Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
French
Size:
100K - 1M
License:
File size: 3,681 Bytes
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"""Allocine Dataset: A Large-Scale French Movie Reviews Dataset."""
import json
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
@misc{blard2019allocine,
author = {Blard, Theophile},
title = {french-sentiment-analysis-with-bert},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}},
}
"""
_DESCRIPTION = """\
Allocine Dataset: A Large-Scale French Movie Reviews Dataset.
This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr.
It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k).
"""
class AllocineConfig(datasets.BuilderConfig):
"""BuilderConfig for Allocine."""
def __init__(self, **kwargs):
"""BuilderConfig for Allocine.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(AllocineConfig, self).__init__(**kwargs)
class AllocineDataset(datasets.GeneratorBasedBuilder):
"""Allocine Dataset: A Large-Scale French Movie Reviews Dataset."""
_DOWNLOAD_URL = "https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/raw/master/allocine_dataset/data.tar.bz2"
_TRAIN_FILE = "train.jsonl"
_VAL_FILE = "val.jsonl"
_TEST_FILE = "test.jsonl"
BUILDER_CONFIGS = [
AllocineConfig(
name="allocine",
version=datasets.Version("1.0.0"),
description="Allocine Dataset: A Large-Scale French Movie Reviews Dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"review": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["neg", "pos"]),
}
),
supervised_keys=None,
homepage="https://github.com/TheophileBlard/french-sentiment-analysis-with-bert",
citation=_CITATION,
task_templates=[TextClassification(text_column="review", label_column="label")],
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(self._DOWNLOAD_URL)
data_dir = "data"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": f"{data_dir}/{self._TRAIN_FILE}",
"files": dl_manager.iter_archive(archive_path),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": f"{data_dir}/{self._VAL_FILE}",
"files": dl_manager.iter_archive(archive_path),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": f"{data_dir}/{self._TEST_FILE}",
"files": dl_manager.iter_archive(archive_path),
},
),
]
def _generate_examples(self, filepath, files):
"""Generate Allocine examples."""
for path, file in files:
if path == filepath:
for id_, row in enumerate(file):
data = json.loads(row.decode("utf-8"))
review = data["review"]
label = "neg" if data["polarity"] == 0 else "pos"
yield id_, {"review": review, "label": label}
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