genderperformr 1.1

GenderPerformr

# 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)