---
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](http://arxiv.org/abs/2406.11192)
 - 🎮 Github Repo: https://github.com/UmeanNever/B2NER .
 - 📀 Data: See below data section. You can download from here (in "Files and versions" tab) or [Google Drive](https://drive.google.com/file/d/11Wt4RU48i06OruRca2q_MsgpylzNDdjN/view?usp=drive_link).
 - 💾 Model (LoRA Adapters): See [7B model](https://huggingface.co/Umean/B2NER-Internlm2.5-7B-LoRA) and [20B model](https://huggingface.co/Umean/B2NER-Internlm2-20B-LoRA). You may refer to the github repo for quick demo usage.

See github repo for more information about data usage and this work.

# 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. **The preprocessed test datasets (7 for Chinese NER and 7 for English NER) used for Open NER OOD evaluation in our paper are also included in the released dataset** to facilitate convenient evaluation for future research.  

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 features 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 undergo any data selection or pruning.  
 - `B2NERD_raw`: The raw collected datasets with raw entity labels. It goes through basic format preprocessing but without further standardization.

You can download the data from here (in "Files and versions" tab) or [Google Drive](https://drive.google.com/file/d/1JW3ZZPlJ5vm_upn0msihI9FQjo4TmZDI/view?usp=sharing). Current data is uploaded as .zip for convenience. We are considering upload raw data files for better preview.  
Please ensure that you have the proper licenses to access the raw datasets in our collection. 

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  |

<img src="https://cdn-uploads.huggingface.co/production/uploads/655c6b1abfb531437a54c0e6/NIQWzYvwRxbMVgJf1KDzL.png" width="1000"/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/655c6b1abfb531437a54c0e6/9UuY9EuA7R5PvasddMObQ.png" width="1000"/>

More 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}
}
```