readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: xlnet-base-cased_fold_1_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_1_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: 1.7812
  - F1: 0.8161
  
  ## 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   | 288  | 0.3938          | 0.8019 |
  | 0.4444        | 2.0   | 576  | 0.3945          | 0.8086 |
  | 0.4444        | 3.0   | 864  | 0.4738          | 0.8245 |
  | 0.2504        | 4.0   | 1152 | 0.6641          | 0.8123 |
  | 0.2504        | 5.0   | 1440 | 0.8714          | 0.7863 |
  | 0.159         | 6.0   | 1728 | 0.9177          | 0.8179 |
  | 0.0832        | 7.0   | 2016 | 1.1719          | 0.8129 |
  | 0.0832        | 8.0   | 2304 | 1.2858          | 0.8146 |
  | 0.046         | 9.0   | 2592 | 1.2557          | 0.8181 |
  | 0.046         | 10.0  | 2880 | 1.3332          | 0.8033 |
  | 0.0313        | 11.0  | 3168 | 1.2840          | 0.8112 |
  | 0.0313        | 12.0  | 3456 | 1.4164          | 0.8175 |
  | 0.0246        | 13.0  | 3744 | 1.3709          | 0.8143 |
  | 0.0173        | 14.0  | 4032 | 1.4319          | 0.8179 |
  | 0.0173        | 15.0  | 4320 | 1.5706          | 0.8195 |
  | 0.0138        | 16.0  | 4608 | 1.6072          | 0.8230 |
  | 0.0138        | 17.0  | 4896 | 1.7454          | 0.8192 |
  | 0.0016        | 18.0  | 5184 | 1.7281          | 0.8099 |
  | 0.0016        | 19.0  | 5472 | 1.7692          | 0.8151 |
  | 0.0088        | 20.0  | 5760 | 1.7376          | 0.8132 |
  | 0.0081        | 21.0  | 6048 | 1.7715          | 0.8086 |
  | 0.0081        | 22.0  | 6336 | 1.7400          | 0.8152 |
  | 0.0053        | 23.0  | 6624 | 1.7845          | 0.8099 |
  | 0.0053        | 24.0  | 6912 | 1.8096          | 0.8150 |
  | 0.0062        | 25.0  | 7200 | 1.7812          | 0.8161 |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  