Update README.md

README.md

@@ -189,7 +189,7 @@ import torch
 import numpy as np
 from tqdm.auto import tqdm
 from sentence_transformers import SentenceTransformer
-from sentence_transformers.util import batch_to_device
+from sentence_transformers.util import batch_to_device, cos_sim
 
 # Load the model
 model = SentenceTransformer("TechWolf/JobBERT-v2")
@@ -230,11 +230,26 @@ job_titles = [
 # Get embeddings
 embeddings = encode(model, job_titles)
 
-# Calculate similarity matrix
-similarities = np.dot(embeddings, embeddings.T)
+# Calculate cosine similarity matrix
+similarities = cos_sim(embeddings, embeddings)
 print(similarities)
 ```
 
+The output will be a similarity matrix where each value represents the cosine similarity between two job titles:
+
+```
+tensor([[1.0000, 0.8723, 0.4821, 0.5447],
+        [0.8723, 1.0000, 0.4822, 0.5019],
+        [0.4821, 0.4822, 1.0000, 0.4328],
+        [0.5447, 0.5019, 0.4328, 1.0000]])
+```
+
+In this example:
+- The diagonal values are 1.0000 (perfect similarity with itself)
+- 'Software Engineer' and 'Senior Software Developer' have high similarity (0.8723)
+- 'Product Manager' and 'Data Scientist' show lower similarity with other roles
+- All values range between 0 and 1, where higher values indicate greater similarity
+
 ### Example Use Cases
 
 1. **Job Title Matching**: Find similar job titles for standardization or matching