- Libraries
- sentence-transformers
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] - Transformers
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, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/deepset-mxbai-embed-de-large-v1")
model = AutoModelForMultimodalLM.from_pretrained("mixedbread-ai/deepset-mxbai-embed-de-large-v1") - Transformers.js
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'); - Inference
-
- Notebooks
- Google Colab
- Kaggle
