brightmart/text_classification

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

7940 stars · 2532 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of deep learning baseline models for text classification in NLP, implemented in TensorFlow. It covers classic architectures like FastText, TextCNN, TextRNN, seq2seq with attention, and memory networks, with support for multi-label and multi-class classification.

Use cases

• classify text documents into categories with deep learning
• train a multi-label text classification model
• get baseline models for sentence classification
• compare classic text classification architectures like TextCNN and FastText
• build a sentiment or topic classifier in TensorFlow
• experiment with memory networks for NLP tasks

When to choose

• you need classic baseline models for text classification research or prototyping
• you want TensorFlow implementations of TextCNN, TextRNN, FastText, or memory networks
• you need multi-label classification support with reference implementations

When to avoid

• you need state-of-the-art transformer-based classifiers like BERT fine-tuning
• you want a production-ready, actively maintained NLP framework
• you prefer PyTorch or modern LLM-based approaches

Facets

library · maturity maintenance

machine-learning nlp deep-learning machine-learning deep-learning python text-classification fasttext textcnn textrnn multi-label-classification tensorflow attention-mechanism memory-networks baseline-models natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="brightmart/text_classification")