readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - sibyl
  datasets:
  - yelp_polarity
  metrics:
  - accuracy
  model-index:
  - name: bert-base-uncased-yelp_polarity
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: yelp_polarity
        type: yelp_polarity
        args: plain_text
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9516052631578947
  ---
  
  <!-- 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. -->
  
  # bert-base-uncased-yelp_polarity
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the yelp_polarity dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3222
  - Accuracy: 0.9516
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 1
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 277200
  - training_steps: 2772000
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.8067        | 0.0   | 2000  | 0.8241          | 0.4975   |
  | 0.5482        | 0.01  | 4000  | 0.3507          | 0.8591   |
  | 0.3427        | 0.01  | 6000  | 0.3750          | 0.9139   |
  | 0.4133        | 0.01  | 8000  | 0.5520          | 0.9016   |
  | 0.4301        | 0.02  | 10000 | 0.3803          | 0.9304   |
  | 0.3716        | 0.02  | 12000 | 0.4168          | 0.9337   |
  | 0.4076        | 0.03  | 14000 | 0.5042          | 0.9170   |
  | 0.3674        | 0.03  | 16000 | 0.4806          | 0.9268   |
  | 0.3813        | 0.03  | 18000 | 0.4227          | 0.9261   |
  | 0.3723        | 0.04  | 20000 | 0.3360          | 0.9418   |
  | 0.3876        | 0.04  | 22000 | 0.3255          | 0.9407   |
  | 0.3351        | 0.04  | 24000 | 0.3283          | 0.9404   |
  | 0.34          | 0.05  | 26000 | 0.3489          | 0.9430   |
  | 0.3006        | 0.05  | 28000 | 0.3302          | 0.9464   |
  | 0.349         | 0.05  | 30000 | 0.3853          | 0.9375   |
  | 0.3696        | 0.06  | 32000 | 0.2992          | 0.9454   |
  | 0.3301        | 0.06  | 34000 | 0.3484          | 0.9464   |
  | 0.3151        | 0.06  | 36000 | 0.3529          | 0.9455   |
  | 0.3682        | 0.07  | 38000 | 0.3052          | 0.9420   |
  | 0.3184        | 0.07  | 40000 | 0.3323          | 0.9466   |
  | 0.3207        | 0.08  | 42000 | 0.3133          | 0.9532   |
  | 0.3346        | 0.08  | 44000 | 0.3826          | 0.9414   |
  | 0.3008        | 0.08  | 46000 | 0.3059          | 0.9484   |
  | 0.3306        | 0.09  | 48000 | 0.3089          | 0.9475   |
  | 0.342         | 0.09  | 50000 | 0.3611          | 0.9486   |
  | 0.3424        | 0.09  | 52000 | 0.3227          | 0.9445   |
  | 0.3044        | 0.1   | 54000 | 0.3130          | 0.9489   |
  | 0.3278        | 0.1   | 56000 | 0.3827          | 0.9368   |
  | 0.288         | 0.1   | 58000 | 0.3080          | 0.9504   |
  | 0.3342        | 0.11  | 60000 | 0.3252          | 0.9471   |
  | 0.3737        | 0.11  | 62000 | 0.4250          | 0.9343   |
  
  
  ### Framework versions
  
  - Transformers 4.10.2
  - Pytorch 1.7.1
  - Datasets 1.6.1
  - Tokenizers 0.10.3
  