shiyybua/NER

Health v2 · maintenance only

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Adoption not part of the score

1054 stars · 398 forks observed · 2026-08-28

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

A Chinese named entity recognition (NER) system built with TensorFlow, using a bidirectional RNN combined with a CRF layer for sequence labeling. It relies on pre-trained word embeddings and provides training and prediction scripts driven by a config file.

Use cases

• recognize named entities in Chinese text
• train a BiRNN-CRF NER model on custom Chinese corpora
• run sequence labeling with TensorFlow
• experiment with word embeddings for Chinese NER
• build a Chinese entity extraction pipeline

When to choose

• you need a simple, readable TensorFlow implementation of BiRNN+CRF for Chinese NER
• you want to train an NER model on your own pre-segmented Chinese data with custom word embeddings
• you are studying classic deep-learning approaches to sequence labeling

When to avoid

• you need a maintained library with active support or a license
• you want modern transformer-based NER or pretrained models out of the box
• your project requires TensorFlow 2.x or recent Python versions

Facets

library · maturity abandoned

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

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markdown · JSON · MCP: product_card(name="shiyybua/NER")