license: apache-2.0
language:
- en
metrics:
- precision
- recall
- f1
base_model:
- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
pipeline_tag: token-classification
library_name: transformers
Fine-tuned NER Model for DiMB-RE
Model Description
This is a fine-tuned Named Entity Recognition (NER) model based on the BiomedNLP-BiomedBERT-base-uncased model, specifically designed for token classification tasks in the biomedical domain. The model has been trained on the DiMB-RE dataset and is optimized to identify spans for 15 different entity type, as well as 13 different trigger type.
Performance
The model has been evaluated on the DiMB-RE using the following metrics:
- NER - P: 0.777, R: 0.745, F1: 0.760
- NER Relaxed - P: 0.852, R: 0.788, F1: 0.819
- TRG - P: 0.691, R: 0.631, F1: 0.660
- TRG Relaxed - P: 0.742, R: 0.678, F1: 0.708
Citation
If you use this model, please cite like below:
'''bibtex @misc{hong2024dimbreminingscientificliterature, title={DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations}, author={Gibong Hong and Veronica Hindle and Nadine M. Veasley and Hannah D. Holscher and Halil Kilicoglu}, year={2024}, eprint={2409.19581}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2409.19581}, } '''