Instructions to use remzicam/privacy_intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remzicam/privacy_intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="remzicam/privacy_intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("remzicam/privacy_intent") model = AutoModelForSequenceClassification.from_pretrained("remzicam/privacy_intent") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0114d60d96f90df5adfdc2d77aa262acf397be304eaf06d7cfd57ec8bf4324bc
- Size of remote file:
- 499 MB
- SHA256:
- d137c74670db3e7e2e8de6e1fc458fbb2c72527ab6cdf7e003d5a0e223764b69
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