MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

<strong>Me</strong>dical <strong>D</strong>ataset for <strong>A</strong>bbreviation Disambiguation for Natural <strong>L</strong>anguage 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<br> 💻 Code<br> 💾 Dataset (Kaggle)<br> 💽 Dataset (Zenodo)<br> 🤗 Pre-trained ELECTRA (Hugging Face) To cite this work, you can download the <code>bibtex</code> here, or copy the text below: <pre><code>@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", } </code></pre> <strong>Downloading the data</strong> 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: <pre><code>wget -nc -P data/ https://zenodo.org/record/4482922/files/full_data.csv.zip </code></pre> If you can't use <code>unzip</code>, you can also download the original csv file (which is 3x larger): <pre><code>wget -nc -P data/ https://zenodo.org/record/4482922/files/full_data.csv </code></pre> If you want to reproduce our pre-training results, you can download only the pre-training data below: <pre><code>wget -nc -P data/ https://zenodo.org/record/4482922/files/train.csv wget -nc -P data/ https://zenodo.org/record/4482922/files/valid.csv wget -nc -P data/ https://zenodo.org/record/4482922/files/test.csv </code></pre> Or download the compress <code>zip</code> file containing all three files: <pre><code class="language-bash">wget -nc -P data/ https://zenodo.org/record/4482922/files/pretrain_subset.zip</code></pre> <strong>Model Quickstart</strong> <em>Using Torch Hub</em> You can directly load LSTM and LSTM-SA with <code>torch.hub</code>: <pre><code class="language-python">import torch lstm = torch.hub.load("McGill-NLP/medal", "lstm") lstm_sa = torch.hub.load("McGill-NLP/medal", "lstm_sa")</code></pre> If you want to use the Electra model, you need to first install transformers: <pre><code>pip install transformers </code></pre> Then, you can load it with <code>torch.hub</code>: <pre><code class="language-python">import torch electra = torch.hub.load("McGill-NLP/medal", "electra")</code></pre> <em>Using Huggingface <code>transformers</code></em> 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: <pre><code class="language-python">from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("McGill-NLP/electra-medal") tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/electra-medal")</code></pre> <strong>License, Terms and Conditions</strong> The ELECTRA model is licensed under Apache 2.0. 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