Instructions to use SetFit/deberta-v3-large__sst2__train-16-5 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-16-5 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-16-5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-5") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34b2406300a9024a62d99cce80c855c19cc9d3cb8046a67d43c14415d54e8cc3
- Size of remote file:
- 1.74 GB
- SHA256:
- cd1280483233613fbf9c211b465d1f61766aca20fcac70520ba317fca807708c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.