%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

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.

Task-oriented finetuning for better embeddings on neural search

PyPI Codecov branch PyPI - Downloads from official pypistats

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. ## [Documentation](https://finetuner.jina.ai/) ## Benchmarks
Model Task Metric Pretrained Finetuned Delta Run it!
BERT Quora Question Answering mRR 0.835 0.967 15.8%

Open In Colab
Recall 0.915 0.963 5.3%
ResNet Visual similarity search on TLL mAP 0.110 0.196 78.2%

Open In Colab
Recall 0.249 0.460 84.7%
CLIP Deep Fashion text-to-image search mRR 0.575 0.676 17.4%

Open In Colab
Recall 0.473 0.564 19.2%
M-CLIP Cross market product recommendation (German) mRR 0.430 0.648 50.7%

Open In Colab
Recall 0.247 0.340 37.7%
PointNet++ ModelNet40 3D Mesh Search mRR 0.791 0.891 12.7%

Open In Colab
Recall 0.154 0.242 57.1%
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++ ## 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]" ``` > ⚠️ 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). ## 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/) ## 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. %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

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.

Task-oriented finetuning for better embeddings on neural search

PyPI Codecov branch PyPI - Downloads from official pypistats

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. ## [Documentation](https://finetuner.jina.ai/) ## Benchmarks
Model Task Metric Pretrained Finetuned Delta Run it!
BERT Quora Question Answering mRR 0.835 0.967 15.8%

Open In Colab
Recall 0.915 0.963 5.3%
ResNet Visual similarity search on TLL mAP 0.110 0.196 78.2%

Open In Colab
Recall 0.249 0.460 84.7%
CLIP Deep Fashion text-to-image search mRR 0.575 0.676 17.4%

Open In Colab
Recall 0.473 0.564 19.2%
M-CLIP Cross market product recommendation (German) mRR 0.430 0.648 50.7%

Open In Colab
Recall 0.247 0.340 37.7%
PointNet++ ModelNet40 3D Mesh Search mRR 0.791 0.891 12.7%

Open In Colab
Recall 0.154 0.242 57.1%
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++ ## 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]" ``` > ⚠️ 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). ## 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/) ## 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. %package help Summary: Development documents and examples for finetuner Provides: python3-finetuner-doc %description help

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.

Task-oriented finetuning for better embeddings on neural search

PyPI Codecov branch PyPI - Downloads from official pypistats

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. ## [Documentation](https://finetuner.jina.ai/) ## Benchmarks
Model Task Metric Pretrained Finetuned Delta Run it!
BERT Quora Question Answering mRR 0.835 0.967 15.8%

Open In Colab
Recall 0.915 0.963 5.3%
ResNet Visual similarity search on TLL mAP 0.110 0.196 78.2%

Open In Colab
Recall 0.249 0.460 84.7%
CLIP Deep Fashion text-to-image search mRR 0.575 0.676 17.4%

Open In Colab
Recall 0.473 0.564 19.2%
M-CLIP Cross market product recommendation (German) mRR 0.430 0.648 50.7%

Open In Colab
Recall 0.247 0.340 37.7%
PointNet++ ModelNet40 3D Mesh Search mRR 0.791 0.891 12.7%

Open In Colab
Recall 0.154 0.242 57.1%
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++ ## 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]" ``` > ⚠️ 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). ## 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/) ## 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. %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 - 0.7.6-1 - Package Spec generated