%global _empty_manifest_terminate_build 0 Name: python-happytransformer Version: 2.4.1 Release: 1 Summary: Happy Transformer is an API built on top of Hugging Face's Transformer library that makes it easy to utilize state-of-the-art NLP models. License: Apache 2.0 URL: https://github.com/EricFillion/happy-transformer Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7a/1c/01318ece577d3a7fdd6d9103dc6dcc9eaf20312aef76a3158c399ec8a207/happytransformer-2.4.1.tar.gz BuildArch: noarch Requires: python3-torch Requires: python3-tqdm Requires: python3-transformers Requires: python3-datasets Requires: python3-sentencepiece Requires: python3-protobuf Requires: python3-dataclasses %description [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Downloads](https://pepy.tech/badge/happytransformer)](https://pepy.tech/project/happytransformer) [![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](http://happytransformer.com) ![PyPI](https://img.shields.io/pypi/v/happytransformer) [![](https://github.com/EricFillion/happy-transformer/workflows/build/badge.svg)](https://github.com/EricFillion/happy-transformer/actions) # Happy Transformer **Documentation and news: [happytransformer.com](http://happytransformer.com)** New Course: Create a text generation web app. Also learn how to fine-tune GPT-Neo [link](https://www.udemy.com/course/nlp-text-generation-python-web-app/?couponCode=LAUNCH) Join our Discord server: [![Support Server](https://img.shields.io/discord/839263772312862740.svg?label=Discord&logo=Discord&colorB=7289da&style=?style=flat-square&logo=appveyor)](https://discord.gg/psVwe3wfTb) ![HappyTransformer](logo.png) Happy Transformer is an package built on top of [Hugging Face's transformer library](https://huggingface.co/transformers/) that makes it easy to utilize state-of-the-art NLP models. ## Features | Public Methods | Basic Usage | Training | |------------------------------------|--------------|------------| | Text Generation | ✔ | ✔ | | Text Classification | ✔ | ✔ | | Word Prediction | ✔ | ✔ | | Question Answering | ✔ | ✔ | | Text-to-Text | ✔ | ✔ | | Next Sentence Prediction | ✔ | | | Token Classification | ✔ | | ## Quick Start ```sh pip install happytransformer ``` ```python from happytransformer import HappyWordPrediction #--------------------------------------# happy_wp = HappyWordPrediction() # default uses distilbert-base-uncased result = happy_wp.predict_mask("I think therefore I [MASK]") print(result) # [WordPredictionResult(token='am', score=0.10172799974679947)] print(result[0].token) # am ``` ## Maintainers - [Eric Fillion](https://github.com/ericfillion) Lead Maintainer - [Ted Brownlow](https://github.com/ted537) Maintainer ## Tutorials [Text generation with training (GPT-Neo)](https://youtu.be/GzHJ3NUVtV4) [Text classification (training)](https://www.vennify.ai/train-text-classification-transformers/) [Text classification (hate speech detection)](https://youtu.be/jti2sPQYzeQ) [Text classification (sentiment analysis)](https://youtu.be/Ew72EAgM7FM) [Word prediction with training (DistilBERT, RoBERTa)](https://youtu.be/AWe0PHsPc_M) [Top T5 Models ](https://www.vennify.ai/top-t5-transformer-models/) [Grammar Correction](https://www.vennify.ai/grammar-correction-python/) [Fine-tune a Grammar Correction Model](https://www.vennify.ai/fine-tune-grammar-correction/) %package -n python3-happytransformer Summary: Happy Transformer is an API built on top of Hugging Face's Transformer library that makes it easy to utilize state-of-the-art NLP models. Provides: python-happytransformer BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-happytransformer [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Downloads](https://pepy.tech/badge/happytransformer)](https://pepy.tech/project/happytransformer) [![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](http://happytransformer.com) ![PyPI](https://img.shields.io/pypi/v/happytransformer) [![](https://github.com/EricFillion/happy-transformer/workflows/build/badge.svg)](https://github.com/EricFillion/happy-transformer/actions) # Happy Transformer **Documentation and news: [happytransformer.com](http://happytransformer.com)** New Course: Create a text generation web app. Also learn how to fine-tune GPT-Neo [link](https://www.udemy.com/course/nlp-text-generation-python-web-app/?couponCode=LAUNCH) Join our Discord server: [![Support Server](https://img.shields.io/discord/839263772312862740.svg?label=Discord&logo=Discord&colorB=7289da&style=?style=flat-square&logo=appveyor)](https://discord.gg/psVwe3wfTb) ![HappyTransformer](logo.png) Happy Transformer is an package built on top of [Hugging Face's transformer library](https://huggingface.co/transformers/) that makes it easy to utilize state-of-the-art NLP models. ## Features | Public Methods | Basic Usage | Training | |------------------------------------|--------------|------------| | Text Generation | ✔ | ✔ | | Text Classification | ✔ | ✔ | | Word Prediction | ✔ | ✔ | | Question Answering | ✔ | ✔ | | Text-to-Text | ✔ | ✔ | | Next Sentence Prediction | ✔ | | | Token Classification | ✔ | | ## Quick Start ```sh pip install happytransformer ``` ```python from happytransformer import HappyWordPrediction #--------------------------------------# happy_wp = HappyWordPrediction() # default uses distilbert-base-uncased result = happy_wp.predict_mask("I think therefore I [MASK]") print(result) # [WordPredictionResult(token='am', score=0.10172799974679947)] print(result[0].token) # am ``` ## Maintainers - [Eric Fillion](https://github.com/ericfillion) Lead Maintainer - [Ted Brownlow](https://github.com/ted537) Maintainer ## Tutorials [Text generation with training (GPT-Neo)](https://youtu.be/GzHJ3NUVtV4) [Text classification (training)](https://www.vennify.ai/train-text-classification-transformers/) [Text classification (hate speech detection)](https://youtu.be/jti2sPQYzeQ) [Text classification (sentiment analysis)](https://youtu.be/Ew72EAgM7FM) [Word prediction with training (DistilBERT, RoBERTa)](https://youtu.be/AWe0PHsPc_M) [Top T5 Models ](https://www.vennify.ai/top-t5-transformer-models/) [Grammar Correction](https://www.vennify.ai/grammar-correction-python/) [Fine-tune a Grammar Correction Model](https://www.vennify.ai/fine-tune-grammar-correction/) %package help Summary: Development documents and examples for happytransformer Provides: python3-happytransformer-doc %description help [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Downloads](https://pepy.tech/badge/happytransformer)](https://pepy.tech/project/happytransformer) [![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](http://happytransformer.com) ![PyPI](https://img.shields.io/pypi/v/happytransformer) [![](https://github.com/EricFillion/happy-transformer/workflows/build/badge.svg)](https://github.com/EricFillion/happy-transformer/actions) # Happy Transformer **Documentation and news: [happytransformer.com](http://happytransformer.com)** New Course: Create a text generation web app. Also learn how to fine-tune GPT-Neo [link](https://www.udemy.com/course/nlp-text-generation-python-web-app/?couponCode=LAUNCH) Join our Discord server: [![Support Server](https://img.shields.io/discord/839263772312862740.svg?label=Discord&logo=Discord&colorB=7289da&style=?style=flat-square&logo=appveyor)](https://discord.gg/psVwe3wfTb) ![HappyTransformer](logo.png) Happy Transformer is an package built on top of [Hugging Face's transformer library](https://huggingface.co/transformers/) that makes it easy to utilize state-of-the-art NLP models. ## Features | Public Methods | Basic Usage | Training | |------------------------------------|--------------|------------| | Text Generation | ✔ | ✔ | | Text Classification | ✔ | ✔ | | Word Prediction | ✔ | ✔ | | Question Answering | ✔ | ✔ | | Text-to-Text | ✔ | ✔ | | Next Sentence Prediction | ✔ | | | Token Classification | ✔ | | ## Quick Start ```sh pip install happytransformer ``` ```python from happytransformer import HappyWordPrediction #--------------------------------------# happy_wp = HappyWordPrediction() # default uses distilbert-base-uncased result = happy_wp.predict_mask("I think therefore I [MASK]") print(result) # [WordPredictionResult(token='am', score=0.10172799974679947)] print(result[0].token) # am ``` ## Maintainers - [Eric Fillion](https://github.com/ericfillion) Lead Maintainer - [Ted Brownlow](https://github.com/ted537) Maintainer ## Tutorials [Text generation with training (GPT-Neo)](https://youtu.be/GzHJ3NUVtV4) [Text classification (training)](https://www.vennify.ai/train-text-classification-transformers/) [Text classification (hate speech detection)](https://youtu.be/jti2sPQYzeQ) [Text classification (sentiment analysis)](https://youtu.be/Ew72EAgM7FM) [Word prediction with training (DistilBERT, RoBERTa)](https://youtu.be/AWe0PHsPc_M) [Top T5 Models ](https://www.vennify.ai/top-t5-transformer-models/) [Grammar Correction](https://www.vennify.ai/grammar-correction-python/) [Fine-tune a Grammar Correction Model](https://www.vennify.ai/fine-tune-grammar-correction/) %prep %autosetup -n happytransformer-2.4.1 %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-happytransformer -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot - 2.4.1-1 - Package Spec generated