Datasets:
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- README.md +8 -0
- construct.py +23 -0
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annotated
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outcomes
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.env
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README.md
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---
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license: cc-by-sa-4.0
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license: cc-by-sa-4.0
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## Health Insurance Appeal Adjudication Benchmark
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This data repository houses manually labeled, and pseudo-labeled background spans corresponding to real external appeal adjudications for coverage denials from U.S. health insurance cases. The data is referenced by and described in a more general work documented here: [https://github.com/TPAFS/hicric](https://github.com/TPAFS/hicric).
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## Contact
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For questions or comments, please reach out to `[email protected]`.`
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construct.py
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from datasets import DatasetDict, load_dataset
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def construct_hf_dataset(repo_name: str):
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"""Construct a HF DatasetDict class from the HICRIC outcome data."""
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# Load the datasets from JSONL files
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train_jsonl_path = "outcomes/train_backgrounds_suff.jsonl"
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test_jsonl_path = "outcomes/test_backgrounds_suff.jsonl"
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train_dataset = load_dataset("json", data_files=train_jsonl_path, split="train")
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test_dataset = load_dataset("json", data_files=test_jsonl_path, split="train")
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# Create a DatasetDict to combine both splits
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dataset = DatasetDict({"train": train_dataset, "test": test_dataset})
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# Save each sub-directory dataset as a separate dataset within a DatasetDict
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dataset.push_to_hub(repo_name, private=True)
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return None
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if __name__ == "__main__":
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construct_hf_dataset("mike-persius/imr-appeals")
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