{ "info": { "author": "Ivan Bondarenko", "author_email": "bond005@yandex.ru", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development", "Topic :: Text Processing", "Topic :: Text Processing :: Linguistic" ], "description": "seq2seq-lstm\n============\n\nThe Seq2Seq-LSTM is a sequence-to-sequence classifier with the\nsklearn-like interface, and it uses the Keras package for neural\nmodeling.\n\nDeveloping of this module was inspired by Francois Chollet's tutorial\n`A ten-minute introduction to sequence-to-sequence learning in Keras\n`_\n\nThe goal of this project is creating a simple Python package with the\nsklearn-like interface for solution of different seq2seq tasks: machine\ntranslation, question answering, decoding phonemes sequence into the\nword sequence, etc.\n\nGetting Started\n---------------\n\nInstalling\n~~~~~~~~~~\n\nTo install this project on your local machine, you should run the\nfollowing commands in Terminal:\n\n.. code::\n\n git clone https://github.com/bond005/seq2seq.git\n cd seq2seq\n sudo python setup.py\n\nYou can also run the tests:\n\n.. code::\n\n python setup.py test\n\nBut I recommend you to use pip and install this package from PyPi:\n\n.. code::\n\n pip install seq2seq-lstm\n\nor (using ``sudo``):\n\n.. code::\n\n sudo pip install seq2seq-lstm\n\nUsage\n~~~~~\n\nAfter installing the Seq2Seq-LSTM can be used as Python package in your\nprojects. For example:\n\n.. code::\n\n from seq2seq import Seq2SeqLSTM # import the Seq2Seq-LSTM package\n seq2seq = Seq2SeqLSTM() # create new sequence-to-sequence transformer\n\nTo see the work of the Seq2Seq-LSTM on a large dataset, you can run a\ndemo\n\n.. code::\n \n python demo/seq2seq_lstm_demo.py\n\nor (with saving model after its training):\n\n.. code::\n \n python demo/seq2seq_lstm_demo.py some_file.pkl\n\nIn this demo, the Seq2Seq-LSTM learns to translate the sentences from\nEnglish into Russian. If you specify the neural model file (for example,\naforementioned ``some_file.pkl``), then the learned neural model will be\nsaved into this file for its loading instead of re-fitting at the next\nrunning.\n\nThe Russian-English sentence pairs from the Tatoeba Project have been\nused as data for unit tests and demo script (see\n`http://www.manythings.org/anki/ `_).", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/bond005/seq2seq", "keywords": "seq2seq,sequence-to-sequence,lstm,nlp,keras,scikit-learn", "license": "Apache License Version 2.0", "maintainer": "", "maintainer_email": "", "name": "seq2seq-lstm", "package_url": "https://pypi.org/project/seq2seq-lstm/", "platform": "", "project_url": "https://pypi.org/project/seq2seq-lstm/", "project_urls": { "Homepage": "https://github.com/bond005/seq2seq" }, "release_url": "https://pypi.org/project/seq2seq-lstm/0.1.4/", "requires_dist": null, "requires_python": "", "summary": "Sequence-to-sequence classifier based on LSTM with the simple sklearn-like interface", "version": "0.1.4" }, "last_serial": 3975254, "releases": { "0.1": [ { "comment_text": "", "digests": { "md5": "cdd7ff50694959e1f42078f70c413551", "sha256": "a4d66414b2b5acd4e5a6d6a0a055334b62a2390bbfca28d7a84fedf81d1e01a8" }, "downloads": -1, "filename": "seq2seq-lstm-0.1.tar.gz", "has_sig": false, "md5_digest": "cdd7ff50694959e1f42078f70c413551", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 14095, "upload_time": "2018-06-16T15:05:34", "url": "https://files.pythonhosted.org/packages/11/42/872c8619fd4a8fdf9c9d729bf011414cdf81877b7cfb040f26ed7cd95830/seq2seq-lstm-0.1.tar.gz" } ], "0.1.1": [ { "comment_text": "", "digests": { "md5": "d94ae077a51f669d153104b3a655ee78", "sha256": "c5e8f97d38708cab75b158c28fe885bbad94c0280f8e23a15e8230c70cdbd740" }, "downloads": -1, "filename": "seq2seq-lstm-0.1.1.tar.gz", "has_sig": false, "md5_digest": "d94ae077a51f669d153104b3a655ee78", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 14165, "upload_time": "2018-06-18T05:43:40", "url": "https://files.pythonhosted.org/packages/07/cb/20103599051c2cc0c32ce0fdb53864cd89c1c2a26358123d2c02d6ddb21c/seq2seq-lstm-0.1.1.tar.gz" } ], "0.1.2": [ { "comment_text": "", "digests": { "md5": "d22b1293ec581d304af478dfc05736b0", "sha256": "cd353fee5bd653929d3ca3a49fec6db1cce26d8b2c17830c1863482d5d59f3f6" }, "downloads": -1, "filename": "seq2seq-lstm-0.1.2.tar.gz", "has_sig": false, "md5_digest": "d22b1293ec581d304af478dfc05736b0", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 14380, "upload_time": "2018-06-18T07:54:29", "url": "https://files.pythonhosted.org/packages/34/d3/842f7edf70b495782a8a94a7962cef1bd2984dd6e1b4846d1aea10032922/seq2seq-lstm-0.1.2.tar.gz" } ], "0.1.3": [ { "comment_text": "", "digests": { "md5": "bfc57bceca2c0f4288894a0060073f32", "sha256": "831bb195ef4500f367cde38d283c97a26bb74ec813d0ca714d9a946d9ee34ed5" }, "downloads": -1, "filename": "seq2seq-lstm-0.1.3.tar.gz", "has_sig": false, "md5_digest": "bfc57bceca2c0f4288894a0060073f32", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 14419, "upload_time": "2018-06-18T08:00:27", "url": "https://files.pythonhosted.org/packages/16/f0/327ff1505631c3d9941e88624da2c2d06da0130f40bce866a1200af364d6/seq2seq-lstm-0.1.3.tar.gz" } ], "0.1.4": [ { "comment_text": "", "digests": { "md5": "26fae6a62b63ce0892b03bf7e1500e21", "sha256": "1b9cbf0e534596e3bb715aed70988b00c484ad2dda07e788ed81be4ce62510c7" }, "downloads": -1, "filename": "seq2seq-lstm-0.1.4.tar.gz", "has_sig": false, "md5_digest": "26fae6a62b63ce0892b03bf7e1500e21", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 14503, "upload_time": "2018-06-19T00:26:36", "url": "https://files.pythonhosted.org/packages/66/65/b81c53f5ddd3753e2ecd916403e4c1a255fdce8bcd961cdab71444c94ec0/seq2seq-lstm-0.1.4.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "26fae6a62b63ce0892b03bf7e1500e21", "sha256": "1b9cbf0e534596e3bb715aed70988b00c484ad2dda07e788ed81be4ce62510c7" }, "downloads": -1, "filename": "seq2seq-lstm-0.1.4.tar.gz", "has_sig": false, "md5_digest": "26fae6a62b63ce0892b03bf7e1500e21", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 14503, "upload_time": "2018-06-19T00:26:36", "url": "https://files.pythonhosted.org/packages/66/65/b81c53f5ddd3753e2ecd916403e4c1a255fdce8bcd961cdab71444c94ec0/seq2seq-lstm-0.1.4.tar.gz" } ] }