readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-nlp-project-ft-google-ds-news
    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-nlp-project-ft-google-ds-news
  
  This model is a fine-tuned version of [jestemleon/bert-nlp-project-google](https://huggingface.co/jestemleon/bert-nlp-project-google) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4440
  - Accuracy: 0.8938
  - F1: 0.8717
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.3989        | 0.37  | 120  | 0.3025          | 0.875    | 0.8444 |
  | 0.3088        | 0.75  | 240  | 0.3063          | 0.8812   | 0.8577 |
  | 0.2669        | 1.12  | 360  | 0.3307          | 0.9      | 0.8801 |
  | 0.2058        | 1.5   | 480  | 0.3502          | 0.9      | 0.8769 |
  | 0.1626        | 1.87  | 600  | 0.3725          | 0.8953   | 0.8719 |
  | 0.1526        | 2.24  | 720  | 0.3813          | 0.8969   | 0.875  |
  | 0.0992        | 2.62  | 840  | 0.4399          | 0.8953   | 0.8738 |
  | 0.101         | 2.99  | 960  | 0.4440          | 0.8938   | 0.8717 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  