readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-base-uncased-ft-google
    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. -->
  
  # bert-base-uncased-ft-google
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [steciuk/google](https://huggingface.co/datasets/steciuk/google) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3195
  - Accuracy: 0.9105
  - F1: 0.9174
  
  and flowing results on the testing set:
  - Accuracy: 0.9096
  - F1: 0.9161
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.3651        | 0.37  | 196  | 0.2641          | 0.8962   | 0.9064 |
  | 0.2765        | 0.75  | 392  | 0.2484          | 0.9019   | 0.9099 |
  | 0.2349        | 1.12  | 588  | 0.2532          | 0.9133   | 0.9205 |
  | 0.2015        | 1.49  | 784  | 0.2692          | 0.9095   | 0.9139 |
  | 0.1817        | 1.86  | 980  | 0.2957          | 0.9095   | 0.9180 |
  | 0.1683        | 2.24  | 1176 | 0.2941          | 0.9143   | 0.9213 |
  | 0.1204        | 2.61  | 1372 | 0.3230          | 0.9143   | 0.9223 |
  | 0.1271        | 2.98  | 1568 | 0.3195          | 0.9105   | 0.9174 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  