propaganda_Linh.ipynb

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            "grid_auto_columns": null,
            "margin": null,
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          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/linhnguyen222/DPS-Silo/blob/master/propaganda_Linh.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NxJIYuUsxIkq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "import numpy as np\n",
        "from tqdm.auto import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHaqYDt0yw3b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHdwutp1zPVm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "73ea485e-ce40-4938-d61b-b4214d08d247"
      },
      "source": [
        "from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "drive.mount(\"/content/drive\", force_remount=True)\n",
        "directory = '/content/drive/My Drive/DSP/data/protechn_corpus_eval/train'\n",
        "testDirectory = '/content/drive/My Drive/DSP/data/protechn_corpus_eval/test'\n",
        "devDirectory = '/content/drive/My Drive/DSP/data/protechn_corpus_eval/dev'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkIT8pOtZ5hG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import glob\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3cQkX0M5waV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "from spacy.lang.en.stop_words import STOP_WORDS\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "import string\n",
        "\n",
        "def preprocess_text(text, remove_stop = True, stem_words = False, remove_mentions_hashtags = True):\n",
        "  \"\"\"\n",
        "  eg:\n",
        "  input: preprocess_text(\"@water #dream hi hello where are you going be there tomorrow happening happen happens\",  \n",
        "  stem_words = True) \n",
        "  output: ['tomorrow', 'happen', 'go', 'hello']\n",
        "  \"\"\"\n",
        "  \n",
        "\n",
        "  if remove_mentions_hashtags:\n",
        "      text = re.sub(r\"@(\\w+)\", \" \", text)\n",
        "      text = re.sub(r\"#(\\w+)\", \" \", text)\n",
        "      \n",
        "  text = re.sub(r\"[^\\x00-\\x7F]+\", \" \", text)\n",
        "  regex = re.compile('[' + re.escape(string.punctuation) + '0-9\\\\r\\\\t\\\\n]') # remove punctuation and numbers\n",
        "  nopunct = regex.sub(\" \", text.lower())\n",
        "  words = (''.join(nopunct)).split()\n",
        "  \n",
        "  if(remove_stop):\n",
        "      words = [w for w in words if w not in STOP_WORDS]\n",
        "      words = [w for w in words if len(w) > 2]  # remove a,an,of etc.\n",
        "      \n",
        "  if(stem_words):\n",
        "      stemmer = PorterStemmer()\n",
        "      words = [stemmer.stem(w) for w in words]\n",
        "      \n",
        "  return list(words) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ezqXJnJpTpC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRe5iM-dEf02",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import spacy\n",
        "from spacy import displacy\n",
        "from spacy.matcher import PhraseMatcher\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "matched_sents = []\n",
        "def collect_sents(matcher, doc, i, matches):\n",
        "    match_id, start, end = matches[i]\n",
        "    span = doc[start:end]  # Matched span\n",
        "    sent = span.sent  # Sentence containing matched span\n",
        "    # Append mock entity for match in displaCy style to matched_sents\n",
        "    # get the match span by ofsetting the start and end of the span with the\n",
        "    # start and end of the sentence in the doc\n",
        "    match_ents = [{\n",
        "        \"start\": span.start_char - sent.start_char,\n",
        "        \"end\": span.end_char - sent.start_char,\n",
        "        \"label\": \"MATCH\",\n",
        "    }]\n",
        "    matched_sents.append({\"text\": sent.text, \"ents\": match_ents, \"id\": i })\n",
        "    # matched_sents = sent.text\n",
        "    # print(matched_sents)\n",
        "\n",
        "\n",
        "# for doc, label, start, end in labels.values:\n",
        "def getMatchSentence(textNlp, matchedString, doc, start, end):\n",
        "  matcher = PhraseMatcher(nlp.vocab)\n",
        "  id = \"{doc}-{start}-end\".format(doc=doc, start=start, end=end)\n",
        "  matcher.add(id, collect_sents, nlp(matchedString)) \n",
        "  matches = matcher(textNlp)\n",
        "  matchSentence = matched_sents[0][\"text\"] if len(matched_sents) != 0  else \"\"\n",
        "\n",
        "  # reformat so that we have the text in its original form\n",
        "  matchSentence = matchSentence.replace(\"`\", \"'\").replace(\"'\", \"\\\"\")\n",
        "  matched_sents.pop(0) if len(matched_sents) != 0  else matched_sents\n",
        " # now we clean the sentence\n",
        "  matchSentence = \" \".join(preprocess_text(matchSentence))\n",
        "  return matchSentence\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6njVkDdqqWF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "29c27b705593483cbf4982e0ac39600d",
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            "30020960981e430286de35f858a14820",
            "24fa115eb17d42679a71d9ad9e0abf6c"
          ]
        },
        "outputId": "4d322c4e-7b0f-49ae-c9d4-341144ab10a2"
      },
      "source": [
        "# Get all text file from train\n",
        "os.chdir(directory)\n",
        "myFiles = glob.glob('*.txt')\n",
        "trainData = pd.DataFrame(columns=[\"doc\", \"label\", \"phrase\", \"sentence\"])\n",
        "for textFileName in tqdm(myFiles):\n",
        "  # Read text and label name\n",
        "  try: \n",
        "    lblFileName = textFileName.replace(\".txt\", \".labels.tsv\")\n",
        "    labels = pd.read_csv(lblFileName, header=None, delim_whitespace=True)\n",
        "    labels.columns = ['doc', 'label', 'start', 'end']\n",
        "    text = open(textFileName).read()\n",
        "\n",
        "    # format text because spaCy doesnt work if the match phrase is in quotation mark\n",
        "    text = text.replace(\"'\", \"`\").replace(\"\\\"\", \"'\")\n",
        "    textNlp= nlp(text)\n",
        "    df = pd.concat([pd.DataFrame([[doc, label, text[start:end].replace(\"\\\"\", \"\").strip(), getMatchSentence(textNlp, text[start:end].replace(\"\\\"\", \"\").strip(), doc, start, end)]], columns=[\"doc\", \"label\", \"phrase\", \"sentence\"]) for doc, label, start, end in labels.values],\n",
        "            ignore_index=True)\n",
        "    trainData = pd.concat([trainData, df], axis=0,ignore_index=True)\n",
        "  except:\n",
        "    continue\n",
        "trainData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "29c27b705593483cbf4982e0ac39600d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=293.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>doc</th>\n",
              "      <th>label</th>\n",
              "      <th>phrase</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7618745059</td>\n",
              "      <td>Whataboutism</td>\n",
              "      <td>but they didn’t prosecute Hillary Clinton</td>\n",
              "      <td>interpreted law case criminal saucier told fox...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7618745059</td>\n",
              "      <td>Whataboutism</td>\n",
              "      <td>did the same thing and weren’t treated as crim...</td>\n",
              "      <td>guys ship thing weren treated criminals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7618745059</td>\n",
              "      <td>Causal_Oversimplification</td>\n",
              "      <td>“They used me as an example because of [the ba...</td>\n",
              "      <td>example backlash hillary clinton continued all...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7618745059</td>\n",
              "      <td>Exaggeration,Minimisation</td>\n",
              "      <td>actively destroyed his life an made it all but...</td>\n",
              "      <td>government actively destroyed life impossible ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>729348908</td>\n",
              "      <td>Appeal_to_fear-prejudice</td>\n",
              "      <td>a holy war between Muslims and non-Muslims</td>\n",
              "      <td>course conversations patel discussed desire ho...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          doc  ...                                           sentence\n",
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              "1  7618745059  ...            guys ship thing weren treated criminals\n",
              "2  7618745059  ...  example backlash hillary clinton continued all...\n",
              "3  7618745059  ...  government actively destroyed life impossible ...\n",
              "4   729348908  ...  course conversations patel discussed desire ho...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 7
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "70OHVlW47vPN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
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            "238bba7b4520413587818503126cd2cf",
            "8a23cee4c1d3416082bae83687fb4a61",
            "1a1611106eea44c5be80a0465c8a2e13",
            "3d3df1ac096e480ebf802f8b943b82b6",
            "e3088565364e4cdc9ba740ff1672532c"
          ]
        },
        "outputId": "7d24d3c8-61a3-495d-a2ac-a28c1a1ec1ac"
      },
      "source": [
        "os.chdir(testDirectory)\n",
        "myTestFiles = glob.glob('*.txt')\n",
        "testData = pd.DataFrame(columns=[\"doc\", \"label\", \"phrase\", \"sentence\"])\n",
        "for textFileName in tqdm(myTestFiles):\n",
        "  # Read text and label name\n",
        "  try: \n",
        "    lblFileName = textFileName.replace(\".txt\", \".labels.tsv\")\n",
        "    labels = pd.read_csv(lblFileName, header=None, delim_whitespace=True)\n",
        "    labels.columns = ['doc', 'label', 'start', 'end']\n",
        "    text = open(textFileName).read()\n",
        "\n",
        "    # format text because spaCy doesnt work if the match phrase is in quotation mark\n",
        "    text = text.replace(\"'\", \"`\").replace(\"\\\"\", \"'\")\n",
        "    textNlp= nlp(text)\n",
        "    df = pd.concat([pd.DataFrame([[doc, label, text[start:end].replace(\"\\\"\", \"\").strip(), getMatchSentence(textNlp, text[start:end].replace(\"\\\"\", \"\").strip(), doc, start, end)]], columns=[\"doc\", \"label\", \"phrase\", \"sentence\"]) for doc, label, start, end in labels.values],\n",
        "            ignore_index=True)\n",
        "    testData = pd.concat([testData, df], axis=0,ignore_index=True)\n",
        "  except:\n",
        "    continue\n",
        "testData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d6fcca9638d4af1be407f2b9e384d1f",
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              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=101.0), HTML(value='')))"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>doc</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Flag-Waving</td>\n",
              "      <td>American people</td>\n",
              "      <td>warned prior dealings ssmu jewish identity sec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Repetition</td>\n",
              "      <td>lie</td>\n",
              "      <td>lew board members passionately advocated favor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Repetition</td>\n",
              "      <td>lied</td>\n",
              "      <td>malevolent bds campaign mcgill belatedly come ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Loaded_Language</td>\n",
              "      <td>frivolous</td>\n",
              "      <td>enlightened progressive school suddenly transf...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Loaded_Language</td>\n",
              "      <td>rampant</td>\n",
              "      <td>instant case mcgill bds campaigners dispensed ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         doc  ...                                           sentence\n",
              "0  999001299  ...  warned prior dealings ssmu jewish identity sec...\n",
              "1  999001299  ...  lew board members passionately advocated favor...\n",
              "2  999001299  ...  malevolent bds campaign mcgill belatedly come ...\n",
              "3  999001299  ...  enlightened progressive school suddenly transf...\n",
              "4  999001299  ...  instant case mcgill bds campaigners dispensed ...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yhnJbaEOJrA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5c668666-993a-4e39-8a5b-1f18b9656dd9"
      },
      "source": [
        "testData[\"sentence\"][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'warned prior dealings ssmu jewish identity secret lest sabotage chances'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwb136RO0tQT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "1723e83af40f48218627f97c1649fdf1",
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            "f606685cddee46d99a37f8572e8713bd",
            "5a5faafe745b41aea3615905b6cb1296",
            "5d52e3816d1b44cbae8fe60ffd4003d0",
            "28b915cc95784a38b086f1cb8a57fc10",
            "d70aedb5ee8847c7a15a9ecac22bd211",
            "653efb9e55f644238c921bd49723a5a7"
          ]
        },
        "outputId": "c223684e-aa4e-4383-fc96-e46371d60f66"
      },
      "source": [
        "os.chdir(devDirectory)\n",
        "myDevFiles = glob.glob('*.txt')\n",
        "devData = pd.DataFrame(columns=[\"doc\", \"label\", \"phrase\", \"sentence\"])\n",
        "for textFileName in tqdm(myDevFiles):\n",
        "  # Read text and label name\n",
        "  try: \n",
        "    lblFileName = textFileName.replace(\".txt\", \".labels.tsv\")\n",
        "    labels = pd.read_csv(lblFileName, header=None, delim_whitespace=True)\n",
        "    labels.columns = ['doc', 'label', 'start', 'end']\n",
        "    text = open(textFileName).read()\n",
        "\n",
        "    # format text because spaCy doesnt work if the match phrase is in quotation mark\n",
        "    text = text.replace(\"'\", \"`\").replace(\"\\\"\", \"'\")\n",
        "    textNlp= nlp(text)\n",
        "    df = pd.concat([pd.DataFrame([[doc, label, text[start:end].replace(\"\\\"\", \"\").strip(), getMatchSentence(textNlp, text[start:end].replace(\"\\\"\", \"\").strip(), doc, start, end)]], columns=[\"doc\", \"label\", \"phrase\", \"sentence\"]) for doc, label, start, end in labels.values],\n",
        "            ignore_index=True)\n",
        "    devData = pd.concat([testData, df], axis=0,ignore_index=True)\n",
        "  except:\n",
        "    continue\n",
        "devData.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1723e83af40f48218627f97c1649fdf1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=57.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>doc</th>\n",
              "      <th>label</th>\n",
              "      <th>phrase</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Flag-Waving</td>\n",
              "      <td>American people</td>\n",
              "      <td>warned prior dealings ssmu jewish identity sec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Repetition</td>\n",
              "      <td>lie</td>\n",
              "      <td>lew board members passionately advocated favor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Repetition</td>\n",
              "      <td>lied</td>\n",
              "      <td>malevolent bds campaign mcgill belatedly come ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Loaded_Language</td>\n",
              "      <td>frivolous</td>\n",
              "      <td>enlightened progressive school suddenly transf...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>999001299</td>\n",
              "      <td>Loaded_Language</td>\n",
              "      <td>rampant</td>\n",
              "      <td>instant case mcgill bds campaigners dispensed ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         doc  ...                                           sentence\n",
              "0  999001299  ...  warned prior dealings ssmu jewish identity sec...\n",
              "1  999001299  ...  lew board members passionately advocated favor...\n",
              "2  999001299  ...  malevolent bds campaign mcgill belatedly come ...\n",
              "3  999001299  ...  enlightened progressive school suddenly transf...\n",
              "4  999001299  ...  instant case mcgill bds campaigners dispensed ...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZZkgpyD-M_1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "971c42c9-1a1a-4312-be9c-45d8981e077c"
      },
      "source": [
        "trainData.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>doc</th>\n",
              "      <th>label</th>\n",
              "      <th>phrase</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5114</td>\n",
              "      <td>5114</td>\n",
              "      <td>5114</td>\n",
              "      <td>5114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>281</td>\n",
              "      <td>18</td>\n",
              "      <td>4403</td>\n",
              "      <td>2674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>783702663</td>\n",
              "      <td>Loaded_Language</td>\n",
              "      <td>autonomy</td>\n",
              "      <td>autonomy life constant prerequisite maintainin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>231</td>\n",
              "      <td>1811</td>\n",
              "      <td>21</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              doc  ...                                           sentence\n",
              "count        5114  ...                                               5114\n",
              "unique        281  ...                                               2674\n",
              "top     783702663  ...  autonomy life constant prerequisite maintainin...\n",
              "freq          231  ...                                                 44\n",
              "\n",
              "[4 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "he-3vjeT1mSP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# labelInfo = trainData[[\"label\"].value_counts()]\n",
        "labelInfo = trainData[[\"label\", \"sentence\"]].groupby(by=[\"label\"]).count().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3sHPKUt40N1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "003c65b6-1f66-48ba-8ee6-5c1480bbe6e4"
      },
      "source": [
        "labelInfo"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Appeal_to_Authority</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Appeal_to_fear-prejudice</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bandwagon</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Black-and-White_Fallacy</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Causal_Oversimplification</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Doubt</td>\n",
              "      <td>423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Exaggeration,Minimisation</td>\n",
              "      <td>398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Flag-Waving</td>\n",
              "      <td>206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Loaded_Language</td>\n",
              "      <td>1811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Name_Calling,Labeling</td>\n",
              "      <td>931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Obfuscation,Intentional_Vagueness,Confusion</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Red_Herring</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Reductio_ad_hitlerum</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Repetition</td>\n",
              "      <td>456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Slogans</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Straw_Men</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Thought-terminating_Cliches</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Whataboutism</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          label  sentence\n",
              "0                           Appeal_to_Authority        91\n",
              "1                      Appeal_to_fear-prejudice       187\n",
              "2                                     Bandwagon        10\n",
              "3                       Black-and-White_Fallacy        97\n",
              "4                     Causal_Oversimplification       170\n",
              "5                                         Doubt       423\n",
              "6                     Exaggeration,Minimisation       398\n",
              "7                                   Flag-Waving       206\n",
              "8                               Loaded_Language      1811\n",
              "9                         Name_Calling,Labeling       931\n",
              "10  Obfuscation,Intentional_Vagueness,Confusion        10\n",
              "11                                  Red_Herring        24\n",
              "12                         Reductio_ad_hitlerum        44\n",
              "13                                   Repetition       456\n",
              "14                                      Slogans       120\n",
              "15                                    Straw_Men        11\n",
              "16                  Thought-terminating_Cliches        70\n",
              "17                                 Whataboutism        55"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9z0BGesytjex",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "78568f2b-ea30-4379-c900-aeb9a29b024d"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.pie(labelInfo, values=\"sentence\", names=\"label\", title=\"Distribution of labels\")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"d1af2b4e-1cfd-49b6-b68d-cbc1c2bd5fad\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"d1af2b4e-1cfd-49b6-b68d-cbc1c2bd5fad\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'd1af2b4e-1cfd-49b6-b68d-cbc1c2bd5fad',\n",
              "                        [{\"domain\": {\"x\": [0.0, 1.0], \"y\": [0.0, 1.0]}, \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"label=%{label}<br>sentence=%{value}\", \"labels\": [\"Appeal_to_Authority\", \"Appeal_to_fear-prejudice\", \"Bandwagon\", \"Black-and-White_Fallacy\", \"Causal_Oversimplification\", \"Doubt\", \"Exaggeration,Minimisation\", \"Flag-Waving\", \"Loaded_Language\", \"Name_Calling,Labeling\", \"Obfuscation,Intentional_Vagueness,Confusion\", \"Red_Herring\", \"Reductio_ad_hitlerum\", \"Repetition\", \"Slogans\", \"Straw_Men\", \"Thought-terminating_Cliches\", \"Whataboutism\"], \"legendgroup\": \"\", \"name\": \"\", \"showlegend\": true, \"type\": \"pie\", \"values\": [91, 187, 10, 97, 170, 423, 398, 206, 1811, 931, 10, 24, 44, 456, 120, 11, 70, 55]}],\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGqgYQuzYo6A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQqElLAcWrWy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "620458b0-e503-4e9d-f8f3-947ba66533c2"
      },
      "source": [
        "# stops = stopwords.words(\"english\")\n",
        "\n",
        "# def normalize(comment, lowercase, remove_stopwords):\n",
        "#   if lowercase:\n",
        "#     comment = comment.lower()\n",
        "#   comment = nlp(comment)\n",
        "#   lemmatized = list()\n",
        "#   for word in comment:\n",
        "#     lemma = word.lemma_.strip()\n",
        "#     if lemma:\n",
        "#       if not remove_stopwords or (remove_stopwords and lemma not in stops):\n",
        "#         lemmatized.append(lemma)\n",
        "#   return \" \".join(lemmatized)\n",
        "# Explorative task using spaCy\n",
        "sentence_text = ' '.join(trainData['sentence'])\n",
        "doc = nlp(' '.join(preprocess_text(sentence_text)))\n",
        "\n",
        "x = doc[0]\n",
        "print('word:', x, '\\n')\n",
        "print('part of speech:', x.pos_, '\\n')\n",
        "print('sentiment:', x.sentiment,'\\n')\n",
        "print('sentiment:', x.sentiment,'\\n')\n",
        "print('word vector:', x.vector)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "word: interpreted \n",
            "\n",
            "part of speech: VERB \n",
            "\n",
            "sentiment: 0.0 \n",
            "\n",
            "sentiment: 0.0 \n",
            "\n",
            "word vector: [ 3.0343883  -3.750167   -0.928719    1.6470629   1.7190307  -0.7121873\n",
            " -0.4338514   4.654498    2.5160146   1.0844514   2.6064878  -0.45748934\n",
            " -0.98389083 -4.4254737   0.20492843  0.316422    0.45347315  3.2947745\n",
            "  0.01253429  0.78905517  0.7577066  -4.753515   -1.4073963   0.04084176\n",
            " -0.96920186  0.40185523 -1.6567066  -1.7462039  -2.4742591  -2.6213813\n",
            " -1.9883845  -0.9284941   2.7043738  -0.99468726 -0.55624056 -1.9708086\n",
            "  3.3207269  -1.1958678   1.464607   -0.37742314  0.41884905  2.3968499\n",
            "  1.6800246   3.3990312   0.70407003 -0.32009593 -1.7207618  -1.9469607\n",
            "  1.992593    1.9678866  -1.0841866  -1.4790103   1.1583052  -0.3702048\n",
            " -1.0147747  -1.5415416   0.04105753 -0.8867867   1.1723249   0.44166028\n",
            " -2.9982855  -4.5992503  -0.40138793  2.969132   -2.283688   -1.8961564\n",
            "  2.6907568  -1.6650016   0.19120526  4.490702    0.7372501   0.68763477\n",
            "  0.21692109  1.5782244   0.598561   -1.4087849   0.6797627  -3.5394475\n",
            " -2.9191709   2.053381    1.9190466   0.46825838  1.1439723  -2.242261\n",
            "  1.658636    0.64120364 -0.35223496 -3.9313707   3.209403    0.8575704\n",
            "  0.7194502  -1.0130857  -0.87532634  2.600073   -2.5897233   2.995784  ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPpCP8w8YnzS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "e672660b-d868-4041-c5f8-78b228f48747"
      },
      "source": [
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def make_wc(word_list):\n",
        "    wordcloud = WordCloud()\n",
        "    wordcloud = WordCloud().generate(text)\n",
        "    # wordcloud.fit_words(dict(Counter(word_list).most_common(40)))\n",
        "\n",
        "    fig=plt.figure(figsize=(10, 10))\n",
        "    plt.imshow(wordcloud)\n",
        "    plt.axis(\"off\")\n",
        "    plt.show()\n",
        "    \n",
        "make_wc([token.text for token in doc if token.pos_ in ['NOUN']])\n",
        "# I already removed stop words but they dont include inflection"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1Q90XsKYE4s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWwK5EvK83eL",
        "colab_type": "text"
      },
      "source": [
        "After this I am trying to test the LSTM model from Multiclass Text Classification using LSTM in Pytorch https://towardsdatascience.com/multiclass-text-classification-using-lstm-in-pytorch-eac56baed8df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4ckeWooSoKe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@Keras model {display-mode: \"form\"}\n",
        "\n",
        "# This code will be hidden when the notebook is loaded.\n",
        "from numpy import array\n",
        "from keras.preprocessing.text import one_hot\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Activation, Dropout, Dense\n",
        "from keras.layers import Flatten, LSTM\n",
        "from keras.layers import GlobalMaxPooling1D\n",
        "from keras.models import Model\n",
        "from keras.layers.embeddings import Embedding\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.layers import Input\n",
        "from keras.layers.merge import Concatenate\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LnjIPqy7lT7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = trainData[[\"sentence\"]]\n",
        "X_test = testData[[\"sentence\"]]\n",
        "X_dev = devData[[\"sentence\"]]\n",
        "Y_train = trainData[[\"label\"]]\n",
        "Y_test = testData[[\"label\"]]\n",
        "Y_dev = devData[[\"label\"]]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZkEt3er1q-G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNLvPjTG2qPk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make the label into binary matrix form\n",
        "def binarizerLabels(labelsFrame):\n",
        "  mlb = OneHotEncoder()\n",
        "  label_vec = labelsFrame.label.values\n",
        "  mlb.fit(label_vec.reshape(-1, 1))\n",
        "  a = mlb.transform(label_vec.reshape(-1, 1)).todense()\n",
        "  return pd.DataFrame(a, columns = mlb.categories_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h82sUM_T6HPk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_train_vec = binarizerLabels(Y_train)\n",
        "Y_dev_vec = binarizerLabels(Y_dev)\n",
        "Y_test_vec = binarizerLabels(Y_test)\n",
        "# binarizerLabels(Y_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxNwox5Z7rcT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocess_text(sen):\n",
        "    # Remove punctuations and numbers\n",
        "    sentence = re.sub('[^a-zA-Z]', ' ', sen)\n",
        "\n",
        "    # Single character removal\n",
        "    sentence = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', sentence)\n",
        "\n",
        "    # Removing multiple spaces\n",
        "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
        "\n",
        "    return sentence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPsWGHK3_VGH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "e7ea6453-ed9a-47d3-bfe0-93603a92d9e8"
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>interpreted law case criminal saucier told fox...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>guys ship thing weren treated criminals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>example backlash hillary clinton continued all...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>government actively destroyed life impossible ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>course conversations patel discussed desire ho...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>bds standards manifestly anti semitic campaign...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5110</th>\n",
              "      <td>bds standards manifestly anti semitic campaign...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5111</th>\n",
              "      <td>vitriolic bds campaign immediately sparked out...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5112</th>\n",
              "      <td>vitriolic bds campaign immediately sparked out...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5113</th>\n",
              "      <td>judging past investigations conducted schools ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5114 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               sentence\n",
              "0     interpreted law case criminal saucier told fox...\n",
              "1               guys ship thing weren treated criminals\n",
              "2     example backlash hillary clinton continued all...\n",
              "3     government actively destroyed life impossible ...\n",
              "4     course conversations patel discussed desire ho...\n",
              "...                                                 ...\n",
              "5109  bds standards manifestly anti semitic campaign...\n",
              "5110  bds standards manifestly anti semitic campaign...\n",
              "5111  vitriolic bds campaign immediately sparked out...\n",
              "5112  vitriolic bds campaign immediately sparked out...\n",
              "5113  judging past investigations conducted schools ...\n",
              "\n",
              "[5114 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxuYYsHOsMbv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8e2f1d0-d831-4387-9943-9c4e09a05dd6"
      },
      "source": [
        "np.mean([len(x.split()) for x in X_train.sentence.values])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.176574110285491"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCZ-slEOUjJE",
        "colab_type": "text"
      },
      "source": [
        "Create text-embedding\n",
        "https://stackabuse.com/python-for-nlp-word-embeddings-for-deep-learning-in-keras/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wptq-GSJUgXG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# convert text to vector\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X_train.sentence.values)\n",
        "\n",
        "X_train_tok = tokenizer.texts_to_sequences(X_train.sentence.values)\n",
        "X_test_tok = tokenizer.texts_to_sequences(X_test.sentence.values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0CoCdb1vAnGB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "\n",
        "maxlen = 14\n",
        "\n",
        "X_train_tok = pad_sequences(X_train_tok, padding='post', maxlen=maxlen)\n",
        "X_test_tok = pad_sequences(X_test_tok, padding='post', maxlen=maxlen)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6fgjDHjBqwp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8c4e4e83-82c8-4c02-de7d-1e5fe2d2cd59"
      },
      "source": [
        "X_train.values[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['interpreted law case criminal saucier told fox news referring prosecuting authorities case didn prosecute hillary clinton'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkLQMU2f_-0a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f3798646-ea60-4fe2-ce9a-8aaaace2dae3"
      },
      "source": [
        "X_train_tok"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  42,  129, 1351, ..., 2844,  148,   24],\n",
              "       [3415, 3416,  123, ...,    0,    0,    0],\n",
              "       [ 958, 1667,  148, ...,    0,    0,    0],\n",
              "       ...,\n",
              "       [ 860, 2707,  778, ..., 4238,   69,  131],\n",
              "       [ 860, 2707,  778, ..., 4238,   69,  131],\n",
              "       [8427,  603, 2461, ...,    0,    0,    0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaYVuVRlcBv0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# using GloVe word embeddings to convert text inputs to their numeric counterparts\n",
        "from numpy import array\n",
        "from numpy import asarray\n",
        "from numpy import zeros\n",
        "\n",
        "embeddings_dictionary = dict()\n",
        "\n",
        "glove_file = open('/content/drive/My Drive/DSP/data/glove.6B.100d.txt', encoding=\"utf8\")\n",
        "\n",
        "for line in glove_file:\n",
        "    records = line.split()\n",
        "    word = records[0]\n",
        "    vector_dimensions = asarray(records[1:], dtype='float32')\n",
        "    embeddings_dictionary[word] = vector_dimensions\n",
        "glove_file.close()\n",
        "\n",
        "embedding_matrix = zeros((vocab_size, 100))\n",
        "for word, index in tokenizer.word_index.items():\n",
        "    embedding_vector = embeddings_dictionary.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2cPpuxzDi9R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "e6f4ab88-781a-44e5-af86-11388c0f58f4"
      },
      "source": [
        "Y_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Whataboutism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Whataboutism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Causal_Oversimplification</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Exaggeration,Minimisation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Appeal_to_fear-prejudice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>Appeal_to_fear-prejudice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5110</th>\n",
              "      <td>Doubt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5111</th>\n",
              "      <td>Doubt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5112</th>\n",
              "      <td>Loaded_Language</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5113</th>\n",
              "      <td>Doubt</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5114 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                          label\n",
              "0                  Whataboutism\n",
              "1                  Whataboutism\n",
              "2     Causal_Oversimplification\n",
              "3     Exaggeration,Minimisation\n",
              "4      Appeal_to_fear-prejudice\n",
              "...                         ...\n",
              "5109   Appeal_to_fear-prejudice\n",
              "5110                      Doubt\n",
              "5111                      Doubt\n",
              "5112            Loaded_Language\n",
              "5113                      Doubt\n",
              "\n",
              "[5114 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ko1DiRWIWsK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "class Model(nn.Module):\n",
        "  def __init__(self, vocab_size, out_size):\n",
        "    super().__init__()\n",
        "    self.embedding = nn.Embedding(vocab_size, 100) # , _weight=torch.tensor(embedding_matrix).to(torch.float))\n",
        "    self.lstm = nn.LSTM(100, 512, 1, batch_first=True)\n",
        "    self.dense = nn.Linear(512, 18)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = self.embedding(x)\n",
        "    h0 = torch.randn(1, x.shape[0], 512).to(torch.float)\n",
        "    c0 = torch.randn(1, x.shape[0], 512).to(torch.float)\n",
        "    _, (h, c) = self.lstm(x, (h0, c0))\n",
        "\n",
        "    return self.dense(h.squeeze(0))\n",
        "\n",
        "\n",
        "model = Model(vocab_size, 18)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mblEDsNYbLO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "8a311c3c-428f-4ded-bad2-6face31c9296"
      },
      "source": [
        "model.dense.weight"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([[-0.0359,  0.0110,  0.0312,  ..., -0.0379,  0.0358,  0.0221],\n",
              "        [-0.0419,  0.0440,  0.0321,  ..., -0.0259, -0.0296, -0.0041],\n",
              "        [-0.0166,  0.0306, -0.0018,  ...,  0.0233, -0.0363, -0.0013],\n",
              "        ...,\n",
              "        [-0.0083, -0.0042,  0.0009,  ..., -0.0061, -0.0143,  0.0312],\n",
              "        [ 0.0313,  0.0174,  0.0372,  ..., -0.0129,  0.0425, -0.0020],\n",
              "        [ 0.0084, -0.0416,  0.0071,  ...,  0.0182, -0.0303,  0.0173]],\n",
              "       requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPHZ8pmMLjg9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "idx2label = list(set(Y_train.label))\n",
        "label2idx = { label: i for i, label in enumerate(idx2label) }\n",
        "Y_train_idx = np.array([\n",
        "    label2idx[l] for l in Y_train.label\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0m0Gfs9NU75",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "fb2f3779-0935-4861-df98-809b8d4b05bf"
      },
      "source": [
        "torch.tensor(Y_train_idx, dtype=torch.int).unsqueeze(-1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[15],\n",
              "        [15],\n",
              "        [ 4],\n",
              "        ...,\n",
              "        [ 8],\n",
              "        [14],\n",
              "        [ 8]], dtype=torch.int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
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          ]
        },
        "outputId": "6e9a8702-7971-4cef-a594-40c4f7ad9708"
      },
      "source": [
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "opt = torch.optim.Adam(model.parameters(), lr=0.0005)\n",
        "\n",
        "class ComposeDataset(Dataset):\n",
        "  def __init__(self, data, labels):\n",
        "    super().__init__()\n",
        "    self.data = data\n",
        "    self.labels = labels\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "  \n",
        "  def __getitem__(self, i):\n",
        "    return self.data[i], self.labels[i]\n",
        "\n",
        "train_dl = DataLoader(ComposeDataset(X_train_tok, Y_train_idx), batch_size=64)\n",
        "\n",
        "for epoch in tqdm(range(10)):\n",
        "  bar = tqdm(train_dl)\n",
        "  losses = []\n",
        "  for x, y in bar:\n",
        "    # Compute the outputs\n",
        "    out = model(x.to(torch.long))\n",
        "    loss = F.cross_entropy(\n",
        "        out,\n",
        "        torch.tensor(y, dtype=torch.long)\n",
        "    ).mean()\n",
        "\n",
        "    # Backpropagate the loss\n",
        "    opt.zero_grad()\n",
        "    loss.backward()\n",
        "    opt.step()\n",
        "    bar.set_postfix(loss=loss.item())\n",
        "    losses.append(loss.item())\n",
        "  \n",
        "  print(np.mean(losses))"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
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              "HBox(children=(FloatProgress(value=0.0, max=80.0), HTML(value='')))"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: UserWarning:\n",
            "\n",
            "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "\n"
          ],
          "name": "stderr"
        },
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          "output_type": "stream",
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            "\n",
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            "\n",
            "1.9679570838809013\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "\n",
            "1.9097572088241577\n",
            "\n"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MecL_48PW03z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fef8c97-acb6-4464-b6e9-08ab81c5d9ef"
      },
      "source": [
        "X_train_tok[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  42,  129, 1351,   58, 1496,  113, 2452, 4241,  305,   42,  513,\n",
              "        2844,  148,   24],\n",
              "       [3415, 3416,  123, 1211, 1102, 1352,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0],\n",
              "       [ 958, 1667,  148,   24,  220, 2453,    3, 1212,  110,  893,    0,\n",
              "           0,    0,    0],\n",
              "       [  71, 4242, 1103,    3,  277,  306, 2845,    0,    0,    0,    0,\n",
              "           0,    0,    0],\n",
              "       [ 221, 4243, 3417, 1668, 1874,   10,   53,  318,  259,  318,    0,\n",
              "           0,    0,    0],\n",
              "       [2454,  124, 4244,  412,  413, 2846,  222,   50, 1875,   15,  141,\n",
              "        1213,  895,    7],\n",
              "       [ 394, 4244,  567,    9, 1669,  318,  896, 1670, 1353, 5749,    9,\n",
              "        2146, 1018,  652],\n",
              "       [ 221, 4243, 3417, 1668, 1874,   10,   53,  318,  259,  318,    0,\n",
              "           0,    0,    0],\n",
              "       [   7, 4245, 1214, 2848,   20,  734, 1104,  319,  182, 3419,    0,\n",
              "           0,    0,    0],\n",
              "       [ 294, 5750, 4245, 5751, 4246,    7,  653,  372, 5752, 5753, 5754,\n",
              "           0,    0,    0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8K09Sp9cObT_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "698cab77-5b96-4189-f589-034e430635e1"
      },
      "source": [
        "model(x.to(torch.long))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.3039,  0.5815, -2.8386,  ..., -1.4306, -0.4179, -0.5159],\n",
              "        [ 0.4462, -0.5817, -3.4246,  ..., -1.1467, -2.1231, -2.2683],\n",
              "        [-0.5101,  0.1208, -2.2486,  ..., -1.8097,  0.9363, -1.9549],\n",
              "        ...,\n",
              "        [-0.4560,  0.0798, -4.3595,  ..., -1.3292, -3.2051, -1.8776],\n",
              "        [-0.1643, -0.3011, -3.6316,  ..., -1.3322, -2.6082, -1.5770],\n",
              "        [-0.3050,  0.5620, -2.8668,  ..., -1.4109, -0.4563, -0.4889]],\n",
              "       grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1B3Y8n28XU8K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b55e93bd-87f9-4935-9359-d057698847ed"
      },
      "source": [
        "a = model(torch.tensor(X_test_tok).to(torch.long)).argmax(dim=-1)\n",
        "a\n",
        "# X_test_tok"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([14, 14, 14,  ..., 14, 14, 14])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kH4GXf455TN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = a.numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqKHIj5B3-dv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30fb4d66-f451-41eb-91b6-0f2d6868402b"
      },
      "source": [
        "Y_test_idx = np.array([\n",
        "    label2idx[l] for l in Y_test.label\n",
        "])\n",
        "Y_test_idx.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1439,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uq0yVEkjw7gS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59ff68e0-0752-45d6-8ad7-d0c49ee87bf3"
      },
      "source": [
        "mse = np.mean((b - Y_test_idx)**2)\n",
        "mse\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "45.90271021542738"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHv6z-vGN16U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6a00d79-5688-4631-d546-c77a543e0939"
      },
      "source": [
        "input = torch.randn(3, 5, requires_grad=True)\n",
        "target = torch.empty(3, dtype=torch.long).random_(5)\n",
        "F.cross_entropy(input, target)\n",
        "input"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0574, -0.2863,  1.2659, -0.2144,  0.5824],\n",
              "        [ 1.4127,  0.0052, -1.9311, -0.3545, -0.5037],\n",
              "        [ 0.1702, -0.0260, -1.3030, -1.2014, -0.3517]], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yR2DrNYY7cLC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7klikWnhBJK",
        "colab_type": "text"
      },
      "source": [
        "Explore the other data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CA2W0KZAhLCS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_txt = open('/content/drive/My Drive/DSP/data/train.txt').read()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v0HePgMTiZeM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "c68bc675-6446-4704-f0e0-cb9ae3901db6"
      },
      "source": [
        "train_txt_data = pd.DataFrame([i.split(\"\\t\") for i in train_txt.split(\"\\n\")], columns=[\"word\", \"label\"])\n",
        "train_txt_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Former</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apostolic</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nuncio</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>to</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>the</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356094</th>\n",
              "      <td>from</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356095</th>\n",
              "      <td>Robert</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356096</th>\n",
              "      <td>Spencer</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356097</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356098</th>\n",
              "      <td></td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>356099 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             word label\n",
              "0          Former     O\n",
              "1       Apostolic     O\n",
              "2          Nuncio     O\n",
              "3              to     O\n",
              "4             the     O\n",
              "...           ...   ...\n",
              "356094       from     O\n",
              "356095     Robert     O\n",
              "356096    Spencer     O\n",
              "356097                 \n",
              "356098             None\n",
              "\n",
              "[356099 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    }
  ]
}