readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: CR_XLNet_5E
    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. -->
  
  # CR_XLNet_5E
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6034
  - Accuracy: 0.9067
  
  ## 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: 16
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5384        | 0.33  | 50   | 0.4165          | 0.8533   |
  | 0.3633        | 0.66  | 100  | 0.3059          | 0.8867   |
  | 0.2642        | 0.99  | 150  | 0.2582          | 0.9267   |
  | 0.2626        | 1.32  | 200  | 0.3324          | 0.9      |
  | 0.1859        | 1.66  | 250  | 0.4076          | 0.9067   |
  | 0.2631        | 1.99  | 300  | 0.4334          | 0.8867   |
  | 0.1449        | 2.32  | 350  | 0.4264          | 0.9      |
  | 0.1815        | 2.65  | 400  | 0.4334          | 0.8933   |
  | 0.1316        | 2.98  | 450  | 0.4436          | 0.9      |
  | 0.0725        | 3.31  | 500  | 0.6165          | 0.9      |
  | 0.0708        | 3.64  | 550  | 0.6737          | 0.8933   |
  | 0.0821        | 3.97  | 600  | 0.5777          | 0.9067   |
  | 0.0381        | 4.3   | 650  | 0.6052          | 0.9      |
  | 0.0441        | 4.64  | 700  | 0.5853          | 0.9133   |
  | 0.0237        | 4.97  | 750  | 0.6034          | 0.9067   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0
  - Datasets 2.3.2
  - Tokenizers 0.13.1
  