readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: bert-base-uncased-misogyny-sexism-4tweets-3e-05-0.05-3-misogyny-sexism-4tweets-3e-05-0.05-3
    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-base-uncased-misogyny-sexism-4tweets-3e-05-0.05-3-misogyny-sexism-4tweets-3e-05-0.05-3
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4264
  - Accuracy: 0.7094
  - F1: 0.7168
  - Precision: 0.6507
  - Recall: 0.7978
  - Mae: 0.2906
  - Tn: 341
  - Fp: 197
  - Fn: 93
  - Tp: 367
  
  ## 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: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    | Tn  | Fp  | Fn  | Tp  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:|
  | 0.2579        | 1.0   | 1346 | 1.0788          | 0.7014   | 0.7038 | 0.6484    | 0.7696 | 0.2986 | 346 | 192 | 106 | 354 |
  | 0.2174        | 2.0   | 2692 | 1.3393          | 0.7134   | 0.7229 | 0.6521    | 0.8109 | 0.2866 | 339 | 199 | 87  | 373 |
  | 0.2098        | 3.0   | 4038 | 1.4264          | 0.7094   | 0.7168 | 0.6507    | 0.7978 | 0.2906 | 341 | 197 | 93  | 367 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  