Update README.md

README.md

@@ -86,15 +86,15 @@ We clean the content of the remaining dataset entries according to the following
 
 # Evaluation 
 
-To evaluate we used Kotlin Humaneval (more infromation here)
+To evaluate we used [Kotlin Humaneval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval)
 
 Fine-tuned model:
 
 |         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
 |:---------------------------:|:----------------------------------------:|
 |           `base model`            |           26.09            |
-|        `fine-tuned model`        |          29.19         |
+|        `fine-tuned model`        |          **29.19**         |
 
 # Ethical Considerations and Limitations
 
-Code Llama and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, Kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.
+CodeLlama-7B-KStack-full and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-KStack-full's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of CodeLlama-7B-KStack-full, developers should perform safety testing and tuning tailored to their specific applications of the model.