This is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity. It gets 98.08% accuracy on the test set.
We used the following hyper-parameters to train the model on one GPU:
num_train_epochs = 2.0 learning_rate = 1e-05 weight_decay = 0.0 adam_epsilon = 1e-08 max_grad_norm = 1.0 per_device_train_batch_size = 32 gradient_accumulation_steps = 1 warmup_steps = 3500 seed = 42
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This model can be loaded on the Inference API on-demand.