| 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"), |
| 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}, |
| 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 |
|
|
