Text Ranking
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
sentence-transformers
English
bert
feature-extraction
reranker
cross-encoder
custom_code
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use jinaai/jina-reranker-v1-turbo-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-reranker-v1-turbo-en with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-reranker-v1-turbo-en", trust_remote_code=True) model = AutoModel.from_pretrained("jinaai/jina-reranker-v1-turbo-en", trust_remote_code=True, device_map="auto") - Transformers.js
How to use jinaai/jina-reranker-v1-turbo-en with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-ranking', 'jinaai/jina-reranker-v1-turbo-en'); - sentence-transformers
How to use jinaai/jina-reranker-v1-turbo-en with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("jinaai/jina-reranker-v1-turbo-en", trust_remote_code=True) 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
Ctrl+K