**Me**dical **D**ataset for **A**bbreviation Disambiguation for Natural **L**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. ?? [Code](https://github.com/BruceWen120/medal) ?? [Dataset (Hugging Face)](https://huggingface.co/datasets/medal) ?? [Dataset (Kaggle)](https://www.kaggle.com/xhlulu/medal-emnlp) ?? [Dataset (Zenodo)](https://zenodo.org/record/4265632) ?? [Paper (ACL)](https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/) ?? [Paper (Arxiv)](https://arxiv.org/abs/2012.13978) ? [Pre-trained ELECTRA (Hugging Face)](https://huggingface.co/xhlu/electra-medal) ## Downloading the data We recommend downloading from Kaggle if you can authenticate through their API. The advantage to Kaggle is that the data is compressed, so it will be faster to download. Links to the data can be found at the top of the readme. First, you will need to create an account on kaggle.com. Afterwards, you will need to install the kaggle API: pip install kaggle Then, you will need to follow the [instructions here](https://github.com/Kaggle/kaggle-api#api-credentials) to add your username and key. Once that's done, you can run: kaggle datasets download xhlulu/medal-emnlp Now, unzip everything and place them inside the `data` directory: unzip -nq crawl-300d-2M-subword.zip -d data mv data/pretrain_sample/* data/ Loading FastText Embeddings For the LSTM models, we will need to use the fastText embeddings. To do so, first download and extract the weights: wget -nc -P data/ https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M-subword.zip unzip -nq data/crawl-300d-2M-subword.zip -d data/ ## Model Quickstart Using Torch Hub You can directly load LSTM and LSTM-SA with `torch.hub`: python import torch lstm = torch.hub.load("BruceWen120/medal