readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: xlnet-base-cased_fold_8_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_8_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: 1.5333
  - F1: 0.8407
  
  ## 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   | 290  | 0.3866          | 0.8172 |
  | 0.4299        | 2.0   | 580  | 0.4215          | 0.8246 |
  | 0.4299        | 3.0   | 870  | 0.4765          | 0.8238 |
  | 0.2564        | 4.0   | 1160 | 0.7283          | 0.8350 |
  | 0.2564        | 5.0   | 1450 | 0.6825          | 0.8363 |
  | 0.1553        | 6.0   | 1740 | 0.9637          | 0.8339 |
  | 0.0893        | 7.0   | 2030 | 1.1392          | 0.8239 |
  | 0.0893        | 8.0   | 2320 | 1.1868          | 0.8231 |
  | 0.0538        | 9.0   | 2610 | 1.2180          | 0.8346 |
  | 0.0538        | 10.0  | 2900 | 1.2353          | 0.8253 |
  | 0.0386        | 11.0  | 3190 | 1.1883          | 0.8317 |
  | 0.0386        | 12.0  | 3480 | 1.2786          | 0.8375 |
  | 0.0289        | 13.0  | 3770 | 1.3725          | 0.8375 |
  | 0.0146        | 14.0  | 4060 | 1.3171          | 0.8463 |
  | 0.0146        | 15.0  | 4350 | 1.2323          | 0.8425 |
  | 0.0182        | 16.0  | 4640 | 1.3169          | 0.8485 |
  | 0.0182        | 17.0  | 4930 | 1.4424          | 0.8336 |
  | 0.0125        | 18.0  | 5220 | 1.4336          | 0.8385 |
  | 0.0102        | 19.0  | 5510 | 1.4888          | 0.8405 |
  | 0.0102        | 20.0  | 5800 | 1.5227          | 0.8419 |
  | 0.0035        | 21.0  | 6090 | 1.4994          | 0.8421 |
  | 0.0035        | 22.0  | 6380 | 1.4845          | 0.8424 |
  | 0.0047        | 23.0  | 6670 | 1.5006          | 0.8422 |
  | 0.0047        | 24.0  | 6960 | 1.5468          | 0.8422 |
  | 0.0042        | 25.0  | 7250 | 1.5333          | 0.8407 |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  