Datasets:
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Annotations Creators:
expert-generated
License:
| import json | |
| import jsonlines | |
| import argparse | |
| import pandas as pd | |
| import pprint | |
| def main(args): | |
| mainDict = {} | |
| with jsonlines.open(args.input_file) as reader: | |
| for obj in reader: | |
| #Check if the value already exists | |
| neutral = "" | |
| entailment = "" | |
| contradiction = "" | |
| prompt = "" | |
| if mainDict.get(obj['promptID'], None): | |
| prompt = obj['sentence1'] | |
| entailment = mainDict[obj['promptID']].get('entailment','') | |
| contradiction = mainDict[obj['promptID']].get('contradiction','') | |
| neutral = mainDict[obj['promptID']].get('neutral','') | |
| if obj['gold_label'] == "neutral": | |
| neutral = obj['sentence2'] | |
| elif obj['gold_label'] == "contradiction": | |
| contradiction = obj['sentence2'] | |
| elif obj['gold_label'] == "entailment": | |
| entailment = obj['sentence2'] | |
| mainDict[obj['promptID']] = {'prompt': prompt, 'entailment' : entailment, 'neutral' : neutral, 'contradiction' : contradiction} | |
| myList = [] | |
| for promptID in mainDict: | |
| myList.append({'sent0':mainDict[promptID]['prompt'], 'sent1':mainDict[promptID]['entailment'], 'hard_neg':mainDict[promptID]['contradiction']}) | |
| df = pd.DataFrame.from_records(myList) | |
| #Drop empty | |
| df.replace("", float("NaN"), inplace=True) | |
| df.dropna(subset = ["sent0","sent1","hard_neg"], inplace=True) | |
| #Save csv | |
| df.to_csv(args.output_file, index=False) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--input_file', help="Input file.", required=True) | |
| parser.add_argument('--output_file', help="Output file.", required=True) | |
| args = parser.parse_args() | |
| main(args) | |