library_name: transformers
license: apache-2.0
language:
- en
tags:
- reranker
- cross-encoder
Trained by Jina AI.
jina-reranker-v1-turbo-en
This model is designed for blazing-fast reranking while maintaining competitive performance. What's more, it leverages the power of our JinaBERT model as their foundation. JinaBERT itself is a unique variant of the BERT architecture that supports the symmetric bidirectional variant of ALiBi. This allows jina-reranker-v1-turbo-en to process significantly longer sequences of text compared to other reranking models, up to an impressive 8,192 tokens.
To achieve the remarkable speed, the jina-reranker-v1-turbo-en employ a technique called knowledge distillation. Here, a complex, but slower, model (like our original jina-reranker-v1-base-en) acts as a teacher, condensing its knowledge into a smaller, faster student model. This student retains most of the teacher's knowledge, allowing it to deliver similar accuracy in a fraction of the time.
Here's a breakdown of the reranker models we provide:
| Model Name | Layers | Hidden Size | Parameters (Millions) |
|---|---|---|---|
| jina-reranker-v1-base-en | 12 | 768 | 137.0 |
| jina-reranker-v1-turbo-en | 6 | 384 | 37.8 |
| jina-reranker-v1-tiny-en | 4 | 384 | 33.0 |
Usage
You can use Jina Reranker models directly from transformers package:
!pip install transformers
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(
'jinaai/jina-reranker-v1-turbo-en', num_labels=1, trust_remote_code=True
)
# Example query and documents
query = "Organic skincare products for sensitive skin"
documents = [
"Eco-friendly kitchenware for modern homes",
"Biodegradable cleaning supplies for eco-conscious consumers",
"Organic cotton baby clothes for sensitive skin",
"Natural organic skincare range for sensitive skin",
"Tech gadgets for smart homes: 2024 edition",
"Sustainable gardening tools and compost solutions",
"Sensitive skin-friendly facial cleansers and toners",
"Organic food wraps and storage solutions",
"All-natural pet food for dogs with allergies",
"Yoga mats made from recycled materials"
]
# construct sentence pairs
sentence_pairs = [[query, doc] for doc in documents]
scores = model.compute_score(sentence_pairs)
Contact
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