Instructions to use SetFit/deberta-v3-large__sst2__train-16-3 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-16-3 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-16-3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-3") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-large__sst2__train-16-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SetFit/deberta-v3-large__sst2__train-16-3: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/SetFit/deberta-v3-large__sst2__train-16-3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SetFit/deberta-v3-large__sst2__train-16-3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SetFit/deberta-v3-large__sst2__train-16-3/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- f94428a4392724123820235a44d4cdfb3a41c6c8b6e1e3cda55cd3a346632cb1
- Size of remote file:
- 1.74 GB
- SHA256:
- bd1787941ac5f2fe78ec514bfe19fefe990cc59ad59673eeeec3cfeb65896351
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