jina-reranker-multilingual

About

What jina-reranker-multilingual is

jina-reranker-v2-base-multilingual Intended Usage & Model Info The Jina Reranker v2 (jina-reranker-v2-base-multilingual) is a transformer-based model that has been fine-tuned for text reranking task, which is a crucial component in many information retrieval systems. It is a cross-encoder model that takes a query and a document pair as input and outputs a score indicating the relevance of the document to the query. The model is trained on a large dataset of query-document pairs and is capable of reranking documents in multiple languages with high accuracy. Compared with the state-of-the-art reranker models, including the previous released jina-reranker-v1-base-en, the Jina Reranker v2 model has demonstrated competitiveness across a series of benchmarks targeting for text retrieval, multilingual capability, function-calling-aware and text-to-SQL-aware reranking, and code retrieval tasks. The jina-reranker-v2-base-multilingual model is capable of handling long texts with a context length of up to 1024 tokens, enabling the processing of extensive inputs. To enable the model to handle long texts that exceed 1024 tokens, the model uses a sliding window approach to chunk the input text into smaller pieces and rerank each chunk separately. The model is also equipped with a flash attention mechanism, which significantly improves the model's performance. Usage This model repository is licenced for research and evaluation purposes under CC-BY-NC-4.0. For commercial usage, please refer to Jina AI's APIs, AWS Sagemaker or Azure Marketplace offerings. Please contact us for any further clarifications. 1. The easiest way to use jina-reranker-v2-base-multilingual is to call Jina AI's Reranker API. 2. You can also use the transformers library to interact with the model programmatically. Before you start, install the transformers and einops libraries: And then: The scores will be a list of floats, where each float represents the relevance score of the corresponding document to the query. Higher scores indicate higher relevance. For instance the returning scores in this case will be: The model gives high relevance scores to the documents that are most relevant to the query regardl

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys jina-reranker-multilingual for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-reranker-multilingual below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"jina-reranker-multilingual","input":"text to embed"}'

Details

Languages, data & research

Licence

Commercial licence needed

Sources

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