Fix examples of loading pretrained models in docstring

pytorch_transformers/modeling_xlnet.py

@@ -712,12 +712,11 @@ class XLNetModel(XLNetPreTrainedModel):
 
     Examples::
 
-        config = XLNetConfig.from_pretrained('xlnet-large-cased')
-        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
-        model = XLNetModel(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 = XLNetTokenizer.from_pretrained('xlnet-large-cased')
+        >>> model = XLNetModel.from_pretrained('xlnet-large-cased')
+        >>> 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):
@@ -1019,17 +1018,16 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
 
     Examples::
 
-        config = XLNetConfig.from_pretrained('xlnet-large-cased')
-        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
-        model = XLNetLMHeadModel(config)
-        # We show how to setup inputs to predict a next token using a bi-directional context.
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is very <mask>")).unsqueeze(0)  # We will predict the masked token
-        perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)
-        perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
-        target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float)  # Shape [1, 1, seq_length] => let's predict one token
-        target_mapping[0, 0, -1] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)
-        outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping)
-        next_token_logits = outputs[0]  # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
+        >>> tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
+        >>> model = XLNetLMHeadModel.from_pretrained('xlnet-large-cased')
+        >>> # We show how to setup inputs to predict a next token using a bi-directional context.
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is very <mask>")).unsqueeze(0)  # We will predict the masked token
+        >>> perm_mask = torch.zeros((1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float)
+        >>> perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
+        >>> target_mapping = torch.zeros((1, 1, input_ids.shape[1]), dtype=torch.float)  # Shape [1, 1, seq_length] => let's predict one token
+        >>> target_mapping[0, 0, -1] = 1.0  # Our first (and only) prediction will be the last token of the sequence (the masked token)
+        >>> outputs = model(input_ids, perm_mask=perm_mask, target_mapping=target_mapping)
+        >>> next_token_logits = outputs[0]  # Output has shape [target_mapping.size(0), target_mapping.size(1), config.vocab_size]
 
     """
     def __init__(self, config):
@@ -1100,14 +1098,12 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
 
     Examples::
 
-        config = XLNetConfig.from_pretrained('xlnet-large-cased')
-        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
-        
-        model = XLNetForSequenceClassification(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 = XLNetTokenizer.from_pretrained('xlnet-large-cased')
+        >>> model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
+        >>> 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):
@@ -1200,15 +1196,13 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
 
     Examples::
 
-        config = XLMConfig.from_pretrained('xlm-mlm-en-2048')
-        tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
-        
-        model = XLMForQuestionAnswering(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 = XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
+        >>> model = XLMForQuestionAnswering.from_pretrained('xlnet-large-cased')
+        >>> 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):