testbed/embeddings-benchmark__mteb/mteb/tasks/Classification/eng/YelpReviewFullClassification.py

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"]
        )