How to use jinaai/jina-embeddings-v2-base-de with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v2-base-de", trust_remote_code=True) 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]
How to use jinaai/jina-embeddings-v2-base-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-embeddings-v2-base-de", trust_remote_code=True)
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-de", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("jinaai/jina-embeddings-v2-base-de", trust_remote_code=True, device_map="auto")
How to use jinaai/jina-embeddings-v2-base-de with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'jinaai/jina-embeddings-v2-base-de');
Alicimo Alicimo