readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: detect-femicide-news-xlmr-nl-fft-freeze2
    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-nl-fft-freeze2
  
  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: 0.4119
  - Accuracy: 0.8571
  - Precision Neg: 0.85
  - Precision Pos: 0.875
  - Recall Neg: 0.9444
  - Recall Pos: 0.7
  - F1 Score Neg: 0.8947
  - F1 Score Pos: 0.7778
  
  ## 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: 24
  - eval_batch_size: 8
  - seed: 1996
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:|
  | 1.3215        | 1.0   | 23   | 1.0782          | 0.75     | 0.7391        | 0.8           | 0.9444     | 0.4        | 0.8293       | 0.5333       |
  | 1.0955        | 2.0   | 46   | 0.9057          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.9344        | 3.0   | 69   | 0.7420          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.8303        | 4.0   | 92   | 0.5952          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.6728        | 5.0   | 115  | 0.5078          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.649         | 6.0   | 138  | 0.4546          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.6008        | 7.0   | 161  | 0.4454          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.5439        | 8.0   | 184  | 0.4495          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.5557        | 9.0   | 207  | 0.4479          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.5637        | 10.0  | 230  | 0.4470          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.5709        | 11.0  | 253  | 0.4500          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.5496        | 12.0  | 276  | 0.4456          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5586        | 13.0  | 299  | 0.4484          | 0.8214   | 0.8095        | 0.8571        | 0.9444     | 0.6        | 0.8718       | 0.7059       |
  | 0.562         | 14.0  | 322  | 0.4435          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5555        | 15.0  | 345  | 0.4427          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5449        | 16.0  | 368  | 0.4404          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.555         | 17.0  | 391  | 0.4384          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5541        | 18.0  | 414  | 0.4383          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5463        | 19.0  | 437  | 0.4379          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5548        | 20.0  | 460  | 0.4357          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5365        | 21.0  | 483  | 0.4342          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5473        | 22.0  | 506  | 0.4308          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.5467        | 23.0  | 529  | 0.4309          | 0.8571   | 0.85          | 0.875         | 0.9444     | 0.7        | 0.8947       | 0.7778       |
  | 0.543         | 24.0  | 552  | 0.4312       