Metadata-Version: 2.1
Name: Manteia
Version: 0.0.27
Summary: deep learning,NLP,classification,text,bert,distilbert,albert,xlnet,roberta,gpt2,torch,pytorch,active learning,augmentation,data
Home-page: https://pypi.org/project/Manteia/
Author: Yves Mercadier
Author-email: manteia.ym001@gmail.com
License: UNKNOWN
Project-URL: Pypi, https://pypi.org/project/Manteia/
Project-URL: Documentation, https://manteia.readthedocs.io/en/latest/index.html
Project-URL: Source Code, https://github.com/ym001/Manteia
Description: Manteia - proclaim the good word
        ================================================================
        
        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__
        
        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)
        
        
        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-medium')
        	text_loader = Create_DataLoader_generation(ds.documents_train[:10000],batch_size=32)
        	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)
        
        
        [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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Development Status :: 1 - Planning
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
