Training procedure Input and output were straight up concatenated due to the nature of how ChatGPT works. Padding chosen was the same as the separator token, if that's not effective - please let me know as I am new to this stuff. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.9203 \| 1.0 \| 1378 \| 5.1668 \| 0.7274 \| \| 4.1368 \| 2.0 \| 2756 \| 4.3841 \| 0.7563 \| \| 3.4554 \| 3.0 \| 4134 \| 3.8068 \| 0.7875 \| \| 2.7598 \| 4.0 \| 5512 \| 3.3097 \| 0.8303 \| \| 2.5879 \| 5.0 \| 6890 \| 3.2156 \| 0.8338 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
MrDivakaruni/dqn-SpaceInvadersNoFrameskip-v4	MrDivakaruni	2022-12-23T10:11:44Z	7	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T10:11:07Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 582.50 +/- 173.63 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MrDivakaruni -f logs/ python enjoy.py --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 MrDivakaruni -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 MrDivakaruni ``` ## 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), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
DaniilSirota/ppo-Huggy	DaniilSirota	2022-12-23T10:00:50Z	14	1	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-23T10:00:38Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # 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: DaniilSirota/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
anuoluwa/ppo-LunarLander-v2	anuoluwa	2022-12-23T09:58:19Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T09:57:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 281.12 +/- 24.57 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
hanq0212/RL_course_unit4_part1	hanq0212	2022-12-23T09:39:18Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T09:28:33Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RL_course_unit4_part1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
gggggxy/ddpm-butterflies-128	gggggxy	2022-12-23T09:14:52Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-12-23T07:25:31Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/gggggxy/ddpm-butterflies-128/tensorboard?#scalars)
gaokaobishuati/ppo-LunarLander-v2	gaokaobishuati	2022-12-23T09:10:53Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T09:10:15Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.40 +/- 20.45 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
chengcshi/ppo-LunarLander-v2	chengcshi	2022-12-23T08:51:24Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T08:50:59Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.95 +/- 17.91 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ArchitaRay/my_awesome_opus_books_model	ArchitaRay	2022-12-23T08:47:05Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-12-23T07:16:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus_books model-index: - name: my_awesome_opus_books_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_opus_books_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 1.5494 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.7524 \| 1.0 \| 6355 \| 1.5629 \| \| 1.7382 \| 2.0 \| 12710 \| 1.5494 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
RayKau/dqn-SpaceInvadersNoFrameskip-v4	RayKau	2022-12-23T07:38:09Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T07:37:34Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 538.00 +/- 204.29 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RayKau -f logs/ python enjoy.py --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 RayKau -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 RayKau ``` ## 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)]) ```
LucHayward/ppo-LunarLander-v2	LucHayward	2022-12-23T07:15:47Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-07T15:05:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.72 +/- 34.73 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
umer07/text	umer07	2022-12-23T06:52:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-12-23T06:52:27Z	--- license: creativeml-openrail-m ---
dfsj/ppo-Huggy	dfsj	2022-12-23T06:52:24Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-23T06:52:13Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # 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: dfsj/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
zhuimengshaonian/bert-ancient-base	zhuimengshaonian	2022-12-23T06:29:17Z	5	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "zh", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-12-18T17:27:51Z	--- language: zh widget: text: '海阔凭鱼跃，天高[MASK]鸟飞。' --- ## Chinese Ancient BERT Model ### Model description The model's architecture is the BERT-base. We trained this model in 4 P100 about 7 days. (batch size = 24, steps = 1M) ### How to use You can use the model directly with a pipeline for text generation: ``` >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='zhuimengshaonian/bert-ancient-base') >>> unmasker("海阔凭鱼跃，天高[MASK]鸟飞。") ```
harikc456/n-SpaceInvadersNoFrameskip-v4	harikc456	2022-12-23T06:21:59Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T06:21:22Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 622.00 +/- 127.44 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga harikc456 -f logs/ python enjoy.py --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 harikc456 -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 harikc456 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
ben-yu/ppo-LunarLander-v2-try-2	ben-yu	2022-12-23T04:58:26Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T04:33:02Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 246.81 +/- 24.67 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
vumichien/whisper-medium-mix-jp-ver2	vumichien	2022-12-23T04:30:04Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-22T02:38:57Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: openai/whisper-medium results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2790 - Wer: 8.3986 - Cer: 5.2582 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\| \| 0.1691 \| 1.01 \| 1000 \| 0.1871 \| 10.1740 \| 6.3509 \| \| 0.0916 \| 2.02 \| 2000 \| 0.1691 \| 8.9797 \| 5.5499 \| \| 0.0452 \| 3.03 \| 3000 \| 0.1902 \| 8.9814 \| 5.5867 \| \| 0.0213 \| 4.04 \| 4000 \| 0.2062 \| 8.9375 \| 5.6531 \| \| 0.0096 \| 5.05 \| 5000 \| 0.2284 \| 8.7331 \| 5.6202 \| \| 0.0041 \| 6.05 \| 6000 \| 0.2395 \| 8.5051 \| 5.3009 \| \| 0.0022 \| 7.06 \| 7000 \| 0.2535 \| 8.5507 \| 5.3640 \| \| 0.001 \| 8.07 \| 8000 \| 0.2656 \| 8.5557 \| 5.3791 \| \| 0.0006 \| 9.08 \| 9000 \| 0.2721 \| 8.4037 \| 5.2739 \| \| 0.0004 \| 10.09 \| 10000 \| 0.2790 \| 8.3986 \| 5.2582 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
hasarinduperera/ppo-LunarLander-v2	hasarinduperera	2022-12-23T02:35:43Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T22:17:10Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.14 +/- 16.06 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
p4b/whisper-small-ko-fl	p4b	2022-12-23T01:35:24Z	3	2	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ko", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-09T19:38:15Z	--- language: - ko license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: Whisper Small Ko(FLUERS) - by p4b results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs ko_kr type: google/fleurs config: ko_kr split: test metrics: - name: Wer type: wer value: 20.251271313191744 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Ko(FLUERS) - by p4b This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the FLUERS Korean dataset. It achieves the following results on the evaluation set: - Loss: 0.2893 - Wer: 19.2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data ### Dataset filtering Some of datas from FLUERS are not used for training and evaluation. Most of filtered datas are not fit to model or including non-korean symbols. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 96 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3016 \| 32.0 \| 800 \| 0.4048 \| 140.4726 \| \| 0.0451 \| 64.0 \| 1600 \| 0.2893 \| 19.2043 \| \| 0.0169 \| 96.0 \| 2400 \| 0.3110 \| 20.2513 \| \| 0.0092 \| 128.0 \| 3200 \| 0.3240 \| 20.0419 \| \| 0.0062 \| 160.0 \| 4000 \| 0.3335 \| 20.0419 \| \| 0.0045 \| 192.0 \| 4800 \| 0.3416 \| 20.0718 \| \| 0.0035 \| 224.0 \| 5600 \| 0.3501 \| 20.1615 \| \| 0.0028 \| 256.0 \| 6400 \| 0.3562 \| 20.3709 \| \| 0.0024 \| 288.0 \| 7200 \| 0.3618 \| 20.0120 \| \| 0.002 \| 320.0 \| 8000 \| 0.3669 \| 20.1017 \| \| 0.0017 \| 352.0 \| 8800 \| 0.3704 \| 20.1914 \| \| 0.0017 \| 384.0 \| 9600 \| 0.3723 \| 20.2513 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.14.0.dev20221208+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
AinTziLLo/Reinforce-model-01	AinTziLLo	2022-12-23T01:11:16Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-12-23T01:10:22Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-model-01 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
AinTziLLo/testpyramidsrnd	AinTziLLo	2022-12-23T00:55:30Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2022-12-23T00:55:20Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # 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: AinTziLLo/testpyramidsrnd 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Arch4ngel/sd-class-butterflies-32	Arch4ngel	2022-12-23T00:14:56Z	2	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-23T00:14:20Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('Arch4ngel/sd-class-butterflies-32') image = pipeline().images[0] image ```
jonatasgrosman/whisper-large-es-cv11	jonatasgrosman	2022-12-22T23:52:02Z	20	1	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "es", "dataset:mozilla-foundation/common_voice_11_0", "doi:10.57967/hf/3596", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-21T20:42:43Z	--- language: - es license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer - cer model-index: - name: Whisper Large Spanish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 es type: mozilla-foundation/common_voice_11_0 config: es split: test args: es metrics: - name: WER type: wer value: 4.673613637544826 - name: CER type: cer value: 1.5573247819517182 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs es_419 type: google/fleurs config: es_419 split: test args: es_419 metrics: - name: WER type: wer value: 5.396216546072705 - name: CER type: cer value: 3.450427960057061 --- # Whisper Large Spanish This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on Spanish using the train split of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). ## Usage ```python from transformers import pipeline transcriber = pipeline( "automatic-speech-recognition", model="jonatasgrosman/whisper-large-es-cv11" ) transcriber.model.config.forced_decoder_ids = ( transcriber.tokenizer.get_decoder_prompt_ids( language="es", task="transcribe" ) ) transcription = transcriber("path/to/my_audio.wav") ``` ## Evaluation I've performed the evaluation of the model using the test split of two datasets, the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (same dataset used for the fine-tuning) and the [Fleurs](https://huggingface.co/datasets/google/fleurs) (dataset not seen during the fine-tuning). As Whisper can transcribe casing and punctuation, I've performed the model evaluation in 2 different scenarios, one using the raw text and the other using the normalized text (lowercase + removal of punctuations). Additionally, for the Fleurs dataset, I've evaluated the model in a scenario where there are no transcriptions of numerical values since the way these values are described in this dataset is different from how they are described in the dataset used in fine-tuning (Common Voice), so it is expected that this difference in the way of describing numerical values will affect the performance of the model for this type of transcription in Fleurs. ### Common Voice 11 \| \| CER \| WER \| \| --- \| --- \| --- \| \| [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) \| 2.43 \| 8.85 \| \| [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization \| 1.56 \| 4.67 \| \| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) \| 3.71 \| 12.34 \| \| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization \| 2.45 \| 6.30 \| ### Fleurs \| \| CER \| WER \| \| --- \| --- \| --- \| \| [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) \| 3.06 \| 9.11 \| \| [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization \| 3.45 \| 5.40 \| \| [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + keep only non-numeric samples \| 1.83 \| 7.57 \| \| [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization + keep only non-numeric samples \| 2.36 \| 4.14 \| \| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) \| 2.30 \| 8.50 \| \| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization \| 2.76 \| 4.79 \| \| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + keep only non-numeric samples \| 1.93 \| 7.33 \| \| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization + keep only non-numeric samples \| 2.50 \| 4.28 \|
SiddharthaM/distilbert-sentiment-new	SiddharthaM	2022-12-22T23:48:19Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-22T23:32:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: distilbert-sentiment-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-sentiment-new This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5872 - Accuracy: 0.7243 - Precision: 0.7192 - Recall: 0.7243 - F1: 0.7175 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 296 \| 0.6038 \| 0.6787 \| 0.7049 \| 0.6787 \| 0.6235 \| \| 0.5926 \| 2.0 \| 592 \| 0.5532 \| 0.7148 \| 0.7118 \| 0.7148 \| 0.6994 \| \| 0.5926 \| 3.0 \| 888 \| 0.5480 \| 0.7243 \| 0.7199 \| 0.7243 \| 0.7144 \| \| 0.4946 \| 4.0 \| 1184 \| 0.5535 \| 0.7300 \| 0.7255 \| 0.7300 \| 0.7220 \| \| 0.4946 \| 5.0 \| 1480 \| 0.5858 \| 0.7186 \| 0.7140 \| 0.7186 \| 0.7146 \| \| 0.4267 \| 6.0 \| 1776 \| 0.5872 \| 0.7243 \| 0.7192 \| 0.7243 \| 0.7175 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
rjac/ppo-LunarLander-v2	rjac	2022-12-22T23:47:14Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T23:40:19Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 248.16 +/- 18.51 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
kadirnar/RRDB_PSNR_x4	kadirnar	2022-12-22T23:07:46Z	0	0	null	[ "Super-Resolution", "computer-vision", "ESRGAN", "gan", "arxiv:2107.10833", "license:apache-2.0", "region:us" ]	null	2022-12-22T22:42:57Z	--- license: apache-2.0 tags: - Super-Resolution - computer-vision - ESRGAN - gan --- ### Model Description [ESRGAN](https://arxiv.org/abs/2107.10833): ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution [Paper Repo](https://github.com/xinntao/ESRGAN): Implementation of paper. ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/RRDB_PSNR_x4', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @InProceedings{wang2018esrgan, author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change}, title = {ESRGAN: Enhanced super-resolution generative adversarial networks}, booktitle = {The European Conference on Computer Vision Workshops (ECCVW)}, month = {September}, year = {2018} } ```
kadirnar/RRDB_ESRGAN_x4	kadirnar	2022-12-22T23:05:06Z	0	2	null	[ "Super-Resolution", "computer-vision", "ESRGAN", "gan", "arxiv:2107.10833", "license:apache-2.0", "region:us" ]	null	2022-12-22T22:42:40Z	--- license: apache-2.0 tags: - Super-Resolution - computer-vision - ESRGAN - gan --- ### Model Description [ESRGAN](https://arxiv.org/abs/2107.10833): ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution [Paper Repo](https://github.com/xinntao/ESRGAN): Implementation of paper. ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/RRDB_ESRGAN_x4', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @InProceedings{wang2018esrgan, author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change}, title = {ESRGAN: Enhanced super-resolution generative adversarial networks}, booktitle = {The European Conference on Computer Vision Workshops (ECCVW)}, month = {September}, year = {2018} } ```
kadirnar/DPED	kadirnar	2022-12-22T22:58:25Z	0	1	null	[ "Super-Resolution", "computer-vision", "RealSR", "gan", "arxiv:2005.01996", "license:apache-2.0", "region:us" ]	null	2022-12-22T22:37:31Z	--- license: apache-2.0 tags: - Super-Resolution - computer-vision - RealSR - gan --- ### Model Description [RealSR](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Ji_Real-World_Super-Resolution_via_Kernel_Estimation_and_Noise_Injection_CVPRW_2020_paper.pdf): Real-World Super-Resolution via Kernel Estimation and Noise Injection. [NTIRE 2020 Challenge on Real-World Image Super-Resolution](https://arxiv.org/abs/2005.01996): Methods and Results [Paper Repo](https://github.com/Tencent/Real-SR): Implementation of paper. ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/DPED', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @article{Lugmayr2020ntire, title={NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results}, author={Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy Chiu, Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete, Michelini Christian, Micheloni Kalpesh, Prajapati Haoyu, Ren Yong, Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Haoning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou}, journal={CVPR Workshops}, year={2020}, } ```
JovialValley/model_broadclass_onSet1.1	JovialValley	2022-12-22T22:51:39Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-22T21:28:20Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - wer model-index: - name: model_broadclass_onSet1.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_broadclass_onSet1.1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2469 - 0 Precision: 1.0 - 0 Recall: 1.0 - 0 F1-score: 1.0 - 0 Support: 24 - 1 Precision: 1.0 - 1 Recall: 1.0 - 1 F1-score: 1.0 - 1 Support: 39 - 2 Precision: 1.0 - 2 Recall: 1.0 - 2 F1-score: 1.0 - 2 Support: 23 - 3 Precision: 1.0 - 3 Recall: 1.0 - 3 F1-score: 1.0 - 3 Support: 12 - Accuracy: 1.0 - Macro avg Precision: 1.0 - Macro avg Recall: 1.0 - Macro avg F1-score: 1.0 - Macro avg Support: 98 - Weighted avg Precision: 1.0 - Weighted avg Recall: 1.0 - Weighted avg F1-score: 1.0 - Weighted avg Support: 98 - Wer: 0.2423 - Mtrix: [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] ## 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: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 80 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| 0 Precision \| 0 Recall \| 0 F1-score \| 0 Support \| 1 Precision \| 1 Recall \| 1 F1-score \| 1 Support \| 2 Precision \| 2 Recall \| 2 F1-score \| 2 Support \| 3 Precision \| 3 Recall \| 3 F1-score \| 3 Support \| Accuracy \| Macro avg Precision \| Macro avg Recall \| Macro avg F1-score \| Macro avg Support \| Weighted avg Precision \| Weighted avg Recall \| Weighted avg F1-score \| Weighted avg Support \| Wer \| Mtrix \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:--------:\|:----------:\|:---------:\|:-----------:\|:--------:\|:----------:\|:---------:\|:-----------:\|:--------:\|:----------:\|:---------:\|:-----------:\|:--------:\|:----------:\|:---------:\|:--------:\|:-------------------:\|:----------------:\|:------------------:\|:-----------------:\|:----------------------:\|:-------------------:\|:---------------------:\|:--------------------:\|:------:\|:---------------------------------------------------------------------------------------:\| \| 2.3722 \| 4.16 \| 100 \| 2.1950 \| 0.2449 \| 1.0 \| 0.3934 \| 24 \| 0.0 \| 0.0 \| 0.0 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.2449 \| 0.0612 \| 0.25 \| 0.0984 \| 98 \| 0.0600 \| 0.2449 \| 0.0964 \| 98 \| 0.9879 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 2.2944 \| 8.33 \| 200 \| 2.1537 \| 0.2449 \| 1.0 \| 0.3934 \| 24 \| 0.0 \| 0.0 \| 0.0 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.2449 \| 0.0612 \| 0.25 \| 0.0984 \| 98 \| 0.0600 \| 0.2449 \| 0.0964 \| 98 \| 0.9879 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 1.9927 \| 12.49 \| 300 \| 1.8879 \| 0.2449 \| 1.0 \| 0.3934 \| 24 \| 0.0 \| 0.0 \| 0.0 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.2449 \| 0.0612 \| 0.25 \| 0.0984 \| 98 \| 0.0600 \| 0.2449 \| 0.0964 \| 98 \| 0.9879 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 1.7175 \| 16.65 \| 400 \| 1.6374 \| 0.2449 \| 1.0 \| 0.3934 \| 24 \| 0.0 \| 0.0 \| 0.0 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.2449 \| 0.0612 \| 0.25 \| 0.0984 \| 98 \| 0.0600 \| 0.2449 \| 0.0964 \| 98 \| 0.9879 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 1.6065 \| 20.82 \| 500 \| 1.5619 \| 0.2449 \| 1.0 \| 0.3934 \| 24 \| 0.0 \| 0.0 \| 0.0 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.2449 \| 0.0612 \| 0.25 \| 0.0984 \| 98 \| 0.0600 \| 0.2449 \| 0.0964 \| 98 \| 0.9879 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 1.5362 \| 24.98 \| 600 \| 1.5019 \| 0.2449 \| 1.0 \| 0.3934 \| 24 \| 0.0 \| 0.0 \| 0.0 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.2449 \| 0.0612 \| 0.25 \| 0.0984 \| 98 \| 0.0600 \| 0.2449 \| 0.0964 \| 98 \| 0.9879 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 1.5599 \| 29.16 \| 700 \| 1.4858 \| 0.2449 \| 1.0 \| 0.3934 \| 24 \| 0.0 \| 0.0 \| 0.0 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.2449 \| 0.0612 \| 0.25 \| 0.0984 \| 98 \| 0.0600 \| 0.2449 \| 0.0964 \| 98 \| 0.9879 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 1.5344 \| 33.33 \| 800 \| 1.4721 \| 0.2759 \| 1.0 \| 0.4324 \| 24 \| 1.0 \| 0.2821 \| 0.4400 \| 39 \| 0.0 \| 0.0 \| 0.0 \| 23 \| 0.0 \| 0.0 \| 0.0 \| 12 \| 0.3571 \| 0.3190 \| 0.3205 \| 0.2181 \| 98 \| 0.4655 \| 0.3571 \| 0.2810 \| 98 \| 0.9919 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 28, 11, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] \| \| 1.4024 \| 37.49 \| 900 \| 1.3532 \| 1.0 \| 1.0 \| 1.0 \| 24 \| 1.0 \| 1.0 \| 1.0 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 0.9742 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| \| 0.9429 \| 41.65 \| 1000 \| 0.9455 \| 0.96 \| 1.0 \| 0.9796 \| 24 \| 0.9744 \| 0.9744 \| 0.9744 \| 39 \| 1.0 \| 0.9565 \| 0.9778 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 0.9796 \| 0.9836 \| 0.9827 \| 0.9829 \| 98 \| 0.9800 \| 0.9796 \| 0.9796 \| 98 \| 0.9084 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] \| \| 0.8955 \| 45.82 \| 1100 \| 0.8890 \| 0.96 \| 1.0 \| 0.9796 \| 24 \| 1.0 \| 0.9744 \| 0.9870 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 0.9898 \| 0.99 \| 0.9936 \| 0.9917 \| 98 \| 0.9902 \| 0.9898 \| 0.9898 \| 98 \| 0.9246 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| \| 0.8708 \| 49.98 \| 1200 \| 0.8304 \| 1.0 \| 1.0 \| 1.0 \| 24 \| 0.975 \| 1.0 \| 0.9873 \| 39 \| 1.0 \| 0.9565 \| 0.9778 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 0.9898 \| 0.9938 \| 0.9891 \| 0.9913 \| 98 \| 0.9901 \| 0.9898 \| 0.9897 \| 98 \| 0.9272 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] \| \| 0.8671 \| 54.16 \| 1300 \| 0.8028 \| 0.96 \| 1.0 \| 0.9796 \| 24 \| 1.0 \| 1.0 \| 1.0 \| 39 \| 1.0 \| 0.9565 \| 0.9778 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 0.9898 \| 0.99 \| 0.9891 \| 0.9893 \| 98 \| 0.9902 \| 0.9898 \| 0.9898 \| 98 \| 0.9211 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 1, 0, 22, 0], [3, 0, 0, 0, 12]] \| \| 0.8383 \| 58.33 \| 1400 \| 0.7804 \| 1.0 \| 1.0 \| 1.0 \| 24 \| 1.0 \| 1.0 \| 1.0 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 0.9170 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| \| 0.7872 \| 62.49 \| 1500 \| 0.7745 \| 0.96 \| 1.0 \| 0.9796 \| 24 \| 1.0 \| 0.9744 \| 0.9870 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 0.9898 \| 0.99 \| 0.9936 \| 0.9917 \| 98 \| 0.9902 \| 0.9898 \| 0.9898 \| 98 \| 0.9439 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| \| 0.7538 \| 66.65 \| 1600 \| 0.7141 \| 0.96 \| 1.0 \| 0.9796 \| 24 \| 1.0 \| 0.9744 \| 0.9870 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 0.9898 \| 0.99 \| 0.9936 \| 0.9917 \| 98 \| 0.9902 \| 0.9898 \| 0.9898 \| 98 \| 0.9267 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| \| 0.6439 \| 70.82 \| 1700 \| 0.5818 \| 1.0 \| 1.0 \| 1.0 \| 24 \| 1.0 \| 1.0 \| 1.0 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 0.8574 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| \| 0.5295 \| 74.98 \| 1800 \| 0.3775 \| 1.0 \| 1.0 \| 1.0 \| 24 \| 1.0 \| 1.0 \| 1.0 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 0.4633 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| \| 0.4184 \| 79.16 \| 1900 \| 0.2507 \| 1.0 \| 1.0 \| 1.0 \| 24 \| 1.0 \| 1.0 \| 1.0 \| 39 \| 1.0 \| 1.0 \| 1.0 \| 23 \| 1.0 \| 1.0 \| 1.0 \| 12 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 1.0 \| 1.0 \| 1.0 \| 98 \| 0.2529 \| [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
kadirnar/yolov7-v0.1	kadirnar	2022-12-22T22:19:19Z	0	1	null	[ "object-detection", "computer-vision", "yolov7", "pypi", "dataset:detection-datasets/coco", "arxiv:2207.02696", "license:gpl-3.0", "region:us" ]	object-detection	2022-12-19T13:58:17Z	--- license: gpl-3.0 tags: - object-detection - computer-vision - yolov7 - pypi datasets: - detection-datasets/coco --- ### Model Description [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696) [YOLOv7-Pip: Packaged version of the Yolov7 repository](https://github.com/kadirnar/yolov7-pip) [Paper Repo: Implementation of paper - YOLOv7](https://github.com/WongKinYiu/yolov7) ### Installation ``` pip install yolov7detect ``` ### Yolov7 Inference ```python import yolov7 # load pretrained or custom model model = yolov7.load('kadirnar/yolov7-v0.1', hf_model=True) # set model parameters model.conf = 0.25 # NMS confidence threshold model.iou = 0.45 # NMS IoU threshold model.classes = None # (optional list) filter by class # set image imgs = 'inference/images' # perform inference results = model(imgs) # inference with larger input size and test time augmentation results = model(img, size=1280, augment=True) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() ``` ### BibTeX Entry and Citation Info ``` @article{wang2022yolov7, title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}, author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, journal={arXiv preprint arXiv:2207.02696}, year={2022} } ```
kadirnar/bsrgan	kadirnar	2022-12-22T22:17:35Z	0	1	null	[ "Super-Resolution", "computer-vision", "bsrgan", "gan", "arxiv:2103.14006", "license:apache-2.0", "region:us" ]	null	2022-12-20T20:16:15Z	--- license: apache-2.0 tags: - Super-Resolution - computer-vision - bsrgan - gan --- ### Model Description [BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .](https://arxiv.org/abs/2103.14006) [BSRGAN-Pip: Packaged version of the BSRGAN repository](https://github.com/kadirnar/bsrgan-pip/) [Paper Repo: Implementation of paper - BSRGAN](https://github.com/cszn/BSRGAN) ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/bsrgan', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ```
kadirnar/yolox_nano-v0.1.1	kadirnar	2022-12-22T22:16:24Z	0	0	null	[ "object-detection", "computer-vision", "yolox", "yolov3", "yolov5", "dataset:detection-datasets/coco", "arxiv:2107.08430", "license:apache-2.0", "region:us" ]	object-detection	2022-12-21T22:06:59Z	--- license: apache-2.0 tags: - object-detection - computer-vision - yolox - yolov3 - yolov5 datasets: - detection-datasets/coco --- ### Model Description [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use. [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) ### Installation ``` pip install yoloxdetect ``` ### Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_nano-v0.1.1", config_path = "configs.yolox_s", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(image='data/images', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```
kadirnar/yolox_m-v0.1.1	kadirnar	2022-12-22T22:15:50Z	0	0	null	[ "object-detection", "computer-vision", "yolox", "yolov3", "yolov5", "dataset:detection-datasets/coco", "arxiv:2107.08430", "license:apache-2.0", "region:us" ]	object-detection	2022-12-21T22:08:22Z	--- license: apache-2.0 tags: - object-detection - computer-vision - yolox - yolov3 - yolov5 datasets: - detection-datasets/coco --- ### Model Description [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use. [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) ### Installation ``` pip install yoloxdetect ``` ### Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_m-v0.1.1", config_path = "configs.yolox_m", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(image='data/images', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```
kadirnar/yolox_l-v0.1.1	kadirnar	2022-12-22T22:15:38Z	0	2	null	[ "object-detection", "computer-vision", "yolox", "yolov3", "yolov5", "dataset:detection-datasets/coco", "arxiv:2107.08430", "license:apache-2.0", "region:us" ]	object-detection	2022-12-21T22:11:13Z	--- license: apache-2.0 tags: - object-detection - computer-vision - yolox - yolov3 - yolov5 datasets: - detection-datasets/coco --- ### Model Description [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use. [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) ### Installation ``` pip install yoloxdetect ``` ### Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_l-v0.1.1", config_path = "configs.yolox_l", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(image='data/images', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```
paragon-analytics/bert_empathy	paragon-analytics	2022-12-22T22:00:09Z	24	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-21T15:56:24Z	--- license: "mit" --- This is a fine-tuned RoBERTa model that takes text (up to a few sentences) and predicts to what extent it contains empathic language. Example classification: ```python import torch import tensorflow as tf from transformers import RobertaTokenizer, RobertaModel from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/bert_empathy") model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_empathy") def roberta(x): encoded_input = tokenizer(x, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = tf.nn.softmax(scores) return scores.numpy()[1] ```
hawkeyedesi/ppo-LunarLander-v2	hawkeyedesi	2022-12-22T21:59:00Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T21:58:38Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo-LunarLander-v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.73 +/- 14.54 name: mean_reward verified: false --- # ppo-LunarLander-v2 Agent playing LunarLander-v2 This is a trained model of a ppo-LunarLander-v2 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 ... ```
arampacha/whisper-large-uk-2	arampacha	2022-12-22T21:32:24Z	42	5	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "uk", "dataset:mozilla-foundation/common_voice_11_0", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-21T23:00:43Z	--- language: - uk license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 - google/fleurs model-index: - name: whisper-large-uk results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: uk split: test args: uk metrics: - name: Wer type: wer value: 10.02262314404669 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Fleurs type: google/fleurs config: uk_ua split: test args: uk_ua metrics: - name: Wer type: wer value: 7.564370215727209 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-uk This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2527 - eval_wer: 10.0226 - eval_runtime: 9610.7996 - eval_samples_per_second: 0.747 - eval_steps_per_second: 0.023 - epoch: 1.8 - step: 1098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 32 - eval_batch_size: 32 - 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 - training_steps: 1500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
kRo0T/dqn-SpaceInvadersNoFrameskip-v4	kRo0T	2022-12-22T20:57:38Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T20:56:59Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 622.00 +/- 199.96 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga kRo0T -f logs/ python enjoy.py --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 kRo0T -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 kRo0T ``` ## 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)]) ```
johnhudzinatr/ppo-Huggy	johnhudzinatr	2022-12-22T20:41:47Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-22T20:41:34Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # 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: johnhudzinatr/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Dahoas/gpt2-rm-static	Dahoas	2022-12-22T20:14:15Z	5	1	transformers	[ "transformers", "pytorch", "gpt2", "text-classification", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2022-12-22T20:03:32Z	[wanbd](https://wandb.ai/dahoas/huggingface/runs/1a38b0tb?workspace=user-dahoas)
Dahoas/gptneo-rm-static	Dahoas	2022-12-22T20:13:35Z	6	1	transformers	[ "transformers", "pytorch", "gpt_neo", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-22T20:09:51Z	[wandb](https://wandb.ai/dahoas/huggingface/runs/e7r5an9w?workspace=user-dahoas)
SiddharthaM/xlm-sentiment-new	SiddharthaM	2022-12-22T19:54:29Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-22T19:24:41Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: xlm-sentiment-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-sentiment-new 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: 0.6166 - Accuracy: 0.7405 - Precision: 0.7375 - Recall: 0.7405 - F1: 0.7386 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 296 \| 0.5519 \| 0.7310 \| 0.7266 \| 0.7310 \| 0.7277 \| \| 0.5719 \| 2.0 \| 592 \| 0.5569 \| 0.75 \| 0.7562 \| 0.75 \| 0.7302 \| \| 0.5719 \| 3.0 \| 888 \| 0.5320 \| 0.7243 \| 0.7269 \| 0.7243 \| 0.7254 \| \| 0.477 \| 4.0 \| 1184 \| 0.5771 \| 0.7300 \| 0.7264 \| 0.7300 \| 0.7276 \| \| 0.477 \| 5.0 \| 1480 \| 0.6051 \| 0.7376 \| 0.7361 \| 0.7376 \| 0.7368 \| \| 0.428 \| 6.0 \| 1776 \| 0.6166 \| 0.7405 \| 0.7375 \| 0.7405 \| 0.7386 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
Akumetsu971/SD_VCM07_Anime_Style	Akumetsu971	2022-12-22T19:35:08Z	0	5	null	[ "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-12-21T02:03:36Z	--- inference: true language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- # SD_VCM07_Anime_Style is an open source Stable Diffusion Embedding on art style of VCM07, by Akumetsu971 (https://www.tiktok.com/@akumetsu971) --- ### Model used to train: wd-v1-3-full-opt.ckpt (https://huggingface.co/hakurei/waifu-diffusion-v1-3) ### Files 3 files available (Best version is V2): -VCM07_style - 4000 steps (more focused on girl) -VCM07_style2 - 4000 steps (allowed to create animals) -Prompt_Blending Script (optional, used for prompt) ### Prompt You need to use DeepDanBooru Tags (https://gigazine.net/gsc_news/en/20221012-automatic1111-stable-diffusion-webui-deep-danbooru/) Elysium_Anime_V2.ckpt (https://huggingface.co/hesw23168/SD-Elysium-Model) Prompt_blending script (https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/tree/main) Embedding was trained with images of girls only. Therefore, getting a boy can be difficult. Adjust weight, negative prompt, etc... ### Human Example Positive Prompt: (VCM07_style2:1.0), (1girl:1.2), looking_at_viewer, (best quality), (masterpiece:1.2), (ultra-detailed),(official art),(an extremely delicate and beautiful), (attractive:1.2), (beautiful detailed eyes), (dynamic colours, vibrant colours), depth of field, god rays, dynamic lighting Negative Prompt: (mediocre:1.2), (average:1.2), (bad:1.2), (wrong:1.2), (error:1.2), (fault:1.2),( badly_drawn:1.2), (poorly_drawn:1.2), ( low_quality:1.2), no_quality, bad_quality, no_resolution, low_resolution, (lowres:1.2), normal_resolution, (disfigured:1.6), (deformed:1.4), (distortion:1.2), bad_anatomy, (no_detail:1.2), low_detail, normal_detail, (scribble:1.2), (rushed:1.2), (unfinished:1.2), blur, blurry, claws, (misplaced:1.2), (disconnected:1.2), nonsense, random, (noise:1.2), (deformation:1.2), 3d, dull, boring, uninteresting, screencap, (text:1.2), (frame:1.1), (out_of_frame:1.2), (title:1.2), (description:1.3), (sexual:1.2), text, error,(logo:1.3), (watermark:1.3), bad_perspective, bad_proportions, cinematic, jpg_artifacts, jpeg_artifacts, extra_leg, missing_leg, extra_arm, missing_arm, long_hand, bad_hands, (mutated_hand:1.2), (extra_finger:1.2), (missing_finger:1.2), broken_finger, (fused_fingers:1.2), extra_feet, missing_feet, fused_feet, long_feet, missing_limbs, extra_limbs, fused_limbs, claw, (extra_digit:1.2), (fewer_digits:1.2), elves_ears, (naked:1.3), (wet:1.2), uncensored, (long_neck:1.2), (weapon:1.5) <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06756-3422664593-(VCM07_style_1.2)%2C%20close-up%2C%20portrait%2C%201girl%2C%20(solo_1.2)%2C%20single%2C%20black_hair%2C%20blue_eyes%2C%20%20long_hair%2C%20looking_at_viewer%2C(best%20qua.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/07023-1420035308-(VCM07_style2_1.0)%2C%20(1girl_1.2)%2C%20looking_at_viewer%2C%20(best%20quality)%2C%20(masterpiece_1.2)%2C%20(ultra-detailed)%2C(official%20art)%2C(an%20extre.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/07045-3879379165-(VCM07_style2_1.0)%2C%201girl%2C%20(pink_hair_1.8)%2C%20(solo_1.2)%2C%20looking_at_viewer%2C%20(best%20quality)%2C%20(masterpiece_1.2)%2C%20(ultra-detailed)%2C(.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06785-1732689013-(VCM07_style_1.2)%2C%20close-up%2C%20portrait%2C%20(solo_1.4)%2C%20(1girl%2C)%2C%20(fox_1.6)%2C%20single%2C%20(pink_hair_1.6)%2C%20blue_eyes%2C%20%20long_hair%2C%20looking_.png" width="50%"/> ### Animals Example For V2, embedding was trained with dogs, cats, foxes. Therefore, it is easier to get these animals. However, it is possible to get frogs, elephants, tigers, lions, etc... I used a method with blend prompt script then I described the anatomy of the animal: eyes, ears, nose, fur, etc... Positive Prompt: (VCM07_style2:1.0), (cat:1.4\|dog:0.5\|fox:0.5), (cat_nose:1.2), (cat_eyes:1.2), (cat_ears:1.2), (solo:1.2), looking_at_viewer, (best quality), (masterpiece:1.2), (ultra-detailed),(official art),(an extremely delicate and beautiful), (attractive:1.2), (beautiful detailed eyes), (dynamic colours, vibrant colours), depth of field, god rays, dynamic lighting Negative Prompt: (mediocre:1.2), (average:1.2), (bad:1.2), (wrong:1.2), (error:1.2), (fault:1.2),( badly_drawn:1.2), (poorly_drawn:1.2), ( low_quality:1.2), no_quality, bad_quality, no_resolution, low_resolution, (lowres:1.2), normal_resolution, (disfigured:1.6), (deformed:1.5), (distortion:1.2), bad_anatomy, (no_detail:1.2), low_detail, normal_detail, (scribble:1.2), (rushed:1.2), (unfinished:1.2), blur, blurry, claws, (misplaced:1.2), (disconnected:1.2), nonsense, random, (noise:1.2), (deformation:1.2), 3d, dull, boring, uninteresting, screencap, (text:1.2), (frame:1.1), (out_of_frame:1.2), (title:1.2), (description:1.3), (sexual:1.2), text, error,(logo:1.3), (watermark:1.3), bad_perspective, bad_proportions, cinematic, jpg_artifacts, jpeg_artifacts, extra_leg, missing_leg, extra_arm, missing_arm, long_hand, bad_hands, (mutated_hand:1.2), (extra_finger:1.2), (missing_finger:1.2), broken_finger, (fused_fingers:1.2), extra_feet, missing_feet, fused_feet, long_feet, missing_limbs, extra_limbs, fused_limbs, claw, (extra_digit:1.2), (fewer_digits:1.2), elves_ears, (naked:1.3), (wet:1.2), uncensored, (long_neck:1.2) <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/07031-4095215986-(VCM07_style2_1.0)%2C%20(cat_1.4_dog_0.5_fox_0.5)%2C%20(cat_nose_1.2)%2C%20(cat_eyes_1.2)%2C%20(cat_ears_1.2)%2C%20(solo_1.2)%2C%20looking_at_viewer%2C%20(b.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06915-1261422051-(VCM07_style2_1.0)%2C%20(cat_0.5_lion_0.5_dog_0.5_fox_1.2)%2C%20looking_at_viewer%2C%20(best%20quality)%2C%20(masterpiece_1.2)%2C%20(ultra-detailed)%2C(.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06994-2098824635-(VCM07_style2_1.0)%2C%20(cat_0.5_lion_0.5_dog_0.5_fox_0.5_tiger_1.4)%2C%20(tiger_ears_1.4)%2C%20(tiger_nose_1.4)%2C%20(white_tiger_1.4)%2C%20(white_.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06961-2420141730-(VCM07_style2_1.1)%2C%20(cat_0.5_lion_0.5_dog_0.5_fox_0.5_monkey_1.4)%2C%20(monkey_ears_1.4)%2C%20(monkey_nose_1.4)%2C%20looking_at_viewer%2C%20(bes.png" width="50%"/> ```
magnomont12/Taxi-v3	magnomont12	2022-12-22T19:13:08Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T19:12:56Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.77 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="magnomont12/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"]) ```
hawkeoni/sd-class-butterflies-32	hawkeoni	2022-12-22T18:56:01Z	0	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-22T18:55:41Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('hawkeoni/sd-class-butterflies-32') image = pipeline().images[0] image ```
NielsV/Q-learning-taxi-v4	NielsV	2022-12-22T18:49:51Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T17:17:07Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Q-learning-taxi-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="NielsV/Q-learning-taxi-v4", 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"]) ```
hanymac/CoreML-Stable-Diffusion-2.1-original-img2img	hanymac	2022-12-22T18:34:21Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-12-22T18:12:54Z	--- license: creativeml-openrail-m ---
aliosmankaya/reg_arr_model_1_dim	aliosmankaya	2022-12-22T18:26:47Z	0	0	sklearn	[ "sklearn", "joblib", "skops", "tabular-classification", "region:us" ]	tabular-classification	2022-11-30T22:22:07Z	--- library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: sepal_length: - 6.3 - 6.5 - 5.6 --- ### Linear Regression Model This Linear Regression model trained on Iris dataset as a regular numpy array with 1-dimensional. Goal is to test this pr -> https://github.com/skops-dev/skops/pull/211
aliosmankaya/reg_arr_model_2_dim	aliosmankaya	2022-12-22T18:26:15Z	0	0	sklearn	[ "sklearn", "joblib", "skops", "tabular-classification", "region:us" ]	tabular-classification	2022-11-30T22:15:02Z	--- library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: sepal_length: - 6.3 - 6.5 - 5.6 sepal_width: - 3.3 - 3.0 - 2.5 --- ### Linear Regression Model This Linear Regression model trained on Iris dataset as a regular numpy array with 2-dimensional. Goal is to test this pr -> https://github.com/skops-dev/skops/pull/211
DavidErikMollberg/whisper-medium-is	DavidErikMollberg	2022-12-22T18:17:06Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-20T13:53:22Z	--- model-index: - name: DavidErikMollberg/whisper-medium-is results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: is_is split: test metrics: - type: wer value: 16.17 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: language-and-voice-lab/samromur_asr type: language-and-voice-lab/samromur_asr config: samromur_asr split: test metrics: - type: wer value: 10.22 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: language-and-voice-lab/althingi_asr type: language-and-voice-lab/althingi_asr config: althingi_asr split: test metrics: - type: wer value: 9.67 name: WER --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Resources for more information: [More Information Needed] # Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ## Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ## Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ## Training Procedure [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] # Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ## Testing Data, Factors & Metrics ### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] ### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] ### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ## Results [More Information Needed] # Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] # Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] # Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>
kRo0T/q-Taxi-v3	kRo0T	2022-12-22T18:06:19Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T17:11:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="kRo0T/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"]) ```
JovialValley/model_broadclass_onSet3	JovialValley	2022-12-22T17:55:47Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-22T14:21:33Z	--- tags: - generated_from_trainer model-index: - name: model_broadclass_onSet3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_broadclass_onSet3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1389 - eval_0_precision: 1.0 - eval_0_recall: 1.0 - eval_0_f1-score: 1.0 - eval_0_support: 23 - eval_1_precision: 0.9697 - eval_1_recall: 0.9697 - eval_1_f1-score: 0.9697 - eval_1_support: 33 - eval_2_precision: 1.0 - eval_2_recall: 1.0 - eval_2_f1-score: 1.0 - eval_2_support: 26 - eval_3_precision: 0.9333 - eval_3_recall: 0.9333 - eval_3_f1-score: 0.9333 - eval_3_support: 15 - eval_accuracy: 0.9794 - eval_macro avg_precision: 0.9758 - eval_macro avg_recall: 0.9758 - eval_macro avg_f1-score: 0.9758 - eval_macro avg_support: 97 - eval_weighted avg_precision: 0.9794 - eval_weighted avg_recall: 0.9794 - eval_weighted avg_f1-score: 0.9794 - eval_weighted avg_support: 97 - eval_wer: 0.1037 - eval_mtrix: [[0, 1, 2, 3], [0, 23, 0, 0, 0], [1, 0, 32, 0, 1], [2, 0, 0, 26, 0], [3, 0, 1, 0, 14]] - eval_runtime: 5.6481 - eval_samples_per_second: 17.174 - eval_steps_per_second: 2.302 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
ben-yu/ppo-LunarLander-v2	ben-yu	2022-12-22T17:50:52Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T17:50:03Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 138.00 +/- 77.89 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
rhakbari/distilbert-base-uncased-finetuned-squad	rhakbari	2022-12-22T17:35:20Z	26	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-11-20T14:39:43Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2194 \| 1.0 \| 5533 \| 1.1700 \| \| 0.9533 \| 2.0 \| 11066 \| 1.1341 \| \| 0.7452 \| 3.0 \| 16599 \| 1.1725 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
NielsV/q-FrozenLake-v1-4x4-noSlippery	NielsV	2022-12-22T17:13:05Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T17:12:53Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="NielsV/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"]) ```
kRo0T/q-FrozenLake-v1-4x4-noSlippery	kRo0T	2022-12-22T17:06:53Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T17:06:43Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="kRo0T/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"]) ```
eduyio/dqn-SpaceInvadersNoFrameskip-v4	eduyio	2022-12-22T16:57:47Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T16:47:24Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 373.50 +/- 170.12 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga eduyio -f logs/ python enjoy.py --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 eduyio -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 eduyio ``` ## 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)]) ```
ataunal/q-FrozenLake-v1-4x4-noSlippery	ataunal	2022-12-22T16:06:04Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T16:05:51Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="ataunal/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"]) ```
yangwang825/etdnn-vox2	yangwang825	2022-12-22T16:04:05Z	5	0	speechbrain	[ "speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "E-TDNN", "en", "dataset:voxceleb", "license:apache-2.0", "region:us" ]	null	2022-12-14T08:37:42Z	--- language: "en" thumbnail: tags: - speechbrain - embeddings - Speaker - Verification - Identification - pytorch - E-TDNN license: "apache-2.0" datasets: - voxceleb metrics: - EER - Accuracy inference: true widget: - example_title: VoxCeleb Speaker id10003 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav - example_title: VoxCeleb Speaker id10004 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav --- # Speaker Identification with E-TDNN embeddings on Voxceleb This repository provides a pretrained E-TDNN model (x-vector) using SpeechBrain. The system can be used to extract speaker embeddings as well. Since we can't find any resource that has SpeechBrain or HuggingFace compatible checkpoints that has only been trained on VoxCeleb2 development data, so we decide to pre-train an E-TDNN system from scratch. # Pipeline description This system is composed of an E-TDNN model (x-vector). It is a combination of convolutional and residual blocks. The embeddings are extracted using temporal statistical pooling. The system is trained with Additive Margin Softmax Loss. We use FBank (16kHz, 25ms frame length, 10ms hop length, 80 filter-bank channels) as the input features. It was trained using initial learning rate of 0.001 and batch size of 512 with linear scheduler for 40 epochs on 4 A100 GPUs. We employ additive noises and reverberation from [MUSAN](http://www.openslr.org/17/) and [RIR](http://www.openslr.org/28/) datasets to enrich the supervised information. The pre-training progress takes approximately seven days for the E-TDNN model. # Performance VoxCeleb1-O is the original verification test set from VoxCeleb1 consisting of 40 speakers. All speakers with names starting with "E" are reserved for testing. VoxCeleb1-E uses the entire VoxCeleb1 dataset, covering 1251 speakers. VoxCeleb1-H is a hard version of evaluation set consisting of 552536 pairs with 1190 speakers with the same nationality and gender. There are 18 nationality-gender combinations each with at least 5 individuals. \| Splits \| Backend \| S-norm \| EER(%) \| minDCF(0.01) \| \|:-------------:\|:--------------:\|:--------------:\|:--------------:\|:--------------:\| \| VoxCeleb1-O \| cosine \| no \| 1.91 \| 0.20 \| \| VoxCeleb1-E \| cosine \| no \| TBD \| TBD \| \| VoxCeleb1-H \| cosine \| no \| TBD \| TBD \| - VoxCeleb1-O: includes 37611 test pairs with 40 speakers. - VoxCeleb1-E: includes 579818 test pairs with 1251 speakers. - VoxCeleb1-H: includes 550894 test pairs with 1190 speakers. # Compute the speaker embeddings The system is trained with recordings sampled at 16kHz (single channel). ```python import torch import torchaudio from speechbrain.pretrained.interfaces import Pretrained from speechbrain.pretrained import EncoderClassifier class Encoder(Pretrained): MODULES_NEEDED = [ "compute_features", "mean_var_norm", "embedding_model" ] def __init__(self, args, kwargs): super().__init__(args, **kwargs) def encode_batch(self, wavs, wav_lens=None, normalize=False): # Manage single waveforms in input if len(wavs.shape) == 1: wavs = wavs.unsqueeze(0) # Assign full length if wav_lens is not assigned if wav_lens is None: wav_lens = torch.ones(wavs.shape[0], device=self.device) # Storing waveform in the specified device wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device) wavs = wavs.float() # Computing features and embeddings feats = self.mods.compute_features(wavs) feats = self.mods.mean_var_norm(feats, wav_lens) embeddings = self.mods.embedding_model(feats, wav_lens) if normalize: embeddings = self.hparams.mean_var_norm_emb( embeddings, torch.ones(embeddings.shape[0], device=self.device) ) return embeddings classifier = Encoder.from_hparams( source="yangwang825/etdnn-vox2" ) signal, fs = torchaudio.load('spk1_snt1.wav') embeddings = classifier.encode_batch(signal) >>> torch.Size([1, 1, 192]) ``` We will release our training results (models, logs, etc) shortly. # References 1. Ravanelli et al., SpeechBrain: A General-Purpose Speech Toolkit, 2021 2. Snyder et al., The JHU Speaker Recognition System for the VOiCES 2019 Challenge, 2019
ihanif/wav2vec2-xls-r-300m-pashto-lm	ihanif	2022-12-22T15:57:25Z	121	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "google/fleurs", "generated_from_trainer", "hf-asr-leaderboard", "pashto", "ps", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-21T12:11:15Z	--- license: apache-2.0 tags: - google/fleurs - generated_from_trainer - automatic-speech-recognition - hf-asr-leaderboard - pashto - ps datasets: - fleurs metrics: - wer model-index: - name: facebook/wav2vec2-xls-r-300m results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs type: google/fleurs args: 'config: ps_af, split: test' metrics: - name: Wer type: wer value: 0.5159447476125512 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # facebook/wav2vec2-xls-r-300m This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/FLEURS - PS_AF dataset. It achieves the following results on the evaluation set: - Loss: 0.9162 - Wer: 0.5159 - Cer: 0.1972 ## 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: 7.5e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:------:\|:---------------:\|:------:\| \| 5.0767 \| 6.33 \| 500 \| 1.0 \| 4.8783 \| 1.0 \| \| 3.1156 \| 12.66 \| 1000 \| 1.0 \| 3.0990 \| 1.0 \| \| 1.3506 \| 18.99 \| 1500 \| 0.2889 \| 1.1056 \| 0.7031 \| \| 0.9997 \| 25.32 \| 2000 \| 0.2301 \| 0.9191 \| 0.5944 \| \| 0.7838 \| 31.65 \| 2500 \| 0.2152 \| 0.8952 \| 0.5556 \| \| 0.6665 \| 37.97 \| 3000 \| 0.2017 \| 0.8908 \| 0.5252 \| \| 0.6265 \| 44.3 \| 3500 \| 0.1954 \| 0.9063 \| 0.5133 \| \| 0.5935 \| 50.63 \| 4000 \| 0.1969 \| 0.9162 \| 0.5156 \| \| 0.5174 \| 56.96 \| 4500 \| 0.1972 \| 0.9287 \| 0.5140 \| \| 0.5462 \| 63.29 \| 5000 \| 0.1974 \| 0.9370 \| 0.5138 \| \| 0.5564 \| 69.62 \| 5500 \| 0.1977 \| 0.9461 \| 0.5148 \| \| 0.5252 \| 75.95 \| 6000 \| 0.9505 \| 0.5118 \| 0.1969 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
bofenghuang/deprecated-whisper-large-v2-cv11-french-punct-plus	bofenghuang	2022-12-22T15:55:48Z	3	1	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "whisper-event", "fr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-22T10:51:45Z	--- license: apache-2.0 language: fr library_name: transformers thumbnail: null tags: - automatic-speech-recognition - hf-asr-leaderboard - whisper-event datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Fine-tuned whisper-large-v2 model for ASR in French results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 8.55 - name: WER (Beam 5) type: wer value: 8.03 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Fleurs type: google/fleurs config: fr_fr split: test args: fr_fr metrics: - name: WER (Greedy) type: wer value: 5.58 - name: WER (Beam 5) type: wer value: 5.26 --- <style> img { display: inline; } </style> ![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey) ![Model size](https://img.shields.io/badge/Params-1550M-lightgrey) ![Language](https://img.shields.io/badge/Language-French-lightgrey) # Fine-tuned whisper-large-v2 model for ASR in French This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2), trained on the mozilla-foundation/common_voice_11_0 fr dataset. When using the model make sure that your speech input is also sampled at 16Khz. This model also predicts casing and punctuation. ## Usage Inference with 🤗 Pipeline ```python import torch from datasets import load_dataset from transformers import pipeline device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load pipeline pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-cv11-french-punct", device=device) # NB: set forced_decoder_ids for generation utils pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", task="transcribe") # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = test_segment["audio"] # NB: decoding option # limit the maximum number of generated tokens to 225 pipe.model.config.max_length = 225 + 1 # sampling # pipe.model.config.do_sample = True # beam search # pipe.model.config.num_beams = 5 # return # pipe.model.config.return_dict_in_generate = True # pipe.model.config.output_scores = True # pipe.model.config.num_return_sequences = 5 # Run generated_sentences = pipe(waveform)["text"] ``` Inference with 🤗 low-level APIs ```python import torch import torchaudio from datasets import load_dataset from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load model model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-cv11-french-punct").to(device) processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-cv11-french-punct", language="french", task="transcribe") # NB: set forced_decoder_ids for generation utils model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="fr", task="transcribe") # 16_000 model_sample_rate = processor.feature_extractor.sampling_rate # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = torch.from_numpy(test_segment["audio"]["array"]) sample_rate = test_segment["audio"]["sampling_rate"] # Resample if sample_rate != model_sample_rate: resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate) waveform = resampler(waveform) # Get feat inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt") input_features = inputs.input_features input_features = input_features.to(device) # Generate generated_ids = model.generate(inputs=input_features, max_new_tokens=225) # greedy # generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5) # beam search # Detokenize generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # Normalise predicted sentences if necessary ```
mobiusmatt/ppo-LunarLander-v2	mobiusmatt	2022-12-22T15:52:01Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T02:26:42Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 290.56 +/- 22.68 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mahmoud-mohey/q-FrozenLake-v1-4x4	mahmoud-mohey	2022-12-22T15:10:17Z	0	0	null	[ "FrozenLake-v1-4x4-slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T15:10:03Z	--- tags: - FrozenLake-v1-4x4-slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-slippery_v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-slippery type: FrozenLake-v1-4x4-slippery metrics: - type: mean_reward value: 0.80 +/- 0.40 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="mahmoud-mohey/q-FrozenLake-v1-4x4-slippery_v2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
enryu43/anifusion_sd_unet	enryu43	2022-12-22T15:04:03Z	6	3	diffusers	[ "diffusers", "diffusers:LDMTextToImagePipeline", "region:us" ]	null	2022-12-21T15:47:20Z	This model is converted with https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py. However, the tokenizer in the diffuser model is wrong, for proper usage, see description at https://medium.com/@enryu9000/anifusion-sd-91a59431a6dd, and instructions/examples at https://github.com/enryu43/anifusion2-stable-diffusion. Also, the original checkpoint in the Latent Diffusion format is available. Installation instructions for webui: https://gist.github.com/enryu43/fccaa7f165ffcb214780d203c565761f
NXBY/ppo-LunarLander-v2	NXBY	2022-12-22T14:38:29Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T14:38:01Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.03 +/- 30.45 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
BabakMahmoudi/ppo-LunarLander-v2	BabakMahmoudi	2022-12-22T14:25:36Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T14:25:14Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.95 +/- 14.28 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
flegese/translation_en_to_sd	flegese	2022-12-22T14:14:16Z	3	0	keras	[ "keras", "tf-keras", "marian", "region:us" ]	null	2022-11-29T07:31:50Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| learning_rate \| 0.001 \| \| decay \| 0.0 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
research-backup/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0	research-backup	2022-12-22T14:05:44Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-26T22:53:45Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.317718253968254 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.27807486631016043 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.27299703264094954 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37965536409116174 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.414 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3157894736842105 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.25925925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6180503239415398 - name: F1 (macro) type: f1_macro value: 0.4614231459717044 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.734037558685446 - name: F1 (macro) type: f1_macro value: 0.2496301382914666 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.41765980498374866 - name: F1 (macro) type: f1_macro value: 0.2976004948922019 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8466300340822146 - name: F1 (macro) type: f1_macro value: 0.5996275083276039 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7790661234722657 - name: F1 (macro) type: f1_macro value: 0.7551725837093879 --- # relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0 RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/analogy.json)): - Accuracy on SAT (full): 0.27807486631016043 - Accuracy on SAT: 0.27299703264094954 - Accuracy on BATS: 0.37965536409116174 - Accuracy on U2: 0.3157894736842105 - Accuracy on U4: 0.25925925925925924 - Accuracy on Google: 0.414 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/classification.json)): - Micro F1 score on BLESS: 0.6180503239415398 - Micro F1 score on CogALexV: 0.734037558685446 - Micro F1 score on EVALution: 0.41765980498374866 - Micro F1 score on K&H+N: 0.8466300340822146 - Micro F1 score on ROOT09: 0.7790661234722657 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.317718253968254 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: nce_logout - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 640 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
zjunlp/KnowPrompt	zjunlp	2022-12-22T13:57:04Z	0	3	null	[ "ch", "license:apache-2.0", "region:us" ]	null	2022-11-29T12:02:20Z	--- tasks: - Relation Extraction widgets: - examples: - name: 1 title: Message-Topic(e1,e2) inputs: - name: token data: ["the", "most", "common", "audits", "were", "about", "waste", "and", "recycling", "."] - name: h data: - name: audits pos: [3, 4] - name: t data: - name: waste pos: [6, 7] - name: 2 title: Product-Producer(e2,e1) inputs: - name: token data: ["the", "ombudsman", "'s", "report", "concluded", "that", "``", "a", "large", "part", "of", "the", "package", "was", "not", "provided", "''", "."] - name: h data: - name: ombudsman pos: [1, 2] - name: t data: - name: report pos: [3, 4] - name: 3 title: Instrument-Agency(e2,e1) inputs: - name: token data: ["many", "professional", "cartomancers", "use", "a", "regular", "deck", "of", "playing", "cards", "for", "divination", "."] - name: h data: - name: cartomancers pos: [2, 3] - name: t data: - name: cards pos: [9, 10] - name: 4 title: Entity-Destination(e1,e2) inputs: - name: token data: ["nasa", "kepler", "mission", "sends", "names", "into", "space", "."] - name: h data: - name: oil pos: [4, 5] - name: t data: - name: ocean pos: [7, 8] - name: 5 title: Cause-Effect(e2,e1) inputs: - name: token data: ["sorace", "was", "unaware", "that", "her", "anger", "was", "caused", "by", "the", "abuse", "."] - name: h data: - name: anger pos: [5, 6] - name: t data: - name: abuse pos: [10, 11] - name: 6 title: Component-Whole(e1,e2) inputs: - name: token data: ["the", "castle", "was", "inside", "a", "museum", "."] - name: h data: - name: castle pos: [1, 2] - name: t data: - name: museum pos: [5, 6] domain: - nlp frameworks: - pytorch backbone: - BERT large metrics: - accuracy license: apache-2.0 language: - ch --- # KnowPrompt:Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction KnowPrompt is used for relational extraction tasks, injecting latent knowledge contained in relation labels into prompt construction with learnable virtual template words and answer words , and synergistically optimize their representation with structured constraints. ## Model description We take the first step to inject latent knowledge contained in relation labels into prompt construction，the knowledge extraction is then implemented with a Prompt-tuning model。The implementation is as follows：virtual template words around entities initialized using aggregate entity embeddings are used as learnable virtual template words to inject entity knowledge; Meanwhile, we leverage label to compute average embeddings as virtual answers words to inject relationship knowledge. In this structure, entities and relations are mutually constrained and virtual template and answer words should be contextually relevant, so we introduce synergistic optimization to correct virtual template and answer words. ![image.png](./model.png) ## Intended uses & limitations This model is used for relationship extraction tasks, and the extracted information can be used for more downstream NLP tasks, such as: information retrieval, conversation generation and Q&A. Please refer to the code example for details on how to use it. The relationship labels in the model training data are limited and can only generalize the relationships in the real world to a certain extent. ## Training data We adopt SemEval as the dataset \| Dataset \| # Train. \| # Val. \| # Test. \| # Rel. \| \| ----------- \| ------------ \| ---------- \| ----------- \| ---------- \| \| SemEval \| 6,507 \| 1,493 \| 2,717 \| 19 \| ## Training procedure ### Training The training is divided into two phases, and the first phase performs collaborative optimization of virtual template words and answer words $$ \mathcal{J}=\mathcal{J}_{[\text {MASK }]}+\lambda \mathcal{J}_{\text {structured }}, $$ $\lambda$is the hyperparameter for weighing the two loss functions;The second stage optimizes all parameters with a smaller learning rate based on the optimized virtual template words and answer words, using only the loss function $\mathcal{J}_{texttt{[MASK]}}$to finetune the parameters for the language model.The hyperparameters are different for different datasets, as shown in the script file in the source code.Taking SemEval as an example, the hyperparameters are set as follows: ``` max_epochs=10 max_sequence_length=256 batch_size=16 learning_rate=3e-5 batch_size=16 t_lambda=0.001 ``` ### Data Evaluation and Results The results of the comparison with other models in standard settings are shown in the following table. \| Methods \| Precision \| \| ----------- \| ------------- \| \| Fine-tuning \| 87.6 \| \| KnowBERT \| 89.1 \| \| MTB \| 89.5 \| \| PTR \| 89.9 \| \| KnowPrompt \| 90.2 (+0.3) \| In low-resource settings，we performed the 8-, 16-, and 32-experiments.K instances of each class are sampled from the initial training and validation sets to form the training and validation sets for the FEW-shot. The results are as follows: \| Split \| Methods \| Precision \| \| ----- \| ----------- \| ------------- \| \| k=8 \| Fine-tuning \| 41.3 \| \| \| GDPNet \| 42.0 \| \| \| PTR \| 70.5 \| \| \| KnowPrompt \| 74.3 (+33.0) \| \| k=16 \| Fine-tuning \| 65.2 \| \| \| GDPNet \| 67.5 \| \| \| PTR \| 81.3 \| \| \| KnowPrompt \| 82.9 (+17.7) \| \| k=32 \| Fine-tuning \| 80.1 \| \| \| GDPNet \| 81.2 \| \| \| PTR \| 84.2 \| \| \| KnowPrompt \| 84.8 (+4.7) \| As 𝐾 decreases from 32 to 8, the improvement in our KnowPrompt over the other three methods increases gradually. #### BibTeX entry and citation info ``` @inproceedings{DBLP:conf/www/ChenZXDYTHSC22, author = {Xiang Chen and Ningyu Zhang and Xin Xie and Shumin Deng and Yunzhi Yao and Chuanqi Tan and Fei Huang and Luo Si and Huajun Chen}, editor = {Fr{\'{e}}d{\'{e}}rique Laforest and Rapha{\"{e}}l Troncy and Elena Simperl and Deepak Agarwal and Aristides Gionis and Ivan Herman and Lionel M{\'{e}}dini}, title = {KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction}, booktitle = {{WWW} '22: The {ACM} Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022}, pages = {2778--2788}, publisher = {{ACM}}, year = {2022}, url = {https://doi.org/10.1145/3485447.3511998}, doi = {10.1145/3485447.3511998}, timestamp = {Tue, 26 Apr 2022 16:02:09 +0200}, biburl = {https://dblp.org/rec/conf/www/ChenZXDYTHSC22.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```bash git clone https://www.modelscope.cn/jeno11/knowprompt_demo.git ```
yizhangliu/ddpm-celebahq-finetuned-butterflies-2epochs	yizhangliu	2022-12-22T13:54:08Z	1	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-22T13:53:44Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('yizhangliu/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
Verne/test-sd-class-butterflies-32	Verne	2022-12-22T13:41:24Z	10	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-22T13:21:06Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('Verne/test-sd-class-butterflies-32') image = pipeline().images[0] image ```
jaydipsen/xlm-roberta-base-finetuned-panx-de	jaydipsen	2022-12-22T13:32:56Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-22T13:08:52Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8638300289723342 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-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 4.11.3 - Pytorch 1.13.0+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
edbeeching/sample_factory_FPS	edbeeching	2022-12-22T13:29:12Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T13:28:07Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: gdrl type: gdrl metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the gdrl environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_FPS ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .home.edward.work.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_FPS ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .gpfsssd.scratch.rech.ajs.utv52ia.godot_rl.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_FPS --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
bsmith0430/ppo-LunarLander-v2	bsmith0430	2022-12-22T13:17:17Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-21T08:07:39Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.93 +/- 18.64 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
sophiaaez/sd-class-butterflies-32	sophiaaez	2022-12-22T13:10:35Z	4	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-22T13:09:09Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('sophiaaez/sd-class-butterflies-32') image = pipeline().images[0] image ```
sgangireddy/whisper-medium-cv-fi-3k	sgangireddy	2022-12-22T12:09:37Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "fi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-21T16:00:59Z	--- language: - fi license: apache-2.0 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper medium Finnish CV 4K results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 fi type: mozilla-foundation/common_voice_11_0 config: fi split: test args: fi metrics: - name: Wer type: wer value: 15.736901620806634 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper medium Finnish CV 4K This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 fi dataset. It achieves the following results on the evaluation set: - Loss: 0.3412 - Wer: 15.7369 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0014 \| 19.0 \| 1000 \| 0.3029 \| 16.3117 \| \| 0.0002 \| 38.01 \| 2000 \| 0.3412 \| 15.7369 \| \| 0.0001 \| 57.01 \| 3000 \| 0.3592 \| 15.8783 \| \| 0.0001 \| 76.01 \| 4000 \| 0.3655 \| 15.8594 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
edbeeching/sample_factory_BallChase	edbeeching	2022-12-22T12:06:43Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T12:06:28Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: gdrl type: gdrl metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the gdrl environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_BallChase ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .home.edward.work.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_BallChase ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .home.edward.work.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_BallChase --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
infinitas9/rl-ppo-Huggy	infinitas9	2022-12-22T12:03:34Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-22T12:03:17Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # 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: infinitas9/rl-ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
George117/q-Taxi-v3	George117	2022-12-22T11:53:07Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T11:52:54Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="George117/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"]) ```
Siddu0406/codeparrot-ds	Siddu0406	2022-12-22T11:48:33Z	6	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-12-22T11:07:14Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: codeparrot-ds results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.8843 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -795, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 9.8843 \| 0 \| ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2
nicky007/DocumentNick	nicky007	2022-12-22T11:42:23Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:cord-layoutlmv3", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-20T11:59:14Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - cord-layoutlmv3 model-index: - name: DocumentNick results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DocumentNick This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5167 - eval_precision: 0.8758 - eval_recall: 0.8922 - eval_f1: 0.8839 - eval_accuracy: 0.8901 - eval_runtime: 10.2752 - eval_samples_per_second: 9.732 - eval_steps_per_second: 1.946 - epoch: 11.95 - step: 526 ## 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
aashay96/indic-gpt	aashay96	2022-12-22T11:39:09Z	54	1	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-20T19:34:27Z	--- license: mit tags: - generated_from_trainer model-index: - name: indic-gpt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # indic-gpt This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an Indian Language(https://ai4bharat.iitm.ac.in/corpora) dataset. Sample Dataset is present on https://huggingface.co/datasets/aashay96/indic-gpt. It achieves the following results on the evaluation set: - Loss: 1.9482 ## Model description Model is trained on multiple Indian Languages - Assamese, bengali, gujarati, Kannada, Malayalam,telugu, tamil, odhiya and punjabi. ## Intended uses & limitations More information needed ## Training and evaluation data TBD - Evaluation on indic_glue ## Training procedure Check the notebook! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.3653 \| 0.3 \| 500 \| 2.2985 \| \| 2.2079 \| 0.61 \| 1000 \| 2.0401 \| \| 2.0396 \| 0.91 \| 1500 \| 1.9482 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
jairNeto/q-FrozenLake-v1-4x4-noSlippery	jairNeto	2022-12-22T11:22:08Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T11:21:55Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.61 +/- 0.49 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="jairNeto/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"]) ```
DPolatajko/osm-nlp-setfit	DPolatajko	2022-12-22T10:54:51Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-12-22T10:54:13Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 7500 with parameters: ``` {'batch_size': 8, '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": 7500, "warmup_steps": 750, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, '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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Isaacgv/ppo-LunarLander-v2	Isaacgv	2022-12-22T10:49:10Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T10:48:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.79 +/- 18.79 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
orenk/ppo-LunarLander-v2	orenk	2022-12-22T10:18:14Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T10:07:55Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.96 +/- 37.26 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
shovall/ppo-LunarLander-v2	shovall	2022-12-22T10:07:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T10:06:54Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.12 +/- 34.61 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
DrishtiSharma/whisper-large-v2-hindi-2k-steps	DrishtiSharma	2022-12-22T09:58:14Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-19T05:52:43Z	--- language: - hi license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large V2 Hindi - Drishti Sharma results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: hi metrics: - name: Wer type: wer value: 10.24860360772823 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large V2 Hindi - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1787 - Wer: 10.2486 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0238 \| 2.44 \| 2000 \| 0.1787 \| 10.2486 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
Scrwed/dqn-SpaceInvadersNoFrameskip-v4	Scrwed	2022-12-22T09:55:48Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-21T10:54:25Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 613.50 +/- 107.96 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Scrwed -f logs/ python enjoy.py --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 Scrwed -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 Scrwed ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.005), ('exploration_fraction', 0.025), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 1e-05), ('learning_starts', 500), ('n_timesteps', 150000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
arnonl/ppo-LunarLander-v2	arnonl	2022-12-22T09:54:58Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T09:54:27Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.00 +/- 17.90 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
adlrocha/dqn_lunar	adlrocha	2022-12-22T09:53:46Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T09:53:19Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.96 +/- 15.08 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mdabbah/ppo-LunarLander-v2	mdabbah	2022-12-22T09:51:38Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T09:51:16Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.16 +/- 23.46 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
threite/ppo-LunarLander-v2	threite	2022-12-22T09:48:07Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T09:47:40Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.45 +/- 21.37 name: mean_reward verified: false --- # MlpPolicy Agent playing LunarLander-v2 This is a trained model of a MlpPolicy 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 ... ```
nachshonc/rl_course_unit1	nachshonc	2022-12-22T09:47:41Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-22T09:47:05Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.06 +/- 23.56 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
DrishtiSharma/whisper-large-v2-marathi	DrishtiSharma	2022-12-22T09:44:16Z	26	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "mr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-15T16:02:23Z	--- language: - mr license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large Marathi - Drishti Sharma results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: mr split: test args: mr metrics: - name: Wer type: wer value: 13.644010767160161 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large Marathi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1975 - Wer: 13.6440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1914 \| 0.81 \| 400 \| 0.1975 \| 13.6440 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
avojarot/sd-class-butterflies-32	avojarot	2022-12-22T09:43:33Z	2	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-22T09:43:03Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('avojarot/sd-class-butterflies-32') image = pipeline().images[0] image ```
steja/whisper-large-sindhi	steja	2022-12-22T09:26:17Z	13	1	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-21T17:49:34Z	--- license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - google/fleurs metrics: - wer model-index: - name: Whisper_large_Sindhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs sd_in type: google/fleurs config: sd_in split: test metrics: - name: Wer type: wer value: 27.692698197817073 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper_large_Sindhi This model is a fine-tuned version of [anuragshas/whisper-large-v2-hi](https://huggingface.co/anuragshas/whisper-large-v2-hi) on the google/fleurs sd_in dataset. It achieves the following results on the evaluation set: - Loss: 0.6382 - Wer: 27.6927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_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: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0005 \| 38.44 \| 500 \| 0.6382 \| 27.6927 \| \| 0.0003 \| 76.89 \| 1000 \| 0.6714 \| 27.8323 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

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

MikkelGodsk

2022-12-23T11:47:36Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T11:46:00Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="MikkelGodsk/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"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

research-backup/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0

research-backup

2022-12-23T11:11:20Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T05:34:46Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6765873015873016 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39037433155080214 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39762611275964393 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5108393551973318 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.678 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32456140350877194 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.881271658882025 - name: F1 (macro) type: f1_macro value: 0.8761005729675923 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7938967136150236 - name: F1 (macro) type: f1_macro value: 0.5852558618542903 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6397616468039004 - name: F1 (macro) type: f1_macro value: 0.6268814527849179 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9546497878556027 - name: F1 (macro) type: f1_macro value: 0.8651843170780902 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8708868693199624 - name: F1 (macro) type: f1_macro value: 0.8734929892090338 --- # relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0 RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39037433155080214 - Accuracy on SAT: 0.39762611275964393 - Accuracy on BATS: 0.5108393551973318 - Accuracy on U2: 0.32456140350877194 - Accuracy on U4: 0.375 - Accuracy on Google: 0.678 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/classification.json)): - Micro F1 score on BLESS: 0.881271658882025 - Micro F1 score on CogALexV: 0.7938967136150236 - Micro F1 score on EVALution: 0.6397616468039004 - Micro F1 score on K&H+N: 0.9546497878556027 - Micro F1 score on ROOT09: 0.8708868693199624 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6765873015873016 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: nce_logout - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 6 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 640 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-e-nce-0/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

ShadoWxShinigamI/SD2-StatuesFigurines

ShadoWxShinigamI

2022-12-23T10:46:24Z

0

13

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-12-23T10:29:23Z

--- license: creativeml-openrail-m --- ##Textual Inversion Embed By ShadoWxShinigamI Use this embed to create statues of objects or people. It mixes well with [MJART](https://huggingface.co/ShadoWxShinigamI/SD-2-MJart) Embed + Hypernetwork if you want to create figurines. Examples:- Just This Embed (Default Nai Negatives, DPM++ 2S a Sampler, 20 Steps) - ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791723454-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791770707-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791861120-633a520aecbd8b19357b4806.png) With [MJART](https://huggingface.co/ShadoWxShinigamI/SD-2-MJart) Embed + Hypernetwork 0.5 strength (Default Nai Negatives, DPM++ 2S a Sampler, 20 Steps) - ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671791996917-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671792022270-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671792063432-633a520aecbd8b19357b4806.png) ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1671792086320-633a520aecbd8b19357b4806.png)

mrsteyk/openchatgpt-neo-125m

mrsteyk

2022-12-23T10:39:07Z

23

1

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neo", "text-generation", "generated_from_trainer", "text generation", "casual-lm", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-12-21T00:27:14Z

--- license: mit language: - en tags: - generated_from_trainer - text generation - pytorch - casual-lm metrics: - accuracy model-index: - name: openchatgpt-neo-r1 results: [] --- # --- Disclaimer --- # "Neo is an incredibly cursed codebase, it should not be used by anyone" (C) co-founder of EleutherAI - Connor Leahy # !!! USE [openchatgpt-neox-125m](https://huggingface.co/mrsteyk/openchatgpt-neox-125m) INSTEAD !!! # --- Archived --- # openchatgpt-neo-r1 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the openchatgpt safe-r1 dataset. It achieves the following results on the evaluation set: - Loss: 3.2156 - Accuracy: 0.8338 ## Model description Finetune based on the inner workings of ChatGPT. I won't elaborate on that. You must have a faint idea of how prompt is made for it to spit anything that's not garbled mess. This is effectively a schizophrenic idea that met the light of day. Practically a collab of 3 students in a virtual shed. ## Intended uses & limitations Intended uses & limitations fall in line with OpenAI's. Dataset used consists of safe texts (i.e. not highly sexual/erotica type stuff). NSFW version of the dataset is not planned to exist at the moment. Keep in mind that this is a 125m version of GPT-Neo. My 1050Ti Mobile couldn't even handle that without gradient thingmabobs. If anyone knows how to effectively finetune larger models on free colabs - feel free to let me know. Pile tokenizer also has one downside compared to native GPT-2/3 - `Assistant`. ## Training and evaluation data Data was split in ratio of 95%/5%. Preproccess included removing mentions of OpenAI wherever it was not deemed appropriete (GPT-2 has one of the appropriete mentions). Whole dataset consists of just shy off 3k input-output pairs. One input has multiple outputs (read as: one message has multiple variants of an answer). <<<1% (3 total) are curated lines (i.e. a huge mistake was spotted that needed corrections). Heavy bias on IT. ## Training procedure Input and output were straight up concatenated due to the nature of how ChatGPT works. Padding chosen was the same as the separator token, if that's not effective - please let me know as I am new to this stuff. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.9203 | 1.0 | 1378 | 5.1668 | 0.7274 | | 4.1368 | 2.0 | 2756 | 4.3841 | 0.7563 | | 3.4554 | 3.0 | 4134 | 3.8068 | 0.7875 | | 2.7598 | 4.0 | 5512 | 3.3097 | 0.8303 | | 2.5879 | 5.0 | 6890 | 3.2156 | 0.8338 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

MrDivakaruni/dqn-SpaceInvadersNoFrameskip-v4

MrDivakaruni

2022-12-23T10:11:44Z

7

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T10:11:07Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 582.50 +/- 173.63 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MrDivakaruni -f logs/ python enjoy.py --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 MrDivakaruni -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 MrDivakaruni ``` ## 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), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

DaniilSirota/ppo-Huggy

DaniilSirota

2022-12-23T10:00:50Z

14

1

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2022-12-23T10:00:38Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **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: DaniilSirota/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

anuoluwa/ppo-LunarLander-v2

anuoluwa

2022-12-23T09:58:19Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T09:57:43Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 281.12 +/- 24.57 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

hanq0212/RL_course_unit4_part1

hanq0212

2022-12-23T09:39:18Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T09:28:33Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RL_course_unit4_part1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

gggggxy/ddpm-butterflies-128

gggggxy

2022-12-23T09:14:52Z

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-12-23T07:25:31Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/gggggxy/ddpm-butterflies-128/tensorboard?#scalars)

gaokaobishuati/ppo-LunarLander-v2

gaokaobishuati

2022-12-23T09:10:53Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T09:10:15Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.40 +/- 20.45 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

chengcshi/ppo-LunarLander-v2

chengcshi

2022-12-23T08:51:24Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T08:50:59Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 262.95 +/- 17.91 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

ArchitaRay/my_awesome_opus_books_model

ArchitaRay

2022-12-23T08:47:05Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-12-23T07:16:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus_books model-index: - name: my_awesome_opus_books_model results: [] ---  # my_awesome_opus_books_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 1.5494 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7524 | 1.0 | 6355 | 1.5629 | | 1.7382 | 2.0 | 12710 | 1.5494 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

RayKau/dqn-SpaceInvadersNoFrameskip-v4

RayKau

2022-12-23T07:38:09Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T07:37:34Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 538.00 +/- 204.29 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RayKau -f logs/ python enjoy.py --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 RayKau -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 RayKau ``` ## 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)]) ```

LucHayward/ppo-LunarLander-v2

LucHayward

2022-12-23T07:15:47Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-07T15:05:25Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.72 +/- 34.73 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

umer07/text

umer07

2022-12-23T06:52:29Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-12-23T06:52:27Z

--- license: creativeml-openrail-m ---

dfsj/ppo-Huggy

dfsj

2022-12-23T06:52:24Z

10

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2022-12-23T06:52:13Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **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: dfsj/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

zhuimengshaonian/bert-ancient-base

zhuimengshaonian

2022-12-23T06:29:17Z

5

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "zh", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-12-18T17:27:51Z

--- language: zh widget: text: '海阔凭鱼跃，天高[MASK]鸟飞。' --- ## Chinese Ancient BERT Model ### Model description The model's architecture is the BERT-base. We trained this model in 4 P100 about 7 days. (batch size = 24, steps = 1M) ### How to use You can use the model directly with a pipeline for text generation: ``` >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='zhuimengshaonian/bert-ancient-base') >>> unmasker("海阔凭鱼跃，天高[MASK]鸟飞。") ```

harikc456/n-SpaceInvadersNoFrameskip-v4

harikc456

2022-12-23T06:21:59Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T06:21:22Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 622.00 +/- 127.44 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga harikc456 -f logs/ python enjoy.py --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 harikc456 -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 harikc456 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

ben-yu/ppo-LunarLander-v2-try-2

ben-yu

2022-12-23T04:58:26Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T04:33:02Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 246.81 +/- 24.67 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

vumichien/whisper-medium-mix-jp-ver2

vumichien

2022-12-23T04:30:04Z

10

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-22T02:38:57Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: openai/whisper-medium results: [] ---  # openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2790 - Wer: 8.3986 - Cer: 5.2582 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:| | 0.1691 | 1.01 | 1000 | 0.1871 | 10.1740 | 6.3509 | | 0.0916 | 2.02 | 2000 | 0.1691 | 8.9797 | 5.5499 | | 0.0452 | 3.03 | 3000 | 0.1902 | 8.9814 | 5.5867 | | 0.0213 | 4.04 | 4000 | 0.2062 | 8.9375 | 5.6531 | | 0.0096 | 5.05 | 5000 | 0.2284 | 8.7331 | 5.6202 | | 0.0041 | 6.05 | 6000 | 0.2395 | 8.5051 | 5.3009 | | 0.0022 | 7.06 | 7000 | 0.2535 | 8.5507 | 5.3640 | | 0.001 | 8.07 | 8000 | 0.2656 | 8.5557 | 5.3791 | | 0.0006 | 9.08 | 9000 | 0.2721 | 8.4037 | 5.2739 | | 0.0004 | 10.09 | 10000 | 0.2790 | 8.3986 | 5.2582 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

hasarinduperera/ppo-LunarLander-v2

hasarinduperera

2022-12-23T02:35:43Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T22:17:10Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.14 +/- 16.06 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

p4b/whisper-small-ko-fl

p4b

2022-12-23T01:35:24Z

3

2

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ko", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-09T19:38:15Z

--- language: - ko license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: Whisper Small Ko(FLUERS) - by p4b results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs ko_kr type: google/fleurs config: ko_kr split: test metrics: - name: Wer type: wer value: 20.251271313191744 ---  # Whisper Small Ko(FLUERS) - by p4b This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the FLUERS Korean dataset. It achieves the following results on the evaluation set: - Loss: 0.2893 - Wer: 19.2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data ### Dataset filtering Some of datas from FLUERS are not used for training and evaluation. Most of filtered datas are not fit to model or including non-korean symbols. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 96 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3016 | 32.0 | 800 | 0.4048 | 140.4726 | | 0.0451 | 64.0 | 1600 | 0.2893 | 19.2043 | | 0.0169 | 96.0 | 2400 | 0.3110 | 20.2513 | | 0.0092 | 128.0 | 3200 | 0.3240 | 20.0419 | | 0.0062 | 160.0 | 4000 | 0.3335 | 20.0419 | | 0.0045 | 192.0 | 4800 | 0.3416 | 20.0718 | | 0.0035 | 224.0 | 5600 | 0.3501 | 20.1615 | | 0.0028 | 256.0 | 6400 | 0.3562 | 20.3709 | | 0.0024 | 288.0 | 7200 | 0.3618 | 20.0120 | | 0.002 | 320.0 | 8000 | 0.3669 | 20.1017 | | 0.0017 | 352.0 | 8800 | 0.3704 | 20.1914 | | 0.0017 | 384.0 | 9600 | 0.3723 | 20.2513 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.14.0.dev20221208+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

AinTziLLo/Reinforce-model-01

AinTziLLo

2022-12-23T01:11:16Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2022-12-23T01:10:22Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-model-01 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

AinTziLLo/testpyramidsrnd

AinTziLLo

2022-12-23T00:55:30Z

10

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2022-12-23T00:55:20Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **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: AinTziLLo/testpyramidsrnd 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Arch4ngel/sd-class-butterflies-32

Arch4ngel

2022-12-23T00:14:56Z

2

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-23T00:14:20Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('Arch4ngel/sd-class-butterflies-32') image = pipeline().images[0] image ```

jonatasgrosman/whisper-large-es-cv11

jonatasgrosman

2022-12-22T23:52:02Z

20

1

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "es", "dataset:mozilla-foundation/common_voice_11_0", "doi:10.57967/hf/3596", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-21T20:42:43Z

--- language: - es license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer - cer model-index: - name: Whisper Large Spanish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 es type: mozilla-foundation/common_voice_11_0 config: es split: test args: es metrics: - name: WER type: wer value: 4.673613637544826 - name: CER type: cer value: 1.5573247819517182 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs es_419 type: google/fleurs config: es_419 split: test args: es_419 metrics: - name: WER type: wer value: 5.396216546072705 - name: CER type: cer value: 3.450427960057061 --- # Whisper Large Spanish This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on Spanish using the train split of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). ## Usage ```python from transformers import pipeline transcriber = pipeline( "automatic-speech-recognition", model="jonatasgrosman/whisper-large-es-cv11" ) transcriber.model.config.forced_decoder_ids = ( transcriber.tokenizer.get_decoder_prompt_ids( language="es", task="transcribe" ) ) transcription = transcriber("path/to/my_audio.wav") ``` ## Evaluation I've performed the evaluation of the model using the test split of two datasets, the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (same dataset used for the fine-tuning) and the [Fleurs](https://huggingface.co/datasets/google/fleurs) (dataset not seen during the fine-tuning). As Whisper can transcribe casing and punctuation, I've performed the model evaluation in 2 different scenarios, one using the raw text and the other using the normalized text (lowercase + removal of punctuations). Additionally, for the Fleurs dataset, I've evaluated the model in a scenario where there are no transcriptions of numerical values since the way these values are described in this dataset is different from how they are described in the dataset used in fine-tuning (Common Voice), so it is expected that this difference in the way of describing numerical values will affect the performance of the model for this type of transcription in Fleurs. ### Common Voice 11 | | CER | WER | | --- | --- | --- | | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) | 2.43 | 8.85 | | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization | 1.56 | 4.67 | | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 3.71 | 12.34 | | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization | 2.45 | 6.30 | ### Fleurs | | CER | WER | | --- | --- | --- | | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) | 3.06 | 9.11 | | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization | 3.45 | 5.40 | | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + keep only non-numeric samples | 1.83 | 7.57 | | [jonatasgrosman/whisper-large-es-cv11](https://huggingface.co/jonatasgrosman/whisper-large-es-cv11) + text normalization + keep only non-numeric samples | 2.36 | 4.14 | | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 2.30 | 8.50 | | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization | 2.76 | 4.79 | | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + keep only non-numeric samples | 1.93 | 7.33 | | [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization + keep only non-numeric samples | 2.50 | 4.28 |

SiddharthaM/distilbert-sentiment-new

SiddharthaM

2022-12-22T23:48:19Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-22T23:32:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: distilbert-sentiment-new results: [] ---  # distilbert-sentiment-new This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5872 - Accuracy: 0.7243 - Precision: 0.7192 - Recall: 0.7243 - F1: 0.7175 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.6038 | 0.6787 | 0.7049 | 0.6787 | 0.6235 | | 0.5926 | 2.0 | 592 | 0.5532 | 0.7148 | 0.7118 | 0.7148 | 0.6994 | | 0.5926 | 3.0 | 888 | 0.5480 | 0.7243 | 0.7199 | 0.7243 | 0.7144 | | 0.4946 | 4.0 | 1184 | 0.5535 | 0.7300 | 0.7255 | 0.7300 | 0.7220 | | 0.4946 | 5.0 | 1480 | 0.5858 | 0.7186 | 0.7140 | 0.7186 | 0.7146 | | 0.4267 | 6.0 | 1776 | 0.5872 | 0.7243 | 0.7192 | 0.7243 | 0.7175 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

rjac/ppo-LunarLander-v2

rjac

2022-12-22T23:47:14Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T23:40:19Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 248.16 +/- 18.51 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

kadirnar/RRDB_PSNR_x4

kadirnar

2022-12-22T23:07:46Z

0

null

[ "Super-Resolution", "computer-vision", "ESRGAN", "gan", "arxiv:2107.10833", "license:apache-2.0", "region:us" ]

null

2022-12-22T22:42:57Z

--- license: apache-2.0 tags: - Super-Resolution - computer-vision - ESRGAN - gan --- ### Model Description [ESRGAN](https://arxiv.org/abs/2107.10833): ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution [Paper Repo](https://github.com/xinntao/ESRGAN): Implementation of paper. ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/RRDB_PSNR_x4', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @InProceedings{wang2018esrgan, author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change}, title = {ESRGAN: Enhanced super-resolution generative adversarial networks}, booktitle = {The European Conference on Computer Vision Workshops (ECCVW)}, month = {September}, year = {2018} } ```

kadirnar/RRDB_ESRGAN_x4

kadirnar

2022-12-22T23:05:06Z

0

2

null

[ "Super-Resolution", "computer-vision", "ESRGAN", "gan", "arxiv:2107.10833", "license:apache-2.0", "region:us" ]

null

2022-12-22T22:42:40Z

--- license: apache-2.0 tags: - Super-Resolution - computer-vision - ESRGAN - gan --- ### Model Description [ESRGAN](https://arxiv.org/abs/2107.10833): ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution [Paper Repo](https://github.com/xinntao/ESRGAN): Implementation of paper. ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/RRDB_ESRGAN_x4', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @InProceedings{wang2018esrgan, author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change}, title = {ESRGAN: Enhanced super-resolution generative adversarial networks}, booktitle = {The European Conference on Computer Vision Workshops (ECCVW)}, month = {September}, year = {2018} } ```

kadirnar/DPED

kadirnar

2022-12-22T22:58:25Z

0

1

null

[ "Super-Resolution", "computer-vision", "RealSR", "gan", "arxiv:2005.01996", "license:apache-2.0", "region:us" ]

null

2022-12-22T22:37:31Z

--- license: apache-2.0 tags: - Super-Resolution - computer-vision - RealSR - gan --- ### Model Description [RealSR](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Ji_Real-World_Super-Resolution_via_Kernel_Estimation_and_Noise_Injection_CVPRW_2020_paper.pdf): Real-World Super-Resolution via Kernel Estimation and Noise Injection. [NTIRE 2020 Challenge on Real-World Image Super-Resolution](https://arxiv.org/abs/2005.01996): Methods and Results [Paper Repo](https://github.com/Tencent/Real-SR): Implementation of paper. ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/DPED', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @article{Lugmayr2020ntire, title={NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results}, author={Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy Chiu, Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete, Michelini Christian, Micheloni Kalpesh, Prajapati Haoyu, Ren Yong, Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Haoning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou}, journal={CVPR Workshops}, year={2020}, } ```

JovialValley/model_broadclass_onSet1.1

JovialValley

2022-12-22T22:51:39Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-22T21:28:20Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - wer model-index: - name: model_broadclass_onSet1.1 results: [] ---  # model_broadclass_onSet1.1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2469 - 0 Precision: 1.0 - 0 Recall: 1.0 - 0 F1-score: 1.0 - 0 Support: 24 - 1 Precision: 1.0 - 1 Recall: 1.0 - 1 F1-score: 1.0 - 1 Support: 39 - 2 Precision: 1.0 - 2 Recall: 1.0 - 2 F1-score: 1.0 - 2 Support: 23 - 3 Precision: 1.0 - 3 Recall: 1.0 - 3 F1-score: 1.0 - 3 Support: 12 - Accuracy: 1.0 - Macro avg Precision: 1.0 - Macro avg Recall: 1.0 - Macro avg F1-score: 1.0 - Macro avg Support: 98 - Weighted avg Precision: 1.0 - Weighted avg Recall: 1.0 - Weighted avg F1-score: 1.0 - Weighted avg Support: 98 - Wer: 0.2423 - Mtrix: [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] ## 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: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 80 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:| | 2.3722 | 4.16 | 100 | 2.1950 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 2.2944 | 8.33 | 200 | 2.1537 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.9927 | 12.49 | 300 | 1.8879 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.7175 | 16.65 | 400 | 1.6374 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.6065 | 20.82 | 500 | 1.5619 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.5362 | 24.98 | 600 | 1.5019 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.5599 | 29.16 | 700 | 1.4858 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.5344 | 33.33 | 800 | 1.4721 | 0.2759 | 1.0 | 0.4324 | 24 | 1.0 | 0.2821 | 0.4400 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.3571 | 0.3190 | 0.3205 | 0.2181 | 98 | 0.4655 | 0.3571 | 0.2810 | 98 | 0.9919 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 28, 11, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] | | 1.4024 | 37.49 | 900 | 1.3532 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.9742 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | | 0.9429 | 41.65 | 1000 | 0.9455 | 0.96 | 1.0 | 0.9796 | 24 | 0.9744 | 0.9744 | 0.9744 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9796 | 0.9836 | 0.9827 | 0.9829 | 98 | 0.9800 | 0.9796 | 0.9796 | 98 | 0.9084 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] | | 0.8955 | 45.82 | 1100 | 0.8890 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9246 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | | 0.8708 | 49.98 | 1200 | 0.8304 | 1.0 | 1.0 | 1.0 | 24 | 0.975 | 1.0 | 0.9873 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.9938 | 0.9891 | 0.9913 | 98 | 0.9901 | 0.9898 | 0.9897 | 98 | 0.9272 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] | | 0.8671 | 54.16 | 1300 | 0.8028 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9891 | 0.9893 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9211 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 1, 0, 22, 0], [3, 0, 0, 0, 12]] | | 0.8383 | 58.33 | 1400 | 0.7804 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.9170 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | | 0.7872 | 62.49 | 1500 | 0.7745 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9439 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | | 0.7538 | 66.65 | 1600 | 0.7141 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9267 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | | 0.6439 | 70.82 | 1700 | 0.5818 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.8574 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | | 0.5295 | 74.98 | 1800 | 0.3775 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.4633 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | | 0.4184 | 79.16 | 1900 | 0.2507 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.2529 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

kadirnar/yolov7-v0.1

kadirnar

2022-12-22T22:19:19Z

0

1

null

[ "object-detection", "computer-vision", "yolov7", "pypi", "dataset:detection-datasets/coco", "arxiv:2207.02696", "license:gpl-3.0", "region:us" ]

object-detection

2022-12-19T13:58:17Z

--- license: gpl-3.0 tags: - object-detection - computer-vision - yolov7 - pypi datasets: - detection-datasets/coco --- ### Model Description [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696) [YOLOv7-Pip: Packaged version of the Yolov7 repository](https://github.com/kadirnar/yolov7-pip) [Paper Repo: Implementation of paper - YOLOv7](https://github.com/WongKinYiu/yolov7) ### Installation ``` pip install yolov7detect ``` ### Yolov7 Inference ```python import yolov7 # load pretrained or custom model model = yolov7.load('kadirnar/yolov7-v0.1', hf_model=True) # set model parameters model.conf = 0.25 # NMS confidence threshold model.iou = 0.45 # NMS IoU threshold model.classes = None # (optional list) filter by class # set image imgs = 'inference/images' # perform inference results = model(imgs) # inference with larger input size and test time augmentation results = model(img, size=1280, augment=True) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() ``` ### BibTeX Entry and Citation Info ``` @article{wang2022yolov7, title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}, author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, journal={arXiv preprint arXiv:2207.02696}, year={2022} } ```

kadirnar/bsrgan

kadirnar

2022-12-22T22:17:35Z

0

1

null

[ "Super-Resolution", "computer-vision", "bsrgan", "gan", "arxiv:2103.14006", "license:apache-2.0", "region:us" ]

null

2022-12-20T20:16:15Z

--- license: apache-2.0 tags: - Super-Resolution - computer-vision - bsrgan - gan --- ### Model Description [BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .](https://arxiv.org/abs/2103.14006) [BSRGAN-Pip: Packaged version of the BSRGAN repository](https://github.com/kadirnar/bsrgan-pip/) [Paper Repo: Implementation of paper - BSRGAN](https://github.com/cszn/BSRGAN) ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/bsrgan', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ```

kadirnar/yolox_nano-v0.1.1

kadirnar

2022-12-22T22:16:24Z

0

null

[ "object-detection", "computer-vision", "yolox", "yolov3", "yolov5", "dataset:detection-datasets/coco", "arxiv:2107.08430", "license:apache-2.0", "region:us" ]

object-detection

2022-12-21T22:06:59Z

--- license: apache-2.0 tags: - object-detection - computer-vision - yolox - yolov3 - yolov5 datasets: - detection-datasets/coco --- ### Model Description [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use. [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) ### Installation ``` pip install yoloxdetect ``` ### Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_nano-v0.1.1", config_path = "configs.yolox_s", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(image='data/images', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```

kadirnar/yolox_m-v0.1.1

kadirnar

2022-12-22T22:15:50Z

0

null

[ "object-detection", "computer-vision", "yolox", "yolov3", "yolov5", "dataset:detection-datasets/coco", "arxiv:2107.08430", "license:apache-2.0", "region:us" ]

object-detection

2022-12-21T22:08:22Z

--- license: apache-2.0 tags: - object-detection - computer-vision - yolox - yolov3 - yolov5 datasets: - detection-datasets/coco --- ### Model Description [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use. [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) ### Installation ``` pip install yoloxdetect ``` ### Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_m-v0.1.1", config_path = "configs.yolox_m", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(image='data/images', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```

kadirnar/yolox_l-v0.1.1

kadirnar

2022-12-22T22:15:38Z

0

2

null

[ "object-detection", "computer-vision", "yolox", "yolov3", "yolov5", "dataset:detection-datasets/coco", "arxiv:2107.08430", "license:apache-2.0", "region:us" ]

object-detection

2022-12-21T22:11:13Z

--- license: apache-2.0 tags: - object-detection - computer-vision - yolox - yolov3 - yolov5 datasets: - detection-datasets/coco --- ### Model Description [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. [YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use. [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) ### Installation ``` pip install yoloxdetect ``` ### Yolox Inference ```python from yoloxdetect import YoloxDetector from yolox.data.datasets import COCO_CLASSES model = YoloxDetector( model_path = "kadirnar/yolox_l-v0.1.1", config_path = "configs.yolox_l", device = "cuda:0", hf_model=True ) model.classes = COCO_CLASSES model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(image='data/images', img_size=640) ``` ### BibTeX Entry and Citation Info ``` @article{yolox2021, title={YOLOX: Exceeding YOLO Series in 2021}, author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, journal={arXiv preprint arXiv:2107.08430}, year={2021} } ```

paragon-analytics/bert_empathy

paragon-analytics

2022-12-22T22:00:09Z

24

1

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-21T15:56:24Z

--- license: "mit" --- This is a fine-tuned RoBERTa model that takes text (up to a few sentences) and predicts to what extent it contains empathic language. Example classification: ```python import torch import tensorflow as tf from transformers import RobertaTokenizer, RobertaModel from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/bert_empathy") model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_empathy") def roberta(x): encoded_input = tokenizer(x, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = tf.nn.softmax(scores) return scores.numpy()[1] ```

hawkeyedesi/ppo-LunarLander-v2

hawkeyedesi

2022-12-22T21:59:00Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T21:58:38Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo-LunarLander-v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.73 +/- 14.54 name: mean_reward verified: false --- # **ppo-LunarLander-v2** Agent playing **LunarLander-v2** This is a trained model of a **ppo-LunarLander-v2** 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 ... ```

arampacha/whisper-large-uk-2

arampacha

2022-12-22T21:32:24Z

42

5

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "uk", "dataset:mozilla-foundation/common_voice_11_0", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-21T23:00:43Z

--- language: - uk license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 - google/fleurs model-index: - name: whisper-large-uk results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: uk split: test args: uk metrics: - name: Wer type: wer value: 10.02262314404669 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Fleurs type: google/fleurs config: uk_ua split: test args: uk_ua metrics: - name: Wer type: wer value: 7.564370215727209 ---  # whisper-large-uk This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2527 - eval_wer: 10.0226 - eval_runtime: 9610.7996 - eval_samples_per_second: 0.747 - eval_steps_per_second: 0.023 - epoch: 1.8 - step: 1098 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 32 - eval_batch_size: 32 - 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 - training_steps: 1500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

kRo0T/dqn-SpaceInvadersNoFrameskip-v4

kRo0T

2022-12-22T20:57:38Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T20:56:59Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 622.00 +/- 199.96 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga kRo0T -f logs/ python enjoy.py --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 kRo0T -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 kRo0T ``` ## 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)]) ```

johnhudzinatr/ppo-Huggy

johnhudzinatr

2022-12-22T20:41:47Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2022-12-22T20:41:34Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **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: johnhudzinatr/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Dahoas/gpt2-rm-static

Dahoas

2022-12-22T20:14:15Z

5

1

transformers

[ "transformers", "pytorch", "gpt2", "text-classification", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2022-12-22T20:03:32Z

[**wanbd**](https://wandb.ai/dahoas/huggingface/runs/1a38b0tb?workspace=user-dahoas)

Dahoas/gptneo-rm-static

Dahoas

2022-12-22T20:13:35Z

6

1

transformers

[ "transformers", "pytorch", "gpt_neo", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-22T20:09:51Z

[**wandb**](https://wandb.ai/dahoas/huggingface/runs/e7r5an9w?workspace=user-dahoas)

SiddharthaM/xlm-sentiment-new

SiddharthaM

2022-12-22T19:54:29Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-22T19:24:41Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: xlm-sentiment-new results: [] ---  # xlm-sentiment-new 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: 0.6166 - Accuracy: 0.7405 - Precision: 0.7375 - Recall: 0.7405 - F1: 0.7386 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5519 | 0.7310 | 0.7266 | 0.7310 | 0.7277 | | 0.5719 | 2.0 | 592 | 0.5569 | 0.75 | 0.7562 | 0.75 | 0.7302 | | 0.5719 | 3.0 | 888 | 0.5320 | 0.7243 | 0.7269 | 0.7243 | 0.7254 | | 0.477 | 4.0 | 1184 | 0.5771 | 0.7300 | 0.7264 | 0.7300 | 0.7276 | | 0.477 | 5.0 | 1480 | 0.6051 | 0.7376 | 0.7361 | 0.7376 | 0.7368 | | 0.428 | 6.0 | 1776 | 0.6166 | 0.7405 | 0.7375 | 0.7405 | 0.7386 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

Akumetsu971/SD_VCM07_Anime_Style

Akumetsu971

2022-12-22T19:35:08Z

0

5

null

[ "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2022-12-21T02:03:36Z

--- inference: true language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- # SD_VCM07_Anime_Style is an open source Stable Diffusion Embedding on art style of VCM07, by Akumetsu971 (https://www.tiktok.com/@akumetsu971) --- ### Model used to train: wd-v1-3-full-opt.ckpt (https://huggingface.co/hakurei/waifu-diffusion-v1-3) ### Files 3 files available (Best version is V2): -VCM07_style - 4000 steps (more focused on girl) -VCM07_style2 - 4000 steps (allowed to create animals) -Prompt_Blending Script (optional, used for prompt) ### Prompt You need to use DeepDanBooru Tags (https://gigazine.net/gsc_news/en/20221012-automatic1111-stable-diffusion-webui-deep-danbooru/) Elysium_Anime_V2.ckpt (https://huggingface.co/hesw23168/SD-Elysium-Model) Prompt_blending script (https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/tree/main) Embedding was trained with images of girls only. Therefore, getting a boy can be difficult. Adjust weight, negative prompt, etc... ### Human Example Positive Prompt: (VCM07_style2:1.0), (1girl:1.2), looking_at_viewer, (best quality), (masterpiece:1.2), (ultra-detailed),(official art),(an extremely delicate and beautiful), (attractive:1.2), (beautiful detailed eyes), (dynamic colours, vibrant colours), depth of field, god rays, dynamic lighting Negative Prompt: (mediocre:1.2), (average:1.2), (bad:1.2), (wrong:1.2), (error:1.2), (fault:1.2),( badly_drawn:1.2), (poorly_drawn:1.2), ( low_quality:1.2), no_quality, bad_quality, no_resolution, low_resolution, (lowres:1.2), normal_resolution, (disfigured:1.6), (deformed:1.4), (distortion:1.2), bad_anatomy, (no_detail:1.2), low_detail, normal_detail, (scribble:1.2), (rushed:1.2), (unfinished:1.2), blur, blurry, claws, (misplaced:1.2), (disconnected:1.2), nonsense, random, (noise:1.2), (deformation:1.2), 3d, dull, boring, uninteresting, screencap, (text:1.2), (frame:1.1), (out_of_frame:1.2), (title:1.2), (description:1.3), (sexual:1.2), text, error,(logo:1.3), (watermark:1.3), bad_perspective, bad_proportions, cinematic, jpg_artifacts, jpeg_artifacts, extra_leg, missing_leg, extra_arm, missing_arm, long_hand, bad_hands, (mutated_hand:1.2), (extra_finger:1.2), (missing_finger:1.2), broken_finger, (fused_fingers:1.2), extra_feet, missing_feet, fused_feet, long_feet, missing_limbs, extra_limbs, fused_limbs, claw, (extra_digit:1.2), (fewer_digits:1.2), elves_ears, (naked:1.3), (wet:1.2), uncensored, (long_neck:1.2), (weapon:1.5) <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06756-3422664593-(VCM07_style_1.2)%2C%20close-up%2C%20portrait%2C%201girl%2C%20(solo_1.2)%2C%20single%2C%20black_hair%2C%20blue_eyes%2C%20%20long_hair%2C%20looking_at_viewer%2C(best%20qua.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/07023-1420035308-(VCM07_style2_1.0)%2C%20(1girl_1.2)%2C%20looking_at_viewer%2C%20(best%20quality)%2C%20(masterpiece_1.2)%2C%20(ultra-detailed)%2C(official%20art)%2C(an%20extre.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/07045-3879379165-(VCM07_style2_1.0)%2C%201girl%2C%20(pink_hair_1.8)%2C%20(solo_1.2)%2C%20looking_at_viewer%2C%20(best%20quality)%2C%20(masterpiece_1.2)%2C%20(ultra-detailed)%2C(.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06785-1732689013-(VCM07_style_1.2)%2C%20close-up%2C%20portrait%2C%20(solo_1.4)%2C%20(1girl%2C)%2C%20(fox_1.6)%2C%20single%2C%20(pink_hair_1.6)%2C%20blue_eyes%2C%20%20long_hair%2C%20looking_.png" width="50%"/> ### Animals Example For V2, embedding was trained with dogs, cats, foxes. Therefore, it is easier to get these animals. However, it is possible to get frogs, elephants, tigers, lions, etc... I used a method with blend prompt script then I described the anatomy of the animal: eyes, ears, nose, fur, etc... Positive Prompt: (VCM07_style2:1.0), (cat:1.4|dog:0.5|fox:0.5), (cat_nose:1.2), (cat_eyes:1.2), (cat_ears:1.2), (solo:1.2), looking_at_viewer, (best quality), (masterpiece:1.2), (ultra-detailed),(official art),(an extremely delicate and beautiful), (attractive:1.2), (beautiful detailed eyes), (dynamic colours, vibrant colours), depth of field, god rays, dynamic lighting Negative Prompt: (mediocre:1.2), (average:1.2), (bad:1.2), (wrong:1.2), (error:1.2), (fault:1.2),( badly_drawn:1.2), (poorly_drawn:1.2), ( low_quality:1.2), no_quality, bad_quality, no_resolution, low_resolution, (lowres:1.2), normal_resolution, (disfigured:1.6), (deformed:1.5), (distortion:1.2), bad_anatomy, (no_detail:1.2), low_detail, normal_detail, (scribble:1.2), (rushed:1.2), (unfinished:1.2), blur, blurry, claws, (misplaced:1.2), (disconnected:1.2), nonsense, random, (noise:1.2), (deformation:1.2), 3d, dull, boring, uninteresting, screencap, (text:1.2), (frame:1.1), (out_of_frame:1.2), (title:1.2), (description:1.3), (sexual:1.2), text, error,(logo:1.3), (watermark:1.3), bad_perspective, bad_proportions, cinematic, jpg_artifacts, jpeg_artifacts, extra_leg, missing_leg, extra_arm, missing_arm, long_hand, bad_hands, (mutated_hand:1.2), (extra_finger:1.2), (missing_finger:1.2), broken_finger, (fused_fingers:1.2), extra_feet, missing_feet, fused_feet, long_feet, missing_limbs, extra_limbs, fused_limbs, claw, (extra_digit:1.2), (fewer_digits:1.2), elves_ears, (naked:1.3), (wet:1.2), uncensored, (long_neck:1.2) <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/07031-4095215986-(VCM07_style2_1.0)%2C%20(cat_1.4_dog_0.5_fox_0.5)%2C%20(cat_nose_1.2)%2C%20(cat_eyes_1.2)%2C%20(cat_ears_1.2)%2C%20(solo_1.2)%2C%20looking_at_viewer%2C%20(b.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06915-1261422051-(VCM07_style2_1.0)%2C%20(cat_0.5_lion_0.5_dog_0.5_fox_1.2)%2C%20looking_at_viewer%2C%20(best%20quality)%2C%20(masterpiece_1.2)%2C%20(ultra-detailed)%2C(.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06994-2098824635-(VCM07_style2_1.0)%2C%20(cat_0.5_lion_0.5_dog_0.5_fox_0.5_tiger_1.4)%2C%20(tiger_ears_1.4)%2C%20(tiger_nose_1.4)%2C%20(white_tiger_1.4)%2C%20(white_.png" width="50%"/> <img src="https://huggingface.co/Akumetsu971/SD_VCM07_Anime_Style/resolve/main/06961-2420141730-(VCM07_style2_1.1)%2C%20(cat_0.5_lion_0.5_dog_0.5_fox_0.5_monkey_1.4)%2C%20(monkey_ears_1.4)%2C%20(monkey_nose_1.4)%2C%20looking_at_viewer%2C%20(bes.png" width="50%"/> ```

magnomont12/Taxi-v3

magnomont12

2022-12-22T19:13:08Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T19:12:56Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.77 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="magnomont12/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"]) ```

hawkeoni/sd-class-butterflies-32

hawkeoni

2022-12-22T18:56:01Z

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-22T18:55:41Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('hawkeoni/sd-class-butterflies-32') image = pipeline().images[0] image ```

NielsV/Q-learning-taxi-v4

NielsV

2022-12-22T18:49:51Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T17:17:07Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Q-learning-taxi-v4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="NielsV/Q-learning-taxi-v4", 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"]) ```

hanymac/CoreML-Stable-Diffusion-2.1-original-img2img

hanymac

2022-12-22T18:34:21Z

0

1

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-12-22T18:12:54Z

--- license: creativeml-openrail-m ---

aliosmankaya/reg_arr_model_1_dim

aliosmankaya

2022-12-22T18:26:47Z

0

sklearn

[ "sklearn", "joblib", "skops", "tabular-classification", "region:us" ]

tabular-classification

2022-11-30T22:22:07Z

--- library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: sepal_length: - 6.3 - 6.5 - 5.6 --- ### Linear Regression Model This Linear Regression model trained on Iris dataset as a regular numpy array with 1-dimensional. Goal is to test this pr -> https://github.com/skops-dev/skops/pull/211

aliosmankaya/reg_arr_model_2_dim

aliosmankaya

2022-12-22T18:26:15Z

0

sklearn

[ "sklearn", "joblib", "skops", "tabular-classification", "region:us" ]

tabular-classification

2022-11-30T22:15:02Z

--- library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: sepal_length: - 6.3 - 6.5 - 5.6 sepal_width: - 3.3 - 3.0 - 2.5 --- ### Linear Regression Model This Linear Regression model trained on Iris dataset as a regular numpy array with 2-dimensional. Goal is to test this pr -> https://github.com/skops-dev/skops/pull/211

DavidErikMollberg/whisper-medium-is

DavidErikMollberg

2022-12-22T18:17:06Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-20T13:53:22Z

--- model-index: - name: DavidErikMollberg/whisper-medium-is results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: is_is split: test metrics: - type: wer value: 16.17 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: language-and-voice-lab/samromur_asr type: language-and-voice-lab/samromur_asr config: samromur_asr split: test metrics: - type: wer value: 10.22 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: language-and-voice-lab/althingi_asr type: language-and-voice-lab/althingi_asr config: althingi_asr split: test metrics: - type: wer value: 9.67 name: WER --- # Model Card for Model ID  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Model Examination](#model-examination-optional) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#technical-specifications-optional) 9. [Citation](#citation-optional) 10. [Glossary](#glossary-optional) 11. [More Information](#more-information-optional) 12. [Model Card Authors](#model-card-authors-optional) 13. [Model Card Contact](#model-card-contact) 14. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Resources for more information:** [More Information Needed] # Uses  ## Direct Use  [More Information Needed] ## Downstream Use [optional]  [More Information Needed] ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations  [More Information Needed] ## Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [More Information Needed] # Evaluation  ## Testing Data, Factors & Metrics ### Testing Data  [More Information Needed] ### Factors  [More Information Needed] ### Metrics  [More Information Needed] ## Results [More Information Needed] # Model Examination [optional]  [More Information Needed] # Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] # Glossary [optional]  [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed] # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> [More Information Needed] </details>

kRo0T/q-Taxi-v3

kRo0T

2022-12-22T18:06:19Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T17:11:49Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="kRo0T/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"]) ```

JovialValley/model_broadclass_onSet3

JovialValley

2022-12-22T17:55:47Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-22T14:21:33Z

--- tags: - generated_from_trainer model-index: - name: model_broadclass_onSet3 results: [] ---  # model_broadclass_onSet3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1389 - eval_0_precision: 1.0 - eval_0_recall: 1.0 - eval_0_f1-score: 1.0 - eval_0_support: 23 - eval_1_precision: 0.9697 - eval_1_recall: 0.9697 - eval_1_f1-score: 0.9697 - eval_1_support: 33 - eval_2_precision: 1.0 - eval_2_recall: 1.0 - eval_2_f1-score: 1.0 - eval_2_support: 26 - eval_3_precision: 0.9333 - eval_3_recall: 0.9333 - eval_3_f1-score: 0.9333 - eval_3_support: 15 - eval_accuracy: 0.9794 - eval_macro avg_precision: 0.9758 - eval_macro avg_recall: 0.9758 - eval_macro avg_f1-score: 0.9758 - eval_macro avg_support: 97 - eval_weighted avg_precision: 0.9794 - eval_weighted avg_recall: 0.9794 - eval_weighted avg_f1-score: 0.9794 - eval_weighted avg_support: 97 - eval_wer: 0.1037 - eval_mtrix: [[0, 1, 2, 3], [0, 23, 0, 0, 0], [1, 0, 32, 0, 1], [2, 0, 0, 26, 0], [3, 0, 1, 0, 14]] - eval_runtime: 5.6481 - eval_samples_per_second: 17.174 - eval_steps_per_second: 2.302 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

ben-yu/ppo-LunarLander-v2

ben-yu

2022-12-22T17:50:52Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T17:50:03Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 138.00 +/- 77.89 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

rhakbari/distilbert-base-uncased-finetuned-squad

rhakbari

2022-12-22T17:35:20Z

26

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-11-20T14:39:43Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1725 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2194 | 1.0 | 5533 | 1.1700 | | 0.9533 | 2.0 | 11066 | 1.1341 | | 0.7452 | 3.0 | 16599 | 1.1725 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

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

NielsV

2022-12-22T17:13:05Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T17:12:53Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="NielsV/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"]) ```

kRo0T/q-FrozenLake-v1-4x4-noSlippery

kRo0T

2022-12-22T17:06:53Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T17:06:43Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="kRo0T/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"]) ```

eduyio/dqn-SpaceInvadersNoFrameskip-v4

eduyio

2022-12-22T16:57:47Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T16:47:24Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 373.50 +/- 170.12 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga eduyio -f logs/ python enjoy.py --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 eduyio -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 eduyio ``` ## 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)]) ```

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

ataunal

2022-12-22T16:06:04Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T16:05:51Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="ataunal/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"]) ```

yangwang825/etdnn-vox2

yangwang825

2022-12-22T16:04:05Z

5

0

speechbrain

[ "speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "E-TDNN", "en", "dataset:voxceleb", "license:apache-2.0", "region:us" ]

null

2022-12-14T08:37:42Z

--- language: "en" thumbnail: tags: - speechbrain - embeddings - Speaker - Verification - Identification - pytorch - E-TDNN license: "apache-2.0" datasets: - voxceleb metrics: - EER - Accuracy inference: true widget: - example_title: VoxCeleb Speaker id10003 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav - example_title: VoxCeleb Speaker id10004 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav --- # Speaker Identification with E-TDNN embeddings on Voxceleb This repository provides a pretrained E-TDNN model (x-vector) using SpeechBrain. The system can be used to extract speaker embeddings as well. Since we can't find any resource that has SpeechBrain or HuggingFace compatible checkpoints that has only been trained on VoxCeleb2 development data, so we decide to pre-train an E-TDNN system from scratch. # Pipeline description This system is composed of an E-TDNN model (x-vector). It is a combination of convolutional and residual blocks. The embeddings are extracted using temporal statistical pooling. The system is trained with Additive Margin Softmax Loss. We use FBank (16kHz, 25ms frame length, 10ms hop length, 80 filter-bank channels) as the input features. It was trained using initial learning rate of 0.001 and batch size of 512 with linear scheduler for 40 epochs on 4 A100 GPUs. We employ additive noises and reverberation from [MUSAN](http://www.openslr.org/17/) and [RIR](http://www.openslr.org/28/) datasets to enrich the supervised information. The pre-training progress takes approximately seven days for the E-TDNN model. # Performance **VoxCeleb1-O** is the original verification test set from VoxCeleb1 consisting of 40 speakers. All speakers with names starting with "E" are reserved for testing. **VoxCeleb1-E** uses the entire VoxCeleb1 dataset, covering 1251 speakers. **VoxCeleb1-H** is a hard version of evaluation set consisting of 552536 pairs with 1190 speakers with the same nationality and gender. There are 18 nationality-gender combinations each with at least 5 individuals. | Splits | Backend | S-norm | EER(%) | minDCF(0.01) | |:-------------:|:--------------:|:--------------:|:--------------:|:--------------:| | VoxCeleb1-O | cosine | no | 1.91 | 0.20 | | VoxCeleb1-E | cosine | no | TBD | TBD | | VoxCeleb1-H | cosine | no | TBD | TBD | - VoxCeleb1-O: includes 37611 test pairs with 40 speakers. - VoxCeleb1-E: includes 579818 test pairs with 1251 speakers. - VoxCeleb1-H: includes 550894 test pairs with 1190 speakers. # Compute the speaker embeddings The system is trained with recordings sampled at 16kHz (single channel). ```python import torch import torchaudio from speechbrain.pretrained.interfaces import Pretrained from speechbrain.pretrained import EncoderClassifier class Encoder(Pretrained): MODULES_NEEDED = [ "compute_features", "mean_var_norm", "embedding_model" ] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def encode_batch(self, wavs, wav_lens=None, normalize=False): # Manage single waveforms in input if len(wavs.shape) == 1: wavs = wavs.unsqueeze(0) # Assign full length if wav_lens is not assigned if wav_lens is None: wav_lens = torch.ones(wavs.shape[0], device=self.device) # Storing waveform in the specified device wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device) wavs = wavs.float() # Computing features and embeddings feats = self.mods.compute_features(wavs) feats = self.mods.mean_var_norm(feats, wav_lens) embeddings = self.mods.embedding_model(feats, wav_lens) if normalize: embeddings = self.hparams.mean_var_norm_emb( embeddings, torch.ones(embeddings.shape[0], device=self.device) ) return embeddings classifier = Encoder.from_hparams( source="yangwang825/etdnn-vox2" ) signal, fs = torchaudio.load('spk1_snt1.wav') embeddings = classifier.encode_batch(signal) >>> torch.Size([1, 1, 192]) ``` We will release our training results (models, logs, etc) shortly. # References 1. Ravanelli et al., SpeechBrain: A General-Purpose Speech Toolkit, 2021 2. Snyder et al., The JHU Speaker Recognition System for the VOiCES 2019 Challenge, 2019

ihanif/wav2vec2-xls-r-300m-pashto-lm

ihanif

2022-12-22T15:57:25Z

121

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "google/fleurs", "generated_from_trainer", "hf-asr-leaderboard", "pashto", "ps", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-21T12:11:15Z

--- license: apache-2.0 tags: - google/fleurs - generated_from_trainer - automatic-speech-recognition - hf-asr-leaderboard - pashto - ps datasets: - fleurs metrics: - wer model-index: - name: facebook/wav2vec2-xls-r-300m results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs type: google/fleurs args: 'config: ps_af, split: test' metrics: - name: Wer type: wer value: 0.5159447476125512 ---  # facebook/wav2vec2-xls-r-300m This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/FLEURS - PS_AF dataset. It achieves the following results on the evaluation set: - Loss: 0.9162 - Wer: 0.5159 - Cer: 0.1972 ## 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: 7.5e-07 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:----:|:------:|:---------------:|:------:| | 5.0767 | 6.33 | 500 | 1.0 | 4.8783 | 1.0 | | 3.1156 | 12.66 | 1000 | 1.0 | 3.0990 | 1.0 | | 1.3506 | 18.99 | 1500 | 0.2889 | 1.1056 | 0.7031 | | 0.9997 | 25.32 | 2000 | 0.2301 | 0.9191 | 0.5944 | | 0.7838 | 31.65 | 2500 | 0.2152 | 0.8952 | 0.5556 | | 0.6665 | 37.97 | 3000 | 0.2017 | 0.8908 | 0.5252 | | 0.6265 | 44.3 | 3500 | 0.1954 | 0.9063 | 0.5133 | | 0.5935 | 50.63 | 4000 | 0.1969 | 0.9162 | 0.5156 | | 0.5174 | 56.96 | 4500 | 0.1972 | 0.9287 | 0.5140 | | 0.5462 | 63.29 | 5000 | 0.1974 | 0.9370 | 0.5138 | | 0.5564 | 69.62 | 5500 | 0.1977 | 0.9461 | 0.5148 | | 0.5252 | 75.95 | 6000 | 0.9505 | 0.5118 | 0.1969 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

bofenghuang/deprecated-whisper-large-v2-cv11-french-punct-plus

bofenghuang

2022-12-22T15:55:48Z

3

1

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "whisper-event", "fr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-22T10:51:45Z

--- license: apache-2.0 language: fr library_name: transformers thumbnail: null tags: - automatic-speech-recognition - hf-asr-leaderboard - whisper-event datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Fine-tuned whisper-large-v2 model for ASR in French results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: fr split: test args: fr metrics: - name: WER (Greedy) type: wer value: 8.55 - name: WER (Beam 5) type: wer value: 8.03 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Fleurs type: google/fleurs config: fr_fr split: test args: fr_fr metrics: - name: WER (Greedy) type: wer value: 5.58 - name: WER (Beam 5) type: wer value: 5.26 --- <style> img { display: inline; } </style> ![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey) ![Model size](https://img.shields.io/badge/Params-1550M-lightgrey) ![Language](https://img.shields.io/badge/Language-French-lightgrey) # Fine-tuned whisper-large-v2 model for ASR in French This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2), trained on the mozilla-foundation/common_voice_11_0 fr dataset. When using the model make sure that your speech input is also sampled at 16Khz. **This model also predicts casing and punctuation.** ## Usage Inference with 🤗 Pipeline ```python import torch from datasets import load_dataset from transformers import pipeline device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load pipeline pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-cv11-french-punct", device=device) # NB: set forced_decoder_ids for generation utils pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", task="transcribe") # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = test_segment["audio"] # NB: decoding option # limit the maximum number of generated tokens to 225 pipe.model.config.max_length = 225 + 1 # sampling # pipe.model.config.do_sample = True # beam search # pipe.model.config.num_beams = 5 # return # pipe.model.config.return_dict_in_generate = True # pipe.model.config.output_scores = True # pipe.model.config.num_return_sequences = 5 # Run generated_sentences = pipe(waveform)["text"] ``` Inference with 🤗 low-level APIs ```python import torch import torchaudio from datasets import load_dataset from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load model model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-cv11-french-punct").to(device) processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-cv11-french-punct", language="french", task="transcribe") # NB: set forced_decoder_ids for generation utils model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="fr", task="transcribe") # 16_000 model_sample_rate = processor.feature_extractor.sampling_rate # Load data ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True) test_segment = next(iter(ds_mcv_test)) waveform = torch.from_numpy(test_segment["audio"]["array"]) sample_rate = test_segment["audio"]["sampling_rate"] # Resample if sample_rate != model_sample_rate: resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate) waveform = resampler(waveform) # Get feat inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt") input_features = inputs.input_features input_features = input_features.to(device) # Generate generated_ids = model.generate(inputs=input_features, max_new_tokens=225) # greedy # generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5) # beam search # Detokenize generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # Normalise predicted sentences if necessary ```

mobiusmatt/ppo-LunarLander-v2

mobiusmatt

2022-12-22T15:52:01Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T02:26:42Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 290.56 +/- 22.68 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mahmoud-mohey/q-FrozenLake-v1-4x4

mahmoud-mohey

2022-12-22T15:10:17Z

0

null

[ "FrozenLake-v1-4x4-slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T15:10:03Z

--- tags: - FrozenLake-v1-4x4-slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-slippery_v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-slippery type: FrozenLake-v1-4x4-slippery metrics: - type: mean_reward value: 0.80 +/- 0.40 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="mahmoud-mohey/q-FrozenLake-v1-4x4-slippery_v2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

enryu43/anifusion_sd_unet

enryu43

2022-12-22T15:04:03Z

6

3

diffusers

[ "diffusers", "diffusers:LDMTextToImagePipeline", "region:us" ]

null

2022-12-21T15:47:20Z

This model is converted with https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py. However, the tokenizer in the diffuser model is wrong, for proper usage, see description at https://medium.com/@enryu9000/anifusion-sd-91a59431a6dd, and instructions/examples at https://github.com/enryu43/anifusion2-stable-diffusion. Also, the original checkpoint in the Latent Diffusion format is available. Installation instructions for webui: https://gist.github.com/enryu43/fccaa7f165ffcb214780d203c565761f

NXBY/ppo-LunarLander-v2

NXBY

2022-12-22T14:38:29Z

2

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T14:38:01Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.03 +/- 30.45 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

BabakMahmoudi/ppo-LunarLander-v2

BabakMahmoudi

2022-12-22T14:25:36Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T14:25:14Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.95 +/- 14.28 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

flegese/translation_en_to_sd

flegese

2022-12-22T14:14:16Z

3

0

keras

[ "keras", "tf-keras", "marian", "region:us" ]

null

2022-11-29T07:31:50Z

research-backup/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0

research-backup

2022-12-22T14:05:44Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-26T22:53:45Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.317718253968254 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.27807486631016043 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.27299703264094954 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37965536409116174 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.414 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3157894736842105 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.25925925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6180503239415398 - name: F1 (macro) type: f1_macro value: 0.4614231459717044 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.734037558685446 - name: F1 (macro) type: f1_macro value: 0.2496301382914666 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.41765980498374866 - name: F1 (macro) type: f1_macro value: 0.2976004948922019 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8466300340822146 - name: F1 (macro) type: f1_macro value: 0.5996275083276039 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7790661234722657 - name: F1 (macro) type: f1_macro value: 0.7551725837093879 --- # relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0 RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/analogy.json)): - Accuracy on SAT (full): 0.27807486631016043 - Accuracy on SAT: 0.27299703264094954 - Accuracy on BATS: 0.37965536409116174 - Accuracy on U2: 0.3157894736842105 - Accuracy on U4: 0.25925925925925924 - Accuracy on Google: 0.414 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/classification.json)): - Micro F1 score on BLESS: 0.6180503239415398 - Micro F1 score on CogALexV: 0.734037558685446 - Micro F1 score on EVALution: 0.41765980498374866 - Micro F1 score on K&H+N: 0.8466300340822146 - Micro F1 score on ROOT09: 0.7790661234722657 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.317718253968254 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: nce_logout - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 640 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-v6-mask-prompt-d-nce-0/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

zjunlp/KnowPrompt

zjunlp

2022-12-22T13:57:04Z

0

3

null

[ "ch", "license:apache-2.0", "region:us" ]

null

2022-11-29T12:02:20Z

--- tasks: - Relation Extraction widgets: - examples: - name: 1 title: Message-Topic(e1,e2) inputs: - name: token data: ["the", "most", "common", "audits", "were", "about", "waste", "and", "recycling", "."] - name: h data: - name: audits pos: [3, 4] - name: t data: - name: waste pos: [6, 7] - name: 2 title: Product-Producer(e2,e1) inputs: - name: token data: ["the", "ombudsman", "'s", "report", "concluded", "that", "``", "a", "large", "part", "of", "the", "package", "was", "not", "provided", "''", "."] - name: h data: - name: ombudsman pos: [1, 2] - name: t data: - name: report pos: [3, 4] - name: 3 title: Instrument-Agency(e2,e1) inputs: - name: token data: ["many", "professional", "cartomancers", "use", "a", "regular", "deck", "of", "playing", "cards", "for", "divination", "."] - name: h data: - name: cartomancers pos: [2, 3] - name: t data: - name: cards pos: [9, 10] - name: 4 title: Entity-Destination(e1,e2) inputs: - name: token data: ["nasa", "kepler", "mission", "sends", "names", "into", "space", "."] - name: h data: - name: oil pos: [4, 5] - name: t data: - name: ocean pos: [7, 8] - name: 5 title: Cause-Effect(e2,e1) inputs: - name: token data: ["sorace", "was", "unaware", "that", "her", "anger", "was", "caused", "by", "the", "abuse", "."] - name: h data: - name: anger pos: [5, 6] - name: t data: - name: abuse pos: [10, 11] - name: 6 title: Component-Whole(e1,e2) inputs: - name: token data: ["the", "castle", "was", "inside", "a", "museum", "."] - name: h data: - name: castle pos: [1, 2] - name: t data: - name: museum pos: [5, 6] domain: - nlp frameworks: - pytorch backbone: - BERT large metrics: - accuracy license: apache-2.0 language: - ch --- # KnowPrompt:Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction KnowPrompt is used for relational extraction tasks, injecting latent knowledge contained in relation labels into prompt construction with learnable virtual template words and answer words , and synergistically optimize their representation with structured constraints. ## Model description We take the first step to inject latent knowledge contained in relation labels into prompt construction，the knowledge extraction is then implemented with a Prompt-tuning model。The implementation is as follows：virtual template words around entities initialized using aggregate entity embeddings are used as learnable virtual template words to inject entity knowledge; Meanwhile, we leverage label to compute average embeddings as virtual answers words to inject relationship knowledge. In this structure, entities and relations are mutually constrained and virtual template and answer words should be contextually relevant, so we introduce synergistic optimization to correct virtual template and answer words. ![image.png](./model.png) ## Intended uses & limitations This model is used for relationship extraction tasks, and the extracted information can be used for more downstream NLP tasks, such as: information retrieval, conversation generation and Q&A. Please refer to the code example for details on how to use it. The relationship labels in the model training data are limited and can only generalize the relationships in the real world to a certain extent. ## Training data We adopt SemEval as the dataset | **Dataset** | **# Train.** | **# Val.** | **# Test.** | **# Rel.** | | ----------- | ------------ | ---------- | ----------- | ---------- | | SemEval | 6,507 | 1,493 | 2,717 | 19 | ## Training procedure ### Training The training is divided into two phases, and the first phase performs collaborative optimization of virtual template words and answer words $$ \mathcal{J}=\mathcal{J}_{[\text {MASK }]}+\lambda \mathcal{J}_{\text {structured }}, $$ $\lambda$is the hyperparameter for weighing the two loss functions;The second stage optimizes all parameters with a smaller learning rate based on the optimized virtual template words and answer words, using only the loss function $\mathcal{J}_{texttt{[MASK]}}$to finetune the parameters for the language model.The hyperparameters are different for different datasets, as shown in the script file in the source code.Taking SemEval as an example, the hyperparameters are set as follows: ``` max_epochs=10 max_sequence_length=256 batch_size=16 learning_rate=3e-5 batch_size=16 t_lambda=0.001 ``` ### Data Evaluation and Results The results of the comparison with other models in standard settings are shown in the following table. | **Methods** | **Precision** | | ----------- | ------------- | | Fine-tuning | 87.6 | | KnowBERT | 89.1 | | MTB | 89.5 | | PTR | 89.9 | | KnowPrompt | 90.2 (+0.3) | In low-resource settings，we performed the 8-, 16-, and 32-experiments.K instances of each class are sampled from the initial training and validation sets to form the training and validation sets for the FEW-shot. The results are as follows: | Split | **Methods** | **Precision** | | ----- | ----------- | ------------- | | k=8 | Fine-tuning | 41.3 | | | GDPNet | 42.0 | | | PTR | 70.5 | | | KnowPrompt | 74.3 (+33.0) | | k=16 | Fine-tuning | 65.2 | | | GDPNet | 67.5 | | | PTR | 81.3 | | | KnowPrompt | 82.9 (+17.7) | | k=32 | Fine-tuning | 80.1 | | | GDPNet | 81.2 | | | PTR | 84.2 | | | KnowPrompt | 84.8 (+4.7) | As 𝐾 decreases from 32 to 8, the improvement in our KnowPrompt over the other three methods increases gradually. #### BibTeX entry and citation info ``` @inproceedings{DBLP:conf/www/ChenZXDYTHSC22, author = {Xiang Chen and Ningyu Zhang and Xin Xie and Shumin Deng and Yunzhi Yao and Chuanqi Tan and Fei Huang and Luo Si and Huajun Chen}, editor = {Fr{\'{e}}d{\'{e}}rique Laforest and Rapha{\"{e}}l Troncy and Elena Simperl and Deepak Agarwal and Aristides Gionis and Ivan Herman and Lionel M{\'{e}}dini}, title = {KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction}, booktitle = {{WWW} '22: The {ACM} Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022}, pages = {2778--2788}, publisher = {{ACM}}, year = {2022}, url = {https://doi.org/10.1145/3485447.3511998}, doi = {10.1145/3485447.3511998}, timestamp = {Tue, 26 Apr 2022 16:02:09 +0200}, biburl = {https://dblp.org/rec/conf/www/ChenZXDYTHSC22.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```bash git clone https://www.modelscope.cn/jeno11/knowprompt_demo.git ```

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

yizhangliu

2022-12-22T13:54:08Z

1

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-22T13:53:44Z

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

Verne/test-sd-class-butterflies-32

Verne

2022-12-22T13:41:24Z

10

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-22T13:21:06Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('Verne/test-sd-class-butterflies-32') image = pipeline().images[0] image ```

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

jaydipsen

2022-12-22T13:32:56Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-12-22T13:08:52Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8638300289723342 ---  # 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 4.11.3 - Pytorch 1.13.0+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3

edbeeching/sample_factory_FPS

edbeeching

2022-12-22T13:29:12Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T13:28:07Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: gdrl type: gdrl metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **gdrl** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_FPS ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .home.edward.work.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_FPS ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .gpfsssd.scratch.rech.ajs.utv52ia.godot_rl.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_FPS --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

bsmith0430/ppo-LunarLander-v2

bsmith0430

2022-12-22T13:17:17Z

2

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-21T08:07:39Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.93 +/- 18.64 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

sophiaaez/sd-class-butterflies-32

sophiaaez

2022-12-22T13:10:35Z

4

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-22T13:09:09Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('sophiaaez/sd-class-butterflies-32') image = pipeline().images[0] image ```

sgangireddy/whisper-medium-cv-fi-3k

sgangireddy

2022-12-22T12:09:37Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "fi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-21T16:00:59Z

--- language: - fi license: apache-2.0 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper medium Finnish CV 4K results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 fi type: mozilla-foundation/common_voice_11_0 config: fi split: test args: fi metrics: - name: Wer type: wer value: 15.736901620806634 ---  # Whisper medium Finnish CV 4K This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 fi dataset. It achieves the following results on the evaluation set: - Loss: 0.3412 - Wer: 15.7369 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0014 | 19.0 | 1000 | 0.3029 | 16.3117 | | 0.0002 | 38.01 | 2000 | 0.3412 | 15.7369 | | 0.0001 | 57.01 | 3000 | 0.3592 | 15.8783 | | 0.0001 | 76.01 | 4000 | 0.3655 | 15.8594 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

edbeeching/sample_factory_BallChase

edbeeching

2022-12-22T12:06:43Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T12:06:28Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: gdrl type: gdrl metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **gdrl** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_BallChase ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .home.edward.work.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_BallChase ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .home.edward.work.godot_rl_agents.venv.bin.g --algo=APPO --env=gdrl --train_dir=./train_dir --experiment=sample_factory_BallChase --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

infinitas9/rl-ppo-Huggy

infinitas9

2022-12-22T12:03:34Z

3

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2022-12-22T12:03:17Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **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: infinitas9/rl-ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

George117/q-Taxi-v3

George117

2022-12-22T11:53:07Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T11:52:54Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="George117/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"]) ```

Siddu0406/codeparrot-ds

Siddu0406

2022-12-22T11:48:33Z

6

0

transformers

[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-12-22T11:07:14Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: codeparrot-ds results: [] ---  # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.8843 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -795, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 9.8843 | 0 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2

nicky007/DocumentNick

nicky007

2022-12-22T11:42:23Z

12

0

transformers

[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:cord-layoutlmv3", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-12-20T11:59:14Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - cord-layoutlmv3 model-index: - name: DocumentNick results: [] ---  # DocumentNick This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5167 - eval_precision: 0.8758 - eval_recall: 0.8922 - eval_f1: 0.8839 - eval_accuracy: 0.8901 - eval_runtime: 10.2752 - eval_samples_per_second: 9.732 - eval_steps_per_second: 1.946 - epoch: 11.95 - step: 526 ## 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

aashay96/indic-gpt

aashay96

2022-12-22T11:39:09Z

54

1

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-12-20T19:34:27Z

--- license: mit tags: - generated_from_trainer model-index: - name: indic-gpt results: [] ---  # indic-gpt This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an Indian Language(https://ai4bharat.iitm.ac.in/corpora) dataset. Sample Dataset is present on https://huggingface.co/datasets/aashay96/indic-gpt. It achieves the following results on the evaluation set: - Loss: 1.9482 ## Model description Model is trained on multiple Indian Languages - Assamese, bengali, gujarati, Kannada, Malayalam,telugu, tamil, odhiya and punjabi. ## Intended uses & limitations More information needed ## Training and evaluation data TBD - Evaluation on indic_glue ## Training procedure Check the notebook! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.3653 | 0.3 | 500 | 2.2985 | | 2.2079 | 0.61 | 1000 | 2.0401 | | 2.0396 | 0.91 | 1500 | 1.9482 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

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

jairNeto

2022-12-22T11:22:08Z

0

null

[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T11:21:55Z

--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.61 +/- 0.49 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="jairNeto/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"]) ```

DPolatajko/osm-nlp-setfit

DPolatajko

2022-12-22T10:54:51Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-12-22T10:54:13Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 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) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  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 7500 with parameters: ``` {'batch_size': 8, '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": 7500, "warmup_steps": 750, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, '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}) ) ``` ## Citing & Authors

Isaacgv/ppo-LunarLander-v2

Isaacgv

2022-12-22T10:49:10Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T10:48:43Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.79 +/- 18.79 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

orenk/ppo-LunarLander-v2

orenk

2022-12-22T10:18:14Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T10:07:55Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.96 +/- 37.26 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

shovall/ppo-LunarLander-v2

shovall

2022-12-22T10:07:30Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T10:06:54Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.12 +/- 34.61 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

DrishtiSharma/whisper-large-v2-hindi-2k-steps

DrishtiSharma

2022-12-22T09:58:14Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-19T05:52:43Z

--- language: - hi license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large V2 Hindi - Drishti Sharma results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: hi metrics: - name: Wer type: wer value: 10.24860360772823 ---  # Whisper Large V2 Hindi - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1787 - Wer: 10.2486 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0238 | 2.44 | 2000 | 0.1787 | 10.2486 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

Scrwed/dqn-SpaceInvadersNoFrameskip-v4

Scrwed

2022-12-22T09:55:48Z

4

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-21T10:54:25Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 613.50 +/- 107.96 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Scrwed -f logs/ python enjoy.py --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 Scrwed -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 Scrwed ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.005), ('exploration_fraction', 0.025), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 1e-05), ('learning_starts', 500), ('n_timesteps', 150000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

arnonl/ppo-LunarLander-v2

arnonl

2022-12-22T09:54:58Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T09:54:27Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.00 +/- 17.90 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

adlrocha/dqn_lunar

adlrocha

2022-12-22T09:53:46Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T09:53:19Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.96 +/- 15.08 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mdabbah/ppo-LunarLander-v2

mdabbah

2022-12-22T09:51:38Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T09:51:16Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.16 +/- 23.46 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

threite/ppo-LunarLander-v2

threite

2022-12-22T09:48:07Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T09:47:40Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 257.45 +/- 21.37 name: mean_reward verified: false --- # **MlpPolicy** Agent playing **LunarLander-v2** This is a trained model of a **MlpPolicy** 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 ... ```

nachshonc/rl_course_unit1

nachshonc

2022-12-22T09:47:41Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-22T09:47:05Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.06 +/- 23.56 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

DrishtiSharma/whisper-large-v2-marathi

DrishtiSharma

2022-12-22T09:44:16Z

26

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "mr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-15T16:02:23Z

--- language: - mr license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large Marathi - Drishti Sharma results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: mr split: test args: mr metrics: - name: Wer type: wer value: 13.644010767160161 ---  # Whisper Large Marathi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1975 - Wer: 13.6440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1914 | 0.81 | 400 | 0.1975 | 13.6440 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

avojarot/sd-class-butterflies-32

avojarot

2022-12-22T09:43:33Z

2

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-22T09:43:03Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # 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('avojarot/sd-class-butterflies-32') image = pipeline().images[0] image ```

steja/whisper-large-sindhi

steja

2022-12-22T09:26:17Z

13

1

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-21T17:49:34Z

--- license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - google/fleurs metrics: - wer model-index: - name: Whisper_large_Sindhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs sd_in type: google/fleurs config: sd_in split: test metrics: - name: Wer type: wer value: 27.692698197817073 ---  # Whisper_large_Sindhi This model is a fine-tuned version of [anuragshas/whisper-large-v2-hi](https://huggingface.co/anuragshas/whisper-large-v2-hi) on the google/fleurs sd_in dataset. It achieves the following results on the evaluation set: - Loss: 0.6382 - Wer: 27.6927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_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: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0005 | 38.44 | 500 | 0.6382 | 27.6927 | | 0.0003 | 76.89 | 1000 | 0.6714 | 27.8323 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2