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mixedbread-ai
/
mxbai-rerank-base-v1

Text Ranking
Transformers
ONNX
Safetensors
Transformers.js
sentence-transformers
English
deberta-v2
text-classification
reranker
text-embeddings-inference
Model card Files Files and versions
xet
Community
10

Instructions to use mixedbread-ai/mxbai-rerank-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mixedbread-ai/mxbai-rerank-base-v1 with Transformers:

    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-rerank-base-v1")
    model = AutoModelForSequenceClassification.from_pretrained("mixedbread-ai/mxbai-rerank-base-v1", device_map="auto")
  • Transformers.js

    How to use mixedbread-ai/mxbai-rerank-base-v1 with Transformers.js:

    // npm i @huggingface/transformers
    import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers';
    
    const tokenizer = await AutoTokenizer.from_pretrained('mixedbread-ai/mxbai-rerank-base-v1');
    const model = await AutoModelForSequenceClassification.from_pretrained('mixedbread-ai/mxbai-rerank-base-v1');
    
    const query = 'Which planet is known as the Red Planet?';
    const documents = [
    	'Mars, known for its reddish appearance, is often referred to as the Red Planet.',
    	'Venus is often called the twin of Earth because of its similar size and proximity.',
    ];
    
    const inputs = tokenizer(new Array(documents.length).fill(query), { text_pair: documents, padding: true, truncation: true });
    const { logits } = await model(inputs);
    console.log(logits.sigmoid().tolist()); // one relevance score per document
  • sentence-transformers

    How to use mixedbread-ai/mxbai-rerank-base-v1 with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("mixedbread-ai/mxbai-rerank-base-v1")
    
    query = "Which planet is known as the Red Planet?"
    passages = [
    	"Venus is often called Earth's twin because of its similar size and proximity.",
    	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
    	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
    ]
    
    scores = model.predict([(query, passage) for passage in passages])
    print(scores)
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
mxbai-rerank-base-v1 / onnx
983 MB
Ctrl+K
Ctrl+K
  • 7 contributors
History: 1 commit
Aamir
Xenova's picture
Xenova HF Staff
Add ONNX weights (#1)
5e69dc1 verified over 2 years ago
  • model.onnx
    739 MB
    xet
    Add ONNX weights (#1) over 2 years ago
  • model_quantized.onnx
    244 MB
    xet
    Add ONNX weights (#1) over 2 years ago
  • quantize_config.json
    1.19 kB
    Add ONNX weights (#1) over 2 years ago