Instructions to use unitary/multilingual-toxic-xlm-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unitary/multilingual-toxic-xlm-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="unitary/multilingual-toxic-xlm-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unitary/multilingual-toxic-xlm-roberta") model = AutoModelForSequenceClassification.from_pretrained("unitary/multilingual-toxic-xlm-roberta", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2abb43d12b6962306d6ed618eac7b484a41f2496f0cb21881d5e02cf1a37a500
- Size of remote file:
- 1.11 GB
- SHA256:
- 52b8d8b84f25e00f4936a79b429ebf13116ea9a0128af8125b16bd6bd1cf8abc
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