Implementation of a part-of-speech tagger for the English language
A part-of-speech tagger model is trained with a corpus containing almost 12,000 English sentences.

Check for detailed Medium story that I wrote: https://medium.com/@cetinsamet/part-of-speech-pos-tagging-8af646a3d5bb

Model (mini model) in the repo is trained using mini data which contains only almost 100 English sentences.
Therefore, it does not perform well enough.

I encourage you to train a new model on your own using the corpus in data/ directory.
My training lasted 32 minutes on a device with 3,1 GHz Intel Core i7 processor.
Training accuracy was 100% where development accuracy was 93%.

$python3 pos_tag.py input-sentence

$cd src
$python3 pos_tag.py "Peace at home, peace in the world."
-> POS tagger is loaded.
-> [('Peace', 'NN'), ('at', 'DT'), ('home', 'NN'), (',', ','), ('peace', 'NN'), ('on', 'IN'), ('earth', 'NN'), ('.', '.')]

P.S. Above example is tested with a model trained on corpus in data/ directory(with almost 12,000 English sentences).

Peace at home, peace in the world.
Mustafa Kemal Atatürk