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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | from __future__ import annotations
from typing import Any
import datasets
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import AbsTaskBitextMining, CrosslingualTask
_LANGUAGES = [
"ben-Beng",
"guj_Gujr",
"hin_Deva",
"kan_Knda",
"mal_Mlym",
"mar_Deva",
"tam_Taml",
"tel_Telu",
"urd_Arab",
"asm_Beng",
"bho_Deva",
"nep_Deva",
"ory_Orya",
"pan_Guru",
"pus_Arab",
"san-Deva",
"awa_Deva",
"bgc_Deva",
"bod_Tibt",
"boy_Deva",
"gbm_Deva",
"gom_Deva",
"hne_Deva",
"raj_Deva",
"mai_Deva",
"mni_Mtei",
"mup_Deva",
"mwr_Deva",
"sat_Olck",
]
_ENG_LANGUAGE = ["eng-Latn"]
_CODE_MAPPING = {
"ben": "bn",
"guj": "gu",
"hin": "hi",
"kan": "kn",
"mal": "ml",
"mar": "mr",
"tam": "ta",
"tel": "te",
"urd": "ur",
"asm": "as",
"bho": "bho",
"nep": "ne",
"ory": "or",
"pan": "pa",
"pus": "ps",
"san": "sa",
"awa": "awa",
"bgc": "bgc",
"bod": "bo",
"boy": "brx",
"gbm": "gbm",
"gom": "gom",
"hne": "hne",
"raj": "hoj",
"mai": "mai",
"mni": "mni",
"mup": "mup",
"mwr": "mwr",
"sat": "sat",
}
_SPLIT = ["validation", "test"]
def get_lang_pairs() -> dict[str, list[str]]:
# add eng-> xx and xx -> eng lang pairs
# Normalize language codes
normalized_languages = [lang.replace("_", "-") for lang in _LANGUAGES]
# Create dictionary for language pairs
language_pairs = {}
for lang in normalized_languages:
lang_code = lang.split("-")[0]
lang_to_eng_key = f"{lang_code}-eng"
eng_to_lang_key = f"eng-{lang_code}"
language_pairs[lang_to_eng_key] = [lang, _ENG_LANGUAGE[0]]
language_pairs[eng_to_lang_key] = [_ENG_LANGUAGE[0], lang]
return language_pairs
_LANGUAGES_MAPPING = get_lang_pairs()
class IndicGenBenchFloresBitextMining(AbsTaskBitextMining, CrosslingualTask):
metadata = TaskMetadata(
name="IndicGenBenchFloresBitextMining",
dataset={
"path": "google/IndicGenBench_flores_in",
"revision": "f8650438298df086750ff4973661bb58a201a5ee",
"trust_remote_code": True,
},
description="Flores-IN dataset is an extension of Flores dataset released as a part of the IndicGenBench by Google",
reference="https://github.com/google-research-datasets/indic-gen-bench/",
type="BitextMining",
category="s2s",
eval_splits=_SPLIT,
eval_langs=_LANGUAGES_MAPPING,
main_score="f1",
date=("2023-10-01", "2024-05-01"),
form=["written"],
domains=["Web", "News"],
task_subtypes=[],
license="CC-BY-SA-4.0",
socioeconomic_status="mixed",
annotations_creators="expert-annotated",
dialect=[],
text_creation="human-translated and localized",
bibtex_citation="""@misc{singh2024indicgenbench,
title={IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages},
author={Harman Singh and Nitish Gupta and Shikhar Bharadwaj and Dinesh Tewari and Partha Talukdar},
year={2024},
eprint={2404.16816},
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
n_samples={"validation": 997, "test": 1012},
avg_character_length={"validation": 126.25, "test": 130.84},
)
def load_data(self, **kwargs: Any) -> None:
"""Load dataset from HuggingFace hub"""
if self.data_loaded:
return
self.dataset = {}
for lang in self.hf_subsets:
langs = lang.split("-")
source_lang = langs[0]
target_lang = langs[1]
if source_lang == "eng":
coded_target_language = _CODE_MAPPING[target_lang]
language = f"en_{coded_target_language}"
else:
coded_source_language = _CODE_MAPPING[source_lang]
language = f"{coded_source_language}_en"
self.dataset[lang] = datasets.load_dataset(
**self.metadata_dict["dataset"],
field="examples",
data_files={
"validation": f"flores_{language}_dev.json",
"test": f"flores_{language}_test.json",
},
)
self.dataset_transform()
self.data_loaded = True
def dataset_transform(self) -> None:
for lang in self.hf_subsets:
for split in _SPLIT:
self.dataset[lang][split] = self.dataset[lang][split].rename_columns(
{"source": "sentence1", "target": "sentence2"}
)
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