Instructions to use bergum/product_description_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/product_description_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bergum/product_description_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bergum/product_description_encoder") model = AutoModel.from_pretrained("bergum/product_description_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1868ba8574fb7c0765de7ab2921b80604c0259a3f8d49e03331cd48c0632ad46
- Size of remote file:
- 90.9 MB
- SHA256:
- 97178079397a98780f061ba4ce84381963d117f2cf3fbe1e3b5579437ea0934c
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