Update README.md

README.md

@@ -1,3 +1,33 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- Umean/B2NERD
+language:
+- en
+- zh
+library_name: peft
+---
+
+# 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 [HuggingFace](https://huggingface.co/datasets/Umean/B2NERD) or [Google Drive](https://drive.google.com/file/d/11Wt4RU48i06OruRca2q_MsgpylzNDdjN/view?usp=drive_link).
+ - 💾 Model (LoRA Adapters): Current repo saves the B2NER model LoRA adapter based on InternLM2.5-7B.
+
+**See github repo for more information about model usage and this work.**
+
+# 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}
+}
+```
+
+