readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base-misogyny-sexism-outdomain-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-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: 1.8950
  - Accuracy: 0.2562
  - F1: 0.1909
  - Precision: 0.1086
  - Recall: 0.7891
  - Mae: 0.7438
  - Tn: 1473
  - Fp: 6297
  - Fn: 205
  - Tp: 767
  
  ## 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  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:----:|:----:|:---:|:---:|
  | 0.3783        | 1.0   | 2229 | 1.3615          | 0.1719   | 0.1998 | 0.1120    | 0.9300 | 0.8281 | 599  | 7171 | 68  | 904 |
  | 0.3252        | 2.0   | 4458 | 1.4051          | 0.2099   | 0.1894 | 0.1069    | 0.8302 | 0.7901 | 1028 | 6742 | 165 | 807 |
  | 0.2737        | 3.0   | 6687 | 1.6457          | 0.2724   | 0.1906 | 0.1088    | 0.7706 | 0.7276 | 1632 | 6138 | 223 | 749 |
  | 0.2446        | 4.0   | 8916 | 1.8950          | 0.2562   | 0.1909 | 0.1086    | 0.7891 | 0.7438 | 1473 | 6297 | 205 | 767 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  