{
    "info": {
        "author": "Onur Celebi",
        "author_email": "celebio@fb.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "fastText |CircleCI|\n===================\n\n`fastText <https://fasttext.cc/>`__ is a library for efficient learning\nof word representations and sentence classification.\n\nIn this document we present how to use fastText in python.\n\nTable of contents\n-----------------\n\n-  `Requirements <#requirements>`__\n-  `Installation <#installation>`__\n-  `Usage overview <#usage-overview>`__\n-  `Word representation model <#word-representation-model>`__\n-  `Text classification model <#text-classification-model>`__\n-  `IMPORTANT: Preprocessing data / encoding\n   conventions <#important-preprocessing-data-encoding-conventions>`__\n-  `More examples <#more-examples>`__\n-  `API <#api>`__\n-  `train_unsupervised parameters <#train_unsupervised-parameters>`__\n-  `train_supervised parameters <#train_supervised-parameters>`__\n-  `model object <#model-object>`__\n\nRequirements\n============\n\n`fastText <https://fasttext.cc/>`__ builds on modern Mac OS and Linux\ndistributions. Since it uses C++11 features, it requires a compiler with\ngood C++11 support. You will need `Python <https://www.python.org/>`__\n(version 2.7 or \u2265 3.4), `NumPy <http://www.numpy.org/>`__ &\n`SciPy <https://www.scipy.org/>`__ and\n`pybind11 <https://github.com/pybind/pybind11>`__.\n\nInstallation\n============\n\nTo install the latest release, you can do :\n\n.. code:: bash\n\n    $ pip install fasttext\n\nor, to get the latest development version of fasttext, you can install\nfrom our github repository :\n\n.. code:: bash\n\n    $ git clone https://github.com/facebookresearch/fastText.git\n    $ cd fastText\n    $ sudo pip install .\n    $ # or :\n    $ sudo python setup.py install\n\nUsage overview\n==============\n\nWord representation model\n-------------------------\n\nIn order to learn word vectors, as `described\nhere <https://fasttext.cc/docs/en/references.html#enriching-word-vectors-with-subword-information>`__,\nwe can use ``fasttext.train_unsupervised`` function like this:\n\n.. code:: py\n\n    import fasttext\n\n    # Skipgram model :\n    model = fasttext.train_unsupervised('data.txt', model='skipgram')\n\n    # or, cbow model :\n    model = fasttext.train_unsupervised('data.txt', model='cbow')\n\nwhere ``data.txt`` is a training file containing utf-8 encoded text.\n\nThe returned ``model`` object represents your learned model, and you can\nuse it to retrieve information.\n\n.. code:: py\n\n    print(model.words)   # list of words in dictionary\n    print(model['king']) # get the vector of the word 'king'\n\nSaving and loading a model object\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nYou can save your trained model object by calling the function\n``save_model``.\n\n.. code:: py\n\n    model.save_model(\"model_filename.bin\")\n\nand retrieve it later thanks to the function ``load_model`` :\n\n.. code:: py\n\n    model = fasttext.load_model(\"model_filename.bin\")\n\nFor more information about word representation usage of fasttext, you\ncan refer to our `word representations\ntutorial <https://fasttext.cc/docs/en/unsupervised-tutorial.html>`__.\n\nText classification model\n-------------------------\n\nIn order to train a text classifier using the method `described\nhere <https://fasttext.cc/docs/en/references.html#bag-of-tricks-for-efficient-text-classification>`__,\nwe can use ``fasttext.train_supervised`` function like this:\n\n.. code:: py\n\n    import fasttext\n\n    model = fasttext.train_supervised('data.train.txt')\n\nwhere ``data.train.txt`` is a text file containing a training sentence\nper line along with the labels. By default, we assume that labels are\nwords that are prefixed by the string ``__label__``\n\nOnce the model is trained, we can retrieve the list of words and labels:\n\n.. code:: py\n\n    print(model.words)\n    print(model.labels)\n\nTo evaluate our model by computing the precision at 1 (P@1) and the\nrecall on a test set, we use the ``test`` function:\n\n.. code:: py\n\n    def print_results(N, p, r):\n        print(\"N\\t\" + str(N))\n        print(\"P@{}\\t{:.3f}\".format(1, p))\n        print(\"R@{}\\t{:.3f}\".format(1, r))\n\n    print_results(*model.test('test.txt'))\n\nWe can also predict labels for a specific text :\n\n.. code:: py\n\n    model.predict(\"Which baking dish is best to bake a banana bread ?\")\n\nBy default, ``predict`` returns only one label : the one with the\nhighest probability. You can also predict more than one label by\nspecifying the parameter ``k``:\n\n.. code:: py\n\n    model.predict(\"Which baking dish is best to bake a banana bread ?\", k=3)\n\nIf you want to predict more than one sentence you can pass an array of\nstrings :\n\n.. code:: py\n\n    model.predict([\"Which baking dish is best to bake a banana bread ?\", \"Why not put knives in the dishwasher?\"], k=3)\n\nOf course, you can also save and load a model to/from a file as `in the\nword representation usage <#saving-and-loading-a-model-object>`__.\n\nFor more information about text classification usage of fasttext, you\ncan refer to our `text classification\ntutorial <https://fasttext.cc/docs/en/supervised-tutorial.html>`__.\n\nCompress model files with quantization\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWhen you want to save a supervised model file, fastText can compress it\nin order to have a much smaller model file by sacrificing only a little\nbit performance.\n\n.. code:: py\n\n    # with the previously trained `model` object, call :\n    model.quantize(input='data.train.txt', retrain=True)\n\n    # then display results and save the new model :\n    print_results(*model.test(valid_data))\n    model.save_model(\"model_filename.ftz\")\n\n``model_filename.ftz`` will have a much smaller size than\n``model_filename.bin``.\n\nFor further reading on quantization, you can refer to `this paragraph\nfrom our blog\npost <https://fasttext.cc/blog/2017/10/02/blog-post.html#model-compression>`__.\n\nIMPORTANT: Preprocessing data / encoding conventions\n----------------------------------------------------\n\nIn general it is important to properly preprocess your data. In\nparticular our example scripts in the `root\nfolder <https://github.com/facebookresearch/fastText>`__ do this.\n\nfastText assumes UTF-8 encoded text. All text must be `unicode for\nPython2 <https://docs.python.org/2/library/functions.html#unicode>`__\nand `str for\nPython3 <https://docs.python.org/3.5/library/stdtypes.html#textseq>`__.\nThe passed text will be `encoded as UTF-8 by\npybind11 <https://pybind11.readthedocs.io/en/master/advanced/cast/strings.html?highlight=utf-8#strings-bytes-and-unicode-conversions>`__\nbefore passed to the fastText C++ library. This means it is important to\nuse UTF-8 encoded text when building a model. On Unix-like systems you\ncan convert text using `iconv <https://en.wikipedia.org/wiki/Iconv>`__.\n\nfastText will tokenize (split text into pieces) based on the following\nASCII characters (bytes). In particular, it is not aware of UTF-8\nwhitespace. We advice the user to convert UTF-8 whitespace / word\nboundaries into one of the following symbols as appropiate.\n\n-  space\n-  tab\n-  vertical tab\n-  carriage return\n-  formfeed\n-  the null character\n\nThe newline character is used to delimit lines of text. In particular,\nthe EOS token is appended to a line of text if a newline character is\nencountered. The only exception is if the number of tokens exceeds the\nMAX\\_LINE\\_SIZE constant as defined in the `Dictionary\nheader <https://github.com/facebookresearch/fastText/blob/master/src/dictionary.h>`__.\nThis means if you have text that is not separate by newlines, such as\nthe `fil9 dataset <http://mattmahoney.net/dc/textdata>`__, it will be\nbroken into chunks with MAX\\_LINE\\_SIZE of tokens and the EOS token is\nnot appended.\n\nThe length of a token is the number of UTF-8 characters by considering\nthe `leading two bits of a\nbyte <https://en.wikipedia.org/wiki/UTF-8#Description>`__ to identify\n`subsequent bytes of a multi-byte\nsequence <https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc>`__.\nKnowing this is especially important when choosing the minimum and\nmaximum length of subwords. Further, the EOS token (as specified in the\n`Dictionary\nheader <https://github.com/facebookresearch/fastText/blob/master/src/dictionary.h>`__)\nis considered a character and will not be broken into subwords.\n\nMore examples\n-------------\n\nIn order to have a better knowledge of fastText models, please consider\nthe main\n`README <https://github.com/facebookresearch/fastText/blob/master/README.md>`__\nand in particular `the tutorials on our\nwebsite <https://fasttext.cc/docs/en/supervised-tutorial.html>`__.\n\nYou can find further python examples in `the doc\nfolder <https://github.com/facebookresearch/fastText/tree/master/python/doc/examples>`__.\n\nAs with any package you can get help on any Python function using the\nhelp function.\n\nFor example\n\n::\n\n    +>>> import fasttext\n    +>>> help(fasttext.FastText)\n\n    Help on module fasttext.FastText in fasttext:\n\n    NAME\n        fasttext.FastText\n\n    DESCRIPTION\n        # Copyright (c) 2017-present, Facebook, Inc.\n        # All rights reserved.\n        #\n        # This source code is licensed under the MIT license found in the\n        # LICENSE file in the root directory of this source tree.\n\n    FUNCTIONS\n        load_model(path)\n            Load a model given a filepath and return a model object.\n\n        tokenize(text)\n            Given a string of text, tokenize it and return a list of tokens\n    [...]\n\nAPI\n===\n\n``train_unsupervised`` parameters\n---------------------------------\n\n.. code:: python\n\n        input             # training file path (required)\n        model             # unsupervised fasttext model {cbow, skipgram} [skipgram]\n        lr                # learning rate [0.05]\n        dim               # size of word vectors [100]\n        ws                # size of the context window [5]\n        epoch             # number of epochs [5]\n        minCount          # minimal number of word occurences [5]\n        minn              # min length of char ngram [3]\n        maxn              # max length of char ngram [6]\n        neg               # number of negatives sampled [5]\n        wordNgrams        # max length of word ngram [1]\n        loss              # loss function {ns, hs, softmax, ova} [ns]\n        bucket            # number of buckets [2000000]\n        thread            # number of threads [number of cpus]\n        lrUpdateRate      # change the rate of updates for the learning rate [100]\n        t                 # sampling threshold [0.0001]\n        verbose           # verbose [2]\n\n``train_supervised`` parameters\n-------------------------------\n\n.. code:: python\n\n        input             # training file path (required)\n        lr                # learning rate [0.1]\n        dim               # size of word vectors [100]\n        ws                # size of the context window [5]\n        epoch             # number of epochs [5]\n        minCount          # minimal number of word occurences [1]\n        minCountLabel     # minimal number of label occurences [1]\n        minn              # min length of char ngram [0]\n        maxn              # max length of char ngram [0]\n        neg               # number of negatives sampled [5]\n        wordNgrams        # max length of word ngram [1]\n        loss              # loss function {ns, hs, softmax, ova} [softmax]\n        bucket            # number of buckets [2000000]\n        thread            # number of threads [number of cpus]\n        lrUpdateRate      # change the rate of updates for the learning rate [100]\n        t                 # sampling threshold [0.0001]\n        label             # label prefix ['__label__']\n        verbose           # verbose [2]\n        pretrainedVectors # pretrained word vectors (.vec file) for supervised learning []\n\n``model`` object\n----------------\n\n``train_supervised``, ``train_unsupervised`` and ``load_model``\nfunctions return an instance of ``_FastText`` class, that we generaly\nname ``model`` object.\n\nThis object exposes those training arguments as properties : ``lr``,\n``dim``, ``ws``, ``epoch``, ``minCount``, ``minCountLabel``, ``minn``,\n``maxn``, ``neg``, ``wordNgrams``, ``loss``, ``bucket``, ``thread``,\n``lrUpdateRate``, ``t``, ``label``, ``verbose``, ``pretrainedVectors``.\nSo ``model.wordNgrams`` will give you the max length of word ngram used\nfor training this model.\n\nIn addition, the object exposes several functions :\n\n.. code:: python\n\n        get_dimension           # Get the dimension (size) of a lookup vector (hidden layer).\n                                # This is equivalent to `dim` property.\n        get_input_vector        # Given an index, get the corresponding vector of the Input Matrix.\n        get_input_matrix        # Get a copy of the full input matrix of a Model.\n        get_labels              # Get the entire list of labels of the dictionary\n                                # This is equivalent to `labels` property.\n        get_line                # Split a line of text into words and labels.\n        get_output_matrix       # Get a copy of the full output matrix of a Model.\n        get_sentence_vector     # Given a string, get a single vector represenation. This function\n                                # assumes to be given a single line of text. We split words on\n                                # whitespace (space, newline, tab, vertical tab) and the control\n                                # characters carriage return, formfeed and the null character.\n        get_subword_id          # Given a subword, return the index (within input matrix) it hashes to.\n        get_subwords            # Given a word, get the subwords and their indicies.\n        get_word_id             # Given a word, get the word id within the dictionary.\n        get_word_vector         # Get the vector representation of word.\n        get_words               # Get the entire list of words of the dictionary\n                                # This is equivalent to `words` property.\n        is_quantized            # whether the model has been quantized\n        predict                 # Given a string, get a list of labels and a list of corresponding probabilities.\n        quantize                # Quantize the model reducing the size of the model and it's memory footprint.\n        save_model              # Save the model to the given path\n        test                    # Evaluate supervised model using file given by path\n        test_label              # Return the precision and recall score for each label.\n\nThe properties ``words``, ``labels`` return the words and labels from\nthe dictionary :\n\n.. code:: py\n\n    model.words         # equivalent to model.get_words()\n    model.labels        # equivalent to model.get_labels()\n\nThe object overrides ``__getitem__`` and ``__contains__`` functions in\norder to return the representation of a word and to check if a word is\nin the vocabulary.\n\n.. code:: py\n\n    model['king']       # equivalent to model.get_word_vector('king')\n    'king' in model     # equivalent to `'king' in model.get_words()`\n\nJoin the fastText community\n---------------------------\n\n-  `Facebook page <https://www.facebook.com/groups/1174547215919768>`__\n-  `Stack\n   overflow <https://stackoverflow.com/questions/tagged/fasttext>`__\n-  `Google\n   group <https://groups.google.com/forum/#!forum/fasttext-library>`__\n-  `GitHub <https://github.com/facebookresearch/fastText>`__\n\n.. |CircleCI| image:: https://circleci.com/gh/facebookresearch/fastText/tree/master.svg?style=svg\n   :target: https://circleci.com/gh/facebookresearch/fastText/tree/master\n\n\n",
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