readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base-misogyny-sexism-decay0.05-fr-outdomain
    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-sexism-decay0.05-fr-outdomain
  
  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: 3.9568
  - Accuracy: 0.2475
  - F1: 0.1825
  - Precision: 0.1038
  - Recall: 0.7551
  - Mae: 0.7525
  
  ## 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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
  | 0.2095        | 1.0   | 2233 | 3.0824          | 0.2329   | 0.1872 | 0.1061    | 0.7942 | 0.7671 |
  | 0.1802        | 2.0   | 4466 | 2.1544          | 0.2577   | 0.1853 | 0.1055    | 0.7593 | 0.7423 |
  | 0.1479        | 3.0   | 6699 | 3.2552          | 0.2276   | 0.1824 | 0.1033    | 0.7747 | 0.7724 |
  | 0.1229        | 4.0   | 8932 | 3.9568          | 0.2475   | 0.1825 | 0.1038    | 0.7551 | 0.7525 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  