Instructions to use bergum/product_title_encoder_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/product_title_encoder_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bergum/product_title_encoder_binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bergum/product_title_encoder_binary") model = AutoModel.from_pretrained("bergum/product_title_encoder_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bergum/product_title_encoder_binary: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/bergum/product_title_encoder_binary/resolve/1a90f6d48a605aefb260d729a627ccdb5af2d69b/pytorch_model.bin
- Command line
-
hf download hf://bergum/product_title_encoder_binary@1a90f6d48a605aefb260d729a627ccdb5af2d69b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bergum/product_title_encoder_binary/resolve/1a90f6d48a605aefb260d729a627ccdb5af2d69b/pytorch_model.bin
90.9 MB
- Xet hash:
- c0e0def2abf9c479396fe9b08493c2d3ecd3e1061b66e6bb1ef932258cd9f808
- Size of remote file:
- 90.9 MB
- SHA256:
- 7b4d6b14a85d69e3413b4f5c6f6f8488af843912596734bfa316b74bbdea3920
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