Uniner 7B All

🚀 UniNER-7B-all

UniNER-7B-all is the best model in the UniNER series, offering high - performance named entity recognition capabilities.

🚀 Quick Start

This model is the best UniNER model. It is trained on the combinations of three data splits:

1. ChatGPT-generated Pile-NER-type data.
2. ChatGPT-generated Pile-NER-definition data.
3. 40 supervised datasets in the Universal NER benchmark (see Fig. 4 in paper), where we randomly sample up to 10K instances from the train split of each dataset.

Note that CrossNER and MIT datasets are excluded from training for OOD evaluation.

Check our paper for more information. Check our repo about how to use the model.

💻 Usage Examples

Basic Usage

The template for inference instances is as follows:

<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);">
<strong>Prompting template:</strong><br/>
A virtual assistant answers questions from a user based on the provided text.<br/>
USER: Text: <span style="color: #d73a49;">{Fill the input text here}</span><br/>
ASSISTANT: I’ve read this text.<br/>
USER: What describes <span style="color: #d73a49;">{Fill the entity type here}</span> in the text?<br/>
ASSISTANT: <span style="color: #0366d6;">(model's predictions in JSON format)</span><br/>
</div>

⚠️ Important Note

Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.

📄 License

This model and its associated data are released under the CC BY-NC 4.0 license. They are primarily used for research purposes.

📚 Documentation

Citation

@article{zhou2023universalner,
      title={UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition}, 
      author={Wenxuan Zhou and Sheng Zhang and Yu Gu and Muhao Chen and Hoifung Poon},
      year={2023},
      eprint={2308.03279},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}