Instructions to use bergum/product_title_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/product_title_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bergum/product_title_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bergum/product_title_encoder") model = AutoModel.from_pretrained("bergum/product_title_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bergum/product_title_encoder: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/bergum/product_title_encoder/resolve/b4e10475025d4cfd717022f1789986aa49f9d991/pytorch_model.bin
- Command line
-
hf download hf://bergum/product_title_encoder@b4e10475025d4cfd717022f1789986aa49f9d991/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bergum/product_title_encoder/resolve/b4e10475025d4cfd717022f1789986aa49f9d991/pytorch_model.bin
90.9 MB
- Xet hash:
- c94424de19c71e7c96f211e47865d30a457b26fd5f9dc454d67b8342d01d04d6
- Size of remote file:
- 90.9 MB
- SHA256:
- fdfc7d567fa56bc5cd94515eb32fb153180b27d00515165ab4ceb03e241b95d9
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