Update README.md

README.md

@@ -9,16 +9,16 @@ tags:
 - AceGPT-7B-Chat
 ---
 # InfectA-Chat
-<!--To prevent adversial effects of infectious diseases, clear and accessible communication, tracking infectious diseases regularly is crucial. InfectA-Chat is a generative model specifically designed to address this need. Built upon the powerful AceGPT-7B-Chat pre-trained model, InfectA-Chat is fine-tuned to track infectious diseases outbreaks in the infectious diseases domain. This makes it a valuable tool for facilitating communication in both Arabic and English, potentially bridging language barriers and fostering a deeper understanding of infectious diseases.-->
+To prevent adversial effects of infectious diseases, clear and accessible communication, tracking infectious diseases regularly is crucial. InfectA-Chat is a generative model specifically designed to address this need. Built upon the powerful AceGPT-7B-Chat pre-trained model, InfectA-Chat is fine-tuned to track infectious diseases outbreaks in the infectious diseases domain. This makes it a valuable tool for facilitating communication in both Arabic and English, potentially bridging language barriers and fostering a deeper understanding of infectious diseases.
 
 # Model Details
-<!--In the fight against infectious diseases in the Middle East, clear and effective communication is paramount. We're excited to announce the release of InfectA-Chat, a generative text model fine-tuned on the AceGPT-7B-Chat model. Designed specifically for the Arabic and English languages, InfectA-Chat excels at following instructions related to infectious disease topics. Notably, our models outperform existing Arabic and state-of-the-art LLMs on Q&A task involving infectious disease instructions while competing with GPT-4. This advancement has the potential to significantly improve communication and disease tracking efforts in the specific region.-->
+In the fight against infectious diseases in the Middle East, clear and effective communication is paramount. We're excited to announce the release of InfectA-Chat, a generative text model fine-tuned on the AceGPT-7B-Chat model. Designed specifically for the Arabic and English languages, InfectA-Chat excels at following instructions related to infectious disease topics. Notably, our models outperform existing Arabic and state-of-the-art LLMs on Q&A task involving infectious disease instructions while competing with GPT-4. This advancement has the potential to significantly improve communication and disease tracking efforts in the specific region.
 
-- **Developed by:** [Korea Institute of Science and Technology]
-- **Language(s) (NLP):** [Arabic, English]
-- **License:** [Creative Commons Attribution 2.0]
-- **Finetuned from model [optional]:** [AceGPT-7B-Chat]
-- **Repository:** [KISTI-AI/InfectA-Chat]
+- **Developed by:** Korea Institute of Science and Technology
+- **Language(s) (NLP):** Arabic, English
+- **License:** Creative Commons Attribution 2.0
+- **Finetuned from model [optional]:** AceGPT-7B-Chat
+- **Repository:** KISTI-AI/InfectA-Chat
 
 # Training Details
 
@@ -26,16 +26,16 @@ tags:
 InfectA-Chat was instruction fine-tuned with 55,400 infectious diseases-related instruction-following data.
 
 ## Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure.
 
 ## Training Hyperparameters
 
-- **Training regime:** [fp32] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** fp32 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 # Evaluation
 
 ## Evaluation Results on Infectious Diseases-related Instruction-Following Dataset
-<!-- Experiments on infectious diseases-related instruction-following data and Arabic MMLU benchmark dataset. ‘STEM’, ‘Humanities’, ‘Social Sciences’, ‘Others’ belong to Arabic MMLU.  -->
+Experiments on infectious diseases-related instruction-following data and Arabic MMLU benchmark dataset. ‘STEM’, ‘Humanities’, ‘Social Sciences’, ‘Others’ belong to Arabic MMLU.
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6516795fb84fb7bc6cc34fd9/CQnUnZUWNqlJIM2F77mde.png)
 
@@ -45,4 +45,4 @@ InfectA-Chat was instruction fine-tuned with 55,400 infectious diseases-related
 
 ## Evaluation Results on Arabic MMLU Benchmark Dataset
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6516795fb84fb7bc6cc34fd9/duwgP2pRuPGqd5o-wCNuy.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6516795fb84fb7bc6cc34fd9/ZbNQ83BkyngiSewxXvik_.png)