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| master | [![Build Status](https://dev.azure.com/best-practices/nlp/_apis/build/status/unit-test-master?branchName=master)](https://dev.azure.com/best-practices/nlp/_build/latest?definitionId=22&branchName=master) | | staging | [![Build Status](https://dev.azure.com/best-practices/nlp/_apis/build/status/unit-test-staging?branchName=staging)](https://dev.azure.com/best-practices/nlp/_build/latest?definitionId=21&branchName=staging) |
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# NLP Best Practices
This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language.
-The following section includes a list of the available scenarios. Each scenario is demonstrated in one or more Jupyter notebook examples that make use of the core code base of models and utilities.
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-## Scenarios
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-| Scenario | Applications | Languages | Models |
-|---| ------------------------ | -------------------------------------------- | ------------------- |
-|[Text Classification](scenarios/text_classification) |Topic Classification|en, zh, ar|BERT|
-|[Named Entity Recognition](scenarios/named_entity_recognition) |Wikipedia NER | en, zh |BERT|
-|[Sentence Similarity](scenarios/sentence_similarity) |STS Benchmark |en|Representation: TF-IDF, Word Embeddings, Doc Embeddings<br>Metrics: Cosine Similarity, Word Mover's Distance|
-|[Embeddings](scenarios/embeddings)| Custom Embeddings Training|en|Word2Vec<br>fastText<br>GloVe|
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## Planning
All feature planning is done via projects, milestones, and issues in this repository.
