readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: detect-femicide-news-xlmr
    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. -->
  
  # detect-femicide-news-xlmr
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0161
  - Accuracy: 0.9973
  - Precision Neg: 0.9975
  - Precision Pos: 0.9967
  - Recall Neg: 0.9988
  - Recall Pos: 0.9933
  - F1 Score Neg: 0.9981
  - F1 Score Pos: 0.9950
  
  ## 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: 1e-05
  - train_batch_size: 128
  - eval_batch_size: 8
  - seed: 1996
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:|
  | 0.2758        | 1.0   | 204  | 0.1001          | 0.9718   | 0.9741        | 0.9654        | 0.9875     | 0.93       | 0.9808       | 0.9474       |
  | 0.0782        | 2.0   | 408  | 0.0505          | 0.9809   | 0.9839        | 0.9729        | 0.99       | 0.9567     | 0.9869       | 0.9647       |
  | 0.0501        | 3.0   | 612  | 0.0272          | 0.9927   | 0.9962        | 0.9834        | 0.9938     | 0.99       | 0.9950       | 0.9867       |
  | 0.0389        | 4.0   | 816  | 0.0201          | 0.9945   | 0.9938        | 0.9966        | 0.9988     | 0.9833     | 0.9963       | 0.9899       |
  | 0.031         | 5.0   | 1020 | 0.0175          | 0.9964   | 0.9963        | 0.9966        | 0.9988     | 0.99       | 0.9975       | 0.9933       |
  | 0.0235        | 6.0   | 1224 | 0.0161          | 0.9973   | 0.9975        | 0.9967        | 0.9988     | 0.9933     | 0.9981       | 0.9950       |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  