Instructions to use Jarbas/m2v-256-LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Jarbas/m2v-256-LaBSE with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Jarbas/m2v-256-LaBSE") - sentence-transformers
How to use Jarbas/m2v-256-LaBSE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jarbas/m2v-256-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] - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
9435278 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:2774774285a109186c0fa04346bb22e5449a7ce728684c7b992510eb6a916672
size 13627759
|