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from __future__ import annotations
from mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class Assin2RTE(AbsTaskPairClassification):
metadata = TaskMetadata(
name="Assin2RTE",
dataset={
"path": "nilc-nlp/assin2",
"revision": "0ff9c86779e06855536d8775ce5550550e1e5a2d",
},
description="Recognizing Textual Entailment part of the ASSIN 2, an evaluation shared task collocated with STIL 2019.",
reference="https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39",
type="PairClassification",
category="s2s",
eval_splits=["test"],
eval_langs=["por-Latn"],
main_score="ap",
date=("2019-01-01", "2019-09-16"), # best guess
form=["written"],
domains=[],
task_subtypes=["Textual Entailment"],
license="Not specified",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
}""",
n_samples={"test": 2448},
avg_character_length={"test": 53.55},
)
def dataset_transform(self):
_dataset = {}
self.dataset = self.stratified_subsampling(
self.dataset,
seed=self.seed,
splits=self.metadata.eval_splits,
label="entailment_judgment",
)
for split in self.metadata.eval_splits:
_dataset[split] = [
{
"sent1": self.dataset[split]["premise"],
"sent2": self.dataset[split]["hypothesis"],
"labels": self.dataset[split]["entailment_judgment"],
}
]
self.dataset = _dataset