mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-16 11:08:23 +06:00
27cf1d97f0
* fix-5181 Padding to max sequence length while truncation to another length was wrong on slow tokenizers * clean up and fix #5155 * fix XLM test * Fix tests for Transfo-XL * logging only above WARNING in tests * switch slow tokenizers tests in @slow * fix Marian truncation tokenization test * style and quality * make the test a lot faster by limiting the sequence length used in tests
123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
# coding=utf-8
|
|
# Copyright 2018 The Google AI Language Team Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
|
|
import unittest
|
|
|
|
from transformers import is_tf_available
|
|
|
|
from .utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_tf, slow
|
|
|
|
|
|
if is_tf_available():
|
|
from transformers import (
|
|
AutoConfig,
|
|
BertConfig,
|
|
TFAutoModel,
|
|
TFBertModel,
|
|
TFAutoModelForPreTraining,
|
|
TFBertForPreTraining,
|
|
TFAutoModelWithLMHead,
|
|
TFBertForMaskedLM,
|
|
TFRobertaForMaskedLM,
|
|
TFAutoModelForSequenceClassification,
|
|
TFBertForSequenceClassification,
|
|
TFAutoModelForQuestionAnswering,
|
|
TFBertForQuestionAnswering,
|
|
)
|
|
|
|
|
|
@require_tf
|
|
class TFAutoModelTest(unittest.TestCase):
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
import h5py
|
|
|
|
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
|
|
|
|
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
for model_name in ["bert-base-uncased"]:
|
|
config = AutoConfig.from_pretrained(model_name)
|
|
self.assertIsNotNone(config)
|
|
self.assertIsInstance(config, BertConfig)
|
|
|
|
model = TFAutoModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
self.assertIsInstance(model, TFBertModel)
|
|
|
|
@slow
|
|
def test_model_for_pretraining_from_pretrained(self):
|
|
import h5py
|
|
|
|
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
|
|
|
|
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
for model_name in ["bert-base-uncased"]:
|
|
config = AutoConfig.from_pretrained(model_name)
|
|
self.assertIsNotNone(config)
|
|
self.assertIsInstance(config, BertConfig)
|
|
|
|
model = TFAutoModelForPreTraining.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
self.assertIsInstance(model, TFBertForPreTraining)
|
|
|
|
@slow
|
|
def test_lmhead_model_from_pretrained(self):
|
|
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
for model_name in ["bert-base-uncased"]:
|
|
config = AutoConfig.from_pretrained(model_name)
|
|
self.assertIsNotNone(config)
|
|
self.assertIsInstance(config, BertConfig)
|
|
|
|
model = TFAutoModelWithLMHead.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
self.assertIsInstance(model, TFBertForMaskedLM)
|
|
|
|
@slow
|
|
def test_sequence_classification_model_from_pretrained(self):
|
|
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
for model_name in ["bert-base-uncased"]:
|
|
config = AutoConfig.from_pretrained(model_name)
|
|
self.assertIsNotNone(config)
|
|
self.assertIsInstance(config, BertConfig)
|
|
|
|
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
self.assertIsInstance(model, TFBertForSequenceClassification)
|
|
|
|
@slow
|
|
def test_question_answering_model_from_pretrained(self):
|
|
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
for model_name in ["bert-base-uncased"]:
|
|
config = AutoConfig.from_pretrained(model_name)
|
|
self.assertIsNotNone(config)
|
|
self.assertIsInstance(config, BertConfig)
|
|
|
|
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
self.assertIsInstance(model, TFBertForQuestionAnswering)
|
|
|
|
def test_from_pretrained_identifier(self):
|
|
model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
|
|
self.assertIsInstance(model, TFBertForMaskedLM)
|
|
self.assertEqual(model.num_parameters(), 14830)
|
|
self.assertEqual(model.num_parameters(only_trainable=True), 14830)
|
|
|
|
def test_from_identifier_from_model_type(self):
|
|
model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER)
|
|
self.assertIsInstance(model, TFRobertaForMaskedLM)
|
|
self.assertEqual(model.num_parameters(), 14830)
|
|
self.assertEqual(model.num_parameters(only_trainable=True), 14830)
|