---
language:
- en
- nl
- fr
- pt
- it
- es
- de
- da
- pl
- af
- multilingual
datasets:
- jigsaw_toxicity_pred
metrics:
- F1 Accuracy
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
---

# 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
```