Instructions to use mixedbread-ai/deepset-mxbai-embed-de-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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")# pip install -U transformers accelerate # 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", device_map="auto") - 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
fix: config.json
updated max_position_embeddings to be the max model input length.
Thanks @ouz-m
@juliuslipp @ouz-m @michaelfeil
I have my concerns that this does not work with Torch: https://github.com/UKPLab/sentence-transformers/issues/2873
How to reproduce:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("mixedbread-ai/deepset-mxbai-embed-de-large-v1")
Very simply, model.safetensors its embeddings.position_embeddings.weight has shape [514, 1024], which can't be loaded into a model with shape [512, 1024].
- Tom Aarsen
Hey @tomaarsen , you are right, the original XMLRoberta was trained with 514 max pos embeddings (see here). You can find the explanation here.
@michaelfeil I think the right fix would to fix Optimum instead of changing the model config. I will look closer into that.