Instructions to use textdetox/xlmr-large-toxicity-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textdetox/xlmr-large-toxicity-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textdetox/xlmr-large-toxicity-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textdetox/xlmr-large-toxicity-classifier") model = AutoModelForSequenceClassification.from_pretrained("textdetox/xlmr-large-toxicity-classifier") - Inference
- Notebooks
- Google Colab
- Kaggle
Request: DOI
Can I have the DOI of this model?
Hello!
The model was developed as a part of TextDetox shared task. However, we do not have yet printed publication about our shared task. But if you need some citation now, you can cite these two papers:
Dementieva, Daryna, Nikolay Babakov, and Alexander Panchenko. "MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages." arXiv e-prints (2024): arXiv-2404.
Bevendorff, Janek, et al. "Overview of PAN 2024: multi-author writing style analysis, multilingual text detoxification, oppositional thinking analysis, and generative AI authorship verification." European Conference on Information Retrieval. Cham: Springer Nature Switzerland, 2024.
Otherwise, we will have a publication specifically about the shared task in 1-2 months.
Best,
Daryna