[BIG] pytorch-transformers => transformers
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thomwolf
164
transformers/__init__.py
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164
transformers/__init__.py
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__version__ = "2.0.0"
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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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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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except:
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pass
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import logging
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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# Files and general utilities
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from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
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cached_path, add_start_docstrings, add_end_docstrings,
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WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
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is_tf_available, is_torch_available)
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from .data import (is_sklearn_available,
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InputExample, InputFeatures, DataProcessor,
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glue_output_modes, glue_convert_examples_to_features,
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glue_processors, glue_tasks_num_labels)
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if is_sklearn_available():
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from .data import glue_compute_metrics
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# Tokenizers
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from .tokenization_utils import (PreTrainedTokenizer)
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from .tokenization_auto import AutoTokenizer
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from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
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from .tokenization_openai import OpenAIGPTTokenizer
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from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
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from .tokenization_gpt2 import GPT2Tokenizer
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from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
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from .tokenization_xlm import XLMTokenizer
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from .tokenization_roberta import RobertaTokenizer
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from .tokenization_distilbert import DistilBertTokenizer
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# Configurations
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from .configuration_utils import PretrainedConfig
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from .configuration_auto import AutoConfig
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from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
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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)
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from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
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AutoModelWithLMHead)
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from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
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BertForMaskedLM, BertForNextSentencePrediction,
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BertForSequenceClassification, BertForMultipleChoice,
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BertForTokenClassification, BertForQuestionAnswering,
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load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
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OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
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load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
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load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
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GPT2LMHeadModel, GPT2DoubleHeadsModel,
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load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
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XLNetForSequenceClassification, XLNetForQuestionAnsweringSimple,
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XLNetForQuestionAnswering,
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load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
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XLMWithLMHeadModel, XLMForSequenceClassification,
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XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
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XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_roberta import (RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
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DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
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# Optimization
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from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
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WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
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# TensorFlow
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if is_tf_available():
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from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
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from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
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TFAutoModelWithLMHead)
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from .modeling_tf_bert import (TFBertPreTrainedModel, TFBertMainLayer, TFBertEmbeddings,
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TFBertModel, TFBertForPreTraining,
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TFBertForMaskedLM, TFBertForNextSentencePrediction,
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TFBertForSequenceClassification, TFBertForMultipleChoice,
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TFBertForTokenClassification, TFBertForQuestionAnswering,
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load_bert_pt_weights_in_tf2,
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TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_tf_gpt2 import (TFGPT2PreTrainedModel, TFGPT2MainLayer,
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TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel,
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load_gpt2_pt_weights_in_tf2,
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TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_tf_openai import (TFOpenAIGPTPreTrainedModel, TFOpenAIGPTMainLayer,
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TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel,
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load_openai_gpt_pt_weights_in_tf2,
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TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_tf_transfo_xl import (TFTransfoXLPreTrainedModel, TFTransfoXLMainLayer,
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TFTransfoXLModel, TFTransfoXLLMHeadModel,
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load_transfo_xl_pt_weights_in_tf2,
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TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer,
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TFXLNetModel, TFXLNetLMHeadModel,
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TFXLNetForSequenceClassification,
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TFXLNetForQuestionAnsweringSimple,
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load_xlnet_pt_weights_in_tf2,
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TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_tf_xlm import (TFXLMPreTrainedModel, TFXLMMainLayer,
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TFXLMModel, TFXLMWithLMHeadModel,
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TFXLMForSequenceClassification,
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TFXLMForQuestionAnsweringSimple,
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load_xlm_pt_weights_in_tf2,
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TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_tf_roberta import (TFRobertaPreTrainedModel, TFRobertaMainLayer,
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TFRobertaModel, TFRobertaForMaskedLM,
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TFRobertaForSequenceClassification,
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load_roberta_pt_weights_in_tf2,
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TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer,
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TFDistilBertModel, TFDistilBertForMaskedLM,
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TFDistilBertForSequenceClassification,
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TFDistilBertForQuestionAnswering,
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load_distilbert_pt_weights_in_tf2,
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TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
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# TF 2.0 <=> PyTorch conversion utilities
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if is_tf_available() and is_torch_available():
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from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
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load_pytorch_checkpoint_in_tf2_model,
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load_pytorch_weights_in_tf2_model,
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load_pytorch_model_in_tf2_model,
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load_tf2_checkpoint_in_pytorch_model,
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load_tf2_weights_in_pytorch_model,
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load_tf2_model_in_pytorch_model)
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if not is_tf_available() and not is_torch_available():
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logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
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"Models won't be available and only tokenizers, configuration"
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"and file/data utilities can be used.")
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