metadata
license: cc-by-nc-4.0
language: en
tags:
- longformer
- clinical
- biomedical
KEPTlongfomer is a medical knowledge enhanced version of Longformer that was further pre-trained using contrastive learning.
Pre-training
We initialized this model from RoBERTa-base-PM-M3-Voc-distill from Facebook bio-lm.
And then pretrained with Hierarchical Self-Alignment Pretrain (HSAP) using Knowledge Graph UMLS. This includes (a) Hierarchy, (b) Synonym, (c) Abbreviation. For more info, see section 3.3 in paper. The learning rate was 5e-5, weight decay was 0.01, adam epsilon was 1e-5.
Usage
Load the model directly from Transformers:
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("whaleloops/KEPTlongformer-PMM3")
config = AutoConfig.from_pretrained("whaleloops/KEPTlongformer-PMM3")
model = AutoModelForMaskedLM.from_pretrained("whaleloops/KEPTlongformer-PMM3", config=config)
See our github for how to use this with prompts on auto ICD coding.
With the following result:
| Metric | Score |
|---|---|
| rec_micro | =0.5844294992252652 |
| rec_macro | =0.12471916602840005 |
| rec_at_8 | =0.4138093882408751 |
| rec_at_75 | =0.8581874197033126 |
| rec_at_50 | =0.8109877644497351 |
| rec_at_5 | =0.2923155353947738 |
| rec_at_15 | =0.586890060777621 |
| prec_micro | =0.6537291416981642 |
| prec_macro | =0.1382069689951297 |
| prec_at_8 | =0.7835112692763938 |
| prec_at_75 | =0.20033214709371291 |
| prec_at_50 | =0.2810260972716489 |
| prec_at_5 | =0.8551008303677343 |
| prec_at_15 | =0.6288256227758008 |
| f1_micro | =0.6171399726721254 |
| f1_macro | =0.13111711325953157 |
| f1_at_8 | =0.54158310388029 |
| f1_at_75 | =0.324835806140454 |
| f1_at_50 | =0.4174099512237087 |
| f1_at_5 | =0.4356905906241822 |
| f1_at_15 | =0.6071345676658747 |
| auc_micro | =0.9653561390964384 |
| auc_macro | =0.8572490224880879 |
| acc_micro | =0.4462779749767132 |
| acc_macro | =0.09732882850157536 |