File size: 1,784 Bytes
83d24b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
N_SAMPLES = 1024
class MovieReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="MovieReviewSentimentClassification",
dataset={
"path": "tblard/allocine",
"revision": "a4654f4896408912913a62ace89614879a549287",
},
description="The Allociné dataset is a French-language dataset for sentiment analysis that contains movie reviews produced by the online community of the Allociné.fr website.",
reference="https://github.com/TheophileBlard/french-sentiment-analysis-with-bert",
type="Classification",
category="s2s",
eval_splits=["validation", "test"],
eval_langs=["fra-Latn"],
main_score="accuracy",
date=("2006-01-01", "2020-01-01"),
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="MIT",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@software{blard2020,
title = {French sentiment analysis with BERT},
author = {Théophile Blard},
url = {https://github.com/TheophileBlard/french-sentiment-analysis-with-bert},
year = {2020},
}
""",
n_samples={"validation": N_SAMPLES, "test": N_SAMPLES},
avg_character_length={"validation": 550.3, "test": 558.1},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_column("review", "text")
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["validation", "test"]
)
|