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import csv
from typing import List
import datasets
LANGUAGES = ["ar", "de", "es", "fr", "hi", "it", "ja", "ko", "pl", "pt", "ta", "zh"]
DATA_PATH = "test.csv"
class XPQAConfig(datasets.BuilderConfig):
def __init__(self, language, **kwargs):
super().__init__(**kwargs)
self.language = language
class XPQA(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = XPQAConfig
BUILDER_CONFIGS = [
XPQAConfig(name=language, language=language) for language in LANGUAGES
]
def _info(self):
return datasets.DatasetInfo(
description="xPQA is a large-scale annotated cross-lingual Product QA dataset.",
features=datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
}
),
homepage="https://github.com/amazon-science/contextual-product-qa/tree/main?tab=readme-ov-file#xpqa",
citation="https://arxiv.org/abs/2305.09249",
)
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
downloaded_file = dl_manager.download_and_extract(DATA_PATH)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": downloaded_file}
),
]
def _generate_examples(self, filepath):
id_ = 0
with open(filepath, newline="") as csvfile:
csvreader = csv.reader(csvfile, delimiter=",")
header = next(csvreader)
lang_pos = header.index("lang")
answer_pos = header.index("answer")
question_pos = header.index("question")
label_pos = header.index("label")
for row in csvreader:
if row[lang_pos] == self.config.language and row[label_pos] == "2":
answer = row[answer_pos]
question = row[question_pos]
if not answer or not question:
continue
yield id_, {"id": id_, "question": question, "answer": answer}
id_ += 1
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