Update README.md

README.md

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 # Evaluation Results: Qwen3-4B-Instruct-2507-PII-RL on PII Masking
 
-This dataset contains evaluation results for the RL-trained model `AdamLucek/Qwen3-4B-Instruct-2507-PII-RL` on the `pii-masking` environment from Prime Intellect's Environment Hub. The model was fine-tuned using reinforcement learning to mask personally identifiable information (PII) in text.
+This dataset contains evaluation results for the RL-trained model [AdamLucek/Qwen3-4B-Instruct-2507-PII-RL](https://huggingface.co/AdamLucek/Qwen3-4B-Instruct-2507-PII-RL) on the [adamlucek/pii-masking](https://app.primeintellect.ai/dashboard/environments/adamlucek/pii-masking) environment from Prime Intellect's Environment Hub. The model was fine-tuned using reinforcement learning to mask personally identifiable information (PII) in text.
 
 ## Evaluation Configuration
 
-- **Environment**: `pii-masking`
-- **Model**: `AdamLucek/Qwen3-4B-Instruct-2507-PII-RL`
-- **Base Model**: `Qwen/Qwen3-4B-Instruct-2507`
+- **Environment**: [adamlucek/pii-masking](https://app.primeintellect.ai/dashboard/environments/adamlucek/pii-masking)
+- **Model**: [AdamLucek/Qwen3-4B-Instruct-2507-PII-RL](https://huggingface.co/AdamLucek/Qwen3-4B-Instruct-2507-PII-RL)
+- **Base Model**: [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
 - **Examples Evaluated**: 50
 - **Rollouts per Example**: 3
 - **Total Samples**: 150
@@ -86,7 +86,7 @@ This dataset contains evaluation results for the RL-trained model `AdamLucek/Qwe
 
 - **Format Compliance**: 100% - The model consistently produces correctly formatted outputs with proper XML tags
 - **Exact Match Rate**: 50% - Half of all outputs match the reference answer exactly
-- **PII Identification**: 56.7% - The model correctly identifies and masks the right number of PII items in most cases
+- **PII Count**: 56.7% - The model correctly includes the right count of PII masks a little more than half the time
 - **Consistency**: Stable performance across all three rollouts (mean rewards: 0.88, 0.89, 0.88)
 
 The reward structure uses a maximum of 1.6 points, combining: