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Kill model archive maps (#4636)

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* Kill model archive maps

* Fixup

* Also kill model_archive_map for MaskedBertPreTrainedModel

* Unhook config_archive_map

* Tokenizers: align with model id changes

* make style && make quality

* Fix CI
This commit is contained in:
Julien Chaumond
2020-06-02 09:39:33 -04:00
committed by GitHub
parent 47a551d17b
commit d4c2cb402d
115 changed files with 792 additions and 1323 deletions
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4
tests/test_modeling_albert.py
View File

@@ -33,7 +33,7 @@ if is_torch_available():
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
@@ -295,6 +295,6 @@ class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = AlbertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

14
tests/test_modeling_auto.py
View File

@@ -40,7 +40,7 @@ if is_torch_available():
AutoModelForTokenClassification,
BertForTokenClassification,
)
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.modeling_auto import (
MODEL_MAPPING,
MODEL_FOR_PRETRAINING_MAPPING,
@@ -56,7 +56,7 @@ class AutoModelTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
@@ -71,7 +71,7 @@ class AutoModelTest(unittest.TestCase):
@slow
def test_model_for_pretraining_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
@@ -87,7 +87,7 @@ class AutoModelTest(unittest.TestCase):
@slow
def test_lmhead_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
@@ -100,7 +100,7 @@ class AutoModelTest(unittest.TestCase):
@slow
def test_sequence_classification_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
@@ -115,7 +115,7 @@ class AutoModelTest(unittest.TestCase):
@slow
def test_question_answering_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
@@ -128,7 +128,7 @@ class AutoModelTest(unittest.TestCase):
@slow
def test_token_classification_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)

8
tests/test_modeling_bart.py
View File

@@ -39,7 +39,7 @@ if is_torch_available():
MBartTokenizer,
)
from transformers.modeling_bart import (
BART_PRETRAINED_MODEL_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
shift_tokens_right,
invert_mask,
_prepare_bart_decoder_inputs,
@@ -261,7 +261,7 @@ class BartTranslationTests(unittest.TestCase):
self.assertEqual(expected_translation_romanian, decoded[0])
def test_mbart_enro_config(self):
mbart_models = ["mbart-large-en-ro"]
mbart_models = ["facebook/mbart-large-en-ro"]
expected = {"scale_embedding": True, "output_past": True}
for name in mbart_models:
config = BartConfig.from_pretrained(name)
@@ -561,7 +561,7 @@ class BartModelIntegrationTests(unittest.TestCase):
@unittest.skip("This is just too slow")
def test_model_from_pretrained(self):
# Forces 1.6GB download from S3 for each model
for model_name in list(BART_PRETRAINED_MODEL_ARCHIVE_MAP.keys()):
for model_name in BART_PRETRAINED_MODEL_ARCHIVE_LIST:
model = BartModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@@ -593,7 +593,7 @@ class BartModelIntegrationTests(unittest.TestCase):
self.assertEqual(EXPECTED_SUMMARY, decoded[0])
def test_xsum_config_generation_params(self):
config = BartConfig.from_pretrained("bart-large-xsum")
config = BartConfig.from_pretrained("facebook/bart-large-xsum")
expected_params = dict(num_beams=6, do_sample=False, early_stopping=True, length_penalty=1.0)
config_params = {k: getattr(config, k, "MISSING") for k, v in expected_params.items()}
self.assertDictEqual(expected_params, config_params)

4
tests/test_modeling_bert.py
View File

@@ -35,7 +35,7 @@ if is_torch_available():
BertForTokenClassification,
BertForMultipleChoice,
)
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
class BertModelTester:
@@ -494,6 +494,6 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = BertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_common.py
View File

@@ -36,7 +36,7 @@ if is_torch_available():
PreTrainedModel,
BertModel,
BertConfig,
BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
top_k_top_p_filtering,
)
@@ -824,7 +824,7 @@ class ModelUtilsTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)

4
tests/test_modeling_ctrl.py
View File

@@ -24,7 +24,7 @@ from .utils import require_torch, slow, torch_device
if is_torch_available():
import torch
from transformers import CTRLConfig, CTRLModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRLLMHeadModel
from transformers import CTRLConfig, CTRLModel, CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLLMHeadModel
@require_torch
@@ -210,7 +210,7 @@ class CTRLModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = CTRLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

2
tests/test_modeling_distilbert.py
View File

@@ -247,6 +247,6 @@ class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
# @slow
# def test_model_from_pretrained(self):
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model = DistilBertModel.from_pretrained(model_name)
# self.assertIsNotNone(model)

4
tests/test_modeling_electra.py
View File

@@ -32,7 +32,7 @@ if is_torch_available():
ElectraForPreTraining,
ElectraForSequenceClassification,
)
from transformers.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
@@ -312,6 +312,6 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ElectraModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_flaubert.py
View File

@@ -32,7 +32,7 @@ if is_torch_available():
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
)
from transformers.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
@@ -387,6 +387,6 @@ class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = FlaubertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_gpt2.py
View File

@@ -28,7 +28,7 @@ if is_torch_available():
from transformers import (
GPT2Config,
GPT2Model,
GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
GPT2LMHeadModel,
GPT2DoubleHeadsModel,
)
@@ -334,7 +334,7 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GPT2Model.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_openai.py
View File

@@ -28,7 +28,7 @@ if is_torch_available():
from transformers import (
OpenAIGPTConfig,
OpenAIGPTModel,
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTLMHeadModel,
OpenAIGPTDoubleHeadsModel,
)
@@ -218,7 +218,7 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = OpenAIGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_reformer.py
View File

@@ -29,7 +29,7 @@ if is_torch_available():
ReformerModelWithLMHead,
ReformerTokenizer,
ReformerLayer,
REFORMER_PRETRAINED_MODEL_ARCHIVE_MAP,
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
import torch
@@ -503,7 +503,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
@slow
def test_model_from_pretrained(self):
for model_name in list(REFORMER_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ReformerModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_roberta.py
View File

@@ -33,7 +33,7 @@ if is_torch_available():
RobertaForTokenClassification,
)
from transformers.modeling_roberta import RobertaEmbeddings, RobertaForMultipleChoice, RobertaForQuestionAnswering
from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.modeling_utils import create_position_ids_from_input_ids
@@ -273,7 +273,7 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = RobertaModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_t5.py
View File

@@ -26,7 +26,7 @@ from .utils import require_torch, slow, torch_device
if is_torch_available():
import torch
from transformers import T5Config, T5Model, T5ForConditionalGeneration
from transformers.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.tokenization_t5 import T5Tokenizer
@@ -372,7 +372,7 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = T5Model.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_albert.py
View File

@@ -30,7 +30,7 @@ if is_tf_available():
TFAlbertForMaskedLM,
TFAlbertForSequenceClassification,
TFAlbertForQuestionAnswering,
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
)
@@ -257,6 +257,6 @@ class TFAlbertModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFAlbertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

10
tests/test_modeling_tf_auto.py
View File

@@ -49,7 +49,7 @@ class TFAutoModelTest(unittest.TestCase):
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# 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)
@@ -66,7 +66,7 @@ class TFAutoModelTest(unittest.TestCase):
self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# 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)
@@ -79,7 +79,7 @@ class TFAutoModelTest(unittest.TestCase):
@slow
def test_lmhead_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# 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)
@@ -92,7 +92,7 @@ class TFAutoModelTest(unittest.TestCase):
@slow
def test_sequence_classification_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# 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)
@@ -105,7 +105,7 @@ class TFAutoModelTest(unittest.TestCase):
@slow
def test_question_answering_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# 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)

2
tests/test_modeling_tf_bert.py
View File

@@ -311,7 +311,7 @@ class TFBertModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
model = TFBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_ctrl.py
View File

@@ -25,7 +25,7 @@ from .utils import require_tf, slow
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel, TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel, TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST
@require_tf
@@ -200,7 +200,7 @@ class TFCTRLModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFCTRLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

2
tests/test_modeling_tf_distilbert.py
View File

@@ -218,6 +218,6 @@ class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
# @slow
# def test_model_from_pretrained(self):
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model = DistilBertModesss.from_pretrained(model_name)
# self.assertIsNotNone(model)

2
tests/test_modeling_tf_electra.py
View File

@@ -221,7 +221,7 @@ class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
# for model_name in list(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# for model_name in TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/electra-small-discriminator"]:
model = TFElectraModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_gpt2.py
View File

@@ -29,7 +29,7 @@ if is_tf_available():
TFGPT2Model,
TFGPT2LMHeadModel,
TFGPT2DoubleHeadsModel,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
shape_list,
)
@@ -323,7 +323,7 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFGPT2Model.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_openai_gpt.py
View File

@@ -29,7 +29,7 @@ if is_tf_available():
TFOpenAIGPTModel,
TFOpenAIGPTLMHeadModel,
TFOpenAIGPTDoubleHeadsModel,
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
)
@@ -235,7 +235,7 @@ class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFOpenAIGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_roberta.py
View File

@@ -32,7 +32,7 @@ if is_tf_available():
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaForQuestionAnswering,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
)
@@ -232,7 +232,7 @@ class TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFRobertaModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_transfo_xl.py
View File

@@ -29,7 +29,7 @@ if is_tf_available():
from transformers import (
TFTransfoXLModel,
TFTransfoXLLMHeadModel,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
)
@@ -209,7 +209,7 @@ class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFTransfoXLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_xlm.py
View File

@@ -31,7 +31,7 @@ if is_tf_available():
TFXLMWithLMHeadModel,
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
)
@@ -308,7 +308,7 @@ class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFXLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_tf_xlnet.py
View File

@@ -33,7 +33,7 @@ if is_tf_available():
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetForQuestionAnsweringSimple,
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
)
@@ -410,7 +410,7 @@ class TFXLNetModelTest(TFModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFXLNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_transfo_xl.py
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@@ -27,7 +27,7 @@ from .utils import require_multigpu, require_torch, slow, torch_device
if is_torch_available():
import torch
from transformers import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel
from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
@@ -214,7 +214,7 @@ class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TransfoXLModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_xlm.py
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@@ -34,7 +34,7 @@ if is_torch_available():
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
)
from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
@@ -425,7 +425,7 @@ class XLMModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_modeling_xlnet.py
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@@ -35,7 +35,7 @@ if is_torch_available():
XLNetForTokenClassification,
XLNetForQuestionAnswering,
)
from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
@@ -508,7 +508,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)

4
tests/test_tokenization_bert_japanese.py
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@@ -127,7 +127,7 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは"), ["こん", "##ばんは", "[UNK]", "こんにちは"])
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-japanese")
tokenizer = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese")
text = tokenizer.encode("ありがとう。", add_special_tokens=False)
text_2 = tokenizer.encode("どういたしまして。", add_special_tokens=False)
@@ -188,7 +188,7 @@ class BertJapaneseCharacterTokenizationTest(TokenizerTesterMixin, unittest.TestC
self.assertListEqual(tokenizer.tokenize("こんにちほ"), ["こ", "ん", "に", "ち", "[UNK]"])
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-japanese-char")
tokenizer = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char")
text = tokenizer.encode("ありがとう。", add_special_tokens=False)
text_2 = tokenizer.encode("どういたしまして。", add_special_tokens=False)