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
Download pytorch_model.bin from SetFit/deberta-v3-large__sst2__train-16-2: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/SetFit/deberta-v3-large__sst2__train-16-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SetFit/deberta-v3-large__sst2__train-16-2/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/SetFit/deberta-v3-large__sst2__train-16-2/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- 4e27f287e50cc548ec0474224049b60e51bbfaf5f31e1550c49249c5bba439cd
- Size of remote file:
- 1.74 GB
- SHA256:
- 305842ecc63c3b565b4e116b3ee1ead156d9ea047ac4be757645bd4f0a1275a2
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