
# 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
```
{"language": ["en", "nl", "fr", "pt", "it", "es", "de", "da", "pl", "af"], "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}]}
transformers pytorch distilbert text-classification en nl fr pt it es de da pl af dataset:jigsaw_toxicity_pred autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #distilbert #text-classification #en #nl #fr #pt #it #es #de #da #pl #af #dataset-jigsaw_toxicity_pred #autotrain_compatible #endpoints_compatible #has_space #region-us 


# citizenlab/distilbert-base-multilingual-cased-toxicity

This is multilingual Distil-Bert model sequence classifier trained based on JIGSAW Toxic Comment Classification Challenge dataset.

## How to use it



## Evaluation

### Accuracy


# citizenlab/distilbert-base-multilingual-cased-toxicity

This is multilingual Distil-Bert model sequence classifier trained based on JIGSAW Toxic Comment Classification Challenge dataset. ## How to use it ## Evaluation ### Accuracy
TAGS
#transformers #pytorch #distilbert #text-classification #en #nl #fr #pt #it #es #de #da #pl #af #dataset-jigsaw_toxicity_pred #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # citizenlab/distilbert-base-multilingual-cased-toxicity

This is multilingual Distil-Bert model sequence classifier trained based on JIGSAW Toxic Comment Classification Challenge dataset. ## How to use it ## Evaluation ### Accuracy
passage: TAGS
#transformers #pytorch #distilbert #text-classification #en #nl #fr #pt #it #es #de #da #pl #af #dataset-jigsaw_toxicity_pred #autotrain_compatible #endpoints_compatible #has_space #region-us 
# citizenlab/distilbert-base-multilingual-cased-toxicity

This is multilingual Distil-Bert model sequence classifier trained based on JIGSAW Toxic Comment Classification Challenge dataset.## How to use it## Evaluation### Accuracy