Instructions to use bergum/product_bullet_point_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/product_bullet_point_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bergum/product_bullet_point_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bergum/product_bullet_point_encoder") model = AutoModel.from_pretrained("bergum/product_bullet_point_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d972c6c033518314ae2e0ced07943eb591ceda595e3eb92ffd88af37a0253b3a
- Size of remote file:
- 90.9 MB
- SHA256:
- 85f34388459113ce496f4cb760dd1dcae638631328a7296da77dc2b75a8d2caa
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