Instructions to use liddlefish/privacyembeddingv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liddlefish/privacyembeddingv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liddlefish/privacyembeddingv2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liddlefish/privacyembeddingv2") model = AutoModel.from_pretrained("liddlefish/privacyembeddingv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0976bf27b65dfbd77862b156fd1175509e86e9cdc438d77bf3d2d36b356ce6e4
- Size of remote file:
- 499 MB
- SHA256:
- f2f81023b0951f098e9559809a0c8c8ff61a9ec1f7013ec2ffaf244047896d6f
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