Improve GPT2 doc (#18787)

* Minor typo in GPT2 doc

* improve gpt2 label doc

* update dim of label in GPT2ForTokenClassification

* add change to tf

src/transformers/models/gpt2/modeling_gpt2.py

@@ -1225,10 +1225,10 @@ def forward(
         r"""
         mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
             Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
-            1[`.
+            1]`.
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
-            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size - 1]` All labels set to
+            `labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to
             `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]`
         mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
             Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
@@ -1519,7 +1519,7 @@ def forward(
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, TokenClassifierOutput]:
         r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

src/transformers/models/gpt2/modeling_tf_gpt2.py

@@ -983,7 +983,7 @@ def call(
         r"""
         mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
             Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
-            1[`.
+            1]`.
 
         Return: