thunlp/ERNIE

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1417 stars · 262 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ERNIE is a PyTorch toolkit and dataset for augmenting pre-trained language models like BERT with knowledge graph entity representations, from the ACL 2019 paper by Tsinghua's THUNLP. It includes pre-training data pipelines and fine-tuning commands for knowledge-driven NLP tasks.

Use cases

• augment BERT with knowledge graph entities
• fine-tune a knowledge-enhanced language model
• run entity typing on FIGER or OpenEntity
• relation classification on FewRel or TACRED
• pretrain a language model on Wikipedia with entity links
• compare knowledge-enhanced vs vanilla BERT performance

When to choose

• you need entity/knowledge-graph-aware language representations for NLP research
• you want to reproduce or extend the ACL 2019 ERNIE paper
• your downstream tasks benefit from informative entity signals, like entity typing or relation classification

When to avoid

• you need a modern, actively maintained LLM toolkit - the codebase targets old PyTorch versions
• you want plug-and-play inference rather than research code requiring custom data pipelines
• your tasks do not benefit from knowledge graph entity information

Facets

library · maturity maintenance

machine-learning nlp llm-training machine-learning deep-learning artificial-intelligence python cross-platform knowledge-graph pretrained-language-model bert entity-typing relation-classification research-code pytorch natural-language-processing linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="thunlp/ERNIE")