Update README.md

README.md

@@ -192,33 +192,25 @@ dataset = load_dataset("Supa-AI/STEM-en-ms", name="data_ms")
 
 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%__           |
+| **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%**             |
 
 ---
 
@@ -226,7 +218,6 @@ This document summarizes the evaluation results for various language models base
 
 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.