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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 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class DutchBookReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="DutchBookReviewSentimentClassification",
description="A Dutch book review for sentiment classification.",
reference="https://github.com/benjaminvdb/DBRD",
dataset={
"path": "benjaminvdb/dbrd",
"revision": "3f756ab4572e071eb53e887ab629f19fa747d39e",
},
type="Classification",
category="s2s",
date=("2019-10-04", "2019-10-04"),
eval_splits=["test"],
eval_langs=["nld-Latn"],
main_score="accuracy",
form=["written"],
domains=["Reviews"],
task_subtypes=["Sentiment/Hate speech"],
license="CC BY-NC-SA 4.0",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""@article{DBLP:journals/corr/abs-1910-00896,
author = {Benjamin, van der Burgh and
Suzan, Verberne},
title = {The merits of Universal Language Model Fine-tuning for Small Datasets
- a case with Dutch book reviews},
journal = {CoRR},
volume = {abs/1910.00896},
year = {2019},
url = {http://arxiv.org/abs/1910.00896},
archivePrefix = {arXiv},
eprint = {1910.00896},
timestamp = {Fri, 04 Oct 2019 12:28:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-00896.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
""",
n_samples={"test": 2224},
avg_character_length={"test": 1443.0},
)
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