Instructions to use khailai/roberta-offensive-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khailai/roberta-offensive-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="khailai/roberta-offensive-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("khailai/roberta-offensive-classifier") model = AutoModelForSequenceClassification.from_pretrained("khailai/roberta-offensive-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from khailai/roberta-offensive-classifier: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/khailai/roberta-offensive-classifier/resolve/main/tf_model.h5
- Command line
-
hf download hf://khailai/roberta-offensive-classifier/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/khailai/roberta-offensive-classifier/resolve/main/tf_model.h5
499 MB
- Xet hash:
- 63f9819b8f82e43cfa360b9b3c1b34158b73efb80b2f86a8060889cb94374053
- Size of remote file:
- 499 MB
- SHA256:
- 162394ea5bae48259dc3c7612f5000756f5dafd5df8e9c66004ec94de476d9bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.