readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base-misogyny-en-out-of-sample-test
    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. -->
  
  # xlm-roberta-base-misogyny-en-out-of-sample-test
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.2143
  - Accuracy: 0.5868
  - F1: 0.5033
  - Precision: 0.5570
  - Recall: 0.4591
  - Mae: 0.4132
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
  | 0.2704        | 1.0   | 1138  | 1.0169          | 0.5924   | 0.4022 | 0.6071    | 0.3007 | 0.4076 |
  | 0.2552        | 2.0   | 2276  | 1.0994          | 0.5845   | 0.5141 | 0.5508    | 0.4820 | 0.4155 |
  | 0.2082        | 3.0   | 3414  | 1.6637          | 0.5853   | 0.4815 | 0.5601    | 0.4222 | 0.4147 |
  | 0.1824        | 4.0   | 4552  | 1.9495          | 0.5606   | 0.4482 | 0.5244    | 0.3914 | 0.4394 |
  | 0.1645        | 5.0   | 5690  | 1.8441          | 0.5792   | 0.4997 | 0.5457    | 0.4608 | 0.4208 |
  | 0.113         | 6.0   | 6828  | 2.3997          | 0.5928   | 0.4766 | 0.5758    | 0.4066 | 0.4072 |
  | 0.0755        | 7.0   | 7966  | 2.9149          | 0.5633   | 0.5223 | 0.5211    | 0.5235 | 0.4367 |
  | 0.0763        | 8.0   | 9104  | 2.8218          | 0.5762   | 0.5159 | 0.5384    | 0.4953 | 0.4238 |
  | 0.0657        | 9.0   | 10242 | 2.9956          | 0.5903   | 0.5068 | 0.5619    | 0.4615 | 0.4097 |
  | 0.0498        | 10.0  | 11380 | 3.2143          | 0.5868   | 0.5033 | 0.5570    | 0.4591 | 0.4132 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.9.0+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  