Update README.md

README.md

@@ -10,7 +10,7 @@ This model is IBM's lightweight, 4-layer toxicity binary classifier for English.
 
 
 ## Feature
-This model offers very low inference latency and is capable of running on CPUs apart from GPUs and AIUs. It features 38 million parameters, reducing the number of hidden layers from 12 to 4, decreasing the hidden size from 768 to 576, and the intermediate size from 3072 to 768, compared to the original RoBERTa model architecture. 
+This model offers very low inference latency and is capable of running on CPUs apart from GPUs and AIUs. It features 38 million parameters, reducing the number of hidden layers from 12 to 4, decreasing the hidden size from 768 to 576, and the intermediate size from 3072 to 768, compared to the original RoBERTa model architecture. The latency on CPU vs accuracy numbers for this model in comparision to others is shown in the chart below. 
 
 ![Description of Image](38m_latency.png)
 
@@ -37,7 +37,7 @@ with torch.no_grad():
    
 ```
 ## Performance Comparison with Other Models
-The model delivers competitive performance with significantly lower inference latency. If a better F1 score is required, please refer to IBM's 12-layer model here. 
+The model outperforms most popular models with significantly lower inference latency. If a better F1 score is required, please refer to IBM's 12-layer model [here](https://huggingface.co/ibm-granite/granite-guardian-hap-125m). 
 
 ![Description of Image](38m_comparison_a.png)
 ![Description of Image](38m_comparison_b.png)