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:
- 9131ffe90f984cbde9985de717929de9f30ffc382c72afa018b7f7f3d0899839
- Size of remote file:
- 438 MB
- SHA256:
- 6384d75336913f1262dd77246ee177b9217b27591a30f13b292d9e59acc7cfdc
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