Update README.md

README.md

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 # Weaver Distilled for MMLU-Pro
 
-A distilled cross-encoder model that captures 98.7% of Weaver's accuracy while reducing verification compute by 99.97%. This model is fine-tuned from ModernBERT-large to predict the correctness of academic reasoning responses, trained on Weaver ensemble scores from 35 different verifiers.
+This is a distilled cross-encoder model based on ModernBERT-large, trained to predict the correctness of answers on MMLU Pro. This specialized verifier was trained on Weaver scores aggregated over 35 different verifiers and reward models.
 
 ## Model Details
 
@@ -26,14 +26,6 @@ A distilled cross-encoder model that captures 98.7% of Weaver's accuracy while r
 - **Training Data**: MMLU-Pro problems with Weaver scores from 35 LM judges and reward models
 - **Task**: Binary classification for answer correctness prediction
 
-## Performance
-
-On MMLU-Pro with Llama 3.1 70B generations:
-<!-- TODO: Update with actual performance numbers -->
-- **Weaver (Full)**: XX.X% accuracy, high compute cost
-- **Weaver (Distilled)**: XX.X% accuracy, 99.97% compute reduction
-- **Majority Voting**: XX.X% accuracy
-
 ## Quick Start
 
 ```python