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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)
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