Regenerate over AutoTPDplus-PROTAC: poi 51->78, combined 52->86, e3 7->24, cell 49->56

README.md

@@ -1,7 +1,7 @@
 # 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 (49-d), e3 (7-d), poi (51-d)), and `manifest.json` recording
+one lookup table plus a PCA side-file per block (assay (8-d), cell (56-d), e3 (24-d), poi (78-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

cell_embeddings_model=sentence-transformer_pooling=mean_pca56.npz

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cell_embeddings_model=sentence-transformer_pooling=mean_pca56_model.npz

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manifest.json

@@ -4,12 +4,12 @@
  "protein_space": "combined",
  "block_dims": {
   "assay_time": 1,
-  "cell": 49,
-  "poi": 51,
-  "e3": 7,
+  "cell": 56,
+  "poi": 78,
+  "e3": 24,
   "assay": 8
  },
- "combined_dim": 52,
+ "combined_dim": 86,
  "models": {
   "cell": "sentence-transformers/all-mpnet-base-v1",
   "assay": "sentence-transformers/all-mpnet-base-v1",
@@ -17,69 +17,69 @@
   "e3": "facebook/esm2_t30_150M_UR50D"
  },
  "files": {
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+  "cell_embeddings_model=sentence-transformer_pooling=mean_pca56.npz": {
    "block": "cell",
    "role": "table",
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-   "bytes": 115290,
-   "dim": 49,
-   "n_keys": 257
+   "sha256": "2d0956119782071a6f5f7348c1e7b4daf1f832603692b3f29222ac9378914770",
+   "bytes": 201066,
+   "dim": 56,
+   "n_keys": 418
   },
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+  "cell_embeddings_model=sentence-transformer_pooling=mean_pca56_model.npz": {
    "block": "cell",
    "role": "pca_model",
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    "full_dim": 768,
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+   "reduced_dim": 56
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   "assay_embeddings_vocab=open20_model=all-mpnet-base-v1_pooling=mean_pca8.npz": {
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