davanstrien/model_cards_with_readmes_with_duplicates

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

NihiLicA

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="NihiLicA/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"]) ```

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

acampillos

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="acampillos/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"]) ```

Salesforce/blip2-opt-2.7b-coco

Salesforce

blip-2

12

15

transformers

0

image-to-text

true

false

mit

['en']

null

0

['vision', 'image-to-text', 'image-captioning', 'visual-question-answering']

false

true

2,053

# BLIP-2, OPT-2.7b, fine-tuned on COCO BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg" alt="drawing" width="600"/> This allows the model to be used for tasks like: - image captioning - visual question answering (VQA) - chat-like conversations by feeding the image and the previous conversation as prompt to the model ## Intended uses & limitations You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).

SRobbins/ppo-SnowballTarget

SRobbins

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

855

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

Krud/microsoft_xtremedistil-l12-h384-uncased-TriviaQA

Krud

bert

14

10

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

958

# result This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-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

NihiLicA/Taxi-v3

NihiLicA

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

362

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

franjamonga/translate

franjamonga

marian

9

13

transformers

0

translation

true

false

apache-2.0

['es', 'en']

null

0

['translation']

false

true

2,356

### spa-eng * source group: Spanish * target group: English * OPUS readme: [spa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md) * model: transformer * source language(s): spa * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.zip) * test set translations: [opus-2020-08-18.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.test.txt) * test set scores: [opus-2020-08-18.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-spaeng.spa.eng | 30.6 | 0.570 | | news-test2008-spaeng.spa.eng | 27.9 | 0.553 | | newstest2009-spaeng.spa.eng | 30.4 | 0.572 | | newstest2010-spaeng.spa.eng | 36.1 | 0.614 | | newstest2011-spaeng.spa.eng | 34.2 | 0.599 | | newstest2012-spaeng.spa.eng | 37.9 | 0.624 | | newstest2013-spaeng.spa.eng | 35.3 | 0.609 | | Tatoeba-test.spa.eng | 59.6 | 0.739 | ### System Info: - hf_name: spa-eng - source_languages: spa - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'en'] - src_constituents: {'spa'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.test.txt - src_alpha3: spa - tgt_alpha3: eng - short_pair: es-en - chrF2_score: 0.7390000000000001 - bleu: 59.6 - brevity_penalty: 0.9740000000000001 - ref_len: 79376.0 - src_name: Spanish - tgt_name: English - train_date: 2020-08-18 00:00:00 - src_alpha2: es - tgt_alpha2: en - prefer_old: False - long_pair: spa-eng - helsinki_git_sha: d2f0910c89026c34a44e331e785dec1e0faa7b82 - transformers_git_sha: f7af09b4524b784d67ae8526f0e2fcc6f5ed0de9 - port_machine: brutasse - port_time: 2020-08-24-18:20

acampillos/q-Taxi-v3

acampillos

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="acampillos/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"]) ```

virto/mt5-small-finetuned-rabbi-kook

virto

mt5

11

2

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,077

# mt5-small-finetuned-rabbi-kook This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 223 | 6.4428 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1 - Datasets 2.9.0 - Tokenizers 0.11.0

jannikskytt/Reinforce-CartPole-v1

jannikskytt

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

HuggingFaceH4/bloomz-7b1

HuggingFaceH4

bloom

8

2

transformers

0

text-generation

true

false

bigscience-bloom-rail-1.0

['ak', 'ar', 'as', 'bm', 'bn', 'ca', 'code', 'en', 'es', 'eu', 'fon', 'fr', 'gu', 'hi', 'id', 'ig', 'ki', 'kn', 'lg', 'ln', 'ml', 'mr', 'ne', 'nso', 'ny', 'or', 'pa', 'pt', 'rn', 'rw', 'sn', 'st', 'sw', 'ta', 'te', 'tn', 'ts', 'tum', 'tw', 'ur', 'vi', 'wo', 'xh', 'yo', 'zh', 'zu']

['bigscience/xP3']

null

0

[]

true

9,473

![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true) # Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 7. [Citation](#citation) # Model Summary > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. - **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) - **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages. - **BLOOMZ & mT0 Model Family:** <div class="max-w-full overflow-auto"> <table> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English. </tr> <tr> <td>Parameters</td> <td>300M</td> <td>580M</td> <td>1.2B</td> <td>3.7B</td> <td>13B</td> <td>560M</td> <td>1.1B</td> <td>1.7B</td> <td>3B</td> <td>7.1B</td> <td>176B</td> </tr> <tr> <td>Finetuned Model</td> <td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td> <td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td> <td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> </tr> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td> </tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td> </tr> <th colspan="12">Original pretrained checkpoints. Not recommended.</th> <tr> <td>Pretrained Model</td> <td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td> <td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td> <td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td> <td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td> <td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td> </tr> </table> </div> # Use ## Intended use We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper: - 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评? - Suggest at least five related search terms to "Mạng neural nhân tạo". - Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish): - Explain in a sentence in Telugu what is backpropagation in neural networks. **Feel free to share your generations in the Community tab!** ## How to use ### CPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-7b1" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-7b1" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU in 8bit <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-7b1" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details>  ### # Limitations **Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*". # Training ## Model - **Architecture:** Same as [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1), also refer to the `config.json` file - **Finetuning steps:** 1000 - **Finetuning tokens:** 4.19 billion - **Finetuning layout:** 1x pipeline parallel, 1x tensor parallel, 64x data parallel - **Precision:** float16 ## Hardware - **CPUs:** AMD CPUs with 512GB memory per node - **GPUs:** 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links - **Communication:** NCCL-communications network with a fully dedicated subnet ## Software - **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) - **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5) - **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # Evaluation We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config. # Citation ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sanali209/imclasif-genres-v001

sanali209

vit

6

106

transformers

0

image-classification

true

false

null

0

['image-classification', 'pytorch', 'huggingpics']

false

true

332

# imclasif-genres-v001 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).

Salesforce/blip2-opt-6.7b-coco

Salesforce

blip-2

14

11

transformers

0

image-to-text

true

false

mit

['en']

null

0

['vision', 'image-to-text', 'image-captioning', 'visual-question-answering']

false

true

2,053

# BLIP-2, OPT-6.7b, fine-tuned on COCO BLIP-2 model, leveraging [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (a large language model with 6.7 billion parameters). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg" alt="drawing" width="600"/> This allows the model to be used for tasks like: - image captioning - visual question answering (VQA) - chat-like conversations by feeding the image and the previous conversation as prompt to the model ## Intended uses & limitations You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).

lyk0013/distilbert-finetuned-imdb

lyk0013

distilbert

10

0

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,160

# distilbert-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.3611 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9507 | 1.0 | 13 | 2.5946 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

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

EdenYav

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="EdenYav/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"]) ```

apatidar0/bert-finetuned-squad

apatidar0

bert

12

9

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

930

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

vvn0/Reinforce-Pixelcopter-PLE-v0

vvn0

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

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

RMAV

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

393

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

EdenYav/Taxi-v3

EdenYav

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="EdenYav/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"]) ```

Salesforce/blip2-flan-t5-xl-coco

Salesforce

blip-2

11

7

transformers

1

image-to-text

true

false

mit

['en']

null

0

['vision', 'image-to-text', 'image-captioning', 'visual-question-answering']

false

true

2,029

# BLIP-2, Flan T5-xl, fine-tuned on COCO BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg" alt="drawing" width="600"/> This allows the model to be used for tasks like: - image captioning - visual question answering (VQA) - chat-like conversations by feeding the image and the previous conversation as prompt to the model ## Intended uses & limitations You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).

LarsLary/xtremedistil-l12-h384-uncased-SQuAD-trained

LarsLary

bert

15

5

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

957

# result This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) on the SQuAD dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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

sanali209/imclasif-quality-v001

sanali209

vit

8

83

transformers

0

image-classification

true

false

null

0

['image-classification', 'pytorch', 'huggingpics']

false

true

333

# imclasif-quality-v001 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).

RMAV/taxi-driver

RMAV

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

362

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

sakistriker/DreamLikeSamKuvshinov

sakistriker

null

22

15

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

4,289

# DreamLikeSamKuvshinov This is a safetensors Diffusers conversion of the model [DreamLikeSamKuvshinov](https://civitai.com/models/1473/dreamlikesamkuvshinov) created by [mattgroy](https://civitai.com/user/mattgroy). ## Model Description (from CivitAI) A mixture of [Dreamlike Diffusion 1.0](https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0), [SamDoesArt V3](https://huggingface.co/Sandro-Halpo/SamDoesArt-V3) and [Kuvshinov style](https://civitai.com/models/1231/kuvshinov-style) models. Created mostly for exploring different character concepts with a focus on drawings, but the mix happened to be pretty good at realistic-ish images, all thanks to wonderful models that it uses. ### Prompt Trigger Words - dreamlikeart - samdoesart - kuvshinov ## Examples (from CivitAI) ### Example 1 ![Example 1](https://huggingface.co/sakistriker/DreamLikeSamKuvshinov/resolve/main/example1.jpg) ``` Positive: (samdoesart:1.2) dreamlikeart beautiful young lady, long hair, perfect face, cyberpunk, masterpiece, intense shadows, ambient light, illustration, thick outlines, highres, drawn by Greg Rutkowski, (Yoji Shinkawa:1.1), (kuvshinov:0.9), <kuvshinov> Negative: (ugly:1.5), (duplicate:1.3), (morbid:1.2), (mutilated:1.2), (mutation), [out of frame], (extra fingers:1.2), (more than two arms), (more than two legs), (missing arms), (missing legs), (poorly drawn hands:1.3), (poorly drawn face:1.3), (deformed:1.2), blurry, (bad anatomy), (bad proportions), (disfigured:1.3), extra limbs, (malformed limbs), mutated hands, (fused fingers), (makeup) Steps: 50 CFG Scale: 10 Seed: 1736018256 Sampler: DPM++ 2M Karras ``` ### Example 2 ![Example 2](https://huggingface.co/sakistriker/DreamLikeSamKuvshinov/resolve/main/example2.jpg) ``` Positive: (samdoesart:1.1) (dreamlikeart:1) long shot of a beautiful young lady, short hair, (medieval:1.2), masterpiece, intense shadows, ambient light, illustration, (thick outlines:1.2), cartoon, highres, drawn by Inoue Takehiko, (Yoji Shinkawa:1.1), (kuvshinov:1) Negative: (ugly:1.5), (duplicate:1.3), (morbid:1.2), (mutilated:1.2), (mutation), [out of frame], (extra fingers:1.2), (more than two arms), (more than two legs), (missing arms), (missing legs), (poorly drawn hands:1.3), (poorly drawn face:1.3), (deformed:1.2), blurry, (bad anatomy), (bad proportions), (disfigured:1.3), extra limbs, (malformed limbs), mutated hands, (fused fingers), (makeup) Steps: 50 CFG Scale: 5 Seed: 2598035884 Sampler: DPM++ 2M Karras ``` ### Example 3 ![Example 3](https://huggingface.co/sakistriker/DreamLikeSamKuvshinov/resolve/main/example3.jpg) ``` Positive: (samdoesart:1.1) (dreamlikeart:1) full body portrait of a beautiful young lady, short hair, (medieval:1.2), masterpiece, intense shadows, ambient light, illustration, (thick outlines:1.2), cartoon, highres, drawn by Inoue Takehiko, (Yoji Shinkawa:0.9), Jakub Rozalski, (kuvshinov:1) Negative: (ugly:1.5), (duplicate:1.3), (morbid:1.2), (mutilated:1.2), (mutation), [out of frame], (extra fingers:1.2), (more than two arms), (more than two legs), (missing arms), (missing legs), (poorly drawn hands:1.3), (poorly drawn face:1.3), (deformed:1.2), blurry, (bad anatomy), (bad proportions), (disfigured:1.3), extra limbs, (malformed limbs), mutated hands, (fused fingers), (makeup) Steps: 50 CFG Scale: 5 Seed: 3399343143 Sampler: DPM++ 2M Karras ``` ### Example 4 ![Example 4](https://huggingface.co/sakistriker/DreamLikeSamKuvshinov/resolve/main/example4.jpg) ``` Positive: (samdoesart:1) (dreamlikeart:1) full body portrait of a beautiful young lady, curly hair, (sci-fi:1.2), masterpiece, intense shadows, ambient light, illustration, (thick outlines:1.2), cartoon, highres, drawn by Inoue Takehiko, (Diego Dayer:0.9), Jakub Rozalski, (kuvshinov:1) <kuvshinov> Negative: (ugly:1.5), (duplicate:1.3), (morbid:1.2), (mutilated:1.2), (mutation), [out of frame], (extra fingers:1.2), (more than two arms), (more than two legs), (missing arms), (missing legs), (poorly drawn hands:1.3), (poorly drawn face:1.3), (deformed:1.2), blurry, (bad anatomy), (bad proportions), (disfigured:1.3), extra limbs, (malformed limbs), mutated hands, (fused fingers), (makeup) Steps: 50 CFG Scale: 7 Seed: 4289815823 Sampler: DPM++ 2M Karras ``` ## Closing All credit goes to the original model's author.

vvn0/ppo-SnowballTarget

vvn0

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

851

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

EgilKarlsen/ApacheBertBaseCase

EgilKarlsen

bert

9

10

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,246

# ApacheBertBaseCase This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2008 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.2938 | 1.0 | 20881 | 0.2663 | | 0.2345 | 2.0 | 41762 | 0.2134 | | 0.2182 | 3.0 | 62643 | 0.2008 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

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

frangiral

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

398

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

sakistriker/XperoEnd1essModel

sakistriker

null

21

37

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

4,595

# Xpero End1ess Model This is a safetensors Diffusers conversion of the model [Xpero End1ess Model](https://civitai.com/models/6231/xpero-end1ess-model) created by [xpero](https://civitai.com/user/xpero). ## Model Description (from CivitAI) This model is a custom blend of various models, presenting many options for generating images, including NSFW. Based on Stable Diffusion 1.5. Primarily focused on the creation of digital art characters. Can easily generate great images in different styles - characters, illustration, anime etc. The model is demanding for VAE, I use [vae-ft-mse-840000-ema-pruned](https://huggingface.co/stabilityai/sd-vae-ft-mse). ## Examples (from CivitAI) ### Example 1 ![Example 1](https://huggingface.co/sakistriker/XperoEnd1essModel/resolve/main/example1.jpg) ``` Positive: a woman in a white top and pink shorts is standing next to a bike with a yellow background and, by Quentin Tarantino, 1girl, bracelet, brown_hair, jewelry, letterboxed, lips, long_hair, makeup, medium_breasts, midriff, nail_polish, navel, necklace, nose, orange_sky, pink_shorts, realistic, shorts, solo, sun, sunset, tattoo,wristband, yellow_background, yellow_sky, beautiful detailed glow, detailed, Cinematic light, intricate detail, highres, detailed facial features, high detail, sharp focus, smooth, aesthetic, extremely detailed, stamp, octane render, Negative: multiple people, lowres, bad anatomy, bad hands, text, error, missing fingers,extra digit, fewer digits, cropped, worstquality, low quality, normal quality,jpegartifacts,signature, watermark, username,blurry,bad feet,cropped,poorly drawn hands,poorly drawn face,mutation,deformed,worst quality,low quality,normal quality,jpeg artifacts,signature,watermark,extra fingers,fewer digits,extra limbs,extra arms,extra legs,malformed limbs,fused fingers,too many fingers,long neck,cross-eyed,mutated hands,polar lowres,bad body,bad proportions,gross proportions,text,error,missing fingers,missing arms,missing legs,extra digit Steps: 22 CFG Scale: 8 Seed: 1116627766 Sampler: Euler-a ``` ### Example 2 ![Example 2](https://huggingface.co/sakistriker/XperoEnd1essModel/resolve/main/example2.jpg) ``` Positive: a woman with blue disheveled hair and piercings, with a dark and a black background, Charlie Bowater, stanley artgerm lau, a character portrait, sots art, sharp focus, smooth, aesthetic, extremely detailed, octane render, 1girl, black_choker, blue_eyes, blurry, choker, earrings, jewelry, lips, nose, piercing, realistic, short_hair, solo, dark industrial background, rtx, upper body, dark makeup, elegant pose, rock clothes, tattoo on arm, leather shirt, navel, beautiful detailed glow, detailed, Cinematic light, intricate detail, highres, detailed facial features, high detail Negative: multiple people, lowres, bad anatomy, bad hands, text, error, missing fingers,extra digit, fewer digits, cropped, worstquality, low quality, normal quality,jpegartifacts,signature, watermark, username,blurry,bad feet,cropped,poorly drawn hands,poorly drawn face,mutation,deformed,worst quality,low quality,normal quality,jpeg artifacts,signature,watermark,extra fingers,fewer digits,extra limbs,extra arms,extra legs,malformed limbs,fused fingers,too many fingers,long neck,cross-eyed,mutated hands,polar lowres,bad body,bad proportions,gross proportions,text,error,missing fingers,missing arms,missing legs,extra digit Steps: 22 CFG Scale: 8 Seed: 4281920120 Sampler: Euler-a ``` ### Example 3 ![Example 3](https://huggingface.co/sakistriker/XperoEnd1essModel/resolve/main/example3.jpg) ``` Positive: 1/2 portrait of beautiful rock girl, punk, slim body, beautiful detailed glow, highres, high detail, smooth, aesthetic, extremely detailed, octane render, detailed facial features, sharp focus, rtx, ambient light, intricate city background, pink hair, expressive eyes Negative: multiple people, lowres, bad anatomy, bad hands, text, error, missing fingers,extra digit, fewer digits, cropped, worstquality, low quality, normal quality,jpegartifacts,signature, watermark, username,blurry,bad feet,cropped,poorly drawn hands,poorly drawn face,mutation,deformed,worst quality,low quality,normal quality,jpeg artifacts,signature,watermark,extra fingers,fewer digits,extra limbs,extra arms,extra legs,malformed limbs,fused fingers,too many fingers,long neck,cross-eyed,mutated hands,polar lowres,bad body,bad proportions,gross proportions,text,error,missing fingers,missing arms,missing legs,extra digit Steps: 24 CFG Scale: 7.5 Seed: 1452262543 Sampler: Euler-a ``` ## Closing All credit goes to the original model's author.

Allen-Young/xtremedistil-l6-h256-uncased-nlp4web1

Allen-Young

bert

15

52

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,030

# result This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on a [NewsQA](https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NewsQA.jsonl.gz) 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

adsjklfsd/pegasus-samsum

adsjklfsd

pegasus

22

0

transformers

0

text2text-generation

true

false

null

['samsum']

null

0

['generated_from_trainer']

true

1,009

# pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Framework versions - Transformers 4.26.0 - Pytorch 1.10.1+cu113 - Datasets 2.9.0 - Tokenizers 0.13.2

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_stsb_256

gokuls

distilbert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,994

# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_stsb_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.4500 - Pearson: 0.1761 - Spearmanr: 0.1778 - Combined Score: 0.1770 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 0.5832 | 1.0 | 1259 | 1.5244 | 0.1737 | 0.1803 | 0.1770 | | 0.2202 | 2.0 | 2518 | 1.4500 | 0.1761 | 0.1778 | 0.1770 | | 0.1249 | 3.0 | 3777 | 1.4720 | 0.1743 | 0.1782 | 0.1762 | | 0.0822 | 4.0 | 5036 | 1.5790 | 0.1581 | 0.1658 | 0.1619 | | 0.0611 | 5.0 | 6295 | 1.4750 | 0.1850 | 0.1905 | 0.1878 | | 0.0477 | 6.0 | 7554 | 1.5776 | 0.1612 | 0.1694 | 0.1653 | | 0.0394 | 7.0 | 8813 | 1.5512 | 0.1648 | 0.1694 | 0.1671 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

jannikskytt/Reinforce-PixelCopter

jannikskytt

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

frangiral/Taxi-v3-Try1

frangiral

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

368

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="frangiral/Taxi-v3-Try1", 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"]) ```

EgilKarlsen/ApacheRoberta

EgilKarlsen

roberta

8

13

transformers

0

fill-mask

true

false

mit

null

0

['generated_from_trainer']

true

1,235

# ApacheRoberta This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.3618 | 1.0 | 18539 | 0.3174 | | 0.2826 | 2.0 | 37078 | 0.2560 | | 0.2633 | 3.0 | 55617 | 0.2315 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

EgilKarlsen/ApacheGPT2

EgilKarlsen

gpt2

9

10

transformers

0

text-generation

true

false

null

0

['generated_from_trainer']

true

1,240

# ApacheGPT2 This model is a fine-tuned version of [EgilKarlsen/gpt2](https://huggingface.co/EgilKarlsen/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3480 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.4368 | 1.0 | 18303 | 0.4028 | | 0.3919 | 2.0 | 36606 | 0.3600 | | 0.3766 | 3.0 | 54909 | 0.3480 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

BenjaminB/test-skops-card-creator-02

BenjaminB

null

6

0

sklearn

0

tabular-classification

false

null

0

['sklearn', 'skops', 'tabular-classification', 'art']

false

true

5,910

# Model description [More Information Needed] ## Intended uses & limitations ### Only for cats ![Untitled 8ea66d](cat_8ea66d.png) ### And for birbs ![tmp9ji6uam1i5pmkd7l.jpg](tmp9ji6uam1i5pmkd7l.jpg) ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |------------------|---------| | constant | | | random_state | | | strategy | prior | </details> ### Model Plot The model plot is below. <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DummyClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">DummyClassifier</label><div class="sk-toggleable__content"><pre>DummyClassifier()</pre></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|---------| | accuracy | 0.9 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ```

Kuray107/RATS_clean

Kuray107

wav2vec2

27

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

4,095

# RATS_clean This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3775 - Wer: 0.1324 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.4025 | 0.42 | 1000 | 0.3019 | 0.1434 | | 0.3939 | 0.85 | 2000 | 0.3044 | 0.1377 | | 0.3808 | 1.27 | 3000 | 0.3053 | 0.1417 | | 0.377 | 1.7 | 4000 | 0.3213 | 0.1414 | | 0.3616 | 2.12 | 5000 | 0.2976 | 0.1449 | | 0.3519 | 2.55 | 6000 | 0.3266 | 0.1482 | | 0.3532 | 2.97 | 7000 | 0.3365 | 0.1455 | | 0.3377 | 3.4 | 8000 | 0.3170 | 0.1409 | | 0.3321 | 3.82 | 9000 | 0.3070 | 0.1398 | | 0.3199 | 4.25 | 10000 | 0.3123 | 0.1366 | | 0.322 | 4.67 | 11000 | 0.3166 | 0.1378 | | 0.3085 | 5.1 | 12000 | 0.3492 | 0.1448 | | 0.2954 | 5.52 | 13000 | 0.3173 | 0.1387 | | 0.3003 | 5.95 | 14000 | 0.3341 | 0.1442 | | 0.2863 | 6.37 | 15000 | 0.3124 | 0.1382 | | 0.2887 | 6.8 | 16000 | 0.3273 | 0.1404 | | 0.2777 | 7.22 | 17000 | 0.3291 | 0.1399 | | 0.2728 | 7.65 | 18000 | 0.3326 | 0.1352 | | 0.2726 | 8.07 | 19000 | 0.3443 | 0.1355 | | 0.2519 | 8.5 | 20000 | 0.3448 | 0.1400 | | 0.3256 | 8.92 | 21000 | 0.3274 | 0.1396 | | 0.3174 | 9.35 | 22000 | 0.3210 | 0.1411 | | 0.3075 | 9.77 | 23000 | 0.3319 | 0.1410 | | 0.2982 | 10.2 | 24000 | 0.3463 | 0.1424 | | 0.2927 | 10.62 | 25000 | 0.3445 | 0.1399 | | 0.3027 | 11.05 | 26000 | 0.3488 | 0.1410 | | 0.2835 | 11.47 | 27000 | 0.3639 | 0.1371 | | 0.2767 | 11.89 | 28000 | 0.3467 | 0.1391 | | 0.2792 | 12.32 | 29000 | 0.3459 | 0.1358 | | 0.2701 | 12.74 | 30000 | 0.3425 | 0.1351 | | 0.269 | 13.17 | 31000 | 0.3790 | 0.1421 | | 0.2567 | 13.59 | 32000 | 0.3613 | 0.1352 | | 0.2626 | 14.02 | 33000 | 0.3586 | 0.1396 | | 0.2436 | 14.44 | 34000 | 0.3694 | 0.1369 | | 0.2557 | 14.87 | 35000 | 0.3715 | 0.1321 | | 0.2468 | 15.29 | 36000 | 0.3970 | 0.1348 | | 0.2463 | 15.72 | 37000 | 0.3675 | 0.1304 | | 0.2362 | 16.14 | 38000 | 0.3690 | 0.1377 | | 0.2388 | 16.57 | 39000 | 0.3775 | 0.1310 | | 0.2314 | 16.99 | 40000 | 0.3601 | 0.1326 | | 0.2315 | 17.42 | 41000 | 0.3633 | 0.1322 | | 0.2334 | 17.84 | 42000 | 0.3794 | 0.1356 | | 0.2255 | 18.27 | 43000 | 0.3670 | 0.1316 | | 0.2222 | 18.69 | 44000 | 0.3778 | 0.1341 | | 0.225 | 19.12 | 45000 | 0.3708 | 0.1331 | | 0.2209 | 19.54 | 46000 | 0.3807 | 0.1332 | | 0.2216 | 19.97 | 47000 | 0.3775 | 0.1324 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1 - Datasets 2.9.0 - Tokenizers 0.13.2

Eulalye921/test_trainer

Eulalye921

bert

9

8

transformers

0

text-classification

true

false

apache-2.0

null

['yelp_review_full']

null

0

['generated_from_trainer']

true

1,320

# test_trainer This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 1.0183 - Accuracy: 0.586 ## 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 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Mandoryan/DQN-LunarLander-v2

Mandoryan

null

19

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

350

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

erniechiew/a2c-AntBulletEnv-v0

erniechiew

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 ... ```

DL82/remylacroix

DL82

null

41

4

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

2,734

### remylacroix Dreambooth model trained by DL82 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: remylacroix (use that on your prompt) ![remylacroix 0](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%281%29.jpg)![remylacroix 1](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%282%29.jpg)![remylacroix 2](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%283%29.jpg)![remylacroix 3](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%284%29.jpg)![remylacroix 4](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%285%29.jpg)![remylacroix 5](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%286%29.jpg)![remylacroix 6](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%287%29.jpg)![remylacroix 7](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%288%29.jpg)![remylacroix 8](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%289%29.jpg)![remylacroix 9](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2810%29.jpg)![remylacroix 10](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2811%29.jpg)![remylacroix 11](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2812%29.jpg)![remylacroix 12](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2813%29.jpg)![remylacroix 13](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2814%29.jpg)![remylacroix 14](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2815%29.jpg)![remylacroix 15](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2816%29.jpg)![remylacroix 16](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2817%29.jpg)![remylacroix 17](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2818%29.jpg)![remylacroix 18](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2819%29.jpg)![remylacroix 19](https://huggingface.co/DL82/remylacroix/resolve/main/concept_images/remylacroix_%2820%29.jpg)

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_256

gokuls

distilbert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,636

# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5279 - Accuracy: 0.1549 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3422 | 1.0 | 218 | 0.5279 | 0.1549 | | 0.305 | 2.0 | 436 | 0.5961 | 0.1268 | | 0.291 | 3.0 | 654 | 0.6364 | 0.0845 | | 0.2816 | 4.0 | 872 | 0.6604 | 0.0986 | | 0.2744 | 5.0 | 1090 | 0.6627 | 0.0845 | | 0.2686 | 6.0 | 1308 | 0.6618 | 0.0986 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

deepneuralnet/lll

deepneuralnet

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 ... ```

SRobbins/ppo-Pyramids_Training

SRobbins

null

20

1

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

840

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

pfunk/Pong-v4-DQPN_p30_pt0.1-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

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

true

1,990

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p30_pt0.1.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p30_pt0.1]" python -m cleanrl_utils.enjoy --exp-name DQPN_p30_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_p30_pt0.1-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p30_pt0.1-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p30_pt0.1-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p30_pt0.1 --start-policy-f 30000 --end-policy-f 30000 --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': 30000, 'env_id': 'Pong-v4', 'evaluation_fraction': 1.0, 'exp_name': 'DQPN_p30_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': 30000, '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'} ```

virto/mt5-base-finetuned-rabbi-kook

virto

mt5

11

5

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,201

# mt5-base-finetuned-rabbi-kook This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.2102 | 1.0 | 3567 | 2.4526 | | 3.0283 | 2.0 | 7134 | 2.3861 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.11.0

SRobbins/q-Taxi-v3

SRobbins

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="SRobbins/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"]) ```

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mnli_256

gokuls

distilbert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,652

# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5082 - Accuracy: 0.6312 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.5216 | 1.0 | 31440 | 0.5047 | 0.6315 | | 0.4566 | 2.0 | 62880 | 0.5097 | 0.6383 | | 0.4188 | 3.0 | 94320 | 0.5243 | 0.6361 | | 0.3943 | 4.0 | 125760 | 0.5328 | 0.6346 | | 0.3777 | 5.0 | 157200 | 0.5345 | 0.6300 | | 0.3658 | 6.0 | 188640 | 0.5392 | 0.6318 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

pfunk/Pong-v4-DQPN_p50_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

2,038

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQPN_p50_pt0.1_tt0.1.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p50_pt0.1_tt0.1]" python -m cleanrl_utils.enjoy --exp-name DQPN_p50_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_p50_pt0.1_tt0.1-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_pt0.1_tt0.1-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p50_pt0.1_tt0.1-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p50_pt0.1_tt0.1 --start-policy-f 50000 --end-policy-f 50000 --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': 50000, 'env_id': 'Pong-v4', 'evaluation_fraction': 1.0, 'exp_name': 'DQPN_p50_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': 50000, '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'} ```

qgallouedec/ppo-MiniGrid-DoorKey-5x5-v0

qgallouedec

null

16

0

stable-baselines3

0

reinforcement-learning

false

null

0

['MiniGrid-DoorKey-5x5-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

2,094

# **PPO** Agent playing **MiniGrid-DoorKey-5x5-v0** This is a trained model of a **PPO** agent playing **MiniGrid-DoorKey-5x5-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env MiniGrid-DoorKey-5x5-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-DoorKey-5x5-v0 -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 ppo --env MiniGrid-DoorKey-5x5-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-DoorKey-5x5-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env MiniGrid-DoorKey-5x5-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env MiniGrid-DoorKey-5x5-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('clip_range', 0.2), ('ent_coef', 0.0), ('env_wrapper', 'gym_minigrid.wrappers.FlatObsWrapper'), ('gae_lambda', 0.95), ('gamma', 0.99), ('learning_rate', 0.00025), ('n_envs', 8), ('n_epochs', 10), ('n_steps', 128), ('n_timesteps', 100000.0), ('normalize', True), ('policy', 'MlpPolicy'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```

0RisingStar0/HighRiseMixV1

0RisingStar0

null

11

0

diffusers

12

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

1,548

<p align="center"><img src="https://huggingface.co/0RisingStar0/HighRiseMixV1/resolve/main/00401-2269441947-(masterpiece%2C%20excellent%20quality%2C%20high%20quality%2C%20highres%20_%201.5)%2C%20(1girl%2C%20solo)%2C%20solo%20focus%2C%20sky%2C%20city%2C%20skyscrapers%2C%20pavement%2C%20tree.png"> <img src="https://huggingface.co/0RisingStar0/HighRiseMixV1/resolve/main/13.png"></p> <b>V2 is out! : https://huggingface.co/0RisingStar0/HighRiseMixV2</b> <center><b>HighRiseMixV1</b></center> U-Net mixed model <b>specialized for city and skyscrapers background.</b> <b>FP16 Pruned version</b>(No EMA). (Quality change may occur in very small details on buildings' textures) <b>Recommended prompts : </b> (masterpiece, best quality, excellent quality), ((1girl, solo)), sky, city, (skyscrapers), trees, pavement, lens flare EasyNegative, moss, phone, man, pedestrians, extras, border, outside border, white border (EasyNegative is a negative embedding : https://huggingface.co/datasets/gsdf/EasyNegative) <b>Recommended settings : </b> Sampler : DPM++ 2M Karras OR DPM++ SDE Karras Sampling steps : 25 ~ 30 Resolution : 512x768 OR 768x512 CFG Scale : 9 <b> Upscale is a must-do!! </b> Otherwise, you won't get great results. Upscaler : Latent (nearest) Hires steps : 0 Denoise : 0.6 Upscale 2x <b> Mixed models : </b> AbyssOrangeMix2_NSFW, AnythingV4.5, BasilMixFixed, CounterfeitV2.5, EerieOrangeMix2, PowercolorV2 (Thanks to everyone who made above models!) This is my first mixed model being uploaded to public site, so feel free to give feedbacks as you wish, I'll try and work around with it.

mstaron/SingBERTa

mstaron

roberta

7

15

transformers

0

fill-mask

true

false

cc-by-4.0

null

0

[]

false

true

1,606

This model is a RoBERTa model trained on a programming language code - WolfSSL + examples of Singletons diffused with the Linux Kernel code. The model is pre-trained to understand the concep of a singleton in the code The programming language is C/C++, but the actual inference can also use other languages. Using the model to unmask can be done in the following way ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='mstaron/SingBERTa') unmasker("Hello I'm a <mask> model.") ``` To obtain the embeddings for downstream task can be done in the following way: ```python # import the model via the huggingface library from transformers import AutoTokenizer, AutoModelForMaskedLM # load the tokenizer and the model for the pretrained SingBERTa tokenizer = AutoTokenizer.from_pretrained('mstaron/SingBERTa') # load the model model = AutoModelForMaskedLM.from_pretrained("mstaron/SingBERTa") # import the feature extraction pipeline from transformers import pipeline # create the pipeline, which will extract the embedding vectors # the models are already pre-defined, so we do not need to train anything here features = pipeline( "feature-extraction", model=model, tokenizer=tokenizer, return_tensor = False ) # extract the features == embeddings lstFeatures = features('Class SingletonX1') # print the first token's embedding [CLS] # which is also a good approximation of the whole sentence embedding # the same as using np.mean(lstFeatures[0], axis=0) lstFeatures[0][0] ``` In order to use the model, we need to train it on the downstream task.

cedwin/log_c_pt

cedwin

mpnet

13

27

sentence-transformers

0

sentence-similarity

true

false

null

0

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

false

true

3,569

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 2053 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 4106, "warmup_steps": 411, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

bigcode/santacoder-fast-inference

bigcode

gpt_bigcode

4

34

transformers

0

text-generation

true

false

openrail

['code']

['bigcode/the-stack']

null

0

[]

true

3,394

# SantaCoder ![banner](https://huggingface.co/datasets/bigcode/admin/resolve/main/banner.png) Play with the model on the [SantaCoder Space Demo](https://huggingface.co/spaces/bigcode/santacoder-demo). # Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [License](#license) 6. [Citation](#citation) # Model Summary This is the Megatron-version of [SantaCoder](https://huggingface.co/bigcode/santacoder). We refer the reader to the [SantaCoder model page](https://huggingface.co/bigcode/santacoder) for full documentation about this model - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Project Website:** [bigcode-project.org](www.bigcode-project.org) - **Paper:** [🎅SantaCoder: Don't reach for the stars!🌟](https://t.co/YV3pzUbYOr) - **Point of Contact:** [[email protected]](mailto:[email protected]) - **Languages:** Python, Java, and JavaScript There are two versions (branches) of the model: * `main`: Uses the `gpt_bigcode` model. [Requires the bigcode fork of transformers](https://github.com/bigcode-project/transformers). * `main_custom`: Packaged with its modeling code. Requires `transformers>=4.27`. Alternatively, it can run on older versions by setting the configuration parameter `activation_function = "gelu_pytorch_tanh"`. # Use ## Intended use The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. `# the following function computes the sqrt`) or write a function signature and docstring and let the model complete the function body. ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/santacoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code in Python, Java, and JavaScript. 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 ## Model - **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective - **Pretraining steps:** 600K - **Pretraining tokens:** 236 billion - **Precision:** float16 ## Hardware - **GPUs:** 96 Tesla V100 - **Training time:** 6.2 days - **Total FLOPS:** 2.1 x 10e21 ## Software - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) - **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # License The model is licenses under the CodeML Open RAIL-M v0.1 license. You can find the full license [here](https://huggingface.co/spaces/bigcode/license).

lukee/a2c-PandaReachDense-v2

lukee

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 ... ```

PeterBanning71/t5-small-finetuned-xsum

PeterBanning71

t5

14

12

transformers

0

summarization

true

false

apache-2.0

null

['xsum']

null

0

['summarization', 'generated_from_trainer']

true

1,489

# t5-small-finetuned-amazon-en-es This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4226 - Rouge1: 29.0135 - Rouge2: 8.2985 - Rougel: 22.9598 - Rougelsum: 22.954 - Gen Len: 18.8244 ## 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.6843 | 1.0 | 12753 | 2.4482 | 28.6347 | 7.9907 | 22.5689 | 22.567 | 18.8236 | | 2.6439 | 2.0 | 25506 | 2.4226 | 29.0135 | 8.2985 | 22.9598 | 22.954 | 18.8244 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

vaibhav9/mini5-a

vaibhav9

bert

14

8

transformers

0

question-answering

true

false

null

0

['generated_from_trainer']

true

1,170

# mini5-a This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5849 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 52 | 1.5947 | | No log | 2.0 | 104 | 1.5901 | | No log | 3.0 | 156 | 1.5849 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

lnros/poca-SoccerTwos

lnros

null

21

369

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

839

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

qgallouedec/ppo-CarRacing-v0

qgallouedec

null

16

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,589

# **PPO** Agent playing **CarRacing-v0** This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env CarRacing-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo --env CarRacing-v0 -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 ppo --env CarRacing-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo --env CarRacing-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env CarRacing-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env CarRacing-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('clip_range', 0.2), ('ent_coef', 0.0), ('env_wrapper', [{'rl_zoo3.wrappers.FrameSkip': {'skip': 2}}, {'gym.wrappers.resize_observation.ResizeObservation': {'shape': 64}}, {'gym.wrappers.gray_scale_observation.GrayScaleObservation': {'keep_dim': True}}]), ('frame_stack', 2), ('gae_lambda', 0.95), ('gamma', 0.99), ('learning_rate', 'lin_1e-4'), ('max_grad_norm', 0.5), ('n_envs', 8), ('n_epochs', 10), ('n_steps', 512), ('n_timesteps', 4000000.0), ('normalize', "{'norm_obs': False, 'norm_reward': True}"), ('policy', 'CnnPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.GELU, ' 'net_arch=dict(pi=[256], vf=[256]), )'), ('sde_sample_freq', 4), ('use_sde', True), ('vf_coef', 0.5), ('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})]) ```

sb3/ppo-CarRacing-v0

sb3

null

16

1

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,565

# **PPO** Agent playing **CarRacing-v0** This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env CarRacing-v0 -orga sb3 -f logs/ python -m rl_zoo3.enjoy --algo ppo --env CarRacing-v0 -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 ppo --env CarRacing-v0 -orga sb3 -f logs/ python -m rl_zoo3.enjoy --algo ppo --env CarRacing-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo --env CarRacing-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env CarRacing-v0 -f logs/ -orga sb3 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('clip_range', 0.2), ('ent_coef', 0.0), ('env_wrapper', [{'rl_zoo3.wrappers.FrameSkip': {'skip': 2}}, {'gym.wrappers.resize_observation.ResizeObservation': {'shape': 64}}, {'gym.wrappers.gray_scale_observation.GrayScaleObservation': {'keep_dim': True}}]), ('frame_stack', 2), ('gae_lambda', 0.95), ('gamma', 0.99), ('learning_rate', 'lin_1e-4'), ('max_grad_norm', 0.5), ('n_envs', 8), ('n_epochs', 10), ('n_steps', 512), ('n_timesteps', 4000000.0), ('normalize', "{'norm_obs': False, 'norm_reward': True}"), ('policy', 'CnnPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.GELU, ' 'net_arch=dict(pi=[256], vf=[256]), )'), ('sde_sample_freq', 4), ('use_sde', True), ('vf_coef', 0.5), ('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})]) ```

mutisya/wav2vec2-300m-swa-r22-1K-lambda-fine-v1

mutisya

wav2vec2

28

3

transformers

0

automatic-speech-recognition

true

false

null

['common_voice_9_0']

null

0

['generated_from_trainer']

true

12,270

# wav2vec2-300m-swa-r22-1K-lambda-fine-v1 This model is a fine-tuned version of [mutisya/wav2vec2-300m-swa-r22-1K-lambda-pre-v1](https://huggingface.co/mutisya/wav2vec2-300m-swa-r22-1K-lambda-pre-v1) on the common_voice_9_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2218 - Wer: 0.2379 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.8849 | 0.06 | 400 | 3.1614 | 1.0 | | 2.2947 | 0.11 | 800 | 1.2490 | 0.9366 | | 0.8926 | 0.17 | 1200 | 0.7215 | 0.6875 | | 0.6727 | 0.22 | 1600 | 0.5753 | 0.5788 | | 0.6056 | 0.28 | 2000 | 0.5337 | 0.5421 | | 0.5423 | 0.33 | 2400 | 0.4949 | 0.5352 | | 0.5089 | 0.39 | 2800 | 0.4556 | 0.4857 | | 0.5041 | 0.44 | 3200 | 0.4575 | 0.4864 | | 0.4719 | 0.5 | 3600 | 0.4391 | 0.4503 | | 0.4613 | 0.55 | 4000 | 0.4145 | 0.4609 | | 0.4468 | 0.61 | 4400 | 0.3956 | 0.4461 | | 0.4331 | 0.67 | 4800 | 0.5997 | 0.4302 | | 0.4207 | 0.72 | 5200 | 0.5058 | 0.4434 | | 0.4015 | 0.78 | 5600 | 0.3794 | 0.4228 | | 0.4117 | 0.83 | 6000 | 0.3523 | 0.4077 | | 0.3943 | 0.89 | 6400 | 0.3392 | 0.4030 | | 0.3894 | 0.94 | 6800 | 0.3377 | 0.3983 | | 0.3888 | 1.0 | 7200 | 0.3425 | 0.3899 | | 0.3515 | 1.05 | 7600 | 0.3666 | 0.3872 | | 0.3437 | 1.11 | 8000 | 0.3806 | 0.3760 | | 0.3446 | 1.16 | 8400 | 0.3440 | 0.3732 | | 0.3422 | 1.22 | 8800 | 1.4329 | 0.3821 | | 0.3511 | 1.27 | 9200 | 0.3203 | 0.3672 | | 0.3458 | 1.33 | 9600 | 0.3142 | 0.3723 | | 0.3347 | 1.39 | 10000 | 0.3037 | 0.3636 | | 0.3459 | 1.44 | 10400 | 0.3210 | 0.3669 | | 0.3271 | 1.5 | 10800 | 0.3145 | 0.3593 | | 0.3343 | 1.55 | 11200 | 0.3055 | 0.3531 | | 0.3293 | 1.61 | 11600 | 0.3111 | 0.3646 | | 0.3163 | 1.66 | 12000 | 0.3305 | 0.3617 | | 0.319 | 1.72 | 12400 | 0.3259 | 0.3522 | | 0.3294 | 1.77 | 12800 | 0.3156 | 0.3513 | | 0.3155 | 1.83 | 13200 | 0.2889 | 0.3400 | | 0.3128 | 1.88 | 13600 | 0.3019 | 0.3453 | | 0.3179 | 1.94 | 14000 | 0.3019 | 0.3489 | | 0.305 | 2.0 | 14400 | 0.2957 | 0.3434 | | 0.2627 | 2.05 | 14800 | 0.3254 | 0.3501 | | 0.2676 | 2.11 | 15200 | 0.2905 | 0.3351 | | 0.2687 | 2.16 | 15600 | 0.2966 | 0.3414 | | 0.2711 | 2.22 | 16000 | 0.3002 | 0.3451 | | 0.2797 | 2.27 | 16400 | 0.2849 | 0.3407 | | 0.2859 | 2.33 | 16800 | 1.0602 | 0.3286 | | 0.2756 | 2.38 | 17200 | 0.2934 | 0.3383 | | 0.2881 | 2.44 | 17600 | 0.4832 | 0.3424 | | 0.2683 | 2.49 | 18000 | 0.3135 | 0.3292 | | 0.2694 | 2.55 | 18400 | 0.2816 | 0.3339 | | 0.2644 | 2.6 | 18800 | 0.2860 | 0.3225 | | 0.2689 | 2.66 | 19200 | 0.2796 | 0.3288 | | 0.2559 | 2.72 | 19600 | 0.2795 | 0.3298 | | 0.2741 | 2.77 | 20000 | 0.2707 | 0.3249 | | 0.2619 | 2.83 | 20400 | 0.2639 | 0.3178 | | 0.2633 | 2.88 | 20800 | 0.2819 | 0.3196 | | 0.2594 | 2.94 | 21200 | 0.2853 | 0.3232 | | 0.2529 | 2.99 | 21600 | 0.2548 | 0.3134 | | 0.2404 | 3.05 | 22000 | 0.2959 | 0.3101 | | 0.2329 | 3.1 | 22400 | 0.2627 | 0.3150 | | 0.2289 | 3.16 | 22800 | 0.2679 | 0.3138 | | 0.2224 | 3.21 | 23200 | 0.2628 | 0.3117 | | 0.2218 | 3.27 | 23600 | 0.2699 | 0.3125 | | 0.2175 | 3.33 | 24000 | 0.3142 | 0.3083 | | 0.2233 | 3.38 | 24400 | 0.2634 | 0.3143 | | 0.2199 | 3.44 | 24800 | 0.2498 | 0.3083 | | 0.2097 | 3.49 | 25200 | 0.2616 | 0.3113 | | 0.2281 | 3.55 | 25600 | 0.2605 | 0.3112 | | 0.2222 | 3.6 | 26000 | 0.2573 | 0.3128 | | 0.2313 | 3.66 | 26400 | 0.2600 | 0.3046 | | 0.231 | 3.71 | 26800 | 0.2559 | 0.3105 | | 0.224 | 3.77 | 27200 | 0.2542 | 0.3047 | | 0.2214 | 3.82 | 27600 | 0.2642 | 0.3026 | | 0.2172 | 3.88 | 28000 | 0.2486 | 0.2947 | | 0.2159 | 3.93 | 28400 | 0.2543 | 0.3008 | | 0.2231 | 3.99 | 28800 | 0.2688 | 0.3019 | | 0.2036 | 4.05 | 29200 | 0.2658 | 0.3054 | | 0.1994 | 4.1 | 29600 | 0.3157 | 0.2985 | | 0.1908 | 4.16 | 30000 | 0.2503 | 0.2944 | | 0.1802 | 4.21 | 30400 | 0.2479 | 0.3017 | | 0.1957 | 4.27 | 30800 | 0.3001 | 0.2938 | | 0.1919 | 4.32 | 31200 | 0.2544 | 0.2954 | | 0.1923 | 4.38 | 31600 | 0.2653 | 0.2961 | | 0.1908 | 4.43 | 32000 | 0.2738 | 0.3008 | | 0.1929 | 4.49 | 32400 | 0.2583 | 0.2941 | | 0.1843 | 4.54 | 32800 | 0.2459 | 0.2882 | | 0.1942 | 4.6 | 33200 | 0.2830 | 0.2941 | | 0.1921 | 4.66 | 33600 | 0.2751 | 0.2939 | | 0.1914 | 4.71 | 34000 | 0.2554 | 0.2898 | | 0.1902 | 4.77 | 34400 | 0.2896 | 0.2932 | | 0.1925 | 4.82 | 34800 | 0.2418 | 0.2868 | | 0.1757 | 4.88 | 35200 | 0.2369 | 0.2844 | | 0.1825 | 4.93 | 35600 | 0.2769 | 0.2920 | | 0.1844 | 4.99 | 36000 | 0.2451 | 0.2858 | | 0.1645 | 5.04 | 36400 | 0.2514 | 0.2807 | | 0.1562 | 5.1 | 36800 | 0.2551 | 0.2805 | | 0.1634 | 5.15 | 37200 | 0.2666 | 0.2832 | | 0.1705 | 5.21 | 37600 | 0.2355 | 0.2830 | | 0.1672 | 5.26 | 38000 | 0.3299 | 0.2818 | | 0.1638 | 5.32 | 38400 | 0.2663 | 0.2833 | | 0.1559 | 5.38 | 38800 | 0.3040 | 0.2844 | | 0.1599 | 5.43 | 39200 | 0.3044 | 0.2759 | | 0.1629 | 5.49 | 39600 | 0.2440 | 0.2772 | | 0.1651 | 5.54 | 40000 | 0.2493 | 0.2807 | | 0.1589 | 5.6 | 40400 | 0.2447 | 0.2803 | | 0.1611 | 5.65 | 40800 | 0.2503 | 0.2814 | | 0.1595 | 5.71 | 41200 | 0.2447 | 0.2739 | | 0.1614 | 5.76 | 41600 | 0.2316 | 0.2744 | | 0.1523 | 5.82 | 42000 | 0.2259 | 0.2670 | | 0.1544 | 5.87 | 42400 | 0.2499 | 0.2742 | | 0.1579 | 5.93 | 42800 | 0.2360 | 0.2746 | | 0.1563 | 5.99 | 43200 | 0.2353 | 0.2697 | | 0.1495 | 6.04 | 43600 | 0.2480 | 0.2753 | | 0.1374 | 6.1 | 44000 | 0.2342 | 0.2679 | | 0.1363 | 6.15 | 44400 | 0.2396 | 0.2687 | | 0.1368 | 6.21 | 44800 | 0.2544 | 0.2717 | | 0.1378 | 6.26 | 45200 | 0.2342 | 0.2686 | | 0.1326 | 6.32 | 45600 | 0.2393 | 0.2695 | | 0.1383 | 6.37 | 46000 | 0.2322 | 0.2681 | | 0.1396 | 6.43 | 46400 | 0.2890 | 0.2694 | | 0.1347 | 6.48 | 46800 | 0.2377 | 0.2644 | | 0.1314 | 6.54 | 47200 | 0.2277 | 0.2642 | | 0.1289 | 6.59 | 47600 | 0.2316 | 0.2667 | | 0.1343 | 6.65 | 48000 | 0.3072 | 0.2663 | | 0.1301 | 6.71 | 48400 | 0.2744 | 0.2658 | | 0.1312 | 6.76 | 48800 | 0.2836 | 0.2687 | | 0.1337 | 6.82 | 49200 | 0.2402 | 0.2632 | | 0.1353 | 6.87 | 49600 | 0.2439 | 0.2637 | | 0.1351 | 6.93 | 50000 | 0.2439 | 0.2619 | | 0.131 | 6.98 | 50400 | 0.2154 | 0.2609 | | 0.1229 | 7.04 | 50800 | 0.2383 | 0.2606 | | 0.1164 | 7.09 | 51200 | 0.2296 | 0.2562 | | 0.1089 | 7.15 | 51600 | 0.2280 | 0.2614 | | 0.1103 | 7.2 | 52000 | 0.2241 | 0.2576 | | 0.115 | 7.26 | 52400 | 0.2306 | 0.2605 | | 0.1168 | 7.32 | 52800 | 0.2200 | 0.2574 | | 0.115 | 7.37 | 53200 | 0.2235 | 0.2564 | | 0.1102 | 7.43 | 53600 | 0.2186 | 0.2593 | | 0.1076 | 7.48 | 54000 | 0.2587 | 0.2573 | | 0.1112 | 7.54 | 54400 | 0.2318 | 0.2566 | | 0.114 | 7.59 | 54800 | 0.2253 | 0.2531 | | 0.1056 | 7.65 | 55200 | 0.2240 | 0.2541 | | 0.1097 | 7.7 | 55600 | 0.2342 | 0.2519 | | 0.1089 | 7.76 | 56000 | 0.2173 | 0.2525 | | 0.1143 | 7.81 | 56400 | 0.3170 | 0.2541 | | 0.1116 | 7.87 | 56800 | 0.3029 | 0.2537 | | 0.1088 | 7.92 | 57200 | 0.2835 | 0.2537 | | 0.1071 | 7.98 | 57600 | 0.2136 | 0.2484 | | 0.1031 | 8.04 | 58000 | 0.2211 | 0.2496 | | 0.0952 | 8.09 | 58400 | 0.2259 | 0.2524 | | 0.097 | 8.15 | 58800 | 0.2258 | 0.2479 | | 0.0977 | 8.2 | 59200 | 0.2224 | 0.2489 | | 0.0965 | 8.26 | 59600 | 0.2207 | 0.2484 | | 0.0904 | 8.31 | 60000 | 0.2179 | 0.2473 | | 0.1005 | 8.37 | 60400 | 0.2254 | 0.2471 | | 0.0917 | 8.42 | 60800 | 0.2186 | 0.2480 | | 0.0937 | 8.48 | 61200 | 0.2149 | 0.2452 | | 0.0957 | 8.53 | 61600 | 0.2254 | 0.2481 | | 0.0934 | 8.59 | 62000 | 0.2215 | 0.2473 | | 0.1034 | 8.65 | 62400 | 0.2204 | 0.2472 | | 0.0907 | 8.7 | 62800 | 0.2288 | 0.2447 | | 0.0893 | 8.76 | 63200 | 0.2292 | 0.2446 | | 0.0905 | 8.81 | 63600 | 0.2207 | 0.2454 | | 0.0874 | 8.87 | 64000 | 0.2261 | 0.2448 | | 0.0915 | 8.92 | 64400 | 0.2280 | 0.2430 | | 0.0912 | 8.98 | 64800 | 0.2278 | 0.2420 | | 0.0906 | 9.03 | 65200 | 0.2290 | 0.2417 | | 0.0821 | 9.09 | 65600 | 0.2223 | 0.2409 | | 0.0848 | 9.14 | 66000 | 0.2241 | 0.2406 | | 0.0785 | 9.2 | 66400 | 0.2299 | 0.2400 | | 0.0761 | 9.25 | 66800 | 0.2289 | 0.2408 | | 0.082 | 9.31 | 67200 | 0.2297 | 0.2409 | | 0.0755 | 9.37 | 67600 | 0.2300 | 0.2398 | | 0.0771 | 9.42 | 68000 | 0.2275 | 0.2391 | | 0.078 | 9.48 | 68400 | 0.2236 | 0.2388 | | 0.0735 | 9.53 | 68800 | 0.2219 | 0.2382 | | 0.0758 | 9.59 | 69200 | 0.2215 | 0.2395 | | 0.0793 | 9.64 | 69600 | 0.2217 | 0.2383 | | 0.0712 | 9.7 | 70000 | 0.2224 | 0.2383 | | 0.0762 | 9.75 | 70400 | 0.2196 | 0.2374 | | 0.0724 | 9.81 | 70800 | 0.2208 | 0.2376 | | 0.0783 | 9.86 | 71200 | 0.2214 | 0.2378 | | 0.0816 | 9.92 | 71600 | 0.2213 | 0.2378 | | 0.0702 | 9.98 | 72000 | 0.2218 | 0.2378 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

Hatman/ddpm-celebahq-finetuned-few-shot-universe

Hatman

null

6

0

diffusers

0

unconditional-image-generation

true

false

mit

null

0

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

false

true

408

# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) A model from google/ddpm-celebahq-256 finetuned using the huggan/few-shot-universe dataset ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Hatman/ddpm-celebahq-finetuned-few-shot-universe') image = pipeline().images[0] image ```

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

austinmw

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="austinmw/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"]) ```

michalcisek5/q-Taxi-v3

michalcisek5

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

368

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="michalcisek5/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"]) ```

austinmw/q-Taxi-v3

austinmw

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="austinmw/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"]) ```

davanstrien/autotrain-encyclopaedia-illustrations-blog-post-3327992159

davanstrien

convnext

5

2

transformers

0

image-classification

true

false

null

['davanstrien/autotrain-data-encyclopaedia-illustrations-blog-post']

{'emissions': 4.608389135708385}

0

1

0

['autotrain', 'vision', 'image-classification']

false

true

245

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3327992159 - CO2 Emissions (in grams): 4.6084 ## Validation Metrics - Loss: 0.040 - Accuracy: 0.992 - Precision: 0.994 - Recall: 0.998 - AUC: 0.993 - F1: 0.996

hectorjelly/Ledbest_FC

hectorjelly

null

20

369

ml-agents

1

reinforcement-learning

false

null

0

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

false

true

840

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

CoreyMorris/poca-SoccerTwos-be-the-goldfish

CoreyMorris

null

20

372

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

861

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

Hamid-reza/mt5-small-finetuned-digikala-titleGen

Hamid-reza

mt5

18

6

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

1,918

# mt5-small-finetuned-digikala-titleGen This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8801 - Rouge1: 70.3489 - Rouge2: 43.245 - Rougel: 34.6608 - Rougelsum: 34.6608 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 7.5555 | 1.0 | 847 | 3.2594 | 45.6729 | 19.6446 | 31.5974 | 31.5974 | | 4.1386 | 2.0 | 1694 | 3.0347 | 58.3021 | 32.8172 | 33.9012 | 33.9012 | | 3.7449 | 3.0 | 2541 | 2.9665 | 66.731 | 40.8991 | 34.2203 | 34.2203 | | 3.5575 | 4.0 | 3388 | 2.9102 | 65.598 | 39.4081 | 34.5116 | 34.5116 | | 3.4062 | 5.0 | 4235 | 2.8944 | 69.6081 | 42.8707 | 34.6622 | 34.6622 | | 3.3408 | 6.0 | 5082 | 2.8888 | 70.2123 | 42.8639 | 34.5669 | 34.5669 | | 3.3025 | 7.0 | 5929 | 2.8801 | 70.3489 | 43.245 | 34.6608 | 34.6608 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

dbaibak/poca-SoccerTwos

dbaibak

null

20

371

ml-agents

1

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

austinmw/q-FrozenLake-v1-8x8-slippery

austinmw

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

395

# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="austinmw/q-FrozenLake-v1-8x8-slippery", 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"]) ```

victorivus/ppo-LunarLander-v2

victorivus

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 ... ```

stelladk/Reinforce-CartPole-v1

stelladk

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

xiaozhangMJXXZ/schoolanime

xiaozhangMJXXZ

null

2

0

null

0

null

false

null

0

[]

false

true

2,133

https://civitai.com/models/7189/school-anime For me, this model is my favorite, and of course I hope you will like it 78 versions later, the current model is good at creating good light and shadow effects, cool background details, and is compatible with many special tags. (If you want to get a cool image, I recommend you refer to the example diagram for tag writing, this model has a very high upper limit and a very low lower limit) On second thought, I'd better list directly how to use it, after all, there is an old saying in China:”授人以鱼不如授人以渔。“(It is better to teach a man how to fish than to give him fish) (((calligraphy_brush, limited palette))), (extremely detailed CG unity 8k wallpaper, masterpiece, best quality, ultra-detailed, best shadow), high resolution, (detailed background), ((加入你喜欢的天气, 或场景Add your favorite weather, or scene)) (beautiful detailed face, beautiful detailed eyes), (perfect_hands, perfect_body, perfect_anatomy), (arm + hand + 1thumb + 4finger), High contrast, (best illumination, an extremely delicate and beautiful), ((cinematic light)), hyper detail, (illustration), dramatic light, intricate details, dynamic angle, 1 girl, (((solo))), ((你喜欢的发型以及头发的颜色可以用“+”来链接它们Your favorite hairstyle and hair color can be linked with "+")), ((加入你想要的人物细节，就比如在示例图里的那样Add the character details you want, as in the example diagram)) Everything can be changed to your liking. Make pictures that you like! If possible, I really hope you can share in the comments section! Of course, nsfw content is also suitable, and I recommend that you use it with some erotic lora models I've generated a lot of comparison charts on CFG for you to refer to, and my personal favourite is in the 3.5-11.5 range. It works well in close-up or distant faces. I recommend this for tag writing(it's in Chinese）元素同典：确实不完全科学的魔导书 (qq.com) Of course, you can use this even if you are not familiar with Chinese 元素法典第二卷——Novel AI 元素魔法全收录 (qq.com) if you use this model to generate pictures that you like, I really, really, really, really hope you share them in the comments, I'll definitely give you a thumbs up on ！！！！

foxanthis/data-kds

foxanthis

gpt_neo

13

3

transformers

0

text-generation

true

false

null

0

['generated_from_trainer']

true

929

# data-kds This model is a fine-tuned version of [flax-community/gpt-neo-125M-code-clippy](https://huggingface.co/flax-community/gpt-neo-125M-code-clippy) 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: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

deprem-ml/deprem-ner

deprem-ml

bert

13

197

transformers

23

token-classification

true

false

apache-2.0

['tr']

null

0

[]

false

true

1,453

## deprem-ner 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" ``` from transformers import pipeline ner_pipe = pipeline("token-classification","deprem-ml/deprem-ner") predictions = ner_pipe(""Lütfen yardım Akevler mahallesi Rüzgar sokak Tuncay apartmanı zemin kat Antakya akrabalarım göçük altında #hatay #Afad"") ``` 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.

foxanthis/data-deepit-kds

foxanthis

gpt_neo

17

2

transformers

0

text-generation

true

false

null

0

['generated_from_trainer']

true

1,287

# data-deepit-kds This model is a fine-tuned version of [flax-community/gpt-neo-125M-code-clippy](https://huggingface.co/flax-community/gpt-neo-125M-code-clippy) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 3 | 0.5930 | | No log | 2.0 | 6 | 0.3968 | | No log | 3.0 | 9 | 0.3333 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Deisler/a2c-PandaReachDense-v2

Deisler

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 ... ```

Mandoryan/PPO-LunarLander-v2

Mandoryan

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 ... ```

scribis/italian-literature-model-mini

scribis

gpt2

9

5

transformers

0

text-generation

false

true

false

mit

['it']

null

0

['generated_from_keras_callback', 'text_generator']

true

1,583

# italian-literature-model-mini This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.7067 - Validation Loss: 5.6842 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 15686, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.7065 | 5.6842 | 0 | | 5.7065 | 5.6842 | 1 | | 5.7067 | 5.6842 | 2 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

konrad-wesub/roberta-base-iphone-2

konrad-wesub

xlm-roberta

9

0

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,258

# roberta-base-iphone-2 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: 0.1359 - Accuracy: 0.9833 ## 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 27 | 0.2765 | 0.8333 | | No log | 2.0 | 54 | 0.1359 | 0.9833 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

deetsml/dummy-model

deetsml

bart

14

20

transformers

0

text-classification

true

false

null

['en']

null

0

['text-classification', 'transformers']

false

true

3,573

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 14756 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "accuracy", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 14756, "warmup_steps": 1476, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BartModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

MultiversexPeeps/wave-concepts

MultiversexPeeps

null

21

9

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

863

### Wave Concepts Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Information on this model will be here: https://civitai.com/user/duskfallcrew If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk wvebg1 (use that on your prompt)

Seyfelislem/wspr-sm-ar4

Seyfelislem

whisper

8

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,275

# wspr-sm-ar4 This model is a fine-tuned version of [Seyfelislem/wspr-sm-ar3](https://huggingface.co/Seyfelislem/wspr-sm-ar3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3664 - Wer: 58.7933 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 150 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0972 | 1.0 | 150 | 0.3664 | 58.7933 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.11.0 - Datasets 2.9.1.dev0 - Tokenizers 0.12.1

vivals/babanaltaf

vivals

null

19

5

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

418

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

SfinOe/stable-diffusion-v1.5

SfinOe

null

22

3

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

13,362

# Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion) ### Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Currently six Stable Diffusion checkpoints are provided, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-1-to-v1-5.png) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

EgilKarlsen/ApacheGPTNEO

EgilKarlsen

gpt_neo

9

5

transformers

0

text-generation

true

false

null

0

['generated_from_trainer']

true

1,246

# ApacheGPTNEO This model is a fine-tuned version of [EgilKarlsen/GPTNEO](https://huggingface.co/EgilKarlsen/GPTNEO) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2460 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.2787 | 1.0 | 18303 | 0.2792 | | 0.24 | 2.0 | 36606 | 0.2524 | | 0.2044 | 3.0 | 54909 | 0.2460 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

mrm8488/xlm-v-base-finetuned-xglue-xnli

mrm8488

xlm-roberta

11

5

transformers

0

text-classification

true

false

mit

null

['xglue']

null

0

['generated_from_trainer', 'xnli']

true

2,704

# XLM-V (base) fine-tuned on XNLI This model is a fine-tuned version of [XLM-V (base)](https://huggingface.co/facebook/xlm-v-base) on the XNLI (XGLUE) dataset. It achieves the following results on the evaluation set: - Loss: 0.6511 - Accuracy: 0.7403 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0994 | 0.08 | 1000 | 1.0966 | 0.3697 | | 1.0221 | 0.16 | 2000 | 1.0765 | 0.4560 | | 0.8437 | 0.24 | 3000 | 0.8472 | 0.6179 | | 0.6997 | 0.33 | 4000 | 0.7650 | 0.6804 | | 0.6304 | 0.41 | 5000 | 0.7227 | 0.7007 | | 0.5972 | 0.49 | 6000 | 0.7430 | 0.6977 | | 0.5886 | 0.57 | 7000 | 0.7365 | 0.7066 | | 0.5585 | 0.65 | 8000 | 0.6819 | 0.7223 | | 0.5464 | 0.73 | 9000 | 0.7222 | 0.7046 | | 0.5289 | 0.81 | 10000 | 0.7290 | 0.7054 | | 0.5298 | 0.9 | 11000 | 0.6824 | 0.7221 | | 0.5241 | 0.98 | 12000 | 0.6650 | 0.7268 | | 0.4806 | 1.06 | 13000 | 0.6861 | 0.7308 | | 0.4715 | 1.14 | 14000 | 0.6619 | 0.7304 | | 0.4645 | 1.22 | 15000 | 0.6656 | 0.7284 | | 0.4443 | 1.3 | 16000 | 0.7026 | 0.7270 | | 0.4582 | 1.39 | 17000 | 0.7055 | 0.7225 | | 0.4456 | 1.47 | 18000 | 0.6592 | 0.7361 | | 0.44 | 1.55 | 19000 | 0.6816 | 0.7329 | | 0.4419 | 1.63 | 20000 | 0.6772 | 0.7357 | | 0.4403 | 1.71 | 21000 | 0.6745 | 0.7319 | | 0.4348 | 1.79 | 22000 | 0.6678 | 0.7338 | | 0.4355 | 1.87 | 23000 | 0.6614 | 0.7365 | | 0.4295 | 1.96 | 24000 | 0.6511 | 0.7403 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

bhpardo/clasificador-amazonproducts2

bhpardo

bert

10

7

transformers

0

text-classification

true

false

null

['amazon_reviews_multi']

null

0

['classification', 'generated_from_trainer']

true

1,389

# clasificador-amazonproducts2 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.2356 - Accuracy: 0.5563 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2045 | 1.0 | 658 | 1.0496 | 0.5845 | | 0.9569 | 2.0 | 1316 | 1.0380 | 0.5704 | | 0.7637 | 3.0 | 1974 | 1.2356 | 0.5563 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

summervent/speller-t5-909

summervent

t5

21

15

transformers

0

text2text-generation

true

false

null

0

['generated_from_trainer']

true

2,259

# speller-t5-909 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: 0.0814 - Rouge1: 18.2203 - Rouge2: 5.9322 - Rougel: 17.7966 - Rougelsum: 18.2203 - Gen Len: 42.0424 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.3022 | 0.1 | 1500 | 0.1563 | 18.2203 | 5.9322 | 17.7966 | 18.2203 | 43.4492 | | 0.2274 | 0.2 | 3000 | 0.1311 | 18.2203 | 5.9322 | 17.7966 | 18.2203 | 42.3814 | | 0.2001 | 0.31 | 4500 | 0.1128 | 18.2203 | 5.9322 | 17.7966 | 18.2203 | 41.9407 | | 0.1757 | 0.41 | 6000 | 0.1063 | 18.2203 | 5.9322 | 17.7966 | 18.2203 | 42.2542 | | 0.1612 | 0.51 | 7500 | 0.1002 | 17.9379 | 5.0847 | 17.5141 | 17.7966 | 42.339 | | 0.1718 | 0.61 | 9000 | 0.0921 | 18.2203 | 5.9322 | 17.7966 | 18.2203 | 42.0508 | | 0.1678 | 0.72 | 10500 | 0.0834 | 17.7966 | 5.0847 | 17.3729 | 17.7966 | 41.9831 | | 0.1407 | 0.82 | 12000 | 0.0793 | 18.2203 | 5.9322 | 17.7966 | 18.2203 | 42.2119 | | 0.1447 | 0.92 | 13500 | 0.0814 | 18.2203 | 5.9322 | 17.7966 | 18.2203 | 42.0424 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

kmposkid1/ppo-Huggy

kmposkid1

null

32

8

ml-agents

0

reinforcement-learning

false

null

0

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

false

true

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: kmposkid1/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

petergoldstein/ppo-LunarLander-v2

petergoldstein

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 ... ```

MultiversexPeeps/art-of-wave

MultiversexPeeps

null

21

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

866

### Art of Wave Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Information on this model will be here: https://civitai.com/user/duskfallcrew If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk wvert1 (use that on your prompt)

atatavana/rhenus_model

atatavana

layoutlmv3

20

35

transformers

0

token-classification

true

false

cc-by-nc-sa-4.0

null

['sroie']

null

0

['generated_from_trainer']

true

3,564

# rhenus_model This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset. It achieves the following results on the evaluation set: - Loss: 0.1560 - Precision: 0.8057 - Recall: 0.8397 - F1: 0.8223 - Accuracy: 0.9734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.96 | 100 | 0.7818 | 0.0909 | 0.0042 | 0.0081 | 0.8569 | | No log | 3.92 | 200 | 0.5681 | 0.2442 | 0.0886 | 0.1300 | 0.8708 | | No log | 5.88 | 300 | 0.4568 | 0.2803 | 0.1857 | 0.2234 | 0.8913 | | No log | 7.84 | 400 | 0.3759 | 0.5053 | 0.4051 | 0.4496 | 0.9196 | | 0.5952 | 9.8 | 500 | 0.2987 | 0.6560 | 0.6034 | 0.6286 | 0.9456 | | 0.5952 | 11.76 | 600 | 0.2585 | 0.6721 | 0.6920 | 0.6819 | 0.9456 | | 0.5952 | 13.73 | 700 | 0.2016 | 0.7247 | 0.7553 | 0.7397 | 0.9595 | | 0.5952 | 15.69 | 800 | 0.2053 | 0.704 | 0.7426 | 0.7228 | 0.9573 | | 0.5952 | 17.65 | 900 | 0.1845 | 0.7782 | 0.7848 | 0.7815 | 0.9667 | | 0.1097 | 19.61 | 1000 | 0.1917 | 0.75 | 0.7848 | 0.7670 | 0.9623 | | 0.1097 | 21.57 | 1100 | 0.1897 | 0.8099 | 0.8270 | 0.8184 | 0.9695 | | 0.1097 | 23.53 | 1200 | 0.1848 | 0.7901 | 0.8101 | 0.8 | 0.9684 | | 0.1097 | 25.49 | 1300 | 0.1533 | 0.8016 | 0.8523 | 0.8262 | 0.9734 | | 0.1097 | 27.45 | 1400 | 0.1534 | 0.8204 | 0.8481 | 0.8340 | 0.9750 | | 0.0384 | 29.41 | 1500 | 0.1879 | 0.8024 | 0.8397 | 0.8206 | 0.9695 | | 0.0384 | 31.37 | 1600 | 0.1550 | 0.816 | 0.8608 | 0.8378 | 0.9750 | | 0.0384 | 33.33 | 1700 | 0.1598 | 0.8279 | 0.8523 | 0.8399 | 0.9756 | | 0.0384 | 35.29 | 1800 | 0.1643 | 0.8148 | 0.8354 | 0.825 | 0.9728 | | 0.0384 | 37.25 | 1900 | 0.1558 | 0.792 | 0.8354 | 0.8131 | 0.9728 | | 0.02 | 39.22 | 2000 | 0.1699 | 0.7944 | 0.8312 | 0.8124 | 0.9717 | | 0.02 | 41.18 | 2100 | 0.1558 | 0.8138 | 0.8481 | 0.8306 | 0.9750 | | 0.02 | 43.14 | 2200 | 0.1566 | 0.8024 | 0.8397 | 0.8206 | 0.9728 | | 0.02 | 45.1 | 2300 | 0.1617 | 0.8049 | 0.8354 | 0.8199 | 0.9734 | | 0.02 | 47.06 | 2400 | 0.1571 | 0.8016 | 0.8354 | 0.8182 | 0.9723 | | 0.014 | 49.02 | 2500 | 0.1560 | 0.8057 | 0.8397 | 0.8223 | 0.9734 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.2.2 - Tokenizers 0.13.2

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

eshwarprasadS

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

402

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

eshwarprasadS/taxi_qlearner

eshwarprasadS

null

5

0

null

0

reinforcement-learning

false

null

0

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

true

373

# **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="eshwarprasadS/taxi_qlearner", 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"]) ```

SRobbins/a2c-AntBulletEnv-v0

SRobbins

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 ... ```

DragonProgrammer/dqn-SpaceInvadersNoFrameskip-v4

DragonProgrammer

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

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

true

2,242

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

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

gatardochi

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="gatardochi/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"]) ```

gatardochi/q-Taxi-v3

gatardochi

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="gatardochi/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"]) ```