Instructions to use citizenlab/distilbert-base-multilingual-cased-toxicity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use citizenlab/distilbert-base-multilingual-cased-toxicity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="citizenlab/distilbert-base-multilingual-cased-toxicity")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("citizenlab/distilbert-base-multilingual-cased-toxicity") model = AutoModelForSequenceClassification.from_pretrained("citizenlab/distilbert-base-multilingual-cased-toxicity", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#3
by Horenskyih - opened
Model converted by the transformers' pt_to_tf CLI. All converted model outputs and hidden layers were validated against its PyTorch counterpart.
Maximum crossload output difference=3.576e-07; Maximum crossload hidden layer difference=5.722e-06;
Maximum conversion output difference=3.576e-07; Maximum conversion hidden layer difference=5.722e-06;