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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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metrics: |
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- precision |
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- recall |
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- f1 |
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base_model: |
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- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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pipeline_tag: token-classification |
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library_name: transformers |
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--- |
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# Fine-tuned NER Model for DiMB-RE |
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## Model Description |
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This is a fine-tuned **Named Entity Recognition (NER)** model based on the [BiomedNLP-BiomedBERT-base-uncased](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) model, specifically designed for span prediction tasks for extracting entity and trigger mentions for diet, human metabolism and microbiome field. The model has been trained on the DiMB-RE dataset and is optimized to identify spans for 15 different entity types, as well as 13 different trigger types. |
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<!-- ### Key Features: |
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- **Language**: English |
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- **Task**: Token classification for Named Entity Recognition (NER) |
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- **Base Model**: BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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- **Domains**: Biomedical, Clinical, Scientific --> |
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## Performance |
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The model has been evaluated on the DiMB-RE using the following metrics: |
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- **NER** - P: 0.777, R: 0.745, F1: 0.760 |
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- **NER Relaxed** - P: 0.852, R: 0.788, F1: 0.819 |
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- **TRG** - P: 0.691, R: 0.631, F1: 0.660 |
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- **TRG Relaxed** - P: 0.742, R: 0.678, F1: 0.708 |
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## Citation |
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If you use this model, please cite like below: |
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```bibtex |
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@misc{hong2024dimbreminingscientificliterature, |
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title={DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations}, |
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author={Gibong Hong and Veronica Hindle and Nadine M. Veasley and Hannah D. Holscher and Halil Kilicoglu}, |
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year={2024}, |
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eprint={2409.19581}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2409.19581}, |
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} |
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``` |
