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/twitter-xlm-roberta-base-sentiment-finetunned
  
  This is multilingual XLM-Roberta model sequence classifier fine tunned and based on [Cardiff NLP Group](cardiffnlp/twitter-roberta-base-sentiment) sentiment classification model.
  
  ## How to use it
  
  ```python
  from transformers import pipeline
  
  model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned"
  
  sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
  sentiment_classifier("this is a lovely message")
  > [{'label': 'Positive', 'score': 0.9918450713157654}]
  
  sentiment_classifier("you are an idiot and you and your family should go back to your country")
  > [{'label': 'Negative', 'score': 0.9849833846092224}]
  
  ```
  
  ## Evaluation
  
  ```
                precision    recall  f1-score   support
  
      Negative       0.57      0.14      0.23        28
       Neutral       0.78      0.94      0.86       132
      Positive       0.89      0.80      0.85        51
  
      accuracy                           0.80       211
     macro avg       0.75      0.63      0.64       211
  weighted avg       0.78      0.80      0.77       211
  ```
  
  