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")# 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") - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
3620d8a | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:7b4d6b14a85d69e3413b4f5c6f6f8488af843912596734bfa316b74bbdea3920
size 90888945
|