Instructions to use Jarbas/m2v-256-multilingual-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Jarbas/m2v-256-multilingual-e5-large with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Jarbas/m2v-256-multilingual-e5-large") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use Jarbas/m2v-256-multilingual-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jarbas/m2v-256-multilingual-e5-large") 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
fbf7e4f | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:e50d57f2617dfe4425aa46562197e4953b6f10875ed63c32f1859a91fa544170
size 17081245
|