Instructions to use SetFit/deberta-v3-large__sst2__train-8-3 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-8-3 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-8-3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-3") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03c88f049ae404d4011f558e725b8e1383dc721b0b1e35183a1afe4f3c8e0ac4
- Size of remote file:
- 3.06 kB
- SHA256:
- abaa47e67aac9fb638ab9c8db25492ed36f865957d9a0545e4a593088cbf8699
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.