Tensorflow doc changes on loss output size

src/transformers/modeling_tf_outputs.py

@@ -175,7 +175,7 @@ class TFCausalLMOutput(ModelOutput):
     Base class for causal language model (or autoregressive) outputs.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(n,)`, `optional`, where n is the number of non-masked labels, returned when :obj:`labels` is provided):
             Language modeling loss (for next-token prediction).
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
@@ -204,7 +204,7 @@ class TFCausalLMOutputWithPast(ModelOutput):
     Base class for causal language model (or autoregressive) outputs.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(n,)`, `optional`, where n is the number of non-masked labels, returned when :obj:`labels` is provided):
             Language modeling loss (for next-token prediction).
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
@@ -240,7 +240,7 @@ class TFMaskedLMOutput(ModelOutput):
     Base class for masked language models outputs.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(n,)`, `optional`, where n is the number of non-masked labels, returned when :obj:`labels` is provided):
             Masked language modeling (MLM) loss.
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
@@ -269,7 +269,7 @@ class TFSeq2SeqLMOutput(ModelOutput):
     Base class for sequence-to-sequence language models outputs.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(n,)`, `optional`, where n is the number of non-masked labels, returned when :obj:`labels` is provided):
             Language modeling loss.
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
@@ -321,7 +321,7 @@ class TFNextSentencePredictorOutput(ModelOutput):
     Base class for outputs of models predicting if two sentences are consecutive or not.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`next_sentence_label` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(n,)`, `optional`, where n is the number of non-masked labels, returned when :obj:`next_sentence_label` is provided):
             Next sentence prediction loss.
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, 2)`):
             Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
@@ -351,7 +351,7 @@ class TFSequenceClassifierOutput(ModelOutput):
     Base class for outputs of sentence classification models.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(batch_size, )`, `optional`, returned when :obj:`labels` is provided):
             Classification (or regression if config.num_labels==1) loss.
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).
@@ -432,7 +432,7 @@ class TFMultipleChoiceModelOutput(ModelOutput):
     Base class for outputs of multiple choice models.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape `(batch_size, )`, `optional`, returned when :obj:`labels` is provided):
             Classification loss.
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
             `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
@@ -463,7 +463,7 @@ class TFTokenClassifierOutput(ModelOutput):
     Base class for outputs of token classification models.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
+        loss (:obj:`tf.Tensor` of shape :obj:`(n,)`, `optional`, where n is the number of unmasked labels, returned when ``labels`` is provided) :
             Classification loss.
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
             Classification scores (before SoftMax).
@@ -492,7 +492,7 @@ class TFQuestionAnsweringModelOutput(ModelOutput):
     Base class for outputs of question answering models.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(batch_size, )`, `optional`, returned when :obj:`start_positions` and :obj:`end_positions` are provided):
             Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
         start_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
             Span-start scores (before SoftMax).
@@ -579,7 +579,7 @@ class TFSequenceClassifierOutputWithPast(ModelOutput):
     Base class for outputs of sentence classification models.
 
     Args:
-        loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+        loss (:obj:`tf.Tensor` of shape :obj:`(batch_size, )`, `optional`, returned when :obj:`labels` is provided):
             Classification (or regression if config.num_labels==1) loss.
         logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
             Classification (or regression if config.num_labels==1) scores (before SoftMax).

src/transformers/models/blenderbot/modeling_tf_blenderbot.py

@@ -1322,7 +1322,7 @@ def call(
         **kwargs,
     ):
         r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+        labels (:obj:`tf.tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
             config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
             (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.

src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py

@@ -1297,7 +1297,7 @@ def call(
         **kwargs,
     ):
         r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+        labels (:obj:`tf.tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
             config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
             (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.

src/transformers/models/marian/modeling_tf_marian.py

@@ -1314,7 +1314,7 @@ def call(
         **kwargs,
     ):
         r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+        labels (:obj:`tf.tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
             config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
             (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.

src/transformers/models/pegasus/modeling_tf_pegasus.py

@@ -1328,7 +1328,7 @@ def call(
         **kwargs,
     ):
         """
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+        labels (:obj:`tf.tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Labels for computing the masked language modeling loss. Indices should either be in ``[0, ...,
             config.vocab_size]`` or -100 (see ``input_ids`` docstring). Tokens with indices set to ``-100`` are ignored
             (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.