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Conference paper Open Access

MeDAL

Wen, Zhi; Lu, Xing Han; Reddy, Siva

Medical Dataset for Abbreviation Disambiguation for Natural Language Understanding (MeDAL) is a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. It was published at the ClinicalNLP workshop at EMNLP.

📜 Paper
💻 Code
💾 Dataset (Kaggle)
💽 Dataset (Zenodo)
🤗 Pre-trained ELECTRA (Hugging Face)

 

Downloading the data

We recommend downloading from Zenodo if you do not want to authenticate through Kaggle. The downside to Zenodo is that the data is uncompressed, so it will take more time to download. Links to the data can be found at the top of the readme. To download from Zenodo, simply do:

wget -nc -P data/ https://zenodo.org/record/4276178/files/full_data.csv

If you want to reproduce our pre-training results, you can download only the pre-training data below:

wget -nc -P data/ https://zenodo.org/record/4276178/files/train.csv
wget -nc -P data/ https://zenodo.org/record/4276178/files/valid.csv
wget -nc -P data/ https://zenodo.org/record/4276178/files/test.csv

 

Model Quickstart

Using Torch Hub

You can directly load LSTM and LSTM-SA with torch.hub:

import torch

lstm = torch.hub.load("BruceWen120/medal", "lstm")
lstm_sa = torch.hub.load("BruceWen120/medal", "lstm_sa")

If you want to use the Electra model, you need to first install transformers:

pip install transformers

Then, you can load it with torch.hub:

import torch
electra = torch.hub.load("BruceWen120/medal", "electra")

Using Huggingface transformers

If you are only interested in the pre-trained ELECTRA weights (without the disambiguation head), you can load it directly from the Hugging Face Repository:

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("xhlu/electra-medal")
tokenizer = AutoTokenizer.from_pretrained("xhlu/electra-medal")

 

Citation

Download the bibtex here, or copy the text below:

@inproceedings{wen-etal-2020-medal,
    title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
    author = "Wen, Zhi and Lu, Xing Han and Reddy, Siva",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
    pages = "130--135",
}

 

License, Terms and Conditions

The ELECTRA model is licensed under Apache 2.0. The license for the libraries used in this project (transformers, pytorch, etc.) can be found in their respective GitHub repository. Our model is released under a MIT license.

The original dataset was retrieved and modified from the NLM website. By using this dataset, you are bound by the terms and conditions specified by NLM:

INTRODUCTION

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Files (21.1 GB)
Name Size
full_data.csv
md5:6aafd66617438cee6e4fb43a473166a7
15.2 GB Download
test.csv
md5:7ba9fd2e9fa5b4069370bafa42418a85
1.2 GB Download
train.csv
md5:28d5c3e0b4d80f95b7e20cebfc783379
3.5 GB Download
valid.csv
md5:28e450e8c5d5542c6b3ba98876adf621
1.2 GB Download
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