dalinvip/cnn-lstm-bilstm-deepcnn-clstm-in-pytorch resource

Health v2 · maintenance only

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1449 stars · 346 forks observed · 2026-08-28

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

A PyTorch implementation collection of neural network models (CNN, BiLSTM, BiGRU, DeepCNN, CLSTM) for text classification tasks like sentiment analysis on SST-1/SST-2 datasets. It serves as a reference and learning resource for building and comparing sequence and convolutional text classification architectures.

Use cases

• classify sentences into sentiment categories with CNN in pytorch
• implement BiLSTM text classification in pytorch
• compare CNN vs LSTM vs GRU on SST-2 benchmark
• learn how to build text classification models with torchtext
• get baseline accuracy numbers for sentence classification models
• train a deep CNN for sentence classification

When to choose

• you want reference PyTorch implementations of classic text classification models
• you need baseline results on SST-1/SST-2 sentiment datasets
• you are learning how CNNs and RNNs are applied to NLP in PyTorch

When to avoid

• you need production-ready, actively maintained NLP pipelines
• you want transformer or BERT-based models (see the author's BERT repo instead)
• you need compatibility with recent PyTorch versions without code changes
• you require serious hyperparameter tuning or state-of-the-art accuracy

Facets

learning-resource · maturity maintenance

machine-learning deep-learning nlp deep-learning tutorials python pytorch text-classification cnn lstm bilstm gru torchtext sentiment-analysis sst natural-language-processing linux macos

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