readme: ---
  pipeline_type: "text-classification"
  
  widget:
  - text: "this is a lovely message"
    example_title: "Example 1"
    multi_class: false
  - text: "you are an idiot and you and your family should go back to your country"
    example_title: "Example 2"
    multi_class: false
  
  
  language: 
    - en
    - nl
    - fr
    - pt
    - it
    - es
    - de
    - da
    - pl
    - af
    
  datasets:
  - jigsaw_toxicity_pred
  metrics:
  - F1 Accuracy
  ---
  
  # citizenlab/distilbert-base-multilingual-cased-toxicity
  
  This is multilingual Distil-Bert model sequence classifier trained based on [JIGSAW Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) dataset.
  
  ## How to use it
  
  ```python
  from transformers import pipeline
  
  model_path = "citizenlab/distilbert-base-multilingual-cased-toxicity"
  
  toxicity_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
  toxicity_classifier("this is a lovely message")
  > [{'label': 'not_toxic', 'score': 0.9954179525375366}]
  
  toxicity_classifier("you are an idiot and you and your family should go back to your country")
  > [{'label': 'toxic', 'score': 0.9948776960372925}]
  
  ```
  
  ## Evaluation
  
  ### Accuracy
  
  ```
    Accuracy Score = 0.9425
  F1 Score (Micro) = 0.9450549450549449
  F1 Score (Macro) = 0.8491432341169309
  ```