Update README.md

README.md

@@ -2,7 +2,7 @@
 license: cc-by-4.0
 datasets:
 - Salesforce/xlam-function-calling-60k
-- MadeAgents/XLAM-7.5k-Irrelevance
+- MadeAgents/xlam-irrelevance-7.5k
 base_model:
 - Qwen/Qwen2.5-Coder-1.5B-Instruct
 ---
@@ -13,24 +13,26 @@ base_model:
 We're excited to release lightweight Hammer 2.0 models ([0.5B](https://huggingface.co/MadeAgents/Hammer2.0-0.5b) ,   [1.5B](https://huggingface.co/MadeAgents/Hammer2.0-1.5b) ,   [3B](https://huggingface.co/MadeAgents/Hammer2.0-3b) ,  and  [7B](https://huggingface.co/MadeAgents/Hammer2.0-7b)) with strong function calling capability, which empower developers to build personalized, on-device agentic applications.
 
 ## Model Details
-Hammer2.0 finetuned based on [Qwen 2.5 series](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) and [Qwen 2.5 coder series](https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f) using function masking techniques. It's trained using the [APIGen Function Calling Datasets](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) containing 60,000 samples, supplemented by [XLAM-7.5k-Irrelevance](https://huggingface.co/datasets/MadeAgents/XLAM-7.5k-Irrelevance) we generated. Hammer2.0 has achieved exceptional performances across numerous function calling benchmarks. For detailed data construction, training methods, and evaluation strategies, please refer to our paper [Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://arxiv.org/abs/2410.04587) and the [Hammer GitHub repository](https://github.com/MadeAgents/Hammer) .
+Hammer2.0 finetuned based on [Qwen 2.5 series](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) and [Qwen 2.5 coder series](https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f) using function masking techniques. It's trained using the [APIGen Function Calling Datasets](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) containing 60,000 samples, supplemented by [xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k) we generated. Hammer2.0 has achieved exceptional performances across numerous function calling benchmarks. For more details, please refer to [Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://arxiv.org/abs/2410.04587) and [Hammer GitHub repository](https://github.com/MadeAgents/Hammer) .
 
 ## Evaluation
-The evaluation results of Hammer 2.0 series on the Berkeley Function-Calling Leaderboard (BFCL) are presented in the following table:
+The evaluation results of Hammer 2.0 models on the Berkeley Function-Calling Leaderboard (BFCL-v3) are presented in the following table:
 <div style="text-align: center;">
     <img src="v2_figures/bfcl.PNG" alt="overview" width="1000" style="margin: auto;">
 </div>
 
+Our Hammer 2.0 series consistently achieves corresponding best performance at comparable scales. The 7B model outperforms most function calling enchanced models, and the 1.5B model also achieves unexpected performance.
+
+In addition, we evaluated the Hammer 2.0 models on other academic benchmarks to further demonstrate the generalization ability of our models.
 
-In addition, we evaluated Hammer2.0 on other academic benchmarks to further show our model's generalization ability:
 <div style="text-align: center;">
     <img src="v2_figures/others.PNG" alt="overview" width="1000" style="margin: auto;">
 </div>
 
-On comparison, Hammer 2.0 outperforms models with similar sizes and even surpass many larger models overall. 
+Hammer 2.0 models showcase highly stable performance, suggesting the robustness of Hammer 2.0 series. In contrast, the baseline approaches display varying levels of effectiveness. 
 
 ## Requiements
-The code of Hammer2.0 models have been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`.
+The code of Hammer 2.0 models have been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`.
 
 ## How to Use
 This is a simple example of how to use our model.