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:
This is a version of it that has the required gender data and includes a trained model. It was tested on Windows 7x64 with Python 3.6.2 with the following libraries installed
docopt==0.6.2
numpy==1.15.3
regex==2018.8.29
scikit-learn==0.20.0
scipy==1.1.0
tensorboard==1.11.0
tensorflow==1.11.0
tensorflow-gpu==1.11.0
and Ubuntu 14.04LTSx64 with Python 3.6.6 with the following libraries installed
docopt==0.6.2
numpy==1.15.3
regex==2018.8.29
scikit-learn==0.20.0
scipy==1.1.0
tensorboard==1.10.0
tensorflow==1.10.0
Model accuracy
precision recall f1-score support
female 0.938 0.865 0.900 1733
male 0.986 0.961 0.973 10818
neutral 0.802 0.959 0.874 2074
micro avg 0.949 0.949 0.949 14625
macro avg 0.909 0.928 0.916 14625
weighted avg 0.954 0.949 0.950 14625
- This repository can run on Ubuntu 14.04 LTS & Mac OSX 10.x (not tested on other OSs)
- Tested only on Python 3.5
chicksexer depends on NumPy and Scipy, Python packages for scientific computing. You might need to have them installed prior to installing chicksexer.
You can install chicksexer by:
pip install chicksexerchicksexer 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)
The gender classifier is implemented using Character-level Multilayer LSTM. The architecture is roughly as follows:
- Character Embedding Layer
- 1st LSTM Layer
- 2nd LSTM Layer
- Pooling Layer
- Fully Connected Layer
The 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.
Names with gender annotation are obtained from the sources as follows: