Update README.md

README.md

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 ---
 license: gpl-3.0
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+
+Pre-trained word embeddings using the text of published clinical case reports. 
+
+## Getting started
+
+```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_100d.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')
+
+```