Metadata-Version: 2.1
Name: agreementr
Version: 1.5
Summary: agreementr
Home-page: UNKNOWN
Author: Junjie Wu
Author-email: wujj38@mail2.sysu.edu.cn
License: UNKNOWN
Description: # Agreementr
        
        ## Intro
        Agreementr is a package used to predict the value of agreement of texts.
        
        It is based on a fine tuned BERT model.
        ## Install 
        
        ### Use pip
        If `pip` is installed, agreementr could be installed directly from it:
        
            pip install agreementr
        
        ### Dependencies
        	python>=3.6.0
        	torch>=0.4.1
        	numpy
        	pandas
        	unidecode
        	pytorch-pretrained-bert
        	pytorch-transformers
        	
        
        
        ## Usage and Example
        
        ### Notes: During your first usage, the package will download a model file automatically, which is about 400MB.
        
        ### `predict`
        `predict` is the core method of this package, 
        which takes a single text of a list of texts, and returns a list of raw values in `[1,5]` (higher means more agreement, while lower means less).
        
        ### Simplest usage
        
        You may directly import `agreementr` and use the default predict method, e.g.:
        
            >>> import agreementr
            >>> agreementr.predict(["I am totally agree with you"])
            [4.3568916]
            
        ### Construct from class
        Alternatively, you may also construct the object from class, where you could customize the model path and device:
         
        	>>> from agreementr import Agreementr
        	>>> ar = Agreementr()
        	
        	# Predict a single text
        	>>> ar.predict(["I am totally agree with you"])
        	[4.3568916]
        	
        	# Predict a list of texts
        	>>> preds = ar.predict(['I am totally agree with you','I hate you'])
            >>> f"Raw values are {preds}"
            [4.3568916 2.42935]
        
        
        
        More detail on how to construct the object is available in docstrings.
        
        ### Model using multiprocessing when preprocessing a large dataset into BERT input features 
        If you want to use several cpu cores via multiprocessing while preprocessing a large dataset, you may construct the object via
        
            >>> ar = Agreementr(CPU_COUNT=cpu_cpunt, CHUNKSIZE=chunksize)
        
        If you want to faster the code through multi gpus, you may construct the object via
        
            >>> ar = Agreementr(is_paralleled=False, BATCH_SIZE = batch_size)
        
        
        ## Contact
        Junjie Wu (wujj38@mail2.sysu.edu.cn)
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
