---
license: apache-2.0
language:
- en
metrics:
- precision
- recall
- f1
base_model:
- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
pipeline_tag: text-classification
library_name: transformers
---

# Fine-tuned FD Model for DiMB-RE

## Model Description
This is a fine-tuned **Factuality Detection (FD)** model based on the [BiomedNLP-BiomedBERT-base-uncased](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) model, specifically designed for sentence classification task to assign factuality level for extracted relations for diet, human metabolism and microbiome field. The model has been trained on the DiMB-RE dataset and is optimized to infer factuality with 3 factuality level.

<!-- ### Key Features:
- **Language**: English
- **Task**: Token classification for Named Entity Recognition (NER)
- **Base Model**: BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
- **Domains**: Biomedical, Clinical, Scientific -->

## Performance
The model has been evaluated on the DiMB-RE using the following metrics:
- **Relation with Factuality (w/ GOLD relations)** - P: 0.926, R: 0.843, F1: 0.883
- **Relation with Factuality (Strict, end-to-end w/ predicted entities and relations)** - P: 0.399, R: 0.322, F1: 0.356
- **Relation with Factuality (Relaxed, end-to-end w/ predicted entities and relations)** - P: 0.440, R: 0.355, F1: 0.393

## 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}, 
}
```