Updated LLM eval leaderboard in Read.me

README.md

@@ -52,6 +52,7 @@ dataset_info:
     num_bytes: 34663548
     num_examples: 614
   download_size: 69119656
+  
   dataset_size: 69327096.0
 tags:
 - mathematics
@@ -74,9 +75,8 @@ language:
 - en
 - ms
 ---
-**STEM_Dataset_eng_ms**
 
-**A Bilingual Dataset for Evaluating Reasoning Skills in STEM Subjects**
+# **A Bilingual Dataset for Evaluating Reasoning Skills in STEM Subjects**
 
 This dataset provides a comprehensive evaluation set for tasks assessing reasoning skills in Science, Technology, Engineering, and Mathematics (STEM) subjects. It features questions in both English and Malay, catering to a diverse audience.
 
@@ -103,6 +103,8 @@ The dataset is comprised of two configurations: `data_en` (English) and `data_ms
 * **Options:** Possible answer choices for the question, with keys (e.g., "A", "B", "C", "D") and corresponding text.
 * **Answers:** Correct answer to the question, represented by the key of the correct option (e.g., "C").
 
+---
+
 ## Data Instance Example
 
 ```json
@@ -161,11 +163,15 @@ The dataset is derived from a combination of resources, including:
 * **Release Date:** December 27, 2024
 * **Contact:** We welcome any feedback or corrections to improve the dataset quality.
 
-**License**
+---
+
+# License
 
 This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
 
-**Getting Started**
+---
+
+# Getting Started
 
 You can access the dataset on Hugging Face using the following commands:
 
@@ -180,6 +186,52 @@ dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_en")
 # For Malay data
 dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_ms")
 ```
+---
+
+# Bilingual STEM Dataset LLM Leaderboard
+
+This document summarizes the evaluation results for various language models based on **5-shot** and **First Token Accuracy**. The evaluation was conducted across four configurations:
+
+- **en_withfigures**: English prompts including figures.
+- **en_withoutfigures**: English prompts excluding figures.
+- **ms_withfigures**: Malay prompts including figures.
+- **ms_withoutfigures**: Malay prompts excluding figures.
+
+---
+
+## Results Table
+
+| **Model**                       | **en_withfigures** | **en_withoutfigures** | **ms_withfigures** | **ms_withoutfigures** |
+|---------------------------------|--------------------|-----------------------|--------------------|-----------------------|
+| **gemini-2.0-flash-exp**        | __63.70%__         | **75.16%**           | __63.36%__         | **75.47%**           |
+| **gemini-1.5-flash**            | __49.66%__         | __67.39%__           | __50.00%__         | __64.28%__           |
+| **Qwen/Qwen2-VL-72B-Instruct**  | __58.22%__         | __69.25%__           | __57.53%__         | __63.66%__           |
+| **gpt-4o**                      | __47.95%__         | __66.15%__           | __50.00%__         | __68.01%__           |
+| **gpt-4o-mini**                 | __41.10%__         | __55.90%__           | __38.36%__         | __52.80%__           |
+| **pixtral-large-2411**          | __42.81%__         | __64.29%__           | __35.27%__         | __60.87%__           |
+| **pixtral-12b-2409**            | __24.66%__         | __48.45%__           | __24.66%__         | __39.13%__           |
+| **DeepSeek-V3**                 | None              | **79.19%**           | None              | **76.40%**           |
+| **Qwen2.5-72B-Instruct**        | None              | __74.53%__           | None              | __72.98%__           |
+| **Meta-Llama-3.3-70B-Instruct** | None              | __67.08%__           | None              | __58.07%__           |
+| **Llama-3.2-90B-Vision-Instruct** | None            | __65.22%__           | None              | __58.07%__           |
+| **sail/Sailor2-20B-Chat**       | None              | __66.46%__           | None              | __61.68%__           |
+| **mallam-small**                | None              | __61.49%__           | None              | __55.28%__           |
+| **mistral-large-latest**        | None              | __60.56%__           | None              | __53.42%__           |
+| **google/gemma-2-27b-it**       | None              | __58.07%__           | None              | __57.76%__           |
+| **SeaLLMs-v3-7B-Chat**          | None              | __50.93%__           | None              | __45.96%__           |
+
+---
+
+## Notes
+
+In the repository, there is an `eval.py` script that can be used to run the evaluation for any other LLM.
+
+
+- The evaluation results are based on the specific dataset and methodology employed.
+- The "First Token Accuracy" metric emphasizes the accuracy of predicting the initial token correctly.
+- Further analysis might be needed to determine the models' suitability for specific tasks.
+
+---
 
 **Contributors**
 - [Gele](https://huggingface.co/Geleliong)