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.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+test.csv filter=lfs diff=lfs merge=lfs -text

test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1647f87a48a5537a9449af065d9eed41a42ab6fbd6203c7cf964374af8f5cab4
+size 89978573

xpqa.py

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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]:
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": DATA_PATH}
+            ),
+        ]
+
+    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