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import os |
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import json |
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import pandas as pd |
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def main(input_json, input_txt, output_dir): |
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meta_df = pd.read_csv("output/migration/split_data/split_dev10.texts.meta.csv") |
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text_ids = meta_df["text_id"].to_list() |
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with open(input_json, encoding="utf-8") as f: |
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json_predictions = json.load(f) |
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with open(input_txt, encoding="utf-8") as f: |
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txt_predictions = f.read().split("\n\n") |
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for t_id, json_p, txt_p in zip(text_ids, json_predictions, txt_predictions): |
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if int(t_id) % 100 == 0: |
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print(t_id) |
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prediction_dir = f"{output_dir}/{t_id}" |
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if not os.path.isdir(prediction_dir): |
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os.makedirs(prediction_dir) |
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prediction_file_json = f"{prediction_dir}/lome_{t_id}.comm.json" |
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prediction_file_txt = f"{prediction_dir}/lome_{t_id}.comm.txt" |
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with open(prediction_file_json, "w", encoding="utf-8") as f_out: |
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json.dump([json_p], f_out) |
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with open(prediction_file_txt, "w", encoding="utf-8") as f_out: |
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f_out.write(txt_p + "\n\n") |
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if __name__ == "__main__": |
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main( |
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input_json="/home/gossminn/WorkSyncs/Code/fn-for-social-frames/output/migration/lome/lome_migration_concat.comm.json", |
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input_txt="/home/gossminn/WorkSyncs/Code/fn-for-social-frames/output/migration/lome/lome_migration_concat.comm.txt", |
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output_dir="output/migration/lome/multilabel/lome_0shot/pavia" |
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) |
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