Update README.md

README.md

@@ -6,7 +6,7 @@ We present a large classification model trained on a manually curated real-world
 benchmark for advancing research in toxicity detection and classification.
 We started with the original weights from the [WavLM base plus](https://arxiv.org/abs/2110.13900) and fine-tuned it 
 with 2,374 hours of voice chat audio clips for multilabel classification. The audio clips are automatically labeled using a 
-synthetic data pipeline described in [our blog post]( https://research.roblox.com/tech-blog/2024/07/deploying-ml-for-voice-safety). A single output can have multiple labels. 
+synthetic data pipeline described in [our blog post](https://research.roblox.com/tech-blog/2024/06/deploying-ml-for-voice-safety). A single output can have multiple labels. 
 The model outputs a n by 6 output tensor where the inferred labels are `Profanity`, `DatingAndSexting`, `Racist`, 
 `Bullying`, `Other`, `NoViolation`. `Other` consists of policy violation categories with low prevalence such as drugs 
 and alcohol or self-harm that are combined into a single category.