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, AutoModel tokenizer = AutoTokenizer.from_pretrained("liddlefish/PrivacyEmbedder") model = AutoModel.from_pretrained("liddlefish/PrivacyEmbedder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b02a52959b3eb8a47ab1c97eef1e4c86845ef4b12b465730942867871ddcf1af
- Size of remote file:
- 499 MB
- SHA256:
- d01c5aeec2a46ed9b7b5c7efb00b15f341fe37c2ba29419825d3728feefd7131
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