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RoBERTa token classification

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[WIP] copy paste bert token classification for roberta
This commit is contained in:
Matt Maybeno
2019-10-23 21:05:13 -07:00
committed by Julien Chaumond
parent 5b6cafb11b
commit 66085a1321
5 changed files with 158 additions and 1 deletions
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19
transformers/tests/modeling_roberta_test.py
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@@ -24,7 +24,8 @@ from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification)
from transformers import (RobertaConfig, RobertaModel, RobertaForMaskedLM,
RobertaForSequenceClassification, RobertaForTokenClassification)
from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
@@ -156,6 +157,22 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
[self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
def create_and_check_roberta_for_token_classification(self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = RobertaForTokenClassification(config=config)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,

15
transformers/tests/modeling_tf_roberta_test.py
View File

@@ -30,6 +30,7 @@ if is_tf_available():
import numpy
from transformers.modeling_tf_roberta import (TFRobertaModel, TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@@ -154,6 +155,20 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_roberta_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = TFRobertaForTokenClassification(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.seq_length, self.num_labels])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,