File size: 4,089 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 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | from __future__ import annotations
from datasets import load_dataset
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import MultilingualTask
from ....abstasks.AbsTaskPairClassification import AbsTaskPairClassification
class XStance(MultilingualTask, AbsTaskPairClassification):
metadata = TaskMetadata(
name="XStance",
dataset={
"path": "ZurichNLP/x_stance",
"revision": "810604b9ad3aafdc6144597fdaa40f21a6f5f3de",
},
description="A Multilingual Multi-Target Dataset for Stance Detection in French, German, and Italian.",
reference="https://github.com/ZurichNLP/xstance",
category="s2s",
type="PairClassification",
eval_splits=["test"],
eval_langs={
"de": ["deu-Latn"],
"fr": ["fra-Latn"],
"it": ["ita-Latn"],
},
main_score="ap",
date=("2011-01-01", "2020-12-31"),
form=["written"],
domains=["Social"],
task_subtypes=["Political classification"],
license="cc by-nc 4.0",
socioeconomic_status="medium",
annotations_creators="human-annotated",
dialect=[],
text_creation="created",
bibtex_citation="""
@inproceedings{vamvas2020xstance,
author = "Vamvas, Jannis and Sennrich, Rico",
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection",
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) 16th Conference on Natural Language Processing (KONVENS)",
address = "Zurich, Switzerland",
year = "2020",
month = "jun",
url = "http://ceur-ws.org/Vol-2624/paper9.pdf"
}
""",
n_samples={"test": 2048},
avg_character_length={"test": 152.41}, # length of`sent1` + `sent2`
)
def load_data(self, **kwargs):
"""Load dataset from HuggingFace hub"""
if self.data_loaded:
return
max_n_samples = 2048
self.dataset = {}
path = self.metadata_dict["dataset"]["path"]
revision = self.metadata_dict["dataset"]["revision"]
raw_dataset = load_dataset(path, revision=revision)
def convert_example(example):
return {
"sent1": example["question"],
"sent2": example["comment"],
"labels": 1 if example["label"] == "FAVOR" else 0,
}
for lang in self.metadata.eval_langs:
self.dataset[lang] = {}
for split in self.metadata_dict["eval_splits"]:
# filter by language
self.dataset[lang][split] = raw_dataset[split].filter(
lambda row: row["language"] == lang
)
# reduce samples
if len(self.dataset[lang][split]) > max_n_samples:
# only de + fr are larger than 2048 samples
self.dataset[lang][split] = self.dataset[lang][split].select(
range(max_n_samples)
)
# convert examples
self.dataset[lang][split] = self.dataset[lang][split].map(
convert_example,
remove_columns=self.dataset[lang][split].column_names,
)
self.dataset_transform()
self.data_loaded = True
def dataset_transform(self):
"""Transform dataset into sentence-pair format"""
_dataset = {}
for lang in self.metadata.eval_langs:
_dataset[lang] = {}
for split in self.metadata.eval_splits:
_dataset[lang][split] = [
{
"sent1": self.dataset[lang][split]["sent1"],
"sent2": self.dataset[lang][split]["sent2"],
"labels": self.dataset[lang][split]["labels"],
}
]
self.dataset = _dataset
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