readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - ag_news
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: results
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: ag_news
        type: ag_news
        config: default
        split: train[:40000]
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8951
      - name: F1
        type: f1
        value: 0.8964447542636089
      - name: Precision
        type: precision
        value: 0.8978261707981314
      - name: Recall
        type: recall
        value: 0.896474840596734
  ---
  
  <!-- 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
  
  This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the ag_news dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3320
  - Accuracy: 0.8951
  - F1: 0.8964
  - Precision: 0.8978
  - Recall: 0.8965
  
  ## 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: 0.0003
  - train_batch_size: 64
  - eval_batch_size: 64
  - 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     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.2783        | 1.0   | 625  | 0.3046          | 0.8949   | 0.8960 | 0.8970    | 0.8963 |
  | 0.1878        | 2.0   | 1250 | 0.3139          | 0.8954   | 0.8971 | 0.8995    | 0.8965 |
  | 0.1311        | 3.0   | 1875 | 0.3320          | 0.8951   | 0.8964 | 0.8978    | 0.8965 |
  
  
  ### Framework versions
  
  - Transformers 4.22.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  