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