joel4u/spanie

A span-based joint named entity recognition (NER) and relation extraction model with Roberta

SpanIE

A span-based joint named entity recognition (NER) and relation extraction model with RoBERTa

This code repository has been restructured based on JointIE for the purpose of dependency transition models. It mainly references the following models and codes:

Generalizing Natural Language Analysis through Span-relation Representations (ACL2020). [paper] (https://arxiv.org/abs/1911.03822)

LSTM/BERT-CRF Model for Named Entity Recognition (or Sequence Labeling) code

Environment

torch==2.2.0

Transformer==4.37.2

python==3.8.0

Train and Evaluate Models

Train and evaluate model with default configure.（RoBERTa-Large, Learing rate 1e-5）

python transformers_trainer.py --dataset scierc

Results with Default Configure on Test Set

Dataset	NER (F1)	Relation (F1)
SciERC	70.98（best）	46.19
SciERC	69.87	47.27（best）
NYT24(NYT)	96.52（best）	84.90
NYT24(NYT)	96.21	85.06（best）
NYT29	内容6	内容6
WebNLG	97.94	92.64(best)
ACE2004_fold1	90.79 (best)	49.79
ACE2004_fold1	89.70	60.81 (best)
ACE2004_fold2	90.11(best)	51.11
ACE2004_fold2	88.04	53.98(best)
ACE2004_fold3	90.00(best)	42.39
ACE2004_fold3	88.86	46.77(best)
ACE2004_fold4	92.03(best)	46.72
ACE2004_fold4	91.04	48.67(best)
ACE2004_fold5	88.32(best)	45.16
ACE2004_fold5	87.26	46.35(best)
ACE2004_NER（avg best）	90.25	47.03
ACE2004_RE（avg best）	88.98	51.32
ACE2005_NRE(best)	90.12	62.43
ACE2005_RE(best)	89.51	64.84