seq2seq-lstm 0.1.6

Sequence-to-sequence classifier based on LSTM with the simple sklearn-like interface

The Seq2Seq-LSTM is a sequence-to-sequence classifier with the sklearn-like interface, and it uses the Keras package for neural modeling.

Developing of this module was inspired by Francois Chollet’s tutorial A ten-minute introduction to sequence-to-sequence learning in Keras

The goal of this project is creating a simple Python package with the sklearn-like interface for solution of different seq2seq tasks: machine translation, question answering, decoding phonemes sequence into the word sequence, etc.

Getting Started

Installing

To install this project on your local machine, you should run the following commands in Terminal:

git clone https://github.com/bond005/seq2seq.git
cd seq2seq
sudo python setup.py

You can also run the tests:

python setup.py test

But I recommend you to use pip and install this package from PyPi:

pip install seq2seq-lstm

or (using sudo):

sudo pip install seq2seq-lstm

Usage

After installing the Seq2Seq-LSTM can be used as Python package in your projects. For example:

from seq2seq import Seq2SeqLSTM  # import the Seq2Seq-LSTM package
seq2seq = Seq2SeqLSTM()  # create new sequence-to-sequence transformer

To see the work of the Seq2Seq-LSTM on a large dataset, you can run a demo

python demo/seq2seq_lstm_demo.py

or (with saving model after its training):

python demo/seq2seq_lstm_demo.py some_file.pkl

In this demo, the Seq2Seq-LSTM learns to translate the sentences from English into Russian. If you specify the neural model file (for example, aforementioned some_file.pkl), then the learned neural model will be saved into this file for its loading instead of re-fitting at the next running.

The Russian-English sentence pairs from the Tatoeba Project have been used as data for unit tests and demo script (see http://www.manythings.org/anki/).