How to use mixedbread-ai/deepset-mxbai-embed-de-large-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mixedbread-ai/deepset-mxbai-embed-de-large-v1") 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 mixedbread-ai/deepset-mxbai-embed-de-large-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mixedbread-ai/deepset-mxbai-embed-de-large-v1")
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/deepset-mxbai-embed-de-large-v1") model = AutoModel.from_pretrained("mixedbread-ai/deepset-mxbai-embed-de-large-v1")
How to use mixedbread-ai/deepset-mxbai-embed-de-large-v1 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'mixedbread-ai/deepset-mxbai-embed-de-large-v1');
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