230 lines
9.0 KiB
Python
230 lines
9.0 KiB
Python
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import time
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Dict, List, Optional, Union
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import torch
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from filelock import FileLock
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from torch.utils.data import Dataset
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from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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from ..processors.squad import SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
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logger = logging.get_logger(__name__)
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MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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@dataclass
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class SquadDataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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model_type: str = field(
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default=None, metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)}
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)
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data_dir: str = field(
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default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
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)
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max_seq_length: int = field(
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default=128,
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metadata={
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"help": (
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"The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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doc_stride: int = field(
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default=128,
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metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
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)
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max_query_length: int = field(
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default=64,
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metadata={
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"help": (
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"The maximum number of tokens for the question. Questions longer than this will "
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"be truncated to this length."
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)
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},
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)
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max_answer_length: int = field(
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default=30,
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metadata={
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"help": (
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"The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another."
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)
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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version_2_with_negative: bool = field(
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default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
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)
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null_score_diff_threshold: float = field(
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default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
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)
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n_best_size: int = field(
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default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
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)
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lang_id: int = field(
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default=0,
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metadata={
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"help": (
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"language id of input for language-specific xlm models (see"
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" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
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)
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},
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)
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threads: int = field(default=1, metadata={"help": "multiple threads for converting example to features"})
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class Split(Enum):
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train = "train"
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dev = "dev"
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class SquadDataset(Dataset):
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"""
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This will be superseded by a framework-agnostic approach soon.
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"""
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args: SquadDataTrainingArguments
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features: List[SquadFeatures]
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mode: Split
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is_language_sensitive: bool
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def __init__(
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self,
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args: SquadDataTrainingArguments,
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tokenizer: PreTrainedTokenizer,
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limit_length: Optional[int] = None,
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mode: Union[str, Split] = Split.train,
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is_language_sensitive: Optional[bool] = False,
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cache_dir: Optional[str] = None,
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dataset_format: Optional[str] = "pt",
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):
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self.args = args
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self.is_language_sensitive = is_language_sensitive
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self.processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
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if isinstance(mode, str):
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try:
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mode = Split[mode]
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except KeyError:
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raise KeyError("mode is not a valid split name")
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self.mode = mode
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# Load data features from cache or dataset file
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version_tag = "v2" if args.version_2_with_negative else "v1"
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cached_features_file = os.path.join(
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cache_dir if cache_dir is not None else args.data_dir,
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f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}",
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)
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# Make sure only the first process in distributed training processes the dataset,
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# and the others will use the cache.
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lock_path = cached_features_file + ".lock"
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with FileLock(lock_path):
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if os.path.exists(cached_features_file) and not args.overwrite_cache:
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start = time.time()
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self.old_features = torch.load(cached_features_file)
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# Legacy cache files have only features, while new cache files
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# will have dataset and examples also.
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self.features = self.old_features["features"]
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self.dataset = self.old_features.get("dataset", None)
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self.examples = self.old_features.get("examples", None)
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logger.info(
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f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
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)
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if self.dataset is None or self.examples is None:
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logger.warning(
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f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
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" future run"
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)
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else:
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if mode == Split.dev:
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self.examples = self.processor.get_dev_examples(args.data_dir)
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else:
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self.examples = self.processor.get_train_examples(args.data_dir)
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self.features, self.dataset = squad_convert_examples_to_features(
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examples=self.examples,
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tokenizer=tokenizer,
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max_seq_length=args.max_seq_length,
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doc_stride=args.doc_stride,
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max_query_length=args.max_query_length,
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is_training=mode == Split.train,
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threads=args.threads,
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return_dataset=dataset_format,
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)
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start = time.time()
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torch.save(
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{"features": self.features, "dataset": self.dataset, "examples": self.examples},
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cached_features_file,
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)
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# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
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logger.info(
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f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
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)
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def __len__(self):
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return len(self.features)
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def __getitem__(self, i) -> Dict[str, torch.Tensor]:
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# Convert to Tensors and build dataset
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feature = self.features[i]
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input_ids = torch.tensor(feature.input_ids, dtype=torch.long)
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attention_mask = torch.tensor(feature.attention_mask, dtype=torch.long)
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token_type_ids = torch.tensor(feature.token_type_ids, dtype=torch.long)
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cls_index = torch.tensor(feature.cls_index, dtype=torch.long)
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p_mask = torch.tensor(feature.p_mask, dtype=torch.float)
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is_impossible = torch.tensor(feature.is_impossible, dtype=torch.float)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids,
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}
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if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
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del inputs["token_type_ids"]
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if self.args.model_type in ["xlnet", "xlm"]:
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inputs.update({"cls_index": cls_index, "p_mask": p_mask})
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if self.args.version_2_with_negative:
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inputs.update({"is_impossible": is_impossible})
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if self.is_language_sensitive:
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inputs.update({"langs": (torch.ones(input_ids.shape, dtype=torch.int64) * self.args.lang_id)})
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if self.mode == Split.train:
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start_positions = torch.tensor(feature.start_position, dtype=torch.long)
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end_positions = torch.tensor(feature.end_position, dtype=torch.long)
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inputs.update({"start_positions": start_positions, "end_positions": end_positions})
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return inputs
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