Sentence Similarity
Transformers
PyTorch
TensorFlow
JAX
Safetensors
bert
feature-extraction
sentence_embedding
multilingual
google
labse
text-embeddings-inference
Instructions to use setu4993/smaller-LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use setu4993/smaller-LaBSE with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("setu4993/smaller-LaBSE") model = AutoModel.from_pretrained("setu4993/smaller-LaBSE", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from setu4993/smaller-LaBSE: direct link, hf CLI and curl.
- Browser
- Download file 877 MB
-
https://huggingface.co/setu4993/smaller-LaBSE/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://setu4993/smaller-LaBSE/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/setu4993/smaller-LaBSE/resolve/main/flax_model.msgpack
877 MB
- Xet hash:
- b9721a054949c51dd5c64ccb5d9e4b69d8db409e623cdb02cd528ea73aedd177
- Size of remote file:
- 877 MB
- SHA256:
- fd35ca85ae5361125c52f076201ceee53a8f7b6b95923740707adb2cef327816
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.