Instructions to use eevvgg/StanceBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eevvgg/StanceBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eevvgg/StanceBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eevvgg/StanceBERTa") model = AutoModelForSequenceClassification.from_pretrained("eevvgg/StanceBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 386841e61c8067ead1114dce8247234229151f85443fa6928c8aff59e85e1e24
- Size of remote file:
- 329 MB
- SHA256:
- e939f34865e6847d37e29cbaa7398be45868730f558c2cb7b8dd1d5cce805c2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.