Sentence Similarity
Transformers
PyTorch
TensorFlow
JAX
Safetensors
bert
feature-extraction
sentence_embedding
multilingual
google
text-embeddings-inference
Instructions to use setu4993/LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use setu4993/LaBSE with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("setu4993/LaBSE") model = AutoModel.from_pretrained("setu4993/LaBSE", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from setu4993/LaBSE: direct link, hf CLI and curl.
- Browser
- Download file 1.88 GB
-
https://huggingface.co/setu4993/LaBSE/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://setu4993/LaBSE/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/setu4993/LaBSE/resolve/main/flax_model.msgpack
1.88 GB
- Xet hash:
- 581c85cdaa495008ca572e290c6642b766ec00c9aab052a8c429a7d061f6181d
- Size of remote file:
- 1.88 GB
- SHA256:
- 4cbe50771a6b147d2da0beb6da1d80908a706cec2e2e06a09873649ed183e884
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