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        "author": "Zijian Wang and David Jurgens",
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        "description": "# GenderPerformr\n\n## Intro\nGenderPerformr is the model release from the paper `It\u2019s going to be okay: Measuring Access to Support in Online Communities` by Zijian Wang and David Jurgens (in proceedings of EMNLP 2018).\n\nIt 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).\n\nSee the [project website](http://blablablab.si.umich.edu/projects/support) for full details, including contact information.\n \n## Install \n\n### Use pip\nIf `pip` is installed, genderperformr could be installed directly from it:\n\n\tpip install genderperformr\n### From raw\n`git clone` the project and do:\n\n\tpython setup.py install\n\n### Dependencies\n\tpython>=3.6.0\n\ttorch>=0.4.1\n\tnumpy\n\tunidecode\n\n\n## Usage and Example\n\n### `predict`\n`predict` is the core method of this package, \nwhich 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). \n\n### Simplest usage\n\nYou may directly import `genderperformr` and use the default predict method, e.g.:\n\n    >>> import genderperformr\n    >>> genderperformr.predict(\"AdamMcAdamson\")\n    (0.019139649, 'M')\n    \n### Construct from class\nAlternatively, you may also construct the object from class, where you could customize the model path and device:\n \n\t>>> from genderperformr import GenderPerformr\n\t>>> gp = GenderPerformr()\n\t\n\t# Predict a single username\n\t>>> gp.predict(\"John\")\n\t(0.087956183, 'M')\n\t\n\t# Predict a list of names\n\t>>> probs, labels = gp.predict([\"BarryCA67\", \"pizzamagic\", \"KatieZ22\"])\n    >>> f\"Raw probabilities are {probs}\"\n    Raw probabilities are [0.03398224 0.5439474 0.93964571]\n    >>> f\"Labels are {labels}\"\n    Labels are ['M', 'N', 'F']\n\n\nMore detail on how to construct the object is available in docstrings.\n\n### Model using new data partition \nIf you want to use the model described in Supplemental Material using the new data partition, you may construct the object via\n\n    >>> gp = GenderPerformr(is_new_model=True)\n\nAll other usages remain the same.\n\n\n## Citation\n    @inproceedings{wang2018its,\n           title={It's going to be okay: Measuring Access to Support in Online Communities},\n           author={Wang, Zijian and Jurgens, David},\n           booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},\n           year={2018}\n    }\n    \n## Contact\nZijian Wang (zij<last_name>@stanford.edu)\n\nDavid Jurgens (<last_name>@umich.edu)",
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