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import os |
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import json |
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from tqdm import tqdm |
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import numpy as np |
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from datasets import load_dataset |
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export_dir = 'dataset' |
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os.makedirs(export_dir, exist_ok=True) |
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dataset = load_dataset("conceptnet5", "conceptnet5", split="train") |
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def check(example): |
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if example['sentence'] == '': |
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return False |
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if example['lang'] != 'en': |
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return False |
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if example['rel'] == 'None': |
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return False |
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atom_1 = os.path.basename(example['arg1']) |
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atom_2 = os.path.basename(example['arg2']) |
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for atom in [atom_1, atom_2]: |
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if len(atom) <= 2: |
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return False |
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if len(atom.split(' ')) != 1: |
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return False |
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if len(atom.split('_')) != 1: |
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return False |
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return True |
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dataset = dataset.filter(lambda example: check(example)) |
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relations = list(set(dataset["rel"])) |
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all_word = [os.path.basename(i) for i in dataset['arg1'] + dataset['arg2']] |
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t, c = np.unique(all_word, return_counts=True) |
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freq = {_t: _c for _t, _c in zip(t, c)} |
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def freq_filter(example): |
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if freq[os.path.basename(example['arg1'])] < 5: |
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return False |
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if freq[os.path.basename(example['arg2'])] < 5: |
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return False |
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return True |
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with open(f"{export_dir}/train.jsonl", 'w') as f_train: |
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with open(f"{export_dir}/valid.jsonl", 'w') as f_valid: |
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for r in tqdm(relations): |
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_dataset = dataset.filter(lambda example: example['rel'] == r).shuffle(0) |
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pairs = [[os.path.basename(i['arg1']), os.path.basename(i['arg2'])] for i in _dataset if freq_filter(i)] |
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train_size = int(len(_dataset) * 0.7) |
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f_train.write(json.dumps({ |
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'relation_type': os.path.basename(r), |
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'positives': pairs[:train_size], |
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'negatives': [] |
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})) |
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if len(pairs[train_size:]) > 0: |
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f_valid.write(json.dumps({ |
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'relation_type': os.path.basename(r), |
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'positives': pairs[train_size:], |
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'negatives': [] |
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})) |
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