Instructions to use SetFit/deberta-v3-large__sst2__train-8-9 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-8-9 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-8-9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-9") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-8-9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a92009217dadc54928a6ebbf2943680c17be0671a832e63be656555e5ce7aa2
- Size of remote file:
- 3.06 kB
- SHA256:
- 53ab51dd67d1736b8b13b115e2c16cbbeb9fe79eb084cff1ffeb74af9b585d8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.