Text Ranking
Transformers
ONNX
Safetensors
Transformers.js
sentence-transformers
English
deberta-v2
text-classification
reranker
text-embeddings-inference
Instructions to use mixedbread-ai/mxbai-rerank-xsmall-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mixedbread-ai/mxbai-rerank-xsmall-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-rerank-xsmall-v1") model = AutoModelForSequenceClassification.from_pretrained("mixedbread-ai/mxbai-rerank-xsmall-v1", device_map="auto") - Transformers.js
How to use mixedbread-ai/mxbai-rerank-xsmall-v1 with Transformers.js:
// npm i @huggingface/transformers import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers'; const tokenizer = await AutoTokenizer.from_pretrained('mixedbread-ai/mxbai-rerank-xsmall-v1'); const model = await AutoModelForSequenceClassification.from_pretrained('mixedbread-ai/mxbai-rerank-xsmall-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-xsmall-v1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-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
Add ONNX weights
#1
by Xenova HF Staff - opened
No description provided.
aamirshakir changed pull request status to merged
@Xenova
Did you test feature-extraction with these weights? It throws the following error for me:
tensor-impl.js:50 Uncaught (in promise) TypeError: dims[1] must be an integer, got: undefined
at calculateSize (tensor-impl.js:50:19)
at new Tensor (tensor-impl.js:146:22)
at new Tensor (tensor.js:78:33)
at mean_pooling (tensor.js:774:12)
at Function._call (pipelines.js:1186:84)
at async embed (worker.js:79:16)
at async self.onmessage (worker.js:458:25)
Reproducible here with mixedbread-ai/mxbai-rerank-xsmall-v1. The error applies to all onnx mixedbread embedding model versions.
Note that this is not a feature-extraction (embedding) model, it is a text-classification (reranker) model.
For example usage, please refer to the README (JavaScript example).
Oh of course, my bad - mixedbread-ai/mxbai-embed-large-v1 does indeed work as expected!
Thank you!