Update README.md

README.md

@@ -57,13 +57,13 @@ model-index:
 <br>
 
 <div style="text-align: center;">
-    <h1>Fine-tuned ALBERT Model for Constructiveness Detection in Steam Reviews</h1>
+    <b></b><h1>Fine-tuned ALBERT Model for Constructiveness Detection in Steam Reviews</h1></b>
 </div>
 <hr>
 
 ## <u>Model Summary</u>
 
-This model is a fine-tuned version of **albert-base-v2**, designed to classify whether Steam game reviews are constructive or non-constructive. It leverages the [1.5K Steam Reviews Binary Labeled for Constructiveness dataset](https://huggingface.co/datasets/abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k), containing user-generated game reviews labeled as either:
+This model is a fine-tuned version of **albert-base-v2**, designed to classify whether Steam game reviews are constructive or non-constructive. It leverages the *[1.5K Steam Reviews Binary Labeled for Constructiveness dataset](https://huggingface.co/datasets/abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k)*, containing user-generated game reviews labeled as either:
 - **1 (constructive)**
 - **0 (non-constructive)**
 
@@ -82,13 +82,13 @@ The model can be applied in any scenario where it's important to distinguish bet
 ### Limitations
 
 - **Domain Specificity**: The model was trained on Steam reviews and may not generalize well outside gaming.
-- **Dataset Imbalance**: The training data has an approximate 63%-37% split between non-constructive and constructive reviews.
+- **Dataset Imbalance**: The training data has an approximate **63%-37%** split between non-constructive and constructive reviews.
 
 <hr>
 
 ## <u>Evaluation Results</u>
 
-The model was trained and evaluated using an 80/10/10 Train/Dev/Test split, achieving the following performance metrics during evaluation using the test set:
+The model was trained and evaluated using an **80/10/10 Train/Dev/Test** split, achieving the following performance metrics during evaluation using the test set:
 
 - **Accuracy**: 0.80
 - **Precision**: 0.80
@@ -102,7 +102,7 @@ These results indicate that the model performs reasonably well at identifying th
 ## <u>How to Use</u>
 
 ### Huggingface Space
-Explore and test the model interactively on its [Hugging Face Space](https://huggingface.co/spaces/abullard1/steam-review-constructiveness-classifier).
+Explore and test the model interactively on its *[Hugging Face Space](https://huggingface.co/spaces/abullard1/steam-review-constructiveness-classifier)*.
 
 ### Transformers Library
 To use the model programmatically, use this Python snippet: