File size: 2,977 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 | from __future__ import annotations
import datasets
from mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class FarsTail(AbsTaskPairClassification):
metadata = TaskMetadata(
name="FarsTail",
dataset={
"path": "azarijafari/FarsTail",
"revision": "7335288588f14e5a687d97fc979194c2abe6f4e7",
},
description="This dataset, named FarsTail, includes 10,367 samples which are provided in both the Persian language as well as the indexed format to be useful for non-Persian researchers. The samples are generated from 3,539 multiple-choice questions with the least amount of annotator interventions in a way similar to the SciTail dataset",
reference="https://link.springer.com/article/10.1007/s00500-023-08959-3",
type="PairClassification",
category="s2s",
eval_splits=["test"],
eval_langs=["fas-Arab"],
main_score="ap",
date=("2021-01-01", "2021-07-12"), # best guess
form=["written"],
domains=["Academic"],
task_subtypes=["Textual Entailment"],
license="Not specified",
socioeconomic_status="high",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@article{amirkhani2023farstail,
title={FarsTail: a Persian natural language inference dataset},
author={Amirkhani, Hossein and AzariJafari, Mohammad and Faridan-Jahromi, Soroush and Kouhkan, Zeinab and Pourjafari, Zohreh and Amirak, Azadeh},
journal={Soft Computing},
year={2023},
publisher={Springer},
doi={10.1007/s00500-023-08959-3}
}""",
n_samples={"test": 1029}, # after removing neutral
avg_character_length={"test": 125.84},
)
def load_data(self, **kwargs):
if self.data_loaded:
return
path = self.metadata_dict["dataset"]["path"]
revision = self.metadata_dict["dataset"]["revision"]
data_files = {
"test": f"https://huggingface.co/datasets/{path}/resolve/{revision}/data/Test-word.csv"
}
self.dataset = datasets.load_dataset(
"csv", data_files=data_files, delimiter="\t"
)
self.dataset_transform()
self.data_loaded = True
def dataset_transform(self):
_dataset = {}
self.dataset = self.dataset.filter(lambda x: x["label"] != "n")
self.dataset = self.dataset.map(
lambda example: {"label": 1 if example["label"] == "e" else 0}
)
for split in self.metadata.eval_splits:
_dataset[split] = [
{
"sent1": self.dataset[split]["premise"],
"sent2": self.dataset[split]["hypothesis"],
"labels": self.dataset[split]["label"],
}
]
self.dataset = _dataset
|