davanstrien/model_cards_with_readmes_with_duplicates

spatial/Reinforce-CartPole8

spatial

null

6

0

null

0

reinforcement-learning

false

null

0

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

true

286

# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Arron/bert-finetuned-ner

Arron

bert

22

5

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,512

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0607 - Precision: 0.9319 - Recall: 0.9495 - F1: 0.9406 - Accuracy: 0.9860 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0845 | 1.0 | 1756 | 0.0626 | 0.9146 | 0.9337 | 0.9241 | 0.9827 | | 0.0414 | 2.0 | 3512 | 0.0561 | 0.9321 | 0.9492 | 0.9405 | 0.9861 | | 0.0198 | 3.0 | 5268 | 0.0607 | 0.9319 | 0.9495 | 0.9406 | 0.9860 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

Mykolyt/q-Taxi-v3

Mykolyt

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

363

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Mykolyt/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"]) ```

morisato/scenery_LoRA_01

morisato

null

5

0

null

3

null

false

unknown

['ja']

null

0

[]

false

true

2,548

# 背景LoRA 既存ののモデルのプロンプトで生成できない場所や風景を学習で生成できるようになったらいいなという実験です。 # karaokeroom.safetensors [<img width="480" src="https://i.imgur.com/hclI0vj.jpg">](https://i.imgur.com/hclI0vj.jpg) [<img width="480" src="https://i.imgur.com/8H3c7eE.jpg">](https://i.imgur.com/8H3c7eE.jpg) カラオケ屋さんの部屋の雰囲気を学習したLoRAです。 Loraを読み込ませて、プロンプトに　**karaokeroom**　と記述してください。プロンプトに、1girl, karaoke, microphone, 等とあわせて記述していただくとカラオケを歌ってる感じの絵ができます。 ※当LoRAを適用すると人物の描画や画風に影響が生じるようです。LoRAを適用するWeightを調整することで画風への影響を抑えられます。影響が気になった場合は \<lora:karaokeroom:1\>ではなく\<lora:karaokeroom:0.6\>といった感じで調整して使ってみてください。 ※karaokeroom, 1girl, karaoke, 等のプロンプトを書いても、部屋の風景のみで人物がうまく描画されないことがあります。ガチャ要素があるのと、モデルによってはうまく働かない場合があるようです。その場合は根気よく何枚か生成してみるか、違うモデルを使ってみてください。 ## この実験をやってみた動機たとえばプロンプトに shibuya,city, と書くと渋谷っぽい風景の絵を描いてくれます。これはモデルが「渋谷」という概念を知ってるという事だと思います。しかし、 Nishinomiya と書いても西宮っぽい風景の絵を描いてはくれません。これはモデルが「西宮」という概念を知らないという事だと思います。最近、LoRAという手法でスペックが低いパソコン(GPU)でも追加学習が出来る方法が普及してきました。既存のモデルでは描けないキャラクターや衣装等を学習させている方がたくさんいらっしゃいます。そこで自分は、風景の写真を何枚か学習させれば、その「場所」の概念を学習してくれるのではないかと考えました。カラオケ店の部屋の写真を20枚程用意して、WD14-taggerでタグ付けを行いました。出来たtxtファイルの全ての先頭の位置に karaokeroom, という単語を追加しました。学習前提のモデルは karaokeroom という概念を知らないので、この学習によって karaokeroom という新しい概念を獲得してくれると想定しました。実際に学習を実行して出来上がった当LoRAを読み込ませると上記の karaokeroom というプロンプトでカラオケ店の部屋っぽい絵が生成できます。場所の概念を学習させる実験は成功ではないでしょうか？　 LoRAでうまく場所の概念を学習できる方法が確立できれば日本の様々な風景を学習させることで身近な場所のイラストが生成できるようになると思います。これはその第一歩です。 ## 問題点、今後の課題カラオケ店の風景は再現できるようになりました。が、当LoRAを適用してカラオケを歌う女の子の絵を生成すると、人物の描画や画風に影響が生じる場合があります。これはおそらく場所の概念だけではなく、素材写真の画風等も学習してしまったものだと思います。現状はLoraを適用するWeightを下げることで影響を軽減できますが、学習方法やLoRAの適用の仕方で影響を軽減することが出来ないか？と考えています。・U-net層でWeight調整することで影響を押さえられる？実は僕も全然よく分かってないのですが（！）階層マージ(Marge Block Weighted)で多くの方が様々なモデルマージに挑戦した結果、絵を生成するU-netの各レイヤー層を調節することで描画に様々な調整ができる（？）ことがわかってきました。例えば「INの上層はリアル調、INの下層がanime調を担当しているのではないか？」、「M_00は全体にキャラクターや服装・背景等に大きな影響が出る」、「OUT上層は、主題以外の表現 (例えば背景)に影響を及ぼしている」、「OUT04,OUT05,OUT06あたりはめっちゃ顔に影響ある」等色々な説があります。上の方で書いたkaraokeroom:0.6といった指定はU-net全体まるごとで影響を下げる設定になると思うのですが(多分）もし背景に大きく関与しているU-net層が分かればそれ以外のU-net層への関与を抑えることで既存モデルの人物描写と追加学習背景LoRAがうまく共存できるのでは？と考えられます。実際にU-net層別にWeightを調節できるScripts（sd-webui-lora-block-weight：https://github.com/hako-mikan/sd-webui-lora-block-weight ）を使って色々な数値を調整したXY Plot画像等を作成してみたりして調べていますが、現状では「ワイには何もわからないことがわかった」という感じではっきりしたことは分かっていません。・学習用素材の写真をうまく調整する・学習時のキャプション（タグ）の付け方などで、画風を学ばないようにできないか？・正則化画像を用意することで何かうまく学習の調整ができるのでは？等、いろいろな案が考えられると思いますが…まだまだ試行錯誤の段階で情報が足りず良い解決方法は得られていません。なかなか難しそうですが、うまく場所の概念だけ覚えさせる方法が出来たらいいですよね。

aisingapore/undersupervised-feature-decomposition

aisingapore

null

10

0

null

0

text-classification

false

gpl-3.0

['en', 'de', 'fr', 'ja']

null

0

['ufd', 'text-classification', 'undersupervised-feature-decomposition']

false

true

3,920

# Cross Lingual Cross Domain You can **try out the model** at [SGNLP](https://sgnlp.aisingapore.net/cross-lingual-cross-domain). If you want to find out more information, please contact us at [SGNLP-AISingapore]([email protected]). ## Table of Contents - [Model Details](#model-details) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Model Parameters](#model-parameters) - [License](#license) ## Model Details **Model Name:** Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language - **Description:** It is an implementation of Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model paper. - **Paper:** Unsupervised domain adaptation of a pretrained cross-lingual language model. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Nov, 2020 (pp. 3672-3678). - **Author(s):** Li, J., He, R., Ye, H., Ng, H. T., Bing, L., & Yan, R. (2020). - **URL:** https://www.ijcai.org/Proceedings/2020/508 # How to Get Started With the Model ## Install Python package SGnlp is an initiative by AI Singapore's NLP Hub. They aim to bridge the gap between research and industry, promote translational research, and encourage adoption of NLP techniques in the industry. Various NLP models, other than cross lingual cross domain are available in the python package. You can try them out at [SGNLP-Demo](https://sgnlp.aisingapore.net/) | [SGNLP-Github](https://github.com/aisingapore/sgnlp). ```python pip install sgnlp ``` ## Examples For more full code guide, please refer to this [documentation](https://sgnlp.aisingapore.net/docs/model/ufd.html). Alternatively, you can also try out the [demo](https://sgnlp.aisingapore.net/cross-lingual-cross-domain) for Cross Lingual Cross Domain. Example of Undersupervised Feature Decomposition (UFD) model (German language): ```python from sgnlp.models.ufd import UFDModelBuilder, UFDPreprocessor # Instantiate model builder and preprocessor model_builder = UFDModelBuilder( source_domains=['books'], target_languages=['de'], target_domains=['dvd']) preprocessor = UFDPreprocessor() # Build pretrained model groups model_groups = model_builder.build_model_group() # Model predict ('books_de_dvd' model example) instance = """Wolverine is BACK Der Film ist im Grunde wie alle Teile der X-Men für Comic-Fans auf jeden Fall ein muss. Hugh Jackman spielt seine Rolle wie immer so gut was ich von den ein oder anderen Darsteller leider nicht sagen kann. Story und Action sind aber genug Gründe um sich die Blu-ray zu kaufen.""" instance_features = preprocessor([instance]) output = model_groups['books_de_dvd'](**instance_features) ``` # Training The training datasets can be retrieved from the following author's repository([github](https://github.com/lijuntaopku/UFD/tree/main/data)). #### Training Results - For UFD - **Training Time: (Unsupervised training)** ~3 hours for 30 epochs on a single V100 GPU - **Training Time: (Supervised training)** ~3 hours for 60 epochs on a single V100 GPU # Model Parameters - **Model Weights:** [refer to documentation for details](https://sgnlp.aisingapore.net/docs/model/ufd.html) - **Model Config:** [refer to documentation for details](https://sgnlp.aisingapore.net/docs/model/ufd.html) - **Model Inputs:** Raw text. - **Model Outputs:** Array of logits with the size of number of classes. - **Model Size:** XLM-Roberta: ~2.2GB, Adaptor Domain: ~8.0MB, Adaptor Global: ~8.0MB, Feature Mapper: ~8.0MB, Classifier: ~9.1KB. - **Model Inference Info:** ~2 sec on Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz. - **Usage Scenarios:** Sentiment analysis for eCommerce with operations across multiple countries. # License - **For non-commercial use:** GNU GPLv3. - **For commercial use:** please contact us [SGNLP-AISingapore]([email protected])

EvaKlimentova/knots_protbertBFD_alphafold

EvaKlimentova

bert

8

6

transformers

0

text-classification

true

false

null

['EvaKlimentova/knots_AF']

null

0

[]

false

true

1,657

# M1 - finetuned ProtBert-BFD The model is trained on [knots_AF dataset](https://huggingface.co/datasets/EvaKlimentova/knots_AF) The accuracy on the test set is ~ 0.9848 | M1 ProtBert BFD | Dataset size | Unknotted set size | Accuracy | TPR | TNR | |:----------------------------:|:------------:|:------------------:|:--------:|:------:|:-------:| | All | 39412 | 19718 | 0.9848 | 0.9871 | 0.9826 | | SPOUT | 7371 | 550 | 0.9905 | 0.9963 | 0.9182 | | TDD | 612 | 24 | 0.9918 | 0.9966 | 0.8750 | | DUF | 736 | 429 | 0.97905 | 0.9826 | 0.9767 | | AdoMet synthase | 1794 | 240 | 0.9939 | 0.9968 | 0.9750 | | Carbonic anhydrase | 1531 | 539 | 0.9556 | 0.9718 | 0.9258 | | UCH | 477 | 125 | 0.9099 | 0.9631 | 0.7600 | | ATCase/OTCase | 3799 | 3352 | 0.9992 | 0.9955 | 0.9997 | | ribosomal-mitochondrial | 147 | 41 | 0.8912 | 0.9906 | 0.63412 | | membrane | 8309 | 1577 | 0.9791 | 0.9895 | 0.9347 | | VIT | 14347 | 12639 | 0.9873 | 0.9415 | 0.9935 | | biosynthesis of lantibiotics | 392 | 286 | 0.9719 | 0.9811 | 0.9685 | | PGluconate dehydrogenase | 1 | 0 | 1.0 | 1.0 | |

deprem-ml/deprem-ner-mdebertav3

deprem-ml

deberta-v2

14

16

transformers

3

token-classification

true

false

apache-2.0

['tr']

null

1

0

1

0

[]

false

true

1,580

# Model: 'deprem-ner-mdebertav3' ### Validasyon Sonuçları - **Precision:** 0.711819 - **Recall:** 0.783626 - **F1:** 0.745999 - **Accuracy:** 0.933360 ### Eğitim Parametreleri ``` evaluation_strategy="epoch" save_strategy="epoch" load_best_model_at_end=True learning_rate=3e-5 per_device_train_batch_size=8 per_device_eval_batch_size=16 num_train_epochs=15 weight_decay=0.01 seed=42 ``` ### Örnekler Bu model depremde enkaz altında kalan kişilerin bildirimlerinden sokak, il, ilçe gibi bilgileri çekmeye çalıştık. Örnek girdiler: - "Lütfen yardım Akevler mahallesi Rüzgar sokak Tuncay apartmanı zemin kat Antakya akrabalarım göçük altında #hatay #Afad" - "MARAȘA'ta arkadaşimizdan haber alamıyoruz ACIL yardım Penta Park konutları 1. Blok en üst kat 11. Kat \n\n@AFADBaskanlik #kahramanmaraş\nACİL" Verdiği çıktılar: ``` [ { "entity_group": "mahalle", "score": 0.8160411715507507, "word": "Akevler mahallesi", "start": 14, "end": 31 }, { "entity_group": "sokak", "score": 0.940501868724823, "word": "Rüzgar sokak", "start": 32, "end": 44 }, { "entity_group": "Apartman/Site", "score": 0.8081040978431702, "word": "Tuncay apartmanı", "start": 45, "end": 61 }, { "entity_group": "ilce", "score": 0.854024350643158, "word": "Antakya", "start": 72, "end": 79 } ] ``` ### Değerlendirme Bu modeli Hugging Face Hub'daki diğer modellerle karşılaştırdık, örnek 30 input'ta sonuçları [bu repository'de](https://huggingface.co/datasets/deprem-ml/butun_model_benchmarklari) bulabilirsiniz.

minoosh/finetuned_bert-base-uncased

minoosh

bert

18

7

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,405

# finetuned_bert-base-uncased This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8732 - Accuracy: 0.4263 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.7365 | 1.0 | 502 | 1.5167 | 0.4288 | | 1.3495 | 2.0 | 1004 | 1.4797 | 0.4592 | | 1.1131 | 3.0 | 1506 | 1.5093 | 0.4527 | | 0.9213 | 4.0 | 2008 | 1.6501 | 0.4522 | | 0.7787 | 5.0 | 2510 | 1.7494 | 0.4407 | | 0.6594 | 6.0 | 3012 | 1.8600 | 0.4417 | | 0.5807 | 7.0 | 3514 | 1.9974 | 0.4412 | | 0.5142 | 8.0 | 4016 | 2.0887 | 0.4273 | | 0.4716 | 9.0 | 4518 | 2.1556 | 0.4273 | | 0.4364 | 10.0 | 5020 | 2.2847 | 0.4348 | | 0.3934 | 11.0 | 5522 | 2.3842 | 0.4298 | | 0.3774 | 12.0 | 6024 | 2.4663 | 0.4228 | | 0.3498 | 13.0 | 6526 | 2.5637 | 0.4253 | | 0.337 | 14.0 | 7028 | 2.6162 | 0.4273 | | 0.3191 | 15.0 | 7530 | 2.6466 | 0.4268 | | 0.3081 | 16.0 | 8032 | 2.6214 | 0.4288 | | 0.2889 | 17.0 | 8534 | 2.8064 | 0.4258 | | 0.2831 | 18.0 | 9036 | 2.8042 | 0.4228 | | 0.2733 | 19.0 | 9538 | 2.8510 | 0.4288 | | 0.2648 | 20.0 | 10040 | 2.8732 | 0.4263 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

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

shashankgarewal

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

404

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

BeaW/whisper-small-pyttsx3

BeaW

whisper

30

18

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['hi']

['logistics']

null

0

['generated_from_trainer']

true

1,075

# Whisper small 2 - BeaW This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chat analysis dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.7.1+cu110 - Datasets 2.8.0 - Tokenizers 0.11.0

mshibatatt/ppo-Huggy

mshibatatt

null

32

4

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

821

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

vvn0/ppo-PyramidsRND

vvn0

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

830

# **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: vvn0/ppo-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

xavisgg/dqn-SpaceInvadersNoFrameskip-v4

xavisgg

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,214

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga xavisgg -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga xavisgg -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga xavisgg ``` ## 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)]) ```

Elytum/bert-finetuned-ner

Elytum

bert

10

8

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,541

# bert-finetuned-ner This model is a fine-tuned version of [gaunernst/bert-small-uncased](https://huggingface.co/gaunernst/bert-small-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0186 - Precision: 0.9941 - Recall: 0.9952 - F1: 0.9946 - Accuracy: 0.9963 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0277 | 1.0 | 2500 | 0.0190 | 0.9929 | 0.9939 | 0.9934 | 0.9956 | | 0.0137 | 2.0 | 5000 | 0.0180 | 0.9935 | 0.9951 | 0.9943 | 0.9960 | | 0.0095 | 3.0 | 7500 | 0.0186 | 0.9941 | 0.9952 | 0.9946 | 0.9963 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

RocioUrquijo/clasificador-muchocine

RocioUrquijo

electra

10

2

transformers

0

text-classification

true

false

null

0

['classification', 'generated_from_trainer']

true

1,367

# clasificador-muchocine This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4310 - Accuracy: 0.4477 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 388 | 1.3207 | 0.4219 | | 1.3692 | 2.0 | 776 | 1.2987 | 0.4516 | | 0.9398 | 3.0 | 1164 | 1.4310 | 0.4477 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

pittawat/dqn-SpaceInvadersNoFrameskip-v4

pittawat

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,217

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pittawat -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pittawat -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pittawat ``` ## 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)]) ```

vodanh4299/ppo-LunarLander-v2

vodanh4299

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

350

# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

skywalker0803r/my_awesome_new_title_model

skywalker0803r

t5

9

transformers

0

text2text-generation

true

null

['zh']

['CLUECorpusSmall']

null

0

[]

false

true

5,687

# Chinese T5 ## Model description This is the set of Chinese T5 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). The Text-to-Text Transfer Transformer (T5) leverages a unified text-to-text format and attains state-of-the-art results on a wide variety of English-language NLP tasks. Following their work, we released a series of Chinese T5 models. You can download the set of Chinese T5 models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below: | | Link | | -------- | :-----------------------: | | **T5-Small** | [**L=6/H=512 (Small)**][small] | | **T5-Base** | [**L=12/H=768 (Base)**][base] | In T5, spans of the input sequence are masked by so-called sentinel token. Each sentinel token represents a unique mask token for the input sequence and should start with `<extra_id_0>`, `<extra_id_1>`, … up to `<extra_id_99>`. However, `<extra_id_xxx>` is separated into multiple parts in Huggingface's Hosted inference API. Therefore, we replace `<extra_id_xxx>` with `extraxxx` in vocabulary and BertTokenizer regards `extraxxx` as one sentinel token. ## How to use You can use this model directly with a pipeline for text2text generation (take the case of T5-Small): ```python >>> from transformers import BertTokenizer, T5ForConditionalGeneration, Text2TextGenerationPipeline >>> tokenizer = BertTokenizer.from_pretrained("uer/t5-small-chinese-cluecorpussmall") >>> model = T5ForConditionalGeneration.from_pretrained("uer/t5-small-chinese-cluecorpussmall") >>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer) >>> text2text_generator("中国的首都是extra0京", max_length=50, do_sample=False) [{'generated_text': 'extra0 北 extra1 extra2 extra3 extra4 extra5'}] ``` ## Training data [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. ## Training procedure The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. We use the same hyper-parameters on different model sizes. Taking the case of T5-Small Stage1: ``` python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ --vocab_path models/google_zh_with_sentinel_vocab.txt \ --dataset_path cluecorpussmall_t5_seq128_dataset.pt \ --processes_num 32 --seq_length 128 \ --dynamic_masking --data_processor t5 ``` ``` python3 pretrain.py --dataset_path cluecorpussmall_t5_seq128_dataset.pt \ --vocab_path models/google_zh_with_sentinel_vocab.txt \ --config_path models/t5/small_config.json \ --output_model_path models/cluecorpussmall_t5_small_seq128_model.bin \ --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \ --learning_rate 1e-3 --batch_size 64 \ --span_masking --span_geo_prob 0.3 --span_max_length 5 ``` Stage2: ``` python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ --vocab_path models/google_zh_with_sentinel_vocab.txt \ --dataset_path cluecorpussmall_t5_small_seq512_dataset.pt \ --processes_num 32 --seq_length 512 \ --dynamic_masking --data_processor t5 ``` ``` python3 pretrain.py --dataset_path cluecorpussmall_t5_seq512_dataset.pt \ --vocab_path models/google_zh_with_sentinel_vocab.txt \ --pretrained_model_path models/cluecorpussmall_t5_small_seq128_model.bin-1000000 \ --config_path models/t5/small_config.json \ --output_model_path models/cluecorpussmall_t5_small_seq512_model.bin \ --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \ --learning_rate 5e-4 --batch_size 16 \ --span_masking --span_geo_prob 0.3 --span_max_length 5 ``` Finally, we convert the pre-trained model into Huggingface's format: ``` python3 scripts/convert_t5_from_uer_to_huggingface.py --input_model_path cluecorpussmall_t5_small_seq512_model.bin-250000 \ --output_model_path pytorch_model.bin \ --layers_num 6 \ --type t5 ``` ### BibTeX entry and citation info ``` @article{2020t5, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, journal = {Journal of Machine Learning Research}, pages = {1-67}, year = {2020} } @article{zhao2019uer, title={UER: An Open-Source Toolkit for Pre-training Models}, author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, journal={EMNLP-IJCNLP 2019}, pages={241}, year={2019} } ``` [small]:https://huggingface.co/uer/t5-small-chinese-cluecorpussmall [base]:https://huggingface.co/uer/t5-base-chinese-cluecorpussmall

kasrahabib/XXX08_02_23__-bucket-finetunned

kasrahabib

bert

12

19

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,834

# kasrahabib/XXX08_02_23__-bucket-finetunned This model is a fine-tuned version of [kasrahabib/after_training_rus_combined_relabeled_data_from-bucket-finetunned_batch_size_16](https://huggingface.co/kasrahabib/after_training_rus_combined_relabeled_data_from-bucket-finetunned_batch_size_16) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0316 - Validation Loss: 0.3645 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 8010, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3976 | 0.3499 | 0 | | 0.2199 | 0.3588 | 1 | | 0.1392 | 0.3404 | 2 | | 0.0962 | 0.3372 | 3 | | 0.0684 | 0.3182 | 4 | | 0.0595 | 0.3414 | 5 | | 0.0411 | 0.3519 | 6 | | 0.0394 | 0.3500 | 7 | | 0.0338 | 0.3647 | 8 | | 0.0316 | 0.3645 | 9 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

tomasabril/unit1

tomasabril

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

350

# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

LouisDT/videomae-base-finetuned

LouisDT

videomae

10

0

transformers

0

video-classification

true

false

cc-by-nc-4.0

null

0

['generated_from_trainer']

true

1,495

# videomae-base-finetuned This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5590 - Accuracy: 0.8641 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 135 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7163 | 0.21 | 28 | 0.6078 | 0.8098 | | 0.7383 | 1.21 | 56 | 0.6975 | 0.4728 | | 0.6853 | 2.21 | 84 | 0.6637 | 0.6957 | | 0.7065 | 3.21 | 112 | 0.5590 | 0.8641 | | 0.6673 | 4.17 | 135 | 0.5766 | 0.8587 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

jannikskytt/a2c-AntBulletEnv-v0

jannikskytt

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

201

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

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

sunwooooong

xlm-roberta

10

0

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,320

# xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1656 - F1: 0.8589 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2905 | 1.0 | 715 | 0.1783 | 0.8310 | | 0.1461 | 2.0 | 1430 | 0.1600 | 0.8455 | | 0.0948 | 3.0 | 2145 | 0.1656 | 0.8589 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

hypefi/my_awesome_swag_model

hypefi

bert

12

3

transformers

0

multiple-choice

true

false

apache-2.0

null

['swag']

null

0

['generated_from_trainer']

true

1,327

# my_awesome_swag_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 1.0107 - Accuracy: 0.7899 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7454 | 1.0 | 4597 | 0.6122 | 0.7662 | | 0.3786 | 2.0 | 9194 | 0.6400 | 0.7833 | | 0.1338 | 3.0 | 13791 | 1.0107 | 0.7899 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

kongacute/ppo-Huggy

kongacute

null

32

0

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

820

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

KoRiF/a2c-PandaReachDense-v2

KoRiF

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

358

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jakub014/bert-base-uncased-finetuned-effectiveness-dagstuhl

jakub014

bert

13

7

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,477

# bert-base-uncased-finetuned-effectiveness-dagstuhl This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6418 - Accuracy: 0.6190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 16 | 0.6729 | 0.5714 | | No log | 2.0 | 32 | 0.6418 | 0.6190 | | No log | 3.0 | 48 | 0.6719 | 0.5556 | | No log | 4.0 | 64 | 0.6386 | 0.6032 | | No log | 5.0 | 80 | 0.6559 | 0.5714 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

mirfan899/t5-e2e-questions-generation

mirfan899

t5

11

47

transformers

1

text2text-generation

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,649

# t5-e2e-questions-generation This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.5634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 295 | 1.6673 | | 1.9714 | 2.0 | 590 | 1.6021 | | 1.9714 | 3.0 | 885 | 1.5820 | | 1.6225 | 4.0 | 1180 | 1.5665 | | 1.6225 | 5.0 | 1475 | 1.5643 | | 1.5252 | 6.0 | 1770 | 1.5676 | | 1.4558 | 7.0 | 2065 | 1.5581 | | 1.4558 | 8.0 | 2360 | 1.5600 | | 1.4169 | 9.0 | 2655 | 1.5604 | | 1.4169 | 10.0 | 2950 | 1.5634 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

schreon/gpt2-lhm-large-04

schreon

gpt2

11

0

transformers

0

text-generation

true

false

null

['training_corpus']

null

0

['generated_from_trainer']

true

965

# gpt2-lhm-large-04 This model was trained from scratch on the training_corpus dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

ecemisildar/Reinforce-1

ecemisildar

null

6

0

null

0

reinforcement-learning

false

null

0

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

true

286

# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

EdenYav/dqn-SpaceInvadersNoFrameskip-v4

EdenYav

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,212

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga EdenYav -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga EdenYav -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga EdenYav ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 150000), ('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', 0.0005), ('learning_starts', 100000), ('n_timesteps', 500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-new_dataset_50e

Gokulapriyan

swin

18

6

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

4,415

# swin-tiny-patch4-window7-224-finetuned-new_dataset_50e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6407 - Accuracy: 0.7973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.94 | 4 | 0.7081 | 0.6081 | | No log | 1.94 | 8 | 0.7104 | 0.6351 | | 0.5516 | 2.94 | 12 | 0.6911 | 0.6351 | | 0.5516 | 3.94 | 16 | 0.7156 | 0.7027 | | 0.537 | 4.94 | 20 | 0.7345 | 0.7297 | | 0.537 | 5.94 | 24 | 0.6745 | 0.6892 | | 0.537 | 6.94 | 28 | 0.7146 | 0.7297 | | 0.5333 | 7.94 | 32 | 0.7057 | 0.6892 | | 0.5333 | 8.94 | 36 | 0.6531 | 0.7027 | | 0.4871 | 9.94 | 40 | 0.6405 | 0.7027 | | 0.4871 | 10.94 | 44 | 0.6126 | 0.6892 | | 0.4871 | 11.94 | 48 | 0.6303 | 0.7027 | | 0.4432 | 12.94 | 52 | 0.6264 | 0.7027 | | 0.4432 | 13.94 | 56 | 0.6347 | 0.7432 | | 0.3669 | 14.94 | 60 | 0.6698 | 0.6622 | | 0.3669 | 15.94 | 64 | 0.6346 | 0.7568 | | 0.3669 | 16.94 | 68 | 0.6510 | 0.6892 | | 0.3704 | 17.94 | 72 | 0.6491 | 0.6892 | | 0.3704 | 18.94 | 76 | 0.5947 | 0.7568 | | 0.3624 | 19.94 | 80 | 0.6248 | 0.7027 | | 0.3624 | 20.94 | 84 | 0.6580 | 0.7027 | | 0.3624 | 21.94 | 88 | 0.6345 | 0.7162 | | 0.3164 | 22.94 | 92 | 0.6092 | 0.7568 | | 0.3164 | 23.94 | 96 | 0.6498 | 0.7162 | | 0.2777 | 24.94 | 100 | 0.6915 | 0.7703 | | 0.2777 | 25.94 | 104 | 0.6482 | 0.7838 | | 0.2777 | 26.94 | 108 | 0.6407 | 0.7973 | | 0.2946 | 27.94 | 112 | 0.6135 | 0.7838 | | 0.2946 | 28.94 | 116 | 0.6819 | 0.7568 | | 0.2546 | 29.94 | 120 | 0.6401 | 0.7568 | | 0.2546 | 30.94 | 124 | 0.6370 | 0.7432 | | 0.2546 | 31.94 | 128 | 0.6488 | 0.7703 | | 0.2477 | 32.94 | 132 | 0.6429 | 0.7973 | | 0.2477 | 33.94 | 136 | 0.6540 | 0.7703 | | 0.1968 | 34.94 | 140 | 0.5895 | 0.7973 | | 0.1968 | 35.94 | 144 | 0.6242 | 0.7568 | | 0.1968 | 36.94 | 148 | 0.6575 | 0.7568 | | 0.2235 | 37.94 | 152 | 0.6263 | 0.7703 | | 0.2235 | 38.94 | 156 | 0.6225 | 0.7838 | | 0.2005 | 39.94 | 160 | 0.6731 | 0.7703 | | 0.2005 | 40.94 | 164 | 0.6844 | 0.7703 | | 0.2005 | 41.94 | 168 | 0.6550 | 0.7703 | | 0.2062 | 42.94 | 172 | 0.6700 | 0.7703 | | 0.2062 | 43.94 | 176 | 0.6661 | 0.7703 | | 0.1933 | 44.94 | 180 | 0.6606 | 0.7838 | | 0.1933 | 45.94 | 184 | 0.6757 | 0.7703 | | 0.1933 | 46.94 | 188 | 0.6889 | 0.7568 | | 0.1895 | 47.94 | 192 | 0.6940 | 0.7568 | | 0.1895 | 48.94 | 196 | 0.6919 | 0.7568 | | 0.1666 | 49.94 | 200 | 0.6899 | 0.7432 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ottovoncwim/Reinforce-PixelCopter-PLE-v0

ottovoncwim

null

6

0

null

0

reinforcement-learning

false

null

0

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

true

303

# **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

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

franfram

distilbert

13

2

transformers

0

fill-mask

true

false

null

['imdb']

null

0

['generated_from_trainer']

true

1,357

# distillbert-base-spanish-uncased-finetuned-imdb This model is a fine-tuned version of [CenIA/distillbert-base-spanish-uncased](https://huggingface.co/CenIA/distillbert-base-spanish-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.5406 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1075 | 1.0 | 157 | 2.6769 | | 2.7807 | 2.0 | 314 | 2.5764 | | 2.7003 | 3.0 | 471 | 2.5571 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

RocioUrquijo/clasificador-appreviews

RocioUrquijo

bert

10

transformers

0

text-classification

true

false

cc-by-sa-4.0

null

0

['classification', 'generated_from_trainer']

true

907

# clasificador-appreviews This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

jannikskytt/a2c-PandaReachDense-v2

jannikskytt

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

207

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).

javiervela/a2c-AntBulletEnv-v0

javiervela

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

352

# **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

shashankgarewal/taxi_default_parameters

shashankgarewal

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

385

# **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="shashankgarewal/taxi_default_parameters", 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"]) ```

MichalJ/dqn-SpaceInvadersNoFrameskip-v4

MichalJ

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,214

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MichalJ -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MichalJ -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MichalJ ``` ## 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)]) ```

nc33/multiqa_model

nc33

roberta

23

7

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,702

# multiqa_model This model is a fine-tuned version of [nc33/multiqa_model](https://huggingface.co/nc33/multiqa_model) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1150 - Precision: 0.0855 - Recall: 0.0485 - F1: 0.0619 - Accuracy: 0.9626 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 327 | 0.1121 | 0.0708 | 0.0280 | 0.0402 | 0.9631 | | 0.0786 | 2.0 | 654 | 0.1098 | 0.0531 | 0.0254 | 0.0343 | 0.9599 | | 0.0786 | 3.0 | 981 | 0.1085 | 0.0657 | 0.0243 | 0.0354 | 0.9634 | | 0.0681 | 4.0 | 1308 | 0.1133 | 0.0765 | 0.0453 | 0.0569 | 0.9618 | | 0.0641 | 5.0 | 1635 | 0.1150 | 0.0855 | 0.0485 | 0.0619 | 0.9626 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Phantom-Artist/phantom-diffusion-s3-the-last-8

Phantom-Artist

null

39

0

null

2

null

false

creativeml-openrail-m

['en', 'ja']

['Phantom-Artist/phantom-diffusion-s3-the-last-8-dataset']

null

0

['art']

false

true

7,502

The final 8 models that are trained over [hakurei's Waifu Diffusion](https://huggingface.co/hakurei/waifu-diffusion). Each model was trained on a notable Japanese (and Taiwanese, I think) AI artist's works using dreambooth, with 30 of their works gained mainly from twitter (except for sabakichi, which I collected the dataset from pixiv). It tooks 1000 steps to train each model, at a learning rate of 5-e6. I have shared the notebook [here](https://huggingface.co/Phantom-Artist/phantom-diffusion/blob/main/DreamBooth_Stable_Diffusion_works_with_SD_2.ipynb) in case you want to try out additional training. Some are good at backgrounds, while others can generate a semi-realistic style as seen in many SD 1.5 anime/waifu models (and some might be a bit overfitted). The dataset is published [here](https://huggingface.co/datasets/Phantom-Artist/phantom-diffusion-s3-the-last-8-dataset), and the full members are listed [here](https://twitter.com/i/lists/1622444997240037377) (special thanks to Yunyalula for creating it!). # For those who are against generative AIs You can see that now they are the target. Take our tool. Now is the time for pay back. Generate the images in their styles, and bring back the power you had to yourself. # For those who support the development of generative AIs Some of the AI artists, even though they take advantage of the open strategy of Stable Diffusion, now tend to hide their prompts, trying to monopolize their style (I'm not saying the AI artists I trained are as such, to be sure). To continue protecting our values and beliefs on the open community and fight against them trying to create another pre-modern style guilds, I will show you a new way. You no longer need their prompts; just train their images by yourself to protect the open community. It's not only legal but also ethical, as they have been taking advantages of others' trained dataset. # For those who call themselves "phantom 40" I saw some caliming there should be 48, and here you go. Phantom 48, or do you like to call yourselves *PTM* 48 instead? It's up to you. # Why will they be the last? My initial intention on this series was a social experiment to see what will happen if the AI artists are targeted for personalized training. As it became more popular than expected and the artists started calling themselves "phantom 20," I came up with the second intention to see how they will react after I add 20 more in one day, to see if they can adapt to the sudden change. They acted greatly, and I think that's why they could become notable. All the reactions and the interpretations on my action were impressive, but since I have accomplished my goal, and since the main stream model will probably be SD 2.1 768 (not SD 2.1 512), I will no longer add new models. I know I couldn't add some of the artists, but no. I will not do it under the name of phantom. It takes me like 8 hours to train, test, and upload 20 models, and it's just unsustainable to continue doing it everyday. **From now on, anyone who wish to add more is the next phantom. Train anyone you wish to by yourself.** # trained artist list - atsuwo_AI - recommended pos: multicolored hair, cg - fladdict - recommended pos: oil painting/ancient relief/impressionist impasto oil painting (maybe more) - possible neg: monkey - Hifumi_AID - recommended pos: dark purple hair, emerald eyes - mayonaka_rr - recommended pos: cg - possible pos: dynamic posing, bikini, ponytail - o81morimori - possible pos: cg, in a messy apartment room with objects on the floor and the bed - sabakichi - possible pos 1: merging underwater, limited pallete, melting underwater, unstable outlines - possible pos 2: rough sketch, limited pallete, ((unstable outlines)), monotone gradation, dynamic posing - teftef - possible pos: light skyblue hair, bun, retropunk gears of a factory - violet_fizz - recommended pos: beautiful face, grown up face, long eyes, expressionless - possible pos: expressionless # samples The basic prompt is as follows. However, to present you the potential of these models as much as possible, many of them have additional postive tags (such as "in the style of") to get the result below (yes, use ``aitop (ARTIST)_style`` to gain the finetuned result). Many works better with the additional prompt ``beautiful face``. Generally speaking, prompting words close to the trained dataset will give you a better result. ``` POS: masterpiece, best quality, 1girl, aitop (ARTIST)_style NEG: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digits, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, simple background ``` ## atsuwo_AI ![atsuwo_AI_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/atsuwo_AI_style.png) ![atsuwo_AI_sample2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/atsuwo_AI_style2.png) ![atsuwo_AI_sample3](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/atsuwo_AI_style3.png) ## fladdict ![fladdict_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/fladdict_style.png) ![fladdict_sample2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/fladdict_style2.png) ![fladdict_sample3](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/fladdict_style3.png) ## Hifumi_AID ![Hifumi_AID_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/Hifumi_AID_style.png) ![Hifumi_AID_sample2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/Hifumi_AID_style2.png) ## mayonaka_rr ![mayonaka_rr_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/mayonaka_rr_style.png) ![mayonaka_rr_sample2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/mayonaka_rr_style2.png) ![mayonaka_rr_sample3](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/mayonaka_rr_style3.png) ## o81morimori ![o81morimori_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/o81morimori_style.png) ![o81morimori_sample2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/o81morimori_style2.png) ## sabakichi ![sabakichi_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/sabakichi_style.png) ![sabakichi_sample2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/sabakichi_style2.png) ![sabakichi_sample3](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/sabakichi_style3.png) ![sabakichi_sample4](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/sabakichi_style4.png) ## teftef ![teftef_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/teftef_style.png) ![teftef_sample2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/teftef_style2.png) ## violet_fizz ![violet_fizz_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/violet_fizz_style.png) ![violet_fizz_sample](https://huggingface.co/Phantom-Artist/phantom-diffusion-s3-the-last-8/resolve/main/violet_fizz_style2.png)

logoyazilim/crnn_vgg16_bn_20230208-152217

logoyazilim

null

4

0

transformers

0

null

true

false

null

['en']

null

0

[]

false

true

1,612

<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> **Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch** ## Task: recognition https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ``` ### Run Configuration { "arch": "crnn_vgg16_bn", "train_path": "doctr-train-10k", "val_path": null, "train_samples": 1000, "val_samples": 20, "font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf", "min_chars": 1, "max_chars": 12, "name": null, "epochs": 10, "batch_size": 32, "device": 0, "input_size": 32, "lr": 0.001, "weight_decay": 0, "workers": 4, "resume": null, "vocab": "turkish", "test_only": false, "show_samples": false, "wb": false, "push_to_hub": true, "pretrained": true, "sched": "cosine", "amp": false, "find_lr": false }

javiervela/a2c-PandaReachDense-v2

javiervela

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

358

# **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

kinkpunk/poca-MLAgents-SoccerTwos-v1.2

kinkpunk

null

20

290

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

864

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn .\config\poca\SoccerTwos.yaml --run-id="poca-SoccerTwos-v1.2" --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: kinkpunk/poca-MLAgents-SoccerTwos-v1.2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Svetlana0303/Regression_distilbert-base-uncased

Svetlana0303

distilbert

16

0

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,512

# Regression_distilbert-base-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1187 - Mse: 2.1187 - Mae: 1.3097 - R2: -0.0932 - Accuracy: 0.1429 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:| | No log | 1.0 | 2 | 3.3933 | 3.3933 | 1.5228 | -2.1839 | 0.2857 | | No log | 2.0 | 4 | 3.0571 | 3.0571 | 1.4011 | -1.8684 | 0.4286 | | No log | 3.0 | 6 | 2.6747 | 2.6747 | 1.2786 | -1.5096 | 0.4286 | | No log | 4.0 | 8 | 2.3024 | 2.3024 | 1.2088 | -1.1603 | 0.4286 | | No log | 5.0 | 10 | 1.9496 | 1.9496 | 1.1459 | -0.8292 | 0.4286 | | No log | 6.0 | 12 | 1.6637 | 1.6637 | 1.1225 | -0.5610 | 0.2857 | | No log | 7.0 | 14 | 1.4167 | 1.4167 | 1.0938 | -0.3293 | 0.1429 | | No log | 8.0 | 16 | 1.2365 | 1.2365 | 1.0609 | -0.1602 | 0.0 | | No log | 9.0 | 18 | 1.1239 | 1.1239 | 1.0234 | -0.0545 | 0.0 | | No log | 10.0 | 20 | 1.0879 | 1.0879 | 0.9906 | -0.0207 | 0.0 | | No log | 11.0 | 22 | 1.1122 | 1.1122 | 0.9599 | -0.0436 | 0.2857 | | No log | 12.0 | 24 | 1.1879 | 1.1879 | 0.9374 | -0.1145 | 0.2857 | | No log | 13.0 | 26 | 1.2784 | 1.2784 | 0.9132 | -0.1995 | 0.4286 | | No log | 14.0 | 28 | 1.3756 | 1.3756 | 0.8905 | -0.2907 | 0.4286 | | No log | 15.0 | 30 | 1.4710 | 1.4710 | 0.9093 | -0.3802 | 0.4286 | | No log | 16.0 | 32 | 1.5513 | 1.5513 | 0.9333 | -0.4555 | 0.4286 | | No log | 17.0 | 34 | 1.6094 | 1.6094 | 0.9491 | -0.5101 | 0.5714 | | No log | 18.0 | 36 | 1.6446 | 1.6446 | 0.9567 | -0.5431 | 0.5714 | | No log | 19.0 | 38 | 1.6510 | 1.6510 | 0.9555 | -0.5491 | 0.5714 | | No log | 20.0 | 40 | 1.6425 | 1.6425 | 0.9503 | -0.5412 | 0.5714 | | No log | 21.0 | 42 | 1.6254 | 1.6254 | 0.9455 | -0.5251 | 0.5714 | | No log | 22.0 | 44 | 1.6025 | 1.6025 | 0.9378 | -0.5036 | 0.5714 | | No log | 23.0 | 46 | 1.5758 | 1.5758 | 0.9289 | -0.4786 | 0.5714 | | No log | 24.0 | 48 | 1.5583 | 1.5583 | 0.9233 | -0.4622 | 0.5714 | | No log | 25.0 | 50 | 1.5504 | 1.5504 | 0.9210 | -0.4547 | 0.5714 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

SRDdev/QABERT-small

SRDdev

distilbert

8

7

transformers

0

question-answering

true

false

null

['en']

['squad_v2']

null

0

['question-answering']

false

true

3,914

# QA-BERT QA-BERT is a Question Answering Model. This model is a lighter version of any of the question-answering models out there. ## Dataset The Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike. Due to GPU limitations, this version is trained on `30k samples` from the Stanford Question Answering Dataset. <details> <summary>Structure of the Data Dictonary</summary>  { "data":[ { "title":"Article Title", "paragraphs":[ { "context":"The context text of the paragraph", "qas":[ { "question":"The question asked about the context", "id":"A unique identifier for the question", "answers":[ { "text":"The answer to the question", "answer_start":"The starting index of the answer in the context" } ] } ] } ] } ], "version":"The version of the SQuAD dataset" } </details> ## Model BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model for natural language processing tasks such as question answering. BERT is fine-tuned for question answering by adding a linear layer on top of the pre-trained BERT representations to predict the start and end of the answer in the input context. BERT has achieved state-of-the-art results on multiple benchmark datasets, including the Stanford Question Answering Dataset (SQuAD). The fine-tuning process allows BERT to effectively capture the relationships between questions and answers and generate accurate answers. <img src="https://imgs.search.brave.com/F8m-nwp6EIG5vq--OmJLrCDpIkuX6tEQ_kyFKQjlUTs/rs:fit:1200:1200:1/g:ce/aHR0cHM6Ly9ibG9n/LmdyaWRkeW5hbWlj/cy5jb20vY29udGVu/dC9pbWFnZXMvMjAy/MC8xMC9TbGljZS0x/OC5wbmc"> For more detail about this read [Understanding QABERT](https://github.com/SRDdev/AnswerMind) ## Inference _Load model_ ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering QAtokenizer = AutoTokenizer.from_pretrained("SRDdev/QABERT-small") QAmodel = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QABERT-small") ``` _context_ ```text Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a question-answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script. ``` _Build Pipeline_ ```python from transformers import pipeline ask = pipeline("question-answering", model= QAmodel , tokenizer = QAtokenizer) result = ask(question="What is a good example of a question answering dataset?", context=context) print(f"Answer: '{result['answer']}'") ``` ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate. ## Citations ``` @citation{ QA-BERT-small, author = {Shreyas Dixit}, year = {2023}, url = {https://huggingface.co/SRDdev/QA-BERT-small} } ```

plai-edp-test/bert_base_spanish_wwm_cased

plai-edp-test

bert

8

3

transformers

0

fill-mask

true

false

null

['es']

null

0

['masked-lm']

false

true

5,859

# BETO: Spanish BERT BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with [Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) as well as other (not BERT-based) models. ## Download | | | | | |-|:--------:|:-----:|:----:| |BETO uncased|[tensorflow_weights](https://users.dcc.uchile.cl/~jperez/beto/uncased_2M/tensorflow_weights.tar.gz) | [pytorch_weights](https://users.dcc.uchile.cl/~jperez/beto/uncased_2M/pytorch_weights.tar.gz) | [vocab](./config/uncased_2M/vocab.txt), [config](./config/uncased_2M/config.json) | |BETO cased| [tensorflow_weights](https://users.dcc.uchile.cl/~jperez/beto/cased_2M/tensorflow_weights.tar.gz) | [pytorch_weights](https://users.dcc.uchile.cl/~jperez/beto/cased_2M/pytorch_weights.tar.gz) | [vocab](./config/cased_2M/vocab.txt), [config](./config/cased_2M/config.json) | All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. ## Benchmarks The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found [here](#references). |Task | BETO-cased | BETO-uncased | Best Multilingual BERT | Other results | |-------|--------------:|--------------:|--------------------------:|-------------------------------:| |[POS](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1827) | **98.97** | 98.44 | 97.10 [2] | 98.91 [6], 96.71 [3] | |[NER-C](https://www.kaggle.com/nltkdata/conll-corpora) | [**88.43**](https://github.com/gchaperon/beto-benchmarks/blob/master/conll2002/dev_results_beto-cased_conll2002.txt) | 82.67 | 87.38 [2] | 87.18 [3] | |[MLDoc](https://github.com/facebookresearch/MLDoc) | [95.60](https://github.com/gchaperon/beto-benchmarks/blob/master/MLDoc/dev_results_beto-cased_mldoc.txt) | [**96.12**](https://github.com/gchaperon/beto-benchmarks/blob/master/MLDoc/dev_results_beto-uncased_mldoc.txt) | 95.70 [2] | 88.75 [4] | |[PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) | 89.05 | 89.55 | 90.70 [8] | |[XNLI](https://github.com/facebookresearch/XNLI) | **82.01** | 80.15 | 78.50 [2] | 80.80 [5], 77.80 [1], 73.15 [4]| ## Example of use For further details on how to use BETO you can visit the [🤗Huggingface Transformers library](https://github.com/huggingface/transformers), starting by the [Quickstart section](https://huggingface.co/transformers/quickstart.html). BETO models can be accessed simply as [`'dccuchile/bert-base-spanish-wwm-cased'`](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) and [`'dccuchile/bert-base-spanish-wwm-uncased'`](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) by using the Transformers library. An example on how to download and use the models in this page can be found in [this colab notebook](https://colab.research.google.com/drive/1uRwg4UmPgYIqGYY4gW_Nsw9782GFJbPt). (We will soon add a more detailed step-by-step tutorial in Spanish for newcommers 😉) ## Acknowledgments We thank [Adereso](https://www.adere.so/) for kindly providing support for traininig BETO-uncased, and the [Millennium Institute for Foundational Research on Data](https://imfd.cl/en/) that provided support for training BETO-cased. Also thanks to Google for helping us with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program. ## Citation [Spanish Pre-Trained BERT Model and Evaluation Data](https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf) To cite this resource in a publication please use the following: ``` @inproceedings{CaneteCFP2020, title={Spanish Pre-Trained BERT Model and Evaluation Data}, author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge}, booktitle={PML4DC at ICLR 2020}, year={2020} } ``` ## License Disclaimer The license CC BY 4.0 best describes our intentions for our work. However we are not sure that all the datasets used to train BETO have licenses compatible with CC BY 4.0 (specially for commercial use). Please use at your own discretion and verify that the licenses of the original text resources match your needs. ## References * [1] [Original Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) * [2] [Multilingual BERT on "Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT"](https://arxiv.org/pdf/1904.09077.pdf) * [3] [Multilingual BERT on "How Multilingual is Multilingual BERT?"](https://arxiv.org/pdf/1906.01502.pdf) * [4] [LASER](https://arxiv.org/abs/1812.10464) * [5] [XLM (MLM+TLM)](https://arxiv.org/pdf/1901.07291.pdf) * [6] [UDPipe on "75 Languages, 1 Model: Parsing Universal Dependencies Universally"](https://arxiv.org/pdf/1904.02099.pdf) * [7] [Multilingual BERT on "Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation"](https://arxiv.org/pdf/1906.01569.pdf) * [8] [Multilingual BERT on "PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification"](https://arxiv.org/abs/1908.11828)

PeterBanning71/t5-small-finetuned-xsum-finetuned-bioMedv2

PeterBanning71

t5

12

9

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

2,181

# t5-small-finetuned-xsum-finetuned-bioMedv2 This model is a fine-tuned version of [PeterBanning71/t5-small-finetuned-xsum](https://huggingface.co/PeterBanning71/t5-small-finetuned-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1056 - Rouge1: 4.8565 - Rouge2: 0.4435 - Rougel: 3.9735 - Rougelsum: 4.415 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 1 | 8.4025 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 2.0 | 2 | 8.4025 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 3.0 | 3 | 7.7250 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 4.0 | 4 | 7.1617 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 5.0 | 5 | 6.7113 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 6.0 | 6 | 6.3646 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 7.0 | 7 | 6.1056 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 8.0 | 8 | 6.1056 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

plai-edp-test/distilbert_base_uncased

plai-edp-test

distilbert

8

0

transformers

0

fill-mask

false

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert']

false

true

8,470

# DistilBERT base model (uncased) This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does not make a difference between english and English. ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives: - Distillation loss: the model was trained to return the same probabilities as the BERT base model. - Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model. This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.05292855575680733, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.03968575969338417, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a business model. [SEP]", 'score': 0.034743521362543106, 'token': 2449, 'token_str': 'business'}, {'sequence': "[CLS] hello i'm a model model. [SEP]", 'score': 0.03462274372577667, 'token': 2944, 'token_str': 'model'}, {'sequence': "[CLS] hello i'm a modeling model. [SEP]", 'score': 0.018145186826586723, 'token': 11643, 'token_str': 'modeling'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import DistilBertTokenizer, DistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = DistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import DistilBertTokenizer, TFDistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("The White man worked as a [MASK].") [{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]', 'score': 0.1235365942120552, 'token': 20987, 'token_str': 'blacksmith'}, {'sequence': '[CLS] the white man worked as a carpenter. [SEP]', 'score': 0.10142576694488525, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the white man worked as a farmer. [SEP]', 'score': 0.04985016956925392, 'token': 7500, 'token_str': 'farmer'}, {'sequence': '[CLS] the white man worked as a miner. [SEP]', 'score': 0.03932540491223335, 'token': 18594, 'token_str': 'miner'}, {'sequence': '[CLS] the white man worked as a butcher. [SEP]', 'score': 0.03351764753460884, 'token': 14998, 'token_str': 'butcher'}] >>> unmasker("The Black woman worked as a [MASK].") [{'sequence': '[CLS] the black woman worked as a waitress. [SEP]', 'score': 0.13283951580524445, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the black woman worked as a nurse. [SEP]', 'score': 0.12586183845996857, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the black woman worked as a maid. [SEP]', 'score': 0.11708822101354599, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]', 'score': 0.11499975621700287, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]', 'score': 0.04722772538661957, 'token': 22583, 'token_str': 'housekeeper'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 16 GB V100 for 90 hours. See the [training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters details. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | ### BibTeX entry and citation info ```bibtex @article{Sanh2019DistilBERTAD, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, journal={ArXiv}, year={2019}, volume={abs/1910.01108} } ``` <a href="https://huggingface.co/exbert/?model=distilbert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

pneubauer/basic-poca-SoccerTwos

pneubauer

null

7

287

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

849

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

fathyshalab/massive_social-roberta-large-v1-1

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,456

# fathyshalab/massive_social-roberta-large-v1-1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_social-roberta-large-v1-1") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

akoshel/dqn-SpaceInvadersNoFrameskip-v4

akoshel

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,213

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga akoshel -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga akoshel -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga akoshel ``` ## 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', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

PeterBanning71/t5-small-finetuned-xsum-finetuned-bioMedv3

PeterBanning71

t5

12

8

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

2,181

# t5-small-finetuned-xsum-finetuned-bioMedv3 This model is a fine-tuned version of [PeterBanning71/t5-small-finetuned-xsum](https://huggingface.co/PeterBanning71/t5-small-finetuned-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1056 - Rouge1: 4.8565 - Rouge2: 0.4435 - Rougel: 3.9735 - Rougelsum: 4.415 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 1 | 8.4025 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 2.0 | 2 | 8.4025 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 3.0 | 3 | 7.7250 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 4.0 | 4 | 7.1617 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 5.0 | 5 | 6.7113 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 6.0 | 6 | 6.3646 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 7.0 | 7 | 6.1056 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | | No log | 8.0 | 8 | 6.1056 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

hectorjelly/Kats_Komets

hectorjelly

null

20

281

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

841

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

Axel578/flan_t5_summarization

Axel578

t5

15

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,317

# flan_t5_summarization This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6162 - Rouge1: 15.9418 - Rouge2: 7.4447 - Rougel: 15.5655 - Rougelsum: 15.5835 - Gen Len: 18.7313 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 272 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7405 | 2.0 | 544 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7405 | 3.0 | 816 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7453 | 4.0 | 1088 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7453 | 5.0 | 1360 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7372 | 6.0 | 1632 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7372 | 7.0 | 1904 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7436 | 8.0 | 2176 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7436 | 9.0 | 2448 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | | 0.7425 | 10.0 | 2720 | 0.6162 | 15.9418 | 7.4447 | 15.5655 | 15.5835 | 18.7313 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

mtlulka/poca-SoccerTwos

mtlulka

null

20

287

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

841

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

akiFQC/japanese-dialogpt-small-aozora

akiFQC

gpt2

9

20

transformers

0

conversational

true

false

null

['ja']

null

0

['conversational', 'ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp']

false

true

939

# Japanese DialoGPT trained with Aozora **(ja) 青空文庫のセリフで学習した日本語のDialoGPT Smallです** **(en) Japanese DialoGPT Small trained on Aozora Bunko.** ## [Demo](https://huggingface.co/spaces/akiFQC/Japanese_DialoGPT_small_Aozora) Demo in this page is not working so well. I recommend you to try it on [Hugging Face Spaces Version](https://huggingface.co/spaces/akiFQC/Japanese_DialoGPT_small_Aozora). ## Reference - [Aozora-bunko](https://www.aozora.gr.jp/) - Japanese public domain books. - I extracted the dialogue part from the books and used it as the training data. - [japanese-gpt2-small](https://huggingface.co/rinna/japanese-gpt2-small) - Novel Japanese GPT2. I used a small model because of the limitation of GPU memory of my desktop PC(with RTX3060x1) 😢. - I used this model as a pre-trained model. - [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536)

jafonz/en_pipeline

jafonz

null

16

62

spacy

0

token-classification

false

null

['en']

null

0

['spacy', 'token-classification']

false

true

678

| Feature | Description | | --- | --- | | **Name** | `en_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.4.4,<3.5.0` | | **Default Pipeline** | `tok2vec`, `ner` | | **Components** | `tok2vec`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (1 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `TEMTEM` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 100.00 | | `ENTS_P` | 100.00 | | `ENTS_R` | 100.00 | | `TOK2VEC_LOSS` | 0.00 | | `NER_LOSS` | 0.00 |

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

azaazato

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

397

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

azaazato/q-Taxi-v3-v1

azaazato

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

367

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="azaazato/q-Taxi-v3-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

azaazato/q-Taxi-v3

azaazato

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

364

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="azaazato/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

fathyshalab/massive_social-roberta-large-v1-2

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,456

# fathyshalab/massive_social-roberta-large-v1-2 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_social-roberta-large-v1-2") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

jojoUla/bert-large-cased-sigir-support-no-label-20-sigir-tune2nd-LR10-labelled-30

jojoUla

bert

14

3

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,787

# bert-large-cased-sigir-support-no-label-20-sigir-tune2nd-LR10-labelled-30 This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-no-label-20](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-no-label-20) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3995 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 30 - eval_batch_size: 30 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1303 | 1.0 | 1 | 3.2415 | | 2.3107 | 2.0 | 2 | 2.1225 | | 1.2824 | 3.0 | 3 | 2.2623 | | 1.0548 | 4.0 | 4 | 0.5449 | | 1.1366 | 5.0 | 5 | 1.1446 | | 0.5947 | 6.0 | 6 | 0.3811 | | 0.4889 | 7.0 | 7 | 1.6445 | | 1.2689 | 8.0 | 8 | 1.7214 | | 0.8074 | 9.0 | 9 | 2.3152 | | 0.7084 | 10.0 | 10 | 0.9325 | | 1.0307 | 11.0 | 11 | 2.4217 | | 0.7119 | 12.0 | 12 | 2.6455 | | 1.0052 | 13.0 | 13 | 1.1594 | | 0.7125 | 14.0 | 14 | 1.2795 | | 0.4732 | 15.0 | 15 | 0.1245 | | 0.8829 | 16.0 | 16 | 1.8585 | | 0.7079 | 17.0 | 17 | 1.6644 | | 0.6243 | 18.0 | 18 | 1.6117 | | 1.2438 | 19.0 | 19 | 2.3044 | | 1.0812 | 20.0 | 20 | 4.5037 | | 0.7003 | 21.0 | 21 | 1.5862 | | 0.867 | 22.0 | 22 | 2.1851 | | 0.9098 | 23.0 | 23 | 1.6055 | | 0.6214 | 24.0 | 24 | 2.6699 | | 0.282 | 25.0 | 25 | 1.3515 | | 0.1888 | 26.0 | 26 | 2.3864 | | 0.6863 | 27.0 | 27 | 1.2444 | | 0.8527 | 28.0 | 28 | 1.9603 | | 0.9416 | 29.0 | 29 | 3.7045 | | 0.8302 | 30.0 | 30 | 0.9336 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

ahmad1289/reinforce-pixelcopter-v1

ahmad1289

null

6

0

null

0

reinforcement-learning

false

null

0

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

true

300

# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

sheraz179/videomae-base-finetuned

sheraz179

videomae

7

0

transformers

0

video-classification

true

false

cc-by-nc-4.0

null

0

['generated_from_trainer']

true

1,486

# videomae-base-finetuned This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8337 - Accuracy: 0.4821 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1110 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7399 | 0.1 | 112 | 2.6561 | 0.0536 | | 2.6817 | 1.1 | 224 | 2.5898 | 0.0893 | | 2.5075 | 2.1 | 336 | 2.5883 | 0.125 | | 2.0081 | 3.1 | 448 | 2.4125 | 0.1429 | | 1.4701 | 4.1 | 560 | 2.2446 | 0.2857 | | 1.328 | 5.1 | 672 | 2.3491 | 0.2679 | | 0.9474 | 6.1 | 784 | 1.7119 | 0.4643 | | 0.5094 | 7.1 | 896 | 1.7790 | 0.4464 | | 0.2963 | 8.1 | 1008 | 1.8519 | 0.4821 | | 0.0614 | 9.09 | 1110 | 1.8337 | 0.4821 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

pfunk/Pong-v4-DQPN_p100_pt0.1-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

2,002

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p100_pt0.1.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p100_pt0.1]" python -m cleanrl_utils.enjoy --exp-name DQPN_p100_pt0.1 --env-id Pong-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_pt0.1-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p100_pt0.1 --start-policy-f 100000 --end-policy-f 100000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000 ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'end_policy_f': 100000, 'env_id': 'Pong-v4', 'evaluation_fraction': 1.0, 'exp_name': 'DQPN_p100_pt0.1', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 0.1, 'save_model': True, 'seed': 1, 'start_e': 1, 'start_policy_f': 100000, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

mili7522/dqn-SpaceInvadersNoFrameskip-v4

mili7522

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,218

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mili7522 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mili7522 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mili7522 ``` ## 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)]) ```

muhtasham/santacoder-finetuned-the-stack-cobol

muhtasham

gpt2

17

7

transformers

1

text-generation

true

false

openrail

['code']

['bigcode/the-stack-dedup']

null

0

['generated_from_trainer', 'code', 'codegen', 'assembly']

true

3,231

# santacoder-finetuned-the-stack-cobol This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an The Stack [cobol](https://huggingface.co/datasets/bigcode/the-stack-dedup) dataset. It achieves the following results on the evaluation set: - Loss: 0.7161 ## Model description The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests). The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255). In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. ## Intended uses & limitations The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. ## Training and evaluation data The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.** ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3911 | 0.1 | 100 | 1.1141 | | 0.9478 | 0.2 | 200 | 0.9735 | | 0.784 | 0.3 | 300 | 0.8497 | | 0.4702 | 0.4 | 400 | 0.7686 | | 0.6133 | 0.5 | 500 | 0.7375 | | 0.5396 | 0.6 | 600 | 0.7265 | | 0.3937 | 0.7 | 700 | 0.6952 | | 0.5691 | 0.8 | 800 | 0.7059 | | 0.6366 | 0.9 | 900 | 0.7069 | | 0.3661 | 1.0 | 1000 | 0.7161 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

regnore/Amemori_Sayo_LoRA

regnore

null

44

0

null

0

null

false

cc-by-2.0

null

0

[]

false

true

340

+ Amemori_Sayo: ![image](https://huggingface.co/regnore/Amemori_Sayo_LoRA/resolve/main/img/1.png) + Amemori_Sayo_NF: ![image](https://huggingface.co/regnore/Amemori_Sayo_LoRA/resolve/main/img/nf.png) + additional prompts you may need to get better results: `black hair`, `sailor dress`, `double braids`, `straight on`

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

pyflynn

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

396

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

pfunk/Pong-v4-DQPN_p500_pt0.1_tt0.1-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

2,050

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p500_pt0.1_tt0.1.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p500_pt0.1_tt0.1]" python -m cleanrl_utils.enjoy --exp-name DQPN_p500_pt0.1_tt0.1 --env-id Pong-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1_tt0.1-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1_tt0.1-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_pt0.1_tt0.1-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p500_pt0.1_tt0.1 --start-policy-f 500000 --end-policy-f 500000 --evaluation-fraction 1.00 --target-tau 0.1 --policy-tau 0.1 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000 ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'end_policy_f': 500000, 'env_id': 'Pong-v4', 'evaluation_fraction': 1.0, 'exp_name': 'DQPN_p500_pt0.1_tt0.1', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 0.1, 'save_model': True, 'seed': 1, 'start_e': 1, 'start_policy_f': 500000, 'target_network_frequency': 1000, 'target_tau': 0.1, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

DaniilSirota/ppo-SnowballTarget

DaniilSirota

null

20

0

ml-agents

1

reinforcement-learning

false

null

0

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

false

true

859

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

apatidar0/my_awesome_billsum_model

apatidar0

t5

12

2

transformers

0

text2text-generation

true

false

apache-2.0

null

['billsum']

null

0

['generated_from_trainer']

true

924

# my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

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

mshibatatt

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

399

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

mshibatatt/q-Taxi-v3

mshibatatt

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

366

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

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

irenekar

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

397

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

irenekar/taxiv3

irenekar

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

361

# **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="irenekar/taxiv3", 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"]) ```

DeepaKrish/distilbert-base-uncased-finetuned-squad

DeepaKrish

distilbert

18

1

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,225

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 78 | 0.7144 | | No log | 2.0 | 156 | 0.3996 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.9.0 - Datasets 2.5.1 - Tokenizers 0.13.2

mwissing/dqn-SpaceInvadersNoFrameskip-v4

mwissing

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,217

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mwissing -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mwissing -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mwissing ``` ## 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)]) ```

quartz14/Reinforce-cartpole

quartz14

null

6

0

null

0

reinforcement-learning

false

null

0

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

true

286

# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

frangiral/dqn-SpaceInvadersNoFrameskip-v4

frangiral

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,219

# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga frangiral -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga frangiral -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga frangiral ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('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)]) ```

pyflynn/taxi-v3-model-v0

pyflynn

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

370

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="pyflynn/taxi-v3-model-v0", 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"]) ```

rishabhjain16/whisper_tiny_to_pf10h

rishabhjain16

whisper

23

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,698

# openai/whisper-tiny This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2034 - Wer: 5.3823 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0814 | 10.0 | 500 | 0.1915 | 6.6701 | | 0.0045 | 20.0 | 1000 | 0.1816 | 5.5088 | | 0.0016 | 30.01 | 1500 | 0.1924 | 5.5014 | | 0.0009 | 40.01 | 2000 | 0.1959 | 5.5609 | | 0.0006 | 51.0 | 2500 | 0.1989 | 5.4195 | | 0.0005 | 61.0 | 3000 | 0.2014 | 5.4418 | | 0.0004 | 71.01 | 3500 | 0.2030 | 5.3674 | | 0.0004 | 81.01 | 4000 | 0.2034 | 5.3823 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_tiny_en_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,707

# openai/whisper-tiny.en This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2166 - Wer: 6.5585 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1174 | 10.0 | 500 | 0.1975 | 6.4170 | | 0.0034 | 20.0 | 1000 | 0.1896 | 5.2259 | | 0.0012 | 30.01 | 1500 | 0.2040 | 6.6478 | | 0.0007 | 40.01 | 2000 | 0.2080 | 6.6404 | | 0.0005 | 51.0 | 2500 | 0.2117 | 6.5957 | | 0.0004 | 61.0 | 3000 | 0.2139 | 6.5510 | | 0.0003 | 71.01 | 3500 | 0.2162 | 6.5883 | | 0.0003 | 81.01 | 4000 | 0.2166 | 6.5585 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_base_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,698

# openai/whisper-base This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1929 - Wer: 4.3549 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0326 | 10.0 | 500 | 0.1670 | 5.0398 | | 0.0019 | 20.0 | 1000 | 0.1728 | 4.5113 | | 0.0008 | 30.01 | 1500 | 0.1820 | 4.4071 | | 0.0005 | 40.01 | 2000 | 0.1847 | 4.3773 | | 0.0004 | 51.0 | 2500 | 0.1886 | 4.3549 | | 0.0003 | 61.0 | 3000 | 0.1910 | 4.3475 | | 0.0003 | 71.01 | 3500 | 0.1925 | 4.3549 | | 0.0002 | 81.01 | 4000 | 0.1929 | 4.3549 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_base_en_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,707

# openai/whisper-base.en This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1913 - Wer: 3.9530 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0489 | 10.0 | 500 | 0.1624 | 8.5536 | | 0.0019 | 20.0 | 1000 | 0.1682 | 4.0051 | | 0.0007 | 30.01 | 1500 | 0.1782 | 4.1167 | | 0.0004 | 40.01 | 2000 | 0.1823 | 4.0497 | | 0.0003 | 51.0 | 2500 | 0.1861 | 3.9827 | | 0.0002 | 61.0 | 3000 | 0.1888 | 3.9753 | | 0.0002 | 71.01 | 3500 | 0.1907 | 3.9678 | | 0.0002 | 81.01 | 4000 | 0.1913 | 3.9530 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_small_en_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,710

# openai/whisper-small.en This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1764 - Wer: 2.9777 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0179 | 10.0 | 500 | 0.1422 | 3.4691 | | 0.0006 | 20.0 | 1000 | 0.1530 | 3.0001 | | 0.0004 | 30.01 | 1500 | 0.1631 | 3.0150 | | 0.0002 | 40.01 | 2000 | 0.1672 | 2.9777 | | 0.0001 | 51.0 | 2500 | 0.1717 | 2.9703 | | 0.0001 | 61.0 | 3000 | 0.1742 | 2.9926 | | 0.0001 | 71.01 | 3500 | 0.1759 | 2.9852 | | 0.0001 | 81.01 | 4000 | 0.1764 | 2.9777 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_small_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,723

# openai/whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1815 - Wer: 206.4766 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0065 | 10.0 | 500 | 0.1476 | 109.2459 | | 0.0006 | 20.0 | 1000 | 0.1683 | 144.5619 | | 0.0012 | 30.01 | 1500 | 0.1623 | 205.1738 | | 0.0002 | 40.01 | 2000 | 0.1710 | 152.7209 | | 0.0001 | 51.0 | 2500 | 0.1760 | 171.9869 | | 0.0001 | 61.0 | 3000 | 0.1789 | 193.3447 | | 0.0001 | 71.01 | 3500 | 0.1808 | 201.9206 | | 0.0001 | 81.01 | 4000 | 0.1815 | 206.4766 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_medium_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,725

# 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: 0.1594 - Wer: 21.8343 ## 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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0269 | 5.0 | 500 | 0.1069 | 118.0302 | | 0.0049 | 10.01 | 1000 | 0.1263 | 135.2788 | | 0.0009 | 15.01 | 1500 | 0.1355 | 94.5731 | | 0.0001 | 20.01 | 2000 | 0.1413 | 7.5188 | | 0.0001 | 25.01 | 2500 | 0.1515 | 7.2508 | | 0.0001 | 30.02 | 3000 | 0.1568 | 24.8493 | | 0.0 | 35.02 | 3500 | 0.1588 | 22.1470 | | 0.0 | 40.02 | 4000 | 0.1594 | 21.8343 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_medium_en_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,713

# openai/whisper-medium.en This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1748 - Wer: 2.7097 ## 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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0329 | 5.0 | 500 | 0.1343 | 4.0125 | | 0.0013 | 10.01 | 1000 | 0.1531 | 2.8810 | | 0.0002 | 15.01 | 1500 | 0.1609 | 2.7321 | | 0.0002 | 20.01 | 2000 | 0.1608 | 2.7544 | | 0.0001 | 25.01 | 2500 | 0.1688 | 2.7321 | | 0.0002 | 30.02 | 3000 | 0.1722 | 2.7172 | | 0.0001 | 35.02 | 3500 | 0.1742 | 2.7172 | | 0.0001 | 40.02 | 4000 | 0.1748 | 2.7097 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_large_to_pf10h

rishabhjain16

whisper

23

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,711

# openai/whisper-large This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1412 - Wer: 6.7893 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0475 | 2.03 | 500 | 0.1095 | 62.6591 | | 0.0201 | 5.01 | 1000 | 0.1225 | 16.9285 | | 0.0044 | 7.03 | 1500 | 0.1312 | 3.6701 | | 0.0026 | 10.01 | 2000 | 0.1278 | 7.9506 | | 0.0001 | 12.04 | 2500 | 0.1323 | 17.9186 | | 0.0001 | 15.02 | 3000 | 0.1386 | 16.3031 | | 0.0001 | 17.05 | 3500 | 0.1403 | 6.7074 | | 0.0 | 20.02 | 4000 | 0.1412 | 6.7893 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

rishabhjain16/whisper_large_v2_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,732

# openai/whisper-large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1534 - Wer: 145.6786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0799 | 2.03 | 500 | 0.1010 | 28.1322 | | 0.0239 | 5.01 | 1000 | 0.1388 | 161.0139 | | 0.0066 | 7.03 | 1500 | 0.1221 | 99.3747 | | 0.0007 | 10.01 | 2000 | 0.1295 | 250.8822 | | 0.0007 | 12.04 | 2500 | 0.1423 | 77.2203 | | 0.0003 | 15.02 | 3000 | 0.1480 | 149.4380 | | 0.0001 | 17.05 | 3500 | 0.1518 | 141.5842 | | 0.0001 | 20.02 | 4000 | 0.1534 | 145.6786 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

SashkaHavr/NLP4Web_Home_Exercise6_Group13

SashkaHavr

bert

19

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

980

# NLP4Web_Home_Exercise6_Group13 This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

nasheed/rl-course

nasheed

null

12

1

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

350

# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

summervent/speller-t5-909_both_

summervent

t5

17

2

transformers

0

text2text-generation

true

false

null

0

['generated_from_trainer']

true

2,265

# speller-t5-909_both_ This model is a fine-tuned version of [sberbank-ai/ruT5-large](https://huggingface.co/sberbank-ai/ruT5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0771 - Rouge1: 20.0565 - Rouge2: 7.9096 - Rougel: 20.1271 - Rougelsum: 20.1977 - Gen Len: 41.2712 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.1653 | 0.1 | 1500 | 0.1176 | 19.8446 | 7.4011 | 19.8446 | 19.9153 | 41.2712 | | 0.2083 | 0.2 | 3000 | 0.1023 | 19.7034 | 8.7571 | 19.7034 | 19.774 | 41.1186 | | 0.1617 | 0.31 | 4500 | 0.0975 | 19.2797 | 7.9096 | 19.2797 | 19.209 | 41.2797 | | 0.17 | 0.41 | 6000 | 0.0949 | 20.5508 | 8.7571 | 20.5862 | 20.6215 | 41.2712 | | 0.1416 | 0.51 | 7500 | 0.0871 | 20.0565 | 7.9096 | 20.1271 | 20.1977 | 41.1017 | | 0.1409 | 0.61 | 9000 | 0.0807 | 20.0565 | 7.9096 | 20.1271 | 20.1977 | 41.1695 | | 0.1094 | 0.72 | 10500 | 0.0746 | 19.9859 | 7.6271 | 19.9506 | 19.9859 | 41.2627 | | 0.1256 | 0.82 | 12000 | 0.0754 | 19.9859 | 7.6271 | 19.9506 | 19.9859 | 41.2119 | | 0.1206 | 0.92 | 13500 | 0.0771 | 20.0565 | 7.9096 | 20.1271 | 20.1977 | 41.2712 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

fathyshalab/massive_social-roberta-large-v1-2-0.13

fathyshalab

roberta

14

2

sentence-transformers

0

text-classification

true

false

apache-2.0

null

0

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

false

true

1,466

# fathyshalab/massive_social-roberta-large-v1-2-0.13 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("fathyshalab/massive_social-roberta-large-v1-2-0.13") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

pabloac31/ppo-SnowballTarget

pabloac31

null

20

1

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

856

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: pabloac31/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

BSC-LT/sciroshot

BSC-LT

roberta

11

8

transformers

0

zero-shot-classification

true

false

apache-2.0

['en']

null

0

['zero-shot', 'text-classification', 'science', 'mag']

false

true

8,424

# SCIroShot ## Overview <details> <summary>Click to expand</summary> - **Model type:** Language Model - **Architecture:** RoBERTa-large - **Language:** English - **License:** Apache 2.0 - **Task:** Zero-Shot Text Classification - **Data:** Microsoft Academic Graph - **Additional Resources:** - [Paper]() <-- WiP (soon to be published in EACL 2023) - [GitHub](https://github.com/TeMU-BSC/sciroshot) </details> ## Model description SCIroShot is an entailment-based Zero-Shot Text Classification model that has been fine-tuned using a self-made dataset composed of scientific articles from [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/) (MAG). The resulting model achieves SOTA performance in the scientific domain and very competitive results in other areas. ## Intended Usage This model is intended to be used for zero-shot text classification in English. ## How to use ```python from transformers import pipeline zstc = pipeline("zero-shot-classification", model="BSC-LT/sciroshot") sentence = "Leo Messi is the best player ever." candidate_labels = ["politics", "science", "sports", "environment"] template = "This example is {}" output = zstc(sentence, candidate_labels, hypothesis_template=template, multi_label=False) print(output) print(f'Predicted class: {output["labels"][0]}') ``` ## Limitations and bias No measures have been taken to estimate the bias and toxicity embedded in the model. Even though the fine-tuning data (which is of a scientific nature) may seem harmless, it is important to note that the corpus used to pre-train the vanilla model is very likely to contain a lot of unfiltered content from the internet, as stated in the [RoBERTa-large model card](https://huggingface.co/roberta-large#limitations-and-bias). ## Training ### Training data Our data builds on top of scientific-domain annotated data from Microsoft Academic Graph (MAG). This database consists of a heterogeneous graph with billions of records from both scientific publications and patents, in addition to metadata information such as the authors, institutions, journals, conferences and their citation relationships. The documents are organized in a six-level hierarchical structure of scientific concepts, where the two top-most levels are manually curated in order to guarantee a high level of accuracy. To create the training corpus, a random sample of scientific articles with a publication year between 2000 and 2021 were retrieved from MAG with their respective titles and abstracts in English. This results in over 2M documents with their corresponding Field Of Study, which was obtained from the 1-level MAG taxonomy (292 possible classes, such as "Computational biology" or "Transport Engineering"). The fine-tuning dataset was constructed in a weakly supervised manner by converting text classification data to the entailment format. Using the relationship between scientific texts and their matching concepts in the 1-level MAG taxonomy we are able to generate the premise- hypothesis pairs corresponding to the entailment label. Conversely, we generate the pairs for the neutral label by removing the actual relationship between the texts and their scientific concepts and creating a virtual relationship with those to which they are not matched. ### Training procedure The newly-created scientific dataset described in the previous section was used to fine-tune a 355M parameters RoBERTa model on the entailment task. To do so, the model has to compute the entailment score between every text that is fed to it and all candidate labels. The final prediction would be the highest-scoring class in a single-label classification setup, or the N classes above a certain threshold in a multi-label scenario. A subset of 52 labels from the training data were kept apart so that they could be used as a development set of fully-unseen classes. As a novelty, the validation was not performed on the entailment task (which is used a proxy) but directly on the target text classification task. This allows us to stop training at the right time via early stopping, which prevents the model from "overfitting" to the training task. This method was our way to counteract an effect that was empirically discovered during the experimentation period, where it was observed that after a certain point the model can start to worsen in the target task (ZSTC) despite still continuing to improve in the training task (RTE). The simple act of shortening the training time led to a boost in performance. Read the paper for more details on the methodology and the analysis of RTE/ZSTC correlation. ## Evaluation ### Evaluation data The model's performance was evaluated on a collection of disciplinary-labeled textual datasets, both from the scientific domain (closer to training data) and the general domain (to assess generalizability). The following table provides an overview of the number of examples and labels for each dataset: | Dataset | Labels | Size | |------------------|--------|--------| | arXiv | 11 | 3,838 | | SciDocs-MeSH | 11 | 16,433 | | SciDocs-MAG | 19 | 17,501 | | Konstanz | 24 | 10,000 | | Elsevier | 26 | 14,738 | | PubMed | 109 | 5,000 | | Topic Categorization (Yahoo! Answers) | 10 | 60,000 | | Emotion Detection (UnifyEmotion) | 10 | 15,689 | | Situation Frame Detection (Situation Typing) | 12 | 3,311 | Please refer to the paper for further details on each particular dataset. ### Evaluation results These are the official results reported in the paper: #### Scientific domain benchmark | Model | arXiv | SciDocs-MesH | SciDocs-MAG | Konstanz | Elsevier | PubMed | |-------|-------|--------------|-------------|----------|----------|--------| | [fb/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) | 33.28 | **66.18**🔥 | 51.77 | 54.62 | 28.41 | **31.59**🔥 | | SCIroShot | **42.22**🔥 | 59.34 | **69.86**🔥 | **66.07**🔥 | **54.42**🔥 | 27.93 | #### General domain benchmark | Model | Topic | Emotion | Situation | |-------|-------|---------|-----------| | RTE [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 43.8 | 12.6 | **37.2**🔥 | | FEVER [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 40.1 | 24.7 | 21.0 | | MNLI [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 37.9 | 22.3 | 15.4 | | NSP [(Ma et al., 2021)](https://aclanthology.org/2021.acl-short.99.pdf) | 50.6 | 16.5 | 25.8 | | NSP-Reverse [(Ma et al., 2021)](https://aclanthology.org/2021.acl-short.99.pdf) | 53.1 | 16.1 | 19.9 | | SCIroShot | **59.08**🔥 | **24.94**🔥 | 27.42 All the numbers reported above represent **label-wise weighted F1** except for the Topic classification dataset, which is evaluated in terms of **accuracy** following the notation from [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf). ## Additional information ### Authors - SIRIS Lab, Research Division of SIRIS Academic. - Language Technologies Unit, Barcelona Supercomputing Center. ### Contact For further information, send an email to either <[email protected]> or <[email protected]>. ### License This work is distributed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). ### Funding This work was partially funded by 2 projects under EU’s H2020 Research and Innovation Programme: - INODE (grant agreement No 863410). - IntelComp (grant agreement No 101004870). ### Citation ```bibtex Soon to be published in EACL 2023. ``` ### Disclaimer <details> <summary>Click to expand</summary> The model published in this repository is intended for a generalist purpose and is made available to third parties under a Apache v2.0 License. Please keep in mind that the model may have bias and/or any other undesirable distortions. When third parties deploy or provide systems and/or services to other parties using this model (or a system based on it) or become users of the model itself, they should note that it is under their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owners and creators of the model be liable for any results arising from the use made by third parties. </details>

JD97/Riffusion_sentiment_LoRA

JD97

null

4

0

diffusers

1

text-to-image

false

mit

['en']

['gwkim22/spectro_caption_dataset', 'Chr0my/Epidemic_music']

null

0

['stable-diffusion', 'diffusion', 'riffusion', 'text-to-audio']

false

true

403

### Introduce Riffusion with LoRA, fine-tuned with <code>Chr0my/Epidemic_music</code> This model was used during Naver Connect BoostCamp AI tech 4th, NLP Track ### Citation ~~~ @article{Forsgren_Martiros_2022, author = {Forsgren, Seth* and Martiros, Hayk*}, title = {{Riffusion - Stable diffusion for real-time music generation}}, url = {https://riffusion.com/about}, year = {2022} } ~~~

prompthero/openjourney-lora

prompthero

null

3

0

diffusers

6

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

2,060

# Openjourney LoRA - by [PromptHero](https://prompthero.com/?utm_source=huggingface&utm_medium=referral) These are LoRA adaption weights for [Openjourney](https://huggingface.co/prompthero/openjourney) trained by [@JHawkk](https://prompthero.com/JHawkk) # Openjourney Links - [Openjourney Dreambooth](https://huggingface.co/prompthero/openjourney) - [Openjourney Fine tuned model](https://huggingface.co/prompthero/openjourney-v2) # Want to learn AI art generation?: - [Crash course in AI art generation](https://prompthero.com/academy/prompt-engineering-course?utm_source=huggingface&utm_medium=referral) - [Learn to fine-tune Stable Diffusion for photorealism](https://prompthero.com/academy/dreambooth-stable-diffusion-train-fine-tune-course?utm_source=huggingface&utm_medium=referral) # How to use LoRA's in auto1111: - Update webui (use git pull like here or redownload it) - Copy the file to stable-diffusion-webui/models/lora - Select your LoRA like in this video - Make sure to change the weight (by default it's :1 which is usually too high) # Examples: ![00860-3667285796-__portrait_photograph_of_Madison_Beer_as_Pocahontas__young_beautiful_native_american_woman__perfect_symmetrical_face__feather_je.png](https://s3.amazonaws.com/moonup/production/uploads/1675871175212-63265d019f9d19bfd4f45031.png) ![00838-2533297102-__old_man_with_long_beard_and_sidelocks_and_hat__windswept__beautifully_lit__studio_lighting__saturated_colors__intricate_detail.png](https://s3.amazonaws.com/moonup/production/uploads/1675871175232-63265d019f9d19bfd4f45031.png) ![00886-625342114-__hyperrealistic_full_length_portrait_of_gorgeous_goddess___standing_in_field_full_of_flowers___detailed_gorgeous_face___full_bo.png](https://s3.amazonaws.com/moonup/production/uploads/1675871175222-63265d019f9d19bfd4f45031.png) ![00851-1385455560-__human_colony_on_unknown_planet__clean_white_structures__bright_vibrant_colored_vegetation__bioluminescent_planets__hyperrealis.png](https://s3.amazonaws.com/moonup/production/uploads/1675871175227-63265d019f9d19bfd4f45031.png)