tiny-receipt-vqa-structured-qa-21m / eval /heldout_summary.json
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{
"status": "source_pytorch_reference_not_full_onnx_rerun",
"dataset": "eval/heldout supported phone/address records",
"records_total": 2000,
"routing": "learned_router",
"source_model": "epoch-100 last.pt; heldout was excluded from training and intermediate checkpointing, then used post hoc to compare e79 best.pt with e100 last.pt; e100 last.pt was selected for higher heldout answer exact; the source checkpoint container is not distributed in this package",
"onnx_fp32_agreement_note": "The source PyTorch reference and the final ONNX FP32 rerun agree exactly on all nine reported accuracy metrics across the overall, address, and phone subsets.",
"summary": {
"address": {
"n": 1032,
"answer_exact": 0.9496124031007752,
"recomputed_n": 1032,
"recomputed_answer_exact": 0.9486434108527132,
"target_exact": 0.015503875968992248
},
"overall": {
"n": 2000,
"answer_exact": 0.9705,
"recomputed_n": 2000,
"recomputed_answer_exact": 0.97,
"target_exact": 0.453
},
"phone_number": {
"n": 968,
"answer_exact": 0.9927685950413223,
"recomputed_n": 968,
"recomputed_answer_exact": 0.9927685950413223,
"target_exact": 0.9194214876033058
}
}
}