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")# pip install -U transformers accelerate # 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
Download sample_input.pkl from Adi2K/Priv-Consent: direct link, hf CLI and curl.
- Browser
- Download file 7.46 kB
-
https://huggingface.co/Adi2K/Priv-Consent/resolve/refs%2Fpr%2F1/sample_input.pkl
- Command line
-
hf download hf://Adi2K/Priv-Consent@refs/pr/1/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/Adi2K/Priv-Consent/resolve/refs%2Fpr%2F1/sample_input.pkl
7.46 kB
- Xet hash:
- 8f6867fcfc95ea2c09996275e518898cedcd8062d206660bc722cdac440f22f4
- Size of remote file:
- 7.46 kB
- SHA256:
- 2ff6609aace6ac6ef7f1834260dc8a828ab1607933f7900a61f045e596097a88
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