readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base-misogyny-sexism-outdomain-fr-trans
    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-outdomain-fr-trans
  
  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: 2.2136
  - Accuracy: 0.2278
  - F1: 0.1824
  - Precision: 0.1034
  - Recall: 0.7747
  - Mae: 0.7722
  - Tn: 1238
  - Fp: 6532
  - Fn: 219
  - Tp: 753
  
  ## 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    | Tn   | Fp   | Fn  | Tp  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:----:|:----:|:---:|:---:|
  | 0.3894        | 1.0   | 2233 | 0.9776          | 0.1977   | 0.1936 | 0.1090    | 0.8663 | 0.8023 | 886  | 6884 | 130 | 842 |
  | 0.3352        | 2.0   | 4466 | 1.3608          | 0.1892   | 0.1898 | 0.1067    | 0.8539 | 0.8108 | 824  | 6946 | 142 | 830 |
  | 0.293         | 3.0   | 6699 | 2.0702          | 0.2038   | 0.1928 | 0.1086    | 0.8549 | 0.7962 | 951  | 6819 | 141 | 831 |
  | 0.2653        | 4.0   | 8932 | 2.2136          | 0.2278   | 0.1824 | 0.1034    | 0.7747 | 0.7722 | 1238 | 6532 | 219 | 753 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  