readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base-misogyny-sexism-indomain-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-indomain-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: 0.7902
  - Accuracy: 0.8418
  - F1: 0.1369
  - Precision: 0.2233
  - Recall: 0.0988
  - Mae: 0.1582
  - Tn: 6342
  - Fp: 334
  - Fn: 876
  - Tp: 96
  
  ## 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  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:----:|:---:|:---:|:---:|
  | 0.3636        | 1.0   | 2302 | 0.5509          | 0.8096   | 0.2155 | 0.2262    | 0.2058 | 0.1904 | 5992 | 684 | 772 | 200 |
  | 0.3192        | 2.0   | 4604 | 0.5863          | 0.8235   | 0.1667 | 0.2083    | 0.1389 | 0.1765 | 6163 | 513 | 837 | 135 |
  | 0.2791        | 3.0   | 6906 | 0.6889          | 0.8226   | 0.1566 | 0.1978    | 0.1296 | 0.1774 | 6165 | 511 | 846 | 126 |
  | 0.2314        | 4.0   | 9208 | 0.7902          | 0.8418   | 0.1369 | 0.2233    | 0.0988 | 0.1582 | 6342 | 334 | 876 | 96  |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  