improve docstrings and fix new token classification model

pytorch_pretrained_bert/modeling.py

@@ -559,7 +559,7 @@ class BertModel(PreTrainedBertModel):
                 of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
                 encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
             - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
-                to the last attention block,
+                to the last attention block of shape [batch_size, sequence_length, hidden_size],
         `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
             classifier pretrained on top of the hidden state associated to the first character of the
             input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
@@ -650,8 +650,8 @@ class BertForPreTraining(PreTrainedBertModel):
             sentence classification loss.
         if `masked_lm_labels` or `next_sentence_label` is `None`:
             Outputs a tuple comprising
-            - the masked language modeling logits, and
+            - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
-            - the next sentence classification logits.
+            - the next sentence classification logits of shape [batch_size, 2].
 
     Example usage:
     ```python
@@ -680,7 +680,7 @@ class BertForPreTraining(PreTrainedBertModel):
 
         if masked_lm_labels is not None and next_sentence_label is not None:
             loss_fct = CrossEntropyLoss(ignore_index=-1)
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels(-1))
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
             next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
             total_loss = masked_lm_loss + next_sentence_loss
             return total_loss
@@ -714,7 +714,7 @@ class BertForMaskedLM(PreTrainedBertModel):
         if `masked_lm_labels` is `None`:
             Outputs the masked language modeling loss.
         if `masked_lm_labels` is `None`:
-            Outputs the masked language modeling logits.
+            Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
 
     Example usage:
     ```python
@@ -776,7 +776,7 @@ class BertForNextSentencePrediction(PreTrainedBertModel):
             Outputs the total_loss which is the sum of the masked language modeling loss and the next
             sentence classification loss.
         if `next_sentence_label` is `None`:
-            Outputs the next sentence classification logits.
+            Outputs the next sentence classification logits of shape [batch_size, 2].
 
     Example usage:
     ```python
@@ -838,7 +838,7 @@ class BertForSequenceClassification(PreTrainedBertModel):
         if `labels` is not `None`:
             Outputs the CrossEntropy classification loss of the output with the labels.
         if `labels` is `None`:
-            Outputs the classification logits.
+            Outputs the classification logits of shape [batch_size, num_labels].
 
     Example usage:
     ```python
@@ -872,7 +872,7 @@ class BertForSequenceClassification(PreTrainedBertModel):
         if labels is not None:
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            return loss, logits
+            return loss
         else:
             return logits
 
@@ -904,7 +904,7 @@ class BertForTokenClassification(PreTrainedBertModel):
         if `labels` is not `None`:
             Outputs the CrossEntropy classification loss of the output with the labels.
         if `labels` is `None`:
-            Outputs the classification logits.
+            Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
 
     Example usage:
     ```python
@@ -938,7 +938,7 @@ class BertForTokenClassification(PreTrainedBertModel):
         if labels is not None:
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            return loss, logits
+            return loss
         else:
             return logits
 
@@ -982,7 +982,7 @@ class BertForQuestionAnswering(PreTrainedBertModel):
             Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions.
         if `start_positions` or `end_positions` is `None`:
             Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end
-            position tokens.
+            position tokens of shape [batch_size, sequence_length].
 
     Example usage:
     ```python