Update bert_embedding.py
@@ -27,6 +27,7 @@ class BertEmbedding(ContextualEmbedding):
>>> import torch
>>> from fastNLP import Vocabulary
+ >>> from fastNLP.embeddings import BertEmbedding
>>> vocab = Vocabulary().add_word_lst("The whether is good .".split())
>>> embed = BertEmbedding(vocab, model_dir_or_name='en-base-uncased', requires_grad=False, layers='4,-2,-1')
>>> words = torch.LongTensor([[vocab.to_index(word) for word in "The whether is good .".split()]])
