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BERT TensorFlow

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Lysandre
2020-01-16 15:51:54 -05:00
committed by Lysandre Debut
parent cd77c750c5
commit dbeb7fb4e6
2 changed files with 240 additions and 213 deletions
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src/transformers/modeling_tf_albert.py
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@@ -496,7 +496,7 @@ ALBERT_START_DOCSTRING = r"""
- 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 :obj:`tf.keras.Model.fit()` method which currently requires having 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)`. 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 If you choose this second option, there are three possibilities you can use to gather all the input Tensors

447
src/transformers/modeling_tf_bert.py
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@@ -22,7 +22,7 @@ import numpy as np
import tensorflow as tf import tensorflow as tf
from .configuration_bert import BertConfig from .configuration_bert import BertConfig
from .file_utils import add_start_docstrings from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
@@ -584,13 +584,9 @@ class TFBertPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "bert" base_model_prefix = "bert"
BERT_START_DOCSTRING = r""" The BERT model was proposed in BERT_START_DOCSTRING = r"""
`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer Use it as a regular TF 2.0 Keras Model and
pre-trained using a combination of masked language modeling objective and next sentence prediction
on a large corpus comprising the Toronto Book Corpus and Wikipedia.
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.
.. _`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`: .. _`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`:
@@ -599,21 +595,24 @@ BERT_START_DOCSTRING = r""" The BERT model was proposed in
.. _`tf.keras.Model`: .. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs: .. note::
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.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.
@@ -622,75 +621,72 @@ BERT_START_DOCSTRING = r""" The BERT model was proposed in
""" """
BERT_INPUTS_DOCSTRING = r""" BERT_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, BERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0``
Bert is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.BertTokenizer`. Indices can be obtained using :class:`transformers.BertTokenizer`.
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`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Segment token indices to indicate first and second portions of the inputs. 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 Bert Model transformer outputing raw hidden-states without any specific head on top.", "The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
BERT_START_DOCSTRING, BERT_START_DOCSTRING,
BERT_INPUTS_DOCSTRING,
) )
class TFBertModel(TFBertPreTrainedModel): class TFBertModel(TFBertPreTrainedModel):
r""" r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: Returns:
**last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)`` :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::
@@ -710,6 +706,7 @@ class TFBertModel(TFBertPreTrainedModel):
super().__init__(config, *inputs, **kwargs) super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert") self.bert = TFBertMainLayer(config, name="bert")
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs): def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs) outputs = self.bert(inputs, **kwargs)
return outputs return outputs
@@ -719,21 +716,37 @@ class TFBertModel(TFBertPreTrainedModel):
"""Bert Model with two heads on top as done during the pre-training: """Bert Model with two heads on top as done during the pre-training:
a `masked language modeling` head and a `next sentence prediction (classification)` head. """, a `masked language modeling` head and a `next sentence prediction (classification)` head. """,
BERT_START_DOCSTRING, BERT_START_DOCSTRING,
BERT_INPUTS_DOCSTRING,
) )
class TFBertForPreTraining(TFBertPreTrainedModel): class TFBertForPreTraining(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**prediction_scores**: ```tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
def get_output_embeddings(self):
return self.bert.embeddings
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**seq_relationship_scores**: ```tf.Tensor`` of shape ``(batch_size, sequence_length, 2)`` seq_relationship_scores (: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**: (`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::
@@ -747,19 +760,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
prediction_scores, seq_relationship_scores = outputs[:2] prediction_scores, seq_relationship_scores = outputs[:2]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
def get_output_embeddings(self):
return self.bert.embeddings
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs) outputs = self.bert(inputs, **kwargs)
sequence_output, pooled_output = outputs[:2] sequence_output, pooled_output = outputs[:2]
@@ -774,19 +775,35 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
@add_start_docstrings( @add_start_docstrings(
"""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING """Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING
) )
class TFBertForMaskedLM(TFBertPreTrainedModel): class TFBertForMaskedLM(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**prediction_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)`` super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
def get_output_embeddings(self):
return self.bert.embeddings
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
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 ``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::
@@ -800,18 +817,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
prediction_scores = outputs[0] prediction_scores = outputs[0]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
def get_output_embeddings(self):
return self.bert.embeddings
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs) outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0] sequence_output = outputs[0]
@@ -825,19 +831,31 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
@add_start_docstrings( @add_start_docstrings(
"""Bert Model with a `next sentence prediction (classification)` head on top. """, """Bert Model with a `next sentence prediction (classification)` head on top. """,
BERT_START_DOCSTRING, BERT_START_DOCSTRING,
BERT_INPUTS_DOCSTRING,
) )
class TFBertForNextSentencePrediction(TFBertPreTrainedModel): class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**seq_relationship_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, 2)`` super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
seq_relationship_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`)
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation 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::
@@ -851,15 +869,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
seq_relationship_scores = outputs[0] seq_relationship_scores = outputs[0]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs) outputs = self.bert(inputs, **kwargs)
pooled_output = outputs[1] pooled_output = outputs[1]
@@ -874,19 +884,35 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
"""Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of """Bert 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. """,
BERT_START_DOCSTRING, BERT_START_DOCSTRING,
BERT_INPUTS_DOCSTRING,
) )
class TFBertForSequenceClassification(TFBertPreTrainedModel): class TFBertForSequenceClassification(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**logits**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, config.num_labels)`` super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name="bert")
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"
)
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
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 ``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::
@@ -900,19 +926,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
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.bert = TFBertMainLayer(config, name="bert")
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.bert(inputs, **kwargs) outputs = self.bert(inputs, **kwargs)
pooled_output = outputs[1] pooled_output = outputs[1]
@@ -929,20 +943,45 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
"""Bert Model with a multiple choice classification head on top (a linear layer on top of """Bert Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
BERT_START_DOCSTRING, BERT_START_DOCSTRING,
BERT_INPUTS_DOCSTRING,
) )
class TFBertForMultipleChoice(TFBertPreTrainedModel): class TFBertForMultipleChoice(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**classification_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension super().__init__(config, *inputs, **kwargs)
of the input tensors. (see `input_ids` above).
self.bert = TFBertMainLayer(config, name="bert")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(
self,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (config) and inputs:
classification_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`:
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
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::
@@ -957,27 +996,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
classification_scores = outputs[0] classification_scores = outputs[0]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
def call(
self,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
if isinstance(inputs, (tuple, list)): if isinstance(inputs, (tuple, list)):
input_ids = inputs[0] input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
@@ -1035,19 +1054,35 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
"""Bert Model with a token classification head on top (a linear layer on top of """Bert 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. """,
BERT_START_DOCSTRING, BERT_START_DOCSTRING,
BERT_INPUTS_DOCSTRING,
) )
class TFBertForTokenClassification(TFBertPreTrainedModel): class TFBertForTokenClassification(TFBertPreTrainedModel):
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.bert = TFBertMainLayer(config, name="bert")
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"
)
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
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::
@@ -1061,19 +1096,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
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.bert = TFBertMainLayer(config, name="bert")
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.bert(inputs, **kwargs) outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0] sequence_output = outputs[0]
@@ -1090,21 +1113,36 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
"""Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of """Bert 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`). """,
BERT_START_DOCSTRING, BERT_START_DOCSTRING,
BERT_INPUTS_DOCSTRING,
) )
class TFBertForQuestionAnswering(TFBertPreTrainedModel): class TFBertForQuestionAnswering(TFBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs: def __init__(self, config, *inputs, **kwargs):
**start_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)`` super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name="bert")
self.qa_outputs = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:obj:`~transformers.BertConfig`) and inputs:
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax). Span-start scores (before SoftMax).
**end_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)`` end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax). Span-end 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::
@@ -1118,18 +1156,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel):
outputs = model(input_ids) outputs = model(input_ids)
start_scores, end_scores = outputs[:2] start_scores, end_scores = outputs[:2]
""" """
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name="bert")
self.qa_outputs = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs) outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0] sequence_output = outputs[0]