azure-mlbest-practicesdeep-learningmachine-learningmlflownatural-languagenatural-language-inferencenatural-language-processingnatural-language-understandingnlinlpnlupretrained-modelssotatexttext-classificationtransfomer
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Integrating Mlflow. |
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benchmarks | ||
docs | ||
scenarios | ||
tests | ||
tools | ||
utils_nlp | ||
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AUTHORS.md | ||
CONTRIBUTING.md | ||
LICENSE | ||
README.md | ||
SETUP.md | ||
pyproject.toml |
README.md
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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.
Planning
All feature planning is done via projects, milestones, and issues in this repository.
Getting Started
To get started, navigate to the Setup Guide, where you'll find instructions on how to setup your environment and dependencies.
Contributing
This project welcomes contributions and suggestions. Before contributing, please see our contribution guidelines.