Text Ranking
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
sentence-transformers
multilingual
text-classification
reranker
cross-encoder
custom_code
🇪🇺 Region: EU
Instructions to use jinaai/jina-reranker-v2-base-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-reranker-v2-base-multilingual with Transformers:
# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("jinaai/jina-reranker-v2-base-multilingual", trust_remote_code=True, device_map="auto") - Transformers.js
How to use jinaai/jina-reranker-v2-base-multilingual with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-ranking', 'jinaai/jina-reranker-v2-base-multilingual'); - sentence-transformers
How to use jinaai/jina-reranker-v2-base-multilingual with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("jinaai/jina-reranker-v2-base-multilingual", 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
Long Context Size Chunk
#27
by CVHvn - opened
With reranker model, I found all model only have 512 or 1024 context size. This is very small when compare with embedding model (like jina v3, openai v3). How we can re-ranking relevant chunks when we split chunk with long context size?