Correct output shape for Bert NSP models in docs (#3482)

src/transformers/modeling_bert.py

@@ -845,7 +845,7 @@ class BertForPreTraining(BertPreTrainedModel):
             Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
         prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, 2)`):
+        seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
             Prediction scores of the next sequence prediction (classification) head (scores of True/False
             continuation before SoftMax).
         hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -1048,7 +1048,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
         :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
         loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):
             Next sequence prediction (classification) loss.
-        seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, 2)`):
+        seq_relationship_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
             Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
         hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
             Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)