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--- |
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license: apache-2.0 |
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language: |
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- en |
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inference: false |
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--- |
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<br><br> |
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<p align="center"> |
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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> |
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<p align="center"> |
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<b>The text embedding suit trained by Jina AI, Finetuner team.</b> |
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</p> |
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## Intented Usage & Model Info |
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`jina-embedding-b-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset. |
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This dataset consists of 380 million pairs of sentences, which include both query-document pairs. |
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These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. |
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The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs. |
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The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more. |
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With a standard size of 110 million parameters, |
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the model enables fast inference while delivering better performance than our small model. |
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It is recommended to use a single GPU for inference. |
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Additionally, we provide the following options: |
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- `jina-embedding-s-en-v1`: 35 million parameters. |
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- `jina-embedding-b-en-v1`: 110 million parameters **you are here**. |
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- `jina-embedding-l-en-v1`: 330 million parameters. |
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- `jina-embedding-xl-en-v1`: 1.2 billion parameters (soon). |
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- `jina-embedding-xxl-en-v1`: 6 billion parameters (soon). |
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## Data & Parameters |
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More info will be released together with the technique report. |
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## Metrics |
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We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI: |
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|Name|param |context| |
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|------------------------------|-----|------| |
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|all-minilm-l6-v2|33m |128| |
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|all-mpnet-base-v2 |110m |128| |
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|ada-embedding-002|Unknown/API based |8192| |
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|jina-embedding-s-en-v1|35m |512| |
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|jina-embedding-b-en-v1|110m |512| |
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|jina-embedding-l-en-v1|330m |512| |
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|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact| |
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|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----| |
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|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 | |
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|all-mpnet--base-v2|0.726|0.835|0.78 |0.857|0.8 |0.906|0.513 |0.875|0.656 | |
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|ada-embedding-002|0.698|0.833|0.761|0.861|0.86 |0.903|0.685 |0.876|0.726 | |
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|jina-embedding-s-en-v1|0.738|0.781|0.732|0.833|0.785|0.859|0.471 |0.852|0.567 | |
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|jina-embedding-b-en-v1|0.736|0.804|0.745|0.844|0.793|0.873|0.481 |0.87|0.616 | |
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|jina-embedding-l-en-v1|0.735|0.829|0.759|0.844|0.8|0.888|0.65 |0.876|0.645 | |
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For more tasks and metrics, please checkout [MTEB](https://huggingface.co/spaces/mteb/leaderboard) benchmark. |
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## Usage [WIP] |
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```python |
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!pip install finetuner[text] |
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import finetuner |
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model = finetuner.get_model('jinaai/jina-embedding-b-en-v1') |
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embeddings = model.encode(['sentence 1', 'sentence 2']) |
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``` |
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## Fine-tuning [WIP] |
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Please consider [Finetuner](https://github.com/jina-ai/finetuner). |
