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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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | from __future__ import annotations
from typing import Any
import datasets
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import AbsTaskClassification
_LANGUAGES = {
"asm_Beng": ["asm-Beng"],
"brx_Deva": ["brx-Deva"],
"ben_Beng": ["ben-Beng"],
"doi_Deva": ["doi-Deva"],
"gom_Deva": ["gom-Deva"],
"guj_Gujr": ["guj-Gujr"],
"hin_Deva": ["hin-Deva"],
"kan_Knda": ["kan-Knda"],
"kas_Arab": ["kas-Arab"],
"kas_Deva": ["kas-Deva"],
"mai_Deva": ["mai-Deva"],
"mal_Mlym": ["mal-Mlym"],
"mar_Deva": ["mar-Deva"],
"mni_Beng": ["mni-Beng"],
"mni_Mtei": ["mni-Mtei"],
"npi_Deva": ["npi-Deva"],
"ory_Orya": ["ory-Orya"],
"pan_Guru": ["pan-Guru"],
"san_Deva": ["san-Deva"],
"sat_Olck": ["sat-Olck"],
"snd_Arab": ["snd-Arab"],
"tam_Taml": ["tam-Taml"],
"tel_Telu": ["tel-Telu"],
"urd_Arab": ["urd-Arab"],
}
LANG_MAP = {
("Assamese", "Bengali"): "asm_Beng",
("Bodo", "Devanagari"): "brx_Deva",
("Bangla", "Bengali"): "ben_Beng",
("Konkani", "Devanagari"): "gom_Deva",
("Gujarati", "Gujarati"): "guj_Gujr",
("Hindi", "Devanagari"): "hin_Deva",
("Kannada", "Kannada"): "kan_Knda",
("Maithili", "Devanagari"): "mai_Deva",
("Malayalam", "Malayalam"): "mal_Mlym",
("Marathi", "Devanagari"): "mar_Deva",
("Nepali", "Devanagari"): "npi_Deva",
("Oriya", "Oriya"): "ory_Orya",
("Punjabi", "Gurmukhi"): "pan_Guru",
("Sanskrit", "Devanagari"): "san_Deva",
("Sindhi", "Perso - Arabic"): "snd_Arab",
("Tamil", "Tamil"): "tam_Taml",
("Telugu", "Telugu"): "tel_Telu",
("Urdu", "Perso - Arabic"): "urd_Arab",
("Kashmiri", "Perso - Arabic"): "kas_Arab",
("Kashmiri", "Devanagari"): "kas_Deva",
("Manipuri", "Meetei - Mayek"): "mni_Mtei",
("Manipuri", "Bengali"): "mni_Beng",
("Dogri", "Devanagari"): "doi_Deva",
("Santali", "Ol - Chiki"): "sat_Olck",
}
class IndicLangClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="IndicLangClassification",
dataset={
"path": "ai4bharat/Bhasha-Abhijnaanam",
"revision": "c54a95d9b9d62c891a03bd5da60715df7176b097",
},
description="A language identification test set for native-script as well as Romanized text which spans 22 Indic languages.",
reference="https://arxiv.org/abs/2305.15814",
category="s2s",
type="Classification",
eval_splits=["test"],
eval_langs=[l for langs in _LANGUAGES.values() for l in langs],
main_score="accuracy",
date=("2022-08-01", "2023-01-01"),
form=["written"],
domains=["Web", "Non-fiction"],
task_subtypes=["Language identification"],
license="CC0",
socioeconomic_status="mixed",
annotations_creators="expert-annotated",
dialect=[],
text_creation="created",
bibtex_citation="""@inproceedings{madhani-etal-2023-bhasa,
title = "Bhasa-Abhijnaanam: Native-script and romanized Language Identification for 22 {I}ndic languages",
author = "Madhani, Yash and
Khapra, Mitesh M. and
Kunchukuttan, Anoop",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.71",
doi = "10.18653/v1/2023.acl-short.71",
pages = "816--826"
}""",
n_samples={"test": 30418},
avg_character_length={"test": 106.5},
)
def load_data(self, **kwargs: Any) -> None:
"""Load dataset from HuggingFace hub"""
if self.data_loaded:
return
labels = sorted(list(_LANGUAGES.keys()))
data = datasets.load_dataset(**self.metadata_dict["dataset"])["train"]["data"][
0
]
dataset = {"train": [], "test": []}
for lang, lang_code in LANG_MAP.items():
subset = [
item for item in data if (item["language"], item["script"]) == lang
]
num_test_examples = min(2048, int(len(subset) * 0.7))
subset = datasets.Dataset.from_list(subset).train_test_split(
test_size=num_test_examples, seed=42
)
subset = subset.map(
lambda x: {"lang_code": lang_code, "label": labels.index(lang_code)}
)
dataset["train"].append(subset["train"])
dataset["test"].append(subset["test"])
self.dataset = datasets.DatasetDict(
{
"train": datasets.concatenate_datasets(dataset["train"]),
"test": datasets.concatenate_datasets(dataset["test"]),
}
)
self.dataset_transform()
self.data_loaded = True
def dataset_transform(self) -> None:
self.dataset = self.dataset.remove_columns(["language", "script"])
self.dataset = self.dataset.rename_columns({"native sentence": "text"})
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