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 model.safetensors 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/main/model.safetensors
- Command line
-
hf download hf://bergum/product_title_encoder_binary/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/bergum/product_title_encoder_binary/resolve/main/model.safetensors
90.9 MB
- Xet hash:
- 2f715d87b4d2bedcf4fd6cbd5949aa97c11376832be777e001b9f015cb10bd92
- Size of remote file:
- 90.9 MB
- SHA256:
- 48c199056ad980ea5db8dd9d686c25fee2f5ec2fd7c881eddd23bf38d41c1231
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