SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Add SequenceClassification and MultipleChoice TF models to Electra (#6227)

Browse Source 
* Add SequenceClassification and MultipleChoice TF models to Electra

* Apply style

* Add summary_proj_to_labels to Electra config

* Finally mirroring the PT version of these models

* Apply style

* Fix Electra test
This commit is contained in:
Julien Plu
2020-08-05 15:04:27 +02:00
committed by GitHub
parent 376c02e9a9
commit 33966811bd
4 changed files with 281 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

41
tests/test_modeling_tf_electra.py
View File

@@ -24,10 +24,14 @@ from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_electra import (
TFElectraModel,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraForQuestionAnswering,
)
@@ -138,6 +142,35 @@ class TFElectraModelTester:
}
self.parent.assertListEqual(list(result["prediction_scores"].shape), [self.batch_size, self.seq_length])
def create_and_check_electra_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFElectraForSequenceClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_labels])
def create_and_check_electra_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = TFElectraForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
(logits,) = model(inputs)
result = {"logits": logits.numpy()}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
def create_and_check_electra_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
@@ -210,6 +243,14 @@ class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_electra_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
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_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_electra_for_multiple_choice(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_electra_for_token_classification(*config_and_inputs)