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

In PyTorch Learing Neural Networks Likes CNN、BiLSTM

Repository: https://github.com/dalinvip/cnn-lstm-bilstm-deepcnn-clstm-in-pytorch
Canonical: https://ross.abutalabs.com/products/cnn-lstm-bilstm-deepcnn-clstm-in-pytorch
Language: Python
License: Apache-2.0
License Family: permissive
Topics: cnn-model, cnn-bilstm, lstm-model, gru, model-bilstm, highway-cnn, pytorch, torchtext
Last push: 2023-03-20T02:58:24+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3323, "days_push": 1262, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1449, forks 346 (observed 2026-08-28T04:04:45.657956+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, nlp
- domain: deep-learning, tutorials
- platform: python
- tags: pytorch, text-classification, cnn, lstm, bilstm, gru, torchtext, sentiment-analysis, sst, natural-language-processing, linux, macos

## Member repositories
- dalinvip/cnn-lstm-bilstm-deepcnn-clstm-in-pytorch (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:45.657956+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 2026-08-30T04:35:59.650471+00:00, confidence not recorded.
  - readme: https://github.com/dalinvip/cnn-lstm-bilstm-deepcnn-clstm-in-pytorch (fetched 2026-08-28T04:04:45.657956+00:00, sha 0778001a40ba)
- Data as of 2026-08-30T08:39:29.467469+00:00.