readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: results-yelp
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # results-yelp
  
  This model is a fine-tuned version of [textattack/bert-base-uncased-yelp-polarity](https://huggingface.co/textattack/bert-base-uncased-yelp-polarity) on a filtered and manually reviewed Yelp dataset containing restaurant reviews only.
  It achieves the following results on the evaluation set:
  - Loss: 0.3563
  - Accuracy: 0.9302
  - Precision: 0.9461
  - Recall: 0.9608
  - F1: 0.9534
  
  Note: to use this tokenizer, please use the following code to load all the required files:
  
  `tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", config=AutoConfig.from_pretrained("potatobunny/results-yelp"))`
  
  ## Model description
  
  This model is fine-tuned on a Yelp dataset with labelled data containing a restaurant review (text) and whether it has a positive (1) or negative (0) sentiment.
  
  ## Intended uses & limitations
  
  This is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.
  
  ## Training and evaluation data
  
  The training and evaluation data were both obtained from the same Yelp dataset. The data was split into 70% training and 30% validation.
  
  <!-- ## Training procedure -->
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 3
  
  ### Training results
  
  The training loss obtained was 0.265741667.
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.12.1
  