File size: 2,149 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 50 51 52 53 54 55 56 57 58 59 | from __future__ import annotations
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import AbsTaskClassification
class YelpReviewFullClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="YelpReviewFullClassification",
description="Yelp Review Full is a dataset for sentiment analysis, containing 5 classes corresponding to ratings 1-5.",
reference="https://arxiv.org/abs/1509.01626",
dataset={
"path": "yelp_review_full",
"revision": "c1f9ee939b7d05667af864ee1cb066393154bf85",
},
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["eng-Latn"],
main_score="accuracy",
date=("2015-01-01", "2015-12-31"), # reviews from 2015
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="Other",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@inproceedings{NIPS2015_250cf8b5,
author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Character-level Convolutional Networks for Text Classification},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf},
volume = {28},
year = {2015}
}
""",
n_samples={"test": 50000},
avg_character_length={},
)
@property
def metadata_dict(self) -> dict[str, str]:
metadata_dict = dict(self.metadata)
metadata_dict["n_experiments"] = 10
metadata_dict["samples_per_label"] = 128
return metadata_dict
def dataset_transform(self):
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["test"]
)
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