Update README.md

README.md

@@ -87,7 +87,7 @@ The model’s performance is evaluated on 200 queries created in-house. For more
 
 1. **Cosine Similarity using Word Embeddings**
    - **Description**: Measures semantic similarity by mapping words/phrases to vectors.
-   - **Equation**: Cosine Similarity = \( \frac{\vec{A} \cdot \vec{B}}{||\vec{A}|| \, ||\vec{B}||} \)
+   - **Equation**: Cosine Similarity = ( A • B ) / ( ||A|| ||B|| )
    - **Example**: "The dog chased the cat." vs. "A canine pursued a feline." (High similarity)
 
 2. **Exact Match (EM)**
@@ -99,8 +99,8 @@ The model’s performance is evaluated on 200 queries created in-house. For more
 3. **ROUGE Score**
    - **Description**: Measures the overlap of the longest common subsequences between reference and response texts.
    - **Equation**:
-     - Precision = \( \frac{LCS(R, C)}{\text{Length of } C} \)
-     - Recall = \( \frac{LCS(R, C)}{\text{Length of } R} \)
+     - Precision = Precision = LCS(R, C) / Length of C
+     - Recall = Recall = LCS(R, C) / Length of R
 
 ### Model Evaluation Summary