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from pathlib import Path | ||
MODE_TRAIN = "train" | ||
MODE_DEV = "dev" | ||
MODE_TEST = "test" | ||
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def get_path_df_scores(mode: str, clean_trn: bool = False) -> str: | ||
path = f"df_scores_{mode}.csv" | ||
if clean_trn: | ||
path = "unshuffled_undropped_" + path | ||
return path | ||
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def get_path_predict(mode: str) -> str: | ||
f = { | ||
MODE_TRAIN: "predict_trn.txt", | ||
MODE_DEV: "predict_dev.txt", | ||
MODE_TEST: "predict.txt", | ||
}[mode] | ||
print(f"Mode: {mode}, predict file will be saved to: {f}") | ||
return f | ||
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def get_path_q(path_data: Path, mode: str) -> Path: | ||
file_q = { | ||
MODE_TRAIN: "questions_train.tsv", | ||
MODE_DEV: "questions_dev.tsv", | ||
MODE_TEST: "questions_test.tsv", | ||
}[mode] | ||
return path_data.joinpath("raw/questions", file_q) | ||
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