readme: ---
  language: 
  - nl
  tags:
  - text-classification
  - pytorch
  widget:
  - text: "Ik heb je lief met heel mijn hart"
    example_title: "Non toxic comment 1"
  - text: "Dat is een goed punt, zo had ik het nog niet bekeken."
    example_title: "Non toxic comment 2"
  - text: "Wat de fuck zei je net tegen me, klootzak?"
    example_title: "Toxic comment 1"
  - text: "Rot op, vuile hoerenzoon."
    example_title: "Toxic comment 2"
  license: apache-2.0
  metrics:
  - Accuracy, F1 Score, Recall, Precision
  ---
  # RobBERT-dutch-base-toxic-comments
  
  ## Model description:
  This model was created with the purpose to detect toxic or potentially harmful comments.
  
  For this model, we finetuned a dutch RobBerta-based model called [RobBERT](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge).
  
  The original dataset was translated using the appropriate [MariantMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
  
  The model was trained for 2 epochs, on 90% of the dataset, with the following arguments:
  ```
  training_args = TrainingArguments(
      learning_rate=1e-5,
      per_device_train_batch_size=8,
      per_device_eval_batch_size=8,
      gradient_accumulation_steps=6,
      load_best_model_at_end=True,
      metric_for_best_model="recall",
      epochs=2,
      evaluation_strategy="steps",
      save_strategy="steps",
      save_total_limit=10,
      logging_steps=100,
      eval_steps=250,
      save_steps=250,
      weight_decay=0.001,
      report_to="wandb")
  ```
  
  ## Model Performance:
  
  Model evaluation was done on 1/10th of the dataset, which served as the test dataset.
  
  | Accuracy | F1 Score |  Recall  |  Precision  |
  | --- | --- | --- | --- |
  | 95.63 | 78.80 | 78.99 | 78.61 |
  
  ## Dataset:
  Unfortunately we cannot open-source the dataset, since we are bound by the underlying Jigsaw license.
  