onnx-community/distilbert-multilingual-toxicity-classifier-ONNX
distilbert-multilingual-toxicity-classifier (ONNX)
This is an ONNX version of gravitee-io/distilbert-multilingual-toxicity-classifier. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for text-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline
distillbert-multilingual-toxicity-classifier
This is a DistilBERT-based multilingual toxicity classifier fine-tuned using the gravitee-io/textdetox-multilingual-toxicity-dataset. The model supports a wide range of languages and is trained for toxicity classification ("not-toxic", "toxic").
We perform an 85/15 train-test split per language based on the textdetox dataset. All credits go to the authors of the original corpora.
Performance Overview
Original model
Quantized model (ONNX)
The quantized model maintains high accuracy and generalization on the evaluation set, with negligible performance loss for most languages.
🤗 Usage
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
import numpy as np
# Load model and tokenizer using optimum
model = ORTModelForSequenceClassification.from_pretrained(
"gravitee-io/distilbert-multilingual-toxicity-classifier",
file_name="model.quant.onnx"
)
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/distilbert-multilingual-toxicity-classifier")
# Tokenize input
text = "Your text here"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
# Run inference
outputs = model(**inputs)
logits = outputs.logits
# Optional: convert to probabilities
probs = 1 / (1 + np.exp(-logits))
print(probs)Github Repository
You can check details on how the model was fine-tuned and evaluated on the Github Repository
License
This model is licensed under OpenRAIL++
Citation
@inproceedings{dementieva2024overview,
title={Overview of the Multilingual Text Detoxification Task at PAN 2024},
author={Dementieva, Daryna and Moskovskiy, Daniil and Babakov, Nikolay and Ayele, Abinew Ali and Rizwan, Naquee and Schneider, Frolian and Wang, Xintog and Yimam, Seid Muhie and Ustalov, Dmitry and Stakovskii, Elisei and Smirnova, Alisa and Elnagar, Ashraf and Mukherjee, Animesh and Panchenko, Alexander},
booktitle={Working Notes of CLEF 2024 - Conference and Labs of the Evaluation Forum},
editor={Guglielmo Faggioli and Nicola Ferro and Petra Galu{ {s}}{ {c}}{'a}kov{'a} and Alba Garc{'i}a Seco de Herrera},
year={2024},
organization={CEUR-WS.org}
}
@inproceedings{dementieva-etal-2024-toxicity,
title = "Toxicity Classification in {U}krainian",
author = "Dementieva, Daryna and Khylenko, Valeriia and Babakov, Nikolay and Groh, Georg",
booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.woah-1.19/",
doi = "10.18653/v1/2024.woah-1.19",
pages = "244--255"
}
@inproceedings{DBLP:conf/ecir/BevendorffCCDEFFKMMPPRRSSSTUWZ24,
author = {Janek Bevendorff and et al.},
title = {Overview of {PAN} 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative {AI} Authorship Verification - Extended Abstract},
booktitle = {ECIR 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part {VI}},
series = {Lecture Notes in Computer Science},
volume = {14613},
pages = {3--10},
publisher = {Springer},
year = {2024},
doi = {10.1007/978-3-031-56072-9_1}
}