How to use Horizon-Labs/multilingual-embedding-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Horizon-Labs/multilingual-embedding-base") sentences = [ "How tall is the Eiffel Tower?", "La tour Eiffel mesure 330 mètres.", "Der Eiffelturm ist das höchste Bauwerk von Paris.", "The Statue of Liberty is 93 metres tall.", "Ich esse gern Pizza." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5]
How to use Horizon-Labs/multilingual-embedding-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'Horizon-Labs/multilingual-embedding-base');
https://huggingface.co/Horizon-Labs/multilingual-embedding-base/resolve/main/sentence_bert_config.json
hf download hf://Horizon-Labs/multilingual-embedding-base/sentence_bert_config.json
curl -L -o sentence_bert_config.json https://huggingface.co/Horizon-Labs/multilingual-embedding-base/resolve/main/sentence_bert_config.json
