metadata

license: mit
language:
  - en
  - zh

B2NER

We present B2NERD, a cohesive and efficient dataset that can improve LLMs' generalization on the challenging Open NER task, refined from 54 existing English or Chinese datasets. Our B2NER models, trained on B2NERD, outperform GPT-4 by 6.8-12.0 F1 points and surpass previous methods in 3 out-of-domain benchmarks across 15 datasets and 6 languages.

📖 Paper: Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition
🎮 Code Repo: https://github.com/UmeanNever/B2NER
📀 Data: See below data section. You can download from HuggingFace or [Google Drive](https://drive.google.com/file/d/11Wt4RU48i06OruRca2q_MsgpylzNDdjN/view?
💾 Model (LoRA Adapters): On the way

Data

One of the paper's core contribution is the construction of B2NERD dataset. It's a cohesive and efficient collection refined from 54 English and Chinese datasets and designed for Open NER model training.
We provide 3 versions of our dataset.

B2NERD (Recommended): Contain ~52k samples from 54 Chinese or English datasets. This is the final version of our dataset suitable for out-of-domain / zero-shot NER model training. It has standardized entity definitions and pruned diverse data.
B2NERD_all: Contain ~1.4M samples from 54 datasets. The full-data version of our dataset suitable for in-domain supervised evaluation. It has standardized entity definitions but does not go through any data selection or pruning.
B2NERD_raw: Raw collected datasets with raw entity labels. It goes through basic format preprocessing but without further standardization.

Below are the datasets statistics and source datasets for B2NERD dataset.

Split	Lang.	Datasets	Types	Num	Raw Num
Train	En	19	119	25,403	838,648
	Zh	21	222	26,504	580,513
	Total	40	341	51,907	1,419,161
Test	En	7	85	-	6,466
	Zh	7	60	-	14,257
	Total	14	145	-	20,723

More datset information can be found in the Appendix of paper.

Cite

@article{yang2024beyond,
  title={Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition},
  author={Yang, Yuming and Zhao, Wantong and Huang, Caishuang and Ye, Junjie and Wang, Xiao and Zheng, Huiyuan and Nan, Yang and Wang, Yuran and Xu, Xueying and Huang, Kaixin and others},
  journal={arXiv preprint arXiv:2406.11192},
  year={2024}
}