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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 | from __future__ import annotations
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import AbsTaskClassification
class YahooAnswersTopicsClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="YahooAnswersTopicsClassification",
description="Dataset composed of questions and answers from Yahoo Answers, categorized into topics.",
reference="https://huggingface.co/datasets/yahoo_answers_topics",
dataset={
"path": "yahoo_answers_topics",
"revision": "78fccffa043240c80e17a6b1da724f5a1057e8e5",
},
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["eng-Latn"],
main_score="accuracy",
date=("2022-01-25", "2022-01-25"),
form=["written"],
domains=["Web"],
task_subtypes=["Topic classification"],
license="Not specified",
socioeconomic_status="low",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="",
n_samples={"test": 60000},
avg_character_length={"test": 346.35},
)
@property
def metadata_dict(self) -> dict[str, str]:
metadata_dict = dict(self.metadata)
metadata_dict["n_experiments"] = 10
metadata_dict["samples_per_label"] = 32
return metadata_dict
def dataset_transform(self):
self.dataset = self.dataset.remove_columns(
["id", "question_title", "question_content"]
)
self.dataset = self.dataset.rename_columns(
{"topic": "label", "best_answer": "text"}
)
self.dataset = self.stratified_subsampling(
self.dataset, seed=self.seed, splits=["train", "test"]
)
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