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:
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.
- Downloads last month
- 8
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.