# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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.
""" Auto Model class. """
import logging
from collections import OrderedDict
from .configuration_auto import (
AlbertConfig,
AutoConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
ElectraConfig,
FlaubertConfig,
GPT2Config,
MobileBertConfig,
OpenAIGPTConfig,
RobertaConfig,
T5Config,
TransfoXLConfig,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
)
from .configuration_utils import PretrainedConfig
from .modeling_tf_albert import (
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertModel,
)
from .modeling_tf_bert import (
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertModel,
)
from .modeling_tf_camembert import (
TFCamembertForMaskedLM,
TFCamembertForMultipleChoice,
TFCamembertForQuestionAnswering,
TFCamembertForSequenceClassification,
TFCamembertForTokenClassification,
TFCamembertModel,
)
from .modeling_tf_ctrl import TFCTRLLMHeadModel, TFCTRLModel
from .modeling_tf_distilbert import (
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
from .modeling_tf_electra import (
TFElectraForMaskedLM,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForTokenClassification,
TFElectraModel,
)
from .modeling_tf_flaubert import (
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
from .modeling_tf_gpt2 import TFGPT2LMHeadModel, TFGPT2Model
from .modeling_tf_mobilebert import (
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
from .modeling_tf_openai import TFOpenAIGPTLMHeadModel, TFOpenAIGPTModel
from .modeling_tf_roberta import (
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaModel,
)
from .modeling_tf_t5 import TFT5ForConditionalGeneration, TFT5Model
from .modeling_tf_transfo_xl import TFTransfoXLLMHeadModel, TFTransfoXLModel
from .modeling_tf_xlm import (
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMModel,
TFXLMWithLMHeadModel,
)
from .modeling_tf_xlm_roberta import (
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
)
from .modeling_tf_xlnet import (
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetModel,
)
logger = logging.getLogger(__name__)
TF_MODEL_MAPPING = OrderedDict(
[
(AlbertConfig, TFAlbertModel),
(CamembertConfig, TFCamembertModel),
(CTRLConfig, TFCTRLModel),
(DistilBertConfig, TFDistilBertModel),
(ElectraConfig, TFElectraModel),
(FlaubertConfig, TFFlaubertModel),
(GPT2Config, TFGPT2Model),
(MobileBertConfig, TFMobileBertModel),
(OpenAIGPTConfig, TFOpenAIGPTModel),
(RobertaConfig, TFRobertaModel),
(BertConfig, TFBertModel),
(T5Config, TFT5Model),
(TransfoXLConfig, TFTransfoXLModel),
(XLMConfig, TFXLMModel),
(XLMRobertaConfig, TFXLMRobertaModel),
(XLNetConfig, TFXLNetModel),
]
)
TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
[
(AlbertConfig, TFAlbertForPreTraining),
(CamembertConfig, TFCamembertForMaskedLM),
(CTRLConfig, TFCTRLLMHeadModel),
(DistilBertConfig, TFDistilBertForMaskedLM),
(ElectraConfig, TFElectraForPreTraining),
(FlaubertConfig, TFFlaubertWithLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
(MobileBertConfig, TFMobileBertForPreTraining),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(RobertaConfig, TFRobertaForMaskedLM),
(BertConfig, TFBertForPreTraining),
(T5Config, TFT5ForConditionalGeneration),
(TransfoXLConfig, TFTransfoXLLMHeadModel),
(XLMConfig, TFXLMWithLMHeadModel),
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(XLNetConfig, TFXLNetLMHeadModel),
]
)
TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
[
(AlbertConfig, TFAlbertForMaskedLM),
(CamembertConfig, TFCamembertForMaskedLM),
(CTRLConfig, TFCTRLLMHeadModel),
(DistilBertConfig, TFDistilBertForMaskedLM),
(ElectraConfig, TFElectraForMaskedLM),
(FlaubertConfig, TFFlaubertWithLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
(MobileBertConfig, TFMobileBertForMaskedLM),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(RobertaConfig, TFRobertaForMaskedLM),
(BertConfig, TFBertForMaskedLM),
(T5Config, TFT5ForConditionalGeneration),
(TransfoXLConfig, TFTransfoXLLMHeadModel),
(XLMConfig, TFXLMWithLMHeadModel),
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(XLNetConfig, TFXLNetLMHeadModel),
]
)
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
[
(AlbertConfig, TFAlbertForMultipleChoice),
(CamembertConfig, TFCamembertForMultipleChoice),
(DistilBertConfig, TFDistilBertForMultipleChoice),
(FlaubertConfig, TFFlaubertForMultipleChoice),
(MobileBertConfig, TFMobileBertForMultipleChoice),
(RobertaConfig, TFRobertaForMultipleChoice),
(BertConfig, TFBertForMultipleChoice),
(XLMConfig, TFXLMForMultipleChoice),
(XLMRobertaConfig, TFXLMRobertaForMultipleChoice),
(XLNetConfig, TFXLNetForMultipleChoice),
]
)
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
[
(AlbertConfig, TFAlbertForQuestionAnswering),
(CamembertConfig, TFCamembertForQuestionAnswering),
(DistilBertConfig, TFDistilBertForQuestionAnswering),
(ElectraConfig, TFElectraForQuestionAnswering),
(FlaubertConfig, TFFlaubertForQuestionAnsweringSimple),
(MobileBertConfig, TFMobileBertForQuestionAnswering),
(RobertaConfig, TFRobertaForQuestionAnswering),
(BertConfig, TFBertForQuestionAnswering),
(XLMConfig, TFXLMForQuestionAnsweringSimple),
(XLMRobertaConfig, TFXLMRobertaForQuestionAnswering),
(XLNetConfig, TFXLNetForQuestionAnsweringSimple),
]
)
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
[
(AlbertConfig, TFAlbertForSequenceClassification),
(CamembertConfig, TFCamembertForSequenceClassification),
(DistilBertConfig, TFDistilBertForSequenceClassification),
(FlaubertConfig, TFFlaubertForSequenceClassification),
(MobileBertConfig, TFMobileBertForSequenceClassification),
(RobertaConfig, TFRobertaForSequenceClassification),
(BertConfig, TFBertForSequenceClassification),
(XLMConfig, TFXLMForSequenceClassification),
(XLMRobertaConfig, TFXLMRobertaForSequenceClassification),
(XLNetConfig, TFXLNetForSequenceClassification),
]
)
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
[
(AlbertConfig, TFAlbertForTokenClassification),
(CamembertConfig, TFCamembertForTokenClassification),
(DistilBertConfig, TFDistilBertForTokenClassification),
(ElectraConfig, TFElectraForTokenClassification),
(FlaubertConfig, TFFlaubertForTokenClassification),
(MobileBertConfig, TFMobileBertForTokenClassification),
(RobertaConfig, TFRobertaForTokenClassification),
(BertConfig, TFBertForTokenClassification),
(XLMConfig, TFXLMForTokenClassification),
(XLMRobertaConfig, TFXLMRobertaForTokenClassification),
(XLNetConfig, TFXLNetForTokenClassification),
]
)
[docs]class TFAutoModel(object):
r"""
:class:`~transformers.TFAutoModel` is a generic model class
that will be instantiated as one of the base model classes of the library
when created with the `TFAutoModel.from_pretrained(pretrained_model_name_or_path)`
class method.
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:
- `t5`: TFT5Model (T5 model)
- `distilbert`: TFDistilBertModel (DistilBERT model)
- `roberta`: TFRobertaModel (RoBERTa model)
- `bert`: TFBertModel (Bert model)
- `openai-gpt`: TFOpenAIGPTModel (OpenAI GPT model)
- `gpt2`: TFGPT2Model (OpenAI GPT-2 model)
- `transfo-xl`: TFTransfoXLModel (Transformer-XL model)
- `xlnet`: TFXLNetModel (XLNet model)
- `xlm`: TFXLMModel (XLM model)
- `ctrl`: TFCTRLModel (CTRL model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModel is designed to be instantiated "
"using the `TFAutoModel.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModel.from_config(config)` methods."
)
[docs] @classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights.
It only affects the model's configuration. Use :func:`~transformers.AutoModel.from_pretrained` to load
the model weights
Args:
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: TFDistilBertModel (DistilBERT model)
- isInstance of `roberta` configuration class: TFRobertaModel (RoBERTa model)
- isInstance of `bert` configuration class: TFBertModel (Bert model)
- isInstance of `openai-gpt` configuration class: TFOpenAIGPTModel (OpenAI GPT model)
- isInstance of `gpt2` configuration class: TFGPT2Model (OpenAI GPT-2 model)
- isInstance of `ctrl` configuration class: TFCTRLModel (Salesforce CTRL model)
- isInstance of `transfo-xl` configuration class: TFTransfoXLModel (Transformer-XL model)
- isInstance of `xlnet` configuration class: TFXLNetModel (XLNet model)
- isInstance of `xlm` configuration class: TFXLMModel (XLM model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = TFAutoModel.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in TF_MODEL_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_MAPPING.keys())
)
)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the base model classes of the library
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:
- `t5`: TFT5Model (T5 model)
- `distilbert`: TFDistilBertModel (DistilBERT model)
- `roberta`: TFRobertaModel (RoBERTa model)
- `bert`: TFTFBertModel (Bert model)
- `openai-gpt`: TFOpenAIGPTModel (OpenAI GPT model)
- `gpt2`: TFGPT2Model (OpenAI GPT-2 model)
- `transfo-xl`: TFTransfoXLModel (Transformer-XL model)
- `xlnet`: TFXLNetModel (XLNet model)
- `ctrl`: TFCTRLModel (CTRL model)
Params:
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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
Set to True if the Checkpoint is a PyTorch checkpoint.
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 recieved 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 = TFAutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = TFAutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModel.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 = TFAutoModel.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=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_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 TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_MAPPING.keys())
)
)
[docs]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."
)
[docs] @classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights.
It only affects the model's configuration. Use :func:`~transformers.AutoModel.from_pretrained` to load
the model weights
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())
)
)
[docs] @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:
- `t5`: :class:`~transformers.TFT5ModelWithLMHead` (T5 model)
- `distilbert`: :class:`~transformers.TFDistilBertForMaskedLM` (DistilBERT model)
- `albert`: :class:`~transformers.TFAlbertForPreTraining` (ALBERT model)
- `roberta`: :class:`~transformers.TFRobertaForMaskedLM` (RoBERTa model)
- `bert`: :class:`~transformers.TFBertForPreTraining` (Bert model)
- `openai-gpt`: :class:`~transformers.TFOpenAIGPTLMHeadModel` (OpenAI GPT model)
- `gpt2`: :class:`~transformers.TFGPT2LMHeadModel` (OpenAI GPT-2 model)
- `transfo-xl`: :class:`~transformers.TFTransfoXLLMHeadModel` (Transformer-XL model)
- `xlnet`: :class:`~transformers.TFXLNetLMHeadModel` (XLNet model)
- `xlm`: :class:`~transformers.TFXLMWithLMHeadModel` (XLM model)
- `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())
)
)
[docs]class TFAutoModelWithLMHead(object):
r"""
:class:`~transformers.TFAutoModelWithLMHead` is a generic model class
that will be instantiated as one of the language modeling model classes of the library
when created with the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
class method.
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:
- `t5`: TFT5ForConditionalGeneration (T5 model)
- `distilbert`: TFDistilBertForMaskedLM (DistilBERT model)
- `roberta`: TFRobertaForMaskedLM (RoBERTa model)
- `bert`: TFBertForMaskedLM (Bert model)
- `openai-gpt`: TFOpenAIGPTLMHeadModel (OpenAI GPT model)
- `gpt2`: TFGPT2LMHeadModel (OpenAI GPT-2 model)
- `transfo-xl`: TFTransfoXLLMHeadModel (Transformer-XL model)
- `xlnet`: TFXLNetLMHeadModel (XLNet model)
- `xlm`: TFXLMWithLMHeadModel (XLM model)
- `ctrl`: TFCTRLLMHeadModel (CTRL model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelWithLMHead is designed to be instantiated "
"using the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelWithLMHead.from_config(config)` methods."
)
[docs] @classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights.
It only affects the model's configuration. Use :func:`~transformers.AutoModel.from_pretrained` to load
the model weights
Args:
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
- isInstance of `bert` configuration class: BertModel (Bert model)
- isInstance of `openai-gpt` configuration class: OpenAIGPTModel (OpenAI GPT model)
- isInstance of `gpt2` configuration class: GPT2Model (OpenAI GPT-2 model)
- isInstance of `ctrl` configuration class: CTRLModel (Salesforce CTRL model)
- isInstance of `transfo-xl` configuration class: TransfoXLModel (Transformer-XL model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
- isInstance of `xlm` configuration class: XLMModel (XLM model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
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):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())
)
)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the language modeling model classes of the library
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:
- `t5`: TFT5ForConditionalGeneration (T5 model)
- `distilbert`: TFDistilBertForMaskedLM (DistilBERT model)
- `roberta`: TFRobertaForMaskedLM (RoBERTa model)
- `bert`: TFBertForMaskedLM (Bert model)
- `openai-gpt`: TFOpenAIGPTLMHeadModel (OpenAI GPT model)
- `gpt2`: TFGPT2LMHeadModel (OpenAI GPT-2 model)
- `transfo-xl`: TFTransfoXLLMHeadModel (Transformer-XL model)
- `xlnet`: TFXLNetLMHeadModel (XLNet model)
- `xlm`: TFXLMWithLMHeadModel (XLM model)
- `ctrl`: TFCTRLLMHeadModel (CTRL model)
Params:
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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
Set to True if the Checkpoint is a PyTorch checkpoint.
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 recieved 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 = TFAutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = TFAutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModelWithLMHead.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 = TFAutoModelWithLMHead.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=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_WITH_LM_HEAD_MAPPING.items():
# Not using isinstance() here to do not take into account inheritance
if config_class == type(config):
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__, cls.__name__, ", ".join(c.__name__ for c in TF_MODEL_WITH_LM_HEAD_MAPPING.keys())
)
)
class TFAutoModelForMultipleChoice:
r"""
:class:`~transformers.TFAutoModelForMultipleChoice` is a generic model class
that will be instantiated as one of the multiple choice model classes of the library
when created with the `TFAutoModelForMultipleChoice.from_pretrained(pretrained_model_name_or_path)`
class method.
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:
- `albert`: TFAlbertForMultipleChoice (Albert model)
- `bert`: TFBertForMultipleChoice (Bert model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForMultipleChoice is designed to be instantiated "
"using the `TFAutoModelForMultipleChoice.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForMultipleChoice.from_config(config)` methods."
)
@classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights.
It only affects the model's configuration. Use :func:`~transformers.AutoModel.from_pretrained` to load
the model weights
Args:
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
The model class to instantiate is selected based on the configuration class:
- isInstance of `albert` configuration class: AlbertModel (Albert model)
- isInstance of `bert` configuration class: BertModel (Bert model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = AutoModelForMulitpleChoice.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the multiple choice model classes of the library
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:
- `albert`: TFRobertaForMultiple (Albert model)
- `bert`: TFBertForMultipleChoice (Bert 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()`
Params:
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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
Set to True if the Checkpoint is a PyTorch checkpoint.
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 recieved 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 = TFAutoModelFormultipleChoice.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = TFAutoModelFormultipleChoice.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModelFormultipleChoice.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 = TFAutoModelFormultipleChoice.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=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_MULTIPLE_CHOICE_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 TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.keys()),
)
)
[docs]class TFAutoModelForSequenceClassification(object):
r"""
:class:`~transformers.TFAutoModelForSequenceClassification` is a generic model class
that will be instantiated as one of the sequence classification model classes of the library
when created with the `TFAutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
class method.
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:
- `distilbert`: TFDistilBertForSequenceClassification (DistilBERT model)
- `roberta`: TFRobertaForSequenceClassification (RoBERTa model)
- `bert`: TFBertForSequenceClassification (Bert model)
- `xlnet`: TFXLNetForSequenceClassification (XLNet model)
- `xlm`: TFXLMForSequenceClassification (XLM model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForSequenceClassification is designed to be instantiated "
"using the `TFAutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForSequenceClassification.from_config(config)` methods."
)
[docs] @classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights.
It only affects the model's configuration. Use :func:`~transformers.AutoModel.from_pretrained` to load
the model weights
Args:
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
- isInstance of `bert` configuration class: BertModel (Bert model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
- isInstance of `xlm` configuration class: XLMModel (XLM model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = AutoModelForSequenceClassification.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the sequence classification model classes of the library
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:
- `distilbert`: TFDistilBertForSequenceClassification (DistilBERT model)
- `roberta`: TFRobertaForSequenceClassification (RoBERTa model)
- `bert`: TFBertForSequenceClassification (Bert model)
- `xlnet`: TFXLNetForSequenceClassification (XLNet model)
- `xlm`: TFXLMForSequenceClassification (XLM 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()`
Params:
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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
Set to True if the Checkpoint is a PyTorch checkpoint.
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 recieved 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 = TFAutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = TFAutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModelForSequenceClassification.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 = TFAutoModelForSequenceClassification.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=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_SEQUENCE_CLASSIFICATION_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 TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()),
)
)
[docs]class TFAutoModelForQuestionAnswering(object):
r"""
:class:`~transformers.TFAutoModelForQuestionAnswering` is a generic model class
that will be instantiated as one of the question answering model classes of the library
when created with the `TFAutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
class method.
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:
- `distilbert`: TFDistilBertForQuestionAnswering (DistilBERT model)
- `albert`: TFAlbertForQuestionAnswering (ALBERT model)
- `roberta`: TFRobertaForQuestionAnswering (RoBERTa model)
- `bert`: TFBertForQuestionAnswering (Bert model)
- `xlnet`: TFXLNetForQuestionAnswering (XLNet model)
- `xlm`: TFXLMForQuestionAnswering (XLM model)
This class cannot be instantiated using `__init__()` (throws an error).
"""
def __init__(self):
raise EnvironmentError(
"TFAutoModelForQuestionAnswering is designed to be instantiated "
"using the `TFAutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` or "
"`TFAutoModelForQuestionAnswering.from_config(config)` methods."
)
[docs] @classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights.
It only affects the model's configuration. Use :func:`~transformers.AutoModel.from_pretrained` to load
the model weights
Args:
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
The model class to instantiate is selected based on the configuration class:
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
- isInstance of `albert` configuration class: AlbertModel (ALBERT model)
- isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
- isInstance of `bert` configuration class: BertModel (Bert model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
- isInstance of `xlm` configuration class: XLMModel (XLM model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = TFAutoModelForQuestionAnswering.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the question answering model classes of the library
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:
- `distilbert`: TFDistilBertForQuestionAnswering (DistilBERT model)
- `albert`: TFAlbertForQuestionAnswering (ALBERT model)
- `roberta`: TFRobertaForQuestionAnswering (RoBERTa model)
- `bert`: TFBertForQuestionAnswering (Bert model)
- `xlnet`: TFXLNetForQuestionAnswering (XLNet model)
- `xlm`: TFXLMForQuestionAnswering (XLM 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()`
Params:
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 `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
from_pt: (`Optional`) Boolean
Set to True if the Checkpoint is a PyTorch checkpoint.
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 recieved 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 = TFAutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = TFAutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModelForQuestionAnswering.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 = TFAutoModelForQuestionAnswering.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=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_QUESTION_ANSWERING_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 TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
)
)
[docs]class TFAutoModelForTokenClassification:
def __init__(self):
raise EnvironmentError(
"TFAutoModelForTokenClassification is designed to be instantiated "
"using the `TFAutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path)` or "
"`AutoModelForTokenClassification.from_config(config)` methods."
)
[docs] @classmethod
def from_config(cls, config):
r""" Instantiates one of the base model classes of the library
from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights.
It only affects the model's configuration. Use :func:`~transformers.AutoModel.from_pretrained` to load
the model weights
Args:
config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
The model class to instantiate is selected based on the configuration class:
- isInstance of `bert` configuration class: BertModel (Bert model)
- isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
- isInstance of `distilbert` configuration class: DistilBertModel (DistilBert model)
- isInstance of `roberta` configuration class: RobteraModel (Roberta model)
Examples::
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
model = TFAutoModelForTokenClassification.from_config(config) # E.g. model was saved using `save_pretrained('./test/saved_model/')`
"""
for config_class, model_class in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items():
if isinstance(config, config_class):
return model_class(config)
raise ValueError(
"Unrecognized configuration class {} for this kind of TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r""" Instantiates one of the question answering model classes of the library
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:
- `bert`: BertForTokenClassification (Bert model)
- `xlnet`: XLNetForTokenClassification (XLNet model)
- `distilbert`: DistilBertForTokenClassification (DistilBert model)
- `roberta`: RobertaForTokenClassification (Roberta 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()`
Params:
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 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.
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 = TFAutoModelForTokenClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = TFAutoModelForTokenClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = TFAutoModelForTokenClassification.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 = TFAutoModelForTokenClassification.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_TOKEN_CLASSIFICATION_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 TFAutoModel: {}.\n"
"Model type should be one of {}.".format(
config.__class__,
cls.__name__,
", ".join(c.__name__ for c in TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()),
)
)