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import random
from langcodes import standardize_tag
from rich import print
from models import translate_google, get_google_supported_languages
from tqdm import tqdm
from datasets import load_dataset, Dataset
import asyncio
from tqdm.asyncio import tqdm_asyncio
import os
from datasets_.util import _get_dataset_config_names, _load_dataset
slug_uhura_arc_easy = "masakhane/uhura-arc-easy"
tags_uhura_arc_easy = {
standardize_tag(a.split("_")[0], macro=True): a
for a in _get_dataset_config_names(slug_uhura_arc_easy)
if not a.endswith("unmatched")
}
random.seed(42)
id_sets_train = [
set(_load_dataset(slug_uhura_arc_easy, tag, split="train")["id"])
for tag in tags_uhura_arc_easy.values()
]
common_ids_train = list(sorted(set.intersection(*id_sets_train)))
random.shuffle(common_ids_train)
id_sets_test = [
set(_load_dataset(slug_uhura_arc_easy, tag, split="test")["id"])
for tag in tags_uhura_arc_easy.values()
]
common_ids_test = list(sorted(set.intersection(*id_sets_test)))
random.shuffle(common_ids_test)
slug_uhura_arc_easy_translated = "fair-forward/arc-easy-autotranslated"
tags_uhura_arc_easy_translated = {
standardize_tag(a.split("_")[0], macro=True): a
for a in _get_dataset_config_names(slug_uhura_arc_easy_translated)
}
def add_choices(row):
row["choices"] = row["choices"]["text"]
return row
def load_uhura_arc_easy(language_bcp_47, nr):
if language_bcp_47 in tags_uhura_arc_easy.keys():
ds = _load_dataset(slug_uhura_arc_easy, tags_uhura_arc_easy[language_bcp_47])
ds = ds.map(add_choices)
ds = ds.rename_column("answerKey", "answer")
task = ds["test"].filter(lambda x: x["id"] == common_ids_test[nr])[0]
return "masakhane/uhura-arc-easy", task, "human"
if language_bcp_47 in tags_uhura_arc_easy_translated.keys():
ds = _load_dataset(
slug_uhura_arc_easy_translated,
tags_uhura_arc_easy_translated[language_bcp_47],
)
ds = ds.rename_column("answerKey", "answer")
task = ds["test"].filter(lambda x: x["id"] == common_ids_test[nr])[0]
return "fair-forward/arc-easy-autotranslated", task, "machine"
else:
return None, None, None
def translate_arc(languages):
human_translated = tags_uhura_arc_easy.keys()
untranslated = [
lang
for lang in languages["bcp_47"].values[:100]
if lang not in human_translated and lang in get_google_supported_languages()
]
n_samples = 10
train_ids = common_ids_train[: n_samples + 3]
en_train = _load_dataset(
slug_uhura_arc_easy, subset=tags_uhura_arc_easy["en"], split="train"
)
en_train = en_train.filter(lambda x: x["id"] in train_ids)
test_ids = common_ids_test[:n_samples]
en_test = _load_dataset(
slug_uhura_arc_easy, subset=tags_uhura_arc_easy["en"], split="test"
)
en_test = en_test.filter(lambda x: x["id"] in test_ids)
data = {"train": en_train, "test": en_test}
slug = "fair-forward/arc-easy-autotranslated"
for lang in tqdm(untranslated):
# check if already exists on hub
try:
ds_lang = load_dataset(slug, lang)
except (ValueError, Exception):
print(f"Translating {lang}...")
for split, data_en in data.items():
questions_tr = [
translate_google(q, "en", lang) for q in data_en["question"]
]
questions_tr = asyncio.run(tqdm_asyncio.gather(*questions_tr))
choices_texts_concatenated = []
for choice in data_en["choices"]:
for option in choice["text"]:
choices_texts_concatenated.append(option)
choices_tr = [
translate_google(c, "en", lang) for c in choices_texts_concatenated
]
choices_tr = asyncio.run(tqdm_asyncio.gather(*choices_tr))
# group into chunks of 4
choices_tr = [
choices_tr[i : i + 4] for i in range(0, len(choices_tr), 4)
]
ds_lang = Dataset.from_dict(
{
"id": data_en["id"],
"question": questions_tr,
"choices": choices_tr,
"answerKey": data_en["answerKey"],
}
)
ds_lang.push_to_hub(
slug,
split=split,
config_name=lang,
token=os.getenv("HUGGINGFACE_ACCESS_TOKEN"),
)
ds_lang.to_json(
f"data/translations/arc/{lang}_{split}.json",
lines=False,
force_ascii=False,
indent=2,
)
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