[docs]class TabularConfig:
r""" Config used for tabular combiner
Args:
mlp_division (int): how much to decrease each MLP dim for each additional layer
combine_feat_method (str): The method to combine categorical and numerical features.
See :obj:`TabularFeatCombiner` for details on the supported methods.
mlp_dropout (float): dropout ratio used for MLP layers
numerical_bn (bool): whether to use batchnorm on numerical features
use_simple_classifier (bool): whether to use single layer or MLP as final classifier
mlp_act (str): the activation function to use for finetuning layers
gating_beta (float): the beta hyperparameters used for gating tabular data
see the paper `Integrating Multimodal Information in Large Pretrained Transformers <https://www.aclweb.org/anthology/2020.acl-main.214.pdf>`_ for details
numerical_feat_dim (int): the number of numerical features
cat_feat_dim (int): the number of categorical features
"""
def __init__(self,
num_labels,
mlp_division=4,
combine_feat_method='text_only',
mlp_dropout=0.1,
numerical_bn=True,
use_simple_classifier=True,
mlp_act='relu',
gating_beta=0.2,
numerical_feat_dim=0,
cat_feat_dim=0,
**kwargs
):
self.mlp_division = mlp_division
self.combine_feat_method = combine_feat_method
self.mlp_dropout = mlp_dropout
self.numerical_bn = numerical_bn
self.use_simple_classifier = use_simple_classifier
self.mlp_act = mlp_act
self.gating_beta = gating_beta
self.numerical_feat_dim = numerical_feat_dim
self.cat_feat_dim = cat_feat_dim
self.num_labels = num_labels