Instructions to use SetFit/deberta-v3-large__sst2__train-16-2 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-16-2 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-16-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-2") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e27f287e50cc548ec0474224049b60e51bbfaf5f31e1550c49249c5bba439cd
- Size of remote file:
- 1.74 GB
- SHA256:
- 305842ecc63c3b565b4e116b3ee1ead156d9ea047ac4be757645bd4f0a1275a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.