mindnlp.dataset.text_classification.sogounews 源代码

# Copyright 2022 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
SogouNews load function
"""
# pylint: disable=C0103

import os
import csv
from typing import Union, Tuple
from mindspore.dataset import GeneratorDataset
from mindnlp.utils.download import cache_file
from mindnlp.dataset.register import load
from mindnlp.configs import DEFAULT_ROOT
from mindnlp.utils import untar

csv.field_size_limit(500000)

URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbUkVqNEszd0pHaFE&confirm=t"

MD5 = "0c1700ba70b73f964dd8de569d3fd03e"


[文档]class Sogounews: """ SogouNews dataset source """ def __init__(self, path) -> None: self.path: str = path self._label, self._text = [], [] self._load() def _load(self): csvfile = open(self.path, "r", encoding="utf-8") dict_reader = csv.reader(csvfile) for row in dict_reader: self._label.append(int(row[0])) self._text.append(f"{row[1]} {row[2]}") def __getitem__(self, index): return self._label[index], self._text[index] def __len__(self): return len(self._label)
[文档]@load.register def SogouNews( root: str = DEFAULT_ROOT, split: Union[Tuple[str], str] = ("train", "test"), proxies=None, ): r""" Load the SogouNews dataset Args: root (str): Directory where the datasets are saved. Default:~/.mindnlp split (str|Tuple[str]): Split or splits to be returned. Default:('train', 'test'). proxies (dict): a dict to identify proxies,for example: {"https": "https://127.0.0.1:7890"}. Returns: - **datasets_list** (list) -A list of loaded datasets. If only one type of dataset is specified,such as 'trian', this dataset is returned instead of a list of datasets. Examples: >>> root = "~/.mindnlp" >>> split = ("train", "test") >>> dataset_train,dataset_test = SogouNews(root, split) >>> train_iter = dataset_train.create_tuple_iterator() >>> print(next(train_iter)) """ cache_dir = os.path.join(root, "datasets", "SogouNews") path_dict = { "train": "train.csv", "test": "test.csv", } column_names = ["label", "text"] path_list = [] datasets_list = [] path, _ = cache_file( None, cache_dir=cache_dir, url=URL, md5sum=MD5, download_file_name="sogou_news_csv.tar.gz", proxies=proxies, ) untar(path, cache_dir) if isinstance(split, str): path_list.append(os.path.join(cache_dir, "sogou_news_csv", path_dict[split])) else: for s in split: path_list.append(os.path.join(cache_dir, "sogou_news_csv", path_dict[s])) for path in path_list: datasets_list.append( GeneratorDataset( source=Sogounews(path), column_names=column_names, shuffle=False ) ) if len(path_list) == 1: return datasets_list[0] return datasets_list