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-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mixedbread-ai/mxbai-rerank-large-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-rerank-large-v1") model = AutoModelForSequenceClassification.from_pretrained("mixedbread-ai/mxbai-rerank-large-v1", device_map="auto") - Transformers.js
How to use mixedbread-ai/mxbai-rerank-large-v1 with Transformers.js:
// npm i @huggingface/transformers import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers'; const tokenizer = await AutoTokenizer.from_pretrained('mixedbread-ai/mxbai-rerank-large-v1'); const model = await AutoModelForSequenceClassification.from_pretrained('mixedbread-ai/mxbai-rerank-large-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-large-v1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("mixedbread-ai/mxbai-rerank-large-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) - Notebooks
- Google Colab
- Kaggle
Does this model comes with multilingual support?
#4
by w3iw3i - opened
Hi, Does this model comes with multilingual support?
It seems to work quite well in a couple languages other than English (f.e. German), but wasn’t explicitly trained on multiple languages.
Multilingual reranker coming soon :)
juliuslipp changed discussion status to closed
Thanks! Looking forward to it :)