Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
bert
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use sentence-transformers/LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/LaBSE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/LaBSE") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 581c85cdaa495008ca572e290c6642b766ec00c9aab052a8c429a7d061f6181d
- Size of remote file:
- 1.88 GB
- SHA256:
- 4cbe50771a6b147d2da0beb6da1d80908a706cec2e2e06a09873649ed183e884
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.