| { |
| "schema_version": 2, |
| "protocol": "heldout-cluster-representative-mlm-v2", |
| "settings": { |
| "population": "all-validation", |
| "context_length": 512, |
| "mask_seed": 20260821 |
| }, |
| "evaluation_sequences": 12288, |
| "expected_source_counts": { |
| "uniref90": 4096, |
| "mgnify": 4096, |
| "omg_img": 4096 |
| }, |
| "expected_masked_residues": 414065, |
| "expected_masked_residues_by_source": { |
| "uniref90": 170557, |
| "mgnify": 107860, |
| "omg_img": 135648 |
| }, |
| "reduction": "Mean over all proteins of each protein's mean masked-token negative log-likelihood; equal protein weights give each source one third of the score.", |
| "selection": { |
| "population": "Every protein in the three pinned validation shards, exactly once.", |
| "dataset_selection": "First 4,096 eligible cluster representatives per source in SHA-256 order; their union is excluded from all training source arms.", |
| "receipt_digest_order": [ |
| "uniref90", |
| "mgnify", |
| "omg_img" |
| ], |
| "within_source_order": "Parquet row order" |
| }, |
| "per_protein_randomness": { |
| "generator": "PyTorch 2.13.0 CPU torch.Generator", |
| "digest_serialization": "bytes.fromhex(protein_sha256).rstrip(b'\\x00')", |
| "digest_serialization_reason": "v2 reads bytes(store.index[row]['digest']) from a NumPy S32 scalar, which removes trailing zero bytes. Preserve this behavior to reproduce v2 results; the original full SHA-256 remains stored in the Parquet shard.", |
| "seed_derivation": "int.from_bytes(SHA256(mask_seed.to_bytes(8, 'big') + serialized_digest).digest()[:8], 'big')", |
| "mask_seed": 20260821, |
| "draw_order": "If a crop is needed, draw its offset first. Then draw the mask Bernoulli uniforms over the single framed sequence; draw a fallback target index only if no eligible residue was selected." |
| }, |
| "cropping": { |
| "maximum_residues": 510, |
| "maximum_context_tokens": 512, |
| "offset": "For length > 510, torch.randint(length - 510 + 1, (1,), generator=generator); otherwise zero without an RNG draw.", |
| "framing": "Prepend BOS and append EOS after cropping." |
| }, |
| "masking": { |
| "probability": 0.15, |
| "eligible": "Canonical amino-acid tokens only; exclude BOS, EOS, padding and noncanonical tokens.", |
| "selection": "torch.rand((1, framed_length), generator=generator) < 0.15, intersected with eligible positions.", |
| "minimum_targets": "If any eligible residue exists but none was selected, select one eligible position uniformly with the same generator.", |
| "corruption": "Replace every selected token with <mask>; labels retain the original token IDs at selected positions and are -100 elsewhere." |
| }, |
| "batching": { |
| "batch_size_is_score_setting": false, |
| "implementation_default_batch_size": 32, |
| "policy": "Construct crops and masks separately for every protein before batching; group by cropped length and pad within each batch.", |
| "invariance": "Batch size, evaluation order, GPU count and global RNG state do not select proteins or change their crops and masks. Floating-point execution can introduce small numerical differences." |
| }, |
| "verification": { |
| "sequence_digests_recomputed": 12288, |
| "unique_sequence_digests": 12288, |
| "evaluated_manifest_sha256": "62a3cf7bde05bf2681b1864f5f45993460f1aa98069029deb47fd70ec36f069f", |
| "evaluated_manifest_encoding": "Concatenate each protein's digest after the v2 trailing-zero serialization in receipt_digest_order and Parquet row order, then SHA-256; matches validation_mlm.manifest_sha256.", |
| "canonical_population_sha256": "62968311150cdcb29188c113e7ab83b5b6d757df104fc290e71d8d33b691b48e", |
| "canonical_population_encoding": "Concatenate each full raw 32-byte SHA-256 digest in the same order, then SHA-256; independent of the mmap scalar serialization.", |
| "trailing_zero_digest_counts": { |
| "uniref90": 14, |
| "mgnify": 15, |
| "omg_img": 18 |
| }, |
| "examples_sha256": "a8588f0d4620a5823142c7ac6e7f74bdc3a6a62be2a30bc5e4a6dd48b19ced84", |
| "examples_encoding": "In the same order, concatenate raw protein digest, framed length as uint32 big-endian, corrupted token IDs as int64 little-endian and labels as int64 little-endian; then SHA-256." |
| }, |
| "validation_pool": { |
| "manifest_sha256": "fe1ac0657085ab19fe6f56786006e9eb004ca66bc6c5b81dfd8e6bc3dcfda6ff", |
| "repo_id": "LuminScience/LuminBench-Nano-ESMC", |
| "revision": "bd38448d50d8f426d7b9bd4410b53159ea001259", |
| "sources": { |
| "mgnify": { |
| "bytes": 578470, |
| "maximum_sequence_sha256": "0000d08aea015e75fe4a484cf6f66c2d1d70d164bb0aa38389b22960983d53a2", |
| "minimum_sequence_sha256": "00000003d38602453faede027bc38ed844fb4416607527c22ff799f0d67654f7", |
| "path": "validation/mgnify/shard-00000.parquet", |
| "records": 4096, |
| "residues": 761606, |
| "sha256": "d9532c1e8490059dfb7178d261b85c31fa7306cd186c0348ee6e3e6ce563fb32" |
| }, |
| "omg_img": { |
| "bytes": 724943, |
| "maximum_sequence_sha256": "00010284808cc5f62cf36bdf8e9f8888f86f9ffd6964ca1a836b9dadef48a742", |
| "minimum_sequence_sha256": "0000000e7eb16834d3220c994cf1aeb688780a24f5af085705774d0e94d52b7b", |
| "path": "validation/omg_img/shard-00000.parquet", |
| "records": 4096, |
| "residues": 1027103, |
| "sha256": "a32797838c3338266c62aa61112263cea4515a4a9b977035f88de9cefda08444" |
| }, |
| "uniref90": { |
| "bytes": 926829, |
| "maximum_sequence_sha256": "0003b7ff4ff70e7b21ec32fae89f0792f0d4f86a489346af4877ef8fbad8126b", |
| "minimum_sequence_sha256": "0000000db96f3a7c2cf445eb4fe4c632a9e047d131200e43131695c51dc2ebdd", |
| "path": "validation/uniref90/shard-00000.parquet", |
| "records": 4096, |
| "residues": 1393942, |
| "sha256": "f059d9793eb8c7fc929764a37473205d4819652d41baed0e07799bde1055a0fa" |
| } |
| }, |
| "total_sequences": 12288 |
| }, |
| "historical_results": { |
| "previous_contract": "MLM_VALIDATION_4096.json", |
| "previous_protocol": "heldout-cluster-representative-mlm-v1", |
| "policy": "Preserve historical sampled results and their original settings. Re-evaluate checkpoints under v2 before comparing with full-population scores; do not relabel old scores or reuse v1 cached evaluation receipts." |
| } |
| } |
|
|
