| import json | |
| import os | |
| from itertools import product | |
| from statistics import mean | |
| import pandas as pd | |
| from datasets import load_dataset | |
| def process(split, output): | |
| data = load_dataset("relbert/t_rex", split=split) | |
| df = data.to_pandas() | |
| df.pop('text') | |
| df.pop('title') | |
| df['pairs'] = [[i, j] for i, j in zip(df.pop('head'), df.pop('tail'))] | |
| rel_sim_data = [{ | |
| "relation_type": pred, | |
| "positives": g['pairs'].values.tolist(), | |
| "negatives": [] | |
| } for pred, g in df.groupby("relation") if len(g) >= 2] | |
| with open(output, "w") as f: | |
| f.write('\n'.join([json.dumps(i) for i in rel_sim_data])) | |
| os.makedirs("data", exist_ok=True) | |
| for s in ['train', 'validation', 'test']: | |
| process(split=s, output=f"data/filter_unified.{s}.jsonl") | |