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
- Xet hash:
- b113f40d594f69e6cd7ec73f3ec000438c655ac3cc25c68b3391da6e57553c3e
- Size of remote file:
- 13.6 MB
- SHA256:
- 2774774285a109186c0fa04346bb22e5449a7ce728684c7b992510eb6a916672
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.