Merge pull request #167 from basf/version_bump

version bump

README.md

@@ -16,7 +16,7 @@
 </div>
 
 <div style="text-align: center;">
-    <h1>Mambular: Tabular Deep Learning</h1>
+    <h1>Mambular: Tabular Deep  Made Simple</h1>
 </div>
 
 Mambular is a Python library for tabular deep learning. It includes models that leverage the Mamba (State Space Model) architecture, as well as other popular models like TabTransformer, FTTransformer, TabM and tabular ResNets. Check out our paper `Mambular: A Sequential Model for Tabular Deep Learning`, available [here](https://arxiv.org/abs/2408.06291). Also check out our paper introducing [TabulaRNN](https://arxiv.org/pdf/2411.17207) and analyzing the efficiency of NLP inspired tabular models. 

mambular/__version__.py

@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "0.2.3"
+__version__ = "1.0.0"

mambular/arch_utils/layer_utils/embedding_layer.py

@@ -54,7 +54,7 @@ def __init__(self, num_feature_info, cat_feature_info, config):
                 d_embedding=self.d_model,
                 n_frequencies=getattr(config, "n_frequencies", 48),
                 frequency_init_scale=getattr(config, "frequency_init_scale", 0.01),
-                activation=self.embedding_activation,
+                activation=True,
                 lite=getattr(config, "plr_lite", False),
             )
         elif self.embedding_type == "linear":

mambular/configs/mlp_config.py

@@ -62,7 +62,7 @@ class DefaultMLPConfig:
     weight_decay: float = 1e-06
     lr_factor: float = 0.1
     layer_sizes: list = (256, 128, 32)
-    activation: callable = nn.SELU()
+    activation: callable = nn.ReLU()
     skip_layers: bool = False
     dropout: float = 0.2
     use_glu: bool = False
@@ -76,5 +76,4 @@ class DefaultMLPConfig:
     embedding_bias: bool = False
     layer_norm_after_embedding: bool = False
     d_model: int = 32
-    embedding_type: float = "plr"
     plr_lite: bool = False