Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use kev216/sentence-embedding-LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kev216/sentence-embedding-LaBSE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kev216/sentence-embedding-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] - Transformers
How to use kev216/sentence-embedding-LaBSE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kev216/sentence-embedding-LaBSE") model = AutoModel.from_pretrained("kev216/sentence-embedding-LaBSE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e0feb724908ee0b3d682746df18fcc9a158af6f68caf7d3107cf2bc4e87a296
- Size of remote file:
- 1.88 GB
- SHA256:
- c9e7daf739f87c2168a6d1baffdae5782eceb03eb6de61950284a925234c6865
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