Update README.md

README.md

@@ -62,12 +62,12 @@ drop-in compatibility with comparable sentence transformers cross encoders.
 To train this model, I added teacher logits to the all-nli dataset `dleemiller/all-nli-distill` from the
 `dleemiller/ModernCE-large-nli` model. This significantly improves performance above standard training.
 
-This 17m architecture is based on ModernBERT and is an excellent candidate for lightweight CPU inference.
+This 17m architecture is based on ModernBERT and is an excellent candidate for lightweight **CPU inference**.
 
 ---
 
 ## Features
-- **High performing:** Achieves **80.19%** and 86.50% (Micro F1) on MNLI mismatched and SNLI test.
+- **High performing:** Achieves **80.19%** and **86.50%** (Micro F1) on MNLI mismatched and SNLI test.
 - **Efficient architecture:** Based on the Ettin-17m encoder design (17M parameters), offering faster inference speeds.
 - **Extended context length:** Processes sequences up to 8192 tokens, great for LLM output evals.
 
@@ -124,7 +124,7 @@ Details:
 Fine-tuning was performed on the `dleemiller/all-nli-distill` dataset.
 
 ### Validation Results
-The model achieved the following test set performance after fine-tuning:
+The model achieved the following test set micro f1 performance after fine-tuning:
 - **MNLI Unmatched:** 0.8019
 - **SNLI:** 0.8650