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distilbert-base-multilingual-cased-toxicity
citizenlab/distilbert-base-multilingual-cased-toxicity
distilbert-base-multilingual-cased-toxicity is a text classification model from citizenlab. Use it when you need a label for a piece of text. It is set up for transformers.
This is multilingual Distil-Bert model sequence classifier trained based on JIGSAW Toxic Comment Classification Challenge dataset.
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Dec 2, 2022
- SHA
- b4532a8b
Try a prompt
Datasets
Model card
From the Hugging Face model README
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
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
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Languages
- en, nl, fr, pt, it, es
- Hf Inference
- live
- Created
- Mar 2, 2022
- Updated
- Dec 2, 2022