Instructions to use avrecum/refusal-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avrecum/refusal-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avrecum/refusal-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avrecum/refusal-classifier") model = AutoModelForSequenceClassification.from_pretrained("avrecum/refusal-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4d29cee18efc928fe120332240e5b2e28fcd1d81dcc6811d85fef4cb2197ab1
- Size of remote file:
- 5.24 kB
- SHA256:
- 56e54b1a346b21a1c1f8bc2d05b4771dd78dc579c555d0c90efbcbf0ecdb5bff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.