Instructions to use Adi2K/Priv-Consent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adi2K/Priv-Consent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Adi2K/Priv-Consent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Adi2K/Priv-Consent") model = AutoModelForSequenceClassification.from_pretrained("Adi2K/Priv-Consent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f6867fcfc95ea2c09996275e518898cedcd8062d206660bc722cdac440f22f4
- Size of remote file:
- 7.46 kB
- SHA256:
- 2ff6609aace6ac6ef7f1834260dc8a828ab1607933f7900a61f045e596097a88
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