readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - sibyl
  datasets:
  - ag_news
  metrics:
  - accuracy
  model-index:
  - name: bert-base-uncased-ag_news
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: ag_news
        type: ag_news
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9375
  ---
  
  <!-- 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-ag_news
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3284
  - Accuracy: 0.9375
  
  ## 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: 8
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 7425
  - training_steps: 74250
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.5773        | 0.13  | 2000  | 0.3627          | 0.8875   |
  | 0.3101        | 0.27  | 4000  | 0.2938          | 0.9208   |
  | 0.3076        | 0.4   | 6000  | 0.3114          | 0.9092   |
  | 0.3114        | 0.54  | 8000  | 0.4545          | 0.9008   |
  | 0.3154        | 0.67  | 10000 | 0.3875          | 0.9083   |
  | 0.3095        | 0.81  | 12000 | 0.3390          | 0.9142   |
  | 0.2948        | 0.94  | 14000 | 0.3341          | 0.9133   |
  | 0.2557        | 1.08  | 16000 | 0.4573          | 0.9092   |
  | 0.258         | 1.21  | 18000 | 0.3356          | 0.9217   |
  | 0.2455        | 1.35  | 20000 | 0.3348          | 0.9283   |
  | 0.2361        | 1.48  | 22000 | 0.3218          | 0.93     |
  | 0.254         | 1.62  | 24000 | 0.3814          | 0.9033   |
  | 0.2528        | 1.75  | 26000 | 0.3628          | 0.9158   |
  | 0.2282        | 1.89  | 28000 | 0.3302          | 0.9308   |
  | 0.224         | 2.02  | 30000 | 0.3967          | 0.9225   |
  | 0.174         | 2.15  | 32000 | 0.3669          | 0.9333   |
  | 0.1848        | 2.29  | 34000 | 0.3435          | 0.9283   |
  | 0.19          | 2.42  | 36000 | 0.3552          | 0.93     |
  | 0.1865        | 2.56  | 38000 | 0.3996          | 0.9258   |
  | 0.1877        | 2.69  | 40000 | 0.3749          | 0.9258   |
  | 0.1951        | 2.83  | 42000 | 0.3963          | 0.9258   |
  | 0.1702        | 2.96  | 44000 | 0.3655          | 0.9317   |
  | 0.1488        | 3.1   | 46000 | 0.3942          | 0.9292   |
  | 0.1231        | 3.23  | 48000 | 0.3998          | 0.9267   |
  | 0.1319        | 3.37  | 50000 | 0.4292          | 0.9242   |
  | 0.1334        | 3.5   | 52000 | 0.4904          | 0.9192   |
  
  
  ### Framework versions
  
  - Transformers 4.10.2
  - Pytorch 1.7.1
  - Datasets 1.6.1
  - Tokenizers 0.10.3
  