zjy-ucas/ChineseNER
A neural network model for Chinese named entity recognition observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3501
- days_rel: n/a
- days_push: 2946
- n_releases_24m: 0
Adoption not part of the score
1822 stars · 564 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A TensorFlow demo implementing a bidirectional LSTM-CRF model for Chinese named entity recognition. It includes training and evaluation scripts plus Chinese word embedding data.
Use cases
- recognize named entities in Chinese text
- train a Chinese NER model
- sequence labeling for Chinese characters
- learn LSTM-CRF NER implementation
- extract person and location names from Chinese text
When to choose
- you need a simple reference implementation of BiLSTM-CRF for Chinese NER
- you are studying character-based Chinese NER models
When to avoid
- you need maintained code or modern TensorFlow versions
- you need production-grade multilingual NER
- you need a license for commercial use
Facets
library · maturity abandoned
nlp machine-learning deep-learning machine-learning python named-entity-recognition chinese-nlp lstm-crf tensorflow sequence-labeling natural-language-processing
1 source
- readme: https://github.com/zjy-ucas/ChineseNER · fetched 2026-08-28 · 17db1b7e05df
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| zjy-ucas/ChineseNER | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="zjy-ucas/ChineseNER")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem