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Add AutoModelForPreTraining

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This commit is contained in:
thomwolf
2020-01-24 17:49:02 -05:00
committed by Lysandre Debut
parent ea56d305be
commit 0e31e06a75
5 changed files with 373 additions and 1 deletions
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2
src/transformers/__init__.py
View File

@@ -133,6 +133,7 @@ if is_torch_available():
from .modeling_utils import PreTrainedModel, prune_layer, Conv1D
from .modeling_auto import (
AutoModel,
AutoModelForPreTraining,
AutoModelForSequenceClassification,
AutoModelForQuestionAnswering,
AutoModelWithLMHead,
@@ -267,6 +268,7 @@ if is_tf_available():
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list
from .modeling_tf_auto import (
TFAutoModel,
TFAutoModelForPreTraining,
TFAutoModelForSequenceClassification,
TFAutoModelForQuestionAnswering,
TFAutoModelWithLMHead,

169
src/transformers/modeling_auto.py
View File

@@ -49,6 +49,7 @@ from .modeling_bert import (
BertForSequenceClassification,
BertForTokenClassification,
BertModel,
BertForPreTraining,
)
from .modeling_camembert import (
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
@@ -143,6 +144,24 @@ MODEL_MAPPING = OrderedDict(
]
)
MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
[
(T5Config, T5WithLMHeadModel),
(DistilBertConfig, DistilBertForMaskedLM),
(AlbertConfig, AlbertForMaskedLM),
(CamembertConfig, CamembertForMaskedLM),
(XLMRobertaConfig, XLMRobertaForMaskedLM),
(RobertaConfig, RobertaForMaskedLM),
(BertConfig, BertForPreTraining),
(OpenAIGPTConfig, OpenAIGPTLMHeadModel),
(GPT2Config, GPT2LMHeadModel),
(TransfoXLConfig, TransfoXLLMHeadModel),
(XLNetConfig, XLNetLMHeadModel),
(XLMConfig, XLMWithLMHeadModel),
(CTRLConfig, CTRLLMHeadModel),
]
)
MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
[
(T5Config, T5WithLMHeadModel),
@@ -348,6 +367,156 @@ class AutoModel(object):
)
class AutoModelForPreTraining(object):
r"""
:class:`~transformers.AutoModelForPreTraining` is a generic model class
that will be instantiated as one of the model classes of the library -with the architecture used for pretraining this model– when created with the `AutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)`
class method.
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"AutoModelForPreTraining is designed to be instantiated "
"using the `AutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)` or "
"`AutoModelForPreTraining.from_config(config)` methods."
)
@classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: :class:`~transformers.DistilBertModelForMaskedLM` (DistilBERT model)
- isInstance of `roberta` configuration class: :class:`~transformers.RobertaModelForMaskedLM` (RoBERTa model)
- isInstance of `bert` configuration class: :class:`~transformers.BertForPreTraining` (Bert model)
- isInstance of `openai-gpt` configuration class: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
- isInstance of `gpt2` configuration class: :class:`~transformers.GPT2ModelLMHeadModel` (OpenAI GPT-2 model)
- isInstance of `ctrl` configuration class: :class:`~transformers.CTRLModelLMHeadModel` (Salesforce CTRL model)
- isInstance of `transfo-xl` configuration class: :class:`~transformers.TransfoXLLMHeadModel` (Transformer-XL model)
- isInstance of `xlnet` configuration class: :class:`~transformers.XLNetLMHeadModel` (XLNet model)
- isInstance of `xlm` configuration class: :class:`~transformers.XLMWithLMHeadModel` (XLM model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = AutoModelForPreTraining.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
)
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the model classes of the library -with the architecture used for pretraining this model– from a pre-trained model configuration.
The `from_pretrained()` method takes care of returning the correct model class instance
based on the `model_type` property of the config object, or when it's missing,
falling back to using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `t5`: :class:`~transformers.T5ModelWithLMHead` (T5 model)
- contains `distilbert`: :class:`~transformers.DistilBertForMaskedLM` (DistilBERT model)
- contains `albert`: :class:`~transformers.AlbertForMaskedLM` (ALBERT model)
- contains `camembert`: :class:`~transformers.CamembertForMaskedLM` (CamemBERT model)
- contains `xlm-roberta`: :class:`~transformers.XLMRobertaForMaskedLM` (XLM-RoBERTa model)
- contains `roberta`: :class:`~transformers.RobertaForMaskedLM` (RoBERTa model)
- contains `bert`: :class:`~transformers.BertForPreTraining` (Bert model)
- contains `openai-gpt`: :class:`~transformers.OpenAIGPTLMHeadModel` (OpenAI GPT model)
- contains `gpt2`: :class:`~transformers.GPT2LMHeadModel` (OpenAI GPT-2 model)
- contains `transfo-xl`: :class:`~transformers.TransfoXLLMHeadModel` (Transformer-XL model)
- contains `xlnet`: :class:`~transformers.XLNetLMHeadModel` (XLNet model)
- contains `xlm`: :class:`~transformers.XLMWithLMHeadModel` (XLM model)
- contains `ctrl`: :class:`~transformers.CTRLLMHeadModel` (Salesforce CTRL model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Args:
pretrained_model_name_or_path:
Either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False:
Do not delete incompletely received file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model.
(e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
attribute will be passed to the underlying model's ``__init__`` function.
Examples::
model = AutoModelForPreTraining.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelForPreTraining.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelForPreTraining.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = AutoModelForPreTraining.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
for config_class, model_class in MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in MODEL_FOR_PRETRAINING_MAPPING.keys())
)
)
class AutoModelWithLMHead(object):
r"""
:class:`~transformers.AutoModelWithLMHead` is a generic model class

167
src/transformers/modeling_tf_auto.py
View File

@@ -46,6 +46,7 @@ from .modeling_tf_bert import (
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertModel,
TFBertForPreTraining,
)
from .modeling_tf_ctrl import TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, TFCTRLLMHeadModel, TFCTRLModel
from .modeling_tf_distilbert import (
@@ -125,6 +126,22 @@ TF_MODEL_MAPPING = OrderedDict(
]
)
TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
[
(T5Config, TFT5WithLMHeadModel),
(DistilBertConfig, TFDistilBertForMaskedLM),
(AlbertConfig, TFAlbertForMaskedLM),
(RobertaConfig, TFRobertaForMaskedLM),
(BertConfig, TFBertForPreTraining),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
(TransfoXLConfig, TFTransfoXLLMHeadModel),
(XLNetConfig, TFXLNetLMHeadModel),
(XLMConfig, TFXLMWithLMHeadModel),
(CTRLConfig, TFCTRLLMHeadModel),
]
)
TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
[
(T5Config, TFT5WithLMHeadModel),
@@ -329,6 +346,154 @@ class TFAutoModel(object):
)
class TFAutoModelForPreTraining(object):
r"""
:class:`~transformers.TFAutoModelForPreTraining` is a generic model class
that will be instantiated as one of the model classes of the library -with the architecture used for pretraining this model– when created with the `TFAutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)`
class method.
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForPreTraining is designed to be instantiated "
"using the `TFAutoModelForPreTraining.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForPreTraining.from_config(config)` methods."
)
@classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Args:
config (:class:`~transformers.PretrainedConfig`):
The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: :class:`~transformers.TFDistilBertModelForMaskedLM` (DistilBERT model)
- isInstance of `roberta` configuration class: :class:`~transformers.TFRobertaModelForMaskedLM` (RoBERTa model)
- isInstance of `bert` configuration class: :class:`~transformers.TFBertForPreTraining` (Bert model)
- isInstance of `openai-gpt` configuration class: :class:`~transformers.TFOpenAIGPTLMHeadModel` (OpenAI GPT model)
- isInstance of `gpt2` configuration class: :class:`~transformers.TFGPT2ModelLMHeadModel` (OpenAI GPT-2 model)
- isInstance of `ctrl` configuration class: :class:`~transformers.TFCTRLModelLMHeadModel` (Salesforce CTRL model)
- isInstance of `transfo-xl` configuration class: :class:`~transformers.TFTransfoXLLMHeadModel` (Transformer-XL model)
- isInstance of `xlnet` configuration class: :class:`~transformers.TFXLNetLMHeadModel` (XLNet model)
- isInstance of `xlm` configuration class: :class:`~transformers.TFXLMWithLMHeadModel` (XLM model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = TFAutoModelForPreTraining.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
)
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the model classes of the library -with the architecture used for pretraining this model– from a pre-trained model configuration.
The `from_pretrained()` method takes care of returning the correct model class instance
based on the `model_type` property of the config object, or when it's missing,
falling back to using pattern matching on the `pretrained_model_name_or_path` string.
The model class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `t5`: :class:`~transformers.TFT5ModelWithLMHead` (T5 model)
- contains `distilbert`: :class:`~transformers.TFDistilBertForMaskedLM` (DistilBERT model)
- contains `albert`: :class:`~transformers.TFAlbertForMaskedLM` (ALBERT model)
- contains `roberta`: :class:`~transformers.TFRobertaForMaskedLM` (RoBERTa model)
- contains `bert`: :class:`~transformers.TFBertForPreTraining` (Bert model)
- contains `openai-gpt`: :class:`~transformers.TFOpenAIGPTLMHeadModel` (OpenAI GPT model)
- contains `gpt2`: :class:`~transformers.TFGPT2LMHeadModel` (OpenAI GPT-2 model)
- contains `transfo-xl`: :class:`~transformers.TFTransfoXLLMHeadModel` (Transformer-XL model)
- contains `xlnet`: :class:`~transformers.TFXLNetLMHeadModel` (XLNet model)
- contains `xlm`: :class:`~transformers.TFXLMWithLMHeadModel` (XLM model)
- contains `ctrl`: :class:`~transformers.TFCTRLLMHeadModel` (Salesforce CTRL model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Args:
pretrained_model_name_or_path:
Either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
state_dict: (`optional`) dict:
an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False:
Do not delete incompletely received file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model.
(e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
attribute will be passed to the underlying model's ``__init__`` function.
Examples::
model = TFAutoModelForPreTraining.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = TFAutoModelForPreTraining.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModelForPreTraining.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = TFAutoModelForPreTraining.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
config = kwargs.pop("config", None)
if not isinstance(config, PretrainedConfig):
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
for config_class, model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.items():
if isinstance(config, config_class):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
raise ValueError(
"Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_FOR_PRETRAINING_MAPPING.keys())
)
)
class TFAutoModelWithLMHead(object):
r"""
:class:`~transformers.TFAutoModelWithLMHead` is a generic model class
@@ -383,7 +548,7 @@ class TFAutoModelWithLMHead(object):
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = AutoModelWithLMHead.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModelWithLMHead.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in TF_MODEL_WITH_LM_HEAD_MAPPING.items():
if isinstance(config, config_class):

17
tests/test_modeling_auto.py
View File

@@ -28,6 +28,8 @@ if is_torch_available():
BertConfig,
AutoModel,
BertModel,
AutoModelForPreTraining,
BertForPreTraining,
AutoModelWithLMHead,
BertForMaskedLM,
RobertaForMaskedLM,
@@ -56,6 +58,21 @@ class AutoModelTest(unittest.TestCase):
for value in loading_info.values():
self.assertEqual(len(value), 0)
@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]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForPreTraining.from_pretrained(model_name)
model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
for value in loading_info.values():
self.assertEqual(len(value), 0)
@slow
def test_lmhead_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)

19
tests/test_modeling_tf_auto.py
View File

@@ -28,6 +28,8 @@ if is_tf_available():
BertConfig,
TFAutoModel,
TFBertModel,
TFAutoModelForPreTraining,
TFBertForPreTraining,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFRobertaForMaskedLM,
@@ -57,6 +59,23 @@ class TFAutoModelTest(unittest.TestCase):
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"))
logging.basicConfig(level=logging.INFO)
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[: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):
logging.basicConfig(level=logging.INFO)