Determined22/zh-NER-TF

Health v2 · maintenance only

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Adoption not part of the score

2333 stars · 924 forks observed · 2026-08-28

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

A simple character-based BiLSTM-CRF sequence labeling model for Chinese Named Entity Recognition built with TensorFlow 1.x. It recognizes PERSON, LOCATION, and ORGANIZATION entities and includes training/testing scripts plus preprocessed MSRA-style data files.

Use cases

• recognize person, location, and organization names in Chinese text
• train a BiLSTM-CRF model for Chinese NER
• run sequence labeling on character-level Chinese corpora
• learn how CRF layers work on top of BiLSTM encoders
• train a custom NER model on my own tagged Chinese dataset

When to choose

• you need a minimal, easy-to-read Chinese NER implementation for learning or prototyping
• you want a classic BiLSTM-CRF baseline for PERSON/LOC/ORG tagging in Chinese
• you have data in BIO character-level format and want a simple training pipeline

When to avoid

• you need modern TensorFlow 2.x, PyTorch, or pretrained transformer-based NER models
• you need production-grade performance, maintenance, or a maintained license
• you need entity types beyond PER/LOC/ORG or multilingual support

Facets

library · maturity maintenance

machine-learning nlp machine-learning python named-entity-recognition bilstm-crf sequence-labeling chinese-nlp tensorflow natural-language-processing

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markdown · JSON · MCP: product_card(name="Determined22/zh-NER-TF")