Metadata-Version: 2.1
Name: genderperformr
Version: 1.2
Summary: GenderPerformr
Home-page: https://github.com/zijwang/genderperformr
Author: Zijian Wang and David Jurgens
Author-email: zijwang@stanford.edu
License: UNKNOWN
Description: # GenderPerformr
        
        ## Intro
        GenderPerformr is the model release from the paper `It’s going to be okay: Measuring Access to Support in Online Communities` by Zijian Wang and David Jurgens (in proceedings of EMNLP 2018).
        
        It is the current state-of-the-art method that predicts gender from usernames based on a LSTM model built in PyTorch (as of Sept. 2018).
        
        See the [project website](http://blablablab.si.umich.edu/projects/support) for full details, including contact information.
         
        ## Install 
        
        ### Use pip
        If `pip` is installed, genderperformr could be installed directly from it:
        
        	pip install genderperformr
        ### From raw
        `git clone` the project and do:
        
        	python setup.py install
        
        ### Dependencies
        	python>=3.6.0
        	torch>=0.4.1
        	numpy
        	unidecode
        
        
        ## Usage and Example
        
        ### `predict`
        `predict` is the core method of this package, 
        which takes a single username of a list of usernames, and returns a tuple of raw probabilities in `[0,1]` (0 - Male, 1 - Female), and labels (M - Male, N - Neutral, F - Female, empty string - others). 
        
        ### Simplest usage
        
        You may directly import `genderperformr` and use the default predict method, e.g.:
        
            >>> import genderperformr
            >>> genderperformr.predict("AdamMcAdamson")
            (0.019139649, 'M')
            
        ### Construct from class
        Alternatively, you may also construct the object from class, where you could customize the model path and device:
         
        	>>> from genderperformr import GenderPerformr
        	>>> gp = GenderPerformr()
        	
        	# Predict a single username
        	>>> gp.predict("John")
        	(0.087956183, 'M')
        	
        	# Predict a list of names
        	>>> probs, labels = gp.predict(["BarryCA67", "pizzamagic", "KatieZ22"])
            >>> f"Raw probabilities are {probs}"
            Raw probabilities are [0.03398224 0.5439474 0.93964571]
            >>> f"Labels are {labels}"
            Labels are ['M', 'N', 'F']
        
        
        More detail on how to construct the object is available in docstrings.
        
        ### Model using new data partition 
        If you want to use the model described in Supplemental Material using the new data partition, you may construct the object via
        
            >>> gp = GenderPerformr(is_new_model=True)
        
        All other usages remain the same.
        
        
        ## Citation
            @inproceedings{wang2018its,
                   title={It's going to be okay: Measuring Access to Support in Online Communities},
                   author={Wang, Zijian and Jurgens, David},
                   booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
                   year={2018}
            }
            
        ## Contact
        Zijian Wang (zij<last_name>@stanford.edu)
        
        David Jurgens (<last_name>@umich.edu)
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
