Update Readme.md

README.md

@@ -14,6 +14,10 @@ language:
 
 This work was carried out for the **ICTER Sinhala MMLU Shared Task 2026**, conducted by **UCSC and IIT, Sri Lanka**.
 
+## System architecture
+
+![Team Cognilex Sinhala MMLU training and inference architecture](./architecture.jpeg)
+
 ## Models and resources in this submission
 
 | Repository | What it is |
@@ -90,6 +94,18 @@ Because easy-level MCQs were underrepresented compared with medium- and hard-lev
 | Filtered dataset for the first three MCQ epochs | 46,373 |
 | Expanded, filtered dataset for the fourth MCQ epoch | 47,025 |
 
+#### Subject distribution
+
+Humanities (38.8%) and STEM (29.5%) account for the largest shares of the MCQ dataset.
+
+![MCQ dataset distribution by subject](./subject-distribution.png)
+
+#### Difficulty distribution
+
+Medium questions comprise 52.9% of the MCQ dataset, followed by hard (32.8%) and easy (14.3%) questions.
+
+![MCQ dataset distribution by difficulty](./difficulty-distribution.png)
+
 ## Training methodology
 
 ### Stage 1: Sinhala syllabus adaptation using synthetic Q&A
@@ -273,11 +289,11 @@ We used **Google Colab with an NVIDIA A100 80 GB GPU** for training and inferenc
 
 | Evaluation metric | Result |
 |---|---|
-| Overall score | — |
-| Leaderboard rank | — |
-| Easy-level score | — |
-| Medium-level score | — |
-| Hard-level score | — |
+| Overall score | 68.55 |
+| Easy-level score | 61.21 |
+| Medium-level score | 67.96 |
+| Hard-level score | 76.49 |
+
 
 ## Team members