Instructions to use gokul-pv/deberta-base-OnionOrNot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokul-pv/deberta-base-OnionOrNot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokul-pv/deberta-base-OnionOrNot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokul-pv/deberta-base-OnionOrNot") model = AutoModelForSequenceClassification.from_pretrained("gokul-pv/deberta-base-OnionOrNot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27f00bd7eb24227a08bef84e1b60915c4812f0934a433a9ea57e5a31083d4b5f
- Size of remote file:
- 557 MB
- SHA256:
- efee33656fcd26f0f811db71e97b59607fa74c15c3ad20551ce703ad5438e159
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