Tencent/NeuralNLP-NeuralClassifier

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1922 stars · 404 forks observed · 2026-08-28

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

NeuralClassifier is a PyTorch-based open-source toolkit for neural text classification, with a focus on hierarchical multi-label classification. It provides many text encoders such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet, and Transformer, and also supports binary and multi-class classification.

Use cases

• train a hierarchical multi-label text classifier
• classify documents into a category tree
• run multi-label text classification with pytorch
• compare text encoders like TextCNN and Transformer for classification
• build a binary or multi-class text classifier
• experiment with HMCN for hierarchical classification

When to choose

• you need hierarchical or multi-label text classification out of the box
• you want to benchmark many classic text classification encoders in one toolkit
• you prefer a PyTorch-based, config-driven training pipeline

When to avoid

• you need modern pretrained transformer fine-tuning (e.g., BERT) as the primary workflow
• you need tasks beyond text classification such as NER or QA
• you want a production serving system rather than a training toolkit

Facets

library · maturity maintenance

machine-learning nlp machine-learning deep-learning python text-classification hierarchical-classification multi-label pytorch text-encoders toolkit natural-language-processing

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