Update README.md

README.md

@@ -41,7 +41,7 @@ model_output = model(**encoded_input)
 Sequence embeddings can be produced as follows:
 
 ```python
-def sequence_embeddings(encoded_input, model_output):
+def get_sequence_embeddings(encoded_input, model_output):
     mask = encoded_input['attention_mask'].float()
     d = {k: v for k, v in torch.nonzero(mask).cpu().numpy()} # dict of sep tokens
     # make sep token invisible
@@ -53,7 +53,7 @@ def sequence_embeddings(encoded_input, model_output):
     sum_mask = torch.clamp(mask.sum(1), min=1e-9)
     return sum_embeddings / sum_mask
 
-seq_embeds = sequence_embeddings(encoded_input, model_output)
+seq_embeds = get_sequence_embeddings(encoded_input, model_output)
 ```
 
 ### Fine-tune