Update README.md

README.md

@@ -28,21 +28,21 @@ from gsfm import Vocab, GSFM
 
 # load gsfm vocabulary and model weights
 vocab = Vocab.from_pretrained('maayanlab/gsfm')
-model = GSFM.from_pretrained('maayanlab/gsfm')
+gsfm = GSFM.from_pretrained('maayanlab/gsfm')
 
 # convert gene symbols into token ids
 token_ids = torch.tensor(vocab(['ACE1', 'ACE2']))[None, :]
 
 # use model to predict missing genes from the set
-logits = torch.squeeze(model(token_ids))
+logits = torch.squeeze(gsfm(token_ids))
 top_10 = sorted(zip(logits, vocab.vocab))[-10:]
 top_10
 
 # get gene embedding
-gene_embeddings = model.embedding(token_ids)
+gene_embeddings = gsfm.embedding(token_ids)
 gene_embeddings
 
 # get model middle layer
-gene_set_encoding = model.encode(token_ids)
+gene_set_encoding = gsfm.encode(token_ids)
 gene_set_encoding
 ```