chore!: avoid repeating num_classes for LabelAttentionConfig

not expose LabelAttentionConfig and build it directly from the wrapper
only provide num_heads

Author: meilame-tayebjee

torchTextClassifiers/torchTextClassifiers.py

@@ -50,11 +50,11 @@ class ModelConfig:
     """Base configuration class for text classifiers."""
 
     embedding_dim: int
+    num_classes: int
     categorical_vocabulary_sizes: Optional[List[int]] = None
     categorical_embedding_dims: Optional[Union[List[int], int]] = None
-    num_classes: Optional[int] = None
     attention_config: Optional[AttentionConfig] = None
-    label_attention_config: Optional[LabelAttentionConfig] = None
+    n_heads_label_attention: Optional[int] = None
 
     def to_dict(self) -> Dict[str, Any]:
         return asdict(self)
@@ -142,7 +142,7 @@ def __init__(
         self.embedding_dim = model_config.embedding_dim
         self.categorical_vocabulary_sizes = model_config.categorical_vocabulary_sizes
         self.num_classes = model_config.num_classes
-        self.enable_label_attention = model_config.label_attention_config is not None
+        self.enable_label_attention = model_config.n_heads_label_attention is not None
 
         if self.tokenizer.output_vectorized:
             self.text_embedder = None
@@ -156,7 +156,10 @@ def __init__(
                 embedding_dim=self.embedding_dim,
                 padding_idx=tokenizer.padding_idx,
                 attention_config=model_config.attention_config,
-                label_attention_config=model_config.label_attention_config,
+                label_attention_config=LabelAttentionConfig(
+                    n_head=model_config.n_heads_label_attention,
+                    num_classes=model_config.num_classes,
+                ),
             )
             self.text_embedder = TextEmbedder(
                 text_embedder_config=text_embedder_config,
@@ -697,10 +700,6 @@ def load(cls, path: Union[str, Path], device: str = "auto") -> "torchTextClassif
 
         # Reconstruct model_config
         model_config = ModelConfig.from_dict(metadata["model_config"])
-        if isinstance(model_config.label_attention_config, dict):
-            model_config.label_attention_config = LabelAttentionConfig(
-                **model_config.label_attention_config
-            )
 
         # Create instance
         instance = cls(