Merge branch 'main' into renovate/actions-setup-python-6.x

Author: micedre

.github/workflows/deploy-docs.yml

@@ -27,15 +27,15 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
 
     - name: Set up Python
       uses: actions/setup-python@v6
       with:
         python-version: '3.11'
 
     - name: Install uv
-      uses: astral-sh/setup-uv@v4
+      uses: astral-sh/setup-uv@v7
 
     - name: Install dependencies
       run: |
@@ -51,7 +51,7 @@ jobs:
         uv run sphinx-build -b html docs/source build/html
 
     - name: Upload artifact
-      uses: actions/upload-pages-artifact@v3
+      uses: actions/upload-pages-artifact@v4
       with:
         path: build/html
 

.github/workflows/python-package.yml

@@ -19,12 +19,12 @@ jobs:
         python-version: ["3.11", "3.12", "3.13"]
 
     steps:
-    - uses: actions/checkout@v5
+    - uses: actions/checkout@v6
     - name: Install uv
       uses: astral-sh/setup-uv@v7
       with:
         python-version: ${{ matrix.python-version }}
-        version: 0.9.10
+        version: 0.9.12
         enable-cache: true
         cache-dependency-glob: "uv.lock"
     - name: Install the project

.github/workflows/python-publish.yml

@@ -34,13 +34,13 @@ jobs:
       # url: https://pypi.org/project/YOURPROJECT/${{ github.event.release.name }}
 
     steps:
-       - uses: actions/checkout@v5
+       - uses: actions/checkout@v6
        - name: Install uv
          uses: astral-sh/setup-uv@v7
          with:
           enable-cache: true
           cache-dependency-glob: "uv.lock"
-          version: 0.9.10
+          version: 0.9.12
 
        - name: Change Package Version
          run: |

README.md

@@ -1,6 +1,6 @@
 # torchTextClassifiers
 
-[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://inseeflab.github.io/torchTextClassifiers/)
+[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://inseefrlab.github.io/torchTextClassifiers/)
 
 A unified, extensible framework for text classification with categorical variables built on [PyTorch](https://pytorch.org/) and [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/).
 
@@ -34,8 +34,7 @@ pip install -e .
 
 ## 📖 Documentation
 
-Full documentation is available at: **https://inseeflab.github.io/torchTextClassifiers/**
-
+Full documentation is available at: **https://inseefrlab.github.io/torchTextClassifiers/**
 The documentation includes:
 - **Getting Started**: Installation and quick start guide
 - **Architecture**: Understanding the 3-layer design
@@ -58,3 +57,5 @@ See the [examples/](examples/) directory for:
 ## 📄 License
 
 This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+

docs/source/architecture/overview.md

@@ -270,7 +270,7 @@ custom_head = nn.Sequential(
     nn.Linear(64, 5)
 )
 
-head = ClassificationHead(linear=custom_head)
+head = ClassificationHead(net=custom_head)
 ```
 
 ## Complete Architecture
@@ -611,3 +611,4 @@ torchTextClassifiers provides a **component-based pipeline** for text classifica
 
 Ready to build your classifier? Start with {doc}`../getting_started/quickstart`!
 
+

torchTextClassifiers/model/components/classification_head.py

@@ -24,6 +24,8 @@ def __init__(
         """
         super().__init__()
         if net is not None:
+            self.net = net
+            
             # --- Custom net should either be a Sequential or a Linear ---
             if not (isinstance(net, nn.Sequential) or isinstance(net, nn.Linear)):
                 raise ValueError("net must be an nn.Sequential when provided.")
@@ -43,7 +45,6 @@ def __init__(
                 # --- Extract features ---
                 self.input_dim = first.in_features
                 self.num_classes = last.out_features
-                self.net = net
             else:  # if not Sequential, it is a Linear
                 self.input_dim = net.in_features
                 self.num_classes = net.out_features
@@ -53,23 +54,8 @@ def __init__(
                 input_dim is not None and num_classes is not None
             ), "Either net or both input_dim and num_classes must be provided."
             self.net = nn.Linear(input_dim, num_classes)
-            self.input_dim, self.num_classes = self._get_linear_input_output_dims(self.net)
+            self.input_dim = input_dim
+            self.num_classes = num_classes
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.net(x)
-
-    @staticmethod
-    def _get_linear_input_output_dims(module: nn.Module):
-        """
-        Returns (input_dim, output_dim) for any module containing Linear layers.
-        Works for Linear, Sequential, or nested models.
-        """
-        # Collect all Linear layers recursively
-        linears = [m for m in module.modules() if isinstance(m, nn.Linear)]
-
-        if not linears:
-            raise ValueError("No Linear layers found in the given module.")
-
-        input_dim = linears[0].in_features
-        output_dim = linears[-1].out_features
-        return input_dim, output_dim