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import ast |
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import logging |
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import os |
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import sys |
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from dataclasses import dataclass, field |
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import pandas as pd |
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from sklearn.model_selection import train_test_split |
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from tqdm import tqdm |
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from typing import Dict, List, Optional, Tuple |
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from datasets import load_dataset |
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from transformers import ( |
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HfArgumentParser, |
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) |
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from data_utils import ( |
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filter_by_lang_regex, |
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filter_by_num_tokens, |
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filter_by_num_sents, |
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filter_by_adv, |
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normalizer |
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) |
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logger = logging.getLogger(__name__) |
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@dataclass |
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class DataArguments: |
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""" |
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Arguments to which dataset we are going to set up. |
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""" |
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output_dir: str = field( |
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default=".", |
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metadata={"help": "The output directory where the config will be written."}, |
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) |
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dataset_name: str = field( |
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default=None, |
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metadata={"help": "The name of the dataset to use (via the datasets library)."} |
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) |
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dataset_config_name: Optional[str] = field( |
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} |
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) |
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) |
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cache_dir: Optional[str] = field( |
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default=None, |
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, |
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) |
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def main(): |
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parser = HfArgumentParser([DataArguments]) |
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): |
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data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0] |
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else: |
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data_args = parser.parse_args_into_dataclasses()[0] |
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logging.basicConfig( |
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", |
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datefmt="%m/%d/%Y %H:%M:%S", |
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handlers=[logging.StreamHandler(sys.stdout)], |
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) |
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logger.setLevel(logging.INFO) |
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logger.info(f"Preparing the dataset") |
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if data_args.dataset_name is not None: |
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dataset = load_dataset( |
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data_args.dataset_name, |
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data_args.dataset_config_name, |
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cache_dir=data_args.cache_dir, |
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split="train" |
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) |
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else: |
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data_files = {"train": data_args.train_file} |
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extension = data_args.train_file.split(".")[-1] |
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if extension == "txt": |
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extension = "text" |
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dataset = load_dataset( |
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extension, |
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data_files=data_files, |
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delimiter="\t", |
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cache_dir=data_args.cache_dir, |
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) |
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logger.info(f"dataset: {dataset}") |
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def data_preparation(item_dict): |
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if "text" not in item_dict: |
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return None |
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text = item_dict["text"] |
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status = filter_by_lang_regex(text, ratio=0.75) |
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if not status: |
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return None |
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status = filter_by_num_tokens(text, gt=64) |
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if not status: |
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return None |
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status = filter_by_num_sents(text, gt=2) |
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if not status: |
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return None |
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status = filter_by_adv(text, ratio=50) |
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if not status: |
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return None |
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text = normalizer(text) |
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return {"text": text} |
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data_dict = [] |
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for item in tqdm(dataset, position=0, total=len(dataset)): |
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item = data_preparation(item) |
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if item: |
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data_dict.append(item) |
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data_df = pd.DataFrame(data_dict) |
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logger.info(f"Preparation - [before] consists of {len(dataset)} records!") |
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logger.info(f"Preparation - [after] consists of {len(data_df)} records!") |
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train, test = train_test_split(data_df, test_size=0.01, random_state=101) |
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train = train.reset_index(drop=True) |
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test = test.reset_index(drop=True) |
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logger.info(f"Preparation of [train] set consists of {len(train)} records!") |
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logger.info(f"Preparation of [test] set consists of {len(test)} records!") |
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os.makedirs(data_args.output_dir, exist_ok=True) |
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train.to_csv(os.path.join(data_args.output_dir, "train.csv"), sep="\t", encoding="utf-8", index=False) |
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test.to_csv(os.path.join(data_args.output_dir, "test.csv"), sep="\t", encoding="utf-8", index=False) |
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logger.info(f"Data saved here {data_args.output_dir}") |
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if __name__ == '__main__': |
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main() |
