Instructions to use SetFit/deberta-v3-large__sst2__train-32-0 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-32-0 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-32-0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-32-0") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-32-0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edf9862ae4ca74a801846fad5608b9501b0aefc1715a787e7e361210ca4528a9
- Size of remote file:
- 3.06 kB
- SHA256:
- 4f99373601a57b7ea767844c9a61fc1a0606e8d92b0866313dbfea62fe68276a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.