Instructions to use rasa/LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasa/LaBSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rasa/LaBSE")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rasa/LaBSE") model = AutoModel.from_pretrained("rasa/LaBSE") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 581c85cdaa495008ca572e290c6642b766ec00c9aab052a8c429a7d061f6181d
- Size of remote file:
- 1.88 GB
- SHA256:
- 4cbe50771a6b147d2da0beb6da1d80908a706cec2e2e06a09873649ed183e884
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