Update README.md

README.md

@@ -1,10 +1,13 @@
 ## Model Description
-The model is fine-tuned on the [WavLM base plus](https://arxiv.org/abs/2110.13900) with 2,374 hours of audio clips from 
+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 toxicity detection and classification.
+Our model is fine-tuned on the [WavLM base plus](https://arxiv.org/abs/2110.13900) with 2,374 hours of audio clips from 
 voice chat for multilabel classification. The audio clips are automatically labeled using a synthetic data pipeline 
-described in [our blog post](link to blog post here). 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.
+described in [our blog post](link to blog post here). 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.
+
 
 We evaluated this model on a data set with human annotated labels that contained a total of 9,795 samples with the class 
 distribution shown below. Note that we did not include the "other" category in this evaluation data set. 
@@ -18,7 +21,9 @@ distribution shown below. Note that we did not include the "other" category in t
 |NoViolation | 4185 | 9.93 | 42.73% |
 
 
-If we set the same threshold across all classes and treat the model as a binary classifier across all 4 toxicity classes (`Profanity`, `DatingAndSexting`, `Racist`, `Bullying`), we get a binarized average precision of 94.48%. The precision recall curve is as shown below.
+If we set the same threshold across all classes and treat the model as a binary classifier across all 4 toxicity classes
+(`Profanity`, `DatingAndSexting`, `Racist`, `Bullying`), we get a binarized average precision of 94.48%. The precision 
+recall curve is as shown below.
 
 
 <p align="center">
@@ -35,5 +40,6 @@ To run the inference file, please run the following command:
 ```
 python inference.py --audio_file <your audio file path> --model_path <path to Huggingface model>
 ```
-You can get the model weights either by downloading from the model releases page [here](https://github.com/Roblox/voice-safety-classifier/releases/tag/vs-classifier-v1), or from HuggingFace under `roblox/voice-safety-classifier`. 
-If `model_path` isn’t specified, the model will be loaded directly from HuggingFace.
+You can get the model weights either by downloading from the model releases page [here](https://github.com/Roblox/voice-safety-classifier/releases/tag/vs-classifier-v1), or from HuggingFace under 
+[`roblox/voice-safety-classifier`](https://huggingface.co/Roblox/voice-safety-classifier). If `model_path` isn’t 
+specified, the model will be loaded directly from HuggingFace.