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nlp-tutorial is an educational repository that implements influential natural language processing models in concise PyTorch examples. Most implementations use fewer than 100 lines of executable code, making the architectures easier to inspect and modify. The curriculum begins with neural language models, Word2Vec, and FastText. It then covers TextCNN, recurrent networks, LSTM, bidirectional LSTM, sequence-to-sequence learning, and attention. Later examples implement the Transformer and BERT for translation, classification, and masked-token tasks. Paper links and Google Colab notebooks connect each implementation with its research background and an accessible runtime.
Features
- Concise PyTorch model implementations
- Word embedding and language models
- CNN, RNN, and LSTM examples
- Sequence-to-sequence attention models
- Transformer and BERT implementations
- Research papers and Colab notebooks
Project Samples
Categories
Natural Language Processing (NLP)License
MIT LicenseFollow nlp-tutorial
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