Datasets:
| #!/usr/bin/env python3 | |
| import hashlib | |
| from collections import defaultdict | |
| from promote_and_train_classical import inject, load_all_gold | |
| from held_out_classical import score | |
| from PFLT_FSOT_2_1_aligned import PFLT | |
| def main() -> None: | |
| gold = load_all_gold() | |
| train, test = [], [] | |
| for r in gold: | |
| h = int( | |
| hashlib.sha256(f"{r['source_lang']}:{r['source_word']}".encode()).hexdigest(), | |
| 16, | |
| ) % 10000 | |
| (train if h < 9000 else test).append(r) | |
| print(f"90/10 train={len(train)} test={len(test)}") | |
| p = PFLT( | |
| load_historical=True, | |
| load_classical=False, | |
| load_hieroglyphs=False, | |
| load_domain_lexica=False, | |
| enable_gapfill=True, | |
| ) | |
| inject(p, train) | |
| by = defaultdict(list) | |
| for r in test: | |
| by[r["source_lang"]].append(r) | |
| for lang, rows in sorted(by.items(), key=lambda x: -len(x[1])): | |
| if len(rows) < 20: | |
| continue | |
| s = score(p, rows) | |
| print( | |
| f"{lang:5s} n={len(rows):4d} " | |
| f"exact={s['exact_rate']*100:5.2f}% " | |
| f"partial={s['exact_or_partial_rate']*100:5.2f}%" | |
| ) | |
| s = score(p, test) | |
| print( | |
| f"ALL n={len(test):4d} " | |
| f"exact={s['exact_rate']*100:5.2f}% " | |
| f"partial={s['exact_or_partial_rate']*100:5.2f}%" | |
| ) | |
| if __name__ == "__main__": | |
| main() | |