# thunlp/OpenNRE

An Open-Source Package for Neural Relation Extraction (NRE)

Repository: https://github.com/thunlp/OpenNRE
Canonical: https://ross.abutalabs.com/products/opennre
Language: Python
License: MIT
License Family: permissive
Topics: relation-extraction
Last push: 2024-01-10T11:52:49+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": 3475, "days_push": 966, "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 4467, forks 1049 (observed 2026-08-28T04:08:50.896964+00:00)

## What it is
OpenNRE is an open-source Python toolkit for neural relation extraction, extracting relation triples between entities from plain text. It unifies supervised and distant supervision models, including CNN-based and pre-trained language model approaches, for knowledge graph construction.

## Use cases
- extract relation triples from sentences
- build a knowledge graph from plain text
- train relation extraction models on custom datasets
- use pretrained relation extraction models in production
- run NLP research experiments on relation extraction benchmarks
- identify relationships between named entities in documents

## When to choose
- you need relation extraction for knowledge graph construction
- you want a unified framework to compare NRE models
- you need pretrained models for entity-relation extraction
- you are an NLP researcher benchmarking relation extraction methods

## When to avoid
- you need general-purpose NLP pipelines beyond relation extraction
- you want a maintained project with frequent updates
- you need production-grade entity extraction without training data
- your task is entity recognition rather than relation classification

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, artificial-intelligence
- platform: python
- tags: relation-extraction, knowledge-graph, information-extraction, pretrained-models, natural-language-processing

## Member repositories
- thunlp/OpenNRE (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:50.896964+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 2026-08-29T18:20:38.325354+00:00, confidence not recorded.
  - readme: https://github.com/thunlp/OpenNRE (fetched 2026-08-28T04:08:50.896964+00:00, sha 9196fe768395)
- Data as of 2026-08-30T08:39:29.467469+00:00.