readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: detect-femicide-news-bert-nl-None
    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-bert-nl-None
  
  This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8162
  - Accuracy: 0.75
  - Precision Neg: 0.8235
  - Precision Pos: 0.6364
  - Recall Neg: 0.7778
  - Recall Pos: 0.7
  - F1 Score Neg: 0.8000
  - F1 Score Pos: 0.6667
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:|
  | 0.6636        | 1.0   | 23   | 0.6474          | 0.6429   | 0.8333        | 0.5           | 0.5556     | 0.8        | 0.6667       | 0.6154       |
  | 0.572         | 2.0   | 46   | 0.5653          | 0.6071   | 0.6842        | 0.4444        | 0.7222     | 0.4        | 0.7027       | 0.4211       |
  | 0.502         | 3.0   | 69   | 0.5601          | 0.6786   | 0.8462        | 0.5333        | 0.6111     | 0.8        | 0.7097       | 0.64         |
  | 0.4576        | 4.0   | 92   | 0.5199          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.3803        | 5.0   | 115  | 0.5219          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.3466        | 6.0   | 138  | 0.5125          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.3325        | 7.0   | 161  | 0.4930          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
  | 0.3022        | 8.0   | 184  | 0.5144          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
  | 0.2854        | 9.0   | 207  | 0.5588          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.2797        | 10.0  | 230  | 0.5700          | 0.6786   | 0.7647        | 0.5455        | 0.7222     | 0.6        | 0.7429       | 0.5714       |
  | 0.2645        | 11.0  | 253  | 0.5806          | 0.6786   | 0.7647        | 0.5455        | 0.7222     | 0.6        | 0.7429       | 0.5714       |
  | 0.2411        | 12.0  | 276  | 0.5642          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.2554        | 13.0  | 299  | 0.6364          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
  | 0.2682        | 14.0  | 322  | 0.5656          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.2429        | 15.0  | 345  | 0.6249          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.2368        | 16.0  | 368  | 0.5914          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.2398        | 17.0  | 391  | 0.7456          | 0.6786   | 0.8462        | 0.5333        | 0.6111     | 0.8        | 0.7097       | 0.64         |
  | 0.251         | 18.0  | 414  | 0.5602          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
  | 0.2403        | 19.0  | 437  | 0.5803          | 0.75     | 0.7895        | 0.6667        | 0.8333     | 0.6        | 0.8108       | 0.6316       |
  | 0.2237        | 20.0  | 460  | 0.8165          | 0.6786   | 0.9091        | 0.5294        | 0.5556     | 0.9        | 0.6897       | 0.6667       |
  | 0.2481        | 21.0  | 483  | 0.6195          | 0.7143   | 0.7778        | 0.6           | 0.7778     | 0.6        | 0.7778       | 0.6          |
  | 0.2357        | 22.0  | 506  | 0.7081          | 0.6429   | 0.75          | 0.5           | 0.6667     | 0.6        | 0.7059       | 0.5455       |
  | 0.2227        | 23.0  | 529  | 0.6786          | 0.6786   | 0.8           | 0.5385        | 0.6667     | 0.7        | 0.7273       | 0.6087       |
  | 0.2137        | 24.0  | 552  | 0.6567         