LeeSureman/Flat-Lattice-Transformer

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1003 stars · 170 forks observed · 2026-09-03

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

Reference implementation of the ACL 2020 paper FLAT: Chinese NER Using Flat-Lattice Transformer, built on PyTorch and FastNLP. It trains flat-lattice transformer models for Chinese named entity recognition on datasets like OntoNotes, MSRA, Weibo, and Resume, with optional BERT integration.

Use cases

• run Chinese named entity recognition with a flat-lattice transformer
• reproduce FLAT NER results from the ACL 2020 paper
• train a Chinese NER model on MSRA or OntoNotes
• compare lattice-based transformer NER against BERT baselines
• experiment with character and word lattice embeddings for Chinese NER

When to choose

• you need a strong research-grade Chinese NER model with lattice structure
• you want to reproduce or extend the FLAT ACL 2020 paper
• you work with PyTorch and FastNLP for Chinese sequence labeling

When to avoid

• you need a production-ready NER service with an API
• you want multilingual or non-Chinese NER out of the box
• you need actively maintained code with a license and recent updates

Facets

library · maturity maintenance

nlp machine-learning deep-learning machine-learning deep-learning python named-entity-recognition chinese-nlp transformer lattice-structure research-code acl-2020 pytorch natural-language-processing

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