Fix examples of loading pretrained models in docstring

pytorch_transformers/modeling_bert.py

@@ -643,12 +643,11 @@ class BertModel(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        model = BertModel(config)
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids)
-        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertModel.from_pretrained('bert-base-uncased')
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(input_ids)
+        >>> last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
 
     """
     def __init__(self, config):
@@ -754,13 +753,11 @@ class BertForPreTraining(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
-        model = BertForPreTraining(config)
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids)
-        prediction_scores, seq_relationship_scores = outputs[:2]
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForPreTraining.from_pretrained('bert-base-uncased')
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(input_ids)
+        >>> prediction_scores, seq_relationship_scores = outputs[:2]
 
     """
     def __init__(self, config):
@@ -824,13 +821,11 @@ class BertForMaskedLM(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
-        model = BertForMaskedLM(config)
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, masked_lm_labels=input_ids)
-        loss, prediction_scores = outputs[:2]
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForMaskedLM.from_pretrained('bert-base-uncased')
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(input_ids, masked_lm_labels=input_ids)
+        >>> loss, prediction_scores = outputs[:2]
 
     """
     def __init__(self, config):
@@ -891,13 +886,11 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
-        model = BertForNextSentencePrediction(config)
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids)
-        seq_relationship_scores = outputs[0]
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(input_ids)
+        >>> seq_relationship_scores = outputs[0]
 
     """
     def __init__(self, config):
@@ -951,14 +944,12 @@ class BertForSequenceClassification(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
-        model = BertForSequenceClassification(config)
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, labels=labels)
-        loss, logits = outputs[:2]
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        >>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(input_ids, labels=labels)
+        >>> loss, logits = outputs[:2]
 
     """
     def __init__(self, config):
@@ -1057,15 +1048,13 @@ class BertForMultipleChoice(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
-        model = BertForMultipleChoice(config)
-        choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
-        input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
-        labels = torch.tensor(1).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, labels=labels)
-        loss, classification_scores = outputs[:2]
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForMultipleChoice.from_pretrained('bert-base-uncased')
+        >>> choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
+        >>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
+        >>> labels = torch.tensor(1).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(input_ids, labels=labels)
+        >>> loss, classification_scores = outputs[:2]
 
     """
     def __init__(self, config):
@@ -1127,14 +1116,12 @@ class BertForTokenClassification(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
-        model = BertForTokenClassification(config)
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, labels=labels)
-        loss, scores = outputs[:2]
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForTokenClassification.from_pretrained('bert-base-uncased')
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        >>> labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
+        >>> outputs = model(input_ids, labels=labels)
+        >>> loss, scores = outputs[:2]
 
     """
     def __init__(self, config):
@@ -1203,15 +1190,13 @@ class BertForQuestionAnswering(BertPreTrainedModel):
 
     Examples::
 
-        config = BertConfig.from_pretrained('bert-base-uncased')
-        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
-        model = BertForQuestionAnswering(config)
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        start_positions = torch.tensor([1])
-        end_positions = torch.tensor([3])
-        outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
-        loss, start_scores, end_scores = outputs[:2]
+        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+        >>> model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+        >>> start_positions = torch.tensor([1])
+        >>> end_positions = torch.tensor([3])
+        >>> outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
+        >>> loss, start_scores, end_scores = outputs[:2]
 
     """
     def __init__(self, config):