FM / albert

/usr/local/lib/python3.6/site-packages/transformers/models/albert/

/usr/local/lib/python3.6/site-packages/transformers/models/albert

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_albert.py	8400	0644	edit dl rm
convert_albert_original_tf_checkpoint_to_pytorch.py	2155	0644	edit dl rm
modeling_albert.py	56178	0644	edit dl rm
modeling_flax_albert.py	40363	0644	edit dl rm
modeling_tf_albert.py	63858	0644	edit dl rm
tokenization_albert.py	14707	0644	edit dl rm
tokenization_albert_fast.py	10814	0644	edit dl rm
__init__.py	4377	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/albert/__init__.py (4377B)

# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

from typing import TYPE_CHECKING

from ...utils import (
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)

_import_structure = {
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

if is_sentencepiece_available():
    _import_structure["tokenization_albert"] = ["AlbertTokenizer"]

if is_tokenizers_available():
    _import_structure["tokenization_albert_fast"] = ["AlbertTokenizerFast"]

if is_torch_available():
    _import_structure["modeling_albert"] = [
        "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "AlbertForMaskedLM",
        "AlbertForMultipleChoice",
        "AlbertForPreTraining",
        "AlbertForQuestionAnswering",
        "AlbertForSequenceClassification",
        "AlbertForTokenClassification",
        "AlbertModel",
        "AlbertPreTrainedModel",
        "load_tf_weights_in_albert",
    ]

if is_tf_available():
    _import_structure["modeling_tf_albert"] = [
        "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFAlbertForMaskedLM",
        "TFAlbertForMultipleChoice",
        "TFAlbertForPreTraining",
        "TFAlbertForQuestionAnswering",
        "TFAlbertForSequenceClassification",
        "TFAlbertForTokenClassification",
        "TFAlbertMainLayer",
        "TFAlbertModel",
        "TFAlbertPreTrainedModel",
    ]

if is_flax_available():
    _import_structure["modeling_flax_albert"] = [
        "FlaxAlbertForMaskedLM",
        "FlaxAlbertForMultipleChoice",
        "FlaxAlbertForPreTraining",
        "FlaxAlbertForQuestionAnswering",
        "FlaxAlbertForSequenceClassification",
        "FlaxAlbertForTokenClassification",
        "FlaxAlbertModel",
        "FlaxAlbertPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

if is_sentencepiece_available():
        from .tokenization_albert import AlbertTokenizer

if is_tokenizers_available():
        from .tokenization_albert_fast import AlbertTokenizerFast

if is_torch_available():
        from .modeling_albert import (
            ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForPreTraining,
            AlbertForQuestionAnswering,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertModel,
            AlbertPreTrainedModel,
            load_tf_weights_in_albert,
        )

if is_tf_available():
        from .modeling_tf_albert import (
            TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAlbertForMaskedLM,
            TFAlbertForMultipleChoice,
            TFAlbertForPreTraining,
            TFAlbertForQuestionAnswering,
            TFAlbertForSequenceClassification,
            TFAlbertForTokenClassification,
            TFAlbertMainLayer,
            TFAlbertModel,
            TFAlbertPreTrainedModel,
        )

if is_flax_available():
        from .modeling_flax_albert import (
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForPreTraining,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertModel,
            FlaxAlbertPreTrainedModel,
        )
else:
    import sys

sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # 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. from typing import TYPE_CHECKING from ...utils import ( _LazyModule, is_flax_available, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"], } if is_sentencepiece_available(): _import_structure["tokenization_albert"] = ["AlbertTokenizer"] if is_tokenizers_available(): _import_structure["tokenization_albert_fast"] = ["AlbertTokenizerFast"] if is_torch_available(): _import_structure["modeling_albert"] = [ "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "AlbertForMaskedLM", "AlbertForMultipleChoice", "AlbertForPreTraining", "AlbertForQuestionAnswering", "AlbertForSequenceClassification", "AlbertForTokenClassification", "AlbertModel", "AlbertPreTrainedModel", "load_tf_weights_in_albert", ] if is_tf_available(): _import_structure["modeling_tf_albert"] = [ "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAlbertForMaskedLM", "TFAlbertForMultipleChoice", "TFAlbertForPreTraining", "TFAlbertForQuestionAnswering", "TFAlbertForSequenceClassification", "TFAlbertForTokenClassification", "TFAlbertMainLayer", "TFAlbertModel", "TFAlbertPreTrainedModel", ] if is_flax_available(): _import_structure["modeling_flax_albert"] = [ "FlaxAlbertForMaskedLM", "FlaxAlbertForMultipleChoice", "FlaxAlbertForPreTraining", "FlaxAlbertForQuestionAnswering", "FlaxAlbertForSequenceClassification", "FlaxAlbertForTokenClassification", "FlaxAlbertModel", "FlaxAlbertPreTrainedModel", ] if TYPE_CHECKING: from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig if is_sentencepiece_available(): from .tokenization_albert import AlbertTokenizer if is_tokenizers_available(): from .tokenization_albert_fast import AlbertTokenizerFast if is_torch_available(): from .modeling_albert import ( ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, AlbertForMaskedLM, AlbertForMultipleChoice, AlbertForPreTraining, AlbertForQuestionAnswering, AlbertForSequenceClassification, AlbertForTokenClassification, AlbertModel, AlbertPreTrainedModel, load_tf_weights_in_albert, ) if is_tf_available(): from .modeling_tf_albert import ( TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFAlbertForMaskedLM, TFAlbertForMultipleChoice, TFAlbertForPreTraining, TFAlbertForQuestionAnswering, TFAlbertForSequenceClassification, TFAlbertForTokenClassification, TFAlbertMainLayer, TFAlbertModel, TFAlbertPreTrainedModel, ) if is_flax_available(): from .modeling_flax_albert import ( FlaxAlbertForMaskedLM, FlaxAlbertForMultipleChoice, FlaxAlbertForPreTraining, FlaxAlbertForQuestionAnswering, FlaxAlbertForSequenceClassification, FlaxAlbertForTokenClassification, FlaxAlbertModel, FlaxAlbertPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)