Update README.md

README.md

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 ---
 license: gpl-3.0
 ---
+
+Pre-trained word embeddings using the text of published scientific manuscripts. These embeddings use 300 dimensions and were trained using the fasttext 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 = FastText.load('ft_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')
+
+```