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https://github.com/huggingface/awesome-huggingface

🤗 A list of wonderful open-source projects & applications integrated with Hugging Face libraries.
https://github.com/huggingface/awesome-huggingface

List: awesome-huggingface

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🤗 A list of wonderful open-source projects & applications integrated with Hugging Face libraries.

Host: GitHub
URL: https://github.com/huggingface/awesome-huggingface
Owner: huggingface
License: apache-2.0
Created: 2021-06-10T10:16:48.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-04-29T20:19:45.000Z (8 months ago)
Last Synced: 2024-11-02T13:02:53.779Z (about 1 month ago)
Topics: awesome, awesome-list, huggingface, machine-learning, nlp, transformers
Homepage:
Size: 62.5 KB
Stars: 888
Watchers: 56
Forks: 79
Open Issues: 4
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

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ultimate-awesome - awesome-huggingface - 🤗 A list of wonderful open-source projects & applications integrated with Hugging Face libraries. (Programming Language Lists / Python Lists)

README

        
 





# awesome-huggingface

This is a list of some wonderful open-source projects & applications integrated with Hugging Face libraries.

[How to contribute](https://github.com/huggingface/awesome-huggingface/blob/main/CONTRIBUTING.md)

## 🤗 Official Libraries

*First-party cool stuff made with ❤️ by 🤗 Hugging Face.*

* [transformers](https://github.com/huggingface/transformers) - State-of-the-art natural language processing for Jax, PyTorch and TensorFlow.

* [datasets](https://github.com/huggingface/datasets) - The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools.

* [tokenizers](https://github.com/huggingface/tokenizers) - Fast state-of-the-Art tokenizers optimized for research and production.

* [knockknock](https://github.com/huggingface/knockknock) - Get notified when your training ends with only two additional lines of code.

* [accelerate](https://github.com/huggingface/accelerate) - A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.

* [autonlp](https://github.com/huggingface/autonlp) - Train state-of-the-art natural language processing models and deploy them in a scalable environment automatically.

* [nn_pruning](https://github.com/huggingface/nn_pruning) - Prune a model while finetuning or training.

* [huggingface_hub](https://github.com/huggingface/huggingface_hub) - Client library to download and publish models and other files on the huggingface.co hub.

* [tune](https://github.com/huggingface/tune) - A benchmark for comparing Transformer-based models.

## 👩‍🏫 Tutorials

*Learn how to use Hugging Face toolkits, step-by-step.*

* [Official Course](https://huggingface.co/course) (from Hugging Face) - The official course series provided by 🤗 Hugging Face.

* [transformers-tutorials](https://github.com/nielsrogge/transformers-tutorials) (by @nielsrogge) - Tutorials for applying multiple models on real-world datasets.

## 🧰 NLP Toolkits

*NLP toolkits built upon Transformers. Swiss Army!*

* [AllenNLP](https://github.com/allenai/allennlp) (from AI2) - An open-source NLP research library.

* [Graph4NLP](https://github.com/graph4ai/graph4nlp) - Enabling easy use of Graph Neural Networks for NLP.

* [Lightning Transformers](https://github.com/PyTorchLightning/lightning-transformers) - Transformers with PyTorch Lightning interface.

* [Adapter Transformers](https://github.com/Adapter-Hub/adapter-transformers) - Extension to the Transformers library, integrating adapters into state-of-the-art language models.

* [Obsei](https://github.com/obsei/obsei) - A low-code AI workflow automation tool and performs various NLP tasks in the workflow pipeline. 

* [Trapper](https://github.com/obss/trapper) (from OBSS) - State-of-the-art NLP through transformer models in a modular design and consistent APIs.

* [Flair](https://github.com/flairNLP/flair) - A very simple framework for state-of-the-art NLP.

## 🥡 Text Representation

*Converting a sentence to a vector.*

* [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) (from UKPLab) - Widely used encoders computing dense vector representations for sentences, paragraphs, and images.

* [WhiteningBERT](https://github.com/Jun-jie-Huang/WhiteningBERT) (from Microsoft) - An easy unsupervised sentence embedding approach with whitening.

* [SimCSE](https://github.com/princeton-nlp/SimCSE) (from Princeton) - State-of-the-art sentence embedding with contrastive learning.

* [DensePhrases](https://github.com/princeton-nlp/DensePhrases) (from Princeton) - Learning dense representations of phrases at scale.

## ⚙️ Inference Engines

*Highly optimized inference engines implementing Transformers-compatible APIs.*

* [TurboTransformers](https://github.com/Tencent/TurboTransformers) (from Tencent) - An inference engine for transformers with fast C++ API.

* [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) (from Nvidia) - A script and recipe to run the highly optimized transformer-based encoder and decoder component on NVIDIA GPUs.

* [lightseq](https://github.com/bytedance/lightseq) (from ByteDance) - A high performance inference library for sequence processing and generation implemented in CUDA.

* [FastSeq](https://github.com/microsoft/fastseq) (from Microsoft) - Efficient implementation of popular sequence models (e.g., Bart, ProphetNet) for text generation, summarization, translation tasks etc.

## 🌗 Model Scalability

*Parallelization models across multiple GPUs.*

* [Parallelformers](https://github.com/tunib-ai/parallelformers) (from TUNiB) - A library for model parallel deployment.

* [OSLO](https://github.com/tunib-ai/oslo) (from TUNiB) - A library that supports various features to help you train large-scale models.

* [Deepspeed](https://github.com/microsoft/DeepSpeed) (from Microsoft) - Deepspeed-ZeRO - scales any model size with zero to no changes to the model. [Integrated with HF Trainer](https://huggingface.co/docs/transformers/master/main_classes/deepspeed).

* [fairscale](https://github.com/facebookresearch/fairscale) (from Facebook) - Implements ZeRO protocol as well. [Integrated with HF Trainer](https://huggingface.co/docs/transformers/master/main_classes/trainer#fairscale).

* [ColossalAI](https://github.com/hpcaitech/colossalai) (from Hpcaitech) - A Unified Deep Learning System for Large-Scale Parallel Training (1D, 2D, 2.5D, 3D and sequence parallelism, and ZeRO protocol).

## 🏎️ Model Compression/Acceleration

*Compressing or accelerate models for improved inference speed.*

* [torchdistill](https://github.com/yoshitomo-matsubara/torchdistill) - PyTorch-based modular, configuration-driven framework for knowledge distillation.

* [TextBrewer](https://github.com/airaria/TextBrewer) (from HFL) - State-of-the-art distillation methods to compress language models.

* [BERT-of-Theseus](https://github.com/JetRunner/BERT-of-Theseus) (from Microsoft) - Compressing BERT by progressively replacing the components of the original BERT.

## 🏹️ Adversarial Attack

*Conducting adversarial attack to test model robustness.*

* [TextAttack](https://github.com/QData/TextAttack) (from UVa) -  A Python framework for adversarial attacks, data augmentation, and model training in NLP.

* [TextFlint](https://github.com/textflint/textflint) (from Fudan) - A unified multilingual robustness evaluation toolkit for NLP.

* [OpenAttack](https://github.com/thunlp/OpenAttack) (from THU) - An open-source textual adversarial attack toolkit.

## 🔁 Style Transfer

*Transfer the style of text! Now you know why it's called transformer?*

* [Styleformer](https://github.com/PrithivirajDamodaran/Styleformer) - A neural language style transfer framework to transfer text smoothly between styles.

* [ConSERT](https://github.com/yym6472/ConSERT) - A contrastive framework for self-supervised sentence representation transfer.

## 💢 Sentiment Analysis

*Analyzing the sentiment and emotions of human beings.*

* [conv-emotion](https://github.com/declare-lab/conv-emotion) - Implementation of different architectures for emotion recognition in conversations.

## 🙅 Grammatical Error Correction

*You made a typo! Let me correct it.*

* [Gramformer](https://github.com/PrithivirajDamodaran/Gramformer) - A framework for detecting, highlighting and correcting grammatical errors on natural language text.

## 🗺 Translation

*Translating between different languages.*

* [dl-translate](https://github.com/xhlulu/dl-translate) - A deep learning-based translation library based on HF Transformers.

* [EasyNMT](https://github.com/UKPLab/EasyNMT) (from UKPLab) - Easy-to-use, state-of-the-art translation library and Docker images based on HF Transformers.

## 📖 Knowledge and Entity

*Learning knowledge, mining entities, connecting the world.*

* [PURE](https://github.com/princeton-nlp/PURE) (from Princeton) - Entity and relation extraction from text.

## 🎙 Speech

*Speech processing powered by HF libraries. Need for speech!*

* [s3prl](https://github.com/s3prl/s3prl) - A self-supervised speech pre-training and representation learning toolkit.

* [speechbrain](https://github.com/speechbrain/speechbrain) - A PyTorch-based speech toolkit.

## 🤯 Multi-modality

*Understanding the world from different modalities.*

* [ViLT](https://github.com/dandelin/ViLT) (from Kakao) - A vision-and-language transformer Without convolution or region supervision.

## 🤖 Reinforcement Learning

*Combining RL magic with NLP!*

* [trl](https://github.com/lvwerra/trl) - Fine-tune transformers using Proximal Policy Optimization (PPO) to align with human preferences.

## ❓ Question Answering

*Searching for answers? Transformers to the rescue!*

* [Haystack](https://haystack.deepset.ai/) (from deepset) - End-to-end framework for developing and deploying question-answering systems in the wild.

## 💁 Recommender Systems

*I think this is just right for you!*

* [Transformers4Rec](https://github.com/NVIDIA-Merlin/Transformers4Rec) (from Nvidia) - A flexible and efficient library powered by Transformers for sequential and session-based recommendations.

## ⚖️ Evaluation

*Evaluating model outputs and data quality powered by HF datasets!*

* [Jury](https://github.com/obss/jury) (from OBSS) - Easy to use tool for evaluating NLP model outputs, spesifically for NLG (Natural Language Generation), offering various automated text-to-text metrics.

* [Spotlight](https://github.com/Renumics/spotlight) - Interactively explore your HF dataset with one line of code. Use model results (e.g. embeddings, predictions) to understand critical data segments and model failure modes.

## 🔍 Neural Search

*Search, but with the power of neural networks!*

* [Jina Integration](https://github.com/jina-ai/jina-hub/tree/master/encoders/nlp/TransformerTorchEncoder) - Jina integration of Hugging Face Accelerated API.

* Weaviate Integration [(text2vec)](https://www.semi.technology/developers/weaviate/current/modules/text2vec-transformers.html) [(QA)](https://www.semi.technology/developers/weaviate/current/modules/qna-transformers.html) - Weaviate integration of Hugging Face Transformers.

* [ColBERT](https://github.com/stanford-futuredata/ColBERT) (from Stanford) - A fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.

## ☁ Cloud

*Cloud makes your life easy!*

* [Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html) - Making it easier than ever to train Hugging Face Transformer models in Amazon SageMaker.

## 📱 Hardware

*The infrastructure enabling the magic to happen.*

* [Qualcomm](https://www.qualcomm.com/news/onq/2020/12/02/exploring-ai-capabilities-qualcomm-snapdragon-888-mobile-platform) - Collaboration on enabling Transformers in Snapdragon.

* [Intel](https://github.com/huggingface/tune) - Collaboration with Intel for configuration options.