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A curated list of awesome frameworks, libraries, tools, datasets, tutorials, and research papers for Natural Language Processing (NLP). This list covers a variety of NLP tasks, from text processing and tokenization to state-of-the-art language models and applications like sentiment analysis and machine translation.
https://github.com/awesomelistsio/awesome-nlp

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A curated list of awesome frameworks, libraries, tools, datasets, tutorials, and research papers for Natural Language Processing (NLP). This list covers a variety of NLP tasks, from text processing and tokenization to state-of-the-art language models and applications like sentiment analysis and machine translation.

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# Awesome Natural Language Processing (NLP) [![Awesome Lists](https://srv-cdn.himpfen.io/badges/awesome-lists/awesomelists-flat.svg)](https://github.com/awesomelistsio/awesome)



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> A curated list of awesome frameworks, libraries, tools, datasets, tutorials, and research papers for Natural Language Processing (NLP). This list covers a variety of NLP tasks, from text processing and tokenization to state-of-the-art language models and applications like sentiment analysis and machine translation.



## Contents



- [Frameworks and Libraries](#frameworks-and-libraries)

- [Text Processing and Tokenization](#text-processing-and-tokenization)

- [Pretrained Language Models](#pretrained-language-models)

- [NLP Tasks](#nlp-tasks)

- [Tools and Applications](#tools-and-applications)

- [Datasets](#datasets)

- [Research Papers](#research-papers)

- [Learning Resources](#learning-resources)

- [Books](#books)

- [Community](#community)

- [Contribute](#contribute)

- [License](#license)



## Frameworks and Libraries



- [Hugging Face Transformers](https://huggingface.co/transformers/) - A comprehensive library of state-of-the-art NLP models like BERT, GPT, and RoBERTa.

- [spaCy](https://spacy.io/) - An open-source library for advanced natural language processing in Python.

- [NLTK (Natural Language Toolkit)](https://www.nltk.org/) - A comprehensive library for text processing and analysis.

- [Stanford NLP](https://stanfordnlp.github.io/CoreNLP/) - A suite of NLP tools developed by the Stanford NLP Group.

- [AllenNLP](https://allennlp.org/) - An open-source NLP research library built on top of PyTorch.

- [TextBlob](https://textblob.readthedocs.io/) - A simple library for processing textual data in Python.



## Text Processing and Tokenization



- [Moses Tokenizer](https://github.com/moses-smt/mosesdecoder) - A widely used tokenizer for machine translation tasks.

- [BPE (Byte Pair Encoding)](https://arxiv.org/abs/1508.07909) - A subword tokenization technique used by models like GPT and BERT.

- [SentencePiece](https://github.com/google/sentencepiece) - A language-independent tokenization and text processing library.

- [RegexpTokenizer (NLTK)](https://www.nltk.org/api/nltk.tokenize.html) - A tokenizer that uses regular expressions to split text into tokens.

- [spaCy Tokenizer](https://spacy.io/usage/linguistic-features#tokenization) - A fast and efficient tokenizer integrated within the spaCy library.



## Pretrained Language Models



- [BERT (Bidirectional Encoder Representations from Transformers)](https://arxiv.org/abs/1810.04805) - A Transformer-based model for a variety of NLP tasks.

- [GPT-3 (Generative Pre-trained Transformer 3)](https://arxiv.org/abs/2005.14165) - A powerful generative language model by OpenAI.

- [RoBERTa](https://arxiv.org/abs/1907.11692) - An optimized variant of BERT, focusing on robustly optimized pretraining.

- [T5 (Text-to-Text Transfer Transformer)](https://arxiv.org/abs/1910.10683) - A model that treats every NLP task as a text-to-text problem.

- [XLNet](https://arxiv.org/abs/1906.08237) - A generalized autoregressive pretraining model that outperforms BERT on several tasks.

- [DistilBERT](https://arxiv.org/abs/1910.01108) - A smaller, faster, and lighter version of BERT.



## NLP Tasks



- **Sentiment Analysis**: The process of determining the sentiment (positive, negative, or neutral) of a text.

  - [TextBlob Sentiment Analysis](https://textblob.readthedocs.io/en/dev/quickstart.html#sentiment-analysis)

  - [VADER Sentiment Analysis](https://github.com/cjhutto/vaderSentiment)

- **Named Entity Recognition (NER)**: Identifying and classifying entities in text (e.g., names, dates).

  - [spaCy NER](https://spacy.io/usage/linguistic-features#named-entities)

  - [Stanford NER](https://nlp.stanford.edu/software/CRF-NER.html)

- **Machine Translation**: Translating text from one language to another.

  - [OpenNMT](https://opennmt.net/) - A neural machine translation framework.

  - [Fairseq](https://fairseq.readthedocs.io/en/latest/) - A Facebook AI research framework for sequence-to-sequence models.

- **Text Summarization**: Generating a concise summary of a given text.

  - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation](https://arxiv.org/abs/1910.13461)

  - [PEGASUS](https://arxiv.org/abs/1912.08777) - A pre-trained model specifically designed for text summarization.



## Tools and Applications



- [Gensim](https://radimrehurek.com/gensim/) - A Python library for topic modeling and document similarity.

- [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/) - A suite of NLP tools for linguistic analysis.

- [FastText](https://fasttext.cc/) - A library for efficient text classification and representation learning.

- [Polyglot](https://polyglot.readthedocs.io/) - A multilingual NLP toolkit supporting various languages.

- [LexRank](https://github.com/crabcamp/lexrank) - A text summarization library using graph-based ranking algorithms.



## Datasets



- [GLUE Benchmark](https://gluebenchmark.com/) - A collection of resources for evaluating natural language understanding systems.

- [SQuAD (Stanford Question Answering Dataset)](https://rajpurkar.github.io/SQuAD-explorer/) - A dataset for reading comprehension and question answering tasks.

- [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/) - A dataset for named entity recognition.

- [IMDB Reviews](https://ai.stanford.edu/~amaas/data/sentiment/) - A dataset for sentiment analysis.

- [WikiText](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) - A collection of high-quality text from Wikipedia for language modeling tasks.



## Research Papers



- [Attention Is All You Need (2017)](https://arxiv.org/abs/1706.03762) - The paper that introduced the Transformer architecture, revolutionizing NLP.

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)](https://arxiv.org/abs/1810.04805) - The introduction of the BERT model.

- [GloVe: Global Vectors for Word Representation (2014)](https://nlp.stanford.edu/pubs/glove.pdf) - A model for generating word embeddings.

- [Word2Vec: Efficient Estimation of Word Representations in Vector Space (2013)](https://arxiv.org/abs/1301.3781) - The introduction of Word2Vec, a method for learning word embeddings.

- [ELMo: Deep Contextualized Word Representations (2018)](https://arxiv.org/abs/1802.05365) - A model for contextual word embeddings.



## Learning Resources



- [Coursera: Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing) - A comprehensive course on NLP by Deeplearning.ai.

- [Stanford CS224N: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/) - A popular university course on NLP.

- [Fast.ai NLP Course](https://www.fast.ai/) - A practical course on NLP using the fastai library.

- [Hugging Face Tutorials](https://huggingface.co/course/chapter1) - Official tutorials for using the Hugging Face NLP library.



## Books



- *Speech and Language Processing* by Daniel Jurafsky and James H. Martin - A comprehensive textbook on NLP.

- *Natural Language Processing with Python* by Steven Bird, Ewan Klein, and Edward Loper - An introduction to NLP using Python.

- *Deep Learning for Natural Language Processing* by Palash Goyal, Sumit Pandey, and Karan Jain - A book covering deep learning techniques in NLP.



## Community



- [Reddit: r/NLP](https://www.reddit.com/r/LanguageTechnology/) - A subreddit for discussions on natural language processing.

- [Hugging Face Community](https://discuss.huggingface.co/) - A forum for discussing the Hugging Face NLP library.

- [NLP Summit](https://www.nlpsummit.org/) - An annual conference focused on NLP research and applications.



## Contribute



Contributions are welcome!



## License



[![CC0](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/by-sa.svg)](http://creativecommons.org/licenses/by-sa/4.0/)