Computing Predictions

Prediction

model.predict() accepts a RelationalBatch, a canonical tensor mapping, a simple all-text cell-vector array, or a sequence of one-context inputs.

Contextual Cell Embeddings

model.encode(context) returns one contextual state per cell. Use output_value="target_features" to return the summed target representation used by task heads.

Classification and Regression

Classification applies sigmoid by default. Multiclass task heads apply softmax. Regression and forecasting return raw scalar outputs. Override with activation="identity" whenever calibration or ranking consumes logits.

Batching

Variable-length contexts are padded during sequence collation. All contexts must use the same text embedding width and model contract.

Ablation

Call batch.ablate(positions) to mask caller-selected cells without changing stable node or parent IDs. Compare the resulting prediction with the original; the library never chooses an ablation on the user’s behalf.