automatic import of python-manteia

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+/Manteia-0.0.41.tar.gz
diff --git a/python-manteia.spec b/python-manteia.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-Manteia
+Version:	0.0.41
+Release:	1
+Summary:	deep learning,NLP,classification,text,bert,distilbert,albert,xlnet,roberta,gpt2,torch,pytorch,active learning,augmentation,data
+License:	MIT License
+URL:		https://pypi.org/project/Manteia/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6b/f9/66fae5f7d919d35fa8b246b6d51e95bd5b6bf4093b91974b5ad44fd959d3/Manteia-0.0.41.tar.gz
+BuildArch:	noarch
+
+
+%description
+Designing your neural network to natural language processing. Deep learning has been used extensively in natural language processing (NLP) because
+it is well suited for learning the complex underlying structure of a sentence and semantic proximity of various words.
+Data cleaning, construction model (Bert, Roberta, Distilbert, XLNet, Albert, GPT, GPT2),
+quality measurement training and finally visualization of your results on several dataset ( 20newsgroups, SST-2, PubMed_20k_RCT, DBPedia, Amazon Review Full, Amazon Review Polarity).
+You can install it with pip :
+     __pip install Manteia__
+[Pretraitement]( https://raw.githubusercontent.com/ym001/Manteia/master/docs/images/boxplot.png)
+[Training]( https://raw.githubusercontent.com/ym001/Manteia/master/docs/images/train.png)
+For use with GPU and cuda we recommend the use of [Anaconda](https://www.anaconda.com/open-source) :
+     __conda create -n manteia_env python=3.7__
+     __conda activate manteia_env__
+     __conda install pytorch__
+     __pip install manteia__
+Example of use Classification :
+	from Manteia.Classification import Classification 
+	from Manteia.Model import Model 
+	documents = ['What should you do before criticizing Pac-Man? WAKA WAKA WAKA mile in his shoe.','What did Arnold Schwarzenegger say at the abortion clinic? Hasta last vista, baby.']
+	labels = ['funny','not funny']
+	model = Model(model_name ='roberta')
+	cl=Classification(model,documents,labels,process_classif=True)
+[NoteBook](https://github.com/ym001/Manteia/blob/master/notebook/notebook_Manteia_presentation1.ipynb)
+Example of use Generation :
+	from Manteia.Generation import Generation 
+	from Manteia.Dataset import Dataset
+	from Manteia.Model import *
+	ds=Dataset('Short_Jokes')
+	model       = Model(model_name ='gpt2')
+	text_loader = Create_DataLoader_generation(ds.documents_train[:10000],batch_size=32)
+	model.load_type()
+	model.load_tokenizer()
+	model.load_class()
+	model.devices()
+	model.configuration(text_loader)
+	gn=Generation(model)
+	gn.model.fit_generation(text_loader)
+	output      = model.predict_generation('What did you expect ?')
+	output_text = decode_text(output,model.tokenizer)
+	print(output_text)
+[NoteBook](https://github.com/ym001/Manteia/blob/master/notebook/notebook_Manteia_presentation2.ipynb)
+[Documentation](https://manteia.readthedocs.io/en/latest/#)
+[Pypi](https://pypi.org/project/Manteia/)
+[Source](https://github.com/ym001/Manteia)
+This code is licensed under MIT.
+
+%package -n python3-Manteia
+Summary:	deep learning,NLP,classification,text,bert,distilbert,albert,xlnet,roberta,gpt2,torch,pytorch,active learning,augmentation,data
+Provides:	python-Manteia
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-Manteia
+Designing your neural network to natural language processing. Deep learning has been used extensively in natural language processing (NLP) because
+it is well suited for learning the complex underlying structure of a sentence and semantic proximity of various words.
+Data cleaning, construction model (Bert, Roberta, Distilbert, XLNet, Albert, GPT, GPT2),
+quality measurement training and finally visualization of your results on several dataset ( 20newsgroups, SST-2, PubMed_20k_RCT, DBPedia, Amazon Review Full, Amazon Review Polarity).
+You can install it with pip :
+     __pip install Manteia__
+[Pretraitement]( https://raw.githubusercontent.com/ym001/Manteia/master/docs/images/boxplot.png)
+[Training]( https://raw.githubusercontent.com/ym001/Manteia/master/docs/images/train.png)
+For use with GPU and cuda we recommend the use of [Anaconda](https://www.anaconda.com/open-source) :
+     __conda create -n manteia_env python=3.7__
+     __conda activate manteia_env__
+     __conda install pytorch__
+     __pip install manteia__
+Example of use Classification :
+	from Manteia.Classification import Classification 
+	from Manteia.Model import Model 
+	documents = ['What should you do before criticizing Pac-Man? WAKA WAKA WAKA mile in his shoe.','What did Arnold Schwarzenegger say at the abortion clinic? Hasta last vista, baby.']
+	labels = ['funny','not funny']
+	model = Model(model_name ='roberta')
+	cl=Classification(model,documents,labels,process_classif=True)
+[NoteBook](https://github.com/ym001/Manteia/blob/master/notebook/notebook_Manteia_presentation1.ipynb)
+Example of use Generation :
+	from Manteia.Generation import Generation 
+	from Manteia.Dataset import Dataset
+	from Manteia.Model import *
+	ds=Dataset('Short_Jokes')
+	model       = Model(model_name ='gpt2')
+	text_loader = Create_DataLoader_generation(ds.documents_train[:10000],batch_size=32)
+	model.load_type()
+	model.load_tokenizer()
+	model.load_class()
+	model.devices()
+	model.configuration(text_loader)
+	gn=Generation(model)
+	gn.model.fit_generation(text_loader)
+	output      = model.predict_generation('What did you expect ?')
+	output_text = decode_text(output,model.tokenizer)
+	print(output_text)
+[NoteBook](https://github.com/ym001/Manteia/blob/master/notebook/notebook_Manteia_presentation2.ipynb)
+[Documentation](https://manteia.readthedocs.io/en/latest/#)
+[Pypi](https://pypi.org/project/Manteia/)
+[Source](https://github.com/ym001/Manteia)
+This code is licensed under MIT.
+
+%package help
+Summary:	Development documents and examples for Manteia
+Provides:	python3-Manteia-doc
+%description help
+Designing your neural network to natural language processing. Deep learning has been used extensively in natural language processing (NLP) because
+it is well suited for learning the complex underlying structure of a sentence and semantic proximity of various words.
+Data cleaning, construction model (Bert, Roberta, Distilbert, XLNet, Albert, GPT, GPT2),
+quality measurement training and finally visualization of your results on several dataset ( 20newsgroups, SST-2, PubMed_20k_RCT, DBPedia, Amazon Review Full, Amazon Review Polarity).
+You can install it with pip :
+     __pip install Manteia__
+[Pretraitement]( https://raw.githubusercontent.com/ym001/Manteia/master/docs/images/boxplot.png)
+[Training]( https://raw.githubusercontent.com/ym001/Manteia/master/docs/images/train.png)
+For use with GPU and cuda we recommend the use of [Anaconda](https://www.anaconda.com/open-source) :
+     __conda create -n manteia_env python=3.7__
+     __conda activate manteia_env__
+     __conda install pytorch__
+     __pip install manteia__
+Example of use Classification :
+	from Manteia.Classification import Classification 
+	from Manteia.Model import Model 
+	documents = ['What should you do before criticizing Pac-Man? WAKA WAKA WAKA mile in his shoe.','What did Arnold Schwarzenegger say at the abortion clinic? Hasta last vista, baby.']
+	labels = ['funny','not funny']
+	model = Model(model_name ='roberta')
+	cl=Classification(model,documents,labels,process_classif=True)
+[NoteBook](https://github.com/ym001/Manteia/blob/master/notebook/notebook_Manteia_presentation1.ipynb)
+Example of use Generation :
+	from Manteia.Generation import Generation 
+	from Manteia.Dataset import Dataset
+	from Manteia.Model import *
+	ds=Dataset('Short_Jokes')
+	model       = Model(model_name ='gpt2')
+	text_loader = Create_DataLoader_generation(ds.documents_train[:10000],batch_size=32)
+	model.load_type()
+	model.load_tokenizer()
+	model.load_class()
+	model.devices()
+	model.configuration(text_loader)
+	gn=Generation(model)
+	gn.model.fit_generation(text_loader)
+	output      = model.predict_generation('What did you expect ?')
+	output_text = decode_text(output,model.tokenizer)
+	print(output_text)
+[NoteBook](https://github.com/ym001/Manteia/blob/master/notebook/notebook_Manteia_presentation2.ipynb)
+[Documentation](https://manteia.readthedocs.io/en/latest/#)
+[Pypi](https://pypi.org/project/Manteia/)
+[Source](https://github.com/ym001/Manteia)
+This code is licensed under MIT.
+
+%prep
+%autosetup -n Manteia-0.0.41
+
+%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-Manteia -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.41-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..8f074f2
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+05884dac5da0d4d4ed0059c5c9167bae  Manteia-0.0.41.tar.gz