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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
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SetFit--amazon_massive_intent_en-US/json-test.parquet

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+size 93856

SetFit--amazon_massive_intent_en-US/json-train.parquet

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+size 340268

SetFit--amazon_massive_intent_en-US/json-validation.parquet

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create_dataset.py

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-from datasets import load_dataset
-from huggingface_hub import create_repo, Repository, upload_file
-import os
-import typer
-
-def main(language_label):
-	raw_data = load_dataset("AmazonScience/massive", language_label)
-	raw_data = raw_data.rename_column("utt", "text")
-	raw_data = raw_data.rename_column("intent", "label")
-	raw_data = raw_data.remove_columns(["locale", "partition", "scenario", "annot_utt", 
-    									"slot_method", "worker_id", "judgments"])
-
-	#to get labels
-	labels = raw_data["train"].features["label"]
-	
-	#for uploading to hub
-	repo_name = "amazon_massive_intent_" + language_label
-	create_repo(repo_name, organization="SetFit", repo_type="dataset")
-
-	for split, dataset in raw_data.items():
-		dataset = dataset.map(lambda x: {"label_text": labels.int2str(x["label"])}, num_proc=4)
-		dataset.to_json(f"{split}.jsonl")
-		upload_file(f"{split}.jsonl", path_in_repo=f"{split}.jsonl", repo_id="SetFit/" + repo_name, repo_type="dataset")
-		os.system(f"rm {split}.jsonl")
-
-	upload_file("create_dataset.py", path_in_repo="create_dataset.py", repo_id="SetFit/" + repo_name, repo_type="dataset")
-	
-	
-
-
-if __name__ == "__main__":
-	typer.run(main)
-
-