File size: 2,950 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
from mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
N_SAMPLES = 1000
class SickBrPC(AbsTaskPairClassification):
metadata = TaskMetadata(
name="SICK-BR-PC",
dataset={
"path": "eduagarcia/sick-br",
"revision": "0cdfb1d51ef339011c067688a3b75b82f927c097",
},
description="SICK-BR is a Portuguese inference corpus, human translated from SICK",
reference="https://linux.ime.usp.br/~thalen/SICK_PT.pdf",
type="PairClassification",
category="s2s",
eval_splits=["test"],
eval_langs=["por-Latn"],
main_score="ap",
date=("2018-01-01", "2018-09-01"), # rough estimate
form=["written"],
domains=["Web"],
task_subtypes=["Textual Entailment"],
license="unknown",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="human-translated and localized",
bibtex_citation="""
@inproceedings{real18,
author="Real, Livy
and Rodrigues, Ana
and Vieira e Silva, Andressa
and Albiero, Beatriz
and Thalenberg, Bruna
and Guide, Bruno
and Silva, Cindy
and de Oliveira Lima, Guilherme
and C{\^a}mara, Igor C. S.
and Stanojevi{\'{c}}, Milo{\v{s}}
and Souza, Rodrigo
and de Paiva, Valeria"
year ="2018",
title="SICK-BR: A Portuguese Corpus for Inference",
booktitle="Computational Processing of the Portuguese Language. PROPOR 2018.",
doi ="10.1007/978-3-319-99722-3_31",
isbn="978-3-319-99722-3"
}
""",
n_samples={"test": N_SAMPLES},
avg_character_length={"test": 54.89},
)
def dataset_transform(self):
_dataset = {}
# Do not process the subsets we won't use
self.dataset.pop("train")
self.dataset.pop("validation")
self.dataset = self.stratified_subsampling(
self.dataset,
seed=self.seed,
splits=self.metadata.eval_splits,
label="entailment_label",
n_samples=N_SAMPLES,
)
for split in self.metadata.eval_splits:
print(self.dataset[split]["entailment_label"])
# keep labels 0=entailment and 2=contradiction, and map them as 1 and 0 for binary classification
hf_dataset = self.dataset[split].filter(
lambda x: x["entailment_label"] in [0, 2]
)
hf_dataset = hf_dataset.map(
lambda example: {"label": 0 if example["entailment_label"] == 2 else 1}
)
_dataset[split] = [
{
"sent1": hf_dataset["sentence_A"],
"sent2": hf_dataset["sentence_B"],
"labels": hf_dataset["label"],
}
]
self.dataset = _dataset
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