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diff --git a/python-finetuner.spec b/python-finetuner.spec new file mode 100644 index 0000000..2e669da --- /dev/null +++ b/python-finetuner.spec @@ -0,0 +1,660 @@ +%global _empty_manifest_terminate_build 0 +Name: python-finetuner +Version: 0.7.6 +Release: 1 +Summary: Task-oriented finetuning for better embeddings on neural search. +License: Apache 2.0 +URL: https://github.com/jina-ai/finetuner/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1e/f2/65b859918b1e5e4cbfa6e52fb711087e0f44047426e4e7a9c1eb5894eef0/finetuner-0.7.6.tar.gz +BuildArch: noarch + + +%description +<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://slack.jina.ai"><img src="https://img.shields.io/badge/Slack-3.6k-blueviolet?logo=slack&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/) + +## 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 encode `docarray.DocumentArray` objects with the `finetuner.encode` function, you need to install +`"finetuner[full]"`. This includes a number of additional dependencies, which are necessary for encoding: Torch, +Torchvision and OpenCLIP: + +```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 support-pitch --> +## Support + +- Use [Discussions](https://github.com/jina-ai/finetuner/discussions) to talk about your use cases, questions, and + support queries. +- Join our [Slack community](https://slack.jina.ai) and chat with other Jina AI 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 --> + +%package -n python3-finetuner +Summary: Task-oriented finetuning for better embeddings on neural search. +Provides: python-finetuner +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-finetuner +<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://slack.jina.ai"><img src="https://img.shields.io/badge/Slack-3.6k-blueviolet?logo=slack&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/) + +## 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 encode `docarray.DocumentArray` objects with the `finetuner.encode` function, you need to install +`"finetuner[full]"`. This includes a number of additional dependencies, which are necessary for encoding: Torch, +Torchvision and OpenCLIP: + +```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 support-pitch --> +## Support + +- Use [Discussions](https://github.com/jina-ai/finetuner/discussions) to talk about your use cases, questions, and + support queries. +- Join our [Slack community](https://slack.jina.ai) and chat with other Jina AI 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 --> + +%package help +Summary: Development documents and examples for finetuner +Provides: python3-finetuner-doc +%description help +<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://slack.jina.ai"><img src="https://img.shields.io/badge/Slack-3.6k-blueviolet?logo=slack&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/) + +## 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 encode `docarray.DocumentArray` objects with the `finetuner.encode` function, you need to install +`"finetuner[full]"`. This includes a number of additional dependencies, which are necessary for encoding: Torch, +Torchvision and OpenCLIP: + +```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 support-pitch --> +## Support + +- Use [Discussions](https://github.com/jina-ai/finetuner/discussions) to talk about your use cases, questions, and + support queries. +- Join our [Slack community](https://slack.jina.ai) and chat with other Jina AI 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 --> + +%prep +%autosetup -n finetuner-0.7.6 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-finetuner -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.6-1 +- Package Spec generated |