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README.md
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@@ -11,16 +11,17 @@ We present B2NERD, a cohesive and efficient dataset that can improve LLMs' gener
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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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- 🎮
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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): On the way
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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.
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We provide 3 versions of our dataset.
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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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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): On the way
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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.
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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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