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Commit 0e9899f4 authored by Lysandre's avatar Lysandre Committed by Lysandre Debut
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docs/source/model_doc/camembert.rst
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...@@ -10,49 +10,49 @@ It is a model trained on 138GB of French text. ...@@ -10,49 +10,49 @@ It is a model trained on 138GB of French text.
This implementation is the same as RoBERTa. This implementation is the same as RoBERTa.
``CamembertConfig`` ``CamembertConfig``
~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertConfig .. autoclass:: transformers.CamembertConfig
:members: :members:
``CamembertTokenizer`` ``CamembertTokenizer``
~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertTokenizer .. autoclass:: transformers.CamembertTokenizer
:members: :members:
``CamembertModel`` ``CamembertModel``
~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertModel .. autoclass:: transformers.CamembertModel
:members: :members:
``CamembertForMaskedLM`` ``CamembertForMaskedLM``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForMaskedLM .. autoclass:: transformers.CamembertForMaskedLM
:members: :members:
``CamembertForSequenceClassification`` ``CamembertForSequenceClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForSequenceClassification .. autoclass:: transformers.CamembertForSequenceClassification
:members: :members:
``CamembertForMultipleChoice`` ``CamembertForMultipleChoice``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForMultipleChoice .. autoclass:: transformers.CamembertForMultipleChoice
:members: :members:
``CamembertForTokenClassification`` ``CamembertForTokenClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForTokenClassification .. autoclass:: transformers.CamembertForTokenClassification
:members: :members:
docs/source/model_doc/ctrl.rst
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CTRL CTRL
---------------------------------------------------- ----------------------------------------------------
CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation`_ CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_
by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher. by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
It's a causal (unidirectional) transformer pre-trained using language modeling on a very large It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.). corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
refer to the PyTorch documentation for all matter related to general usage and behavior. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Note: if you fine-tune a CTRL model using the Salesforce code (https://github.com/salesforce/ctrl), Note: if you fine-tune a CTRL model using the Salesforce code (https://github.com/salesforce/ctrl),
you'll be able to convert from TF to our HuggingFace/Transformers format using the you'll be able to convert from TF to our HuggingFace/Transformers format using the
......
src/transformers/modeling_albert.py
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...@@ -494,7 +494,7 @@ class AlbertModel(AlbertPreTrainedModel): ...@@ -494,7 +494,7 @@ class AlbertModel(AlbertPreTrainedModel):
): ):
r""" r"""
Return: Return:
:obj:`Tuple` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` 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:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`): pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
...@@ -726,7 +726,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel): ...@@ -726,7 +726,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy). If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
Returns: Returns:
: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.AlbertConfig`) and inputs:
loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss. Classification (or regression if config.num_labels==1) loss.
logits ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)`` logits ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
...@@ -824,7 +824,7 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel): ...@@ -824,7 +824,7 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
Position outside of the sequence are not taken into account for computing the loss. Position outside of the sequence are not taken into account for computing the loss.
Returns: Returns:
: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.AlbertConfig`) and inputs:
loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_scores ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` start_scores ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
......
src/transformers/modeling_bert.py
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...@@ -546,8 +546,9 @@ class BertPreTrainedModel(PreTrainedModel): ...@@ -546,8 +546,9 @@ class BertPreTrainedModel(PreTrainedModel):
BERT_START_DOCSTRING = r""" BERT_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
refer to the PyTorch documentation for all matter related to general usage and behavior. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters: Parameters:
config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model. config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
...@@ -660,7 +661,7 @@ class BertModel(BertPreTrainedModel): ...@@ -660,7 +661,7 @@ class BertModel(BertPreTrainedModel):
): ):
r""" r"""
Return: Return:
:obj:`Tuple` 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:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` 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:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`): pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
...@@ -1218,7 +1219,7 @@ class BertForMultipleChoice(BertPreTrainedModel): ...@@ -1218,7 +1219,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
of the input tensors. (see `input_ids` above) of the input tensors. (see `input_ids` above)
Returns: Returns:
: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:
loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided): loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided):
Classification loss. Classification loss.
classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`): classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
...@@ -1313,7 +1314,7 @@ class BertForTokenClassification(BertPreTrainedModel): ...@@ -1313,7 +1314,7 @@ class BertForTokenClassification(BertPreTrainedModel):
Indices should be in ``[0, ..., config.num_labels - 1]``. Indices should be in ``[0, ..., config.num_labels - 1]``.
Returns: Returns:
: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:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) : loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
Classification loss. Classification loss.
scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`) scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
...@@ -1409,7 +1410,7 @@ class BertForQuestionAnswering(BertPreTrainedModel): ...@@ -1409,7 +1410,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
Position outside of the sequence are not taken into account for computing the loss. Position outside of the sequence are not taken into account for computing the loss.
Returns: Returns:
: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:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_ctrl.py
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...@@ -291,7 +291,7 @@ class CTRLModel(CTRLPreTrainedModel): ...@@ -291,7 +291,7 @@ class CTRLModel(CTRLPreTrainedModel):
): ):
r""" r"""
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`CTRLConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` 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.
past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
...@@ -478,7 +478,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel): ...@@ -478,7 +478,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
computed for labels in ``[0, ..., config.vocab_size]`` computed for labels in ``[0, ..., config.vocab_size]``
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.CTRLConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided) loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
Language modeling loss. Language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
......
src/transformers/modeling_distilbert.py
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...@@ -351,6 +351,9 @@ class DistilBertPreTrainedModel(PreTrainedModel): ...@@ -351,6 +351,9 @@ class DistilBertPreTrainedModel(PreTrainedModel):
DISTILBERT_START_DOCSTRING = r""" DISTILBERT_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters: Parameters:
config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model. config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
...@@ -416,7 +419,7 @@ class DistilBertModel(DistilBertPreTrainedModel): ...@@ -416,7 +419,7 @@ class DistilBertModel(DistilBertPreTrainedModel):
def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None): def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None):
r""" r"""
Return: Return:
:obj:`Tuple` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.DistilBertConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` 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(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
...@@ -513,7 +516,7 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel): ...@@ -513,7 +516,7 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
in ``[0, ..., config.vocab_size]`` in ``[0, ..., config.vocab_size]``
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` 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.DistilBertConfig`) and inputs:
loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Masked language modeling loss. Masked language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`) prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
...@@ -560,7 +563,7 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel): ...@@ -560,7 +563,7 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
@add_start_docstrings( @add_start_docstrings(
"""DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of """DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """, the pooled output) e.g. for GLUE tasks. """,
DISTILBERT_START_DOCSTRING, DISTILBERT_START_DOCSTRING,
) )
class DistilBertForSequenceClassification(DistilBertPreTrainedModel): class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
...@@ -585,7 +588,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel): ...@@ -585,7 +588,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` 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.DistilBertConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided): loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
Classification (or regression if config.num_labels==1) loss. Classification (or regression if config.num_labels==1) loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`): logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
...@@ -637,7 +640,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel): ...@@ -637,7 +640,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
@add_start_docstrings( @add_start_docstrings(
"""DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of """DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
the hidden-states output to compute `span start logits` and `span end logits`). """, the hidden-states output to compute `span start logits` and `span end logits`). """,
DISTILBERT_START_DOCSTRING, DISTILBERT_START_DOCSTRING,
) )
class DistilBertForQuestionAnswering(DistilBertPreTrainedModel): class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
...@@ -672,7 +675,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel): ...@@ -672,7 +675,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
Position outside of the sequence are not taken into account for computing the loss. Position outside of the sequence are not taken into account for computing the loss.
Returns: Returns:
: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.DistilBertConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
...@@ -736,7 +739,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel): ...@@ -736,7 +739,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
@add_start_docstrings( @add_start_docstrings(
"""DistilBert Model with a token classification head on top (a linear layer on top of """DistilBert Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
DISTILBERT_START_DOCSTRING, DISTILBERT_START_DOCSTRING,
) )
class DistilBertForTokenClassification(DistilBertPreTrainedModel): class DistilBertForTokenClassification(DistilBertPreTrainedModel):
...@@ -758,7 +761,7 @@ class DistilBertForTokenClassification(DistilBertPreTrainedModel): ...@@ -758,7 +761,7 @@ class DistilBertForTokenClassification(DistilBertPreTrainedModel):
Indices should be in ``[0, ..., config.num_labels - 1]``. Indices should be in ``[0, ..., config.num_labels - 1]``.
Returns: Returns:
: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.DistilBertConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) : loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
Classification loss. Classification loss.
scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`) scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
......
src/transformers/modeling_gpt2.py
View file @ 0e9899f4
...@@ -266,8 +266,9 @@ class GPT2PreTrainedModel(PreTrainedModel): ...@@ -266,8 +266,9 @@ class GPT2PreTrainedModel(PreTrainedModel):
GPT2_START_DOCSTRING = r""" GPT2_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
refer to the PyTorch documentation for all matter related to general usage and behavior. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters: Parameters:
config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model. config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model.
...@@ -362,7 +363,7 @@ class GPT2Model(GPT2PreTrainedModel): ...@@ -362,7 +363,7 @@ class GPT2Model(GPT2PreTrainedModel):
): ):
r""" r"""
Return: 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.GPT2Config`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` 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.
past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
...@@ -547,7 +548,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel): ...@@ -547,7 +548,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
computed for labels in ``[0, ..., config.vocab_size]`` computed for labels in ``[0, ..., config.vocab_size]``
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided) loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
Language modeling loss. Language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
...@@ -658,7 +659,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel): ...@@ -658,7 +659,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
of the input tensors. (see `input_ids` above) of the input tensors. (see `input_ids` above)
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided): lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided):
Language modeling loss. Language modeling loss.
mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided): mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided):
......
src/transformers/modeling_openai.py
View file @ 0e9899f4
...@@ -375,7 +375,7 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel): ...@@ -375,7 +375,7 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
): ):
r""" r"""
Return: 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.OpenAIGPTConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` 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.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
...@@ -522,7 +522,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel): ...@@ -522,7 +522,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
computed for labels in ``[0, ..., config.vocab_size]`` computed for labels in ``[0, ..., config.vocab_size]``
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.OpenAIGPTConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided) loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
Language modeling loss. Language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
...@@ -627,7 +627,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel): ...@@ -627,7 +627,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
of the input tensors. (see `input_ids` above) of the input tensors. (see `input_ids` above)
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.OpenAIGPTConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided): lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided):
Language modeling loss. Language modeling loss.
mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided): mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided):
......
src/transformers/modeling_roberta.py
View file @ 0e9899f4
...@@ -401,7 +401,7 @@ class RobertaForMultipleChoice(BertPreTrainedModel): ...@@ -401,7 +401,7 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
of the input tensors. (see `input_ids` above) of the input tensors. (see `input_ids` above)
Returns: Returns:
: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.RobertaConfig`) and inputs:
loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided): loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided):
Classification loss. Classification loss.
classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`): classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
...@@ -497,7 +497,7 @@ class RobertaForTokenClassification(BertPreTrainedModel): ...@@ -497,7 +497,7 @@ class RobertaForTokenClassification(BertPreTrainedModel):
Indices should be in ``[0, ..., config.num_labels - 1]``. Indices should be in ``[0, ..., config.num_labels - 1]``.
Returns: Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.RobertaConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) : loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
Classification loss. Classification loss.
scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`) scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
...@@ -616,7 +616,7 @@ class RobertaForQuestionAnswering(BertPreTrainedModel): ...@@ -616,7 +616,7 @@ class RobertaForQuestionAnswering(BertPreTrainedModel):
Position outside of the sequence are not taken into account for computing the loss. Position outside of the sequence are not taken into account for computing the loss.
Returns: Returns:
: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.RobertaConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_tf_albert.py
View file @ 0e9899f4
...@@ -719,7 +719,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel): ...@@ -719,7 +719,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) 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`):
...@@ -776,7 +776,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel): ...@@ -776,7 +776,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.AlbertConfig`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) 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`):
......
src/transformers/modeling_tf_bert.py
View file @ 0e9899f4
...@@ -668,7 +668,7 @@ class TFBertModel(TFBertPreTrainedModel): ...@@ -668,7 +668,7 @@ class TFBertModel(TFBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: 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)`): 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)`):
...@@ -724,7 +724,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel): ...@@ -724,7 +724,7 @@ 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 (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 (: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)`):
...@@ -780,7 +780,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel): ...@@ -780,7 +780,7 @@ 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 (: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 (: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`):
...@@ -830,7 +830,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel): ...@@ -830,7 +830,7 @@ 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 (: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)`) 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`):
...@@ -886,7 +886,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel): ...@@ -886,7 +886,7 @@ 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 (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)`): 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`):
...@@ -952,7 +952,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel): ...@@ -952,7 +952,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
): ):
r""" r"""
Return: 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)`: 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).
...@@ -1054,7 +1054,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel): ...@@ -1054,7 +1054,7 @@ 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 (: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)`): 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`):
...@@ -1111,7 +1111,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel): ...@@ -1111,7 +1111,7 @@ 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 (: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,)`): 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_ctrl.py
View file @ 0e9899f4
...@@ -436,7 +436,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel): ...@@ -436,7 +436,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) 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 last layer of the model. Sequence of hidden-states at the last layer of the model.
past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
...@@ -509,7 +509,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel): ...@@ -509,7 +509,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) 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).
past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
......
src/transformers/modeling_tf_distilbert.py
View file @ 0e9899f4
...@@ -544,7 +544,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel): ...@@ -544,7 +544,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: 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,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`):
...@@ -618,7 +618,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel): ...@@ -618,7 +618,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
r""" r"""
Returns: Returns:
: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,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`):
...@@ -658,7 +658,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel): ...@@ -658,7 +658,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
@add_start_docstrings( @add_start_docstrings(
"""DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of """DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """, the pooled output) e.g. for GLUE tasks. """,
DISTILBERT_START_DOCSTRING, DISTILBERT_START_DOCSTRING,
) )
class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel): class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
...@@ -682,7 +682,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel): ...@@ -682,7 +682,7 @@ 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 (config) and inputs: :obj:`tuple(torch.FloatTensor)` 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`):
...@@ -740,7 +740,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel): ...@@ -740,7 +740,7 @@ 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 (:obj:`~transformers.BertConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` 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`):
...@@ -777,7 +777,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel): ...@@ -777,7 +777,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel):
@add_start_docstrings( @add_start_docstrings(
"""DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of """DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
the hidden-states output to compute `span start logits` and `span end logits`). """, the hidden-states output to compute `span start logits` and `span end logits`). """,
DISTILBERT_START_DOCSTRING, DISTILBERT_START_DOCSTRING,
) )
class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel): class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
...@@ -795,7 +795,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel): ...@@ -795,7 +795,7 @@ 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 (:obj:`~transformers.BertConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` 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_gpt2.py
View file @ 0e9899f4
...@@ -452,7 +452,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel): ...@@ -452,7 +452,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) 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 last layer of the model. Sequence of hidden-states at the last layer of the model.
past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
...@@ -504,7 +504,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel): ...@@ -504,7 +504,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) 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).
past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
...@@ -585,7 +585,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel): ...@@ -585,7 +585,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
Selected in the range ``[0, input_ids.size(-1) - 1[``. Selected in the range ``[0, input_ids.size(-1) - 1[``.
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
lm_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`): lm_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices, 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).
mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`): mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
......
src/transformers/modeling_tf_openai.py
View file @ 0e9899f4
...@@ -435,7 +435,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel): ...@@ -435,7 +435,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) 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 last layer of the model. Sequence of hidden-states at the last layer of the model.
hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned when ``config.output_hidden_states=True``):
...@@ -483,7 +483,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel): ...@@ -483,7 +483,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) 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).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
...@@ -558,7 +558,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel): ...@@ -558,7 +558,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
Selected in the range ``[0, input_ids.size(-1) - 1[``. Selected in the range ``[0, input_ids.size(-1) - 1[``.
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
lm_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`): lm_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices, 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).
mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`): mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
......
src/transformers/modeling_tf_roberta.py
View file @ 0e9899f4
...@@ -187,7 +187,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel): ...@@ -187,7 +187,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: 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.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)`):
...@@ -269,7 +269,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel): ...@@ -269,7 +269,7 @@ 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 (:obj:`~transformers.BertConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` 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`):
...@@ -346,7 +346,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel): ...@@ -346,7 +346,7 @@ 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 (config) and inputs: :obj:`tuple(torch.FloatTensor)` 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`):
...@@ -402,7 +402,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel): ...@@ -402,7 +402,7 @@ 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 (:obj:`~transformers.BertConfig`) and inputs: :obj:`tuple(torch.FloatTensor)` 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`):
......
src/transformers/modeling_tf_transfo_xl.py
View file @ 0e9899f4
...@@ -695,7 +695,7 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel): ...@@ -695,7 +695,7 @@ 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 (config) and inputs: :obj:`tuple(torch.FloatTensor)` 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:`torch.FloatTensor` 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[torch.FloatTensor]` of length :obj:`config.n_layers`):
...@@ -759,7 +759,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel): ...@@ -759,7 +759,7 @@ 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 (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(torch.FloatTensor)` 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:`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). 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[torch.FloatTensor]` of length :obj:`config.n_layers`):
......
src/transformers/modeling_tf_xlm.py
View file @ 0e9899f4
...@@ -579,7 +579,7 @@ class TFXLMModel(TFXLMPreTrainedModel): ...@@ -579,7 +579,7 @@ class TFXLMModel(TFXLMPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` 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 ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
...@@ -661,7 +661,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel): ...@@ -661,7 +661,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` 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 ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
...@@ -714,7 +714,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel): ...@@ -714,7 +714,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(tf.Tensor)` 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.XLMConfig`) and inputs:
logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, config.num_labels)`): logits (:obj:`tf.Tensor` or :obj:`Numpy array` 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 ``config.output_hidden_states=True``): hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
...@@ -768,7 +768,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel): ...@@ -768,7 +768,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLMConfig`) and inputs:
start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
end_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`): end_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_tf_xlnet.py
View file @ 0e9899f4
...@@ -788,7 +788,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel): ...@@ -788,7 +788,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`tf.Tensor` or :obj:`Numpy array` 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[tf.Tensor]` of length :obj:`config.n_layers`): mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
...@@ -841,7 +841,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel): ...@@ -841,7 +841,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`tf.Tensor` or :obj:`Numpy array` 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[tf.Tensor]` of length :obj:`config.n_layers`): mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
...@@ -910,7 +910,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel): ...@@ -910,7 +910,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:(batch_size, config.num_labels)`): logits (:obj:`tf.Tensor` or :obj:`Numpy array` 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).
mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`): mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
...@@ -970,7 +970,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel): ...@@ -970,7 +970,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Return: Return:
`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:(batch_size, config.num_labels)`): logits (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:(batch_size, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`): mems (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
...@@ -1028,7 +1028,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel): ...@@ -1028,7 +1028,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel):
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
r""" r"""
Returns: Returns:
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (config) and inputs: :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.XLNetConfig`) and inputs:
loss (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): loss (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`): start_scores (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length,)`):
......
src/transformers/modeling_transfo_xl.py
View file @ 0e9899f4
...@@ -673,7 +673,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel): ...@@ -673,7 +673,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None): def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None):
r""" r"""
Return: 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.TransfoXLConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): last_hidden_state (:obj:`torch.FloatTensor` 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[torch.FloatTensor]` of length :obj:`config.n_layers`):
...@@ -860,7 +860,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel): ...@@ -860,7 +860,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
computed for labels in ``[0, ..., config.vocab_size]`` computed for labels in ``[0, ..., config.vocab_size]``
Return: Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.TransfoXLConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided) loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
Language modeling loss. Language modeling loss.
prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
......
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