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--- |
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license: gpl-3.0 |
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--- |
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Pre-trained word embeddings using the text of published biomedical manuscripts. These embeddings use 100 dimensions and were trained using the word2vec algorithm on all available manuscripts found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/ |
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Citation: |
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``` |
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@article{flamholz2022word, |
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title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information}, |
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author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E}, |
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journal={Journal of Biomedical Informatics}, |
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volume={125}, |
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pages={103971}, |
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year={2022}, |
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publisher={Elsevier} |
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} |
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``` |
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## Quick start |
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Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format. |
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First download the files from this archive. Then load the embeddings into Python. |
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```python |
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from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models |
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# Load the model |
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model = Word2Vec.load('w2v_oa_all_100d.bin') |
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# Return 100-dimensional vector representations of each word |
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model.wv.word_vec('diabetes') |
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model.wv.word_vec('cardiac_arrest') |
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model.wv.word_vec('lymphangioleiomyomatosis') |
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# Try out cosine similarity |
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model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease') |
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model.wv.similarity('myocardial_infarction', 'heart_attack') |
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model.wv.similarity('lymphangioleiomyomatosis', 'lam') |
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``` |
