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--- |
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license: mit |
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language: |
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- en |
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- zh |
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--- |
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# B2NER |
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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. |
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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. |
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- 📖 Paper: [Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](http://arxiv.org/abs/2406.11192) |
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- 🎮 Github Repo: https://github.com/UmeanNever/B2NER . |
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- 📀 Data: You can download from here (the B2NERD_data.zip in the "Files and versions" tab). See below data section for more information. |
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- 💾 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. |
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See github repo for more information about data usage and this work. |
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# Data |
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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. |
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We provide 3 versions of our dataset. |
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- **`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. |
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- `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. |
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- `B2NERD_raw`: The raw collected datasets with raw entity labels. It goes through basic format preprocessing but without further standardization. |
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You can download the data from here (the B2NERD_data.zip in the "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. |
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Please ensure that you have the proper licenses to access the raw datasets in our collection. |
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Below are the datasets statistics and source datasets for `B2NERD` dataset. |
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| Split | Lang. | Datasets | Types | Num | Raw Num | |
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|-------|-------|----------|-------|-----|---------| |
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| Train | En | 19 | 119 | 25,403 | 838,648 | |
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| | Zh | 21 | 222 | 26,504 | 580,513 | |
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| | Total | 40 | 341 | 51,907 | 1,419,161 | |
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| Test | En | 7 | 85 | - | 6,466 | |
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| | Zh | 7 | 60 | - | 14,257 | |
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| | Total | 14 | 145 | - | 20,723 | |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/655c6b1abfb531437a54c0e6/NIQWzYvwRxbMVgJf1KDzL.png" width="1000"/> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/655c6b1abfb531437a54c0e6/9UuY9EuA7R5PvasddMObQ.png" width="1000"/> |
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More information can be found in the Appendix of paper. |
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# Cite |
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``` |
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@inproceedings{yang-etal-2025-beyond, |
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title = "Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition", |
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author = "Yang, Yuming and |
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Zhao, Wantong and |
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Huang, Caishuang and |
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Ye, Junjie and |
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Wang, Xiao and |
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Zheng, Huiyuan and |
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Nan, Yang and |
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Wang, Yuran and |
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Xu, Xueying and |
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Huang, Kaixin and |
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Zhang, Yunke and |
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Gui, Tao and |
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Zhang, Qi and |
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Huang, Xuanjing", |
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editor = "Rambow, Owen and |
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Wanner, Leo and |
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Apidianaki, Marianna and |
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Al-Khalifa, Hend and |
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Eugenio, Barbara Di and |
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Schockaert, Steven", |
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booktitle = "Proceedings of the 31st International Conference on Computational Linguistics", |
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month = jan, |
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year = "2025", |
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address = "Abu Dhabi, UAE", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2025.coling-main.725/", |
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pages = "10902--10923" |
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} |
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``` |
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