zjunlp/DeepKE

Health v2 · maintenance only

Adoption not part of the score

4472 stars · 750 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DeepKE is an open-source PyTorch-based knowledge extraction toolkit for knowledge graph construction, supporting named entity recognition, relation extraction, and attribute extraction. It covers standard supervised, low-resource few-shot, document-level, and multimodal scenarios, and includes LLM-based extraction via DeepKE-LLM and OneKE.

Use cases

• extract entities and relations from unstructured text to build a knowledge graph
• train a named entity recognition model with few labeled examples
• perform document-level relation extraction across multiple sentences
• run multimodal entity and relation extraction using visual cues
• use large language models for information extraction
• extract relational triples in Chinese with cnSchema support

When to choose

• building knowledge graphs from text, especially Chinese or low-resource settings
• you need research-grade IE models (NER, RE, triple extraction) with PyTorch
• you want few-shot or multimodal information extraction capabilities
• you want LLM-based extraction with prebuilt OneKE models

When to avoid

• you need a production-ready REST API or GUI out of the box
• your task is unrelated to information extraction or knowledge graphs
• you need a lightweight pure-inference library without training infrastructure

Facets

library · maturity active

nlp machine-learning deep-learning parser machine-learning artificial-intelligence python knowledge-graph information-extraction named-entity-recognition relation-extraction attribute-extraction low-resource few-shot document-level multimodal pytorch chinese-nlp llm knowledge-graph-construction natural-language-processing linux gpu

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