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# coding=utf-8
"""The Multilingual Amazon Reviews Corpus"""
import json
import datasets
from datasets.exceptions import DefunctDatasetError
_DESCRIPTION = """\
Please refer to https://huggingface.co/datasets/defunct-datasets/amazon_reviews_multi.
"""
_LANGUAGES = {
"de": "German",
"en": "English",
"es": "Spanish",
"fr": "French",
"ja": "Japanese",
"zh": "Chinese",
}
_ALL_LANGUAGES = "all_languages"
_VERSION = "1.0.0"
_HOMEPAGE_URL = "https://huggingface.co/datasets/defunct-datasets/amazon_reviews_multi"
_DOWNLOAD_URL = "https://huggingface.co/datasets/buruzaemon/amazon_reviews_multi/resolve/main/{lang}/{split}.jsonl.gz"
class AmazonReviewsMultiConfig(datasets.BuilderConfig):
"""BuilderConfig for AmazonReviewsMultiConfig."""
def __init__(self, languages=None, **kwargs):
super(AmazonReviewsMultiConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
self.languages = languages
class AmazonReviewsMulti(datasets.GeneratorBasedBuilder):
"""The Multilingual Amazon Reviews Corpus"""
BUILDER_CONFIGS = [
AmazonReviewsMultiConfig(
name=_ALL_LANGUAGES,
languages=_LANGUAGES,
description="A collection of Amazon reviews specifically designed to aid research in multilingual text classification.",
)
] + [
AmazonReviewsMultiConfig(
name=lang,
languages=[lang],
description=f"{_LANGUAGES[lang]} examples from a collection of Amazon reviews specifically designed to aid research in multilingual text classification",
)
for lang in _LANGUAGES
]
BUILDER_CONFIG_CLASS = AmazonReviewsMultiConfig
DEFAULT_CONFIG_NAME = _ALL_LANGUAGES
def _info(self):
#raise DefunctDatasetError(
# "Dataset 'amazon_reviews_multi' is defunct and no longer accessible due to the decision of data providers"
#)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"review_id": datasets.Value("string"),
"product_id": datasets.Value("string"),
"reviewer_id": datasets.Value("string"),
"stars": datasets.Value("int32"),
"review_body": datasets.Value("string"),
"review_title": datasets.Value("string"),
"language": datasets.Value("string"),
"product_category": datasets.Value("string"),
}
),
supervised_keys=None,
license=None,
homepage=_HOMEPAGE_URL,
citation=None,
)
def _split_generators(self, dl_manager):
train_urls = [_DOWNLOAD_URL.format(split="train", lang=lang) for lang in self.config.languages]
dev_urls = [_DOWNLOAD_URL.format(split="validation", lang=lang) for lang in self.config.languages]
test_urls = [_DOWNLOAD_URL.format(split="test", lang=lang) for lang in self.config.languages]
train_paths = dl_manager.download_and_extract(train_urls)
dev_paths = dl_manager.download_and_extract(dev_urls)
test_paths = dl_manager.download_and_extract(test_urls)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_paths": train_paths}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"file_paths": dev_paths}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths}),
]
def _generate_examples(self, file_paths):
row_count = 0
for file_path in file_paths:
with open(file_path, "r", encoding="utf-8") as f:
for line in f:
yield row_count, json.loads(line)
row_count += 1
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