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73cc8d2 | 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 import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class BulgarianStoreReviewSentimentClassfication(AbsTaskClassification):
metadata = TaskMetadata(
name="BulgarianStoreReviewSentimentClassfication",
description="Bulgarian online store review dataset for sentiment classification.",
reference="https://doi.org/10.7910/DVN/TXIK9P",
dataset={
"path": "artist/Bulgarian-Online-Store-Feedback-Text-Analysis",
"revision": "701984d6c6efea0e14a1c7850ef70e464c5577c0",
},
type="Classification",
category="s2s",
date=("2018-05-14", "2018-05-14"),
eval_splits=["test"],
eval_langs=["bul-Cyrl"],
main_score="accuracy",
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="cc-by-4.0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@data{DVN/TXIK9P_2018,
author = {Georgieva-Trifonova, Tsvetanka and Stefanova, Milena and Kalchev, Stefan},
publisher = {Harvard Dataverse},
title = {{Dataset for ``Customer Feedback Text Analysis for Online Stores Reviews in Bulgarian''}},
year = {2018},
version = {V1},
doi = {10.7910/DVN/TXIK9P},
url = {https://doi.org/10.7910/DVN/TXIK9P}
}
""",
n_samples={"test": 182},
avg_character_length={"test": 316.7},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_columns(
{"Review": "text", "Category": "label"}
)
labels = self.dataset["train"]["label"]
lab2idx = {lab: idx for idx, lab in enumerate(sorted(set(labels)))}
self.dataset = self.dataset.map(
lambda x: {"label": lab2idx[x["label"]]}, remove_columns=["label"]
)
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