Add example usage script

example_usage.py

@@ -0,0 +1,40 @@
+
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "SURIYA-KP/nomic-embed-text-v2-moe-fine-tuned-depression-symptoms"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Function to get embeddings
+def get_embedding(text):
+    # Tokenize
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+    # Get model output
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Mean pooling
+    token_embeddings = outputs.last_hidden_state
+    attention_mask = inputs['attention_mask']
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    embedding = torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+    return embedding.numpy()[0]
+
+# Example usage
+text1 = "I feel worthless and useless."
+text2 = "I am feeling happy and content today."
+
+emb1 = get_embedding(text1)
+emb2 = get_embedding(text2)
+
+# Calculate cosine similarity
+cos_sim = torch.nn.functional.cosine_similarity(
+    torch.tensor(emb1).unsqueeze(0),
+    torch.tensor(emb2).unsqueeze(0)
+).item()
+
+print(f"Cosine similarity between texts: {cos_sim:.4f}")