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license: apache-2.0
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<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
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<p align="center">
<b>Task-oriented finetuning for better embeddings on neural search</b>
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The text embedding suit trained by [Jina AI](https://github.com/jina-ai), [Finetuner team](https://github.com/jina-ai/finetuner).
## Intented Usage & Model Info
`jina-embedding-s-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
With a compact size of just 35 million parameters,
the model enables lightning-fast inference while still delivering impressive performance.
Additionally, we provide the following options:
- jina-embedding-b-en-v1: 110 million parameters.
- jina-embedding-l-en-v1: 800 million parameters.
- jina-embedding-xl-en-v1: 3 billion parameters.
- jina-embedding-xxl-en-v1: 11 billion parameters.
## Data & Parameters
More info will be released together with the technique report.
## Metrics
## Usage |