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---
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.
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