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--- |
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license: mit |
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datasets: |
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- Umean/B2NERD |
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language: |
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- en |
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- zh |
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library_name: peft |
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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: 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). |
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- 💾 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. |
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**See github repo for more information about model usage and this work.** |
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# Cite |
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``` |
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@article{yang2024beyond, |
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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 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}, |
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journal={arXiv preprint arXiv:2406.11192}, |
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year={2024} |
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} |
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``` |
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