# 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.

## Loading

```python
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.