Instructions to use jpohhhh/biencoder_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jpohhhh/biencoder_embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jpohhhh/biencoder_embedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jpohhhh/biencoder_embedding") model = AutoModel.from_pretrained("jpohhhh/biencoder_embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2663aed2653dd965ff27dbfd801080b00184130e5118ab5d82b2d0e390a961b
- Size of remote file:
- 128 Bytes
- SHA256:
- 8f1477dbc12d625e3d64d8f2f9cb5bf693eb30bccabba930eb66d06d3a3bdd84
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