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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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--- |
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--- |
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# UniNER-7B-type |
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**Description**: A UniNER-7B model trained from LLama-7B using the [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type) without human-labeled data. The data was collected by prompting gpt-3.5-turbo-0301 to label entities from passages and provide entity tags. The data collection prompt is as follows: |
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<div style="background-color: #f6f8fa; padding: 20px; border-radius: 10px; border: 1px solid #e1e4e8; box-shadow: 0 2px 5px rgba(0,0,0,0.1);"> |
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<strong>Instruction:</strong><br/> |
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Given a passage, your task is to extract all entities and identify their entity types. The output should be in a list of tuples of the following format: [("entity 1", "type of entity 1"), ... ].</div> |
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Check our [paper](https://arxiv.org/abs/2308.03279) for more information. Check our [repo](https://github.com/universal-ner/universal-ner) about how to use the model. |
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## Comparison with [UniNER-7B-definition](https://huggingface.co/datasets/Universal-NER/Pile-NER-definition) |
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The UniNER-7B-type model excels when handling entity tags. It performs better on the Universal NER benchmark, which consists of 43 academic datasets across 9 domains. In contrast, UniNER-7B-definition performs better at processing entity types defined in short sentences and is more robust to type paraphrasing. |
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## Inference |
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The template for inference instances is as follows: |
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<div style="background-color: #f6f8fa; padding: 20px; border-radius: 10px; border: 1px solid #e1e4e8; box-shadow: 0 2px 5px rgba(0,0,0,0.1);"> |
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<strong>Prompting template:</strong><br/> |
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A virtual assistant answers questions from a user based on the provided text.<br/> |
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USER: Text: <span style="color: #d73a49;">{Fill the input text here}</span><br/> |
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ASSISTANT: I’ve read this text.<br/> |
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USER: What describes <span style="color: #d73a49;">{Fill the entity type here}</span> in the text?<br/> |
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ASSISTANT: <span style="color: #0366d6;">(model's predictions in JSON format)</span><br/> |
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</div> |
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### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type. |
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## License |
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This model and its associated data are released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes. |
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## Citation |
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```bibtex |
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@article{zhou2023universalner, |
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title={UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition}, |
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author={Wenxuan Zhou and Sheng Zhang and Yu Gu and Muhao Chen and Hoifung Poon}, |
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year={2023}, |
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eprint={2308.03279}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
