Adjust the api from pytorch_pretrained_bert to transformers

README.md

@@ -102,13 +102,13 @@ pip install .
 >>> input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
 >>> token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 >>> model, vocab  = get_pytorch_kobert_model()
->>> all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+>>> sequence_output, pooled_output = model(input_ids, input_mask, token_type_ids)
 >>> pooled_output.shape
 torch.Size([2, 768])
 >>> vocab
 Vocab(size=8002, unk="[UNK]", reserved="['[MASK]', '[SEP]', '[CLS]']")
 >>> # Last Encoding Layer
->>> all_encoder_layers[-1][0]
+>>> sequence_output[0]
 tensor([[-0.2461,  0.2428,  0.2590,  ..., -0.4861, -0.0731,  0.0756],
         [-0.2478,  0.2420,  0.2552,  ..., -0.4877, -0.0727,  0.0754],
         [-0.2472,  0.2420,  0.2561,  ..., -0.4874, -0.0733,  0.0765]],