Metadata-Version: 1.1
Name: seq2seq-lstm
Version: 0.1.5
Summary: Sequence-to-sequence classifier based on LSTM with the simple sklearn-like interface
Home-page: https://github.com/bond005/seq2seq
Author: Ivan Bondarenko
Author-email: bond005@yandex.ru
License: Apache License Version 2.0
Description: 
        seq2seq-lstm
        ============
        
        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
        <https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html>`_
        
        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:
        
        .. code::
        
            git clone https://github.com/bond005/seq2seq.git
            cd seq2seq
            sudo python setup.py
        
        You can also run the tests:
        
        .. code::
        
            python setup.py test
        
        But I recommend you to use pip and install this package from PyPi:
        
        .. code::
        
            pip install seq2seq-lstm
        
        or (using ``sudo``):
        
        .. code::
        
            sudo pip install seq2seq-lstm
        
        Usage
        ~~~~~
        
        After installing the Seq2Seq-LSTM can be used as Python package in your
        projects. For example:
        
        .. code::
        
            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
        
        .. code::
         
            python demo/seq2seq_lstm_demo.py
        
        or (with saving model after its training):
        
        .. code::
         
            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/ <http://www.manythings.org/anki/>`_).
        
        
Keywords: seq2seq,sequence-to-sequence,lstm,nlp,keras,scikit-learn
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing
Classifier: Topic :: Text Processing :: Linguistic
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
