Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
Transformers.js
German
English
xlm-roberta
sentence_embedding
feature_extraction
text-embeddings-inference
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")# 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") - 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
- Xet hash:
- 315d06f40e7eeb72de07a0e344e3f86c32f93a6d46a989c9b062629b362bd7f1
- Size of remote file:
- 488 MB
- SHA256:
- 6c5b63e660a7d008c4c84164729121ed79f64f8380ac61a279eed17c07d2165f
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