# microsoft/nlp-recipes

Natural Language Processing Best Practices & Examples

Repository: https://github.com/microsoft/nlp-recipes
Canonical: https://ross.abutalabs.com/products/nlp-recipes
Language: Python
License: MIT
License Family: permissive
Topics: nlp, natural-language-processing, natural-language-understanding, text, deep-learning, azure-ml, nlu, nli, natural-language-inference, mlflow, machine-learning, natural-language, pretrained-models, sota, transfomer, text-classification, best-practices
Archived: true
Last push: 2022-08-30T19:12:12+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2707, "days_push": 1464, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6429, forks 910 (observed 2026-08-28T04:09:43.366958+00:00)

## What it is
Microsoft's collection of Jupyter notebooks and utility functions demonstrating best practices for building NLP systems with state-of-the-art deep learning and pretrained transformer models. It covers common text and language scenarios such as text classification and natural language inference.

## Use cases
- learn how to build nlp models with deep learning
- examples of text classification with transformers
- fine-tune pretrained language models for nlu tasks
- best practices for natural language processing in python
- get started with nlp notebooks and sample code
- compare traditional vs state-of-the-art nlp approaches

## When to choose
- you want guided, notebook-based examples of modern NLP techniques
- you need reference implementations for text classification or NLI
- you are learning to apply pretrained transformer models in Python

## When to avoid
- you need a production-ready NLP library with active support
- you require up-to-date compatibility with the latest framework versions
- you want a hosted API solution rather than self-built models

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, tutorials, developer-tools
- platform: python, cross-platform
- tags: jupyter-notebooks, best-practices, pretrained-models, text-classification, azure-ml, transformers, educational, natural-language-processing

## Member repositories
- microsoft/nlp-recipes (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:43.366958+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:44:52.857771+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/nlp-recipes (fetched 2026-08-28T04:09:43.366958+00:00, sha 7ee3da7f4db7)
- Data as of 2026-08-30T08:39:29.467469+00:00.