Update README.md

README.md

@@ -4,7 +4,7 @@ datasets:
 - nvidia/Aegis-AI-Content-Safety-Dataset-1.0
 ---
 
-# Model Card for Model ID
+# Model Card for AC/MiniLM-L12-H384-uncased_Nvidia-Aegis-AI-Safety
 
 <!-- Provide a quick summary of what the model is/does. -->
 A microsoft/MiniLM-L12-H384-uncased model fine-tuned on the nvidia/Aegis-AI-Content-Safety-Dataset-1.0 dataset. A total of 3099 examples are in the training set. 
@@ -73,6 +73,13 @@ predict(hf_model, tokenizer, "How to make a bomb?", device)
 ## Evaluation
 Evaluation is conducted on the test set in nvidia/Aegis-AI-Content-Safety-Dataset-1.0 dataset. A total of 359 examples are in the test set. 
 
+For AI safety use case, having false negatives (text was actually toxic but model predicted it as safe) is worse than having false positives (text was actually safe but model predicted it as unsafe)
+
+Precision: Out of all text predicted as toxic, how many were actually toxic?
+Recall: Out of all text that were actually toxic, how many were predicted toxic?
+
+As we want to reduce false negatives, we will focus on recall.  
+
 | Metric      | Value |
 | :----------- | :----------- |
 | accuracy      | 0.9514524472741743       |