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
Download tokenizer.json from Jarbas/m2v-256-multilingual-e5-large: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Jarbas/m2v-256-multilingual-e5-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://Jarbas/m2v-256-multilingual-e5-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Jarbas/m2v-256-multilingual-e5-large/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 569a28d6a2673ea5e44b1c7415b0b7090f5cab1bb4ab3b43f1033c0d5f9cfeaa
- Size of remote file:
- 17.1 MB
- SHA256:
- e50d57f2617dfe4425aa46562197e4953b6f10875ed63c32f1859a91fa544170
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.