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Unverified Commit 64070cbb authored by Jared T Nielsen's avatar Jared T Nielsen Committed by GitHub
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Fix TF input docstrings to refer to tf.Tensor rather than torch.FloatTensor. (#4051)

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README.md
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...@@ -63,7 +63,7 @@ Choose the right framework for every part of a model's lifetime ...@@ -63,7 +63,7 @@ Choose the right framework for every part of a model's lifetime
## Installation ## Installation
This repo is tested on Python 3.6+, PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1 This repo is tested on Python 3.6+, PyTorch 1.0.0+ and TensorFlow 2.0.
You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
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src/transformers/modeling_tf_albert.py
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...@@ -846,7 +846,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel): ...@@ -846,7 +846,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_tf_bert.py
View file @ 64070cbb
...@@ -673,7 +673,7 @@ class TFBertModel(TFBertPreTrainedModel): ...@@ -673,7 +673,7 @@ class TFBertModel(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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)`): 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. 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)`): pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
...@@ -730,7 +730,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel): ...@@ -730,7 +730,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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 (: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). 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)`): seq_relationship_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`):
...@@ -786,7 +786,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel): ...@@ -786,7 +786,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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 (: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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -836,7 +836,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel): ...@@ -836,7 +836,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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)`) 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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -892,7 +892,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel): ...@@ -892,7 +892,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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)`): 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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -967,7 +967,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel): ...@@ -967,7 +967,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
): ):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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)`: 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). `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
...@@ -1069,7 +1069,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel): ...@@ -1069,7 +1069,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -1126,7 +1126,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel): ...@@ -1126,7 +1126,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: :obj:`tuple(tf.Tensor)` 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,)`): start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_tf_distilbert.py
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...@@ -545,7 +545,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel): ...@@ -545,7 +545,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): 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. Sequence of hidden-states at the output of the last layer of the model.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -619,7 +619,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel): ...@@ -619,7 +619,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): 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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -683,7 +683,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel): ...@@ -683,7 +683,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`): 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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -741,7 +741,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel): ...@@ -741,7 +741,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -798,7 +798,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel): ...@@ -798,7 +798,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_tf_electra.py
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...@@ -357,7 +357,7 @@ class TFElectraModel(TFElectraPreTrainedModel): ...@@ -357,7 +357,7 @@ class TFElectraModel(TFElectraPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): 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. Sequence of hidden-states at the output of the last layer of the model.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -418,7 +418,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel): ...@@ -418,7 +418,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
): ):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Prediction scores of the head (scores for each token before SoftMax). Prediction scores of the head (scores for each token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -511,7 +511,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel): ...@@ -511,7 +511,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel):
): ):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): 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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -578,7 +578,7 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel): ...@@ -578,7 +578,7 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel):
): ):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
......
src/transformers/modeling_tf_roberta.py
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...@@ -49,8 +49,8 @@ class TFRobertaEmbeddings(TFBertEmbeddings): ...@@ -49,8 +49,8 @@ class TFRobertaEmbeddings(TFBertEmbeddings):
""" Replace non-padding symbols with their position numbers. Position numbers begin at """ Replace non-padding symbols with their position numbers. Position numbers begin at
padding_idx+1. Padding symbols are ignored. This is modified from fairseq's padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
`utils.make_positions`. `utils.make_positions`.
:param torch.Tensor x: :param tf.Tensor x:
:return torch.Tensor: :return tf.Tensor:
""" """
mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32) mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
incremental_indicies = tf.math.cumsum(mask, axis=1) * mask incremental_indicies = tf.math.cumsum(mask, axis=1) * mask
...@@ -59,8 +59,8 @@ class TFRobertaEmbeddings(TFBertEmbeddings): ...@@ -59,8 +59,8 @@ class TFRobertaEmbeddings(TFBertEmbeddings):
def create_position_ids_from_inputs_embeds(self, inputs_embeds): def create_position_ids_from_inputs_embeds(self, inputs_embeds):
""" We are provided embeddings directly. We cannot infer which are padded so just generate """ We are provided embeddings directly. We cannot infer which are padded so just generate
sequential position ids. sequential position ids.
:param torch.Tensor inputs_embeds: :param tf.Tensor inputs_embeds:
:return torch.Tensor: :return tf.Tensor:
""" """
seq_length = shape_list(inputs_embeds)[1] seq_length = shape_list(inputs_embeds)[1]
...@@ -188,7 +188,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel): ...@@ -188,7 +188,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): 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. 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)`): pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
...@@ -271,7 +271,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel): ...@@ -271,7 +271,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): 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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -349,7 +349,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel): ...@@ -349,7 +349,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`): 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). 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`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -406,7 +406,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel): ...@@ -406,7 +406,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`): scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
...@@ -462,7 +462,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel): ...@@ -462,7 +462,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_tf_t5.py
View file @ 64070cbb
...@@ -797,7 +797,7 @@ T5_INPUTS_DOCSTRING = r""" ...@@ -797,7 +797,7 @@ T5_INPUTS_DOCSTRING = r"""
Used in the cross-attention of the decoder. Used in the cross-attention of the decoder.
decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`): decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default. Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
decoder_past_key_value_states (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): decoder_past_key_value_states (:obj:`tuple(tuple(tf.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains pre-computed key and value hidden-states of the attention blocks. Contains pre-computed key and value hidden-states of the attention blocks.
Can be used to speed up decoding. Can be used to speed up decoding.
If `decoder_past_key_value_states` are used, the user can optionally input only the last `decoder_input_ids` If `decoder_past_key_value_states` are used, the user can optionally input only the last `decoder_input_ids`
...@@ -863,7 +863,7 @@ class TFT5Model(TFT5PreTrainedModel): ...@@ -863,7 +863,7 @@ class TFT5Model(TFT5PreTrainedModel):
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): 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. Sequence of hidden-states at the output of the last layer of the model.
If `decoder_past_key_value_states` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output. If `decoder_past_key_value_states` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
decoder_past_key_value_states (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`, `optional`, returned when ``use_cache=True``): decoder_past_key_value_states (:obj:`tuple(tuple(tf.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`, `optional`, returned when ``use_cache=True``):
Contains pre-computed key and value hidden-states of the attention blocks. Contains pre-computed key and value hidden-states of the attention blocks.
Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input). Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input).
Note that when using `decoder_past_key_value_states`, the model only outputs the last `hidden-state` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`. Note that when using `decoder_past_key_value_states`, the model only outputs the last `hidden-state` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`.
...@@ -985,7 +985,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel): ...@@ -985,7 +985,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
Classification loss (cross entropy). Classification loss (cross entropy).
prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`) 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). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
decoder_past_key_value_states (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`, `optional`, returned when ``use_cache=True``): decoder_past_key_value_states (:obj:`tuple(tuple(tf.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`, `optional`, returned when ``use_cache=True``):
Contains pre-computed key and value hidden-states of the attention blocks. Contains pre-computed key and value hidden-states of the attention blocks.
Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input). Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input).
Note that when using `decoder_past_key_value_states`, the model only outputs the last `prediction_score` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`. Note that when using `decoder_past_key_value_states`, the model only outputs the last `prediction_score` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`.
......
src/transformers/modeling_tf_transfo_xl.py
View file @ 64070cbb
...@@ -671,7 +671,7 @@ TRANSFO_XL_INPUTS_DOCSTRING = r""" ...@@ -671,7 +671,7 @@ TRANSFO_XL_INPUTS_DOCSTRING = r"""
:func:`transformers.PreTrainedTokenizer.encode_plus` for details. :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
`What are input IDs? <../glossary.html#input-ids>`__ `What are input IDs? <../glossary.html#input-ids>`__
mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`): mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `mems` output below). Can be used to speed up sequential decoding. The token ids which have their mems (see `mems` output below). Can be used to speed up sequential decoding. The token ids which have their mems
given to this model should not be passed as input ids as they have already been computed. given to this model should not be passed as input ids as they have already been computed.
...@@ -699,20 +699,20 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel): ...@@ -699,20 +699,20 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the last layer of the model. Sequence of hidden-states at the last layer of the model.
mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`): mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `mems` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(tf.Tensor)`, `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) Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs. Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape Tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`. :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
...@@ -788,20 +788,20 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel): ...@@ -788,20 +788,20 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, labels=None, training=False): def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, labels=None, training=False):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): 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). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`): mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks). Contains pre-computed hidden-states (key and values in the attention blocks).
Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
should not be passed as input ids as they have already been computed. should not be passed as input ids as they have already been computed.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(tf.Tensor)`, `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) Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`. of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs. Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape Tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`. :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
......
src/transformers/modeling_tf_utils.py
View file @ 64070cbb
...@@ -491,7 +491,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin): ...@@ -491,7 +491,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
input_ids: (`optional`) `tf.Tensor` of `dtype=tf.int32` of shape `(batch_size, sequence_length)` input_ids: (`optional`) `tf.Tensor` of `dtype=tf.int32` of shape `(batch_size, sequence_length)`
The sequence used as a prompt for the generation. If `None` the method initializes The sequence used as a prompt for the generation. If `None` the method initializes
it as an empty `torch.LongTensor` of shape `(1,)`. it as an empty `tf.Tensor` of shape `(1,)`.
max_length: (`optional`) int max_length: (`optional`) int
The max length of the sequence to be generated. Between 1 and infinity. Default to 20. The max length of the sequence to be generated. Between 1 and infinity. Default to 20.
......
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