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
docs: update benchmark
#3
by jemfu - opened
README.md
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We evaluated Jina Reranker v2 on multiple benchmarks to ensure top-tier performance and search relevance.
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- NDCG@10 and MRR@10 measure ranking quality, with higher scores indicating better search results
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We evaluated Jina Reranker v2 on multiple benchmarks to ensure top-tier performance and search relevance.
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| Model Name | MKQA(nDCG@10, 26 langs) | BEIR(nDCG@10, 17 datasets) | MLDR(recall@10, 13 langs) | CodeSearchNet (MRR@10, 3 tasks) | AirBench (nDCG@10, zh/en) | ToolBench (recall@3, 3 tasks) | TableSearch (recall@3) |
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| jina-reranker-v2-multilingual | 54.83 | 53.17 | 68.95 | 71.36 | 60.95 | 77.75 | 93.31 |
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| bge-reranker-v2-m3 | 54.17 | 53.65 | 59.73 | 62.86 | 61.28 | 78.46 | 74.86 |
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| mmarco-mMiniLMv2-L12-H384-v1 | 53.37 | 45.40 | 28.91 | 51.78 | 56.46 | 58.39 | 53.60 |
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| jina-reranker-v1-base-en | - | 52.45 | - | - | - | 74.13 | 72.89 |
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- NDCG@10 and MRR@10 measure ranking quality, with higher scores indicating better search results
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