Update README.md

README.md

@@ -1,3 +1,48 @@
----
-license: mit
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+---
+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)
+ - 🎮 Code Repo: https://github.com/UmeanNever/B2NER
+ - 📀 Data: See below data section. You can download from [HuggingFace](https://huggingface.co/datasets/Umean/B2NERD) 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}
+}
+```