README.md

metadata

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 sentiment classification model.

How to use it

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