# neubig/nn4nlp-code

Code Samples from Neural Networks for NLP

Repository: https://github.com/neubig/nn4nlp-code
Canonical: https://ross.abutalabs.com/products/nn4nlp-code
Language: Python
License: NOASSERTION
License Family: other
Last push: 2020-01-27T21:24:54+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3293, "days_push": 2410, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1311, forks 478 (observed 2026-08-28T04:04:20.005118+00:00)

## What it is
A collection of Python code examples accompanying the CMU 11-747 'Neural Networks for NLP' course by Graham Neubig and collaborators. It demonstrates neural network techniques applied to NLP tasks for educational purposes.

## Use cases
- learn neural networks for NLP with code examples
- study course materials for CMU neural networks for NLP
- find reference implementations of NLP deep learning models
- supplement self-study of neural NLP techniques
- explore example code for sequence models in NLP

## When to choose
- you want worked code examples to accompany learning neural NLP concepts
- you are following the CMU 11-747 course or similar curriculum
- you prefer small educational samples over full production frameworks

## When to avoid
- you need a production-ready NLP library or framework
- you expect maintained, up-to-date code with recent releases
- you need comprehensive documentation or support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp, deep-learning
- domain: deep-learning, tutorials
- platform: python
- tags: course-materials, code-examples, cmu, educational, natural-language-processing

## Member repositories
- neubig/nn4nlp-code (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:20.005118+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 2026-08-30T04:49:51.482967+00:00, confidence not recorded.
  - readme: https://github.com/neubig/nn4nlp-code (fetched 2026-08-28T04:04:20.005118+00:00, sha cff4b95b0808)
- Data as of 2026-08-30T08:39:29.467469+00:00.