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VictorSanh/roberta-base-finetuned-yelp-polarity VictorSanh/roberta-base-finetuned-yelp-polarity
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Contributed by

VictorSanh Victor Sanh
2 models

How to use this model directly from the πŸ€—/transformers library:

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from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VictorSanh/roberta-base-finetuned-yelp-polarity") model = AutoModelForSequenceClassification.from_pretrained("VictorSanh/roberta-base-finetuned-yelp-polarity")


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