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TF RoBERTa

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Lysandre
2020-01-20 13:31:04 -05:00
committed by Lysandre Debut
parent 3e1bc27e1b
commit b28020f590
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docs/source/model_doc/roberta.rst
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@@ -71,3 +71,10 @@ TFRobertaForSequenceClassification
.. autoclass:: transformers.TFRobertaForSequenceClassification .. autoclass:: transformers.TFRobertaForSequenceClassification
:members: :members:
TFRobertaForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRobertaForTokenClassification
:members:

269
src/transformers/modeling_tf_roberta.py
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@@ -104,41 +104,29 @@ class TFRobertaPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "roberta" base_model_prefix = "roberta"
ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in ROBERTA_START_DOCSTRING = r"""
`RoBERTa: A Robustly Optimized BERT Pretraining Approach`_ This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Use it as a regular TF 2.0 Keras Model and
Veselin Stoyanov. It is based on Google's BERT model released in 2018.
It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
objective and training with much larger mini-batches and learning rates.
This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained
models.
This model is a tf.keras.Model `tf.keras.Model`_ sub-class. Use it as a regular TF 2.0 Keras Model and
refer to the TF 2.0 documentation for all matter related to general usage and behavior. refer to the TF 2.0 documentation for all matter related to general usage and behavior.
.. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`: .. note::
https://arxiv.org/abs/1907.11692
.. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs:
TF 2.0 models accepts two formats as inputs: TF 2.0 models accepts two formats as inputs:
- 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:
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})` :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters: Parameters:
config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the
@@ -147,75 +135,78 @@ ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
""" """
ROBERTA_INPUTS_DOCSTRING = r""" ROBERTA_INPUTS_DOCSTRING = r"""
Inputs: Args:
**input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``: input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Indices of input sequence tokens in the vocabulary.
To match pre-training, RoBERTa input sequence should be formatted with <s> and </s> tokens as follows:
Indices can be obtained using :class:`transformers.RobertaTokenizer`.
(a) For sequence pairs:
``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
(b) For single sequences:
``tokens: <s> the dog is hairy . </s>``
Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with
the ``add_special_tokens`` parameter set to ``True``.
RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
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:`Numpy array` or :obj:`tf.Tensor` 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` need to be trained) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Optional segment token indices to indicate first and second portions of the inputs. `What are attention masks? <../glossary.html#attention-mask>`__
This embedding matrice is not trained (not pretrained during RoBERTa pretraining), you will have to train it token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
during finetuning. Segment token indices to indicate first and second portions of the inputs.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``: `What are token type IDs? <../glossary.html#token-type-ids>`__
position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` 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:`Numpy array` or :obj:`tf.Tensor` 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)``: inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `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 RoBERTa Model transformer outputing raw hidden-states without any specific head on top.", "The bare RoBERTa Model transformer outputing raw hidden-states without any specific head on top.",
ROBERTA_START_DOCSTRING, ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
) )
class TFRobertaModel(TFRobertaPreTrainedModel): class TFRobertaModel(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)`` super().__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name="roberta")
def call(self, inputs, **kwargs):
r"""
Returns:
: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)`):
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**: ``tf.Tensor`` of shape ``(batch_size, hidden_size)`` pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token) Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification) layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence. the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape ``(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**: (`optional`, returned when ``config.output_attentions=True``) attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
list of ``tf.Tensor`` (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
: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 heads. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples:: Examples::
@@ -229,13 +220,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name="roberta")
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs) outputs = self.roberta(inputs, **kwargs)
return outputs return outputs
@@ -272,21 +257,34 @@ class TFRobertaLMHead(tf.keras.layers.Layer):
@add_start_docstrings( @add_start_docstrings(
"""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING """RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING
) )
class TFRobertaForMaskedLM(TFRobertaPreTrainedModel): class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``tf.Tensor`` of shape ``(1,)``: super().__init__(config, *inputs, **kwargs)
Masked language modeling loss.
**prediction_scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` self.roberta = TFRobertaMainLayer(config, name="roberta")
self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
def get_output_embeddings(self):
return self.lm_head.decoder
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape ``(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**: (`optional`, returned when ``config.output_attentions=True``) attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
list of ``tf.Tensor`` (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
: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 heads. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples:: Examples::
@@ -300,18 +298,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
outputs = model(input_ids, masked_lm_labels=input_ids) outputs = model(input_ids, masked_lm_labels=input_ids)
prediction_scores = outputs[0] prediction_scores = outputs[0]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
def get_output_embeddings(self):
return self.lm_head.decoder
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs) outputs = self.roberta(inputs, **kwargs)
sequence_output = outputs[0] sequence_output = outputs[0]
@@ -351,19 +338,31 @@ class TFRobertaClassificationHead(tf.keras.layers.Layer):
"""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer """RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
on top of the pooled output) e.g. for GLUE tasks. """, on top of the pooled output) e.g. for GLUE tasks. """,
ROBERTA_START_DOCSTRING, ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
) )
class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel): class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**logits**: ``tf.Tensor`` of shape ``(batch_size, config.num_labels)`` super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.classifier = TFRobertaClassificationHead(config, name="classifier")
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax). Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape ``(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**: (`optional`, returned when ``config.output_attentions=True``) attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
list of ``tf.Tensor`` (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
: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 heads. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples:: Examples::
@@ -378,16 +377,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
logits = outputs[0] logits = outputs[0]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.classifier = TFRobertaClassificationHead(config, name="classifier")
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs) outputs = self.roberta(inputs, **kwargs)
sequence_output = outputs[0] sequence_output = outputs[0]
@@ -402,19 +392,34 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
"""RoBERTa Model with a token classification head on top (a linear layer on top of """RoBERTa 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. """,
ROBERTA_START_DOCSTRING, ROBERTA_START_DOCSTRING,
ROBERTA_INPUTS_DOCSTRING,
) )
class TFRobertaForTokenClassification(TFRobertaPreTrainedModel): class TFRobertaForTokenClassification(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)`` super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax). Classification scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``) hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings) tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape ``(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**: (`optional`, returned when ``config.output_attentions=True``) attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
list of ``Numpy array`` or ``tf.Tensor`` (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
: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 heads. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples:: Examples::
@@ -428,19 +433,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
scores = outputs[0] scores = outputs[0]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs) outputs = self.roberta(inputs, **kwargs)
sequence_output = outputs[0] sequence_output = outputs[0]