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Lysandre
2020-01-20 16:57:54 -05:00
committed by Lysandre Debut
parent 48ac24020d
commit 0e9899f451
23 changed files with 110 additions and 106 deletions
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16
src/transformers/modeling_tf_bert.py
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@@ -668,7 +668,7 @@ class TFBertModel(TFBertPreTrainedModel):
def call(self, inputs, **kwargs):
r"""
Returns:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
@@ -724,7 +724,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
prediction_scores (: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).
seq_relationship_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`):
@@ -780,7 +780,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :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).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -830,7 +830,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
seq_relationship_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`)
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -886,7 +886,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -952,7 +952,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
classification_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`:
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
@@ -1054,7 +1054,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
@@ -1111,7 +1111,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel):
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):