Add method forward to ModelGloVe

source/models.py

@@ -16,11 +16,15 @@ def __init__(
             device: str,
             vocabulary_size: int,
             embedding_size: int,
+            x_max: float,
+            alpha: float,
             padding_idx: int = None
         ):
         super().__init__()
         self.device = device
         self.filepath = os.path.join(PROJECT_DIRECTORY_PATH, "data", "cbow", "model.pt")
+        self.x_max = x_max
+        self.alpha = alpha
         self.padding_idx = padding_idx
         # init embedding layers
         self.main_embeddings = torch.nn.Embedding(
@@ -49,6 +53,35 @@ def __init__(
         # send model to device
         self.to(self.device)
 
+    def save(self):
+        os.makedirs(os.path.dirname(self.filepath), exist_ok=True)
+        torch.save(self.state_dict(), self.filepath)
+
+    def load(self):
+        self.load_state_dict(torch.load(self.filepath, map_location=self.device))
+
+    def get_embeddings(self) -> np.ndarray:
+        embeddings: np.ndarray = (self.main_embeddings.weight.cpu().detach() + self.context_embeddings.weight.cpu().detach()).numpy()
+        # if padding is used, set it to 1 to avoid division by zero
+        if self.padding_idx is not None:
+            embeddings[self.padding_idx] = 1
+        embeddings = utils.normalize(embeddings, axis=1, keepdims=True)
+        return embeddings
+
+    def forward(self, word_index: torch.Tensor, context_index: torch.Tensor, cooccurrence_count: torch.Tensor):
+        # dot product calculation
+        dot_product: torch.Tensor = (self.main_embeddings(word_index) * self.context_embeddings(context_index)).sum(dim=1)
+        # prediction
+        prediction = dot_product + self.main_bias[word_index] + self.context_bias[context_index]
+        # weighted loss calculation
+        x_scaled = (cooccurrence_count / self.x_max).pow(self.alpha)
+        weighted_error = (x_scaled.clamp(0, 1) * (prediction - cooccurrence_count.log()) ** 2).mean()
+        return weighted_error
+
+    def validate(self, validation_dataloader: datahandler.loaders.ValidationLoader) -> float:
+        validation_dataloader.evaluate_analogies(self.get_embeddings(), quiet=True)
+        return validation_dataloader.analogies_accuracy()
+
 
 class ModelCBOW(torch.nn.Module):
     def __init__(