readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlm-roberta-base-misogyny-sexism-tweets-AMI2018_ELG
    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-tweets-AMI2018_ELG
  
  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: 0.8141
  - Accuracy: 0.6824
  - F1: 0.6749
  - Precision: 0.6388
  - Recall: 0.7152
  - Mae: 0.3176
  - Tn: 352
  - Fp: 186
  - Fn: 131
  - Tp: 329
  
  ## 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  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:|
  | 0.5156        | 1.0   | 250  | 0.6834          | 0.6613   | 0.5888 | 0.6685    | 0.5261 | 0.3387 | 418 | 120 | 218 | 242 |
  | 0.3854        | 2.0   | 500  | 0.6871          | 0.6743   | 0.6667 | 0.6311    | 0.7065 | 0.3257 | 348 | 190 | 135 | 325 |
  | 0.3146        | 3.0   | 750  | 0.6641          | 0.6884   | 0.6810 | 0.6447    | 0.7217 | 0.3116 | 355 | 183 | 128 | 332 |
  | 0.2222        | 4.0   | 1000 | 0.8141          | 0.6824   | 0.6749 | 0.6388    | 0.7152 | 0.3176 | 352 | 186 | 131 | 329 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  