DSKSD/DeepNLP-models-Pytorch resource

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2944 stars · 638 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

learning-resource · maturity maintenance

nlp machine-learning deep-learning deep-learning tutorials education python pytorch cs224n jupyter-notebooks word-embeddings rnn attention ner machine-translation text-classification language-model natural-language-processing

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