# jiesutd/NCRFpp

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Repository: https://github.com/jiesutd/NCRFpp
Canonical: https://ross.abutalabs.com/products/ncrfpp
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pytorch, ner, sequence-labeling, crf, lstm-crf, char-rnn, char-cnn, named-entity-recognition, part-of-speech-tagger, chunking, neural-networks, nbest, lstm, cnn, natural-language-processing, artificial-intelligence
Last push: 2022-06-30T09:54:21+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": 3192, "days_push": 1525, "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 1899, forks 436 (observed 2026-08-28T04:05:51.406327+00:00)

## What it is
NCRF++ is a PyTorch-based toolkit for neural sequence labeling tasks such as NER, POS tagging, and word segmentation. Model architectures combining character/word LSTM, GRU, or CNN encoders with softmax or CRF inference layers are fully configurable via a configuration file without writing code.

## Use cases
- train a named entity recognition model
- build a POS tagger with LSTM-CRF
- run Chinese word segmentation
- compare CRF vs softmax decoding for sequence labeling
- add character-level features to a tagger
- get n-best sequences from a sequence labeler
- reproduce state-of-the-art NER results

## When to choose
- you need a configurable, code-free pipeline for NER, POS, or chunking
- you want LSTM/CNN encoders with CRF inference in PyTorch
- you need fast batched training and n-best decoding for tagging tasks

## When to avoid
- you need transformer-based pretrained models like BERT for tagging
- you require tasks beyond sequence labeling
- you need a actively maintained library with recent updates

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, nlp, deep-learning
- domain: machine-learning, deep-learning
- platform: python, cross-platform
- tags: pytorch, sequence-labeling, named-entity-recognition, crf, lstm-cnn, pos-tagging, word-segmentation, chunking, nbest-decoding, natural-language-processing

## Member repositories
- jiesutd/NCRFpp (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:51.406327+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 2026-08-30T03:12:11.225548+00:00, confidence not recorded.
  - readme: https://github.com/jiesutd/NCRFpp (fetched 2026-08-28T04:05:51.406327+00:00, sha b485efe1786f)
- Data as of 2026-08-30T08:39:29.467469+00:00.