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        "author": "Kensuke Muraki",
        "author_email": "kensk8er1017@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: Linguistic"
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        "description": "chicksexer - Python package for gender classification\n=================================================================\n\n![Chicksexer](images/chicksexer.jpg?raw=true \"Title\")\n\n`chicksexer` is a Python package that performs **gender classification**. It receives a string of person name and returns the probability estimate of its gender as follows:\n\n```python\n>>> from chicksexer import predict_gender\n>>> predict_gender('John Smith')\n{'female': 0.0027230381965637207, 'male': 0.9972769618034363}\n```\n\nSeveral merits of using the classifier instead of simply looking up known male/female names are:\n\n* Sometimes simple name lookup does not work. For instance, \"Miki\" is a Japanese female name, but also a Croatian male name.\n* Can predict the gender of a name that does not exist in the list of male/female names.\n* Can deal with a typo in a name relatively easily.\n\nYou can also get an estimate as a simple string as follows:\n\n```python\n>>> predict_gender('Oliver Butterfield', return_proba=False)\n'male'\n>>> predict_gender('Naila Ata', return_proba=False)\n'female'\n>>> predict_gender('Saldivar Anderson', return_proba=False)\n'neutral'\n>>> predict_gender('Ponyo', return_proba=False)  # name of a character from the film\n'male'\n>>> predict_gender('Ponya', return_proba=False)  # modify the name such that it sounds like a female name\n'female'\n>>> predict_gender('Miki Suzuki', return_proba=True)  # Suzuki here is a Japanese surname so Miki is a female name\n{'female': 0.9997618066990981, 'male': 0.00023819330090191215}\n>>> predict_gender('Miki Adami\u0107', return_proba=True)  # Adami\u0107 is a Croatian surname so Miki is a male name\n{'female': 0.16958969831466675, 'male': 0.8304103016853333}\n>>> predict_gender('Jessica')\n{'female': 0.999996105068476, 'male': 3.894931523973355e-06}\n>>> predict_gender('Jesssica')  # typo in Jessica\n{'female': 0.9999851534785194, 'male': 1.4846521480649244e-05}\n```\n\nIf you want to predict the gender of multiple names, use `predict_genders` (plural) function instead:\n\n```python\n>>> from chicksexer import predict_genders\n>>> predict_genders(['Ichiro Suzuki', 'Haruki Murakami'])\n[{'female': 3.039836883544922e-05, 'male': 0.9999696016311646},\n {'female': 1.2040138244628906e-05, 'male': 0.9999879598617554}]\n>>> predict_genders(['Ichiro Suzuki', 'Haruki Murakami'], return_proba=False)\n['male', 'male']\n```\n\nInstallation\n------------\n- This repository can run on Ubuntu 14.04 LTS & Mac OSX 10.x (not tested on other OSs)\n- Tested only on Python 3.5\n\n`chicksexer` depends on [NumPy and Scipy](https://www.scipy.org/install.html), Python packages for scientific computing. You might need to have them installed prior to installing `chicksexer`.\n\nYou can install `chicksexer` by:\n\n```bash\npip install chicksexer\n```\n\n`chicksexer` also depends on `tensorflow` package. In default, it tries to install the CPU-only version of `tensorflow`. If you want to use GPU, you need to install `tensorflow` with GPU support by yourself. (C.f. [Installing Tensorflow](https://www.tensorflow.org/install/))\n\nModel Architecture\n------------------\nThe gender classifier is implemented using Character-level Multilayer LSTM. The architecture is roughly as follows:\n\n1. Character Embedding Layer\n2. 1st LSTM Layer\n3. 2nd LSTM Layer\n4. Pooling Layer\n5. Fully Connected Layer\n\nThe fully connected layer outputs the probability of a name bing a male name. For the details, look at `_build_graph()` method in `chicksexer/_classifier.py`, which implements the computational graph of the architecture in `tensorflow`.\n\nTraining Data\n-------------\nNames with gender annotation are obtained from the sources as follows:\n\n* [Dbpedia Person Data](http://downloads.dbpedia.org/2015-10/core-i18n/en/persondata_en.tql.bz2)\n* [Popular baby names in the US](https://www.ssa.gov/oact/babynames/limits.html)\n* [Names dataset curated by Milos Bejda](https://mbejda.github.io/)\n",
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        "keywords": "natural-language-processing machine-learning tensorflow deep-learning recurrent-neural-networks lstm nlp python neural-network character-embeddings data-science gender-classification",
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