Create model.py

model.py

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+import torch
+import torch.nn as nn
+import fasttext
+
+class SimpleMultilingualClassifier(nn.Module):
+    def __init__(self, embedding_files, num_classes, embedding_dim=100):
+        super().__init__()
+        self.embedding_files = embedding_files
+        self.embedding_dim = embedding_dim
+        self.linear = nn.Linear(embedding_dim, num_classes)
+        self.language_models = {}
+        for lang, path in embedding_files.items():
+            self.language_models[lang] = fasttext.load_model(path)
+
+    def get_embedding(self, text, lang):
+        if lang in self.language_models:
+            return torch.tensor(self.language_models[lang].get_sentence_vector(text))
+        else:
+            raise ValueError(f"Language '{lang}' not supported.")
+
+    def forward(self, text, lang):
+        embedding = self.get_embedding(text, lang)
+        return self.linear(embedding)
+
+    def predict(self, text, lang, class_labels):
+        self.eval()
+        with torch.no_grad():
+            output = self.forward(text, lang).unsqueeze(0) # Add batch dimension
+            probabilities = torch.softmax(output, dim=-1)
+            predicted_class_index = torch.argmax(probabilities, dim=-1).item()
+            return class_labels[predicted_class_index]
+
+# Example usage (you'd need to define your classes and supported languages)
+if __name__ == '__main__':
+    embedding_files = {
+        'en': 'fasttext_embeddings/cc.en.100.bin',
+        'fr': 'fasttext_embeddings/cc.fr.100.bin'
+    }
+    num_classes = 3  # Example number of classes
+    class_labels = ["positive", "negative", "neutral"]
+    model = SimpleMultilingualClassifier(embedding_files, num_classes)
+
+    # Dummy prediction
+    text_en = "This is a great movie."
+    lang_en = 'en'
+    prediction_en = model.predict(text_en, lang_en, class_labels)
+    print(f"English Prediction: {prediction_en}")
+
+    text_fr = "C'est un film incroyable."
+    lang_fr = 'fr'
+    prediction_fr = model.predict(text_fr, lang_fr, class_labels)
+    print(f"French Prediction: {prediction_fr}")