Update README.md

README.md

@@ -12,17 +12,31 @@ We present B2NERD, a cohesive and efficient dataset that can improve LLMs' gener
 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 .
+ - 🎮 GitHub Repo: https://github.com/UmeanNever/B2NER .
  - 📀 Data: You can download from here (the B2NERD_data.zip in the "Files and versions" tab). See below data section for more information.
  - 💾 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.
+**Feature Highlights:**
+ - Curated dataset (B2NERD) refined from the largest bilingual NER dataset collection to date for training Open NER models.  
+ - Achieves SoTA OOD NER performance across multiple benchmarks with light-weight LoRA adapters (<=50MB). 
+ - Uses simple natural language format prompt, achieving 4X faster inference speed than previous SoTA which use complex prompts. 
+ - Easy integration with other IE tasks by adopting UIE-style instructions. 
+ - Provides a universal entity taxonomy that guides the definition and label naming of new entities.
+ - We have open-sourced our data, code, and models, and provided easy-to-follow usage instructions.  
+
+| Model | Avg. F1 on OOD English datasets  | Avg. F1 on OOD Chinese datasets | Avg. F1 on OOD multilingual dataset
+|-------|------------------------|------------------------|--|
+| Previous SoTA | 69.1    |  42.7     | 36.6
+| GPT         | 60.1    |  54.7     | 31.8
+| B2NER | **72.1**    |  **61.3**     | **43.3**
+
+See our [GitHub Repo](https://github.com/UmeanNever/B2NER) for more information on 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.  
+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. See the tables below for our train/test splits and dataset statistics.
 
 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` (**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 training data, while also including separate unpruned test 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.