File size: 3,360 Bytes
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | from __future__ import annotations
import datasets
from mteb.abstasks import MultilingualTask
from mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
_LANGS = {
"de": ["deu-Latn"],
"en": ["eng-Latn"],
"fr": ["fra-Latn"],
"it": ["ita-Latn"],
}
class RTE3(MultilingualTask, AbsTaskPairClassification):
metadata = TaskMetadata(
name="RTE3",
dataset={
"path": "maximoss/rte3-multi",
"revision": "d94f96ca5a6798e20f5a77e566f7a288dc6138d7",
},
description="Recognising Textual Entailment Challenge (RTE-3) aim to provide the NLP community with a benchmark to test progress in recognizing textual entailment",
reference="https://aclanthology.org/W07-1401/",
category="s2s",
type="PairClassification",
eval_splits=["test"],
eval_langs=_LANGS,
main_score="ap",
date=("2023-03-25", "2024-04-15"),
form=["written"],
domains=["News", "Web", "Encyclopaedic"],
task_subtypes=["Textual Entailment"],
license="cc-by-4.0",
socioeconomic_status="mixed",
annotations_creators="expert-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@inproceedings{giampiccolo-etal-2007-third,
title = "The Third {PASCAL} Recognizing Textual Entailment Challenge",
author = "Giampiccolo, Danilo and
Magnini, Bernardo and
Dagan, Ido and
Dolan, Bill",
booktitle = "Proceedings of the {ACL}-{PASCAL} Workshop on Textual Entailment and Paraphrasing",
month = jun,
year = "2007",
address = "Prague",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W07-1401",
pages = "1--9",
}
""",
n_samples={"test": 1923}, # sum of 4 languages after neutral filtering
avg_character_length={"test": 124.79},
)
def load_data(self, **kwargs):
"""Load dataset from HuggingFace hub"""
if self.data_loaded:
return
self.dataset = datasets.load_dataset(
self.metadata.dataset["path"], revision=self.metadata.dataset["revision"]
)
self.dataset_transform()
self.data_loaded = True
def dataset_transform(self):
_dataset = {}
for lang in self.langs:
_dataset[lang] = {}
for split in self.metadata.eval_splits:
# keep target language
hf_dataset = self.dataset[split].filter(lambda x: x["language"] == lang)
# keep labels 0=entailment and 2=contradiction, and map them as 1 and 0 for binary classification
hf_dataset = hf_dataset.filter(lambda x: x["label"] in [0, 2])
hf_dataset = hf_dataset.map(
lambda example: {"label": 0 if example["label"] == 2 else 1}
)
_dataset[lang][split] = [
{
"sent1": hf_dataset["premise"],
"sent2": hf_dataset["hypothesis"],
"labels": hf_dataset["label"],
}
]
self.dataset = _dataset
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