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ElectraForQuestionAnswering (#4913) 

* ElectraForQuestionAnswering

* udate __init__

* add test for electra qa model

* add ElectraForQuestionAnswering in auto models

* add ElectraForQuestionAnswering in all_model_classes

* fix outputs, input_ids defaults to None

* add ElectraForQuestionAnswering in docs

* remove commented line
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Suraj Patil 2020-06-11 00:47:52 +05:30 committed by GitHub
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7
docs/source/model_doc/electra.rst
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@ -106,6 +106,13 @@ ElectraForTokenClassification
:members:
ElectraForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.ElectraForQuestionAnswering
:members:
TFElectraModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

1
src/transformers/__init__.py
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@ -319,6 +319,7 @@ if is_torch_available():
ElectraForTokenClassification,
ElectraPreTrainedModel,
ElectraForSequenceClassification,
ElectraForQuestionAnswering,
ElectraModel,
load_tf_weights_in_electra,
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,

2
src/transformers/modeling_auto.py
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@ -78,6 +78,7 @@ from .modeling_distilbert import (
from .modeling_electra import (
ElectraForMaskedLM,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
@ -237,6 +238,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
(XLNetConfig, XLNetForQuestionAnsweringSimple),
(FlaubertConfig, FlaubertForQuestionAnsweringSimple),
(XLMConfig, XLMForQuestionAnsweringSimple),
(ElectraConfig, ElectraForQuestionAnswering),
]
)

116
src/transformers/modeling_electra.py
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@ -742,3 +742,119 @@ class ElectraForTokenClassification(ElectraPreTrainedModel):
output += discriminator_hidden_states[1:]
return output # (loss), scores, (hidden_states), (attentions)
@add_start_docstrings(
"""ELECTRA 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`). """,
ELECTRA_INPUTS_DOCSTRING,
)
class ElectraForQuestionAnswering(ElectraPreTrainedModel):
config_class = ElectraConfig
base_model_prefix = "electra"
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.electra = ElectraModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
):
r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
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.
start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax).
end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
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.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (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.
Examples::
from transformers import ElectraTokenizer, ElectraForQuestionAnswering
import torch
tokenizer = ElectraTokenizer.from_pretrained('google/electra-base-discriminator')
model = ElectraForQuestionAnswering.from_pretrained('google/electra-base-discriminator')
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
input_ids = tokenizer.encode(question, text)
start_scores, end_scores = model(torch.tensor([input_ids]))
all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
"""
discriminator_hidden_states = self.electra(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
)
sequence_output = discriminator_hidden_states[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
outputs = (start_logits, end_logits,) + discriminator_hidden_states[1:]
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
outputs = (total_loss,) + outputs
return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions)

36
tests/test_modeling_electra.py
View File

@ -31,6 +31,7 @@ if is_torch_available():
ElectraForTokenClassification,
ElectraForPreTraining,
ElectraForSequenceClassification,
ElectraForQuestionAnswering,
)
from transformers.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
@ -45,6 +46,7 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
ElectraForMaskedLM,
ElectraForTokenClassification,
ElectraForSequenceClassification,
ElectraForQuestionAnswering,
)
if is_torch_available()
else ()
@ -276,6 +278,36 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
self.check_loss_output(result)
def create_and_check_electra_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = ElectraForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
loss, start_logits, end_logits = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
@ -318,6 +350,10 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_electra_for_sequence_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_electra_for_question_answering(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: