Instructions to use SetFit/deberta-v3-large__sst2__train-16-0 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-16-0 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-16-0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-0") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SetFit/deberta-v3-large__sst2__train-16-0: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/SetFit/deberta-v3-large__sst2__train-16-0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SetFit/deberta-v3-large__sst2__train-16-0/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/SetFit/deberta-v3-large__sst2__train-16-0/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- a9bacfcd1abda123ef1251ea0d4031389698cd065083dcc67522af252e02d383
- Size of remote file:
- 1.74 GB
- SHA256:
- 5778b7ca85b3961f5f94b42744abadb0913ed3bddc0559186b5459e2d8eaa727
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