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.gitattributes

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-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
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-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
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README.md

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-#amazon reviews multi chinese
-
-This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the Chinese language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.

train.jsonl → SetFit--amazon_reviews_multi_zh/json-test.parquet

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-size 70681483
+oid sha256:31a527d794016a68ddfae08ad1fdc23497a010c7267482a921b8458f2d442af9
+size 604012

SetFit--amazon_reviews_multi_zh/json-train.parquet

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SetFit--amazon_reviews_multi_zh/json-validation.parquet

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create_dataset.py

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-from datasets import load_dataset
-
-
-
-def main():
-	raw_data = load_dataset("amazon_reviews_multi", "zh")
-	raw_data = raw_data.rename_column("review_id", "id")
-	raw_data = raw_data.rename_column("review_body", "text")
-	raw_data = raw_data.remove_columns(["product_id", "reviewer_id", "review_title", "language", "product_category"])
-
-	for split, dataset in raw_data.items():
-		dataset = dataset.map(lambda x: {"label": x["stars"]-1}, num_proc=4)
-		dataset = dataset.map(lambda x: {"label_text": str(x["label"])}, num_proc=4)	
-		dataset = dataset.remove_columns(["stars"])
-		dataset.to_json(f"{split}.jsonl")
-
-if __name__ == "__main__":
-	main()
-
-