Configure TabICL2 model architecture

tab_icl2_config(
  embed_dim = 128L,
  col_n_block = 3L,
  row_n_block = 3L,
  icl_n_block = 12L,
  col_n_head = 8L,
  row_n_head = 8L,
  icl_n_head = 8L,
  feature_group_size = 3L,
  col_n_cls = 4L,
  row_n_cls = 128L,
  num_quantiles = 30L,
  ...
)

Arguments

embed_dim: A positive integer. Embedding dimension shared across all transformer stages.
col_n_block: A positive integer. Number of column-wise transformer blocks.
row_n_block: A positive integer. Number of row-wise transformer blocks.
icl_n_block: A positive integer. Number of in-context-learning transformer blocks.
col_n_head: A positive integer. Number of attention heads for column blocks.
row_n_head: A positive integer. Number of attention heads for row blocks.
icl_n_head: A positive integer. Number of attention heads for ICL blocks.
feature_group_size: A positive integer. Size of feature groups for cyclic repeated grouping.
col_n_cls: A positive integer. Number of CLS tokens per column, concatenated to produce the row representation fed into the ICL stage.
row_n_cls: A positive integer. Number of inducing vectors used in the induced self-attention column blocks.
num_quantiles: A positive integer (>= 5). Number of quantiles predicted by the regression head.
...: Additional named arguments stored in the returned config object. Use this for architecture settings not covered by the named parameters above (e.g., arguments added in newer versions of the model).

Value

A list with class "tab_icl2_config" containing one element per architecture parameter, all coerced to integer.

Examples

tab_icl2_config()
#> architecture config for `tab_icl2()`
tab_icl2_config(embed_dim = 256L, icl_n_block = 8L)
#> architecture config for `tab_icl2()`
#> 
#> non-default arguments:
#> • `embed_dim`: 256
#> • `icl_n_block`: 8