readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: BERT-evidence-types
    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. -->
  
  # BERT-evidence-types
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the evidence types dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.8008
  - Macro f1: 0.4227
  - Weighted f1: 0.6976
  - Accuracy: 0.7154
  - Balanced accuracy: 0.3876
  
  ## Training and evaluation data
  
  The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
  | 1.1148        | 1.0   | 125  | 1.0531          | 0.2566   | 0.6570      | 0.6705   | 0.2753            |
  | 0.7546        | 2.0   | 250  | 0.9725          | 0.3424   | 0.6947      | 0.7002   | 0.3334            |
  | 0.4757        | 3.0   | 375  | 1.1375          | 0.3727   | 0.7113      | 0.7184   | 0.3680            |
  | 0.2637        | 4.0   | 500  | 1.3585          | 0.3807   | 0.6836      | 0.6910   | 0.3805            |
  | 0.1408        | 5.0   | 625  | 1.6605          | 0.3785   | 0.6765      | 0.6872   | 0.3635            |
  | 0.0856        | 6.0   | 750  | 1.9703          | 0.3802   | 0.6890      | 0.7047   | 0.3704            |
  | 0.0502        | 7.0   | 875  | 2.1245          | 0.4067   | 0.6995      | 0.7169   | 0.3751            |
  | 0.0265        | 8.0   | 1000 | 2.2676          | 0.3756   | 0.6816      | 0.6925   | 0.3647            |
  | 0.0147        | 9.0   | 1125 | 2.4286          | 0.4052   | 0.6887      | 0.7062   | 0.3803            |
  | 0.0124        | 10.0  | 1250 | 2.5773          | 0.4084   | 0.6853      | 0.7040   | 0.3695            |
  | 0.0111        | 11.0  | 1375 | 2.5941          | 0.4146   | 0.6915      | 0.7085   | 0.3834            |
  | 0.0076        | 12.0  | 1500 | 2.6124          | 0.4157   | 0.6936      | 0.7078   | 0.3863            |
  | 0.0067        | 13.0  | 1625 | 2.7050          | 0.4139   | 0.6925      | 0.7108   | 0.3798            |
  | 0.0087        | 14.0  | 1750 | 2.6695          | 0.4252   | 0.7009      | 0.7169   | 0.3920            |
  | 0.0056        | 15.0  | 1875 | 2.7357          | 0.4257   | 0.6985      | 0.7161   | 0.3868            |
  | 0.0054        | 16.0  | 2000 | 2.7389          | 0.4249   | 0.6955      | 0.7116   | 0.3890            |
  | 0.0051        | 17.0  | 2125 | 2.7767          | 0.4197   | 0.6967      | 0.7146   | 0.3863            |
  | 0.004         | 18.0  | 2250 | 2.7947          | 0.4211   | 0.6977      | 0.7154   | 0.3876            |
  | 0.0041        | 19.0  | 2375 | 2.8030          | 0.4204   | 0.6953      | 0.7131   | 0.3855            |
  | 0.0042        | 20.0  | 2500 | 2.8008          | 0.4227   | 0.6976      | 0.7154   | 0.3876            |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  