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
Commit ·
e615b58
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Parent(s): e8585d2
upload flax model
Browse files- flax_model.msgpack +3 -0
flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:4cbe50771a6b147d2da0beb6da1d80908a706cec2e2e06a09873649ed183e884
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size 1883714625
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