readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base-misogyny-sexism-out-of-sample-test-opt-bal
    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-out-of-sample-test-opt-bal
  
  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: 3.2811
  - Accuracy: 0.6022
  - F1: 0.5689
  - Precision: 0.5624
  - Recall: 0.5756
  - Mae: 0.3978
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
  | 0.4434        | 1.0   | 1100  | 0.8792          | 0.5752   | 0.4897 | 0.5414    | 0.4469 | 0.4248 |
  | 0.3592        | 2.0   | 2200  | 1.0511          | 0.5882   | 0.4597 | 0.5723    | 0.3841 | 0.4118 |
  | 0.3351        | 3.0   | 3300  | 0.8862          | 0.5639   | 0.5437 | 0.5199    | 0.5698 | 0.4361 |
  | 0.2649        | 4.0   | 4400  | 1.5065          | 0.5931   | 0.5467 | 0.5556    | 0.5381 | 0.4069 |
  | 0.2252        | 5.0   | 5500  | 1.2637          | 0.5766   | 0.6084 | 0.5261    | 0.7212 | 0.4234 |
  | 0.2234        | 6.0   | 6600  | 1.6854          | 0.5832   | 0.5419 | 0.5432    | 0.5405 | 0.4168 |
  | 0.2288        | 7.0   | 7700  | 1.7353          | 0.5985   | 0.5917 | 0.5517    | 0.6380 | 0.4015 |
  | 0.2008        | 8.0   | 8800  | 1.8444          | 0.6152   | 0.5693 | 0.5814    | 0.5577 | 0.3848 |
  | 0.1765        | 9.0   | 9900  | 2.4235          | 0.5833   | 0.5508 | 0.5417    | 0.5601 | 0.4167 |
  | 0.2334        | 10.0  | 11000 | 2.0034          | 0.6002   | 0.5635 | 0.5611    | 0.5659 | 0.3998 |
  | 0.1561        | 11.0  | 12100 | 2.3651          | 0.5897   | 0.5772 | 0.5445    | 0.6142 | 0.4103 |
  | 0.1759        | 12.0  | 13200 | 2.8745          | 0.5855   | 0.5742 | 0.5402    | 0.6128 | 0.4145 |
  | 0.1306        | 13.0  | 14300 | 2.7506          | 0.5904   | 0.5830 | 0.5442    | 0.6278 | 0.4096 |
  | 0.1443        | 14.0  | 15400 | 2.7292          | 0.6061   | 0.5549 | 0.5725    | 0.5383 | 0.3939 |
  | 0.1124        | 15.0  | 16500 | 2.6693          | 0.6119   | 0.5744 | 0.5745    | 0.5742 | 0.3881 |
  | 0.0886        | 16.0  | 17600 | 2.8332          | 0.6052   | 0.5708 | 0.5661    | 0.5756 | 0.3948 |
  | 0.078         | 17.0  | 18700 | 3.0623          | 0.6054   | 0.5693 | 0.5668    | 0.5718 | 0.3946 |
  | 0.0955        | 18.0  | 19800 | 3.1543          | 0.5965   | 0.5725 | 0.5538    | 0.5925 | 0.4035 |
  | 0.0689        | 19.0  | 20900 | 3.3443          | 0.5971   | 0.5763 | 0.5537    | 0.6009 | 0.4029 |
  | 0.0669        | 20.0  | 22000 | 3.2811          | 0.6022   | 0.5689 | 0.5624    | 0.5756 | 0.3978 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.9.0+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  