Update README.md

README.md

@@ -7,8 +7,8 @@ license: cc-by-nc-4.0
 </p>
 <p align="center">
   <a href="https://apigen-pipeline.github.io/">[Homepage]</a>  |  
-  <a href="https://coder.deepseek.com/">[Paper]</a> | 
-  <a href="https://coder.deepseek.com/">[Dataset]</a> | 
+  <a href="https://arxiv.org/abs/2406.18518">[Paper]</a> | 
+  <a href="https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k">[Dataset]</a> | 
   <a href="https://github.com/SalesforceAIResearch/xLAM">[Github]</a>
 </p>
 <hr>
@@ -75,7 +75,7 @@ We mainly test our function-calling models on the [Berkeley Function-Calling Lea
 <p>Our <code>xLAM-7b-fc-r</code> secures the 3rd place with an overall accuracy of 88.24% on the leaderboard, outperforming many strong models. Notably, our <code>xLAM-1b-fc-r</code> model is the only tiny model with less than 2B parameters on the leaderboard, but still achieves a competitive overall accuracy of 78.94% and outperforming GPT3-Turbo and many larger models.
 Both models exhibit balanced performance across various categories, showing their strong function-calling capabilities despite their small sizes.</p>
 
-See our [paper](https://arxiv.org/abs/2406.18518) for more detailed analysis.
+See our [paper](https://arxiv.org/abs/2406.18518) and Github [repo](https://github.com/SalesforceAIResearch/xLAM) for more detailed analysis.
 
 
 ## Usage