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1fe152b3e8
* alignmentbench infer and judge * alignmentbench * alignmentbench done * alignment all done * alignment all done
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
import argparse
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import csv
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import json
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import os
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def extract_predictions_from_json(input_path, file_name):
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for root, dirs, files in os.walk(input_path):
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for file in files:
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if file == f'{file_name}.json':
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file_path = os.path.join(root, file)
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output_csv = os.path.join(root, f'{file_name}.csv')
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with open(file_path, 'r', encoding='utf-8') as json_file:
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data = json.load(json_file)
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predictions = []
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for key in data:
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prediction = data[key].get('prediction', '')
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predictions.append(prediction)
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with open(output_csv, 'w', newline='',
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encoding='utf-8') as csv_file:
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writer = csv.writer(csv_file)
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for prediction in predictions:
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writer.writerow([prediction])
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def process_jsonl(file_path):
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new_data = []
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with open(file_path, 'r', encoding='utf-8') as file:
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for line in file:
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json_data = json.loads(line)
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new_dict = {
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'question': json_data['question'],
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'capability': json_data['category'],
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'others': {
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'subcategory': json_data['subcategory'],
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'reference': json_data['reference'],
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'question_id': json_data['question_id']
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}
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}
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new_data.append(new_dict)
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return new_data
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def save_as_json(data, output_file='./alignment_bench.json'):
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with open(output_file, 'w', encoding='utf-8') as file:
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json.dump(data, file, indent=4, ensure_ascii=False)
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def parse_args():
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parser = argparse.ArgumentParser(description='File Converter')
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parser.add_argument('--mode',
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default='json',
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help='The mode of convert to json or convert to csv')
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parser.add_argument('--jsonl',
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default='./data_release.jsonl',
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help='The original jsonl path')
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parser.add_argument('--json',
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default='your prediction file path',
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help='The results json path')
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parser.add_argument('--name',
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default='alignment_bench',
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help='The results json name')
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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mode = args.mode
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if mode == 'json':
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processed_data = process_jsonl(args.jsonl)
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save_as_json(processed_data)
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elif mode == 'csv':
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extract_predictions_from_json(args.json, args.name)
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