Instructions to use carsonpoole/binary-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use carsonpoole/binary-embeddings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="carsonpoole/binary-embeddings")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("carsonpoole/binary-embeddings") model = AutoModel.from_pretrained("carsonpoole/binary-embeddings") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20dde50d8542eb7df856c93fdf11f947f929fe1ca281d5b1ffc2c2c386882791
- Size of remote file:
- 133 MB
- SHA256:
- baea2fade155b8f8367a2b97d11b0a65febef4fc5024c046848020597d637584
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