readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - imdb
  metrics:
  - accuracy
  model-index:
  - name: IMDB_XLNET_5E
    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: 0.94
  ---
  
  <!-- 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. -->
  
  # IMDB_XLNET_5E
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the imdb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3195
  - Accuracy: 0.94
  
  ## 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: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.3192        | 0.63  | 50   | 0.2033          | 0.94     |
  | 0.196         | 1.27  | 100  | 0.2036          | 0.9467   |
  | 0.1651        | 1.9   | 150  | 0.2106          | 0.9267   |
  | 0.0628        | 2.53  | 200  | 0.3531          | 0.92     |
  | 0.0865        | 3.16  | 250  | 0.2186          | 0.9533   |
  | 0.0436        | 3.8   | 300  | 0.2718          | 0.9533   |
  | 0.0254        | 4.43  | 350  | 0.3195          | 0.94     |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  