From e4a6f1a9e763c4be7e9b121905ac04a668f82b8a Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 5 May 2023 03:26:57 +0000 Subject: automatic import of python-finetuner --- .gitignore | 1 + python-finetuner.spec | 660 ++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 662 insertions(+) create mode 100644 python-finetuner.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..7bfebc5 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/finetuner-0.7.6.tar.gz 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 +

+ +

+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 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelTaskMetricPretrainedFinetunedDeltaRun it!
BERTQuora Question AnsweringmRR0.8350.96715.8%

Open In Colab

Recall0.9150.9635.3%
ResNetVisual similarity search on TLLmAP0.1100.19678.2%

Open In Colab

Recall0.2490.46084.7%
CLIPDeep Fashion text-to-image searchmRR0.5750.67617.4%

Open In Colab

Recall0.4730.56419.2%
M-CLIPCross market product recommendation (German)mRR0.4300.64850.7%

Open In Colab

Recall0.2470.34037.7%
PointNet++ModelNet40 3D Mesh SearchmRR0.7910.89112.7%

Open In Colab

Recall0.1540.24257.1%
+ +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 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelTaskMetricPretrainedFinetunedDeltaRun it!
BERTQuora Question AnsweringmRR0.8350.96715.8%

Open In Colab

Recall0.9150.9635.3%
ResNetVisual similarity search on TLLmAP0.1100.19678.2%

Open In Colab

Recall0.2490.46084.7%
CLIPDeep Fashion text-to-image searchmRR0.5750.67617.4%

Open In Colab

Recall0.4730.56419.2%
M-CLIPCross market product recommendation (German)mRR0.4300.64850.7%

Open In Colab

Recall0.2470.34037.7%
PointNet++ModelNet40 3D Mesh SearchmRR0.7910.89112.7%

Open In Colab

Recall0.1540.24257.1%
+ +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 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelTaskMetricPretrainedFinetunedDeltaRun it!
BERTQuora Question AnsweringmRR0.8350.96715.8%

Open In Colab

Recall0.9150.9635.3%
ResNetVisual similarity search on TLLmAP0.1100.19678.2%

Open In Colab

Recall0.2490.46084.7%
CLIPDeep Fashion text-to-image searchmRR0.5750.67617.4%

Open In Colab

Recall0.4730.56419.2%
M-CLIPCross market product recommendation (German)mRR0.4300.64850.7%

Open In Colab

Recall0.2470.34037.7%
PointNet++ModelNet40 3D Mesh SearchmRR0.7910.89112.7%

Open In Colab

Recall0.1540.24257.1%
+ +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 diff --git a/sources b/sources new file mode 100644 index 0000000..f964eb9 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +1c77f1fa473ac93396da9643bdb4beaf finetuner-0.7.6.tar.gz -- cgit v1.2.3