<br><br>
<p align="center">
<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">
</p>
<p align="center">
<b>Task-oriented finetuning for better embeddings on neural search</b>
</p>
<p align=center>
<a href="https://pypi.org/project/finetuner/"><img alt="PyPI" src="https://img.shields.io/pypi/v/finetuner?label=Release&style=flat-square"></a>
<a href="https://codecov.io/gh/jina-ai/finetuner"><img alt="Codecov branch" src="https://img.shields.io/codecov/c/github/jina-ai/finetuner/main?logo=Codecov&logoColor=white&style=flat-square"></a>
<a href="https://pypistats.org/packages/finetuner"><img alt="PyPI - Downloads from official pypistats" src="https://img.shields.io/pypi/dm/finetuner?style=flat-square"></a>
<a href="https://discord.jina.ai"><img src="https://img.shields.io/discord/1106542220112302130?logo=discord&logoColor=white&style=flat-square"></a>
</p>
<!-- start elevator-pitch -->
Fine-tuning is an effective way to improve performance on [neural search](https://jina.ai/news/what-is-neural-search-and-learn-to-build-a-neural-search-engine/) tasks.
However, setting up and performing fine-tuning can be very time-consuming and resource-intensive.
Jina AI's Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud.
With Finetuner, you can easily enhance the performance of pre-trained models,
making them production-ready [without extensive labeling](https://jina.ai/news/fine-tuning-with-low-budget-and-high-expectations/) or expensive hardware.
🎏 **Better embeddings**: Create high-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems,
clustering, duplication detection, anomaly detection, or other uses.
⏰ **Low budget, high expectations**: Bring considerable improvements to model performance, making the most out of as little as a few hundred training samples, and finish fine-tuning in as little as an hour.
📈 **Performance promise**: Enhance the performance of pre-trained models so that they deliver state-of-the-art performance on
domain-specific applications.
🔱 **Simple yet powerful**: Easy access to 40+ mainstream loss functions, 10+ optimizers, layer pruning, weight
freezing, dimensionality reduction, hard-negative mining, cross-modal models, and distributed training.
☁ **All-in-cloud**: Train using our GPU infrastructure, manage runs, experiments, and artifacts on Jina AI Cloud
without worrying about resource availability, complex integration, or infrastructure costs.
<!-- end elevator-pitch -->
## [Documentation](https://finetuner.jina.ai/)
## Pretrained Text Embedding Models
| name | parameter | dimension | Huggingface |
|------------------------|-----------|-----------|--------------------------------------------------------|
| jina-embedding-t-en-v1 | 14m | 312 | [link](https://huggingface.co/jinaai/jina-embedding-t-en-v1) |
| jina-embedding-s-en-v1 | 35m | 512 | [link](https://huggingface.co/jinaai/jina-embedding-s-en-v1) |
| jina-embedding-b-en-v1 | 110m | 768 | [link](https://huggingface.co/jinaai/jina-embedding-b-en-v1) |
| jina-embedding-l-en-v1 | 330m | 1024 | [link](https://huggingface.co/jinaai/jina-embedding-l-en-v1) |
## Benchmarks
<table>
<thead>
<tr>
<th>Model</th>
<th>Task</th>
<th>Metric</th>
<th>Pretrained</th>
<th>Finetuned</th>
<th>Delta</th>
<th>Run it!</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">BERT</td>
<td rowspan="2"><a href="https://www.kaggle.com/c/quora-question-pairs">Quora</a> Question Answering</td>
<td>mRR</td>
<td>0.835</td>
<td>0.967</td>
<td><span style="color:green">15.8%</span></td>
<td rowspan="2"><p align=center><a href="https://colab.research.google.com/drive/1Ui3Gw3ZL785I7AuzlHv3I0-jTvFFxJ4_?usp=sharing"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p></td>
</tr>
<tr>
<td>Recall</td>
<td>0.915</td>
<td>0.963</td>
<td><span style="color:green">5.3%</span></td>
</tr>
<tr>
<td rowspan="2">ResNet</td>
<td rowspan="2">Visual similarity search on <a href="https://sites.google.com/view/totally-looks-like-dataset">TLL</a></td>
<td>mAP</td>
<td>0.110</td>
<td>0.196</td>
<td><span style="color:green">78.2%</span></td>
<td rowspan="2"><p align=center><a href="https://colab.research.google.com/drive/1QuUTy3iVR-kTPljkwplKYaJ-NTCgPEc_?usp=sharing"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p></td>
</tr>
<tr>
<td>Recall</td>
<td>0.249</td>
<td>0.460</td>
<td><span style="color:green">84.7%</span></td>
</tr>
<tr>
<td rowspan="2">CLIP</td>
<td rowspan="2"><a href="https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html">Deep Fashion</a> text-to-image search</td>
<td>mRR</td>
<td>0.575</td>
<td>0.676</td>
<td><span style="color:green">17.4%</span></td>
<td rowspan="2"><p align=center><a href="https://colab.research.google.com/drive/1yKnmy2Qotrh3OhgwWRsMWPFwOSAecBxg?usp=sharing"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p></td>
</tr>
<tr>
<td>Recall</td>
<td>0.473</td>
<td>0.564</td>
<td><span style="color:green">19.2%</span></td>
</tr>
<tr>
<td rowspan="2">M-CLIP</td>
<td rowspan="2"><a href="https://xmrec.github.io/">Cross market</a> product recommendation (German)</td>
<td>mRR</td>
<td>0.430</td>
<td>0.648</td>
<td><span style="color:green">50.7%</span></td>
<td rowspan="2"><p align=center><a href="https://colab.research.google.com/drive/10Wldbu0Zugj7NmQyZwZzuorZ6SSAhtIo"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p></td>
</tr>
<tr>
<td>Recall</td>
<td>0.247</td>
<td>0.340</td>
<td><span style="color:green">37.7%</span></td>
</tr>
<tr>
<td rowspan="2">PointNet++</td>
<td rowspan="2"><a href="https://modelnet.cs.princeton.edu/">ModelNet40</a> 3D Mesh Search</td>
<td>mRR</td>
<td>0.791</td>
<td>0.891</td>
<td><span style="color:green">12.7%</span></td>
<td rowspan="2"><p align=center><a href="https://colab.research.google.com/drive/1lIMDFkUVsWMshU-akJ_hwzBfJ37zLFzU?usp=sharing"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p></td>
</tr>
<tr>
<td>Recall</td>
<td>0.154</td>
<td>0.242</td>
<td><span style="color:green">57.1%</span></td>
</tr>
</tbody>
</table>
<sub><sup>All metrics were evaluated for k@20 after training for 5 epochs using the Adam optimizer with learning rates of 1e-4 for ResNet, 1e-7 for CLIP and 1e-5 for the BERT models, 5e-4 for PointNet++</sup></sub>
<!-- start install-instruction -->
## Install
Make sure you have Python 3.8+ installed. Finetuner can be installed via `pip` by executing:
```bash
pip install -U finetuner
```
If you want to submit a fine-tuning job on the cloud, please use
```bash
pip install "finetuner[full]"
```
<!-- end install-instruction -->
> ⚠️ Starting with version 0.5.0, Finetuner computing is performed on Jina AI Cloud. The last local version is `0.4.1`.
> This version is still available for installation via `pip`. See [Finetuner git tags and releases](https://github.com/jina-ai/finetuner/releases).
<!-- start finetuner-articles -->
## Articles about Finetuner
Check out our published blogposts and tutorials to see Finetuner in action!
- [Fine-tuning with Low Budget and High Expectations](https://jina.ai/news/fine-tuning-with-low-budget-and-high-expectations/)
- [Hype and Hybrids: Search is more than Keywords and Vectors](https://jina.ai/news/hype-and-hybrids-multimodal-search-means-more-than-keywords-and-vectors-2/)
- [Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models](https://jina.ai/news/improving-search-quality-non-english-queries-fine-tuned-multilingual-clip-models/)
- [How Much Do We Get by Finetuning CLIP?](https://jina.ai/news/applying-jina-ai-finetuner-to-clip-less-data-smaller-models-higher-performance/)
<!-- end finetuner-articles -->
<!-- start citations -->
If you find Jina Embeddings useful in your research, please cite the following paper:
```text
@misc{günther2023jina,
title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
year={2023},
eprint={2307.11224},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!-- end citations -->
<!-- start support-pitch -->
## Support
- Use [Discussions](https://github.com/jina-ai/finetuner/discussions) to talk about your use cases, questions, and
support queries.
- Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
- Join our [Engineering All Hands](https://youtube.com/playlist?list=PL3UBBWOUVhFYRUa_gpYYKBqEAkO4sxmne) meet-up to discuss your use case and learn Jina AI new features.
- **When?** The second Tuesday of every month
- **Where?**
Zoom ([see our public events calendar](https://calendar.google.com/calendar/embed?src=c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com&ctz=Europe%2FBerlin)/[.ical](https://calendar.google.com/calendar/ical/c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com/public/basic.ics))
and [live stream on YouTube](https://youtube.com/c/jina-ai)
- Subscribe to the latest video tutorials on our [YouTube channel](https://youtube.com/c/jina-ai)
## Join Us
Finetuner is backed by [Jina AI](https://jina.ai) and licensed under [Apache-2.0](./LICENSE).
[We are actively hiring](https://jobs.jina.ai) AI engineers and solution engineers to build the next generation of
open-source AI ecosystems.
<!-- end support-pitch -->
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"description": "<br><br>\n\n<p align=\"center\">\n<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\">\n</p>\n\n\n<p align=\"center\">\n<b>Task-oriented finetuning for better embeddings on neural search</b>\n</p>\n\n<p align=center>\n<a href=\"https://pypi.org/project/finetuner/\"><img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/finetuner?label=Release&style=flat-square\"></a>\n<a href=\"https://codecov.io/gh/jina-ai/finetuner\"><img alt=\"Codecov branch\" src=\"https://img.shields.io/codecov/c/github/jina-ai/finetuner/main?logo=Codecov&logoColor=white&style=flat-square\"></a>\n<a href=\"https://pypistats.org/packages/finetuner\"><img alt=\"PyPI - Downloads from official pypistats\" src=\"https://img.shields.io/pypi/dm/finetuner?style=flat-square\"></a>\n<a href=\"https://discord.jina.ai\"><img src=\"https://img.shields.io/discord/1106542220112302130?logo=discord&logoColor=white&style=flat-square\"></a>\n</p>\n\n<!-- start elevator-pitch -->\n\nFine-tuning is an effective way to improve performance on [neural search](https://jina.ai/news/what-is-neural-search-and-learn-to-build-a-neural-search-engine/) tasks.\nHowever, setting up and performing fine-tuning can be very time-consuming and resource-intensive.\n\nJina AI's Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud.\nWith Finetuner, you can easily enhance the performance of pre-trained models,\nmaking them production-ready [without extensive labeling](https://jina.ai/news/fine-tuning-with-low-budget-and-high-expectations/) or expensive hardware.\n\n\ud83c\udf8f **Better embeddings**: Create high-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems,\nclustering, duplication detection, anomaly detection, or other uses.\n\n\u23f0 **Low budget, high expectations**: Bring considerable improvements to model performance, making the most out of as little as a few hundred training samples, and finish fine-tuning in as little as an hour.\n\n\ud83d\udcc8 **Performance promise**: Enhance the performance of pre-trained models so that they deliver state-of-the-art performance on \ndomain-specific applications.\n\n\ud83d\udd31 **Simple yet powerful**: Easy access to 40+ mainstream loss functions, 10+ optimizers, layer pruning, weight\nfreezing, dimensionality reduction, hard-negative mining, cross-modal models, and distributed training. \n\n\u2601 **All-in-cloud**: Train using our GPU infrastructure, manage runs, experiments, and artifacts on Jina AI Cloud\nwithout worrying about resource availability, complex integration, or infrastructure costs.\n\n<!-- end elevator-pitch -->\n\n## [Documentation](https://finetuner.jina.ai/)\n\n## Pretrained Text Embedding Models\n\n| name | parameter | dimension | Huggingface |\n|------------------------|-----------|-----------|--------------------------------------------------------|\n| jina-embedding-t-en-v1 | 14m | 312 | [link](https://huggingface.co/jinaai/jina-embedding-t-en-v1) |\n| jina-embedding-s-en-v1 | 35m | 512 | [link](https://huggingface.co/jinaai/jina-embedding-s-en-v1) |\n| jina-embedding-b-en-v1 | 110m | 768 | [link](https://huggingface.co/jinaai/jina-embedding-b-en-v1) |\n| jina-embedding-l-en-v1 | 330m | 1024 | [link](https://huggingface.co/jinaai/jina-embedding-l-en-v1) |\n\n## Benchmarks\n\n<table>\n<thead>\n <tr>\n <th>Model</th>\n <th>Task</th>\n <th>Metric</th>\n <th>Pretrained</th>\n <th>Finetuned</th>\n <th>Delta</th>\n <th>Run it!</th>\n </tr>\n</thead>\n<tbody>\n <tr>\n <td rowspan=\"2\">BERT</td>\n <td rowspan=\"2\"><a href=\"https://www.kaggle.com/c/quora-question-pairs\">Quora</a> Question Answering</td>\n <td>mRR</td>\n <td>0.835</td>\n <td>0.967</td>\n <td><span style=\"color:green\">15.8%</span></td>\n <td rowspan=\"2\"><p align=center><a href=\"https://colab.research.google.com/drive/1Ui3Gw3ZL785I7AuzlHv3I0-jTvFFxJ4_?usp=sharing\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a></p></td>\n </tr>\n <tr>\n <td>Recall</td>\n <td>0.915</td>\n <td>0.963</td>\n <td><span style=\"color:green\">5.3%</span></td>\n </tr>\n <tr>\n <td rowspan=\"2\">ResNet</td>\n <td rowspan=\"2\">Visual similarity search on <a href=\"https://sites.google.com/view/totally-looks-like-dataset\">TLL</a></td>\n <td>mAP</td>\n <td>0.110</td>\n <td>0.196</td>\n <td><span style=\"color:green\">78.2%</span></td>\n <td rowspan=\"2\"><p align=center><a href=\"https://colab.research.google.com/drive/1QuUTy3iVR-kTPljkwplKYaJ-NTCgPEc_?usp=sharing\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a></p></td>\n </tr>\n <tr>\n <td>Recall</td>\n <td>0.249</td>\n <td>0.460</td>\n <td><span style=\"color:green\">84.7%</span></td>\n </tr>\n <tr>\n <td rowspan=\"2\">CLIP</td>\n <td rowspan=\"2\"><a href=\"https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html\">Deep Fashion</a> text-to-image search</td>\n <td>mRR</td>\n <td>0.575</td>\n <td>0.676</td>\n <td><span style=\"color:green\">17.4%</span></td>\n <td rowspan=\"2\"><p align=center><a href=\"https://colab.research.google.com/drive/1yKnmy2Qotrh3OhgwWRsMWPFwOSAecBxg?usp=sharing\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a></p></td>\n </tr>\n <tr>\n <td>Recall</td>\n <td>0.473</td>\n <td>0.564</td>\n <td><span style=\"color:green\">19.2%</span></td>\n </tr>\n <tr>\n <td rowspan=\"2\">M-CLIP</td>\n <td rowspan=\"2\"><a href=\"https://xmrec.github.io/\">Cross market</a> product recommendation (German)</td>\n <td>mRR</td>\n <td>0.430</td>\n <td>0.648</td>\n <td><span style=\"color:green\">50.7%</span></td>\n <td rowspan=\"2\"><p align=center><a href=\"https://colab.research.google.com/drive/10Wldbu0Zugj7NmQyZwZzuorZ6SSAhtIo\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a></p></td>\n </tr>\n <tr>\n <td>Recall</td>\n <td>0.247</td>\n <td>0.340</td>\n <td><span style=\"color:green\">37.7%</span></td>\n </tr>\n <tr>\n <td rowspan=\"2\">PointNet++</td>\n <td rowspan=\"2\"><a href=\"https://modelnet.cs.princeton.edu/\">ModelNet40</a> 3D Mesh Search</td>\n <td>mRR</td>\n <td>0.791</td>\n <td>0.891</td>\n <td><span style=\"color:green\">12.7%</span></td>\n <td rowspan=\"2\"><p align=center><a href=\"https://colab.research.google.com/drive/1lIMDFkUVsWMshU-akJ_hwzBfJ37zLFzU?usp=sharing\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a></p></td>\n </tr>\n <tr>\n <td>Recall</td>\n <td>0.154</td>\n <td>0.242</td>\n <td><span style=\"color:green\">57.1%</span></td>\n </tr>\n\n</tbody>\n</table>\n\n<sub><sup>All metrics were evaluated for k@20 after training for 5 epochs using the Adam optimizer with learning rates of 1e-4 for ResNet, 1e-7 for CLIP and 1e-5 for the BERT models, 5e-4 for PointNet++</sup></sub>\n\n<!-- start install-instruction -->\n\n## Install\n\nMake sure you have Python 3.8+ installed. Finetuner can be installed via `pip` by executing:\n\n```bash\npip install -U finetuner\n```\n\nIf you want to submit a fine-tuning job on the cloud, please use\n\n```bash\npip install \"finetuner[full]\"\n```\n\n<!-- end install-instruction -->\n\n> \u26a0\ufe0f Starting with version 0.5.0, Finetuner computing is performed on Jina AI Cloud. The last local version is `0.4.1`. \n> This version is still available for installation via `pip`. See [Finetuner git tags and releases](https://github.com/jina-ai/finetuner/releases).\n\n<!-- start finetuner-articles -->\n## Articles about Finetuner\n\nCheck out our published blogposts and tutorials to see Finetuner in action!\n\n- [Fine-tuning with Low Budget and High Expectations](https://jina.ai/news/fine-tuning-with-low-budget-and-high-expectations/)\n- [Hype and Hybrids: Search is more than Keywords and Vectors](https://jina.ai/news/hype-and-hybrids-multimodal-search-means-more-than-keywords-and-vectors-2/)\n- [Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models](https://jina.ai/news/improving-search-quality-non-english-queries-fine-tuned-multilingual-clip-models/)\n- [How Much Do We Get by Finetuning CLIP?](https://jina.ai/news/applying-jina-ai-finetuner-to-clip-less-data-smaller-models-higher-performance/)\n\n<!-- end finetuner-articles -->\n\n<!-- start citations -->\nIf you find Jina Embeddings useful in your research, please cite the following paper:\n\n```text\n@misc{g\u00fcnther2023jina,\n title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models}, \n author={Michael G\u00fcnther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},\n year={2023},\n eprint={2307.11224},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\n```\n<!-- end citations -->\n\n<!-- start support-pitch -->\n## Support\n\n- Use [Discussions](https://github.com/jina-ai/finetuner/discussions) to talk about your use cases, questions, and\n support queries.\n- Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.\n- Join our [Engineering All Hands](https://youtube.com/playlist?list=PL3UBBWOUVhFYRUa_gpYYKBqEAkO4sxmne) meet-up to discuss your use case and learn Jina AI new features.\n - **When?** The second Tuesday of every month\n - **Where?**\n Zoom ([see our public events calendar](https://calendar.google.com/calendar/embed?src=c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com&ctz=Europe%2FBerlin)/[.ical](https://calendar.google.com/calendar/ical/c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com/public/basic.ics))\n and [live stream on YouTube](https://youtube.com/c/jina-ai)\n- Subscribe to the latest video tutorials on our [YouTube channel](https://youtube.com/c/jina-ai)\n\n## Join Us\n\nFinetuner is backed by [Jina AI](https://jina.ai) and licensed under [Apache-2.0](./LICENSE). \n\n[We are actively hiring](https://jobs.jina.ai) AI engineers and solution engineers to build the next generation of\nopen-source AI ecosystems.\n\n<!-- end support-pitch -->",
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