fixed doc_strings

pytorch_pretrained_bert/modeling.py

@@ -569,10 +569,10 @@ class BertModel(PreTrainedBertModel):
     # Already been converted into WordPiece token ids
     input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
-        num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
+    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
     model = modeling.BertModel(config=config)
     all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
@@ -658,10 +658,10 @@ class BertForPreTraining(PreTrainedBertModel):
     # Already been converted into WordPiece token ids
     input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = BertConfig(vocab_size=32000, hidden_size=512,
-        num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
     model = BertForPreTraining(config)
     masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
@@ -721,10 +721,10 @@ class BertForMaskedLM(PreTrainedBertModel):
     # Already been converted into WordPiece token ids
     input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = BertConfig(vocab_size=32000, hidden_size=512,
-        num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
     model = BertForMaskedLM(config)
     masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask)
@@ -785,8 +785,8 @@ class BertForNextSentencePrediction(PreTrainedBertModel):
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
     token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = BertConfig(vocab_size=32000, hidden_size=512,
-        num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
     model = BertForNextSentencePrediction(config)
     seq_relationship_logits = model(input_ids, token_type_ids, input_mask)
@@ -845,10 +845,10 @@ class BertForSequenceClassification(PreTrainedBertModel):
     # Already been converted into WordPiece token ids
     input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = BertConfig(vocab_size=32000, hidden_size=512,
-        num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
     num_labels = 2
 
@@ -989,10 +989,10 @@ class BertForQuestionAnswering(PreTrainedBertModel):
     # Already been converted into WordPiece token ids
     input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 2, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = BertConfig(vocab_size=32000, hidden_size=512,
-        num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
+    config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
     model = BertForQuestionAnswering(config)
     start_logits, end_logits = model(input_ids, token_type_ids, input_mask)