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    "info": {
        "author": "Gaurav",
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            "License :: OSI Approved :: MIT License",
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        "description": "## Natural Language Toolkit for Indic Languages (iNLTK)\n\n[![Gitter](https://badges.gitter.im/inltk/community.svg)](https://gitter.im/inltk/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n\niNLTK aims to provide out of the box support for various NLP tasks \nthat an application developer might need for Indic languages.\n\n![Alt Text](inltk/static/inltk.gif)\n\n### Installation\n\n```bash\npip install http://download.pytorch.org/whl/cpu/torch-1.0.0-cp36-cp36m-linux_x86_64.whl\npip install inltk\n```\n\niNLTK runs on CPU, as is the desired behaviour for most\nof the Deep Learning models in production.\n\nThe first command above will install pytorch-cpu, which, as the name suggests,\n does not have cuda support. \n\n`Note: inltk is currently supported only on Linux with Python >= 3.6`\n\n### Installation on windows (experimental)\n```bash\npip install https://download.pytorch.org/whl/cpu/torch-1.1.0-cp36-cp36m-win_amd64.whl\npip install inltk\n```\n\n### Supported languages\n\n| Language | Code <code-of-language> |\n|:--------:|:----:|\n|   Hindi  |  hi  |\n|  Punjabi |  pa  |\n| Sanskrit |  sa  |\n| Gujarati |  gu  |\n|  Kannada |  kn  |\n| Malayalam |  ml  |\n|  Nepali  |  ne  |\n|   Odia   |  or  |\n|  Marathi |  mr  |\n|  Bengali |  bn  |\n|   Tamil  |  ta  |\n|   Urdu  |  ur  |\n\n### Usage\n\n**Setup the language**\n\n```bash\nfrom inltk.inltk import setup\n\nsetup('<code-of-language>') // if you wanted to use hindi, then setup('hi')\n```\n\n`Note: You need to run setup('<code-of-language>') when you use a language \nfor the FIRST TIME ONLY. This will download all the necessary models required\nto do inference for that language.`\n\n**Tokenize**\n\n```bash\nfrom inltk.inltk import tokenize\n\ntokenize(text ,'<code-of-language>') // where text is string in <code-of-language>\n```\n\n**Get Embedding Vectors**\n\nThis returns an array of \"Embedding vectors\", containing 400 Dimensional representation for \nevery token in the text.\n\n\n```\nfrom inltk.inltk import get_embedding_vectors\n\nvectors = get_embedding_vectors(text, '<code-of-language>') // where text is string in <code-of-language>\n\nExample:\n\n>> vectors = get_embedding_vectors('\u092d\u093e\u0930\u0924', 'hi')\n>> vectors[0].shape\n(400,)\n\n>> get_embedding_vectors('\u0a1c\u0a3f\u0a39\u0a28\u0a3e\u0a02 \u0a24\u0a4b\u0a02 \u0a27\u0a3e\u0a24\u0a35\u0a40 \u0a05\u0a32\u0a4c\u0a39 \u0a26\u0a3e \u0a06\u0a30\u0a25\u0a15','pa')\n[array([-0.894777, -0.140635, -0.030086, -0.669998, ...,  0.859898,  1.940608,  0.09252 ,  1.043363], dtype=float32), array([ 0.290839,  1.459981, -0.582347,  0.27822 , ..., -0.736542, -0.259388,  0.086048,  0.736173], dtype=float32), array([ 0.069481, -0.069362,  0.17558 , -0.349333, ...,  0.390819,  0.117293, -0.194081,  2.492722], dtype=float32), array([-0.37837 , -0.549682, -0.497131,  0.161678, ...,  0.048844, -1.090546,  0.154555,  0.925028], dtype=float32), array([ 0.219287,  0.759776,  0.695487,  1.097593, ...,  0.016115, -0.81602 ,  0.333799,  1.162199], dtype=float32), array([-0.31529 , -0.281649, -0.207479,  0.177357, ...,  0.729619, -0.161499, -0.270225,  2.083801], dtype=float32), array([-0.501414,  1.337661, -0.405563,  0.733806, ..., -0.182045, -1.413752,  0.163339,  0.907111], dtype=float32), array([ 0.185258, -0.429729,  0.060273,  0.232177, ..., -0.537831, -0.51664 , -0.249798,  1.872428], dtype=float32)]\n>> vectors = get_embedding_vectors('\u0a1c\u0a3f\u0a39\u0a28\u0a3e\u0a02 \u0a24\u0a4b\u0a02 \u0a27\u0a3e\u0a24\u0a35\u0a40 \u0a05\u0a32\u0a4c\u0a39 \u0a26\u0a3e \u0a06\u0a30\u0a25\u0a15','pa')\n>> len(vectors)\n8\n\n```\n\nTo get a feel of embeddings, checkout\n [this visualization of subset of Hindi Embedding vectors](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-hindi/master/language-model/embedding_projector_config.json)\n\n**Predict Next 'n' words**\n\n```bash\nfrom inltk.inltk import predict_next_words\n\npredict_next_words(text , n, '<code-of-language>') \n\n// text --> string in <code-of-language>\n// n --> number of words you want to predict (integer)\n```\n\n`Note: You can also pass a fourth parameter, randomness, to predict_next_words.\nIt has a default value of 0.8`\n\n**Identify language**\n\nNote: If you update the version of iNLTK, you need to run \n`reset_language_identifying_models` before identifying language.\n\n```bash\nfrom inltk.inltk import identify_language, reset_language_identifying_models\n\nreset_language_identifying_models() # only if you've updated iNLTK version\nidentify_language(text)\n\n// text --> string in one of the supported languages\n\nExample:\n\n>> identify_language('\u0928\u094d\u092f\u093e\u092f\u0926\u0930\u094d\u0936\u0928\u092e\u094d \u092d\u093e\u0930\u0924\u0940\u092f\u0926\u0930\u094d\u0936\u0928\u0947\u0937\u0941 \u0905\u0928\u094d\u092f\u0924\u092e\u092e\u094d\u0964 \u0935\u0948\u0926\u093f\u0915\u0926\u0930\u094d\u0936\u0928\u0947\u0937\u0941 ')\n'sanskrit'\n\n```\n\n**Remove foreign languages**\n\n```bash\nfrom inltk.inltk import remove_foreign_languages\n\nremove_foreign_languages(text, '<code-of-language>')\n\n// text --> string in one of the supported languages\n// <code-of-language> --> code of that language whose words you want to retain\n\nExample:\n\n>> remove_foreign_languages('\u0935\u093f\u0915\u093f\u092a\u0940\u0921\u093f\u092f\u093e \u0938\u092d\u0940 \u0935\u093f\u0937\u092f\u094b\u0902 \u0a07\u0a71\u0a15 \u0a05\u0a32\u0a4c\u0a15\u0a3f\u0a15 \u0a28\u0a1c\u0a3c\u0a3e\u0a30\u0a3e \u0a2c\u0a71\u0a1d\u0a3e \u0a39\u0a4b\u0a07\u0a06 \u0a38\u0a3e\u0a39\u0a2e\u0a23\u0a47 \u0a06 \u0a16\u0a32\u0a4b\u0a02\u0a26\u0a3e \u0a38\u0a40 \u092a\u0930 \u092a\u094d\u0930\u093e\u092e\u093e\u0923\u093f\u0915 \u0914\u0930 \u7ef4\u57fa\u767e\u79d1:\u5173\u4e8e\u4e2d\u6587\u7ef4\u57fa\u767e\u79d1 \u0909\u092a\u092f\u094b\u0917, \u092a\u0930\u093f\u0935\u0930\u094d\u0924\u0928 \u7ef4\u57fa\u767e\u79d1:\u5173\u4e8e\u4e2d\u6587\u7ef4\u57fa\u767e\u79d1', 'hi')\n['\u2581\u0935\u093f\u0915\u093f\u092a\u0940\u0921\u093f\u092f\u093e', '\u2581\u0938\u092d\u0940', '\u2581\u0935\u093f\u0937\u092f\u094b\u0902', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581', '<unk>', '\u2581\u092a\u0930', '\u2581\u092a\u094d\u0930\u093e\u092e\u093e\u0923\u093f\u0915', '\u2581\u0914\u0930', '\u2581', '<unk>', ':', '<unk>', '\u2581\u0909\u092a\u092f\u094b\u0917', ',', '\u2581\u092a\u0930\u093f\u0935\u0930\u094d\u0924\u0928', '\u2581', '<unk>', ':', '<unk>']\n```\n\nEvery word other than that of host language will become `<unk>` and `\u2581` signifies `space character`\n\nCheckout [this notebook](https://drive.google.com/file/d/0B3K0rqnCfC9pbVpSWk9Ndm5raGRCdjV6cGxVN1BGWFhTTlA0/view?usp=sharing)\n by [Amol Mahajan](https://www.linkedin.com/in/amolmahajan0804/) where he uses iNLTK to remove foreign characters from\n [iitb_en_hi_parallel corpus](http://www.cfilt.iitb.ac.in/iitb_parallel/iitb_corpus_download/)\n\n#### Repositories containing models used in iNLTK\n\n| Language | Repository                                                      | Perplexity of Language model | Wikipedia Articles Dataset |   Classification accuracy   |     Classification Kappa score    |\n|:--------:|-----------------------------------------------------------------|:----------------------------:|:--------------------------:|:---------------------------:|:---------------------------------:|\n|   Hindi  | [NLP for Hindi](https://github.com/goru001/nlp-for-hindi)       |              ~36             |       55,000 articles      |  ~79 (News Classification)  | ~30 (Movie Review Classification) |\n|  Punjabi | [NLP for Punjabi](https://github.com/goru001/nlp-for-punjabi)   |              ~13             |       44,000 articles      | ~89 (News Classification)   |     ~60 (News Classification)     |\n| Sanskrit | [NLP for Sanskrit](https://github.com/goru001/nlp-for-sanskrit) |              ~6              |       22,273 articles      | ~70 (Shloka Classification) |    ~56 (Shloka Classification)    |\n| Gujarati | [NLP for Gujarati](https://github.com/goru001/nlp-for-gujarati) |              ~34             |       31,913 articles      |  ~91 (News Classification)  |     ~85 (News Classification)     |\n|  Kannada | [NLP for Kannada](https://github.com/goru001/nlp-for-kannada)   |              ~70             |       32,997 articles      |  ~94 (News Classification)  |     ~90 (News Classification)     |\n| Malayalam | [NLP for Malayalam](https://github.com/goru001/nlp-for-malyalam) |              ~26             |       12,388 articles      |  ~94 (News Classification)  |     ~91 (News Classification)     |\n|  Nepali  | [NLP for Nepali](https://github.com/goru001/nlp-for-nepali)     |              ~32             |       38,757 articles      |  ~97 (News Classification)  |     ~96 (News Classification)     |\n|   Odia   | [NLP for Odia](https://github.com/goru001/nlp-for-odia)         |              ~27             |       17,781 articles      |  ~95 (News Classification)  |     ~92 (News Classification)     |\n|  Marathi | [NLP for Marathi](https://github.com/goru001/nlp-for-marathi)   |              ~18             |       85,537 articles      |  ~91 (News Classification)  |     ~84 (News Classification)     |\n|  Bengali | [NLP for Bengali](https://github.com/goru001/nlp-for-bengali)   |              ~41             |       72,374 articles      |  ~94 (News Classification)  |     ~92 (News Classification)     |\n|   Tamil  | [NLP for Tamil](https://github.com/goru001/nlp-for-tamil)       |              ~20             |       >127,000 articles    |  ~97 (News Classification)  |     ~95 (News Classification)     |\n|   Urdu  | [NLP for Urdu](https://github.com/anuragshas/nlp-for-urdu)       |              ~13             |       >150,000 articles    |  ~94 (News Classification)  |     ~90 (News Classification)     |\n\n### Contributing\n\n**Add a new language support for iNLTK**\n\nIf you would like to add support for language of your own choice to iNLTK,\n please start with checking/raising a issue [here](https://github.com/goru001/inltk/issues)\n\nPlease checkout the steps I'd [mentioned here for Telugu](https://github.com/goru001/inltk/issues/1)\nto begin with. They should be almost similar for other languages as well.\n\n**Improving models/Using models for your own research**\n\nIf you would like to take iNLTK's models and refine them with your own \ndataset or build your own custom models on top of it, please check out the \nrepositories in the above table for the language of your choice. The repositories above \ncontain links to datasets, pretrained models, classifiers and all of the code for that.\n\n**Add new functionality**\n\nIf you wish for a particular functionality in iNLTK - Start by checking/raising a issue [here](https://github.com/goru001/inltk/issues)\n\n### What's next (and being worked upon)\n\n`Shout out if you want to help :)`\n\n* Add [Telugu](https://github.com/goru001/inltk/issues/1) \nand [Maithili](https://github.com/goru001/inltk/issues/10) support\n* Add NER support\n* Add Textual Entailment support\n* Add English to iNLTK\n\n\n### What's next - (and NOT being worked upon)\n\n`Shout out if you want to lead :)`\n\n* Work on a unified model for all the languages\n* Add translations - to and from languages in iNLTK + English\n\n### Appreciation for iNLTK \n\n* [By Jeremy Howard on Twitter](https://twitter.com/jeremyphoward/status/1111318198891110402)\n* [By Vincent Boucher on LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:6517137647310241792/)\n* [By Kanimozhi](https://www.linkedin.com/feed/update/urn:li:activity:6517277916030701568), [By Soham](https://www.linkedin.com/feed/update/urn:li:activity:6513084638955696128) on LinkedIn\n\n\n\n\n",
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