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  ---
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  license: apache-2.0
 
 
 
 
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  ---
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- **How to use the model**
 
 
 
 
 
 
 
 
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  To use the model with `transformer` package, see the example below:
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  ```python
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  outputs = llm.generate([chat_prompt], sampling_params)
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  response_text = outputs[0].outputs[0].text
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  print(response_text)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  ---
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  license: apache-2.0
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+ language:
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+ - en
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+ base_model:
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+ - aaditya/Llama3-OpenBioLLM-8B
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  ---
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+ # OpenBioLLM-Text2Graph-8B
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+
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+ This model is a biomedical annotation model designed to generate named entity annotations from unlabeled biomedical text.
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+ It was introduced in the paper [GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://arxiv.org/abs/2504.00676).
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+
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+ This model enables **high-throughput, cost-efficient synthetic biomedical NER data generation**, serving as the synthetic annotation backbone for [GLiNER-BioMed models](https://huggingface.co/collections/knowledgator/gliner-biomed-67ecf1b7cc62e673dbc8b57f).
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+
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+
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+ ## Usage
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  To use the model with `transformer` package, see the example below:
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  ```python
 
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  outputs = llm.generate([chat_prompt], sampling_params)
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  response_text = outputs[0].outputs[0].text
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  print(response_text)
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+ ```
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+
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+ ## Citation
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+
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+ If you use this model, please cite:
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+
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+ ```bibtex
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+ @misc{yazdani2025glinerbiomedsuiteefficientmodels,
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+ title={GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition},
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+ author={Anthony Yazdani and Ihor Stepanov and Douglas Teodoro},
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+ year={2025},
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+ eprint={2504.00676},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2504.00676},
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+ }
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  ```