Update README.md

README.md

@@ -38,7 +38,7 @@ This model is a fine-tuned IndoBERT transformer for performing sentiment analysi
 
 ## 🧠 Model Description
 
-The model is built upon [`indobert-base-p1`](https://huggingface.co/indobenchmark/indobert-base-p1), a BERT-based transformer pre-trained on over 220 million Indonesian words. The fine-tuning process was done on 7500 samples containing balanced sentiment labels related to online academic services.
+The model is built upon [`indobert-base-p2`](https://huggingface.co/indobenchmark/indobert-base-p2), a BERT-based transformer pre-trained on over 220 million Indonesian words. The fine-tuning process was done on 7500 samples containing balanced sentiment labels related to online academic services.
 
 - **Label classes**: Positive, Neutral, Negative
 - **Preprocessing**: Case folding, punctuation removal, stopword removal, stemming, tokenization (using IndoBERT tokenizer)
@@ -90,18 +90,18 @@ The model is built upon [`indobert-base-p1`](https://huggingface.co/indobenchmar
 | Precision | 87%   |
 | Recall    | 89%   |
 
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6157d43f013078aa50b55498/XHIlRUdocdHHG578yaKN7.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6157d43f013078aa50b55498/pJo7M_qjPoNMyZ6WbfbA4.png)
 
 
 ## 💻 Deployment Context
 
-This model was integrated into a Django-based sentiment dashboard application with:
+This model was integrated into [`a Django-based sentiment dashboard application`](https://github.com/ShinyQ/Django_Thesis-Sentiboard-University-Sentiment-App) with:
 - A custom Twitter crawler
 - Real-time sentiment classification
 - Wordclouds and sentiment breakdowns by time period
 - Admin tools for filtering, deleting, and exporting data
 
+
 ## 🧪 Functional Testing & UAT
 
 ### Functional testing (black-box)