readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: xlnet-base-cased_fold_4_binary_v1
    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. -->
  
  # xlnet-base-cased_fold_4_binary_v1
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5724
  - F1: 0.8315
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 25
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 1.0   | 289  | 0.4043          | 0.8009 |
  | 0.4373        | 2.0   | 578  | 0.4093          | 0.8260 |
  | 0.4373        | 3.0   | 867  | 0.5084          | 0.8206 |
  | 0.2707        | 4.0   | 1156 | 0.5945          | 0.8087 |
  | 0.2707        | 5.0   | 1445 | 0.6389          | 0.8251 |
  | 0.1691        | 6.0   | 1734 | 0.8131          | 0.8156 |
  | 0.1012        | 7.0   | 2023 | 0.9865          | 0.8190 |
  | 0.1012        | 8.0   | 2312 | 1.1356          | 0.8342 |
  | 0.0506        | 9.0   | 2601 | 1.0624          | 0.8369 |
  | 0.0506        | 10.0  | 2890 | 1.2604          | 0.8255 |
  | 0.0384        | 11.0  | 3179 | 1.2648          | 0.8183 |
  | 0.0384        | 12.0  | 3468 | 1.3763          | 0.8158 |
  | 0.0318        | 13.0  | 3757 | 1.4966          | 0.8217 |
  | 0.0221        | 14.0  | 4046 | 1.3889          | 0.8250 |
  | 0.0221        | 15.0  | 4335 | 1.4014          | 0.8284 |
  | 0.0145        | 16.0  | 4624 | 1.5321          | 0.8289 |
  | 0.0145        | 17.0  | 4913 | 1.4914          | 0.8233 |
  | 0.0172        | 18.0  | 5202 | 1.3946          | 0.8314 |
  | 0.0172        | 19.0  | 5491 | 1.5032          | 0.8269 |
  | 0.0135        | 20.0  | 5780 | 1.5111          | 0.8328 |
  | 0.0087        | 21.0  | 6069 | 1.4899          | 0.8318 |
  | 0.0087        | 22.0  | 6358 | 1.5562          | 0.8311 |
  | 0.0061        | 23.0  | 6647 | 1.5384          | 0.8327 |
  | 0.0061        | 24.0  | 6936 | 1.5798          | 0.8304 |
  | 0.0052        | 25.0  | 7225 | 1.5724          | 0.8315 |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  