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| """Parse train.py run.log → (bpb, tps_avg, factual). | |
| bpb priority order: | |
| 1. val_bpb from [VAL] line (cleanest signal, but OOMs on 6GB cards) | |
| 2. train_bpb from the LAST step= line (proxy when val fails — not held-out | |
| but monotone with model capability over a 5-min budget) | |
| """ | |
| import re, sys | |
| txt = open(sys.argv[1]).read() | |
| m = re.search(r'val_bpb:\s+([\d\.]+)', txt) | |
| if m: | |
| bpb = m.group(1) | |
| else: | |
| step_lines = re.findall(r'^step=\d+\s+loss=[\d\.]+\s+bpb=([\d\.]+)', txt, re.M) | |
| bpb = f'~{step_lines[-1]}' if step_lines else 'NA' | |
| tps_vals = [int(m.group(1)) for m in re.finditer(r'tps=(\d+)', txt)] | |
| tps_avg = f'{sum(tps_vals)/len(tps_vals):.0f}' if tps_vals else 'NA' | |
| m = re.search(r'factual_english_hits:\s+(\d+/\d+)', txt) | |
| factual = m.group(1) if m else 'NA' | |
| print(f"{bpb}\t{tps_avg}\t{factual}") | |