Instructions to use Cameron/BERT-SBIC-offensive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cameron/BERT-SBIC-offensive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cameron/BERT-SBIC-offensive")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cameron/BERT-SBIC-offensive") model = AutoModelForSequenceClassification.from_pretrained("Cameron/BERT-SBIC-offensive") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30d1455aa8aa6ef039210f949db38c9d1f07cdfe87351b45384844f837a5bd7c
- Size of remote file:
- 433 MB
- SHA256:
- b7f32cc7a1790b5124373903c10bbd1855a29ac094c0eb1310e5f363c2b533dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.