# shiyybua/NER

基于tensorflow深度学习的中文的命名实体识别

Repository: https://github.com/shiyybua/NER
Canonical: https://ross.abutalabs.com/products/ner
Language: Python
License Family: other
Last push: 2018-03-11T13:03:32+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3284, "days_push": 3097, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1054, forks 398 (observed 2026-08-28T04:03:24.033642+00:00)

## What it is
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
- artifact type: library
- maturity: abandoned
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, deep-learning
- platform: python
- tags: named-entity-recognition, chinese-nlp, tensorflow, birnn, crf, sequence-labeling, natural-language-processing

## Member repositories
- shiyybua/NER (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:24.033642+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:59:03.066971+00:00, confidence not recorded.
  - readme: https://github.com/shiyybua/NER (fetched 2026-08-28T04:03:24.033642+00:00, sha cbb8d84824af)
- Data as of 2026-08-30T08:39:29.467469+00:00.