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Apply quality and style requirements once again
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@ -31,6 +31,7 @@ from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, Open
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from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
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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_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_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
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# Configurations
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# Configurations
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from .configuration_utils import PretrainedConfig
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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 import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
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@ -55,6 +56,7 @@ from .data import (
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xnli_processors,
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xnli_processors,
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xnli_tasks_num_labels,
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xnli_tasks_num_labels,
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)
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)
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# Files and general utilities
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# Files and general utilities
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from .file_utils import (
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from .file_utils import (
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CONFIG_NAME,
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CONFIG_NAME,
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@ -71,8 +73,10 @@ from .file_utils import (
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is_tf_available,
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is_tf_available,
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is_torch_available,
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is_torch_available,
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)
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)
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# Model Cards
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# Model Cards
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from .modelcard import ModelCard
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from .modelcard import ModelCard
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# TF 2.0 <=> PyTorch conversion utilities
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# TF 2.0 <=> PyTorch conversion utilities
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from .modeling_tf_pytorch_utils import (
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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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convert_tf_weight_name_to_pt_weight_name,
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@ -83,6 +87,7 @@ from .modeling_tf_pytorch_utils import (
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load_tf2_model_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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load_tf2_weights_in_pytorch_model,
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)
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)
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# Pipelines
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# Pipelines
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from .pipelines import (
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from .pipelines import (
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CsvPipelineDataFormat,
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CsvPipelineDataFormat,
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@ -108,6 +113,7 @@ from .tokenization_openai import OpenAIGPTTokenizer
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from .tokenization_roberta import RobertaTokenizer
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from .tokenization_roberta import RobertaTokenizer
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from .tokenization_t5 import T5Tokenizer
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from .tokenization_t5 import T5Tokenizer
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from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
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from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
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# Tokenizers
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# Tokenizers
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from .tokenization_utils import PreTrainedTokenizer
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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 import XLMTokenizer
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@ -30,8 +30,7 @@ from .modeling_tf_roberta import (
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
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TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {}
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}
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CAMEMBERT_START_DOCSTRING = r""" The CamemBERT model was proposed in
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CAMEMBERT_START_DOCSTRING = r""" The CamemBERT model was proposed in
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@ -52,6 +52,7 @@ from utils_squad import (
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write_predictions,
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write_predictions,
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write_predictions_extended,
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write_predictions_extended,
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)
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)
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# The follwing import is the official SQuAD evaluation script (2.0).
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# The follwing import is the official SQuAD evaluation script (2.0).
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# You can remove it from the dependencies if you are using this script outside of the library
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# You can remove it from the dependencies if you are using this script outside of the library
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# We've added it here for automated tests (see examples/test_examples.py file)
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# We've added it here for automated tests (see examples/test_examples.py file)
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@ -21,6 +21,7 @@ import logging
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import math
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import math
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from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
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from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
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# Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
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# Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
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from utils_squad_evaluate import find_all_best_thresh_v2, get_raw_scores, make_qid_to_has_ans
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from utils_squad_evaluate import find_all_best_thresh_v2, get_raw_scores, make_qid_to_has_ans
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