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