# rockyzhengwu/FoolNLTK

A Chinese Nature Language Toolkit

Repository: https://github.com/rockyzhengwu/FoolNLTK
Canonical: https://ross.abutalabs.com/products/foolnltk
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2020-02-17T01:39:50+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3184, "days_push": 2390, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1678, forks 387 (observed 2026-08-28T04:05:21.000149+00:00)

## What it is
FoolNLTK is a Python toolkit for Chinese natural language processing built on a BiLSTM model. It provides high-accuracy word segmentation, part-of-speech tagging, and named entity recognition, with support for user-defined dictionaries and custom model training.

## Use cases
- segment chinese text into words
- tag parts of speech in chinese sentences
- extract named entities like locations from chinese text
- add custom words to a chinese tokenizer dictionary
- train a custom chinese nlp model
- batch process chinese text files

## When to choose
- you need accurate chinese word segmentation rather than the fastest
- you need pos tagging and ner for chinese in one toolkit
- you want to train or fine-tune your own chinese nlp model

## When to avoid
- you need windows or non-python-3 support
- you need actively maintained software with recent updates
- you need the fastest possible segmentation speed

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning
- domain: machine-learning, localization
- platform: python
- tags: chinese-word-segmentation, part-of-speech-tagging, named-entity-recognition, bilstm, user-dictionary, natural-language-processing, linux

## Member repositories
- rockyzhengwu/FoolNLTK (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:21.000149+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 2026-08-30T03:41:34.511985+00:00, confidence not recorded.
  - readme: https://github.com/rockyzhengwu/FoolNLTK (fetched 2026-08-28T04:05:21.000149+00:00, sha 523aae1dd789)
  - registry_pypi: https://pypi.org/pypi/foolnltk/json (fetched 2026-08-29T11:14:55.187690+00:00, sha 1711cd9be2bc)
- Data as of 2026-08-30T08:39:29.467469+00:00.