pytorch bart en arxiv:2408.16493 base_model:dmis-lab/ANGEL_pretrained base_model:finetune:dmis-lab/ANGEL_pretrained license:gpl-3.0 region:us
---
base_model: dmis-lab/ANGEL_pretrained
language:
- en
license: gpl-3.0
metrics:
- accuracy
---

# Model Card for ANGEL_mm
This model card provides detailed information about the ANGEL_mm model, designed for biomedical entity linking.


# Model Details

#### Model Description
- **Developed by:** Chanhwi Kim, Hyunjae Kim, Sihyeon Park, Jiwoo Lee, Mujeen Sung, Jaewoo Kang
- **Model type:** Generative Biomedical Entity Linking Model
- **Language(s):** English
- **License:** GPL-3.0
- **Finetuned from model:** BART-large (Base architecture)

#### Model Sources

- **Github Repository:** https://github.com/dmis-lab/ANGEL
- **Paper:** https://arxiv.org/pdf/2408.16493


# Direct Use
ANGEL_mm is a tool specifically designed for biomedical entity linking, with a focus on identifying and linking disease mentions within Medmentions ST21pv datasets.
To use this model, you need to set up a virtual environment and the inference code.
Start by cloning our [ANGEL GitHub repository](https://github.com/dmis-lab/ANGEL). 
Then, run the following script to set up the environment:
```bash
bash script/environment/set_environment.sh
```

Then, if you want to run the model on a single sample, no preprocessing is required. 
Simply execute the run_sample.sh script:

```bash
bash script/inference/run_sample.sh mm
```

To modify the sample with your own example, refer to the [Direct Use](https://github.com/dmis-lab/ANGEL?tab=readme-ov-file#direct-use) section in our GitHub repository.
If you're interested in training or evaluating the model, check out the [Fine-tuning](https://github.com/dmis-lab/ANGEL?tab=readme-ov-file#fine-tuning) section and [Evaluation](https://github.com/dmis-lab/ANGEL?tab=readme-ov-file#evaluation) section.
# Training

#### Training Data
The model was trained on the Medmentions ST21pv dataset, which includes annotated disease entities.

#### Training Procedure
Positive-only Pre-training: Initial training using only positive examples, following the standard approach.
Negative-aware Training: Subsequent training incorporated negative examples to improve the model's discriminative capabilities.

# Evaluation

### Testing Data
The model was evaluated using Medmentions ST21pv dataset.

### Metrics
Accuracy at Top-1 (Acc@1): Measures the percentage of times the model's top prediction matches the correct entity.

### Scores

<table border="1" cellspacing="0" cellpadding="5" style="width: 100%; text-align: center; border-collapse: collapse; margin-left: 0;">
  <thead>
    <tr>
      <th><b>Dataset</b></th>
      <th><b>BioSYN</b><br>(Sung et al., 2020)</th>
      <th><b>SapBERT</b><br>(Liu et al., 2021)</th>
      <th><b>GenBioEL</b><br>(Yuan et al., 2022b)</th>
      <th><b>ANGEL<br>(Ours)</b></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td><b>Medmentions ST21pv</b></td>
      <td>-</td>
      <td>-</td>
      <td>70.7</td>
      <td><b>73.3</b></td>
    </tr>
  </tbody>
</table>

The scores of GenBioEL were reproduced.

Also we excluded the performance of BioSYN and SapBERT, as they were evaluated separately on the chemical and disease subsets, differing from our settings.

# Citation
If you use the ANGEL_mm model, please cite:

```bibtex
@article{kim2024learning,
  title={Learning from Negative Samples in Generative Biomedical Entity Linking},
  author={Kim, Chanhwi and Kim, Hyunjae and Park, Sihyeon and Lee, Jiwoo and Sung, Mujeen and Kang, Jaewoo},
  journal={arXiv preprint arXiv:2408.16493},
  year={2024}
}
```

# Contact
For questions or issues, please contact chanhwi_kim@korea.ac.kr.