Create README.md

README.md

@@ -0,0 +1,53 @@
+---
+license: cc
+language:
+- en
+---
+
+---
+
+# UniNER-7B-definition
+
+**Description**: A UniNER-7B model trained from LLama-7B using the [Pile-NER-definition data](https://huggingface.co/datasets/Universal-NER/Pile-NER-definition) without human-labeled data. The data was collected by prompting gpt-3.5-turbo-0301 to label entities from passages and provide short-sentence definitions. The data collection prompt 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>Instruction:</strong><br/>
+Given a paragraph, your task is to extract all entities and concepts,
+and define their type using a short sentence. The output should be in the following format:
+[("entity", "definition of entity type in a short sentence"), ... ]
+</div>
+
+Check our [paper](https://arxiv.org/abs/2308.03279) for more information.
+
+## Comparison with [UniNER-7B-type](https://huggingface.co/Universal-NER/UniNER-7B-type)
+The UniNER-7B-type model, trained on Pile-NER-type, excels in recognizing common and short NER tags (e.g., person, location) and performs better on NER datasets. On the other hand, UniNER-7B-definition demonstrates superior capabilities in understanding short-sentence definitions of entity types. Additionally, it exhibits enhanced robustness against variations in type paraphrasing.
+
+## Inference
+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>
+
+### 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](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes.
+
+## Citation
+
+```bibtex
+@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}
+}
+```