# DSKSD/DeepNLP-models-Pytorch

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

Repository: https://github.com/DSKSD/DeepNLP-models-Pytorch
Canonical: https://ross.abutalabs.com/products/deepnlp-models-pytorch
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: pytorch, nlp, deep-nlp-models, stanford-univ, rnn, neural-network, deep-learning, cs-224n, natural-language-processing
Last push: 2019-10-15T03:26:36+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": 3305, "days_push": 2514, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2944, forks 638 (observed 2026-08-28T04:07:31.130952+00:00)

## What it is
A collection of Jupyter Notebook implementations of deep NLP models from Stanford's CS-224n course, written in PyTorch. It covers models like word2vec, GloVe, NER classifiers, dependency parsing, RNN language models, and neural machine translation with attention.

## Use cases
- learn deep NLP models from cs-224n with pytorch code
- implement word2vec skip-gram with negative sampling from scratch
- study neural machine translation with attention in a notebook
- understand RNN language models with worked examples
- see a CNN text classification implementation in pytorch
- study companion for stanford cs-224n lectures

## When to choose
- you are following the CS-224n course and want runnable PyTorch code for each model
- you learn best from small, self-contained notebooks tied to papers
- you want reference implementations of classic NLP models like GloVe, NER, and seq2seq attention

## When to avoid
- you are a PyTorch beginner needing step-by-step framework tutorials
- you need production-ready, maintained NLP libraries
- you want modern transformer or LLM-based models, as the repo predates them and is no longer actively developed

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: deep-learning, tutorials, education
- platform: python
- tags: pytorch, cs224n, jupyter-notebooks, word-embeddings, rnn, attention, ner, machine-translation, text-classification, language-model, natural-language-processing

## Member repositories
- DSKSD/DeepNLP-models-Pytorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:31.130952+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 2026-08-30T07:32:54.838363+00:00, confidence not recorded.
  - readme: https://github.com/DSKSD/DeepNLP-models-Pytorch (fetched 2026-08-28T04:07:31.130952+00:00, sha 5531b78ebdc1)
- Data as of 2026-08-30T08:39:29.467469+00:00.