update app.py

app.py

@@ -8,13 +8,19 @@ model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-Nli-Matryosh
 # Define the labels for NLI
 labels = ["contradiction", "entailment", "neutral"]
 
+# Define the Matryoshka dimension
+matryoshka_dim = 64
+
 # Function to compute similarity and classify relationship
 def predict(sentence1, sentence2):
     sentences = [sentence1, sentence2]
     embeddings = model.encode(sentences)
     
+    # Shrink the embedding dimensions
+    embeddings = embeddings[..., :matryoshka_dim]
+    
     # Compute cosine similarity between the two sentences
-    similarity_score = util.pytorch_cos_sim(embeddings[0], embeddings[1])
+    similarity_score = util.cos_sim(embeddings[0], embeddings[1])
     
     # Placeholder logic for NLI (needs to be replaced with actual model inference)
     # This is just an example; in reality, you need a classifier trained for NLI
@@ -45,10 +51,10 @@ examples = [
 gr.Interface(
     fn=predict,
     title="Arabic Sentence Similarity and NLI Classification",
-    description="Compute the semantic similarity and classify the relationship between two Arabic sentences using an Arabic Matryoshka Embedding model.",
+    description="Compute the semantic similarity and classify the relationship between two Arabic sentences using a SentenceTransformer model.",
     inputs=inputs,
     examples=examples,
     outputs=outputs,
     cache_examples=False,
-    article="Author: OMER NACAR. Model from Hugging Face Hub: Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka",
+    article="Author: Your Name. Model from Hugging Face Hub: Omartificial-Intelligence-Space/Arabic-Nli-Matryoshka",
 ).launch(debug=True, share=True)