01_Code_Mixing_demo.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/Code-Mixing/blob/main/01_Code_Mixing_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "### Studying CSS patterns of Indian Speakers in Code-Mixed Tweets\n",
        "\n",
        "In this notebook, we will learn one of the most useful ways of processing Code-Mixed text from twitter (by utilizing word-level language identification), and then analyze initial patterns in the data that consists of tweets from/about Indian speakers.\n",
        "\n",
        " Code walkthrugh video : https://huggingface.co/docs/transformers/model_doc/m2m_100"
      ],
      "metadata": {
        "id": "xSQdCdTEYcCh"
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    {
      "cell_type": "markdown",
      "source": [
        "### Downloading and Preparing Data"
      ],
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      "cell_type": "markdown",
      "source": [
        "#### 1. About Data\n",
        "\n",
        "- We are using a custom dataset that was created for this class specifically. It has 510 tweets from/about Indian speakers.\n",
        "- We have sampled some code-mixed English-Hindi tweets in roman script from [Silent Flame's NER Code-Mixed Corpus](https://github.com/SilentFlame/Named-Entity-Recognition).\n",
        "- We have extracted tweets related to the query `india` from Twitter, using [twitter's developer API](https://developer.twitter.com/en/docs/twitter-api/tweets/search/introduction)."
      ],
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            "--2022-08-10 07:22:33--  https://raw.githubusercontent.com/mohdsanadzakirizvi/plaksha_rasa/main/assignments/plaksha_tweet_data_1.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 67390 (66K) [text/plain]\n",
            "Saving to: ‘plaksha_tweet_data_1.csv’\n",
            "\n",
            "\rplaksha_tweet_data_   0%[                    ]       0  --.-KB/s               \rplaksha_tweet_data_ 100%[===================>]  65.81K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2022-08-10 07:22:33 (53.9 MB/s) - ‘plaksha_tweet_data_1.csv’ saved [67390/67390]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://raw.githubusercontent.com/mohdsanadzakirizvi/plaksha_rasa/main/assignments/plaksha_tweet_data_1.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Exploring the Data"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "import pandas as pd\n",
        "\n",
        "tweetdf = pd.read_csv('plaksha_tweet_data_1.csv')\n",
        "tweetdf"
      ],
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              "                                                 Tweet\n",
              "0    RT @RavinderKapur2: Narendra Modi and Devendra...\n",
              "1    RT @MahuaMoitra: It is perhaps India’s greates...\n",
              "2    RT @ActualidadRT: Pakistán e India ignoran a W...\n",
              "3    @kimpaim A Índia de Taubaté é  a que estava no...\n",
              "4    RT @Zee_Hindustan: Shehnaaz Gill ने 2019 में ह...\n",
              "..                                                 ...\n",
              "506  RT @ZEE5India: We couldn’t find a better way t...\n",
              "507  Yeduurappa is honest, Jay Shah is honest, Mopa...\n",
              "508  election agenda toh #RamMandir hai kabse bol r...\n",
              "509  Bengal Arson: 2 Kids Among 8 Killed in Birbhum...\n",
              "510  We take so much pain in studying for CA and as...\n",
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              "      <td>RT @Zee_Hindustan: Shehnaaz Gill ने 2019 में ह...</td>\n",
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      "source": [
        "We notice that there are certain inconsistencies in the raw tweets, let's do a basic round of preprocessing to fix that. We will normalize the text by first converting everything to lower case and then removing URLs and non-alphabetic characters."
      ],
      "metadata": {
        "id": "ot8-2ieFrhaQ"
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      "cell_type": "code",
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        "import re\n",
        "\n",
        "def text_cleaner(text):\n",
        "    #converting to lowercase\n",
        "    newString = text.lower()\n",
        "    #removing links\n",
        "    newString = re.sub(r'(https|http)?:\\/\\/(\\w|\\.|\\/|\\?|\\=|\\&|\\%)*\\b', '', newString) \n",
        "    #fetching alphabetic characters\n",
        "    newString = re.sub('[^a-zA-Z#@]', ' ', newString)\n",
        "    return newString"
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        "tweetdf['cleaned_text'] = tweetdf['Tweet'].apply(text_cleaner)\n",
        "\n",
        "tweetdf.head()"
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              "                                               Tweet  \\\n",
              "0  RT @RavinderKapur2: Narendra Modi and Devendra...   \n",
              "1  RT @MahuaMoitra: It is perhaps India’s greates...   \n",
              "2  RT @ActualidadRT: Pakistán e India ignoran a W...   \n",
              "3  @kimpaim A Índia de Taubaté é  a que estava no...   \n",
              "4  RT @Zee_Hindustan: Shehnaaz Gill ने 2019 में ह...   \n",
              "\n",
              "                                        cleaned_text  \n",
              "0  rt @ravinderkapur   narendra modi and devendra...  \n",
              "1  rt @mahuamoitra  it is perhaps india s greates...  \n",
              "2  rt @actualidadrt  pakist n e india ignoran a w...  \n",
              "3  @kimpaim a  ndia de taubat     a que estava no...  \n",
              "4  rt @zee hindustan  shehnaaz gill              ...  "
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              "      <td>RT @Zee_Hindustan: Shehnaaz Gill ने 2019 में ह...</td>\n",
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      "cell_type": "markdown",
      "source": [
        "Now that we've cleaned the tweets, let's build a word cloud and see what kind of word frequency distribution we have in the underlying data."
      ],
      "metadata": {
        "id": "G6Ahhe3MsHCc"
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    },
    {
      "cell_type": "code",
      "source": [
        "#Import libraries\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "#Generate word cloud\n",
        "total_text = \" \".join(tweetdf.cleaned_text.tolist())\n",
        "wc = WordCloud(width=400, height=330, max_words=100, background_color='white').generate_from_text(total_text)\n",
        "plt.figure(figsize=(12, 8))\n",
        "plt.imshow(wc, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "O_t8Joa2O1Ch",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "b5fad271-94ef-47a5-e9b7-5163bf88abb5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above visualization gives a rudimentary overview of topic/word distribution in the data. We can see some of the topics that are recurring in the data. We also notice that lot of high-frequency words are code-mixed words from Hindi written in roman script."
      ],
      "metadata": {
        "id": "ZpKc-_GHsQxx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Installing LID-tool\n",
        "\n"
      ],
      "metadata": {
        "id": "A5US2Fa5xxZu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### About LID-tool \n",
        "\n",
        "- It is a word level language identification tool for identifying Code-Mixed text of languages (like Hindi etc.) written in roman script and mixed with English.\n",
        "\n",
        "- At a broader level, we utilize a ML classifier that's trained using [MALLET](https://mimno.github.io/Mallet/index) to generate word level probabilities for language tags. We then utilize these probabilities along with the context information of surrounding words to generate language tags for each word of the input. \n",
        "\n",
        "- We also use hand-crafted dictionaries as look-up tables to cover unique, corner and conflicting cases to give a robust language identification tool. \n",
        "\n",
        "- Note: LID is shorthand for [Language Identification](https://en.wikipedia.org/wiki/Language_identification).\n",
        "\n",
        "Now that we've learned about the tool, let's install it!\n"
      ],
      "metadata": {
        "id": "xcQ2FHebtu9s"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "M0BoSxoLBluy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "082ada5e-6d2a-45a8-f202-f05bdc31debc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'LID-tool'...\n",
            "remote: Enumerating objects: 54, done.\u001b[K\n",
            "remote: Counting objects: 100% (54/54), done.\u001b[K\n",
            "remote: Compressing objects: 100% (47/47), done.\u001b[K\n",
            "remote: Total 54 (delta 6), reused 39 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (54/54), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/microsoft/LID-tool.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls LID-tool/"
      ],
      "metadata": {
        "id": "EPchoMWaB7EZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6caf8ec-c385-4c0f-aa25-831cab02adc9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classifiers\t    getLanguage.py  sampleinp.txt\t  tests\n",
            "CODE_OF_CONDUCT.md  images\t    sampleinp.txt_tagged  tmp\n",
            "config.ini\t    LICENSE\t    sampleoutp.txt\t  Train_Custom_LID.md\n",
            "dictionaries\t    README.md\t    SECURITY.md\t\t  utils\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
        "!unzip mallet-2.0.8.zip"
      ],
      "metadata": {
        "id": "O4aytLu-Vpo7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a72eb51-334d-42cf-8cbe-8efc96a62b96"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-10 07:22:35--  http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
            "Resolving mallet.cs.umass.edu (mallet.cs.umass.edu)... 128.119.246.70\n",
            "Connecting to mallet.cs.umass.edu (mallet.cs.umass.edu)|128.119.246.70|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://mallet.cs.umass.edu/dist/mallet-2.0.8.zip [following]\n",
            "--2022-08-10 07:22:35--  https://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
            "Connecting to mallet.cs.umass.edu (mallet.cs.umass.edu)|128.119.246.70|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16184794 (15M) [application/zip]\n",
            "Saving to: ‘mallet-2.0.8.zip’\n",
            "\n",
            "mallet-2.0.8.zip    100%[===================>]  15.43M  59.0MB/s    in 0.3s    \n",
            "\n",
            "2022-08-10 07:22:35 (59.0 MB/s) - ‘mallet-2.0.8.zip’ saved [16184794/16184794]\n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/grmm/types/PottsTableFactor.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/grmm/util/RememberTokenizationPipe.class  \n",
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            "   creating: mallet-2.0.8/class/cc/mallet/optimize/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/AGIS.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizableCollection$ByGradientValue.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizationException.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizer$ByBatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizerEvaluator$ByBatchGradient.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OrthantWiseLimitedMemoryBFGS.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/StochasticMetaAscent.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/optimize/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/tests/TestOptimizable$SimplePoly.class  \n",
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            "   creating: mallet-2.0.8/class/cc/mallet/pipe/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AddClassifierTokenPredictions$TokenClassifiers.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AddClassifierTokenPredictions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Array2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AugmentableFeatureVectorAddConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AugmentableFeatureVectorLogScale.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$BranchingInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$GateKeepingInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$PeekingInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequence2CharNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequence2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceArray2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceLowercase.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML$ParserGetter.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML$TagStripper.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveUUEncodedBlocks.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceReplace.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSubsequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Classification2ConfidencePredictingFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Csv2Array.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Csv2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Directory2FileIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureCountPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureDocFreqPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureSequence2AugmentableFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureSequence2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureSequenceConvolution.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureValueString2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureVectorConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureVectorSequence2FeatureVectors$FeatureVectorIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureVectorSequence2FeatureVectors.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Filename2CharSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FilterEmptyFeatureVectors$FilteringPipeInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FilterEmptyFeatureVectors.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FixedVocabTokenizer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Input2CharSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/InstanceListTrimFeaturesByCount.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/iterator/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ArrayDataAndTargetIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ArrayIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ConcatenatedInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/CsvIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/DBInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/EmptyInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/FileIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/FileListIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/FileUriIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/LineGroupIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/LineIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ParenGroupIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/PatternMatchIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/PipeExtendedIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/PipeInputIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/RandomFeatureVectorIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/RandomTokenSequenceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/SegmentIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/SelectiveFileLineIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/SimpleFileLineIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/StringArrayIterator.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/iterator/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/tests/TestPatternMatchIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/UnlabeledFileIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/LineGroupString2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/MakeAmpersandXMLFriendly.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/NGramPreprocessor$ReplacementSet.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/NGramPreprocessor.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Noop.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Pipe$SimplePipeInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Pipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PipeException.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PipeUtils.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PrintInput.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PrintInputAndTarget.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PrintTokenSequenceFeatures.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SaveDataInSource.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SelectiveSGML2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SerialPipes$Predicate.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SerialPipes.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SGML2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SimpleTaggerSentence2StringTokenization.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SimpleTaggerSentence2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SimpleTokenizer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SourceLocation2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/StringAddNewLineDelimiter.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/StringList2FeatureSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SvmLight2FeatureVectorAndLabel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Target2Double.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Target2LabelSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TargetRememberLastLabel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TargetStringToFeatures.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestInstancePipe$Array2ArrayIterator.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestIterators.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestPipeUtils$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestPipeUtils$StupidPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestPipeUtils.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestRainbowStyle.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestSGML2TokenSequence$Array2ArrayIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestSGML2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestSpacePipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Token2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2FeatureSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2FeatureSequenceWithBigrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2FeatureVectorSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2TokenInstances$TokenInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2TokenInstances.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceLowercase.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceMatchDataAndTarget.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceParseFeatureString.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceRemoveNonAlpha.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceRemoveStopPatterns.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceRemoveStopwords.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/tsf/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/CountMatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/CountMatchesAlignedWithOffsets.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/CountMatchesMatching.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/FeaturesInWindow.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/FeaturesOfFirstMention.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/LexiconMembership.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/OffsetConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/OffsetFeatureConjunction.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/OffsetPropertyConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/RegexMatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/SequencePrintingPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/Target2BIOFormat.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/TestOffsetConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/TestOffsetFeatureConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/TestSequencePrintingPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenFirstPosition.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenText.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextCharNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextCharPrefix.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextCharSuffix.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TrieLexiconMembership$TrieLexicon.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TrieLexiconMembership.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/WordVectors.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/ValueString2FeatureVector.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/regression/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/CoordinateDescent.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/LeastSquares.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/LinearRegression.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/LinearRegressionTrainer.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/regression/tui/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/tui/Regression.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/tui/RegressionImporter.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/casutton/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/casutton/ner/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/casutton/ner/ConllNer2003Sentence2TokenSequence.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/mccallum/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/ConllNer2003Sentence2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/TokenSequenceDocHeader.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/TUI.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/upenn/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/MaxEntShell.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/FeatureWindow.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/LengthBins.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/ListMember.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/LongRegexMatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/NEPipes.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/weili/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/weili/ner/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/weili/ner/enron/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/weili/ner/enron/EnronMessage2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/weili/ner/enron/TUI.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/weili/ner/WordTransformation.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/topics/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/AbstractTopicReports.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/DMROptimizable.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/DMRTopicModel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/HierarchicalLDA$NCRPNode.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/HierarchicalLDA.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/HierarchicalPAM.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/JSONTopicReports.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LabeledLDA.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LDA$1WordProb.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LDA.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LDAHyper$Topication.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LDAHyper.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LDAStream.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/MarginalProbEstimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/MultinomialHMM.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/NPTopicModel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/PAM4L$IDSorter.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/PAM4L.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/ParallelTopicModel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/PolylingualTopicModel$TopicAssignment.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/PolylingualTopicModel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/RTopicModel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/SimpleLDA.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/TopicalNGrams$1WordProb.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/TopicalNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/TopicAssignment.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/TopicInferencer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/TopicModelDiagnostics$TopicScores.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/TopicModelDiagnostics.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/TopicReports.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/topics/tui/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/tui/DMRLoader.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/tui/EvaluateTopics.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/tui/HierarchicalLDATUI.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/tui/InferTopics.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/tui/TopicTrainer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/tui/Vectors2Topics.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/WeightedTopicModel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/WordEmbeddingRunnable.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/WordEmbeddings.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/WorkerRunnable.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/types/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Alphabet.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/AlphabetCarrying.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/AlphabetFactory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ArrayListSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ArraySequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/AugmentableFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/BiNormalSeparation$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/BiNormalSeparation.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/CachedMetric.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ChainedInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ConstantMatrix.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/CrossValidationIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/DenseMatrix.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/DenseVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Dirichlet$Estimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Dirichlet$MethodOfMomentsEstimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Dirichlet.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/EuclideanDistance.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ExpGain$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ExpGain.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureConjunction$List.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureConjunction.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureCounter.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureCounts$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureCounts.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureInducer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureSelection.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureSelector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureSequenceWithBigrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureVectorSequence$Iterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/FeatureVectorSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/GainRatio$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/GainRatio.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/GradientGain$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/GradientGain.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/HashedSparseVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/IDSorter.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/IndexedSparseVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InfiniteDistance.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InfoGain$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InfoGain.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Instance.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InstanceList$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InstanceList$CrossValidationIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InstanceList$NotYetSetPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InstanceList.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InstanceListTUI.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/InvertedIndex.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/KLGain.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Label.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/LabelAlphabet.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Labeler.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Labeling.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Labelings.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Labels.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/LabelSequence$Iterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/LabelSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/LabelsSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/LabelVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/LogNumber.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ManhattenDistance.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Matrix.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Matrix2.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Matrixn.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/MatrixOps.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Metric.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Minkowski.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/MultiInstanceList$MultiIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/MultiInstanceList.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$Estimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$LaplaceEstimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$Logged.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$MAPEstimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$MEstimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$MLEstimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/NormalizedDotProductMetric.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/NullLabel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PagedInstanceList.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PartiallyRankedFeatureVector$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PartiallyRankedFeatureVector$PerLabelFactory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PartiallyRankedFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PerLabelFeatureCounts$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PerLabelFeatureCounts.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PerLabelInfoGain$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PerLabelInfoGain.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PropertyHolder.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/RankedFeatureVector$EntryWithOriginalIndex.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/RankedFeatureVector$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/RankedFeatureVector$PerLabelFactory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/RankedFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/ROCData.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Sequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/SequencePair.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/SequencePairAlignment.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/SingleInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/SparseMatrixn.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/SparseVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/StringEditFeatureVectorSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/StringEditVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/StringKernel.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/types/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestAlphabet.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestAugmentableFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestBiNormalSeparation$BinaryTestData.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestBiNormalSeparation.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestFeatureSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestHashedSparseVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestIndexedSparseVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestInstanceListWeights.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestLabelAlphabet$Labelee.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestLabelAlphabet.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestLabelsSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestLabelVector.class  \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "RH9ZOmQZKcZq"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install twitter-text-python"
      ],
      "metadata": {
        "id": "f30H9SpvC77G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7715bc6-bbe9-40a9-d9bb-007685bc1e97"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting twitter-text-python\n",
            "  Downloading twitter-text-python-1.1.1.tar.gz (11 kB)\n",
            "Building wheels for collected packages: twitter-text-python\n",
            "  Building wheel for twitter-text-python (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for twitter-text-python: filename=twitter_text_python-1.1.1-py3-none-any.whl size=10038 sha256=bb1e9707586d2da5878d381a370fd09166984626f09f2bd75ad50aa8d4707ecb\n",
            "  Stored in directory: /root/.cache/pip/wheels/b9/da/73/f52b2caab4e30520ebefdc029b00cffb1901cc02a7cdad2db3\n",
            "Successfully built twitter-text-python\n",
            "Installing collected packages: twitter-text-python\n",
            "Successfully installed twitter-text-python-1.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir LID-tool/mallet-2.0.8\n",
        "!cp -r mallet-2.0.8 LID-tool/"
      ],
      "metadata": {
        "id": "jeUYohvIF2la"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd LID-tool/"
      ],
      "metadata": {
        "id": "BB5tzuYhWFJH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23b533e8-3ed7-41f0-d916-3dc9f617f5dd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/LID-tool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv dictionaries/dict1hi.txt dictionaries/dicthi.txt\n",
        "!touch dictionaries/dict1hi.txt\n",
        "\n",
        "!mv dictionaries/dict1hinmov.txt dictionaries/dicthinmov.txt\n",
        "!touch dictionaries/dict1hinmov.txt"
      ],
      "metadata": {
        "id": "L01iXVFtZGS2"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you've run all the above cells without an error then your setup of LID-tool is complete!"
      ],
      "metadata": {
        "id": "5KdwTtgptNHp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Running LID on the Tweets\n",
        "\n",
        "Now that we have installed LID-tool, let's run it on a bunch of sample sentences that are given in the tool. The following are the sentences we want to be language tagged:"
      ],
      "metadata": {
        "id": "JMHHESJOSExB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cat sampleinp.txt"
      ],
      "metadata": {
        "id": "m9nr-v7HolLO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c7e3aee-2ee3-4808-9be2-4a700a36b2f2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\tYeh mera pehla sentence hai\r\n",
            "2\tAur ye dusra\r\n",
            "3\tMain kya karoon\r\n",
            "4\tThis is main sentence"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Initial run on sample tweets\n",
        "\n"
      ],
      "metadata": {
        "id": "K2IeruNDSJdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm sampleinp.txt_tagged"
      ],
      "metadata": {
        "id": "Tq1GNFaSiFfy"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python getLanguage.py sampleinp.txt"
      ],
      "metadata": {
        "id": "cZEh6NMeFM89",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67622726-2f47-47e9-f87b-59915a83315b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HINDI dictionary created\n",
            "ENGLISH dictionary created\n",
            "sampleinp.txt\n",
            "['getLanguage.py', 'sampleinp.txt']\n",
            "time for mallet classification 1.7504160404205322\n",
            "Yeh\ten\t0.3590321271096717\thi\t0.6409678728903284\n",
            "mera\ten\t0.012166092658460071\thi\t0.98783390734154\n",
            "pehla\ten\t3.3597386600962424E-4\thi\t0.9996640261339904\n",
            "sentence\ten\t0.999999999\thi\t1e-09\n",
            "hai\ten\t0.999999999\thi\t1e-09 \n",
            "---------------------------------\n",
            "\n",
            "time for mallet classification 1.2779970169067383\n",
            "Aur\ten\t0.832483388331911\thi\t0.16751661166808904\n",
            "ye\ten\t0.999999999\thi\t1e-09\n",
            "dusra\ten\t0.009362950333435751\thi\t0.9906370496665643 \n",
            "---------------------------------\n",
            "\n",
            "time for mallet classification 1.2611382007598877\n",
            "Main\ten\t0.999999999\thi\t1e-09\n",
            "kya\ten\t0.999999999\thi\t1e-09\n",
            "karoon\ten\t0.004832763543168134\thi\t0.9951672364568319 \n",
            "---------------------------------\n",
            "\n",
            "time for mallet classification 1.5319948196411133\n",
            "This\ten\t0.999999999\thi\t1e-09\n",
            "is\ten\t0.999999999\thi\t1e-09\n",
            "main\ten\t0.999999999\thi\t1e-09\n",
            "sentence\ten\t0.999999999\thi\t1e-09 \n",
            "---------------------------------\n",
            "\n",
            "written to sampleinp.txt_tagged\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice that in the third sentence, the words `main` and `kya` have high English probabilities even though they are actually Hindi words in that sentence's context. While the same word `main` is actually an English word in the context of fourth sentence and accordingly has high English probabilities.\n",
        "\n",
        "Let's see what this means for out final language tag output:"
      ],
      "metadata": {
        "id": "6jeK0-Ytt8Yi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cat sampleinp.txt_tagged"
      ],
      "metadata": {
        "id": "ccKjnDCYSDb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3deba1d-1ad8-4f7c-8549-c3953ddc1d34"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "##1\tYeh mera pehla sentence hai\n",
            "1\tYeh/HI mera/HI pehla/HI sentence/EN hai/EN \n",
            "##2\tAur ye dusra\n",
            "2\tAur/EN ye/EN dusra/HI \n",
            "##3\tMain kya karoon\n",
            "3\tMain/EN kya/EN karoon/HI \n",
            "##4\tThis is main sentence\n",
            "4\tThis/EN is/EN main/EN sentence/EN \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, the LID-tool has performed well by tagging `main` in th fourth sentence as EN. But, it has mistaked the words `main` and `kya` as EN in the third sentence. \n",
        "\n",
        "All is not bad though, since it has correctly predicted the word `karoon` as HI in the third sentence with high probability.\n",
        "\n",
        "Let's see how we can improve the above output by utilizing the concept of dictionaries."
      ],
      "metadata": {
        "id": "a7-IXXmDuk_g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Improving the output by utilizing dictionaries\n",
        "\n",
        "The LID-tool has two major components: an ML model trained on n-gram features and a couple of word lists (dictionaries) in both the languages. \n",
        "\n",
        "![](https://raw.githubusercontent.com/microsoft/LID-tool/main/images/info_flow_new_lid.PNG)\n",
        "\n"
      ],
      "metadata": {
        "id": "971uJICRSSVh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's make slight change in our dictionaries. Go to `LID-tool/dictionaries/dict1hinmov.txt` file and the words \"kya\" and \"main\" in separate lines one at a time. Then let's rerun the pipeline and check what changes."
      ],
      "metadata": {
        "id": "juXMv2y4cx5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm sampleinp.txt_tagged\n",
        "!rm dictionaries/memoize_dict.pkl"
      ],
      "metadata": {
        "id": "OohQZ4Nv0yuS"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python getLanguage.py sampleinp.txt"
      ],
      "metadata": {
        "id": "RpBm7hWBSHz9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e0a3c07-2acf-4270-a4ad-efc7c47c3b4d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HINDI dictionary created\n",
            "ENGLISH dictionary created\n",
            "sampleinp.txt\n",
            "['getLanguage.py', 'sampleinp.txt']\n",
            "time for mallet classification 1.5303964614868164\n",
            "Yeh\ten\t0.3590321271096717\thi\t0.6409678728903284\n",
            "mera\ten\t0.012166092658460071\thi\t0.98783390734154\n",
            "pehla\ten\t3.3597386600962424E-4\thi\t0.9996640261339904\n",
            "sentence\ten\t0.999999999\thi\t1e-09\n",
            "hai\ten\t0.999999999\thi\t1e-09 \n",
            "---------------------------------\n",
            "\n",
            "time for mallet classification 1.182682991027832\n",
            "Aur\ten\t0.832483388331911\thi\t0.16751661166808904\n",
            "ye\ten\t0.999999999\thi\t1e-09\n",
            "dusra\ten\t0.009362950333435751\thi\t0.9906370496665643 \n",
            "---------------------------------\n",
            "\n",
            "time for mallet classification 1.1295812129974365\n",
            "Main\ten\t0.999999999\thi\t1e-09\n",
            "kya\ten\t0.999999999\thi\t1e-09\n",
            "karoon\ten\t0.004832763543168134\thi\t0.9951672364568319 \n",
            "---------------------------------\n",
            "\n",
            "time for mallet classification 1.0733158588409424\n",
            "This\ten\t0.999999999\thi\t1e-09\n",
            "is\ten\t0.999999999\thi\t1e-09\n",
            "main\ten\t0.999999999\thi\t1e-09\n",
            "sentence\ten\t0.999999999\thi\t1e-09 \n",
            "---------------------------------\n",
            "\n",
            "written to sampleinp.txt_tagged\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat sampleinp.txt_tagged"
      ],
      "metadata": {
        "id": "Om9kdiyu03n7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14874106-efac-4a1e-dcc4-a12f3d9f0580"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "##1\tYeh mera pehla sentence hai\n",
            "1\tYeh/HI mera/HI pehla/HI sentence/EN hai/EN \n",
            "##2\tAur ye dusra\n",
            "2\tAur/EN ye/EN dusra/HI \n",
            "##3\tMain kya karoon\n",
            "3\tMain/EN kya/EN karoon/HI \n",
            "##4\tThis is main sentence\n",
            "4\tThis/EN is/EN main/EN sentence/EN \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The addition of dictionaries in the project was an engineering decision that was taken after considering the empirical results, which showed that the dictionaries complemented the performance of the ML-based classifier (MALLET) for certain corner-cases. \n",
        "\n",
        "Here are some of the problems that this method solved:\n",
        "\n",
        "**1.Dealing with “common words” that can belong to either of the languages.**\n",
        "\n",
        "For example, the English word `“to”` is one of the ways in which the Hindi word `“तो”` or `“तू”` is spelt when written in `roman script` so the word “to” will be classified differently in the following two sentences:\n",
        "\n",
        "\n",
        "**Input:**       I have to get back to my advisor\n",
        "\n",
        "**Output:**    I/EN have/EN ***to/EN*** get/EN back/EN to/EN my/EN advisor/EN \n",
        "\n",
        "**Input:**       Bhai to kabhi nahi sudhrega\n",
        "\n",
        "**Output:**    Bhai/HI ***to/HI*** kabhi/HI nahi/HI sudhrega/HI\n",
        "\n",
        "\n",
        "In this case, we make sure that the word “to” is present in both the dictionaries and the LID is supposed to focus more on the combination of ML probabilities and Context (surrounding words) to tag the language. For instance, the probability values \"1e-09\" and \"0.999999999\" indicate the word is present in dictionary(s).\n",
        "\n",
        "**2.Words that surely belong to only one language.** \n",
        "\n",
        "For example, words like “bhai”, “nahi”, “kabhi” in Hindi and words like “advisor”, “get” etc. in English. In this case, we utilize the relevant dictionary to force tag it to the correct language even if the ML classifier says otherwise.\n",
        "\n",
        "So the questions that you have to ask yourself while creating the dictionaries are: \n",
        "\n",
        "1. “Are there certain words that can be spelt the same way in both the languages?” And, \n",
        "2. “Are there common words in one language that surely can’t be used in the other language?”\n",
        "\n",
        "These are just a couple of things that we looked at while building this tool, but given your specific use-case you can consider more such engineering use-cases and customize the dictionaries accordingly. \n",
        "\n",
        "- [Implementation in Code](https://github.com/microsoft/LID-tool/blob/f6528ebd8ac77b561f10d3659799f3f8894deb09/getLanguage.py#L147)\n",
        "- [More details in Docs](https://github.com/microsoft/LID-tool/blob/main/Train_Custom_LID.md)"
      ],
      "metadata": {
        "id": "12nbKb4IhjoQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analyzing Code-Mixing Patterns in Tweets"
      ],
      "metadata": {
        "id": "WKAz_MqPScon"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Getting Language Tagged Tweets\n",
        "\n",
        "Due to the limitations of free version of Colab and in the interest of time, we have already computed language tags for all the tweets in the dataset. Let's fetch them!"
      ],
      "metadata": {
        "id": "sQ0s91GhROgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/mohdsanadzakirizvi/plaksha_rasa/main/assignments/tagged_tweets.txt"
      ],
      "metadata": {
        "id": "NKBzXytnRSf-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a10ba4ff-5e81-4d4c-af6a-53a848ea7433"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-10 07:22:58--  https://raw.githubusercontent.com/mohdsanadzakirizvi/plaksha_rasa/main/assignments/tagged_tweets.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 140771 (137K) [text/plain]\n",
            "Saving to: ‘tagged_tweets.txt’\n",
            "\n",
            "tagged_tweets.txt   100%[===================>] 137.47K  --.-KB/s    in 0.003s  \n",
            "\n",
            "2022-08-10 07:22:58 (47.5 MB/s) - ‘tagged_tweets.txt’ saved [140771/140771]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('tagged_tweets.txt') as fp:\n",
        "  tagged_tweets = fp.readlines()\n",
        "\n",
        "tagged_tweets[:5]"
      ],
      "metadata": {
        "id": "f5xeZ740xozV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cc215d5-2562-4b62-aceb-023f430995f2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['##1\\trt @tathagata   welcome to the most cultured state in india which gave birth to raja ram mohun roy vidyasagar  swami vivekananda  tagore an\\n',\n",
              " '1\\trt/EN @tathagata/OTHER welcome/EN to/EN the/EN most/EN cultured/EN state/EN in/EN india/EN which/EN gave/EN birth/EN to/EN raja/HI ram/HI mohun/HI roy/HI vidyasagar/HI swami/HI vivekananda/HI tagore/HI an/EN \\n',\n",
              " '##2\\t#antimuslimwomencong ye  sahi h #naishhaasan ji aap ne #tripletalaq ko khatm krne ke liye  ladai ki ab jab khatm kr dia toh ab  keha rahi ho ki faida bhi do #naishhaasan ab  kya tum #politician banna  chahati ho kya   apni raajniti me aane ki roti sakne ke lite aag laga rahi ho\\n',\n",
              " '2\\t#antimuslimwomencong/OTHER ye/EN sahi/HI h/HI #naishhaasan/OTHER ji/HI aap/HI ne/EN #tripletalaq/OTHER ko/HI khatm/HI krne/HI ke/HI liye/HI ladai/HI ki/HI ab/EN jab/HI khatm/HI kr/HI dia/HI toh/HI ab/EN keha/HI rahi/HI ho/EN ki/HI faida/HI bhi/HI do/EN #naishhaasan/OTHER ab/EN kya/HI tum/HI #politician/OTHER banna/HI chahati/HI ho/EN kya/HI apni/HI raajniti/HI me/EN aane/HI ki/HI roti/HI sakne/HI ke/HI lite/EN aag/HI laga/HI rahi/HI ho/EN \\n',\n",
              " '##3\\trt @adgpi  a nine officer delegation of united arab emirates army #uaearmy is on a visit to #india to participate in army to army staff tal\\n']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(tagged_tweets)"
      ],
      "metadata": {
        "id": "H03yI335y3aH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3505125f-6c7a-407e-c688-8572d5e5f133"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "966"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Basic aggregation metrics\n",
        "\n",
        "Let's find out the number of tweets in each of the categories: Mostly En, Mostly Hi and Code-Mixed. We will also cluster the tweets later based on it."
      ],
      "metadata": {
        "id": "yg9XDOBZSiRX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# threshold of 70% of total word length\n",
        "thres = 0.7\n",
        "\n",
        "outdf = []\n",
        "\n",
        "# aggregate word frequencies\n",
        "for twt in tagged_tweets:\n",
        "  en_fr, hi_fr = 0, 0\n",
        "  if not twt.startswith('##'):\n",
        "    tags = twt.split()\n",
        "    taglen = len(tags)\n",
        "    for t in tags:\n",
        "      if 'HI' in t:\n",
        "        hi_fr += 1\n",
        "      if 'EN' in t:\n",
        "        en_fr += 1\n",
        "    \n",
        "    if hi_fr/taglen >= thres:\n",
        "      outdf.append((twt, 0, 1, 0))\n",
        "    elif en_fr/taglen >= thres:\n",
        "      outdf.append((twt, 1, 0, 0))\n",
        "    else:\n",
        "      outdf.append((twt, 0, 0, 1))\n",
        "\n",
        "# create the output dataframe\n",
        "outdf = pd.DataFrame(outdf, columns = ['Tweet', 'EN', 'HI', 'CM'])\n",
        "outdf.head()"
      ],
      "metadata": {
        "id": "hn6RJJigyhG8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "04afee77-eb2d-4b7f-b17f-2129d587e3bd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Tweet  EN  HI  CM\n",
              "0  1\\trt/EN @tathagata/OTHER welcome/EN to/EN the...   0   0   1\n",
              "1  2\\t#antimuslimwomencong/OTHER ye/EN sahi/HI h/...   0   0   1\n",
              "2  3\\trt/EN @adgpi/OTHER a/EN nine/EN officer/EN ...   1   0   0\n",
              "3  4\\trt/EN @ani/OTHER west/EN bengal/EN sit/EN i...   1   0   0\n",
              "4  5\\trt/EN @hate/OTHER stopper/EN jack/EN uppal/...   1   0   0"
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              "<style scoped>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So now we have tagged each tweet based on whether it has mostly EN, HI or Code-Mixed words. \n",
        "\n",
        "**P.S.** You can play with the threshold value `thresh` given above and see how things change. Go crazy!\n",
        "\n",
        "Let's now aggregate how many tweets fall in each category!"
      ],
      "metadata": {
        "id": "0T0lf5-wx7lP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outdf.sum()"
      ],
      "metadata": {
        "id": "L0KlqkwU2fcQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "725721ad-288e-463f-f689-13d4cc867d11"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tweet    1\\trt/EN @tathagata/OTHER welcome/EN to/EN the...\n",
              "EN                                                     199\n",
              "HI                                                      53\n",
              "CM                                                     231\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Do you want to see tweets falling in a particular category? Just replace the 'CM' with the required tag in code in the following line:"
      ],
      "metadata": {
        "id": "62I2Dz2s2t19"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outdf[outdf['EN'] == 1]"
      ],
      "metadata": {
        "id": "WOagAddx2396",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "0d0df408-be06-4fc4-cc65-7bdef0031939"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                                 Tweet  EN  HI  CM\n",
              "2    3\\trt/EN @adgpi/OTHER a/EN nine/EN officer/EN ...   1   0   0\n",
              "3    4\\trt/EN @ani/OTHER west/EN bengal/EN sit/EN i...   1   0   0\n",
              "4    5\\trt/EN @hate/OTHER stopper/EN jack/EN uppal/...   1   0   0\n",
              "5    6\\trt/EN @monica/OTHER america/EN @david/OTHER...   1   0   0\n",
              "6    7\\tsir/EN two/EN gang/EN rape/EN in/EN days/EN...   1   0   0\n",
              "..                                                 ...  ..  ..  ..\n",
              "473  1\\trt/EN @piyushgoyal/OTHER making/EN india/EN...   1   0   0\n",
              "476  4\\tscheduled/EN to/EN teach/EN my/EN first/EN ...   1   0   0\n",
              "477  5\\trt/EN @worcester/OTHER uni/EN graduate/EN a...   1   0   0\n",
              "479  7\\trt/EN @zee/OTHER india/EN we/EN couldn/EN t...   1   0   0\n",
              "482  10\\tbengal/HI arson/EN kids/EN among/EN killed...   1   0   0\n",
              "\n",
              "[199 rows x 4 columns]"
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              "<p>199 rows × 4 columns</p>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Extracting user mentions and hashtags\n",
        "\n",
        "We now have a general idea of the distribution of EN, HI and CM heavy tweets.\n",
        " Let's try to find some explaination of what might be the reason of tweets having the prominence of one of the above languages.\n",
        "\n",
        "In order to do that, we can take advantage of one information that we have present in our tweets: hashtags (starting with `#` and user mentions (starting with `@`)"
      ],
      "metadata": {
        "id": "j8nTdgnYSnlW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tagdf = []\n",
        "\n",
        "# extract user mentions and tags\n",
        "for twt in tagged_tweets:\n",
        "  hashtags, users = [], []\n",
        "  if not twt.startswith('##'):\n",
        "    tags = twt.split()\n",
        "    taglen = len(tags)\n",
        "    for t in tags:\n",
        "      if 'OTHER' in t:\n",
        "        tok = t.split('/')[0]\n",
        "        if t.startswith('@'):\n",
        "          users.append(tok)\n",
        "        if t.startswith('#'):\n",
        "          hashtags.append(tok)\n",
        "    tagdf.append((twt, hashtags, users))\n",
        "\n",
        "# create the output dataframe\n",
        "tagdf = pd.DataFrame(tagdf, columns = ['Tweet', 'Hashtags', 'Users'])\n",
        "tagdf.head()"
      ],
      "metadata": {
        "id": "nrd7ZVZoSvPz",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d02737de-dada-4b38-ef6e-76eca822433c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Tweet  \\\n",
              "0  1\\trt/EN @tathagata/OTHER welcome/EN to/EN the...   \n",
              "1  2\\t#antimuslimwomencong/OTHER ye/EN sahi/HI h/...   \n",
              "2  3\\trt/EN @adgpi/OTHER a/EN nine/EN officer/EN ...   \n",
              "3  4\\trt/EN @ani/OTHER west/EN bengal/EN sit/EN i...   \n",
              "4  5\\trt/EN @hate/OTHER stopper/EN jack/EN uppal/...   \n",
              "\n",
              "                                            Hashtags                   Users  \n",
              "0                                                 []            [@tathagata]  \n",
              "1  [#antimuslimwomencong, #naishhaasan, #tripleta...                      []  \n",
              "2                                 [#uaearmy, #india]                [@adgpi]  \n",
              "3                                          [#birhum]                  [@ani]  \n",
              "4                                 [#thekashmirfiles]  [@hate, @royal, @phil]  "
            ],
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              "      <td>3\\trt/EN @adgpi/OTHER a/EN nine/EN officer/EN ...</td>\n",
              "      <td>[#uaearmy, #india]</td>\n",
              "      <td>[@adgpi]</td>\n",
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              "      <th>3</th>\n",
              "      <td>4\\trt/EN @ani/OTHER west/EN bengal/EN sit/EN i...</td>\n",
              "      <td>[#birhum]</td>\n",
              "      <td>[@ani]</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5\\trt/EN @hate/OTHER stopper/EN jack/EN uppal/...</td>\n",
              "      <td>[#thekashmirfiles]</td>\n",
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              "                                                     [key], {});\n",
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    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Hypothesis\n",
        "\n",
        "Now that we have tagged the tweets by both the prominent language and the hashtags + user mentions, let's merge the two dataframes and look at the bigger picture!"
      ],
      "metadata": {
        "id": "kdvW5bm9S2Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "finaldf = outdf.merge(tagdf)\n",
        "\n",
        "finaldf.sample(20)"
      ],
      "metadata": {
        "id": "jsGE2ZBmS64t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "406296d3-95bf-415e-fc13-fb6089ffb469"
      },
      "execution_count": 27,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                                 Tweet  EN  HI  CM  \\\n",
              "335  2\\t#rahulgandhi/OTHER ji/HI month/EN phele/HI ...   0   0   1   \n",
              "320  7\\ten/HI instagram/HI muy/EN #blacklivesmatter...   0   0   1   \n",
              "254  1\\t@rmpars/OTHER is/EN a/EN fun/EN person/EN y...   1   0   0   \n",
              "501  5\\t#barish/OTHER bhi/HI confused/EN hai/HI abh...   0   0   1   \n",
              "237  4\\ttrue/EN shakal/HI nahin/HI milti/HI drama/H...   0   0   1   \n",
              "21   1\\t@naomi/OTHER i/EN don/EN t/EN understand/EN...   1   0   0   \n",
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        "- On running the above script multiple times, we notice that there are patterns dictating when a tweet has Code-mixing vs. when it's just all English or all Hindi.\n",
        "\n",
        "- One clear pattern is that the tweets that are from formal settings like those by news agencies or organisations (@reuters, @geologytime, @ani etc.) tend to use pure English in their tweets even when they are talking about incidents in India. It is true for even Indian news agencies.\n",
        "\n",
        "- Conversely, we see lot of Code-Mixed words or majority of Hindi words in tweets that are by individual accounts. These are everyday folks just expressing their honest opinion on certain topics (having hashtags of major incidents in Indian society). Since, Code-mixing occurs naturally to them in their speech and day to day informal interactions, there's a tendency to do the same online. Which is interesting because they aren't just going for all Hindi words or even an all Hindi script but are heavily mixing both the script and words with Roman English.\n",
        "\n",
        "- Another interesting pattern is that some organisations tend to use Code-Mixing when they are advertising a product or service (ex. @jio). Which is probably a marketing hack of making the customer feel \"closer\" to the product by speaking in \"their colloquial language\" which is usually full of Code-Mixing.\n",
        "\n",
        "- The above set of early hypothesis corroborates with previous studies of Code-Mixing that it's a spoken language phenomenon that tends to happen more in informal contexts like speech, social media and chat apps like Whatsapp, Messenger etc.\n",
        "\n",
        "- Please note that the above are but qualitative observations, we can perform more rigorous quantitative analyses to come up with solid conclusions from the data.\n",
        "\n",
        "- Do you see any other patterns? Thoughts?"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "### Resources\n",
        "\n",
        "You can refer the following resources for more details about the project:\n",
        "\n",
        "- [GitHub Page](https://github.com/microsoft/LID-tool)\n",
        "- [Papers](https://github.com/microsoft/LID-tool#papers)"
      ],
      "metadata": {
        "id": "qWmLyWIAnAxl"
      }
    }
  ]
}