Model Card for ANGEL_pretrained

This model card provides detailed information about the ANGEL_pretrained 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

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

Direct Use

ANGEL_pretrained is pretrained with UMLS dataset. We recommand to finetune this model to downstream dataset rather directly use. If you still want to run the model on a single sample, no preprocessing is required. Simply execute the run_sample.sh script:

bash script/inference/run_sample.sh pretrained

To modify the sample with your own example, refer to the Direct Use section in our GitHub repository. If you're interested in training or evaluating the model, check out the Fine-tuning section and Evaluation section.

Training Details

Training Data

The model was pretrained on the UMLS-2020-AA dataset.

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 multiple biomedical datasets, including NCBI-disease, BC5CDR, COMETA, AAP, and MedMentions. The fine-tuned scores have also been included.

Metrics

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

Results

Model	NCBI-disease	BC5CDR	COMETA	AAP	MedMentions ST21pv	Average
GenBioEL_pretrained	58.2	33.1	42.4	50.6	10.6	39.0
ANGEL_pretrained (Ours)	64.6	49.7	46.8	61.5	18.2	48.2
GenBioEL_pt_ft	91.0	93.1	80.9	89.3	70.7	85.0
ANGEL_pt_ft (Ours)	92.8	94.5	82.8	90.2	73.3	86.7

• In this table, "pt" refers to pre-training, where the model is trained on a large dataset (UMLS in this case), and "ft" refers to fine-tuning, where the model is further refined on specific datasets.

In the pre-training phase, ANGEL was trained using UMLS dataset entities that were similar to a given word based on TF-IDF scores but had different CUIs (Concept Unique Identifiers). This negative-aware pre-training approach improved its performance across the benchmarks, leading to an average score of 48.2, which is 9.2 points higher than the GenBioEL pre-trained model, which scored 39.0 on average.

The performance improvement continued during the fine-tuning phase. After fine-tuning, ANGEL achieved an average score of 86.7, surpassing the GenBioEL model's average score of 85.0, representing a further 1.7 point improvement. The ANGEL model consistently outperformed GenBioEL across all datasets in this phase. The results demonstrate that the negative-aware training introduced by ANGEL not only enhances performance during pre-training but also carries over into fine-tuning, helping the model generalize better to unseen data.

Citation

If you use the ANGEL_ncbi model, please cite:

@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.