Update README.md

README.md

@@ -14,19 +14,22 @@ library_name: peft
 #   example_title: "Chinese Example"
 ---
 
-# B2NER 
+This is the B2NER model's LoRA adapter based on [InternLM2.5-7B](https://huggingface.co/internlm/internlm2_5-7b).  
+**See github repo for quick demo usage and more information about this work.**
+
+## 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).
+ - 🎮 Code Repo: We provide codes for both training and inference at https://github.com/UmeanNever/B2NER
+ - 📀 Data: See [B2NERD](https://huggingface.co/datasets/Umean/B2NERD).
  - 💾 Model (LoRA Adapters): Current repo saves the B2NER model LoRA adapter based on InternLM2.5-7B. See [20B model](https://huggingface.co/Umean/B2NER-Internlm2-20B-LoRA) for a 20B adapter.
 
 **See github repo for more information about model usage and this work.**
 
-# Cite
+## Cite
 ```
 @article{yang2024beyond,
   title={Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition},