Instructions to use SetFit/deberta-v3-large__sst2__train-8-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/deberta-v3-large__sst2__train-8-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/deberta-v3-large__sst2__train-8-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-2") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fea19c30fe9c54060b2d744231f5d635c6ec2554ad230bba8286f9a84878f34a
- Size of remote file:
- 1.74 GB
- SHA256:
- 7a11d47e0e884157c96cb3da70726ebcd2989381236b03d9d22340d683580514
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.