--- license: apache-2.0 datasets: - berkeley-nest/Nectar language: - en --- # 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.