# Use cases
This model is used to deep clean the Rhino dataset, making it a higher quality dataset. This model achieved an average MSE loss of 0.095 during training.
We recommend to use the sigmoid function to turn the logits into probabilities:
```python
1 / (1 + torch.exp(logits))
```
# Training
Using trl's RewardTrainer, this model was trained on berkeley-nest/Nectar. The dataset is curated on-the-fly during training, as explained in the Rhino repo.

{"language": ["en"], "license": "apache-2.0", "datasets": ["berkeley-nest/Nectar"]}
transformers pytorch mistral text-classification en dataset:berkeley-nest/Nectar license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us