readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imdb
  metrics:
  - accuracy
  - f1
  model-index:
  - name: finetuned-base_mini
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: imdb
        type: imdb
        config: plain_text
        split: train
        args: plain_text
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9076
      - name: F1
        type: f1
        value: 0.9515621723631789
  ---
  
  <!-- 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. -->
  
  # finetuned-base_mini
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the imdb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3938
  - Accuracy: 0.9076
  - F1: 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: 3e-05
  - train_batch_size: 128
  - eval_batch_size: 128
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.354         | 2.55  | 500  | 0.2300          | 0.9116   | 0.9538 |
  | 0.2086        | 5.1   | 1000 | 0.3182          | 0.8815   | 0.9370 |
  | 0.1401        | 7.65  | 1500 | 0.2160          | 0.9241   | 0.9605 |
  | 0.0902        | 10.2  | 2000 | 0.4684          | 0.8722   | 0.9317 |
  | 0.0654        | 12.76 | 2500 | 0.4885          | 0.8747   | 0.9332 |
  | 0.043         | 15.31 | 3000 | 0.3938          | 0.9076   | 0.9516 |
  
  
  ### Framework versions
  
  - Transformers 4.25.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  