wehub-resource-sync
147 lines
6.0 KiB
Python
147 lines
6.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2021 The HuggingFace Inc. 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 argparse
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import os
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from datasets import load_dataset
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from transformers import AutoTokenizer
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--output_dir",
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default="wiki",
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type=str,
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required=False,
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument("--dataset_name", default="wikipedia", type=str, required=False, help="dataset name")
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parser.add_argument(
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"--dataset_config_name", default="20200501.en", type=str, required=False, help="dataset config name"
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)
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parser.add_argument("--tokenizer_name", default="roberta-base", type=str, required=False, help="tokenizer name")
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parser.add_argument(
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"--max_seq_length",
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default=512,
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type=int,
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help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--line_by_line",
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type=bool,
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default=False,
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help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
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)
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parser.add_argument("--preprocessing_num_workers", default=20, type=int, help="multi-processing number.")
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parser.add_argument(
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"--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
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)
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def main(args):
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if args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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# Get the datasets:
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if args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
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# Load pretrained tokenizer
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if args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name)
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# First we tokenize all the texts.
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column_names = raw_datasets["train"].column_names
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text_column_name = "text" if "text" in column_names else column_names[0]
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if args.line_by_line:
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# When using line_by_line, we just tokenize each nonempty line.
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padding = False
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def tokenize_function(examples):
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# Remove empty lines
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examples[text_column_name] = [
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line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()
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]
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return tokenizer(
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examples[text_column_name],
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padding=padding,
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truncation=True,
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max_length=args.max_seq_length,
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# We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
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# receives the `special_tokens_mask`.
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return_special_tokens_mask=True,
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)
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tokenized_datasets = raw_datasets.map(
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tokenize_function,
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batched=True,
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num_proc=args.preprocessing_num_workers,
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remove_columns=[text_column_name],
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load_from_cache_file=not args.overwrite_cache,
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desc="Running tokenizer on dataset line_by_line",
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)
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else:
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# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
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# We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
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# efficient when it receives the `special_tokens_mask`.
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def tokenize_function(examples):
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return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
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tokenized_datasets = raw_datasets.map(
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tokenize_function,
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batched=True,
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num_proc=args.preprocessing_num_workers,
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remove_columns=column_names,
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load_from_cache_file=not args.overwrite_cache,
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desc="Running tokenizer on every text in dataset",
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)
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# Main data processing function that will concatenate all texts from our dataset and generate chunks of
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# max_seq_length.
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def group_texts(examples):
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# Concatenate all texts.
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concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
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total_length = len(concatenated_examples[list(examples.keys())[0]])
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# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
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# customize this part to your needs.
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if total_length >= args.max_seq_length:
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total_length = (total_length // args.max_seq_length) * args.max_seq_length
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# Split by chunks of max_len.
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result = {
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k: [t[i : i + args.max_seq_length] for i in range(0, total_length, args.max_seq_length)]
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for k, t in concatenated_examples.items()
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}
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return result
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# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
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# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
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# might be slower to preprocess.
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tokenized_datasets = tokenized_datasets.map(
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group_texts,
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batched=True,
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num_proc=args.preprocessing_num_workers,
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load_from_cache_file=not args.overwrite_cache,
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desc=f"Grouping texts in chunks of {args.max_seq_length}",
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)
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tokenized_datasets.save_to_disk(args.output_dir)
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if __name__ == "__main__":
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args = parser.parse_args()
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main(args)
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