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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d92d8f898c3b48fb04bc7b6edc0b39ffa07045c314b5e65dadf94b82f06d4fa
- Size of remote file:
- 499 MB
- SHA256:
- 28ab9642aed9a2157fa07ca3166c279a2c380e511927cb361631c8d977f1f5a1
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