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Update README.md #3

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## Model Description
We present a large classification model trained on a manually curated real-world dataset that can be used as a new benchmark for advancing research in voice 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.
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.
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