File size: 2,561 Bytes
83d24b2 | 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 | from __future__ import annotations
from collections import Counter
import datasets
from datasets import DatasetDict
from mteb.abstasks import AbsTaskClassification, MultilingualTask
from mteb.abstasks.TaskMetadata import TaskMetadata
class TurkicClassification(MultilingualTask, AbsTaskClassification):
metadata = TaskMetadata(
name="TurkicClassification",
description="A dataset of news classification in three Turkic languages.",
dataset={
"path": "Electrotubbie/classification_Turkic_languages",
"revision": "db1a67c1bdd54fbb8536af026dc8596f00f9c41d",
},
reference="https://huggingface.co/datasets/Electrotubbie/classification_Turkic_languages/",
type="Classification",
category="s2s",
eval_splits=["train"],
eval_langs={
"ky": ["kir-Cyrl"],
"kk": ["kaz-Cyrl"],
"ba": ["bak-Cyrl"],
},
main_score="accuracy",
date=("2023-02-16", "2023-09-03"),
form=["written"],
domains=["News"],
task_subtypes=["Topic classification"],
license="Not specified",
socioeconomic_status="low",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
""",
n_samples={"train": 193056},
avg_character_length={"train": 1103.13},
)
def transform_data(self, dataset, lang):
dataset_lang = DatasetDict()
label_count = Counter(dataset["train"]["label"])
dataset_lang["train"] = dataset["train"].filter(
lambda example: example["lang"] == lang
and label_count[example["label"]] >= 20
)
dataset_lang = self.stratified_subsampling(
dataset_lang, seed=self.seed, splits=["train"]
)
return dataset_lang["train"]
def load_data(self, **kwargs):
"""Load dataset from HuggingFace hub"""
if self.data_loaded:
return
dataset = {}
metadata = self.metadata_dict.get("dataset", None)
full_dataset = datasets.load_dataset(**metadata)
full_dataset = full_dataset.rename_columns(
{"processed_text": "text", "category": "label"}
)
for lang in self.langs:
dataset[lang] = DatasetDict()
filtered_dataset = self.transform_data(full_dataset, lang)
dataset[lang]["train"] = filtered_dataset
self.dataset = dataset
self.dataset_transform()
self.data_loaded = True
|