fix ernie api docstring (#630)

Author: Steffy-zxf

paddlenlp/transformers/ernie/modeling.py

@@ -420,7 +420,7 @@ def forward(self,
                 model = ErnieForSequenceClassification.from_pretrained('ernie-1.0')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
 
         """
@@ -505,7 +505,7 @@ def forward(self,
                 model = ErnieForQuestionAnswering.from_pretrained('ernie-1.0')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
         """
 
@@ -601,7 +601,7 @@ def forward(self,
                 model = ErnieForTokenClassification.from_pretrained('ernie-1.0')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
         """
         sequence_output, _ = self.ernie(
@@ -742,7 +742,7 @@ def forward(self,
                 model = ErnieForTokenClassification.from_pretrained('ernie-1.0')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
 
         """

paddlenlp/transformers/ernie_gram/modeling.py

@@ -272,7 +272,7 @@ def forward(self,
                 model = ErnieGramModel.from_pretrained('ernie-gram-zh')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 sequence_output, pooled_output = model(**inputs)
 
         """
@@ -376,7 +376,7 @@ def forward(self,
                 model = ErnieGramForTokenClassification.from_pretrained('ernie-gram-zh')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
         """
         sequence_output, _ = self.ernie_gram(
@@ -460,7 +460,7 @@ def forward(self,
                 model = ErnieGramForQuestionAnswering.from_pretrained('ernie-gram-zh')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
         """
 
@@ -554,7 +554,7 @@ def forward(self,
                 model = ErnieGramForSequenceClassification.from_pretrained('ernie-gram-zh')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
 
         """

paddlenlp/transformers/roberta/modeling.py

@@ -325,7 +325,7 @@ def forward(self,
                 model = RobertaModel.from_pretrained('roberta-wwm-ext')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 sequence_output, pooled_output = model(**inputs)
 
         """
@@ -413,7 +413,7 @@ def forward(self, input_ids, token_type_ids=None):
                 model = RobertaForSequenceClassification.from_pretrained('roberta-wwm-ext')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
 
         """
@@ -509,7 +509,7 @@ def forward(self,
                 model = RobertaForSequenceClassification.from_pretrained('roberta-wwm-ext')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
 
         """
@@ -603,7 +603,7 @@ def forward(self,
                 model = RobertaForTokenClassification.from_pretrained('roberta-wwm-ext')
 
                 inputs = tokenizer("这是个测试样例")
-                inputs = {k:paddle.to_tensor(v) for (k, v) in inputs.items()}
+                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                 logits = model(**inputs)
 
         """