format string | tackai_version string | protein_space string | block_dims dict | combined_dim int64 | models dict | files dict |
|---|---|---|---|---|---|---|
tack-fusion-context/v1 | 1.0.0 | combined | {
"assay_time": 1,
"cell": 47,
"poi": 51,
"e3": 7,
"assay": 8
} | 52 | {
"cell": "sentence-transformers/all-mpnet-base-v1",
"assay": "sentence-transformers/all-mpnet-base-v1",
"poi": "facebook/esm2_t30_150M_UR50D",
"e3": "facebook/esm2_t30_150M_UR50D"
} | {
"cell_embeddings_model=sentence-transformer_pooling=mean_pca47.npz": {
"block": "cell",
"role": "table",
"sha256": "cfa457dbf422d6aae5e5f04fc8501b67041e4eac1462244e785efb01cf20cf2f",
"bytes": 88078,
"dim": 47,
"n_keys": 201
},
"cell_embeddings_model=sentence-transformer_pooling=mean_pca4... |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
ailab-bio/TACK-Context-Encoding
PCA-reduced context embedding tables for the tackai fusion surrogates (tackai.fusion):
one lookup table plus a PCA side-file per block (assay (8-d), cell (47-d), e3 (7-d), poi (51-d)), and manifest.json recording
each file's sha256, byte size and dimensions. Published from a protein_space="combined" encoder; from_pretrained reproduces that by default.
These tables are inputs to every fusion model, never the output of one — a fitted ensemble is only meaningful against the exact tables it was built with, so this repo is versioned by commit rather than by file content.
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from tackai.fusion.data import FusionData
data = FusionData.from_pretrained("ailab-bio/TACK-Context-Encoding")
context = data.encode_context({"poi_seq": ..., "e3_seq": ..., "cell_id": ..., "assay": ...})
from_pretrained installs these files into TACKAI_CACHE and verifies them against this
repo's manifest.json before use.
Generated by tackai.fusion.context.stage_context_tables (tackai version
1.0.0) — do not hand-edit; re-stage and re-upload instead.
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