
# RoBERTa-base-finetuned-yelp-polarity

This is a [RoBERTa-base](https://huggingface.co/roberta-base) checkpoint fine-tuned on binary sentiment classifcation from [Yelp polarity](https://huggingface.co/nlp/viewer/?dataset=yelp_polarity).
It gets **98.08%** accuracy on the test set.

## Hyper-parameters

We used the following hyper-parameters to train the model on one GPU:
```python
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
```

{"language": "en", "datasets": ["yelp_polarity"]}
transformers pytorch jax safetensors roberta text-classification en dataset:yelp_polarity autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us 


# 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.

## Hyper-parameters

We used the following hyper-parameters to train the model on one GPU:


# 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. ## Hyper-parameters

We used the following hyper-parameters to train the model on one GPU:
TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us 
 # 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. ## Hyper-parameters

We used the following hyper-parameters to train the model on one GPU:
passage: TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us 
# 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.## Hyper-parameters

We used the following hyper-parameters to train the model on one GPU: