Instructions to use liddlefish/PrivacyEmbedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liddlefish/PrivacyEmbedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liddlefish/PrivacyEmbedder")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("liddlefish/PrivacyEmbedder") model = AutoModelForMultimodalLM.from_pretrained("liddlefish/PrivacyEmbedder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e50f60ea08a8e319beac3e798c8722679c03612461ed0a2e8c2a0e4f515fdd8a
- Size of remote file:
- 499 MB
- SHA256:
- 8bba109060f8f5c4e2e3bff447c3b78750e13bf3272ce89f1aa0cace7a593ff4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.