---
license: gpl-3.0
---

Pre-trained word embeddings using the text of published biomedical manuscripts. These embeddings use 300 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/

Citation:

```
@article{flamholz2022word,
  title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information},
  author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E},
  journal={Journal of Biomedical Informatics},
  volume={125},
  pages={103971},
  year={2022},
  publisher={Elsevier}
}
```

## Quick start

Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.

First download the files from this archive. Then load the embeddings into Python.


```python

from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models

# Load the model
model = Word2Vec.load('w2v_oa_all_300d.bin')

# Return 100-dimensional vector representations of each word
model.wv.word_vec('diabetes')
model.wv.word_vec('cardiac_arrest')
model.wv.word_vec('lymphangioleiomyomatosis')

# Try out cosine similarity
model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease')
model.wv.similarity('myocardial_infarction', 'heart_attack')
model.wv.similarity('lymphangioleiomyomatosis', 'lam')

```