Instructions to use SetFit/deberta-v3-large__sst2__train-8-5 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-8-5 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-8-5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-5") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44c2532ec36603ff3b9f29ddb7c733c889f2e9cc6dc780c0a7b56e62828d461d
- Size of remote file:
- 1.74 GB
- SHA256:
- 0b5fabd92ef1dfda773582174d0a9f1c2c142e670d50a35bb2973263eee8da52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.