532 lines
17 KiB
Python
Executable File
532 lines
17 KiB
Python
Executable File
# flake8: noqa
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# There's no way to ignore "F401 '...' imported but unused" warnings in this
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# module, but to preserve other warnings. So, don't check this module at all.
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__version__ = "2.9.1"
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# Work around to update TensorFlow's absl.logging threshold which alters the
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# default Python logging output behavior when present.
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# see: https://github.com/abseil/abseil-py/issues/99
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# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
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try:
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import absl.logging
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except ImportError:
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pass
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else:
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absl.logging.set_verbosity("info")
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absl.logging.set_stderrthreshold("info")
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absl.logging._warn_preinit_stderr = False
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import logging
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# Benchmarking
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from .benchmark_utils import (
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Frame,
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Memory,
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MemoryState,
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MemorySummary,
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MemoryTrace,
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UsedMemoryState,
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bytes_to_human_readable,
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start_memory_tracing,
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stop_memory_tracing,
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)
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# Configurations
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from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
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from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, AutoConfig
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from .configuration_bart import BartConfig
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from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
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from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
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from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
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from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
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from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig
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from .configuration_encoder_decoder import EncoderDecoderConfig
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from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
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from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
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from .configuration_marian import MarianConfig
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from .configuration_mmbt import MMBTConfig
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from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
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from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
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from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
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from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
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from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
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from .configuration_utils import PretrainedConfig
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from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
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from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
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from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
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from .data import (
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DataProcessor,
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InputExample,
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InputFeatures,
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SingleSentenceClassificationProcessor,
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SquadExample,
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SquadFeatures,
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SquadV1Processor,
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SquadV2Processor,
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glue_convert_examples_to_features,
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glue_output_modes,
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glue_processors,
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glue_tasks_num_labels,
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is_sklearn_available,
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squad_convert_examples_to_features,
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xnli_output_modes,
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xnli_processors,
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xnli_tasks_num_labels,
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)
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# Files and general utilities
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from .file_utils import (
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CONFIG_NAME,
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MODEL_CARD_NAME,
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PYTORCH_PRETRAINED_BERT_CACHE,
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PYTORCH_TRANSFORMERS_CACHE,
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TF2_WEIGHTS_NAME,
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TF_WEIGHTS_NAME,
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TRANSFORMERS_CACHE,
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WEIGHTS_NAME,
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add_end_docstrings,
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add_start_docstrings,
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cached_path,
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is_tf_available,
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is_torch_available,
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)
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from .hf_argparser import HfArgumentParser
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# Model Cards
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from .modelcard import ModelCard
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# TF 2.0 <=> PyTorch conversion utilities
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from .modeling_tf_pytorch_utils import (
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convert_tf_weight_name_to_pt_weight_name,
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load_pytorch_checkpoint_in_tf2_model,
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load_pytorch_model_in_tf2_model,
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load_pytorch_weights_in_tf2_model,
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load_tf2_checkpoint_in_pytorch_model,
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load_tf2_model_in_pytorch_model,
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load_tf2_weights_in_pytorch_model,
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)
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# Pipelines
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from .pipelines import (
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CsvPipelineDataFormat,
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FeatureExtractionPipeline,
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FillMaskPipeline,
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JsonPipelineDataFormat,
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NerPipeline,
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PipedPipelineDataFormat,
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Pipeline,
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PipelineDataFormat,
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QuestionAnsweringPipeline,
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SummarizationPipeline,
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TextClassificationPipeline,
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TextGenerationPipeline,
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TokenClassificationPipeline,
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TranslationPipeline,
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pipeline,
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)
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# Tokenizers
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from .tokenization_albert import AlbertTokenizer
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from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
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from .tokenization_bart import BartTokenizer, MBartTokenizer
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from .tokenization_bert import BasicTokenizer, BertTokenizer, BertTokenizerFast, WordpieceTokenizer
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from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
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from .tokenization_camembert import CamembertTokenizer
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from .tokenization_ctrl import CTRLTokenizer
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from .tokenization_distilbert import DistilBertTokenizer, DistilBertTokenizerFast
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from .tokenization_electra import ElectraTokenizer, ElectraTokenizerFast
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from .tokenization_flaubert import FlaubertTokenizer
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from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast
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from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
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from .tokenization_reformer import ReformerTokenizer
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from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
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from .tokenization_t5 import T5Tokenizer
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from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer, TransfoXLTokenizerFast
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from .tokenization_utils import PreTrainedTokenizer
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from .tokenization_xlm import XLMTokenizer
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from .tokenization_xlm_roberta import XLMRobertaTokenizer
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from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
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from .trainer_utils import EvalPrediction
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from .training_args import TrainingArguments
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from .training_args_tf import TFTrainingArguments
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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if is_sklearn_available():
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from .data import glue_compute_metrics, xnli_compute_metrics
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# Modeling
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if is_torch_available():
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from .modeling_utils import PreTrainedModel, prune_layer, Conv1D, top_k_top_p_filtering, apply_chunking_to_forward
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from .modeling_auto import (
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AutoModel,
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AutoModelForPreTraining,
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AutoModelForSequenceClassification,
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AutoModelForQuestionAnswering,
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AutoModelWithLMHead,
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AutoModelForTokenClassification,
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AutoModelForMultipleChoice,
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ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
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MODEL_MAPPING,
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MODEL_FOR_PRETRAINING_MAPPING,
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MODEL_WITH_LM_HEAD_MAPPING,
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
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)
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from .modeling_bert import (
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BertPreTrainedModel,
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BertModel,
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BertForPreTraining,
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BertForMaskedLM,
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BertForNextSentencePrediction,
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BertForSequenceClassification,
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BertForMultipleChoice,
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BertForTokenClassification,
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BertForQuestionAnswering,
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load_tf_weights_in_bert,
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BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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BertLayer,
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)
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from .modeling_openai import (
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OpenAIGPTPreTrainedModel,
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OpenAIGPTModel,
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OpenAIGPTLMHeadModel,
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OpenAIGPTDoubleHeadsModel,
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load_tf_weights_in_openai_gpt,
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_transfo_xl import (
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TransfoXLPreTrainedModel,
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TransfoXLModel,
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TransfoXLLMHeadModel,
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AdaptiveEmbedding,
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load_tf_weights_in_transfo_xl,
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TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_gpt2 import (
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GPT2PreTrainedModel,
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GPT2Model,
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GPT2LMHeadModel,
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GPT2DoubleHeadsModel,
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load_tf_weights_in_gpt2,
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GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_ctrl import CTRLPreTrainedModel, CTRLModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
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from .modeling_xlnet import (
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XLNetPreTrainedModel,
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XLNetModel,
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XLNetLMHeadModel,
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XLNetForSequenceClassification,
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XLNetForTokenClassification,
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XLNetForMultipleChoice,
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XLNetForQuestionAnsweringSimple,
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XLNetForQuestionAnswering,
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load_tf_weights_in_xlnet,
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XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_xlm import (
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XLMPreTrainedModel,
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XLMModel,
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XLMWithLMHeadModel,
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XLMForSequenceClassification,
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XLMForTokenClassification,
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XLMForQuestionAnswering,
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XLMForQuestionAnsweringSimple,
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XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_bart import (
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BartForSequenceClassification,
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BartModel,
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BartForConditionalGeneration,
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BART_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_marian import MarianMTModel
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from .tokenization_marian import MarianTokenizer
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from .modeling_roberta import (
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RobertaForMaskedLM,
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RobertaModel,
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RobertaForSequenceClassification,
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RobertaForMultipleChoice,
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RobertaForTokenClassification,
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RobertaForQuestionAnswering,
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_distilbert import (
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DistilBertPreTrainedModel,
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DistilBertForMaskedLM,
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DistilBertModel,
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DistilBertForSequenceClassification,
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DistilBertForQuestionAnswering,
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DistilBertForTokenClassification,
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_camembert import (
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CamembertForMaskedLM,
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CamembertModel,
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CamembertForSequenceClassification,
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CamembertForMultipleChoice,
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CamembertForTokenClassification,
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CamembertForQuestionAnswering,
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CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_encoder_decoder import EncoderDecoderModel
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from .modeling_t5 import (
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T5PreTrainedModel,
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T5Model,
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T5ForConditionalGeneration,
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load_tf_weights_in_t5,
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T5_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_albert import (
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AlbertPreTrainedModel,
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AlbertModel,
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AlbertForPreTraining,
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AlbertForMaskedLM,
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AlbertForSequenceClassification,
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AlbertForQuestionAnswering,
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AlbertForTokenClassification,
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load_tf_weights_in_albert,
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ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_xlm_roberta import (
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XLMRobertaForMaskedLM,
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XLMRobertaModel,
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XLMRobertaForMultipleChoice,
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XLMRobertaForSequenceClassification,
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XLMRobertaForTokenClassification,
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XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_mmbt import ModalEmbeddings, MMBTModel, MMBTForClassification
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from .modeling_flaubert import (
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FlaubertModel,
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FlaubertWithLMHeadModel,
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FlaubertForSequenceClassification,
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FlaubertForQuestionAnswering,
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FlaubertForQuestionAnsweringSimple,
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FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_electra import (
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ElectraForPreTraining,
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ElectraForMaskedLM,
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ElectraForTokenClassification,
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ElectraPreTrainedModel,
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ElectraModel,
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load_tf_weights_in_electra,
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ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_reformer import (
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ReformerAttention,
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ReformerLayer,
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ReformerModel,
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ReformerModelWithLMHead,
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REFORMER_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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# Optimization
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from .optimization import (
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AdamW,
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get_constant_schedule,
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get_constant_schedule_with_warmup,
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get_cosine_schedule_with_warmup,
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get_cosine_with_hard_restarts_schedule_with_warmup,
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get_linear_schedule_with_warmup,
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)
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# Trainer
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from .trainer import Trainer, set_seed, torch_distributed_zero_first, EvalPrediction
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from .data.data_collator import DefaultDataCollator, DataCollator, DataCollatorForLanguageModeling
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from .data.datasets import GlueDataset, TextDataset, LineByLineTextDataset, GlueDataTrainingArguments
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# TensorFlow
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if is_tf_available():
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from .modeling_tf_utils import (
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TFPreTrainedModel,
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TFSharedEmbeddings,
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TFSequenceSummary,
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shape_list,
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tf_top_k_top_p_filtering,
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)
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from .modeling_tf_auto import (
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TFAutoModel,
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TFAutoModelForPreTraining,
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TFAutoModelForMultipleChoice,
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TFAutoModelForSequenceClassification,
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TFAutoModelForQuestionAnswering,
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TFAutoModelWithLMHead,
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TFAutoModelForTokenClassification,
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TF_ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
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TF_MODEL_MAPPING,
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TF_MODEL_FOR_PRETRAINING_MAPPING,
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TF_MODEL_WITH_LM_HEAD_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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)
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from .modeling_tf_bert import (
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TFBertPreTrainedModel,
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TFBertMainLayer,
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TFBertEmbeddings,
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TFBertModel,
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TFBertForPreTraining,
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TFBertForMaskedLM,
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TFBertForNextSentencePrediction,
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TFBertForSequenceClassification,
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TFBertForMultipleChoice,
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TFBertForTokenClassification,
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TFBertForQuestionAnswering,
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TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_gpt2 import (
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TFGPT2PreTrainedModel,
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TFGPT2MainLayer,
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TFGPT2Model,
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TFGPT2LMHeadModel,
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TFGPT2DoubleHeadsModel,
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TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_openai import (
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TFOpenAIGPTPreTrainedModel,
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TFOpenAIGPTMainLayer,
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TFOpenAIGPTModel,
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TFOpenAIGPTLMHeadModel,
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TFOpenAIGPTDoubleHeadsModel,
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TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_transfo_xl import (
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TFTransfoXLPreTrainedModel,
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TFTransfoXLMainLayer,
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TFTransfoXLModel,
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TFTransfoXLLMHeadModel,
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TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
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TFAdaptiveEmbedding,
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)
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from .modeling_tf_xlnet import (
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TFXLNetPreTrainedModel,
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TFXLNetMainLayer,
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TFXLNetModel,
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TFXLNetLMHeadModel,
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TFXLNetForSequenceClassification,
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TFXLNetForTokenClassification,
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TFXLNetForQuestionAnsweringSimple,
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TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_xlm import (
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TFXLMPreTrainedModel,
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TFXLMMainLayer,
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TFXLMModel,
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TFXLMWithLMHeadModel,
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TFXLMForSequenceClassification,
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TFXLMForQuestionAnsweringSimple,
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TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_xlm_roberta import (
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TFXLMRobertaForMaskedLM,
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TFXLMRobertaModel,
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TFXLMRobertaForSequenceClassification,
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TFXLMRobertaForTokenClassification,
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TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_roberta import (
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TFRobertaPreTrainedModel,
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TFRobertaMainLayer,
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TFRobertaModel,
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TFRobertaForMaskedLM,
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TFRobertaForSequenceClassification,
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TFRobertaForTokenClassification,
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TFRobertaForQuestionAnswering,
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TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_camembert import (
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TFCamembertModel,
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TFCamembertForMaskedLM,
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TFCamembertForSequenceClassification,
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TFCamembertForTokenClassification,
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TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_flaubert import (
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TFFlaubertModel,
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TFFlaubertWithLMHeadModel,
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TFFlaubertForSequenceClassification,
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TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_distilbert import (
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TFDistilBertPreTrainedModel,
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TFDistilBertMainLayer,
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TFDistilBertModel,
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TFDistilBertForMaskedLM,
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TFDistilBertForSequenceClassification,
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TFDistilBertForTokenClassification,
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TFDistilBertForQuestionAnswering,
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TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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from .modeling_tf_ctrl import (
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TFCTRLPreTrainedModel,
|
|
TFCTRLModel,
|
|
TFCTRLLMHeadModel,
|
|
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
|
|
)
|
|
|
|
from .modeling_tf_albert import (
|
|
TFAlbertPreTrainedModel,
|
|
TFAlbertMainLayer,
|
|
TFAlbertModel,
|
|
TFAlbertForPreTraining,
|
|
TFAlbertForMaskedLM,
|
|
TFAlbertForMultipleChoice,
|
|
TFAlbertForSequenceClassification,
|
|
TFAlbertForQuestionAnswering,
|
|
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
|
|
)
|
|
|
|
from .modeling_tf_t5 import (
|
|
TFT5PreTrainedModel,
|
|
TFT5Model,
|
|
TFT5ForConditionalGeneration,
|
|
TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP,
|
|
)
|
|
|
|
from .modeling_tf_electra import (
|
|
TFElectraPreTrainedModel,
|
|
TFElectraModel,
|
|
TFElectraForPreTraining,
|
|
TFElectraForMaskedLM,
|
|
TFElectraForTokenClassification,
|
|
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
|
|
)
|
|
|
|
# Optimization
|
|
from .optimization_tf import WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator
|
|
|
|
# Trainer
|
|
from .trainer_tf import TFTrainer
|
|
|
|
|
|
if not is_tf_available() and not is_torch_available():
|
|
logger.warning(
|
|
"Neither PyTorch nor TensorFlow >= 2.0 have been found."
|
|
"Models won't be available and only tokenizers, configuration"
|
|
"and file/data utilities can be used."
|
|
)
|