Text Classification
Transformers
Safetensors
sentence-transformers
multilingual
qwen2_vl
feature-extraction
vidore
reranker
custom_code
🇪🇺 Region: EU
Instructions to use jinaai/jina-reranker-m0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-reranker-m0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jinaai/jina-reranker-m0", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("jinaai/jina-reranker-m0", trust_remote_code=True) model = AutoModel.from_pretrained("jinaai/jina-reranker-m0", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use jinaai/jina-reranker-m0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-reranker-m0", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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README.md
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| **Architecture** | Vision Language Model | Cross-Encoder |
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| **Base model** | Qwen2-VL-2B | Jina-XLM-RoBERTa |
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| **Parameters** | 2.
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| **Max context length** | 10,240 tokens (query + document) | 8,192 tokens |
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| **Image processing** | 768 × 28 × 28 patches (dynamic resolution) | ❌ |
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| **Multilingual support** | 29+ languages | Multiple languages |
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| **Architecture** | Vision Language Model | Cross-Encoder |
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| **Base model** | Qwen2-VL-2B | Jina-XLM-RoBERTa |
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| **Parameters** | 2.4 B | 278 M |
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| **Max context length** | 10,240 tokens (query + document) | 8,192 tokens |
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| **Image processing** | 768 × 28 × 28 patches (dynamic resolution) | ❌ |
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| **Multilingual support** | 29+ languages | Multiple languages |
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