- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
If you choose this second option, there are three possibilities you can use to gather all the input Tensors
in the first positional argument :
- a single Tensor with input_ids only and nothing else: `model(inputs_ids)
- a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associaed to the input names given in the docstring:
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
config (:class:`~transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
config (:class:`~transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
...
@@ -391,53 +381,75 @@ OPENAI_GPT_START_DOCSTRING = r""" OpenAI GPT model was proposed in
...
@@ -391,53 +381,75 @@ OPENAI_GPT_START_DOCSTRING = r""" OpenAI GPT model was proposed in
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
"""
OPENAI_GPT_INPUTS_DOCSTRING=r""" Inputs:
OPENAI_GPT_INPUTS_DOCSTRING=r"""
**input_ids**: ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Args:
Indices of input sequence tokens in the vocabulary.
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
Indices of input sequence tokens in the vocabulary.
the right rather than the left.
Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
Indices can be obtained using :class:`transformers.GPT2Tokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices.
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional`) ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs).
`What are attention masks? <../glossary.html#attention-mask>`__
The embeddings from these tokens will be summed with the respective token embeddings.
token_type_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices)
Segment token indices to indicate first and second portions of the inputs.
**position_ids**: (`optional`) ```Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
`What are token type IDs? <../glossary.html#token-type-ids>`_
position_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Indices of positions of each input sequence tokens in the position embeddings.
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
`What are position IDs? <../glossary.html#position-ids>`_
head_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules.
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
**inputs_embeds**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
input_embeds (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
than the model's internal embedding lookup matrix.
training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
(if set to :obj:`False`) for evaluation.
"""
"""
@add_start_docstrings(
@add_start_docstrings(
"The bare OpenAI GPT transformer model outputing raw hidden-states without any specific head on top.",
"The bare OpenAI GPT transformer model outputing raw hidden-states without any specific head on top.",
**mc_token_ids**: (`optional`, default to index of the last token of the input) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, num_choices)``:
mc_token_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
Index of the classification token in each input sequence.
Index of the classification token in each input sequence.
Selected in the range ``[0, input_ids.size(-1) - 1[``.
Selected in the range ``[0, input_ids.size(-1) - 1[``.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
Return:
**lm_prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length, config.vocab_size)``
:obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:obj:`~transformers.GPT2Config`) and inputs:
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**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)``
mc_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax).
Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
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)`):
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
Contains pre-computed hidden-states (key and values in the attention blocks).
of shape ``(batch_size, sequence_length, hidden_size)``:
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.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_hidden_states=True``):
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)`.
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**: (`optional`, returned when ``config.output_attentions=True``)
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
Tuple of :obj:`tf.Tensor` (one for each layer) of shape
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
