AI_in_Health_Project_Code_Redoan.ipynb

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      "execution_count": null,
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        "colab": {
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        },
        "id": "Z4_CxqX4PY-w",
        "outputId": "5b5b42ef-4431-476b-a00b-8d48e95ac325"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "# Note: change your \"path to folder\" to the path connecting to your google drive mimic folder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
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      },
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          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0 MB 5.4 MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[K     |████████████████████████████████| 880 kB 37.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.6.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.6.0-py3-none-any.whl (84 kB)\n",
            "\u001b[K     |████████████████████████████████| 84 kB 2.7 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 32.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 35.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.2.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=5b732e964ef49173b183cc9ba33d2e46369047fc423dae49ceea93f6196ceebe\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.6.0 pyyaml-6.0 sacremoses-0.0.53 tokenizers-0.12.1 transformers-4.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import pandas as pd\n",
        "import random\n",
        "import numpy as np\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn import feature_extraction, model_selection, naive_bayes, pipeline, manifold, preprocessing, feature_selection, metrics\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
        "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
        "from sklearn.model_selection import train_test_split\n",
        "from gensim.models import word2vec\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import spacy\n",
        "import keras\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import *\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.regularizers import L1L2\n",
        "import string\n",
        "import logging\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import models, layers, preprocessing as kprocessing\n",
        "from tensorflow.keras import backend as K\n",
        "import transformers"
      ],
      "metadata": {
        "id": "HZ9_-TacA46l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fe7be54-d11c-4605-946c-d86b99a0c16f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessSHAC(dataFolder):\n",
        "  rootFilePath = '/content/drive/MyDrive/\"Relation Extraction\"/Annotations/' + dataFolder + '/mimic'\n",
        "  fileNames = !ls $rootFilePath\n",
        "  textFiles = []\n",
        "  annotationFiles = []\n",
        "  for line in fileNames:\n",
        "    for fileName in line.split():\n",
        "      if fileName.endswith('.ann'):\n",
        "        annotationFiles.append(fileName)\n",
        "      elif fileName.endswith('.txt'):\n",
        "        textFiles.append(fileName)\n",
        "\n",
        "  textFiles = sorted(textFiles)\n",
        "  annotationFiles = sorted(annotationFiles)\n",
        "  dataFrameList = []\n",
        "  for currentTextFileName in textFiles:\n",
        "    print(currentTextFileName, end = \" \")\n",
        "    # currentTextFileName = '0301.txt'\n",
        "    with open('/content/drive/MyDrive/Relation Extraction/Annotations/' + dataFolder + '/mimic/' + currentTextFileName, 'r') as text:\n",
        "      currentPrefix = currentTextFileName.split('.')[0]\n",
        "      currentAnnotationFile = ''\n",
        "      for annFile in annotationFiles:\n",
        "        if annFile.startswith(currentPrefix):\n",
        "          currentAnnotationFile = annFile\n",
        "          break\n",
        "        \n",
        "      with open('/content/drive/MyDrive/Relation Extraction/Annotations/' + dataFolder + '/mimic/' + currentAnnotationFile, 'r') as ann:\n",
        "        rawTextData = text.read()\n",
        "        rawAnnData = ann.read()\n",
        "        if rawTextData.strip() != '' and rawAnnData.strip() != '' :\n",
        "          cursor = 0\n",
        "          annLines = rawAnnData.splitlines()\n",
        "          triggers = {}\n",
        "          events = {}\n",
        "          values = {}\n",
        "          for line in annLines:\n",
        "            dataValues = line.strip().split()\n",
        "            if dataValues[0].startswith(\"T\"):\n",
        "              start_pos = dataValues[2]\n",
        "              end_pos = dataValues[3]\n",
        "              trigger = ''\n",
        "              trigerStartPos = 4\n",
        "              if ';' in dataValues[3]:\n",
        "                while ';' in dataValues[trigerStartPos]:\n",
        "                  trigerStartPos += 1\n",
        "                end_pos = dataValues[trigerStartPos]\n",
        "                trigerStartPos += 1\n",
        "              for idx in range(trigerStartPos, len(dataValues)):\n",
        "                trigger += dataValues[idx]\n",
        "                if idx != len(dataValues):\n",
        "                  trigger += ' '\n",
        "                \n",
        "              triggers[dataValues[0]] = {\n",
        "                  'start_pos': int(start_pos),\n",
        "                  'end_pos': int(end_pos),\n",
        "                  'trigger': trigger,\n",
        "                  'type': dataValues[1]\n",
        "              }\n",
        "            elif dataValues[0].startswith(\"E\"):\n",
        "              eventSeq = []\n",
        "              for idx in range(1,len(dataValues)):\n",
        "                eventSeq.append({\n",
        "                  'relationType': dataValues[idx].split(':')[0],\n",
        "                  'triggerId': dataValues[idx].split(':')[1]\n",
        "                })\n",
        "              events[dataValues[0]] = eventSeq\n",
        "            # elif dataValues[0].startswith(\"A\"):\n",
        "            \n",
        "          defaultPositionCursorDistance = 15\n",
        "          sentNum = 1\n",
        "          sentenceInfoList = []\n",
        "          cursor = 0\n",
        "          sentenceList = sent_tokenize(rawTextData.replace('\\n','/\\/').replace('  ', ' |_|'))\n",
        "          for sentence in sentenceList:\n",
        "            sentence = sentence.replace('/\\/', ' ').replace('|_|', ' ') \n",
        "            lim = len(sentence)\n",
        "            if lim <= 0:\n",
        "              continue\n",
        "            if re.search('Social History:', sentence, re.IGNORECASE):\n",
        "              sentence = re.split('Social History:', sentence, flags=re.IGNORECASE)[1]\n",
        "              cursor += defaultPositionCursorDistance\n",
        "              lim -= defaultPositionCursorDistance + 1\n",
        "            # else:\n",
        "            #   cursor += 1\n",
        "            start_pos = cursor\n",
        "            cursor += lim\n",
        "            end_pos = cursor\n",
        "            cursor += 1\n",
        "            newSentenceInfo = {\n",
        "                'start_pos': start_pos,\n",
        "                'end_pos': end_pos,\n",
        "                'sentence': sentence,\n",
        "                'sentNum': sentNum\n",
        "            }\n",
        "            sentenceInfoList.append(newSentenceInfo)\n",
        "            sentNum += 1\n",
        "            # print(newSentenceInfo, lim, cursor)\n",
        "          \n",
        "          # print(sentenceInfoList)\n",
        "          # print(events)\n",
        "          # print(triggers)\n",
        "          for event in events.keys():\n",
        "            eventSeq = events[event]\n",
        "            sentNum = -1\n",
        "            sentence = None\n",
        "            cursor = 0\n",
        "            for sentenceInfo in sentenceInfoList:\n",
        "              if (sentenceInfo['start_pos'] <= triggers[eventSeq[0]['triggerId']]['start_pos'] - 1) and (sentenceInfo['end_pos'] >= triggers[eventSeq[len(eventSeq) - 1]['triggerId']]['end_pos'] - 1):\n",
        "                # print(sentenceInfo)\n",
        "                sentNum = sentenceInfo['sentNum']\n",
        "                sentence = sentenceInfo['sentence']\n",
        "                cursor = sentenceInfo['start_pos']\n",
        "                break\n",
        "            newRowDefaults = {\n",
        "                'sentence': sentence,\n",
        "                'T1': triggers[eventSeq[0]['triggerId']]['trigger'],\n",
        "                'T1_starting_postion': triggers[eventSeq[0]['triggerId']]['start_pos'] - cursor,\n",
        "                'T1_ending_position': triggers[eventSeq[0]['triggerId']]['end_pos'] - cursor - 1,\n",
        "                'T1_type': triggers[eventSeq[0]['triggerId']]['type'],\n",
        "            }\n",
        "            for idx  in range(1, len(eventSeq)):\n",
        "              trigger = eventSeq[idx]\n",
        "              newRow = {\n",
        "                'id': currentPrefix+'_'+event+ sentence[slice(5)] if sentence != None else '',\n",
        "              }\n",
        "              newRow = {**newRow, **newRowDefaults}\n",
        "              nextTrigger = {\n",
        "                'T2': triggers[trigger['triggerId']]['trigger'],\n",
        "                'T2_starting_postion': triggers[trigger['triggerId']]['start_pos'] - cursor,\n",
        "                'T2_ending_position': triggers[trigger['triggerId']]['end_pos'] - cursor - 1,\n",
        "                'T2_type': triggers[trigger['triggerId']]['type'],\n",
        "                'relation_type': trigger['relationType'],\n",
        "              }\n",
        "              newRow = {**newRow, **nextTrigger}\n",
        "              dataFrameList.append(newRow)\n",
        "            \n",
        "        ann.close()\n",
        "      text.close()\n",
        "  return pd.DataFrame(dataFrameList)"
      ],
      "metadata": {
        "id": "lwc1EPCSkU-H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainDataSource = preprocessSHAC(dataFolder = 'train')\n",
        "devDataSource = preprocessSHAC(dataFolder = 'dev')"
      ],
      "metadata": {
        "id": "_L3b3mfMjiQW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62d236df-f1b7-4b3b-e903-06c7bd56d2ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0101.txt 0102.txt 0103.txt 0104.txt 0105.txt 0106.txt 0107.txt 0108.txt 0109.txt 0110.txt 0111.txt 0112.txt 0113.txt 0114.txt 0115.txt 0116.txt 0117.txt 0118.txt 0119.txt 0120.txt 0121.txt 0122.txt 0123.txt 0124.txt 0125.txt 0126.txt 0127.txt 0128.txt 0129.txt 0130.txt 0131.txt 0132.txt 0133.txt 0134.txt 0135.txt 0136.txt 0137.txt 0138.txt 0139.txt 0140.txt 0141.txt 0142.txt 0143.txt 0144.txt 0145.txt 0146.txt 0147.txt 0148.txt 0149.txt 0150.txt 0301.txt 0302.txt 0303.txt 0304.txt 0305.txt 0306.txt 0307.txt 0308.txt 0309.txt 0310.txt 0311.txt 0312.txt 0313.txt 0314.txt 0315.txt 0316.txt 0317.txt 0318.txt 0319.txt 0320.txt 0321.txt 0322.txt 0323.txt 0324.txt 0325.txt 0326.txt 0327.txt 0328.txt 0329.txt 0330.txt 0331.txt 0332.txt 0333.txt 0334.txt 0335.txt 0336.txt 0337.txt 0338.txt 0339.txt 0340.txt 0341.txt 0342.txt 0343.txt 0344.txt 0345.txt 0346.txt 0347.txt 0348.txt 0349.txt 0350.txt 0401.txt 0402.txt 0406.txt 0407.txt 0408.txt 0409.txt 0410.txt 0412.txt 0413.txt 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              "                id                                           sentence  \\\n",
              "0     0403_E1  Liv    Lives with his caring and devoted parents at...   \n",
              "1     0403_E1  Liv    Lives with his caring and devoted parents at...   \n",
              "2     0403_E2 No h        No history of alcohol, tobacco or drug use.   \n",
              "3     0403_E3 No h        No history of alcohol, tobacco or drug use.   \n",
              "4     0403_E4 No h        No history of alcohol, tobacco or drug use.   \n",
              "...            ...                                                ...   \n",
              "1172  2916_E4 Live   Lives with wife Retired Former tobacco use, s...   \n",
              "1173  2916_E4 Live   Lives with wife Retired Former tobacco use, s...   \n",
              "1174  2916_E4 Live   Lives with wife Retired Former tobacco use, s...   \n",
              "1175  2916_E5 Live   Lives with wife Retired Former tobacco use, s...   \n",
              "1176  2916_E5 Live   Lives with wife Retired Former tobacco use, s...   \n",
              "\n",
              "                          T1  T1_starting_postion  T1_ending_position  \\\n",
              "0                     Lives                     2                   6   \n",
              "1                     Lives                     2                   6   \n",
              "2                   tobacco                    25                  31   \n",
              "3                  drug use                    36                  43   \n",
              "4                   alcohol                    16                  22   \n",
              "...                      ...                  ...                 ...   \n",
              "1172                Alcohol                    74                  80   \n",
              "1173                Alcohol                    74                  80   \n",
              "1174                Alcohol                    74                  80   \n",
              "1175  recreational drug use                   113                 133   \n",
              "1176  recreational drug use                   113                 133   \n",
              "\n",
              "           T1_type                                    T2  T2_starting_postion  \\\n",
              "0     LivingStatus                                Lives                     2   \n",
              "1     LivingStatus  with his caring and devoted parents                     8   \n",
              "2          Tobacco                           No history                     2   \n",
              "3             Drug                           No history                     2   \n",
              "4          Alcohol                           No history                     2   \n",
              "...            ...                                   ...                  ...   \n",
              "1172       Alcohol                    [**4-15**] drinks                    83   \n",
              "1173       Alcohol               [**4-15**] drinks/week                    83   \n",
              "1174       Alcohol                                /week                   100   \n",
              "1175          Drug                         recreational                   113   \n",
              "1176          Drug                               Denies                   106   \n",
              "\n",
              "      T2_ending_position     T2_type relation_type  \n",
              "0                      6  StatusTime        Status  \n",
              "1                     42  TypeLiving          Type  \n",
              "2                     11  StatusTime        Status  \n",
              "3                     11  StatusTime        Status  \n",
              "4                     11  StatusTime        Status  \n",
              "...                  ...         ...           ...  \n",
              "1172                  99      Amount        Amount  \n",
              "1173                 104  StatusTime        Status  \n",
              "1174                 104   Frequency     Frequency  \n",
              "1175                 124        Type          Type  \n",
              "1176                 111  StatusTime        Status  \n",
              "\n",
              "[1177 rows x 11 columns]"
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              "      <td>Status</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0403_E3 No h</td>\n",
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              "      <td>36</td>\n",
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              "      <td>0403_E4 No h</td>\n",
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              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>Alcohol</td>\n",
              "      <td>No history</td>\n",
              "      <td>2</td>\n",
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              "      <td>Alcohol</td>\n",
              "      <td>[**4-15**] drinks</td>\n",
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              "      <td>Status</td>\n",
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              "      <td>2916_E4 Live</td>\n",
              "      <td>Lives with wife Retired Former tobacco use, s...</td>\n",
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              "      <td>74</td>\n",
              "      <td>80</td>\n",
              "      <td>Alcohol</td>\n",
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              "      <td>100</td>\n",
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        "devDataSource.to_csv('devDataset.csv')"
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        "outputId": "5bc9e5b4-f5bf-4885-bab5-ef83d089ea92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     0\n",
              "sentence               3\n",
              "T1                     0\n",
              "T1_starting_postion    0\n",
              "T1_ending_position     0\n",
              "T1_type                0\n",
              "T2                     0\n",
              "T2_starting_postion    0\n",
              "T2_ending_position     0\n",
              "T2_type                0\n",
              "relation_type          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainDataSource.pivot_table(index=['relation_type', 'T2_type','T1_type'], aggfunc='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sQVuaqdRuFfZ",
        "outputId": "4fb55eeb-5551-44f1-bd6d-a933c6ac4571"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           T1  T1_ending_position  \\\n",
              "relation_type T2_type      T1_type                                  \n",
              "Amount        Amount       Alcohol        315                 315   \n",
              "                           Drug            10                  10   \n",
              "                           Tobacco        558                 558   \n",
              "Amount2       Amount       Alcohol          7                   7   \n",
              "                           Tobacco         27                  27   \n",
              "Amount3       Amount       Tobacco          1                   1   \n",
              "Duration      Duration     Alcohol         84                  84   \n",
              "                           Drug            23                  23   \n",
              "                           Employment      49                  49   \n",
              "                           LivingStatus    37                  37   \n",
              "                           Tobacco        296                 296   \n",
              "Duration2     Duration     Alcohol          1                   1   \n",
              "                           Tobacco          5                   5   \n",
              "Frequency     Frequency    Alcohol        322                 322   \n",
              "                           Drug            24                  24   \n",
              "                           Tobacco        196                 196   \n",
              "Frequency2    Frequency    Alcohol          4                   4   \n",
              "History       History      Alcohol        147                 147   \n",
              "                           Drug           100                 100   \n",
              "                           Employment      49                  49   \n",
              "                           LivingStatus    19                  19   \n",
              "                           Tobacco        304                 304   \n",
              "History2      History      Alcohol          1                   1   \n",
              "Method        Method       Drug           193                 193   \n",
              "                           Tobacco          5                   5   \n",
              "Method2       Method       Drug             2                   2   \n",
              "Status        StatusEmploy Employment     980                 980   \n",
              "              StatusTime   Alcohol       1292                1292   \n",
              "                           Drug           985                 985   \n",
              "                           LivingStatus   959                 959   \n",
              "                           Tobacco       1230                1230   \n",
              "Status2       StatusEmploy Employment       1                   1   \n",
              "              StatusTime   Alcohol          2                   2   \n",
              "                           Drug             1                   1   \n",
              "                           Tobacco          2                   2   \n",
              "Type          Type         Alcohol        186                 186   \n",
              "                           Drug           663                 663   \n",
              "                           Employment     695                 695   \n",
              "                           Tobacco        153                 153   \n",
              "              TypeLiving   LivingStatus   959                 959   \n",
              "Type2         Type         Alcohol          8                   8   \n",
              "                           Drug            20                  20   \n",
              "                           Employment      12                  12   \n",
              "                           Tobacco          2                   2   \n",
              "Type3         Type         Alcohol          1                   1   \n",
              "                           Drug             2                   2   \n",
              "                           Employment       1                   1   \n",
              "\n",
              "                                         T1_starting_postion    T2  \\\n",
              "relation_type T2_type      T1_type                                   \n",
              "Amount        Amount       Alcohol                       315   315   \n",
              "                           Drug                           10    10   \n",
              "                           Tobacco                       558   558   \n",
              "Amount2       Amount       Alcohol                         7     7   \n",
              "                           Tobacco                        27    27   \n",
              "Amount3       Amount       Tobacco                         1     1   \n",
              "Duration      Duration     Alcohol                        84    84   \n",
              "                           Drug                           23    23   \n",
              "                           Employment                     49    49   \n",
              "                           LivingStatus                   37    37   \n",
              "                           Tobacco                       296   296   \n",
              "Duration2     Duration     Alcohol                         1     1   \n",
              "                           Tobacco                         5     5   \n",
              "Frequency     Frequency    Alcohol                       322   322   \n",
              "                           Drug                           24    24   \n",
              "                           Tobacco                       196   196   \n",
              "Frequency2    Frequency    Alcohol                         4     4   \n",
              "History       History      Alcohol                       147   147   \n",
              "                           Drug                          100   100   \n",
              "                           Employment                     49    49   \n",
              "                           LivingStatus                   19    19   \n",
              "                           Tobacco                       304   304   \n",
              "History2      History      Alcohol                         1     1   \n",
              "Method        Method       Drug                          193   193   \n",
              "                           Tobacco                         5     5   \n",
              "Method2       Method       Drug                            2     2   \n",
              "Status        StatusEmploy Employment                    980   980   \n",
              "              StatusTime   Alcohol                      1292  1292   \n",
              "                           Drug                          985   985   \n",
              "                           LivingStatus                  959   959   \n",
              "                           Tobacco                      1230  1230   \n",
              "Status2       StatusEmploy Employment                      1     1   \n",
              "              StatusTime   Alcohol                         2     2   \n",
              "                           Drug                            1     1   \n",
              "                           Tobacco                         2     2   \n",
              "Type          Type         Alcohol                       186   186   \n",
              "                           Drug                          663   663   \n",
              "                           Employment                    695   695   \n",
              "                           Tobacco                       153   153   \n",
              "              TypeLiving   LivingStatus                  959   959   \n",
              "Type2         Type         Alcohol                         8     8   \n",
              "                           Drug                           20    20   \n",
              "                           Employment                     12    12   \n",
              "                           Tobacco                         2     2   \n",
              "Type3         Type         Alcohol                         1     1   \n",
              "                           Drug                            2     2   \n",
              "                           Employment                      1     1   \n",
              "\n",
              "                                         T2_ending_position  \\\n",
              "relation_type T2_type      T1_type                            \n",
              "Amount        Amount       Alcohol                      315   \n",
              "                           Drug                          10   \n",
              "                           Tobacco                      558   \n",
              "Amount2       Amount       Alcohol                        7   \n",
              "                           Tobacco                       27   \n",
              "Amount3       Amount       Tobacco                        1   \n",
              "Duration      Duration     Alcohol                       84   \n",
              "                           Drug                          23   \n",
              "                           Employment                    49   \n",
              "                           LivingStatus                  37   \n",
              "                           Tobacco                      296   \n",
              "Duration2     Duration     Alcohol                        1   \n",
              "                           Tobacco                        5   \n",
              "Frequency     Frequency    Alcohol                      322   \n",
              "                           Drug                          24   \n",
              "                           Tobacco                      196   \n",
              "Frequency2    Frequency    Alcohol                        4   \n",
              "History       History      Alcohol                      147   \n",
              "                           Drug                         100   \n",
              "                           Employment                    49   \n",
              "                           LivingStatus                  19   \n",
              "                           Tobacco                      304   \n",
              "History2      History      Alcohol                        1   \n",
              "Method        Method       Drug                         193   \n",
              "                           Tobacco                        5   \n",
              "Method2       Method       Drug                           2   \n",
              "Status        StatusEmploy Employment                   980   \n",
              "              StatusTime   Alcohol                     1292   \n",
              "                           Drug                         985   \n",
              "                           LivingStatus                 959   \n",
              "                           Tobacco                     1230   \n",
              "Status2       StatusEmploy Employment                     1   \n",
              "              StatusTime   Alcohol                        2   \n",
              "                           Drug                           1   \n",
              "                           Tobacco                        2   \n",
              "Type          Type         Alcohol                      186   \n",
              "                           Drug                         663   \n",
              "                           Employment                   695   \n",
              "                           Tobacco                      153   \n",
              "              TypeLiving   LivingStatus                 959   \n",
              "Type2         Type         Alcohol                        8   \n",
              "                           Drug                          20   \n",
              "                           Employment                    12   \n",
              "                           Tobacco                        2   \n",
              "Type3         Type         Alcohol                        1   \n",
              "                           Drug                           2   \n",
              "                           Employment                     1   \n",
              "\n",
              "                                         T2_starting_postion    id  sentence  \n",
              "relation_type T2_type      T1_type                                            \n",
              "Amount        Amount       Alcohol                       315   315       315  \n",
              "                           Drug                           10    10        10  \n",
              "                           Tobacco                       558   558       540  \n",
              "Amount2       Amount       Alcohol                         7     7         7  \n",
              "                           Tobacco                        27    27        27  \n",
              "Amount3       Amount       Tobacco                         1     1         1  \n",
              "Duration      Duration     Alcohol                        84    84        84  \n",
              "                           Drug                           23    23        22  \n",
              "                           Employment                     49    49        49  \n",
              "                           LivingStatus                   37    37        37  \n",
              "                           Tobacco                       296   296       286  \n",
              "Duration2     Duration     Alcohol                         1     1         1  \n",
              "                           Tobacco                         5     5         5  \n",
              "Frequency     Frequency    Alcohol                       322   322       322  \n",
              "                           Drug                           24    24        24  \n",
              "                           Tobacco                       196   196       191  \n",
              "Frequency2    Frequency    Alcohol                         4     4         4  \n",
              "History       History      Alcohol                       147   147       142  \n",
              "                           Drug                          100   100        95  \n",
              "                           Employment                     49    49        49  \n",
              "                           LivingStatus                   19    19        19  \n",
              "                           Tobacco                       304   304       287  \n",
              "History2      History      Alcohol                         1     1         1  \n",
              "Method        Method       Drug                          193   193       193  \n",
              "                           Tobacco                         5     5         5  \n",
              "Method2       Method       Drug                            2     2         2  \n",
              "Status        StatusEmploy Employment                    980   980       975  \n",
              "              StatusTime   Alcohol                      1292  1292      1286  \n",
              "                           Drug                          985   985       980  \n",
              "                           LivingStatus                  959   959       958  \n",
              "                           Tobacco                      1230  1230      1209  \n",
              "Status2       StatusEmploy Employment                      1     1         1  \n",
              "              StatusTime   Alcohol                         2     2         2  \n",
              "                           Drug                            1     1         1  \n",
              "                           Tobacco                         2     2         2  \n",
              "Type          Type         Alcohol                       186   186       186  \n",
              "                           Drug                          663   663       660  \n",
              "                           Employment                    695   695       692  \n",
              "                           Tobacco                       153   153       150  \n",
              "              TypeLiving   LivingStatus                  959   959       958  \n",
              "Type2         Type         Alcohol                         8     8         8  \n",
              "                           Drug                           20    20        20  \n",
              "                           Employment                     12    12        11  \n",
              "                           Tobacco                         2     2         2  \n",
              "Type3         Type         Alcohol                         1     1         1  \n",
              "                           Drug                            2     2         2  \n",
              "                           Employment                      1     1         1  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>T1</th>\n",
              "      <th>T1_ending_position</th>\n",
              "      <th>T1_starting_postion</th>\n",
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              "      <th>T2_ending_position</th>\n",
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              "      <th>id</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relation_type</th>\n",
              "      <th>T2_type</th>\n",
              "      <th>T1_type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>Tobacco</th>\n",
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              "      <td>558</td>\n",
              "      <td>558</td>\n",
              "      <td>558</td>\n",
              "      <td>558</td>\n",
              "      <td>540</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">Amount</th>\n",
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              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
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              "      <th>Amount3</th>\n",
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              "      <th>Tobacco</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Duration</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">Duration</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
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              "      <td>22</td>\n",
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              "      <th>Employment</th>\n",
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              "      <td>49</td>\n",
              "      <td>49</td>\n",
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              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
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              "    <tr>\n",
              "      <th>LivingStatus</th>\n",
              "      <td>37</td>\n",
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              "      <td>37</td>\n",
              "      <td>37</td>\n",
              "      <td>37</td>\n",
              "      <td>37</td>\n",
              "      <td>37</td>\n",
              "      <td>37</td>\n",
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              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>296</td>\n",
              "      <td>286</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">Duration2</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Duration</th>\n",
              "      <th>Alcohol</th>\n",
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              "      <th>Tobacco</th>\n",
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              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Frequency</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">Frequency</th>\n",
              "      <th>Alcohol</th>\n",
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              "      <td>322</td>\n",
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              "      <td>24</td>\n",
              "      <td>24</td>\n",
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              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>196</td>\n",
              "      <td>191</td>\n",
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              "    <tr>\n",
              "      <th>Frequency2</th>\n",
              "      <th>Frequency</th>\n",
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              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <th rowspan=\"5\" valign=\"top\">History</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">History</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>147</td>\n",
              "      <td>147</td>\n",
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              "      <td>147</td>\n",
              "      <td>147</td>\n",
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              "      <td>147</td>\n",
              "      <td>142</td>\n",
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              "    <tr>\n",
              "      <th>Drug</th>\n",
              "      <td>100</td>\n",
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              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>95</td>\n",
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              "    <tr>\n",
              "      <th>Employment</th>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LivingStatus</th>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>304</td>\n",
              "      <td>304</td>\n",
              "      <td>304</td>\n",
              "      <td>304</td>\n",
              "      <td>304</td>\n",
              "      <td>304</td>\n",
              "      <td>304</td>\n",
              "      <td>287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>History2</th>\n",
              "      <th>History</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Method</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Method</th>\n",
              "      <th>Drug</th>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Method2</th>\n",
              "      <th>Method</th>\n",
              "      <th>Drug</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Status</th>\n",
              "      <th>StatusEmploy</th>\n",
              "      <th>Employment</th>\n",
              "      <td>980</td>\n",
              "      <td>980</td>\n",
              "      <td>980</td>\n",
              "      <td>980</td>\n",
              "      <td>980</td>\n",
              "      <td>980</td>\n",
              "      <td>980</td>\n",
              "      <td>975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">StatusTime</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>1292</td>\n",
              "      <td>1292</td>\n",
              "      <td>1292</td>\n",
              "      <td>1292</td>\n",
              "      <td>1292</td>\n",
              "      <td>1292</td>\n",
              "      <td>1292</td>\n",
              "      <td>1286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Drug</th>\n",
              "      <td>985</td>\n",
              "      <td>985</td>\n",
              "      <td>985</td>\n",
              "      <td>985</td>\n",
              "      <td>985</td>\n",
              "      <td>985</td>\n",
              "      <td>985</td>\n",
              "      <td>980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LivingStatus</th>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>1230</td>\n",
              "      <td>1230</td>\n",
              "      <td>1230</td>\n",
              "      <td>1230</td>\n",
              "      <td>1230</td>\n",
              "      <td>1230</td>\n",
              "      <td>1230</td>\n",
              "      <td>1209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Status2</th>\n",
              "      <th>StatusEmploy</th>\n",
              "      <th>Employment</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">StatusTime</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Drug</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Type</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">Type</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Drug</th>\n",
              "      <td>663</td>\n",
              "      <td>663</td>\n",
              "      <td>663</td>\n",
              "      <td>663</td>\n",
              "      <td>663</td>\n",
              "      <td>663</td>\n",
              "      <td>663</td>\n",
              "      <td>660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Employment</th>\n",
              "      <td>695</td>\n",
              "      <td>695</td>\n",
              "      <td>695</td>\n",
              "      <td>695</td>\n",
              "      <td>695</td>\n",
              "      <td>695</td>\n",
              "      <td>695</td>\n",
              "      <td>692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TypeLiving</th>\n",
              "      <th>LivingStatus</th>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>959</td>\n",
              "      <td>958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Type2</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">Type</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Drug</th>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Employment</th>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tobacco</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Type3</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">Type</th>\n",
              "      <th>Alcohol</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Drug</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Employment</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwTo1Kdrt648",
        "outputId": "98651786-6e8c-4084-bd89-7f925694619b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.0.tar.gz (79 kB)\n",
            "\u001b[?25l\r\u001b[K     |████▏                           | 10 kB 19.5 MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 20 kB 9.0 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 30 kB 5.8 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 40 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 51 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 61 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 71 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 79 kB 3.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.18.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.64.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.11.0+cu113)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.12.0+cu113)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.4.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (3.2.5)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 33.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.6.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->sentence-transformers) (4.2.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (3.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (6.0)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.0.53)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (4.11.3)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.12.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (21.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2019.12.20)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.8.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.4)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence-transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.0-py3-none-any.whl size=120747 sha256=b17fa6dcf75bcfe4f0a2eb301e3aad8ffb6ec86f2f4cf54d051c89e075165312\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/c0/df/b6873ab7aac3f2465aa9144b6b4c41c4391cfecc027c8b07e7\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.0 sentencepiece-0.1.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleanTrainDF = trainDataSource.dropna().reset_index()\n",
        "cleanDevDF = devDataSource.dropna().reset_index()"
      ],
      "metadata": {
        "id": "YrX67EFM2sl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleanTrainDF['fileName'] = cleanTrainDF['id'].apply(lambda x: x.split(\"_\")[0])\n",
        "cleanDevDF['fileName'] = cleanDevDF['id'].apply(lambda x: x.split(\"_\")[0])\n",
        "\n",
        "def splitDataSource(df, smallerSideMinimumPercentage):\n",
        "  allFiles = df['fileName'].unique()\n",
        "  testFiles = []\n",
        "  while((df[df['fileName'].isin(testFiles)].shape[0] * 100/df.shape[0]) < smallerSideMinimumPercentage):\n",
        "    randomChoice = random.choices(allFiles, k=1)\n",
        "    while(randomChoice[0] in testFiles):\n",
        "      randomChoice = random.choices(allFiles, k=1)\n",
        "    testFiles.append(randomChoice[0])\n",
        "  return testFiles"
      ],
      "metadata": {
        "id": "A6Z8aL9-u7Gj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for file in cleanTrainDF['fileName'].unique():\n",
        "  if(file in cleanDevDF['fileName']):\n",
        "    print(file, end = \" \")"
      ],
      "metadata": {
        "id": "ay0DQdy9kGj7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleanTrainDF['relation_type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9RFgV-ng-94",
        "outputId": "c3cca8f9-2e60-4c51-bb94-70163ae6658e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Status', 'Amount', 'Frequency', 'Type', 'Method', 'Duration',\n",
              "       'History', 'Type2', 'Type3', 'Amount2', 'Duration2', 'Frequency2',\n",
              "       'Status2', 'History2', 'Amount3', 'Method2'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleanTrainDF.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IyI6Vut830hE",
        "outputId": "1fde4339-77b3-42cc-b68d-14a0d4d9bbd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10823, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "def getDistilBertEmbeddings(df, feature_name = 'sentence'):\n",
        "  model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1')\n",
        "  return model.encode(df.reset_index()[feature_name])"
      ],
      "metadata": {
        "id": "LRQGoRq4t5SN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install spacy\n",
        "# !pip install spacy_sentence_bert"
      ],
      "metadata": {
        "id": "hSekGBvu3GuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ! pip install -U pip setuptools wheel\n",
        "# ! pip install -U spacy\n",
        "# ! python -m spacy download en_core_web_sm"
      ],
      "metadata": {
        "id": "sqd9k3Q6pJ2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getCorpus(sentences):\n",
        "  nlp = spacy.load('en_core_web_sm')\n",
        "  corpus = []\n",
        "  for row in range(0, len(sentences)):\n",
        "    str_tokens=[]\n",
        "    tokens= nlp(sentences[row]).ents\n",
        "    for i in range(0, len(tokens)):\n",
        "      str_tokens.append(tokens[i].text)\n",
        "    corpus.append(list(str_tokens))\n",
        "  return corpus\n",
        "\n",
        "def getSpacy_Word2Vec_Corpus(df):\n",
        "  corpus=getCorpus(df.reset_index()['sentence'])\n",
        "  return word2vec.Word2Vec(corpus, min_count=1, size=100)"
      ],
      "metadata": {
        "id": "0CyIhAk8dIIY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def customtokenize(str):\n",
        "    tokens=nltk.word_tokenize(str)\n",
        "    ntokens = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "    words = ntokens.tokenize(str)\n",
        "    nostop = list(filter(lambda token: token not in stopwords.words('english'), words))\n",
        "    lemmatized=[lemmatizer.lemmatize(word) for word in nostop ]\n",
        "    return lemmatized\n",
        "\n",
        "def getTFIDFMatrix(df):\n",
        "  vectorizer = TfidfVectorizer(tokenizer=customtokenize)\n",
        "  return vectorizer.fit_transform(df['sentence'])"
      ],
      "metadata": {
        "id": "zmWt-Lbklvp0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getBagOfWords(df):\n",
        "  CountVec = CountVectorizer(ngram_range=(1,1), stop_words='english')\n",
        "  Count_data = CountVec.fit_transform(df['sentence'])\n",
        "  return pd.DataFrame(Count_data.toarray(),columns=CountVec.get_feature_names())"
      ],
      "metadata": {
        "id": "TPB0N-Lypa20"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "completeDF = pd.concat([cleanTrainDF, cleanDevDF])\n",
        "testFiles = splitDataSource(completeDF, 30)\n",
        "newTestDF = completeDF[completeDF['fileName'].isin(testFiles)]\n",
        "newTrainDF = completeDF[~completeDF['fileName'].isin(testFiles)]\n",
        "validFiles = splitDataSource(newTrainDF, 10)\n",
        "newValidDF = newTrainDF[newTrainDF['fileName'].isin(validFiles)]\n",
        "newTrainDF = newTrainDF[~newTrainDF['fileName'].isin(validFiles)]"
      ],
      "metadata": {
        "id": "Q3_zrly3jhVb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_Train_BOW = getBagOfWords(newTrainDF)\n",
        "new_Valid_BOW = getBagOfWords(newValidDF)\n",
        "new_Test_BOW = getBagOfWords(newTestDF)"
      ],
      "metadata": {
        "id": "4IUnNF5_khNG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf28c734-f582-4b46-893d-9801583619c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_Train_Spacy_Word2Vec = getSpacy_Word2Vec_Corpus(newTrainDF)\n",
        "new_Valid_Spacy_Word2Vec = getSpacy_Word2Vec_Corpus(newValidDF)\n",
        "new_Test_Spacy_Word2Vec = getSpacy_Word2Vec_Corpus(newTestDF)"
      ],
      "metadata": {
        "id": "XExufaTXljT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "amountTrainDF = cleanTrainDF[(cleanTrainDF['relation_type'] == 'Amount')]\n",
        "historyTrainDF = cleanTrainDF[(cleanTrainDF['relation_type'] == 'History')]\n",
        "\n",
        "amountDevDF = cleanDevDF[(cleanDevDF['relation_type'] == 'Amount')]\n",
        "historyDevDF = cleanDevDF[(cleanDevDF['relation_type'] == 'History')]\n",
        "\n",
        "twoClassCleanTrainDF = pd.concat([amountTrainDF.sample(n=(min(amountTrainDF.shape[0], historyTrainDF.shape[0]))), historyTrainDF.sample(n=(min(amountTrainDF.shape[0], historyTrainDF.shape[0])))])\n",
        "twoClassCleanDevDF = pd.concat([amountTrainDF.sample(n=(min(amountDevDF.shape[0], historyDevDF.shape[0]))), historyDevDF.sample(n=(min(amountDevDF.shape[0], historyDevDF.shape[0])))])"
      ],
      "metadata": {
        "id": "hFE6yu2GS0e1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# groupedByTarget = twoClassCleanTrainDF.groupby('relation_type')\n",
        "# testDF = groupedByTarget.apply(lambda x: x.sample(groupedByTarget.size().min()).reset_index(drop=True))\n",
        "# testDF.index.name='relation_group'\n",
        "twoClassCleanTrainDF['relation_type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cikwt2GyGS1Y",
        "outputId": "452e3e90-2354-42d8-efd7-2f2dc89e1aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Amount     592\n",
              "History    592\n",
              "Name: relation_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# twoClassCleanTrainDF\n",
        "# twoClassCleanDevDF\n",
        "trainWord2Vec = getSpacy_Word2Vec_Corpus(twoClassCleanTrainDF)\n",
        "testWord2Vec = getSpacy_Word2Vec_Corpus(twoClassCleanDevDF)\n",
        "\n",
        "w2v_weights = trainWord2Vec.wv.vectors\n",
        "vocab_size, embedding_size = w2v_weights.shape\n"
      ],
      "metadata": {
        "id": "dvuSzrYDzQDz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def word2token(word, w2v_model):\n",
        "    try:\n",
        "        return w2v_model.wv.vocab[word].index\n",
        "    except KeyError:\n",
        "        return 0\n",
        "def token2word(token, w2v_model):\n",
        "    return w2v_model.wv.index2word[token]"
      ],
      "metadata": {
        "id": "2kt2hHaOvSm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "MAX_SEQUENCE_LENGTH = 50\n",
        "\n",
        "\n",
        "class SequenceIterator:\n",
        "    def __init__(self, dataset, seq_length, w2v_model, feature_name):\n",
        "        self.dataset = dataset\n",
        "\n",
        "        self.translator = str.maketrans('', '', string.punctuation + '–')\n",
        "        self.categories, self.ccount = np.unique(dataset.relation_type, return_counts=True)\n",
        "        \n",
        "        self.seq_length = seq_length\n",
        "        self.w2v_model = w2v_model\n",
        "        self.feature_name = feature_name\n",
        "        \n",
        "    def __iter__(self):\n",
        "        for news, cat in zip(self.dataset[self.feature_name], self.dataset.relation_type):\n",
        "            # Make all characters lower-case\n",
        "            news = news.lower()\n",
        "            \n",
        "            # Clean string of all punctuation\n",
        "            news = news.translate(self.translator)\n",
        "\n",
        "            words = np.array([word2token(w, self.w2v_model) for w in news.split(' ') if w != ''])\n",
        "                                \n",
        "            yield (words, cat)\n",
        "\n",
        "def convertText2Feature(df, feature_name='sentence'):\n",
        "  word2Vec = getSpacy_Word2Vec_Corpus(df)\n",
        "\n",
        "  w2v_weights = word2Vec.wv.vectors\n",
        "  vocab_size, embedding_size = w2v_weights.shape\n",
        "  sequences = SequenceIterator(df, MAX_SEQUENCE_LENGTH, word2Vec, feature_name)\n",
        "\n",
        "  # Used for generating the labels in the set\n",
        "  cat_dict = {k: v for k, v in zip(sequences.categories, range(len(sequences.categories)))}\n",
        "\n",
        "  set_x = []\n",
        "  set_y = []\n",
        "  for w, c in sequences:\n",
        "      set_x.append(w)\n",
        "      set_y.append(cat_dict[c])\n",
        "      \n",
        "  # Padding sequences with 0.\n",
        "  set_x = pad_sequences(set_x, maxlen=MAX_SEQUENCE_LENGTH, padding='pre', value=0)\n",
        "  set_y = np.array(set_y)\n",
        "\n",
        "  return cat_dict, set_x, set_y, w2v_weights, vocab_size, embedding_size"
      ],
      "metadata": {
        "id": "kADn9P70towS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_dict, test_x, test_y, w2v_weights, vocab_size, embedding_size = convertText2Feature(twoClassCleanDevDF)\n",
        "cat_dict, set_x, set_y, w2v_weights, vocab_size, embedding_size = convertText2Feature(twoClassCleanTrainDF)"
      ],
      "metadata": {
        "id": "gXhNnThnOs8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "VALID_PER = 0.15\n",
        "\n",
        "total_samples = set_x.shape[0]\n",
        "n_val = int(VALID_PER * total_samples)\n",
        "n_train = total_samples - n_val\n",
        "\n",
        "random_i = random.sample(range(total_samples), total_samples)\n",
        "train_x = set_x[random_i[:n_train]]\n",
        "train_y = set_y[random_i[:n_train]]\n",
        "val_x = set_x[random_i[n_train:n_train+n_val]]\n",
        "val_y = set_y[random_i[n_train:n_train+n_val]]\n",
        "categories, ccount = np.unique(train_y, return_counts=True)\n",
        "\n",
        "print(\"Train Shapes - X: {} - Y: {}\".format(train_x.shape, train_y.shape))\n",
        "print(\"Val Shapes - X: {} - Y: {}\".format(val_x.shape, val_y.shape))\n",
        "\n",
        "n_categories = len(categories)"
      ],
      "metadata": {
        "id": "i0SZCTvn21CS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55b996a9-a5a1-465b-93a7-09087fbb8ee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Shapes - X: (1007, 50) - Y: (1007,)\n",
            "Val Shapes - X: (177, 50) - Y: (177,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "values, counts = np.unique(train_y, return_counts=True)\n",
        "print(values, counts)\n",
        "values, counts = np.unique(val_y, return_counts=True)\n",
        "print(values, counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGzjh31-WYf2",
        "outputId": "1a3ffb52-be40-42b5-821b-4146ab08b0a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1] [499 508]\n",
            "[0 1] [93 84]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(n_categories,\n",
        "                activation='softmax',\n",
        "                kernel_regularizer=L1L2(l1=0.0, l2=0.1),\n",
        "                input_dim=train_x.shape[1]))\n",
        "model.compile(optimizer='sgd',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.summary()\n",
        "model.fit(train_x, train_y, epochs=100, validation_data=(val_x, val_y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnJnimd5XWXj",
        "outputId": "39639770-bea5-4c2c-af2e-24215b7a9c69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 2)                 102       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 102\n",
            "Trainable params: 102\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "32/32 [==============================] - 1s 10ms/step - loss: 57.1160 - accuracy: 0.4717 - val_loss: 62.4981 - val_accuracy: 0.4294\n",
            "Epoch 2/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 44.5806 - accuracy: 0.5164 - val_loss: 42.2809 - val_accuracy: 0.4915\n",
            "Epoch 3/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 51.4555 - accuracy: 0.4786 - val_loss: 77.8959 - val_accuracy: 0.4350\n",
            "Epoch 4/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 49.4067 - accuracy: 0.5055 - val_loss: 51.8241 - val_accuracy: 0.4915\n",
            "Epoch 5/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 51.1186 - accuracy: 0.5124 - val_loss: 94.7910 - val_accuracy: 0.4859\n",
            "Epoch 6/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 51.3645 - accuracy: 0.5035 - val_loss: 75.4437 - val_accuracy: 0.4915\n",
            "Epoch 7/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 56.1056 - accuracy: 0.5174 - val_loss: 50.4069 - val_accuracy: 0.4746\n",
            "Epoch 8/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 47.0415 - accuracy: 0.4906 - val_loss: 87.1926 - val_accuracy: 0.4407\n",
            "Epoch 9/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 50.8768 - accuracy: 0.4926 - val_loss: 49.0427 - val_accuracy: 0.4294\n",
            "Epoch 10/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 54.9275 - accuracy: 0.4886 - val_loss: 52.4546 - val_accuracy: 0.3842\n",
            "Epoch 11/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 53.2126 - accuracy: 0.5074 - val_loss: 75.3159 - val_accuracy: 0.4689\n",
            "Epoch 12/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 52.8466 - accuracy: 0.4856 - val_loss: 34.7423 - val_accuracy: 0.4746\n",
            "Epoch 13/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 53.3441 - accuracy: 0.5164 - val_loss: 56.0886 - val_accuracy: 0.4463\n",
            "Epoch 14/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 48.8356 - accuracy: 0.5025 - val_loss: 63.1776 - val_accuracy: 0.4520\n",
            "Epoch 15/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 46.3183 - accuracy: 0.5134 - val_loss: 57.8523 - val_accuracy: 0.4463\n",
            "Epoch 16/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 47.6465 - accuracy: 0.4816 - val_loss: 59.6771 - val_accuracy: 0.4294\n",
            "Epoch 17/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 41.1799 - accuracy: 0.5402 - val_loss: 51.1247 - val_accuracy: 0.4463\n",
            "Epoch 18/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 53.3253 - accuracy: 0.4985 - val_loss: 64.5592 - val_accuracy: 0.4859\n",
            "Epoch 19/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 42.4992 - accuracy: 0.5323 - val_loss: 72.5986 - val_accuracy: 0.4237\n",
            "Epoch 20/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 48.0360 - accuracy: 0.5045 - val_loss: 44.6826 - val_accuracy: 0.4576\n",
            "Epoch 21/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 49.1569 - accuracy: 0.4935 - val_loss: 59.2400 - val_accuracy: 0.4520\n",
            "Epoch 22/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 49.6199 - accuracy: 0.5104 - val_loss: 74.3205 - val_accuracy: 0.4463\n",
            "Epoch 23/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 45.8049 - accuracy: 0.5233 - val_loss: 78.2005 - val_accuracy: 0.4746\n",
            "Epoch 24/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 48.6371 - accuracy: 0.5114 - val_loss: 85.5076 - val_accuracy: 0.4350\n",
            "Epoch 25/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 56.4573 - accuracy: 0.4926 - val_loss: 93.9852 - val_accuracy: 0.4633\n",
            "Epoch 26/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 48.6755 - accuracy: 0.5204 - val_loss: 72.4762 - val_accuracy: 0.4407\n",
            "Epoch 27/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 52.5352 - accuracy: 0.4975 - val_loss: 67.2086 - val_accuracy: 0.5198\n",
            "Epoch 28/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 58.7190 - accuracy: 0.4955 - val_loss: 70.9386 - val_accuracy: 0.5028\n",
            "Epoch 29/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 48.1249 - accuracy: 0.5055 - val_loss: 42.0138 - val_accuracy: 0.4463\n",
            "Epoch 30/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 48.6640 - accuracy: 0.4926 - val_loss: 52.6783 - val_accuracy: 0.4972\n",
            "Epoch 31/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 51.6601 - accuracy: 0.5074 - val_loss: 103.0442 - val_accuracy: 0.4746\n",
            "Epoch 32/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 55.7001 - accuracy: 0.4906 - val_loss: 65.2327 - val_accuracy: 0.4576\n",
            "Epoch 33/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 45.6832 - accuracy: 0.4985 - val_loss: 58.8830 - val_accuracy: 0.4633\n",
            "Epoch 34/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 54.9640 - accuracy: 0.5104 - val_loss: 68.8331 - val_accuracy: 0.4633\n",
            "Epoch 35/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 47.6391 - accuracy: 0.5144 - val_loss: 37.7432 - val_accuracy: 0.4746\n",
            "Epoch 36/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 53.3404 - accuracy: 0.4896 - val_loss: 56.8810 - val_accuracy: 0.4689\n",
            "Epoch 37/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 47.6073 - accuracy: 0.5214 - val_loss: 58.8165 - val_accuracy: 0.5085\n",
            "Epoch 38/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 50.8807 - accuracy: 0.4975 - val_loss: 122.1073 - val_accuracy: 0.3898\n",
            "Epoch 39/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 54.2778 - accuracy: 0.4896 - val_loss: 56.2107 - val_accuracy: 0.4802\n",
            "Epoch 40/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 47.3504 - accuracy: 0.4836 - val_loss: 62.5254 - val_accuracy: 0.5028\n",
            "Epoch 41/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 45.4320 - accuracy: 0.5283 - val_loss: 40.1179 - val_accuracy: 0.4350\n",
            "Epoch 42/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 42.7816 - accuracy: 0.5134 - val_loss: 52.6131 - val_accuracy: 0.4350\n",
            "Epoch 43/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 48.8920 - accuracy: 0.4955 - val_loss: 36.5908 - val_accuracy: 0.5254\n",
            "Epoch 44/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 46.1616 - accuracy: 0.5114 - val_loss: 50.3225 - val_accuracy: 0.4294\n",
            "Epoch 45/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 55.4439 - accuracy: 0.5134 - val_loss: 59.5563 - val_accuracy: 0.4746\n",
            "Epoch 46/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 59.4544 - accuracy: 0.4667 - val_loss: 91.7313 - val_accuracy: 0.4746\n",
            "Epoch 47/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 53.5423 - accuracy: 0.5015 - val_loss: 57.5343 - val_accuracy: 0.4689\n",
            "Epoch 48/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 48.6701 - accuracy: 0.4926 - val_loss: 76.2565 - val_accuracy: 0.4576\n",
            "Epoch 49/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 46.9130 - accuracy: 0.5124 - val_loss: 52.8571 - val_accuracy: 0.4689\n",
            "Epoch 50/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 46.5668 - accuracy: 0.4886 - val_loss: 67.7152 - val_accuracy: 0.4463\n",
            "Epoch 51/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 50.3733 - accuracy: 0.5184 - val_loss: 44.8913 - val_accuracy: 0.5480\n",
            "Epoch 52/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 52.8328 - accuracy: 0.5015 - val_loss: 45.7566 - val_accuracy: 0.5311\n",
            "Epoch 53/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 52.9815 - accuracy: 0.4995 - val_loss: 89.5514 - val_accuracy: 0.4746\n",
            "Epoch 54/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 54.6211 - accuracy: 0.5035 - val_loss: 96.6943 - val_accuracy: 0.3785\n",
            "Epoch 55/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 46.6635 - accuracy: 0.5233 - val_loss: 56.3010 - val_accuracy: 0.5085\n",
            "Epoch 56/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 48.4145 - accuracy: 0.5204 - val_loss: 118.7527 - val_accuracy: 0.4915\n",
            "Epoch 57/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 53.9601 - accuracy: 0.4796 - val_loss: 53.9969 - val_accuracy: 0.5028\n",
            "Epoch 58/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 43.9716 - accuracy: 0.5114 - val_loss: 57.4862 - val_accuracy: 0.4972\n",
            "Epoch 59/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 54.1380 - accuracy: 0.5074 - val_loss: 42.5407 - val_accuracy: 0.4576\n",
            "Epoch 60/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 51.8163 - accuracy: 0.5144 - val_loss: 73.5069 - val_accuracy: 0.4746\n",
            "Epoch 61/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 49.0097 - accuracy: 0.5194 - val_loss: 111.3642 - val_accuracy: 0.5085\n",
            "Epoch 62/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 52.7417 - accuracy: 0.5084 - val_loss: 68.9987 - val_accuracy: 0.4068\n",
            "Epoch 63/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 44.5848 - accuracy: 0.5104 - val_loss: 58.4946 - val_accuracy: 0.5254\n",
            "Epoch 64/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 51.1991 - accuracy: 0.5084 - val_loss: 61.5539 - val_accuracy: 0.4633\n",
            "Epoch 65/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 44.0388 - accuracy: 0.5303 - val_loss: 58.8261 - val_accuracy: 0.4802\n",
            "Epoch 66/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 44.3052 - accuracy: 0.5164 - val_loss: 54.0915 - val_accuracy: 0.4576\n",
            "Epoch 67/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 49.6827 - accuracy: 0.5184 - val_loss: 54.2980 - val_accuracy: 0.4972\n",
            "Epoch 68/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 47.5653 - accuracy: 0.5144 - val_loss: 98.8343 - val_accuracy: 0.5028\n",
            "Epoch 69/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 50.8026 - accuracy: 0.5233 - val_loss: 116.4508 - val_accuracy: 0.4407\n",
            "Epoch 70/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 60.6735 - accuracy: 0.4727 - val_loss: 90.0531 - val_accuracy: 0.4294\n",
            "Epoch 71/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 49.7179 - accuracy: 0.4955 - val_loss: 49.7453 - val_accuracy: 0.4350\n",
            "Epoch 72/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 53.7295 - accuracy: 0.4916 - val_loss: 41.3277 - val_accuracy: 0.5085\n",
            "Epoch 73/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 52.1110 - accuracy: 0.4906 - val_loss: 51.0387 - val_accuracy: 0.4746\n",
            "Epoch 74/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 44.6767 - accuracy: 0.5134 - val_loss: 103.6697 - val_accuracy: 0.4859\n",
            "Epoch 75/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 50.3329 - accuracy: 0.5313 - val_loss: 61.9406 - val_accuracy: 0.4520\n",
            "Epoch 76/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 48.9839 - accuracy: 0.5005 - val_loss: 99.7045 - val_accuracy: 0.4294\n",
            "Epoch 77/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 55.3873 - accuracy: 0.4896 - val_loss: 50.4544 - val_accuracy: 0.4633\n",
            "Epoch 78/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 47.4547 - accuracy: 0.5164 - val_loss: 101.5340 - val_accuracy: 0.4576\n",
            "Epoch 79/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 58.1278 - accuracy: 0.5035 - val_loss: 76.0508 - val_accuracy: 0.4689\n",
            "Epoch 80/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 48.7237 - accuracy: 0.5243 - val_loss: 64.1769 - val_accuracy: 0.4407\n",
            "Epoch 81/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 41.4570 - accuracy: 0.5432 - val_loss: 87.2776 - val_accuracy: 0.4859\n",
            "Epoch 82/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 46.0400 - accuracy: 0.5055 - val_loss: 110.7912 - val_accuracy: 0.4407\n",
            "Epoch 83/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 55.6480 - accuracy: 0.5114 - val_loss: 45.8108 - val_accuracy: 0.5198\n",
            "Epoch 84/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 46.9389 - accuracy: 0.5055 - val_loss: 59.1571 - val_accuracy: 0.4859\n",
            "Epoch 85/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 45.5324 - accuracy: 0.5293 - val_loss: 77.4138 - val_accuracy: 0.4068\n",
            "Epoch 86/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 50.7015 - accuracy: 0.4886 - val_loss: 77.1876 - val_accuracy: 0.4407\n",
            "Epoch 87/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 54.0580 - accuracy: 0.4955 - val_loss: 48.7080 - val_accuracy: 0.5367\n",
            "Epoch 88/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 48.4092 - accuracy: 0.4886 - val_loss: 64.3416 - val_accuracy: 0.4294\n",
            "Epoch 89/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 46.4011 - accuracy: 0.5045 - val_loss: 102.5384 - val_accuracy: 0.4802\n",
            "Epoch 90/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 57.5428 - accuracy: 0.4846 - val_loss: 110.1224 - val_accuracy: 0.4520\n",
            "Epoch 91/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 49.1671 - accuracy: 0.5223 - val_loss: 39.7475 - val_accuracy: 0.4859\n",
            "Epoch 92/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 51.1971 - accuracy: 0.5035 - val_loss: 38.1131 - val_accuracy: 0.4633\n",
            "Epoch 93/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 46.7610 - accuracy: 0.5164 - val_loss: 52.2112 - val_accuracy: 0.4746\n",
            "Epoch 94/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 45.4897 - accuracy: 0.5134 - val_loss: 59.8873 - val_accuracy: 0.4633\n",
            "Epoch 95/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 49.3980 - accuracy: 0.4935 - val_loss: 70.1314 - val_accuracy: 0.4915\n",
            "Epoch 96/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 50.4907 - accuracy: 0.4965 - val_loss: 81.4885 - val_accuracy: 0.4520\n",
            "Epoch 97/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 50.5626 - accuracy: 0.5084 - val_loss: 108.1282 - val_accuracy: 0.4237\n",
            "Epoch 98/100\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 50.7724 - accuracy: 0.4955 - val_loss: 76.3056 - val_accuracy: 0.5141\n",
            "Epoch 99/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 53.9510 - accuracy: 0.4945 - val_loss: 74.3179 - val_accuracy: 0.4576\n",
            "Epoch 100/100\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 49.5504 - accuracy: 0.5035 - val_loss: 76.8932 - val_accuracy: 0.4915\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc391bc45d0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_probs = model.predict(test_x)\n",
        "preds = np.argmax(predicted_probs, axis=-1)"
      ],
      "metadata": {
        "id": "a4AMy3laX4Ja"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1de9eb51-08f7-4c72-a10c-45957bb060d9",
        "id": "JwkqwInhX4Jf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.74      0.65        78\n",
            "           1       0.64      0.46      0.54        78\n",
            "\n",
            "    accuracy                           0.60       156\n",
            "   macro avg       0.61      0.60      0.59       156\n",
            "weighted avg       0.61      0.60      0.59       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gnb = MultinomialNB()\n",
        "preds = gnb.fit(set_x, set_y).predict(test_x)"
      ],
      "metadata": {
        "id": "vvaJoPV-kWc_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74bb4197-eac3-4665-e8a7-0f5f82d701be",
        "id": "xqLgdeHskWc_"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.56      0.60      0.58        78\n",
            "           1       0.57      0.53      0.55        78\n",
            "\n",
            "    accuracy                           0.56       156\n",
            "   macro avg       0.56      0.56      0.56       156\n",
            "weighted avg       0.56      0.56      0.56       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "ijLGkjfyIjou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = DecisionTreeClassifier()\n",
        "preds = model.fit(set_x, set_y).predict(test_x)"
      ],
      "metadata": {
        "id": "QEIX-VINIll3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b0c657a-b354-4de0-bdb1-55839ecc77fd",
        "id": "udw_Hy5tIll4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.46      0.51      0.48        78\n",
            "           1       0.45      0.40      0.42        78\n",
            "\n",
            "    accuracy                           0.46       156\n",
            "   macro avg       0.45      0.46      0.45       156\n",
            "weighted avg       0.45      0.46      0.45       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "7KlsFfMpJXUH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "preds = model.fit(set_x, set_y).predict(test_x)"
      ],
      "metadata": {
        "id": "AOxKcbqOJXUI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3970abf0-3ccb-4921-e06c-753952170736",
        "id": "xjSaH9UHJXUI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.49      0.46      0.48        78\n",
            "           1       0.49      0.53      0.51        78\n",
            "\n",
            "    accuracy                           0.49       156\n",
            "   macro avg       0.49      0.49      0.49       156\n",
            "weighted avg       0.49      0.49      0.49       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC(kernel=\"poly\", degree=1, C=0.1, gamma=0.1)\n",
        "preds = model.fit(set_x, set_y).predict(test_x)"
      ],
      "metadata": {
        "id": "ea8TCU_NKVWf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sr9CQjhKeEQ",
        "outputId": "c56b5bb5-3474-412b-da34-aab7354e5acb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.96      0.66        78\n",
            "           1       0.40      0.03      0.05        78\n",
            "\n",
            "    accuracy                           0.49       156\n",
            "   macro avg       0.45      0.49      0.35       156\n",
            "weighted avg       0.45      0.49      0.35       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "# Keras Embedding layer with Word2Vec weights initialization\n",
        "model.add(Embedding(input_dim=vocab_size,\n",
        "                    output_dim=embedding_size,\n",
        "                    weights=[w2v_weights],\n",
        "                    input_length=MAX_SEQUENCE_LENGTH,\n",
        "                    mask_zero=True,\n",
        "                    trainable=False))\n",
        "\n",
        "model.add(Bidirectional(LSTM(100)))\n",
        "model.add(Dense(n_categories, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=100, batch_size=64,\n",
        "                    validation_data=(val_x, val_y), verbose=1)"
      ],
      "metadata": {
        "id": "ZlcWZl4K3rLa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21e1b110-81ed-44f5-a46a-b171e0459bce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 15s 372ms/step - loss: 0.6931 - accuracy: 0.4965 - val_loss: 0.6930 - val_accuracy: 0.4802\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 2s 149ms/step - loss: 0.6919 - accuracy: 0.5869 - val_loss: 0.6907 - val_accuracy: 0.5763\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 2s 149ms/step - loss: 0.6899 - accuracy: 0.6058 - val_loss: 0.6904 - val_accuracy: 0.6158\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.6869 - accuracy: 0.6167 - val_loss: 0.6891 - val_accuracy: 0.5932\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.6821 - accuracy: 0.6276 - val_loss: 0.6924 - val_accuracy: 0.6158\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.6761 - accuracy: 0.6276 - val_loss: 0.6990 - val_accuracy: 0.5593\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.6732 - accuracy: 0.6246 - val_loss: 0.6952 - val_accuracy: 0.6045\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.6689 - accuracy: 0.6256 - val_loss: 0.6842 - val_accuracy: 0.5932\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 2s 141ms/step - loss: 0.6632 - accuracy: 0.6216 - val_loss: 0.6807 - val_accuracy: 0.6158\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.6590 - accuracy: 0.6236 - val_loss: 0.6887 - val_accuracy: 0.5932\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.6543 - accuracy: 0.6316 - val_loss: 0.6797 - val_accuracy: 0.6158\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 2s 142ms/step - loss: 0.6497 - accuracy: 0.6296 - val_loss: 0.6818 - val_accuracy: 0.6045\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.6480 - accuracy: 0.6276 - val_loss: 0.6753 - val_accuracy: 0.6158\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.6434 - accuracy: 0.6306 - val_loss: 0.6736 - val_accuracy: 0.6158\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 2s 151ms/step - loss: 0.6394 - accuracy: 0.6276 - val_loss: 0.6755 - val_accuracy: 0.6102\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 2s 149ms/step - loss: 0.6363 - accuracy: 0.6356 - val_loss: 0.6828 - val_accuracy: 0.5989\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.6341 - accuracy: 0.6336 - val_loss: 0.6824 - val_accuracy: 0.6045\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.6309 - accuracy: 0.6435 - val_loss: 0.6771 - val_accuracy: 0.5989\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 2s 142ms/step - loss: 0.6292 - accuracy: 0.6425 - val_loss: 0.6781 - val_accuracy: 0.5989\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.6275 - accuracy: 0.6445 - val_loss: 0.6880 - val_accuracy: 0.5876\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.6271 - accuracy: 0.6425 - val_loss: 0.6899 - val_accuracy: 0.6045\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 2s 150ms/step - loss: 0.6247 - accuracy: 0.6544 - val_loss: 0.6848 - val_accuracy: 0.5932\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.6219 - accuracy: 0.6455 - val_loss: 0.6970 - val_accuracy: 0.5932\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 2s 142ms/step - loss: 0.6175 - accuracy: 0.6554 - val_loss: 0.6837 - val_accuracy: 0.5932\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.6160 - accuracy: 0.6514 - val_loss: 0.6966 - val_accuracy: 0.6102\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 2s 151ms/step - loss: 0.6149 - accuracy: 0.6495 - val_loss: 0.6979 - val_accuracy: 0.5932\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 2s 151ms/step - loss: 0.6184 - accuracy: 0.6495 - val_loss: 0.6999 - val_accuracy: 0.6045\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.6141 - accuracy: 0.6495 - val_loss: 0.6905 - val_accuracy: 0.5876\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 2s 142ms/step - loss: 0.6106 - accuracy: 0.6574 - val_loss: 0.7080 - val_accuracy: 0.5989\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 2s 141ms/step - loss: 0.6058 - accuracy: 0.6683 - val_loss: 0.6988 - val_accuracy: 0.5876\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 2s 141ms/step - loss: 0.6167 - accuracy: 0.6544 - val_loss: 0.6993 - val_accuracy: 0.5989\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.6082 - accuracy: 0.6643 - val_loss: 0.7007 - val_accuracy: 0.5989\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 2s 156ms/step - loss: 0.6053 - accuracy: 0.6703 - val_loss: 0.7008 - val_accuracy: 0.6045\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.6093 - accuracy: 0.6643 - val_loss: 0.7120 - val_accuracy: 0.5819\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.6016 - accuracy: 0.6723 - val_loss: 0.7056 - val_accuracy: 0.6102\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 2s 141ms/step - loss: 0.5994 - accuracy: 0.6703 - val_loss: 0.7030 - val_accuracy: 0.5876\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.5974 - accuracy: 0.6822 - val_loss: 0.7141 - val_accuracy: 0.5989\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5988 - accuracy: 0.6743 - val_loss: 0.7246 - val_accuracy: 0.5989\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 3s 165ms/step - loss: 0.5985 - accuracy: 0.6743 - val_loss: 0.7214 - val_accuracy: 0.5819\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 3s 166ms/step - loss: 0.5928 - accuracy: 0.6783 - val_loss: 0.7158 - val_accuracy: 0.5932\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.5913 - accuracy: 0.6882 - val_loss: 0.7177 - val_accuracy: 0.5989\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5924 - accuracy: 0.6822 - val_loss: 0.7259 - val_accuracy: 0.5989\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.5930 - accuracy: 0.6872 - val_loss: 0.7282 - val_accuracy: 0.5706\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5874 - accuracy: 0.6951 - val_loss: 0.7297 - val_accuracy: 0.5989\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.5912 - accuracy: 0.6832 - val_loss: 0.7283 - val_accuracy: 0.5932\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.5839 - accuracy: 0.6922 - val_loss: 0.7374 - val_accuracy: 0.5763\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 2s 151ms/step - loss: 0.5875 - accuracy: 0.6882 - val_loss: 0.7275 - val_accuracy: 0.5932\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 2s 153ms/step - loss: 0.5838 - accuracy: 0.6922 - val_loss: 0.7225 - val_accuracy: 0.5932\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 2s 154ms/step - loss: 0.5822 - accuracy: 0.6931 - val_loss: 0.7476 - val_accuracy: 0.5932\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 2s 151ms/step - loss: 0.5802 - accuracy: 0.6941 - val_loss: 0.7328 - val_accuracy: 0.6102\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.5807 - accuracy: 0.6991 - val_loss: 0.7420 - val_accuracy: 0.6045\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 2s 150ms/step - loss: 0.5788 - accuracy: 0.6981 - val_loss: 0.7585 - val_accuracy: 0.6045\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.5809 - accuracy: 0.6922 - val_loss: 0.7392 - val_accuracy: 0.5932\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5749 - accuracy: 0.7031 - val_loss: 0.7399 - val_accuracy: 0.6102\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 2s 142ms/step - loss: 0.5733 - accuracy: 0.7071 - val_loss: 0.7495 - val_accuracy: 0.6102\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.5788 - accuracy: 0.6922 - val_loss: 0.7818 - val_accuracy: 0.5763\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5847 - accuracy: 0.6941 - val_loss: 0.7356 - val_accuracy: 0.6102\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5685 - accuracy: 0.7110 - val_loss: 0.7491 - val_accuracy: 0.6102\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5745 - accuracy: 0.6991 - val_loss: 0.7460 - val_accuracy: 0.6045\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.5701 - accuracy: 0.6941 - val_loss: 0.7522 - val_accuracy: 0.6045\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 2s 150ms/step - loss: 0.5684 - accuracy: 0.6961 - val_loss: 0.7573 - val_accuracy: 0.6045\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5716 - accuracy: 0.6991 - val_loss: 0.7635 - val_accuracy: 0.6102\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5655 - accuracy: 0.7130 - val_loss: 0.7647 - val_accuracy: 0.5876\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5702 - accuracy: 0.7011 - val_loss: 0.7701 - val_accuracy: 0.5932\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5659 - accuracy: 0.7041 - val_loss: 0.7833 - val_accuracy: 0.5989\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 2s 148ms/step - loss: 0.5685 - accuracy: 0.7080 - val_loss: 0.7535 - val_accuracy: 0.6045\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5644 - accuracy: 0.7001 - val_loss: 0.7941 - val_accuracy: 0.5819\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5673 - accuracy: 0.7061 - val_loss: 0.7605 - val_accuracy: 0.6045\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5632 - accuracy: 0.7021 - val_loss: 0.7807 - val_accuracy: 0.5932\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 2s 149ms/step - loss: 0.5589 - accuracy: 0.7071 - val_loss: 0.7778 - val_accuracy: 0.5989\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.5618 - accuracy: 0.7061 - val_loss: 0.7874 - val_accuracy: 0.5989\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 2s 147ms/step - loss: 0.5610 - accuracy: 0.7031 - val_loss: 0.7721 - val_accuracy: 0.5932\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 2s 147ms/step - loss: 0.5622 - accuracy: 0.7071 - val_loss: 0.7849 - val_accuracy: 0.6158\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5589 - accuracy: 0.7100 - val_loss: 0.7756 - val_accuracy: 0.6102\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5569 - accuracy: 0.7110 - val_loss: 0.7771 - val_accuracy: 0.6102\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 2s 147ms/step - loss: 0.5593 - accuracy: 0.7080 - val_loss: 0.7939 - val_accuracy: 0.6045\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.5554 - accuracy: 0.7170 - val_loss: 0.8005 - val_accuracy: 0.5819\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 2s 142ms/step - loss: 0.5521 - accuracy: 0.7071 - val_loss: 0.8005 - val_accuracy: 0.5989\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5541 - accuracy: 0.7071 - val_loss: 0.8016 - val_accuracy: 0.5932\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.5647 - accuracy: 0.7130 - val_loss: 0.8226 - val_accuracy: 0.5819\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 2s 143ms/step - loss: 0.5628 - accuracy: 0.7011 - val_loss: 0.7711 - val_accuracy: 0.5989\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 2s 149ms/step - loss: 0.5542 - accuracy: 0.7180 - val_loss: 0.8052 - val_accuracy: 0.5763\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5638 - accuracy: 0.6991 - val_loss: 0.7719 - val_accuracy: 0.5989\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5501 - accuracy: 0.7100 - val_loss: 0.7870 - val_accuracy: 0.5989\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5499 - accuracy: 0.7120 - val_loss: 0.7933 - val_accuracy: 0.6045\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5563 - accuracy: 0.7061 - val_loss: 0.7886 - val_accuracy: 0.5876\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5545 - accuracy: 0.7130 - val_loss: 0.7790 - val_accuracy: 0.5989\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5485 - accuracy: 0.7130 - val_loss: 0.7935 - val_accuracy: 0.5989\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5429 - accuracy: 0.7140 - val_loss: 0.7985 - val_accuracy: 0.6102\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 2s 154ms/step - loss: 0.5456 - accuracy: 0.7110 - val_loss: 0.8066 - val_accuracy: 0.6102\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 2s 151ms/step - loss: 0.5410 - accuracy: 0.7170 - val_loss: 0.8239 - val_accuracy: 0.6045\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 2s 145ms/step - loss: 0.5406 - accuracy: 0.7150 - val_loss: 0.8177 - val_accuracy: 0.6102\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.5472 - accuracy: 0.7150 - val_loss: 0.8274 - val_accuracy: 0.5989\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 2s 150ms/step - loss: 0.5497 - accuracy: 0.7140 - val_loss: 0.8148 - val_accuracy: 0.5819\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 2s 151ms/step - loss: 0.5404 - accuracy: 0.7160 - val_loss: 0.8011 - val_accuracy: 0.5989\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.5500 - accuracy: 0.7080 - val_loss: 0.7897 - val_accuracy: 0.6158\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 2s 147ms/step - loss: 0.5423 - accuracy: 0.7130 - val_loss: 0.8005 - val_accuracy: 0.6045\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 2s 150ms/step - loss: 0.5431 - accuracy: 0.7071 - val_loss: 0.8107 - val_accuracy: 0.5989\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 2s 146ms/step - loss: 0.5431 - accuracy: 0.7051 - val_loss: 0.8084 - val_accuracy: 0.6102\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 2s 144ms/step - loss: 0.5379 - accuracy: 0.7090 - val_loss: 0.8136 - val_accuracy: 0.6158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_probs = model.predict(test_x)\n",
        "preds = np.argmax(predicted_probs, axis=-1)"
      ],
      "metadata": {
        "id": "3ONAR99t4Pez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "id": "-M8_sIJo5Jny",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1607fb5f-ca73-47e2-ea0d-930888a2bcba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.46      0.52        78\n",
            "           1       0.56      0.69      0.62        78\n",
            "\n",
            "    accuracy                           0.58       156\n",
            "   macro avg       0.58      0.58      0.57       156\n",
            "weighted avg       0.58      0.58      0.57       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gensim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFVLSIoJOSFK",
        "outputId": "a93b1ac8-9405-46b2-8182-b654cadbd73c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (3.6.0)\n",
            "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim) (6.0.0)\n",
            "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.15.0)\n",
            "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K7ms2W9-gANG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gensim\n",
        "def getText2Seq(corpus):\n",
        "  lst_corpus = []\n",
        "  for string in corpus:\n",
        "      lst_words = string.split()\n",
        "      lst_grams = [\" \".join(lst_words[i:i+1]) for i in range(0, \n",
        "                  len(lst_words), 1)]\n",
        "      lst_corpus.append(lst_grams)\n",
        "  bigrams_detector = gensim.models.phrases.Phrases(lst_corpus, \n",
        "                 delimiter=\" \".encode(), min_count=5, threshold=10)\n",
        "  bigrams_detector = gensim.models.phrases.Phraser(bigrams_detector)\n",
        "  trigrams_detector = gensim.models.phrases.Phrases(bigrams_detector[lst_corpus], \n",
        "              delimiter=\" \".encode(), min_count=5, threshold=10)\n",
        "  trigrams_detector = gensim.models.phrases.Phraser(trigrams_detector)\n",
        "      \n",
        "  lst_corpus = list(bigrams_detector[lst_corpus])\n",
        "  lst_corpus = list(trigrams_detector[lst_corpus])\n",
        "\n",
        "  tokenizer = kprocessing.text.Tokenizer(lower=True, split=' ', \n",
        "                     oov_token=\"NaN\", \n",
        "                     filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n')\n",
        "  tokenizer.fit_on_texts(lst_corpus)\n",
        "  dic_vocabulary = tokenizer.word_index\n",
        "\n",
        "  lst_text2seq = tokenizer.texts_to_sequences(lst_corpus)\n",
        "\n",
        "  return lst_corpus, kprocessing.sequence.pad_sequences(lst_text2seq, maxlen=15,\n",
        "              padding=\"post\", truncating=\"post\"), dic_vocabulary\n",
        "  \n"
      ],
      "metadata": {
        "id": "sdAWVeg2pXb1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w2vec_train_corpus, X_train_w2vec, dic_vocabulary_train_liar = getText2Seq(twoClassCleanTrainDF['sentence'])\n",
        "w2vec_test_corpus, X_test_w2vec, dic_vocabulary_test_liar = getText2Seq(twoClassCleanDevDF['sentence'])"
      ],
      "metadata": {
        "id": "hJuAirDUNeSq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nlp = gensim.models.word2vec.Word2Vec(w2vec_train_corpus, size=300,   \n",
        "            window=8, min_count=1, sg=1, iter=30)"
      ],
      "metadata": {
        "id": "PKH-Tbtmn-F6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings = np.zeros((len(dic_vocabulary_train_liar)+1, 300))\n",
        "for word,idx in dic_vocabulary_train_liar.items():\n",
        "    try:\n",
        "        embeddings[idx] =  nlp[word]\n",
        "    except:\n",
        "        pass"
      ],
      "metadata": {
        "id": "IxHx5Xksn2gS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f1653a6-5ded-46d2-c009-f2f608d6932d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(n_categories,\n",
        "                activation='softmax',\n",
        "                kernel_regularizer=L1L2(l1=0.0, l2=0.1),\n",
        "                input_dim=X_train_w2vec.shape[1]))\n",
        "model.compile(optimizer='sgd',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.summary()\n",
        "model.fit(X_train_w2vec, set_y, epochs=100, validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9491ced0-abef-4f7f-fda9-dbf1b19db280",
        "id": "PHiaJMHThZnX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 2)                 32        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 32\n",
            "Trainable params: 32\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "34/34 [==============================] - 1s 7ms/step - loss: 4325.5132 - accuracy: 0.5249 - val_loss: 5685.7236 - val_accuracy: 0.2353\n",
            "Epoch 2/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4119.7285 - accuracy: 0.5305 - val_loss: 4361.1147 - val_accuracy: 0.4622\n",
            "Epoch 3/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4653.6865 - accuracy: 0.5249 - val_loss: 552.0146 - val_accuracy: 0.9076\n",
            "Epoch 4/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5172.7490 - accuracy: 0.5127 - val_loss: 17204.4531 - val_accuracy: 0.0000e+00\n",
            "Epoch 5/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4616.2515 - accuracy: 0.5305 - val_loss: 5343.7046 - val_accuracy: 0.6134\n",
            "Epoch 6/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5191.2314 - accuracy: 0.5127 - val_loss: 288.0647 - val_accuracy: 0.9328\n",
            "Epoch 7/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4604.7397 - accuracy: 0.5155 - val_loss: 315.1862 - val_accuracy: 0.9076\n",
            "Epoch 8/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5610.6299 - accuracy: 0.4986 - val_loss: 3153.8640 - val_accuracy: 0.5714\n",
            "Epoch 9/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4793.3003 - accuracy: 0.5192 - val_loss: 20128.8145 - val_accuracy: 0.0504\n",
            "Epoch 10/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5117.7021 - accuracy: 0.5258 - val_loss: 5596.3916 - val_accuracy: 0.3025\n",
            "Epoch 11/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5403.5591 - accuracy: 0.5221 - val_loss: 382.1455 - val_accuracy: 0.8908\n",
            "Epoch 12/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 5625.7681 - accuracy: 0.4958 - val_loss: 16256.6406 - val_accuracy: 0.0420\n",
            "Epoch 13/100\n",
            "34/34 [==============================] - 0s 2ms/step - loss: 4803.3271 - accuracy: 0.5136 - val_loss: 19691.8594 - val_accuracy: 0.0504\n",
            "Epoch 14/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4862.5444 - accuracy: 0.5211 - val_loss: 4133.6548 - val_accuracy: 0.3950\n",
            "Epoch 15/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4696.4238 - accuracy: 0.5080 - val_loss: 29812.1289 - val_accuracy: 0.0252\n",
            "Epoch 16/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4647.5493 - accuracy: 0.5390 - val_loss: 10139.8613 - val_accuracy: 0.1765\n",
            "Epoch 17/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5056.9487 - accuracy: 0.5230 - val_loss: 14899.0820 - val_accuracy: 0.0504\n",
            "Epoch 18/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5535.9507 - accuracy: 0.5164 - val_loss: 24907.8047 - val_accuracy: 0.0252\n",
            "Epoch 19/100\n",
            "34/34 [==============================] - 0s 3ms/step - loss: 4683.5488 - accuracy: 0.5211 - val_loss: 65.7204 - val_accuracy: 0.9580\n",
            "Epoch 20/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4977.8052 - accuracy: 0.4977 - val_loss: 758.8779 - val_accuracy: 0.8067\n",
            "Epoch 21/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4371.9731 - accuracy: 0.5399 - val_loss: 2369.9880 - val_accuracy: 0.6218\n",
            "Epoch 22/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4639.4341 - accuracy: 0.5042 - val_loss: 51.9713 - val_accuracy: 0.9916\n",
            "Epoch 23/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5729.8022 - accuracy: 0.5033 - val_loss: 147.2430 - val_accuracy: 0.9328\n",
            "Epoch 24/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4772.3433 - accuracy: 0.4967 - val_loss: 9919.2070 - val_accuracy: 0.2185\n",
            "Epoch 25/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5098.3354 - accuracy: 0.5174 - val_loss: 753.7220 - val_accuracy: 0.8235\n",
            "Epoch 26/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4941.0854 - accuracy: 0.4986 - val_loss: 16777.0312 - val_accuracy: 0.0252\n",
            "Epoch 27/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4945.3643 - accuracy: 0.5042 - val_loss: 9249.2021 - val_accuracy: 0.1429\n",
            "Epoch 28/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4604.8359 - accuracy: 0.5136 - val_loss: 14128.7422 - val_accuracy: 0.0000e+00\n",
            "Epoch 29/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4424.0244 - accuracy: 0.5286 - val_loss: 2434.9988 - val_accuracy: 0.6134\n",
            "Epoch 30/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5055.3755 - accuracy: 0.5211 - val_loss: 35901.1758 - val_accuracy: 0.0000e+00\n",
            "Epoch 31/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5372.3511 - accuracy: 0.5211 - val_loss: 618.8942 - val_accuracy: 0.8992\n",
            "Epoch 32/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4567.2725 - accuracy: 0.5202 - val_loss: 8174.8276 - val_accuracy: 0.2269\n",
            "Epoch 33/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4981.7026 - accuracy: 0.5099 - val_loss: 23537.0762 - val_accuracy: 0.0672\n",
            "Epoch 34/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4839.4580 - accuracy: 0.5061 - val_loss: 4898.2095 - val_accuracy: 0.3025\n",
            "Epoch 35/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 4690.8115 - accuracy: 0.5202 - val_loss: 5420.9072 - val_accuracy: 0.2941\n",
            "Epoch 36/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 5079.4844 - accuracy: 0.4761 - val_loss: 13264.1982 - val_accuracy: 0.0504\n",
            "Epoch 37/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4534.4019 - accuracy: 0.5286 - val_loss: 2205.2456 - val_accuracy: 0.6134\n",
            "Epoch 38/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4240.1323 - accuracy: 0.5380 - val_loss: 19086.4922 - val_accuracy: 0.1597\n",
            "Epoch 39/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5252.0522 - accuracy: 0.5080 - val_loss: 2500.7600 - val_accuracy: 0.6134\n",
            "Epoch 40/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4549.5342 - accuracy: 0.5192 - val_loss: 2185.1753 - val_accuracy: 0.5546\n",
            "Epoch 41/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4194.6548 - accuracy: 0.5362 - val_loss: 1354.7312 - val_accuracy: 0.7479\n",
            "Epoch 42/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 5498.2671 - accuracy: 0.5268 - val_loss: 27.9452 - val_accuracy: 1.0000\n",
            "Epoch 43/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5377.3154 - accuracy: 0.5089 - val_loss: 7456.4927 - val_accuracy: 0.2353\n",
            "Epoch 44/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4390.2837 - accuracy: 0.5333 - val_loss: 9200.0449 - val_accuracy: 0.2017\n",
            "Epoch 45/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4691.5518 - accuracy: 0.5136 - val_loss: 24615.3984 - val_accuracy: 0.0084\n",
            "Epoch 46/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4029.5610 - accuracy: 0.5427 - val_loss: 35.6489 - val_accuracy: 0.9916\n",
            "Epoch 47/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 5982.6719 - accuracy: 0.4930 - val_loss: 13731.3721 - val_accuracy: 0.0840\n",
            "Epoch 48/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4806.3955 - accuracy: 0.5324 - val_loss: 1428.2778 - val_accuracy: 0.8319\n",
            "Epoch 49/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4648.4243 - accuracy: 0.5174 - val_loss: 2634.6487 - val_accuracy: 0.5210\n",
            "Epoch 50/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4881.2490 - accuracy: 0.5070 - val_loss: 30540.8281 - val_accuracy: 0.0084\n",
            "Epoch 51/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4933.9331 - accuracy: 0.5183 - val_loss: 17710.6309 - val_accuracy: 0.0168\n",
            "Epoch 52/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5351.2632 - accuracy: 0.5127 - val_loss: 212.1781 - val_accuracy: 0.9496\n",
            "Epoch 53/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4657.5859 - accuracy: 0.5146 - val_loss: 1232.0785 - val_accuracy: 0.8151\n",
            "Epoch 54/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4622.5073 - accuracy: 0.5249 - val_loss: 18375.3223 - val_accuracy: 0.0168\n",
            "Epoch 55/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4738.1919 - accuracy: 0.5315 - val_loss: 3402.7881 - val_accuracy: 0.4286\n",
            "Epoch 56/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4497.5791 - accuracy: 0.5352 - val_loss: 12969.7764 - val_accuracy: 0.0672\n",
            "Epoch 57/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4001.8376 - accuracy: 0.5249 - val_loss: 6086.4434 - val_accuracy: 0.1429\n",
            "Epoch 58/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 5041.6470 - accuracy: 0.5380 - val_loss: 863.9386 - val_accuracy: 0.8403\n",
            "Epoch 59/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4631.3721 - accuracy: 0.5117 - val_loss: 3133.6907 - val_accuracy: 0.5210\n",
            "Epoch 60/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4094.3474 - accuracy: 0.5324 - val_loss: 21042.2402 - val_accuracy: 0.0084\n",
            "Epoch 61/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 4762.5117 - accuracy: 0.5221 - val_loss: 3297.9136 - val_accuracy: 0.4118\n",
            "Epoch 62/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 4919.2593 - accuracy: 0.4995 - val_loss: 725.2380 - val_accuracy: 0.8151\n",
            "Epoch 63/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 4764.7998 - accuracy: 0.5183 - val_loss: 304.8342 - val_accuracy: 0.8319\n",
            "Epoch 64/100\n",
            "34/34 [==============================] - 0s 7ms/step - loss: 4972.0371 - accuracy: 0.5192 - val_loss: 1026.3739 - val_accuracy: 0.7311\n",
            "Epoch 65/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4832.3237 - accuracy: 0.5211 - val_loss: 5758.8906 - val_accuracy: 0.2689\n",
            "Epoch 66/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 4704.5669 - accuracy: 0.5089 - val_loss: 10519.1973 - val_accuracy: 0.1849\n",
            "Epoch 67/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 5338.2588 - accuracy: 0.5042 - val_loss: 17603.5742 - val_accuracy: 0.0420\n",
            "Epoch 68/100\n",
            "34/34 [==============================] - 0s 11ms/step - loss: 4430.2046 - accuracy: 0.5183 - val_loss: 75.6232 - val_accuracy: 0.9832\n",
            "Epoch 69/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5087.0474 - accuracy: 0.5333 - val_loss: 32.5180 - val_accuracy: 1.0000\n",
            "Epoch 70/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4765.1484 - accuracy: 0.5033 - val_loss: 7485.3604 - val_accuracy: 0.3445\n",
            "Epoch 71/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5639.9136 - accuracy: 0.4948 - val_loss: 38572.1172 - val_accuracy: 0.0000e+00\n",
            "Epoch 72/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4983.4956 - accuracy: 0.5286 - val_loss: 116.4002 - val_accuracy: 0.9664\n",
            "Epoch 73/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4638.8965 - accuracy: 0.5164 - val_loss: 2456.7000 - val_accuracy: 0.6218\n",
            "Epoch 74/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4708.1392 - accuracy: 0.5268 - val_loss: 38.3108 - val_accuracy: 0.9916\n",
            "Epoch 75/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 5173.6626 - accuracy: 0.5286 - val_loss: 5473.0293 - val_accuracy: 0.4706\n",
            "Epoch 76/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5103.0571 - accuracy: 0.5099 - val_loss: 11673.2529 - val_accuracy: 0.1176\n",
            "Epoch 77/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 5121.0293 - accuracy: 0.5183 - val_loss: 13934.9814 - val_accuracy: 0.0084\n",
            "Epoch 78/100\n",
            "34/34 [==============================] - 0s 8ms/step - loss: 4541.1411 - accuracy: 0.5305 - val_loss: 1067.3577 - val_accuracy: 0.7815\n",
            "Epoch 79/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4579.5239 - accuracy: 0.5117 - val_loss: 1691.9083 - val_accuracy: 0.6134\n",
            "Epoch 80/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4886.9648 - accuracy: 0.5089 - val_loss: 2158.4880 - val_accuracy: 0.5882\n",
            "Epoch 81/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4845.8271 - accuracy: 0.5239 - val_loss: 19807.3613 - val_accuracy: 0.1092\n",
            "Epoch 82/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4484.8936 - accuracy: 0.5258 - val_loss: 2040.1084 - val_accuracy: 0.6050\n",
            "Epoch 83/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4964.0342 - accuracy: 0.5333 - val_loss: 3648.5137 - val_accuracy: 0.5882\n",
            "Epoch 84/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5678.6455 - accuracy: 0.4995 - val_loss: 169.3666 - val_accuracy: 0.9412\n",
            "Epoch 85/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5338.9341 - accuracy: 0.5089 - val_loss: 13183.8086 - val_accuracy: 0.0924\n",
            "Epoch 86/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4508.1382 - accuracy: 0.5192 - val_loss: 1421.7489 - val_accuracy: 0.7395\n",
            "Epoch 87/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5178.4087 - accuracy: 0.4845 - val_loss: 21306.8418 - val_accuracy: 0.0252\n",
            "Epoch 88/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5265.5996 - accuracy: 0.5174 - val_loss: 780.9306 - val_accuracy: 0.8992\n",
            "Epoch 89/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4993.8638 - accuracy: 0.5164 - val_loss: 6861.4980 - val_accuracy: 0.3782\n",
            "Epoch 90/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4325.9966 - accuracy: 0.5221 - val_loss: 3000.7373 - val_accuracy: 0.6303\n",
            "Epoch 91/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5503.5620 - accuracy: 0.4977 - val_loss: 14523.5420 - val_accuracy: 0.1261\n",
            "Epoch 92/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 5035.5503 - accuracy: 0.5230 - val_loss: 16372.9961 - val_accuracy: 0.1681\n",
            "Epoch 93/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4526.4268 - accuracy: 0.5333 - val_loss: 42132.8320 - val_accuracy: 0.0000e+00\n",
            "Epoch 94/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5407.6094 - accuracy: 0.5296 - val_loss: 9630.2861 - val_accuracy: 0.3361\n",
            "Epoch 95/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4533.1992 - accuracy: 0.5164 - val_loss: 11692.1025 - val_accuracy: 0.2101\n",
            "Epoch 96/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4798.7773 - accuracy: 0.5362 - val_loss: 2373.9346 - val_accuracy: 0.6723\n",
            "Epoch 97/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 4966.5396 - accuracy: 0.5089 - val_loss: 1164.1261 - val_accuracy: 0.8151\n",
            "Epoch 98/100\n",
            "34/34 [==============================] - 0s 5ms/step - loss: 4377.9302 - accuracy: 0.5437 - val_loss: 5569.9287 - val_accuracy: 0.4034\n",
            "Epoch 99/100\n",
            "34/34 [==============================] - 0s 4ms/step - loss: 5149.9453 - accuracy: 0.5324 - val_loss: 14964.2480 - val_accuracy: 0.1345\n",
            "Epoch 100/100\n",
            "34/34 [==============================] - 0s 6ms/step - loss: 4957.2368 - accuracy: 0.5089 - val_loss: 5133.3320 - val_accuracy: 0.3613\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc38ba80050>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_probs = model.predict(X_test_w2vec)\n",
        "preds = np.argmax(predicted_probs, axis=-1)"
      ],
      "metadata": {
        "id": "YzPZlMw0hZnc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5695705c-bddd-4564-fc15-7481eb91c13b",
        "id": "OJFcoU2thZnc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.52      0.64      0.57        78\n",
            "           1       0.53      0.41      0.46        78\n",
            "\n",
            "    accuracy                           0.53       156\n",
            "   macro avg       0.53      0.53      0.52       156\n",
            "weighted avg       0.53      0.53      0.52       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gnb = MultinomialNB()\n",
        "preds = gnb.fit(X_train_w2vec, set_y).predict(X_test_w2vec)"
      ],
      "metadata": {
        "id": "Lvyj5w9jhZnc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8108caa1-a41b-4294-bc7b-def8f725a6ca",
        "id": "lQcZBhkrhZnc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.47      0.44      0.45        78\n",
            "           1       0.47      0.50      0.48        78\n",
            "\n",
            "    accuracy                           0.47       156\n",
            "   macro avg       0.47      0.47      0.47       156\n",
            "weighted avg       0.47      0.47      0.47       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = DecisionTreeClassifier()\n",
        "preds = model.fit(X_train_w2vec, set_y).predict(X_test_w2vec)"
      ],
      "metadata": {
        "id": "qgaCH4HvhZnd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "322d0963-b20a-432a-ceb2-4c7c3c693042",
        "id": "hyeEKiBShZnd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.55      0.65      0.60        78\n",
            "           1       0.57      0.46      0.51        78\n",
            "\n",
            "    accuracy                           0.56       156\n",
            "   macro avg       0.56      0.56      0.55       156\n",
            "weighted avg       0.56      0.56      0.55       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "preds = model.fit(X_train_w2vec, set_y).predict(X_test_w2vec)"
      ],
      "metadata": {
        "id": "v6VQtsDBhZnd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04da0278-e8cf-472f-e352-24e0ef0cb482",
        "id": "nK9X5EQChZnd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.47      0.52        78\n",
            "           1       0.55      0.64      0.59        78\n",
            "\n",
            "    accuracy                           0.56       156\n",
            "   macro avg       0.56      0.56      0.55       156\n",
            "weighted avg       0.56      0.56      0.55       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC(kernel=\"poly\", degree=1, C=0.1, gamma=0.1)\n",
        "preds = model.fit(X_train_w2vec, set_y).predict(X_test_w2vec)"
      ],
      "metadata": {
        "id": "c0w4SIwdhZnd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27c25721-1d4e-4637-9782-135d55807a15",
        "id": "jsmD8XhlhZnd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.51      1.00      0.67        78\n",
            "           1       1.00      0.03      0.05        78\n",
            "\n",
            "    accuracy                           0.51       156\n",
            "   macro avg       0.75      0.51      0.36       156\n",
            "weighted avg       0.75      0.51      0.36       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def attention_layer(inputs, neurons):\n",
        "    x = layers.Permute((2,1))(inputs)\n",
        "    x = layers.Dense(neurons, activation=\"softmax\")(x)\n",
        "    x = layers.Permute((2,1), name=\"attention\")(x)\n",
        "    x = layers.multiply([inputs, x])\n",
        "    return x\n",
        "\n",
        "x_in = layers.Input(shape=(15,))\n",
        "x = layers.Embedding(input_dim=embeddings.shape[0],  \n",
        "                     output_dim=embeddings.shape[1], \n",
        "                     weights=[embeddings],\n",
        "                     input_length=15, trainable=False)(x_in)\n",
        "x = attention_layer(x, neurons=15)\n",
        "x = layers.Bidirectional(layers.LSTM(units=15, dropout=0.2, \n",
        "                         return_sequences=True))(x)\n",
        "x = layers.Bidirectional(layers.LSTM(units=15, dropout=0.2))(x)\n",
        "x = layers.Dense(64, activation='relu')(x)\n",
        "y_out = layers.Dense(2, activation='softmax')(x)\n",
        "model = models.Model(x_in, y_out)\n",
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "zx0oxPqIorDc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a26a9a59-635a-46ab-8007-c198adac7914"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 15)]         0           []                               \n",
            "                                                                                                  \n",
            " embedding_1 (Embedding)        (None, 15, 300)      985500      ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " permute (Permute)              (None, 300, 15)      0           ['embedding_1[0][0]']            \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 300, 15)      240         ['permute[0][0]']                \n",
            "                                                                                                  \n",
            " attention (Permute)            (None, 15, 300)      0           ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " multiply (Multiply)            (None, 15, 300)      0           ['embedding_1[0][0]',            \n",
            "                                                                  'attention[0][0]']              \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirectional  (None, 15, 30)      37920       ['multiply[0][0]']               \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " bidirectional_2 (Bidirectional  (None, 30)          5520        ['bidirectional_1[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 64)           1984        ['bidirectional_2[0][0]']        \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 2)            130         ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1,031,294\n",
            "Trainable params: 45,794\n",
            "Non-trainable params: 985,500\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.run_functions_eagerly(True)"
      ],
      "metadata": {
        "id": "itonQC8XcgmB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## encode y\n",
        "dic_y_mapping = {n:label for n,label in \n",
        "                 enumerate(np.unique(twoClassCleanTrainDF['relation_type'].values))}\n",
        "inverse_dic = {v:k for k,v in dic_y_mapping.items()}\n",
        "y_train = np.array([inverse_dic[y] for y in twoClassCleanTrainDF['relation_type'].values])\n",
        "## train\n",
        "training = model.fit(x=X_train_w2vec, y=y_train, batch_size=128, \n",
        "                     epochs=10, shuffle=True, verbose=0, \n",
        "                     validation_split=0.1)\n",
        "## plot loss and accuracy\n",
        "metrics = [k for k in training.history.keys() if (\"loss\" not in k) and (\"val\" not in k)]\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True)\n",
        "ax[0].set(title=\"Training\")\n",
        "ax11 = ax[0].twinx()\n",
        "ax[0].plot(training.history['loss'], color='black')\n",
        "ax[0].set_xlabel('Epochs')\n",
        "ax[0].set_ylabel('Loss', color='black')\n",
        "for metric in metrics:\n",
        "    ax11.plot(training.history[metric], label=metric)\n",
        "ax11.set_ylabel(\"Score\", color='steelblue')\n",
        "ax11.legend()\n",
        "ax[1].set(title=\"Validation\")\n",
        "ax22 = ax[1].twinx()\n",
        "ax[1].plot(training.history['val_loss'], color='black')\n",
        "ax[1].set_xlabel('Epochs')\n",
        "ax[1].set_ylabel('Loss', color='black')\n",
        "for metric in metrics:\n",
        "     ax22.plot(training.history['val_'+metric], label=metric)\n",
        "ax22.set_ylabel(\"Score\", color=\"steelblue\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TvlgB7G0ovXM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "0b709e21-f618-4b6a-c5da-82b09f6f9a0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_prob = model.predict(X_test_w2vec)\n",
        "predicted = [dic_y_mapping[np.argmax(pred)] for pred in \n",
        "             predicted_prob]"
      ],
      "metadata": {
        "id": "SBODNPyx2p3p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64463c07-81ee-44dc-c936-f1f49241837f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "y_test = twoClassCleanDevDF['relation_type'].values\n",
        "classes = np.unique(y_test)\n",
        "y_test_array = pd.get_dummies(y_test, drop_first=False).values\n",
        "    \n",
        "## Accuracy, Precision, Recall\n",
        "accuracy = metrics.accuracy_score(y_test, predicted)\n",
        "# auc = metrics.roc_auc_score(y_test_liar, predicted_prob, \n",
        "#                             multi_class=\"ovr\")\n",
        "print(\"Accuracy:\",  round(accuracy,2))\n",
        "# print(\"Auc:\", round(auc,2))\n",
        "print(\"Detail:\")\n",
        "print(metrics.classification_report(y_test, predicted))\n",
        "    \n",
        "## Plot confusion matrix\n",
        "cm = metrics.confusion_matrix(y_test, predicted)\n",
        "fig, ax = plt.subplots()\n",
        "sns.heatmap(cm, annot=True, fmt='d', ax=ax, cmap=plt.cm.Blues, \n",
        "            cbar=False)\n",
        "ax.set(xlabel=\"Pred\", ylabel=\"True\", xticklabels=classes, \n",
        "       yticklabels=classes, title=\"Confusion matrix\")\n",
        "plt.yticks(rotation=0)\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
        "## Plot roc\n",
        "for i in range(len(classes)):\n",
        "    fpr, tpr, thresholds = metrics.roc_curve(y_test_array[:,i],  \n",
        "                           predicted_prob[:,i])\n",
        "    ax[0].plot(fpr, tpr, lw=3, \n",
        "              label='{0} (area={1:0.2f})'.format(classes[i], \n",
        "                              metrics.auc(fpr, tpr))\n",
        "               )\n",
        "ax[0].plot([0,1], [0,1], color='navy', lw=3, linestyle='--')\n",
        "ax[0].set(xlim=[-0.05,1.0], ylim=[0.0,1.05], \n",
        "          xlabel='False Positive Rate', \n",
        "          ylabel=\"True Positive Rate (Recall)\", \n",
        "          title=\"Receiver operating characteristic\")\n",
        "ax[0].legend(loc=\"lower right\")\n",
        "ax[0].grid(True)\n",
        "    \n",
        "## Plot precision-recall curve\n",
        "for i in range(len(classes)):\n",
        "    precision, recall, thresholds = metrics.precision_recall_curve(\n",
        "                 y_test_array[:,i], predicted_prob[:,i])\n",
        "    ax[1].plot(recall, precision, lw=3, \n",
        "               label='{0} (area={1:0.2f})'.format(classes[i], \n",
        "                                  metrics.auc(recall, precision))\n",
        "              )\n",
        "ax[1].set(xlim=[0.0,1.05], ylim=[0.0,1.05], xlabel='Recall', \n",
        "          ylabel=\"Precision\", title=\"Precision-Recall curve\")\n",
        "ax[1].legend(loc=\"best\")\n",
        "ax[1].grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "WQJjHfzG2vEi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "outputId": "4cb54d3a-dbeb-46d7-b186-a73a1eb9084e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.49\n",
            "Detail:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Amount       0.48      0.33      0.39        78\n",
            "     History       0.49      0.64      0.56        78\n",
            "\n",
            "    accuracy                           0.49       156\n",
            "   macro avg       0.49      0.49      0.47       156\n",
            "weighted avg       0.49      0.49      0.47       156\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_dict"
      ],
      "metadata": {
        "id": "cG8sQimOJbSU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6273099-1ee8-43e6-a334-ce7758f995f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Amount': 0, 'History': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainBertEmbeddings = getDistilBertEmbeddings(twoClassCleanTrainDF)\n",
        "testBertEmbeddings = getDistilBertEmbeddings(twoClassCleanDevDF)\n",
        "\n",
        "trainBertY = twoClassCleanTrainDF['relation_type'].apply(lambda x: cat_dict[x]).values\n",
        "testBertY = twoClassCleanDevDF['relation_type'].apply(lambda x: cat_dict[x]).values\n",
        "\n",
        "train_x, val_x, train_y, val_y = train_test_split(trainBertEmbeddings, trainBertY, test_size=0.15, random_state=42)"
      ],
      "metadata": {
        "id": "8b30yetaKC9i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
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    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(n_categories,\n",
        "                activation='softmax',\n",
        "                kernel_regularizer=L1L2(l1=0.0, l2=0.1),\n",
        "                input_dim=train_x.shape[1]))\n",
        "model.compile(optimizer='sgd',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.summary()\n",
        "model.fit(trainBertEmbeddings, trainBertY, epochs=100, validation_data=(val_x, val_y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3822f934-e7bf-4fc9-a1ff-74dc07205796",
        "id": "2UVr99RELg91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_6 (Dense)             (None, 2)                 1538      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,538\n",
            "Trainable params: 1,538\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            " 4/37 [==>...........................] - ETA: 0s - loss: 1.1178 - accuracy: 0.4297"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37/37 [==============================] - 1s 21ms/step - loss: 1.0867 - accuracy: 0.4375 - val_loss: 1.0574 - val_accuracy: 0.4157\n",
            "Epoch 2/100\n",
            "37/37 [==============================] - 1s 23ms/step - loss: 1.0303 - accuracy: 0.4434 - val_loss: 1.0045 - val_accuracy: 0.4157\n",
            "Epoch 3/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.9818 - accuracy: 0.4561 - val_loss: 0.9592 - val_accuracy: 0.4719\n",
            "Epoch 4/100\n",
            "37/37 [==============================] - 1s 22ms/step - loss: 0.9400 - accuracy: 0.4873 - val_loss: 0.9204 - val_accuracy: 0.5169\n",
            "Epoch 5/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.9042 - accuracy: 0.5279 - val_loss: 0.8870 - val_accuracy: 0.5337\n",
            "Epoch 6/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.8733 - accuracy: 0.5701 - val_loss: 0.8584 - val_accuracy: 0.5955\n",
            "Epoch 7/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.8467 - accuracy: 0.6047 - val_loss: 0.8338 - val_accuracy: 0.6348\n",
            "Epoch 8/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.8238 - accuracy: 0.6427 - val_loss: 0.8127 - val_accuracy: 0.6404\n",
            "Epoch 9/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.8041 - accuracy: 0.6554 - val_loss: 0.7945 - val_accuracy: 0.6629\n",
            "Epoch 10/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.7870 - accuracy: 0.6816 - val_loss: 0.7788 - val_accuracy: 0.7022\n",
            "Epoch 11/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.7724 - accuracy: 0.6833 - val_loss: 0.7654 - val_accuracy: 0.6910\n",
            "Epoch 12/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.7598 - accuracy: 0.7052 - val_loss: 0.7538 - val_accuracy: 0.6966\n",
            "Epoch 13/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.7490 - accuracy: 0.7086 - val_loss: 0.7438 - val_accuracy: 0.7079\n",
            "Epoch 14/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.7396 - accuracy: 0.7264 - val_loss: 0.7352 - val_accuracy: 0.7135\n",
            "Epoch 15/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.7315 - accuracy: 0.7221 - val_loss: 0.7279 - val_accuracy: 0.7191\n",
            "Epoch 16/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.7245 - accuracy: 0.7255 - val_loss: 0.7216 - val_accuracy: 0.7360\n",
            "Epoch 17/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.7185 - accuracy: 0.7297 - val_loss: 0.7161 - val_accuracy: 0.7303\n",
            "Epoch 18/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.7134 - accuracy: 0.7390 - val_loss: 0.7113 - val_accuracy: 0.7303\n",
            "Epoch 19/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.7089 - accuracy: 0.7441 - val_loss: 0.7072 - val_accuracy: 0.7416\n",
            "Epoch 20/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.7051 - accuracy: 0.7458 - val_loss: 0.7038 - val_accuracy: 0.7472\n",
            "Epoch 21/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.7017 - accuracy: 0.7492 - val_loss: 0.7007 - val_accuracy: 0.7472\n",
            "Epoch 22/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6989 - accuracy: 0.7475 - val_loss: 0.6982 - val_accuracy: 0.7584\n",
            "Epoch 23/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6965 - accuracy: 0.7534 - val_loss: 0.6960 - val_accuracy: 0.7584\n",
            "Epoch 24/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6943 - accuracy: 0.7458 - val_loss: 0.6942 - val_accuracy: 0.7416\n",
            "Epoch 25/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.6925 - accuracy: 0.7449 - val_loss: 0.6925 - val_accuracy: 0.7472\n",
            "Epoch 26/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6909 - accuracy: 0.7458 - val_loss: 0.6911 - val_accuracy: 0.7472\n",
            "Epoch 27/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6895 - accuracy: 0.7551 - val_loss: 0.6900 - val_accuracy: 0.7416\n",
            "Epoch 28/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6883 - accuracy: 0.7517 - val_loss: 0.6890 - val_accuracy: 0.7416\n",
            "Epoch 29/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6874 - accuracy: 0.7551 - val_loss: 0.6881 - val_accuracy: 0.7303\n",
            "Epoch 30/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.6865 - accuracy: 0.7517 - val_loss: 0.6873 - val_accuracy: 0.7360\n",
            "Epoch 31/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6857 - accuracy: 0.7432 - val_loss: 0.6865 - val_accuracy: 0.7584\n",
            "Epoch 32/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6851 - accuracy: 0.7500 - val_loss: 0.6859 - val_accuracy: 0.7584\n",
            "Epoch 33/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6845 - accuracy: 0.7500 - val_loss: 0.6854 - val_accuracy: 0.7528\n",
            "Epoch 34/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6841 - accuracy: 0.7466 - val_loss: 0.6850 - val_accuracy: 0.7472\n",
            "Epoch 35/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6837 - accuracy: 0.7492 - val_loss: 0.6847 - val_accuracy: 0.7416\n",
            "Epoch 36/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6833 - accuracy: 0.7483 - val_loss: 0.6844 - val_accuracy: 0.7416\n",
            "Epoch 37/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6830 - accuracy: 0.7407 - val_loss: 0.6840 - val_accuracy: 0.7584\n",
            "Epoch 38/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6827 - accuracy: 0.7492 - val_loss: 0.6838 - val_accuracy: 0.7584\n",
            "Epoch 39/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6825 - accuracy: 0.7500 - val_loss: 0.6836 - val_accuracy: 0.7528\n",
            "Epoch 40/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.6823 - accuracy: 0.7492 - val_loss: 0.6834 - val_accuracy: 0.7584\n",
            "Epoch 41/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6822 - accuracy: 0.7551 - val_loss: 0.6834 - val_accuracy: 0.7360\n",
            "Epoch 42/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6820 - accuracy: 0.7416 - val_loss: 0.6832 - val_accuracy: 0.7528\n",
            "Epoch 43/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.6818 - accuracy: 0.7407 - val_loss: 0.6831 - val_accuracy: 0.7528\n",
            "Epoch 44/100\n",
            "37/37 [==============================] - 1s 23ms/step - loss: 0.6818 - accuracy: 0.7500 - val_loss: 0.6831 - val_accuracy: 0.7303\n",
            "Epoch 45/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6817 - accuracy: 0.7407 - val_loss: 0.6830 - val_accuracy: 0.7472\n",
            "Epoch 46/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6816 - accuracy: 0.7424 - val_loss: 0.6829 - val_accuracy: 0.7416\n",
            "Epoch 47/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6815 - accuracy: 0.7525 - val_loss: 0.6829 - val_accuracy: 0.7360\n",
            "Epoch 48/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6814 - accuracy: 0.7466 - val_loss: 0.6828 - val_accuracy: 0.7360\n",
            "Epoch 49/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6814 - accuracy: 0.7475 - val_loss: 0.6828 - val_accuracy: 0.7416\n",
            "Epoch 50/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6814 - accuracy: 0.7416 - val_loss: 0.6828 - val_accuracy: 0.7360\n",
            "Epoch 51/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6813 - accuracy: 0.7449 - val_loss: 0.6827 - val_accuracy: 0.7360\n",
            "Epoch 52/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6813 - accuracy: 0.7475 - val_loss: 0.6828 - val_accuracy: 0.7247\n",
            "Epoch 53/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6813 - accuracy: 0.7483 - val_loss: 0.6829 - val_accuracy: 0.7303\n",
            "Epoch 54/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6813 - accuracy: 0.7390 - val_loss: 0.6828 - val_accuracy: 0.7247\n",
            "Epoch 55/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6812 - accuracy: 0.7432 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 56/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6812 - accuracy: 0.7466 - val_loss: 0.6826 - val_accuracy: 0.7360\n",
            "Epoch 57/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7432 - val_loss: 0.6826 - val_accuracy: 0.7416\n",
            "Epoch 58/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7508 - val_loss: 0.6826 - val_accuracy: 0.7360\n",
            "Epoch 59/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6812 - accuracy: 0.7534 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 60/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7483 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 61/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7432 - val_loss: 0.6826 - val_accuracy: 0.7247\n",
            "Epoch 62/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7508 - val_loss: 0.6826 - val_accuracy: 0.7247\n",
            "Epoch 63/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7449 - val_loss: 0.6826 - val_accuracy: 0.7303\n",
            "Epoch 64/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.6811 - accuracy: 0.7475 - val_loss: 0.6828 - val_accuracy: 0.7303\n",
            "Epoch 65/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7399 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 66/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6810 - accuracy: 0.7399 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 67/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7492 - val_loss: 0.6828 - val_accuracy: 0.7303\n",
            "Epoch 68/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7475 - val_loss: 0.6828 - val_accuracy: 0.7303\n",
            "Epoch 69/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6810 - accuracy: 0.7466 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 70/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7458 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 71/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6811 - accuracy: 0.7458 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 72/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6810 - accuracy: 0.7458 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 73/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7466 - val_loss: 0.6828 - val_accuracy: 0.7303\n",
            "Epoch 74/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7407 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 75/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6810 - accuracy: 0.7458 - val_loss: 0.6827 - val_accuracy: 0.7303\n",
            "Epoch 76/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7348 - val_loss: 0.6826 - val_accuracy: 0.7303\n",
            "Epoch 77/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7432 - val_loss: 0.6826 - val_accuracy: 0.7360\n",
            "Epoch 78/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.6811 - accuracy: 0.7399 - val_loss: 0.6825 - val_accuracy: 0.7360\n",
            "Epoch 79/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.6810 - accuracy: 0.7458 - val_loss: 0.6825 - val_accuracy: 0.7360\n",
            "Epoch 80/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6811 - accuracy: 0.7432 - val_loss: 0.6825 - val_accuracy: 0.7360\n",
            "Epoch 81/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6810 - accuracy: 0.7390 - val_loss: 0.6825 - val_accuracy: 0.7360\n",
            "Epoch 82/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.6811 - accuracy: 0.7382 - val_loss: 0.6825 - val_accuracy: 0.7416\n",
            "Epoch 83/100\n",
            "37/37 [==============================] - 1s 17ms/step - loss: 0.6811 - accuracy: 0.7475 - val_loss: 0.6826 - val_accuracy: 0.7360\n",
            "Epoch 84/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6810 - accuracy: 0.7441 - val_loss: 0.6826 - val_accuracy: 0.7360\n",
            "Epoch 85/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7466 - val_loss: 0.6825 - val_accuracy: 0.7360\n",
            "Epoch 86/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.6810 - accuracy: 0.7356 - val_loss: 0.6825 - val_accuracy: 0.7472\n",
            "Epoch 87/100\n",
            "37/37 [==============================] - 2s 46ms/step - loss: 0.6810 - accuracy: 0.7416 - val_loss: 0.6825 - val_accuracy: 0.7416\n",
            "Epoch 88/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6810 - accuracy: 0.7382 - val_loss: 0.6825 - val_accuracy: 0.7416\n",
            "Epoch 89/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7441 - val_loss: 0.6824 - val_accuracy: 0.7472\n",
            "Epoch 90/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.6811 - accuracy: 0.7432 - val_loss: 0.6824 - val_accuracy: 0.7472\n",
            "Epoch 91/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7458 - val_loss: 0.6825 - val_accuracy: 0.7472\n",
            "Epoch 92/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6810 - accuracy: 0.7508 - val_loss: 0.6825 - val_accuracy: 0.7360\n",
            "Epoch 93/100\n",
            "37/37 [==============================] - 1s 19ms/step - loss: 0.6811 - accuracy: 0.7483 - val_loss: 0.6826 - val_accuracy: 0.7303\n",
            "Epoch 94/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6810 - accuracy: 0.7365 - val_loss: 0.6825 - val_accuracy: 0.7360\n",
            "Epoch 95/100\n",
            "37/37 [==============================] - 1s 21ms/step - loss: 0.6811 - accuracy: 0.7483 - val_loss: 0.6826 - val_accuracy: 0.7247\n",
            "Epoch 96/100\n",
            "37/37 [==============================] - 1s 18ms/step - loss: 0.6811 - accuracy: 0.7492 - val_loss: 0.6826 - val_accuracy: 0.7303\n",
            "Epoch 97/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6810 - accuracy: 0.7449 - val_loss: 0.6826 - val_accuracy: 0.7247\n",
            "Epoch 98/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6811 - accuracy: 0.7407 - val_loss: 0.6826 - val_accuracy: 0.7303\n",
            "Epoch 99/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6811 - accuracy: 0.7475 - val_loss: 0.6826 - val_accuracy: 0.7303\n",
            "Epoch 100/100\n",
            "37/37 [==============================] - 1s 20ms/step - loss: 0.6811 - accuracy: 0.7466 - val_loss: 0.6825 - val_accuracy: 0.7360\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc387531b50>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_probs = model.predict(testBertEmbeddings)\n",
        "preds = np.argmax(predicted_probs, axis=-1)"
      ],
      "metadata": {
        "id": "OXlQRBaLLg97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47a236a4-e6cc-4a3c-bacc-eef17d6a0e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(testBertY, preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35057e54-0a96-471c-d719-0fc725b2a78a",
        "id": "F8bVzCACLg97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.62      0.69        78\n",
            "           1       0.68      0.82      0.74        78\n",
            "\n",
            "    accuracy                           0.72       156\n",
            "   macro avg       0.73      0.72      0.71       156\n",
            "weighted avg       0.73      0.72      0.71       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.decomposition import NMF\n",
        "# nmf = NMF(init='random', random_state=0)\n",
        "# gnb = MultinomialNB()\n",
        "# preds = gnb.fit(trainBertEmbeddings, trainBertY).predict(testBertEmbeddings)"
      ],
      "metadata": {
        "id": "O_DrQXOaLg97"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print(metrics.classification_report(testBertY, preds))"
      ],
      "metadata": {
        "id": "oBD1Xl1PLg97"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = DecisionTreeClassifier()\n",
        "preds = model.fit(trainBertEmbeddings, trainBertY).predict(testBertEmbeddings)"
      ],
      "metadata": {
        "id": "paGqsOadLg97"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(testBertY, preds))"
      ],
      "metadata": {
        "id": "YGJEd8nKLg97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f41fa98c-318e-4293-b324-6f97bb21a863"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.76      0.71        78\n",
            "           1       0.72      0.62      0.66        78\n",
            "\n",
            "    accuracy                           0.69       156\n",
            "   macro avg       0.69      0.69      0.68       156\n",
            "weighted avg       0.69      0.69      0.68       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "preds = model.fit(trainBertEmbeddings, trainBertY).predict(testBertEmbeddings)"
      ],
      "metadata": {
        "id": "sxSUFlY7Lg97"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(testBertY, preds))"
      ],
      "metadata": {
        "id": "YZBdCWBFMpOv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e3eb149-40e9-4241-de46-6e35446f510a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.68      0.74        78\n",
            "           1       0.72      0.83      0.77        78\n",
            "\n",
            "    accuracy                           0.76       156\n",
            "   macro avg       0.76      0.76      0.75       156\n",
            "weighted avg       0.76      0.76      0.75       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC(kernel=\"poly\", degree=1, C=0.1, gamma=0.1)\n",
        "preds = model.fit(trainBertEmbeddings, trainBertY).predict(testBertEmbeddings)"
      ],
      "metadata": {
        "id": "bXsIIc4MLg98"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(testBertY, preds))"
      ],
      "metadata": {
        "id": "L0O4V98sLg98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a002bf26-33f6-48a2-82e6-5562bc291919"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.45      0.58        78\n",
            "           1       0.62      0.90      0.73        78\n",
            "\n",
            "    accuracy                           0.67       156\n",
            "   macro avg       0.72      0.67      0.66       156\n",
            "weighted avg       0.72      0.67      0.66       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bertModel = Sequential()\n",
        "input_dim, output_dim = trainBertEmbeddings.shape\n",
        "\n",
        "# Keras Embedding layer with Word2Vec weights initialization\n",
        "bertModel.add(Embedding(input_dim=input_dim,\n",
        "                    output_dim=output_dim,\n",
        "                    input_length=768,\n",
        "                    mask_zero=True,\n",
        "                    trainable=False))\n",
        "\n",
        "bertModel.add(Bidirectional(LSTM(100)))\n",
        "bertModel.add(Dense(n_categories, activation='softmax'))\n",
        "\n",
        "bertModel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = bertModel.fit(train_x, train_y, epochs=20, batch_size=64,\n",
        "                    validation_data=(val_x, val_y), verbose=1)"
      ],
      "metadata": {
        "id": "lmFhw1_GN60G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63b99e64-d2db-4fb9-e41e-32c516cbca1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 326s 20s/step - loss: 0.7072 - accuracy: 0.5020 - val_loss: 0.6962 - val_accuracy: 0.4551\n",
            "Epoch 2/20\n",
            "16/16 [==============================] - 346s 22s/step - loss: 0.6960 - accuracy: 0.4940 - val_loss: 0.7067 - val_accuracy: 0.4551\n",
            "Epoch 3/20\n",
            "16/16 [==============================] - 354s 22s/step - loss: 0.6952 - accuracy: 0.5119 - val_loss: 0.6912 - val_accuracy: 0.5449\n",
            "Epoch 4/20\n",
            "16/16 [==============================] - 392s 25s/step - loss: 0.6948 - accuracy: 0.4920 - val_loss: 0.6949 - val_accuracy: 0.4551\n",
            "Epoch 5/20\n",
            "16/16 [==============================] - 350s 22s/step - loss: 0.6969 - accuracy: 0.5080 - val_loss: 0.6946 - val_accuracy: 0.4551\n",
            "Epoch 6/20\n",
            "16/16 [==============================] - 366s 23s/step - loss: 0.6938 - accuracy: 0.4821 - val_loss: 0.6950 - val_accuracy: 0.4551\n",
            "Epoch 7/20\n",
            "16/16 [==============================] - 368s 23s/step - loss: 0.6933 - accuracy: 0.5080 - val_loss: 0.6938 - val_accuracy: 0.4551\n",
            "Epoch 8/20\n",
            "16/16 [==============================] - 342s 21s/step - loss: 0.6934 - accuracy: 0.5080 - val_loss: 0.6953 - val_accuracy: 0.4551\n",
            "Epoch 9/20\n",
            "16/16 [==============================] - 380s 24s/step - loss: 0.6945 - accuracy: 0.4722 - val_loss: 0.6937 - val_accuracy: 0.4551\n",
            "Epoch 10/20\n",
            "16/16 [==============================] - 350s 22s/step - loss: 0.6932 - accuracy: 0.5080 - val_loss: 0.6967 - val_accuracy: 0.4551\n",
            "Epoch 11/20\n",
            "16/16 [==============================] - 344s 21s/step - loss: 0.6935 - accuracy: 0.5080 - val_loss: 0.6939 - val_accuracy: 0.4551\n",
            "Epoch 12/20\n",
            "16/16 [==============================] - 345s 22s/step - loss: 0.6935 - accuracy: 0.4563 - val_loss: 0.6940 - val_accuracy: 0.4551\n",
            "Epoch 13/20\n",
            "16/16 [==============================] - 372s 24s/step - loss: 0.6938 - accuracy: 0.4841 - val_loss: 0.6939 - val_accuracy: 0.4551\n",
            "Epoch 14/20\n",
            "16/16 [==============================] - 350s 22s/step - loss: 0.6932 - accuracy: 0.5080 - val_loss: 0.6957 - val_accuracy: 0.4551\n",
            "Epoch 15/20\n",
            "16/16 [==============================] - 339s 21s/step - loss: 0.6935 - accuracy: 0.5080 - val_loss: 0.6935 - val_accuracy: 0.4551\n",
            "Epoch 16/20\n",
            "16/16 [==============================] - 367s 23s/step - loss: 0.6935 - accuracy: 0.5080 - val_loss: 0.6957 - val_accuracy: 0.4551\n",
            "Epoch 17/20\n",
            "16/16 [==============================] - 346s 22s/step - loss: 0.6933 - accuracy: 0.5080 - val_loss: 0.6940 - val_accuracy: 0.4551\n",
            "Epoch 18/20\n",
            "16/16 [==============================] - 353s 22s/step - loss: 0.6940 - accuracy: 0.5080 - val_loss: 0.6945 - val_accuracy: 0.4551\n",
            "Epoch 19/20\n",
            "16/16 [==============================] - 344s 22s/step - loss: 0.6937 - accuracy: 0.5080 - val_loss: 0.6945 - val_accuracy: 0.4551\n",
            "Epoch 20/20\n",
            "16/16 [==============================] - 351s 22s/step - loss: 0.6933 - accuracy: 0.4980 - val_loss: 0.6928 - val_accuracy: 0.5449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_probs = bertModel.predict(testBertEmbeddings)\n",
        "preds = np.argmax(predicted_probs, axis=-1)"
      ],
      "metadata": {
        "id": "eoEg0PDRMgkB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d1f9f45-6e7e-4bb9-d857-575ff5b8147e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(testBertY, preds))"
      ],
      "metadata": {
        "id": "M49JCywjMgkH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91163aee-02a1-4272-b68f-2b604398c350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      1.00      0.67        78\n",
            "           1       0.00      0.00      0.00        78\n",
            "\n",
            "    accuracy                           0.50       156\n",
            "   macro avg       0.25      0.50      0.33       156\n",
            "weighted avg       0.25      0.50      0.33       156\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_dict, test_x, test_y, w2v_weights, vocab_size, embedding_size = convertText2Feature(twoClassCleanDevDF)\n",
        "cat_dict, set_x, set_y, w2v_weights, vocab_size, embedding_size = convertText2Feature(twoClassCleanTrainDF)\n",
        "\n",
        "cat_dict, set_x_t1, set_y, w2v_weights_t1, vocab_size_t1, embedding_size_t1 = convertText2Feature(twoClassCleanTrainDF, 'T1')\n",
        "cat_dict, set_x_t2, set_y, w2v_weights_t2, vocab_size_t2, embedding_size_t2 = convertText2Feature(twoClassCleanTrainDF, 'T2')\n",
        "\n",
        "VALID_PER = 0.15\n",
        "\n",
        "total_samples = set_x.shape[0]\n",
        "n_val = int(VALID_PER * total_samples)\n",
        "n_train = total_samples - n_val\n",
        "\n",
        "random_i = random.sample(range(total_samples), total_samples)\n",
        "train_x = set_x[random_i[:n_train]]\n",
        "train_x_t1 = set_x_t1[random_i[:n_train]]\n",
        "train_x_t2 = set_x_t2[random_i[:n_train]]\n",
        "train_y = set_y[random_i[:n_train]]\n",
        "val_x = set_x[random_i[n_train:n_train+n_val]]\n",
        "val_x_t1 = set_x_t1[random_i[n_train:n_train+n_val]]\n",
        "val_x_t2 = set_x_t2[random_i[n_train:n_train+n_val]]\n",
        "val_y = set_y[random_i[n_train:n_train+n_val]]\n",
        "categories, ccount = np.unique(train_y, return_counts=True)\n",
        "\n",
        "n_categories = len(categories)"
      ],
      "metadata": {
        "id": "OZr-tcLIsUlD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_x.shape)\n",
        "print(train_x_t1.shape)\n",
        "print(train_x_t2.shape)\n",
        "print(w2v_weights.shape)\n",
        "print(w2v_weights_t1.shape)\n",
        "print(w2v_weights_t2.shape)\n",
        "print(train_y.shape)\n",
        "print(vocab_size)"
      ],
      "metadata": {
        "id": "-F-_LXlPeZ8Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed21e96a-a854-42b5-8427-29a9fc392131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1007, 50)\n",
            "(1007, 50)\n",
            "(1007, 50)\n",
            "(994, 100)\n",
            "(994, 100)\n",
            "(994, 100)\n",
            "(1007,)\n",
            "994\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Keras Embedding layer with Word2Vec weights initialization\n",
        "sentenceInput = Input(shape=(MAX_SEQUENCE_LENGTH,))\n",
        "sentenceInputEmbedding = Embedding(input_dim=vocab_size,\n",
        "                    output_dim=embedding_size,\n",
        "                    weights=[w2v_weights],\n",
        "                    input_length=MAX_SEQUENCE_LENGTH,\n",
        "                    mask_zero=True,\n",
        "                    trainable=False)(sentenceInput)\n",
        "\n",
        "t1Input = Input(shape=(MAX_SEQUENCE_LENGTH,))\n",
        "t1InputEmbedding = Embedding(input_dim=vocab_size_t1,\n",
        "                    output_dim=embedding_size_t1,\n",
        "                    weights=[w2v_weights_t1],\n",
        "                    input_length=MAX_SEQUENCE_LENGTH,\n",
        "                    mask_zero=True,\n",
        "                    trainable=False)(t1Input)\n",
        "\n",
        "t2Input = Input(shape=(MAX_SEQUENCE_LENGTH,))\n",
        "t2InputEmbedding = Embedding(input_dim=vocab_size_t2,\n",
        "                    output_dim=embedding_size_t2,\n",
        "                    weights=[w2v_weights_t2],\n",
        "                    input_length=MAX_SEQUENCE_LENGTH,\n",
        "                    mask_zero=True,\n",
        "                    trainable=False)(t2Input)\n",
        "\n",
        "embeddingInput = tf.keras.layers.Concatenate(axis=1)([sentenceInputEmbedding, t1InputEmbedding, t2InputEmbedding])\n",
        "\n",
        "lstm = (Bidirectional(LSTM(100)))(embeddingInput)\n",
        "output = Dense(n_categories, activation='softmax')(lstm)\n",
        "model = Model(inputs=[sentenceInput, t1Input, t2Input], outputs=[output])\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "Kq8rmpbFPSSH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "262f78d2-f4de-43c0-97e9-7a44fb530408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 50)]         0           []                               \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 50)]         0           []                               \n",
            "                                                                                                  \n",
            " input_4 (InputLayer)           [(None, 50)]         0           []                               \n",
            "                                                                                                  \n",
            " embedding_3 (Embedding)        (None, 50, 100)      99400       ['input_2[0][0]']                \n",
            "                                                                                                  \n",
            " embedding_4 (Embedding)        (None, 50, 100)      99400       ['input_3[0][0]']                \n",
            "                                                                                                  \n",
            " embedding_5 (Embedding)        (None, 50, 100)      99400       ['input_4[0][0]']                \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 150, 100)     0           ['embedding_3[0][0]',            \n",
            "                                                                  'embedding_4[0][0]',            \n",
            "                                                                  'embedding_5[0][0]']            \n",
            "                                                                                                  \n",
            " bidirectional_4 (Bidirectional  (None, 200)         160800      ['concatenate[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " dense_8 (Dense)                (None, 2)            402         ['bidirectional_4[0][0]']        \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 459,402\n",
            "Trainable params: 161,202\n",
            "Non-trainable params: 298,200\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit([train_x, train_x_t1, train_x_t2], train_y, epochs=200, batch_size=64,\n",
        "                    validation_data=([val_x, val_x_t1, val_x_t2], val_y), verbose=1)"
      ],
      "metadata": {
        "id": "qLaRaFJBQfPA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebe9a094-9b67-4123-efe2-5370e58edff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 48s 3s/step - loss: 0.6923 - accuracy: 0.5084 - val_loss: 0.6940 - val_accuracy: 0.4463\n",
            "Epoch 2/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.6897 - accuracy: 0.5094 - val_loss: 0.6903 - val_accuracy: 0.4463\n",
            "Epoch 3/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.6849 - accuracy: 0.5263 - val_loss: 0.6779 - val_accuracy: 0.5537\n",
            "Epoch 4/200\n",
            "16/16 [==============================] - 48s 3s/step - loss: 0.6737 - accuracy: 0.6048 - val_loss: 0.6518 - val_accuracy: 0.6441\n",
            "Epoch 5/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.6716 - accuracy: 0.6137 - val_loss: 0.6329 - val_accuracy: 0.6384\n",
            "Epoch 6/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.6685 - accuracy: 0.6177 - val_loss: 0.6516 - val_accuracy: 0.6328\n",
            "Epoch 7/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.6615 - accuracy: 0.6097 - val_loss: 0.6400 - val_accuracy: 0.6384\n",
            "Epoch 8/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.6540 - accuracy: 0.6216 - val_loss: 0.6427 - val_accuracy: 0.6045\n",
            "Epoch 9/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.6503 - accuracy: 0.6127 - val_loss: 0.6221 - val_accuracy: 0.6441\n",
            "Epoch 10/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.6451 - accuracy: 0.6216 - val_loss: 0.6203 - val_accuracy: 0.6384\n",
            "Epoch 11/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.6437 - accuracy: 0.6167 - val_loss: 0.6112 - val_accuracy: 0.6554\n",
            "Epoch 12/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.6382 - accuracy: 0.6197 - val_loss: 0.6128 - val_accuracy: 0.6610\n",
            "Epoch 13/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.6341 - accuracy: 0.6216 - val_loss: 0.6075 - val_accuracy: 0.6610\n",
            "Epoch 14/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.6319 - accuracy: 0.6306 - val_loss: 0.5996 - val_accuracy: 0.6723\n",
            "Epoch 15/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.6294 - accuracy: 0.6246 - val_loss: 0.5995 - val_accuracy: 0.6780\n",
            "Epoch 16/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.6276 - accuracy: 0.6336 - val_loss: 0.5944 - val_accuracy: 0.6667\n",
            "Epoch 17/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.6241 - accuracy: 0.6365 - val_loss: 0.5886 - val_accuracy: 0.6780\n",
            "Epoch 18/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.6228 - accuracy: 0.6435 - val_loss: 0.5926 - val_accuracy: 0.6554\n",
            "Epoch 19/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.6186 - accuracy: 0.6485 - val_loss: 0.5888 - val_accuracy: 0.6836\n",
            "Epoch 20/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.6210 - accuracy: 0.6524 - val_loss: 0.5943 - val_accuracy: 0.6836\n",
            "Epoch 21/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.6158 - accuracy: 0.6574 - val_loss: 0.5865 - val_accuracy: 0.6554\n",
            "Epoch 22/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.6077 - accuracy: 0.6584 - val_loss: 0.5805 - val_accuracy: 0.6667\n",
            "Epoch 23/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.6073 - accuracy: 0.6634 - val_loss: 0.5815 - val_accuracy: 0.6667\n",
            "Epoch 24/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.6068 - accuracy: 0.6673 - val_loss: 0.5805 - val_accuracy: 0.6723\n",
            "Epoch 25/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.5966 - accuracy: 0.6773 - val_loss: 0.5783 - val_accuracy: 0.6667\n",
            "Epoch 26/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.6001 - accuracy: 0.6792 - val_loss: 0.5750 - val_accuracy: 0.6836\n",
            "Epoch 27/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.6136 - accuracy: 0.6733 - val_loss: 0.6146 - val_accuracy: 0.6554\n",
            "Epoch 28/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.6081 - accuracy: 0.6653 - val_loss: 0.5829 - val_accuracy: 0.6780\n",
            "Epoch 29/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5916 - accuracy: 0.6733 - val_loss: 0.5726 - val_accuracy: 0.6949\n",
            "Epoch 30/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5956 - accuracy: 0.6703 - val_loss: 0.5729 - val_accuracy: 0.6949\n",
            "Epoch 31/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5835 - accuracy: 0.6792 - val_loss: 0.5931 - val_accuracy: 0.6836\n",
            "Epoch 32/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5905 - accuracy: 0.6783 - val_loss: 0.5725 - val_accuracy: 0.6949\n",
            "Epoch 33/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.5910 - accuracy: 0.6802 - val_loss: 0.5862 - val_accuracy: 0.6836\n",
            "Epoch 34/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5787 - accuracy: 0.6872 - val_loss: 0.5795 - val_accuracy: 0.6893\n",
            "Epoch 35/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.5747 - accuracy: 0.6981 - val_loss: 0.5940 - val_accuracy: 0.6893\n",
            "Epoch 36/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5853 - accuracy: 0.6792 - val_loss: 0.5899 - val_accuracy: 0.7006\n",
            "Epoch 37/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5722 - accuracy: 0.7041 - val_loss: 0.5799 - val_accuracy: 0.7119\n",
            "Epoch 38/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5721 - accuracy: 0.7021 - val_loss: 0.5958 - val_accuracy: 0.7062\n",
            "Epoch 39/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.5787 - accuracy: 0.6842 - val_loss: 0.5828 - val_accuracy: 0.7232\n",
            "Epoch 40/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5737 - accuracy: 0.6971 - val_loss: 0.6007 - val_accuracy: 0.7006\n",
            "Epoch 41/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5695 - accuracy: 0.7041 - val_loss: 0.5850 - val_accuracy: 0.7345\n",
            "Epoch 42/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5574 - accuracy: 0.7219 - val_loss: 0.5855 - val_accuracy: 0.7288\n",
            "Epoch 43/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5611 - accuracy: 0.7061 - val_loss: 0.5899 - val_accuracy: 0.7401\n",
            "Epoch 44/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5801 - accuracy: 0.6842 - val_loss: 0.6076 - val_accuracy: 0.7062\n",
            "Epoch 45/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5715 - accuracy: 0.7110 - val_loss: 0.5797 - val_accuracy: 0.7345\n",
            "Epoch 46/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5507 - accuracy: 0.7210 - val_loss: 0.5851 - val_accuracy: 0.7345\n",
            "Epoch 47/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5491 - accuracy: 0.7279 - val_loss: 0.5936 - val_accuracy: 0.7514\n",
            "Epoch 48/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.5496 - accuracy: 0.7080 - val_loss: 0.5875 - val_accuracy: 0.7458\n",
            "Epoch 49/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5466 - accuracy: 0.7100 - val_loss: 0.5998 - val_accuracy: 0.7458\n",
            "Epoch 50/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5443 - accuracy: 0.7130 - val_loss: 0.6133 - val_accuracy: 0.7232\n",
            "Epoch 51/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5479 - accuracy: 0.7110 - val_loss: 0.5924 - val_accuracy: 0.7571\n",
            "Epoch 52/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5426 - accuracy: 0.7200 - val_loss: 0.6165 - val_accuracy: 0.7571\n",
            "Epoch 53/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5340 - accuracy: 0.7210 - val_loss: 0.6287 - val_accuracy: 0.7345\n",
            "Epoch 54/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5376 - accuracy: 0.7200 - val_loss: 0.6208 - val_accuracy: 0.7571\n",
            "Epoch 55/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5383 - accuracy: 0.7190 - val_loss: 0.6186 - val_accuracy: 0.7571\n",
            "Epoch 56/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5295 - accuracy: 0.7279 - val_loss: 0.6415 - val_accuracy: 0.7401\n",
            "Epoch 57/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5256 - accuracy: 0.7130 - val_loss: 0.6451 - val_accuracy: 0.7458\n",
            "Epoch 58/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5701 - accuracy: 0.7259 - val_loss: 0.5806 - val_accuracy: 0.7232\n",
            "Epoch 59/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5644 - accuracy: 0.7051 - val_loss: 0.5854 - val_accuracy: 0.7288\n",
            "Epoch 60/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5588 - accuracy: 0.7001 - val_loss: 0.5925 - val_accuracy: 0.7119\n",
            "Epoch 61/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5487 - accuracy: 0.7219 - val_loss: 0.5898 - val_accuracy: 0.7288\n",
            "Epoch 62/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5485 - accuracy: 0.7120 - val_loss: 0.5786 - val_accuracy: 0.7345\n",
            "Epoch 63/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5368 - accuracy: 0.7180 - val_loss: 0.5971 - val_accuracy: 0.7288\n",
            "Epoch 64/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.5334 - accuracy: 0.7229 - val_loss: 0.6114 - val_accuracy: 0.7232\n",
            "Epoch 65/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5342 - accuracy: 0.7190 - val_loss: 0.5846 - val_accuracy: 0.7514\n",
            "Epoch 66/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5274 - accuracy: 0.7299 - val_loss: 0.6029 - val_accuracy: 0.7458\n",
            "Epoch 67/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5187 - accuracy: 0.7349 - val_loss: 0.5978 - val_accuracy: 0.7627\n",
            "Epoch 68/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5106 - accuracy: 0.7339 - val_loss: 0.6198 - val_accuracy: 0.7458\n",
            "Epoch 69/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5271 - accuracy: 0.7279 - val_loss: 0.6175 - val_accuracy: 0.7345\n",
            "Epoch 70/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5259 - accuracy: 0.7229 - val_loss: 0.6045 - val_accuracy: 0.7684\n",
            "Epoch 71/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5186 - accuracy: 0.7289 - val_loss: 0.6125 - val_accuracy: 0.7514\n",
            "Epoch 72/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5112 - accuracy: 0.7339 - val_loss: 0.6226 - val_accuracy: 0.7627\n",
            "Epoch 73/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5054 - accuracy: 0.7438 - val_loss: 0.6472 - val_accuracy: 0.7514\n",
            "Epoch 74/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.5107 - accuracy: 0.7329 - val_loss: 0.6391 - val_accuracy: 0.7684\n",
            "Epoch 75/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5060 - accuracy: 0.7358 - val_loss: 0.6450 - val_accuracy: 0.7514\n",
            "Epoch 76/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5075 - accuracy: 0.7339 - val_loss: 0.6596 - val_accuracy: 0.7401\n",
            "Epoch 77/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5118 - accuracy: 0.7329 - val_loss: 0.6879 - val_accuracy: 0.7119\n",
            "Epoch 78/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5045 - accuracy: 0.7408 - val_loss: 0.6449 - val_accuracy: 0.7458\n",
            "Epoch 79/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.5028 - accuracy: 0.7349 - val_loss: 0.6571 - val_accuracy: 0.7627\n",
            "Epoch 80/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4969 - accuracy: 0.7329 - val_loss: 0.6739 - val_accuracy: 0.7458\n",
            "Epoch 81/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5014 - accuracy: 0.7358 - val_loss: 0.6805 - val_accuracy: 0.7175\n",
            "Epoch 82/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4998 - accuracy: 0.7408 - val_loss: 0.6651 - val_accuracy: 0.7175\n",
            "Epoch 83/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4993 - accuracy: 0.7378 - val_loss: 0.6668 - val_accuracy: 0.7175\n",
            "Epoch 84/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5110 - accuracy: 0.7368 - val_loss: 0.6147 - val_accuracy: 0.7288\n",
            "Epoch 85/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4958 - accuracy: 0.7428 - val_loss: 0.6682 - val_accuracy: 0.7062\n",
            "Epoch 86/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4989 - accuracy: 0.7339 - val_loss: 0.6801 - val_accuracy: 0.7345\n",
            "Epoch 87/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4863 - accuracy: 0.7388 - val_loss: 0.6825 - val_accuracy: 0.7288\n",
            "Epoch 88/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4873 - accuracy: 0.7388 - val_loss: 0.7348 - val_accuracy: 0.7119\n",
            "Epoch 89/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4881 - accuracy: 0.7408 - val_loss: 0.7313 - val_accuracy: 0.7006\n",
            "Epoch 90/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4923 - accuracy: 0.7378 - val_loss: 0.7268 - val_accuracy: 0.7119\n",
            "Epoch 91/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4884 - accuracy: 0.7398 - val_loss: 0.7064 - val_accuracy: 0.7119\n",
            "Epoch 92/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4885 - accuracy: 0.7378 - val_loss: 0.7443 - val_accuracy: 0.7119\n",
            "Epoch 93/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4907 - accuracy: 0.7349 - val_loss: 0.6942 - val_accuracy: 0.7232\n",
            "Epoch 94/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4878 - accuracy: 0.7408 - val_loss: 0.7091 - val_accuracy: 0.7175\n",
            "Epoch 95/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4882 - accuracy: 0.7378 - val_loss: 0.7199 - val_accuracy: 0.7119\n",
            "Epoch 96/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4855 - accuracy: 0.7438 - val_loss: 0.7258 - val_accuracy: 0.7288\n",
            "Epoch 97/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4789 - accuracy: 0.7468 - val_loss: 0.7209 - val_accuracy: 0.7288\n",
            "Epoch 98/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4725 - accuracy: 0.7507 - val_loss: 0.7438 - val_accuracy: 0.7288\n",
            "Epoch 99/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4714 - accuracy: 0.7527 - val_loss: 0.7489 - val_accuracy: 0.7119\n",
            "Epoch 100/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5046 - accuracy: 0.7249 - val_loss: 0.6897 - val_accuracy: 0.7006\n",
            "Epoch 101/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4893 - accuracy: 0.7478 - val_loss: 0.6620 - val_accuracy: 0.7232\n",
            "Epoch 102/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4755 - accuracy: 0.7478 - val_loss: 0.7253 - val_accuracy: 0.7232\n",
            "Epoch 103/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4808 - accuracy: 0.7468 - val_loss: 0.7494 - val_accuracy: 0.6949\n",
            "Epoch 104/200\n",
            "16/16 [==============================] - 48s 3s/step - loss: 0.5051 - accuracy: 0.7418 - val_loss: 0.9186 - val_accuracy: 0.6836\n",
            "Epoch 105/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.5239 - accuracy: 0.7071 - val_loss: 0.6491 - val_accuracy: 0.6949\n",
            "Epoch 106/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4830 - accuracy: 0.7488 - val_loss: 0.6777 - val_accuracy: 0.7288\n",
            "Epoch 107/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4698 - accuracy: 0.7517 - val_loss: 0.7535 - val_accuracy: 0.7062\n",
            "Epoch 108/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4693 - accuracy: 0.7567 - val_loss: 0.7421 - val_accuracy: 0.7232\n",
            "Epoch 109/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5856 - accuracy: 0.7130 - val_loss: 0.6050 - val_accuracy: 0.6158\n",
            "Epoch 110/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.5677 - accuracy: 0.6862 - val_loss: 0.5696 - val_accuracy: 0.6893\n",
            "Epoch 111/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.5498 - accuracy: 0.7180 - val_loss: 0.5631 - val_accuracy: 0.6893\n",
            "Epoch 112/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5431 - accuracy: 0.7110 - val_loss: 0.5530 - val_accuracy: 0.7006\n",
            "Epoch 113/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.5408 - accuracy: 0.7150 - val_loss: 0.5422 - val_accuracy: 0.7119\n",
            "Epoch 114/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5273 - accuracy: 0.7259 - val_loss: 0.5439 - val_accuracy: 0.7232\n",
            "Epoch 115/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.5295 - accuracy: 0.7229 - val_loss: 0.5589 - val_accuracy: 0.6836\n",
            "Epoch 116/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5327 - accuracy: 0.7160 - val_loss: 0.5481 - val_accuracy: 0.7119\n",
            "Epoch 117/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5150 - accuracy: 0.7279 - val_loss: 0.5552 - val_accuracy: 0.7175\n",
            "Epoch 118/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.5115 - accuracy: 0.7329 - val_loss: 0.5827 - val_accuracy: 0.6780\n",
            "Epoch 119/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.5029 - accuracy: 0.7309 - val_loss: 0.5700 - val_accuracy: 0.7006\n",
            "Epoch 120/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4964 - accuracy: 0.7378 - val_loss: 0.5685 - val_accuracy: 0.7062\n",
            "Epoch 121/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4899 - accuracy: 0.7468 - val_loss: 0.5969 - val_accuracy: 0.7006\n",
            "Epoch 122/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4903 - accuracy: 0.7388 - val_loss: 0.6106 - val_accuracy: 0.6949\n",
            "Epoch 123/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4819 - accuracy: 0.7498 - val_loss: 0.6186 - val_accuracy: 0.6949\n",
            "Epoch 124/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4755 - accuracy: 0.7537 - val_loss: 0.6380 - val_accuracy: 0.7006\n",
            "Epoch 125/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4814 - accuracy: 0.7507 - val_loss: 0.6528 - val_accuracy: 0.7006\n",
            "Epoch 126/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4756 - accuracy: 0.7448 - val_loss: 0.6396 - val_accuracy: 0.6949\n",
            "Epoch 127/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4734 - accuracy: 0.7517 - val_loss: 0.6477 - val_accuracy: 0.6723\n",
            "Epoch 128/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4668 - accuracy: 0.7567 - val_loss: 0.7134 - val_accuracy: 0.6893\n",
            "Epoch 129/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4716 - accuracy: 0.7428 - val_loss: 0.6897 - val_accuracy: 0.7006\n",
            "Epoch 130/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4593 - accuracy: 0.7607 - val_loss: 0.6986 - val_accuracy: 0.6893\n",
            "Epoch 131/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4592 - accuracy: 0.7617 - val_loss: 0.7077 - val_accuracy: 0.6949\n",
            "Epoch 132/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4562 - accuracy: 0.7577 - val_loss: 0.7104 - val_accuracy: 0.6893\n",
            "Epoch 133/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4646 - accuracy: 0.7537 - val_loss: 0.6878 - val_accuracy: 0.7006\n",
            "Epoch 134/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4767 - accuracy: 0.7378 - val_loss: 0.6786 - val_accuracy: 0.6893\n",
            "Epoch 135/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4707 - accuracy: 0.7468 - val_loss: 0.6567 - val_accuracy: 0.7119\n",
            "Epoch 136/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4610 - accuracy: 0.7567 - val_loss: 0.6783 - val_accuracy: 0.6836\n",
            "Epoch 137/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4602 - accuracy: 0.7498 - val_loss: 0.7119 - val_accuracy: 0.7062\n",
            "Epoch 138/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4635 - accuracy: 0.7478 - val_loss: 0.7064 - val_accuracy: 0.6893\n",
            "Epoch 139/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4713 - accuracy: 0.7498 - val_loss: 0.7788 - val_accuracy: 0.6723\n",
            "Epoch 140/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4710 - accuracy: 0.7547 - val_loss: 0.6991 - val_accuracy: 0.6893\n",
            "Epoch 141/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4629 - accuracy: 0.7587 - val_loss: 0.6875 - val_accuracy: 0.7006\n",
            "Epoch 142/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4617 - accuracy: 0.7527 - val_loss: 0.6974 - val_accuracy: 0.7006\n",
            "Epoch 143/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4635 - accuracy: 0.7597 - val_loss: 0.7160 - val_accuracy: 0.6949\n",
            "Epoch 144/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4650 - accuracy: 0.7597 - val_loss: 0.8120 - val_accuracy: 0.6780\n",
            "Epoch 145/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4601 - accuracy: 0.7557 - val_loss: 0.7339 - val_accuracy: 0.6780\n",
            "Epoch 146/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4522 - accuracy: 0.7607 - val_loss: 0.6914 - val_accuracy: 0.7175\n",
            "Epoch 147/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4580 - accuracy: 0.7607 - val_loss: 0.7624 - val_accuracy: 0.6836\n",
            "Epoch 148/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4527 - accuracy: 0.7617 - val_loss: 0.7431 - val_accuracy: 0.6949\n",
            "Epoch 149/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4502 - accuracy: 0.7617 - val_loss: 0.7549 - val_accuracy: 0.7006\n",
            "Epoch 150/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4562 - accuracy: 0.7577 - val_loss: 0.7153 - val_accuracy: 0.7006\n",
            "Epoch 151/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4599 - accuracy: 0.7488 - val_loss: 0.6863 - val_accuracy: 0.7119\n",
            "Epoch 152/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4551 - accuracy: 0.7468 - val_loss: 0.7633 - val_accuracy: 0.6893\n",
            "Epoch 153/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4431 - accuracy: 0.7627 - val_loss: 0.7546 - val_accuracy: 0.6893\n",
            "Epoch 154/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4575 - accuracy: 0.7577 - val_loss: 0.7029 - val_accuracy: 0.7006\n",
            "Epoch 155/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4429 - accuracy: 0.7656 - val_loss: 0.7203 - val_accuracy: 0.7006\n",
            "Epoch 156/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4407 - accuracy: 0.7706 - val_loss: 0.8081 - val_accuracy: 0.6893\n",
            "Epoch 157/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4556 - accuracy: 0.7637 - val_loss: 0.7764 - val_accuracy: 0.7006\n",
            "Epoch 158/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4430 - accuracy: 0.7676 - val_loss: 0.7265 - val_accuracy: 0.7062\n",
            "Epoch 159/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4374 - accuracy: 0.7666 - val_loss: 0.7683 - val_accuracy: 0.7119\n",
            "Epoch 160/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4400 - accuracy: 0.7666 - val_loss: 0.8195 - val_accuracy: 0.6893\n",
            "Epoch 161/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4585 - accuracy: 0.7587 - val_loss: 0.6932 - val_accuracy: 0.7175\n",
            "Epoch 162/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4400 - accuracy: 0.7776 - val_loss: 0.7429 - val_accuracy: 0.7006\n",
            "Epoch 163/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4369 - accuracy: 0.7766 - val_loss: 0.7597 - val_accuracy: 0.7006\n",
            "Epoch 164/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4372 - accuracy: 0.7716 - val_loss: 0.8097 - val_accuracy: 0.7006\n",
            "Epoch 165/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4386 - accuracy: 0.7656 - val_loss: 0.7922 - val_accuracy: 0.7119\n",
            "Epoch 166/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4359 - accuracy: 0.7746 - val_loss: 0.7392 - val_accuracy: 0.6949\n",
            "Epoch 167/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4436 - accuracy: 0.7686 - val_loss: 0.7362 - val_accuracy: 0.6893\n",
            "Epoch 168/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4527 - accuracy: 0.7607 - val_loss: 0.7982 - val_accuracy: 0.6723\n",
            "Epoch 169/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4459 - accuracy: 0.7666 - val_loss: 0.8363 - val_accuracy: 0.6667\n",
            "Epoch 170/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4503 - accuracy: 0.7726 - val_loss: 0.7938 - val_accuracy: 0.6949\n",
            "Epoch 171/200\n",
            "16/16 [==============================] - 50s 3s/step - loss: 0.4386 - accuracy: 0.7696 - val_loss: 0.7652 - val_accuracy: 0.6949\n",
            "Epoch 172/200\n",
            "16/16 [==============================] - 49s 3s/step - loss: 0.4318 - accuracy: 0.7706 - val_loss: 0.7714 - val_accuracy: 0.6893\n",
            "Epoch 173/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4303 - accuracy: 0.7766 - val_loss: 0.7865 - val_accuracy: 0.6949\n",
            "Epoch 174/200\n",
            "16/16 [==============================] - 42s 3s/step - loss: 0.4271 - accuracy: 0.7716 - val_loss: 0.8759 - val_accuracy: 0.6836\n",
            "Epoch 175/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4607 - accuracy: 0.7656 - val_loss: 0.6851 - val_accuracy: 0.6667\n",
            "Epoch 176/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4997 - accuracy: 0.7438 - val_loss: 0.5967 - val_accuracy: 0.6610\n",
            "Epoch 177/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4812 - accuracy: 0.7478 - val_loss: 0.6034 - val_accuracy: 0.6836\n",
            "Epoch 178/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4687 - accuracy: 0.7577 - val_loss: 0.5876 - val_accuracy: 0.7006\n",
            "Epoch 179/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4651 - accuracy: 0.7507 - val_loss: 0.6085 - val_accuracy: 0.7062\n",
            "Epoch 180/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4611 - accuracy: 0.7637 - val_loss: 0.6628 - val_accuracy: 0.7175\n",
            "Epoch 181/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4649 - accuracy: 0.7498 - val_loss: 0.6221 - val_accuracy: 0.7006\n",
            "Epoch 182/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4490 - accuracy: 0.7637 - val_loss: 0.6409 - val_accuracy: 0.7175\n",
            "Epoch 183/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4455 - accuracy: 0.7676 - val_loss: 0.6442 - val_accuracy: 0.7119\n",
            "Epoch 184/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4459 - accuracy: 0.7676 - val_loss: 0.6422 - val_accuracy: 0.7232\n",
            "Epoch 185/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4423 - accuracy: 0.7716 - val_loss: 0.6540 - val_accuracy: 0.7119\n",
            "Epoch 186/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4492 - accuracy: 0.7646 - val_loss: 0.6869 - val_accuracy: 0.6949\n",
            "Epoch 187/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4465 - accuracy: 0.7577 - val_loss: 0.6699 - val_accuracy: 0.7062\n",
            "Epoch 188/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4378 - accuracy: 0.7825 - val_loss: 0.6655 - val_accuracy: 0.7119\n",
            "Epoch 189/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4358 - accuracy: 0.7736 - val_loss: 0.7271 - val_accuracy: 0.6836\n",
            "Epoch 190/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4549 - accuracy: 0.7656 - val_loss: 0.6649 - val_accuracy: 0.7062\n",
            "Epoch 191/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4533 - accuracy: 0.7617 - val_loss: 0.6590 - val_accuracy: 0.7119\n",
            "Epoch 192/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4401 - accuracy: 0.7706 - val_loss: 0.7132 - val_accuracy: 0.6949\n",
            "Epoch 193/200\n",
            "16/16 [==============================] - 43s 3s/step - loss: 0.4595 - accuracy: 0.7646 - val_loss: 0.6538 - val_accuracy: 0.7006\n",
            "Epoch 194/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4467 - accuracy: 0.7696 - val_loss: 0.6656 - val_accuracy: 0.6949\n",
            "Epoch 195/200\n",
            "16/16 [==============================] - 47s 3s/step - loss: 0.4454 - accuracy: 0.7786 - val_loss: 0.6699 - val_accuracy: 0.7006\n",
            "Epoch 196/200\n",
            "16/16 [==============================] - 48s 3s/step - loss: 0.4510 - accuracy: 0.7637 - val_loss: 0.6841 - val_accuracy: 0.7062\n",
            "Epoch 197/200\n",
            "16/16 [==============================] - 45s 3s/step - loss: 0.4434 - accuracy: 0.7726 - val_loss: 0.6664 - val_accuracy: 0.6949\n",
            "Epoch 198/200\n",
            "16/16 [==============================] - 46s 3s/step - loss: 0.4433 - accuracy: 0.7756 - val_loss: 0.6742 - val_accuracy: 0.7006\n",
            "Epoch 199/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4284 - accuracy: 0.7716 - val_loss: 0.6814 - val_accuracy: 0.7119\n",
            "Epoch 200/200\n",
            "16/16 [==============================] - 44s 3s/step - loss: 0.4291 - accuracy: 0.7795 - val_loss: 0.6989 - val_accuracy: 0.6949\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_dict, test_x_t1, test_y, _, _ ,_ = convertText2Feature(twoClassCleanDevDF, 'T1')\n",
        "cat_dict, test_x_t2, test_y, _, _ ,_ = convertText2Feature(twoClassCleanDevDF, 'T2')"
      ],
      "metadata": {
        "id": "tglkxCvq5y9T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_probs = model.predict([test_x, test_x_t1, test_x_t2])\n",
        "preds = np.argmax(predicted_probs, axis=-1)"
      ],
      "metadata": {
        "id": "ggitczqT1wTg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "124d921b-ce6e-496a-9e8d-34c91141f575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(test_y, preds))"
      ],
      "metadata": {
        "id": "YpkG8_Jy1wTh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "386aad67-bd5d-45f7-dc6f-474306d50600"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.71      0.59        78\n",
            "           1       0.51      0.31      0.38        78\n",
            "\n",
            "    accuracy                           0.51       156\n",
            "   macro avg       0.51      0.51      0.49       156\n",
            "weighted avg       0.51      0.51      0.49       156\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(test_y, preds))"
      ],
      "metadata": {
        "id": "bA7M_eVt68L-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1508248e-d5c1-4efd-bdee-a175cc76100e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[55 23]\n",
            " [54 24]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainBertEmbeddings_sentence = getDistilBertEmbeddings(twoClassCleanTrainDF)\n",
        "testBertEmbeddings_sentence = getDistilBertEmbeddings(twoClassCleanDevDF)\n",
        "\n",
        "trainBertEmbeddings_T1 = getDistilBertEmbeddings(twoClassCleanTrainDF, 'T1')\n",
        "testBertEmbeddings_T1 = getDistilBertEmbeddings(twoClassCleanDevDF, 'T1')\n",
        "\n",
        "trainBertEmbeddings_T2 = getDistilBertEmbeddings(twoClassCleanTrainDF, 'T2')\n",
        "testBertEmbeddings_T2 = getDistilBertEmbeddings(twoClassCleanDevDF, 'T2')\n",
        "\n",
        "trainBertY = twoClassCleanTrainDF['relation_type'].apply(lambda x: cat_dict[x]).values\n",
        "testBertY = twoClassCleanDevDF['relation_type'].apply(lambda x: cat_dict[x]).values"
      ],
      "metadata": {
        "id": "ngSrDA177qu8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(trainBertEmbeddings_sentence.shape)\n",
        "print(testBertEmbeddings_sentence.shape)\n",
        "print(trainBertEmbeddings_T1.shape)\n",
        "print(testBertEmbeddings_T1.shape)\n",
        "print(trainBertEmbeddings_T2.shape)\n",
        "print(testBertEmbeddings_T2.shape)"
      ],
      "metadata": {
        "id": "GoQmwRlZ8GAb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2f0223d-a94e-482c-98e3-f260178172f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1184, 768)\n",
            "(156, 768)\n",
            "(1184, 768)\n",
            "(156, 768)\n",
            "(1184, 768)\n",
            "(156, 768)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_x, val_x, train_y, val_y = train_test_split(\n",
        "    np.append(np.append(trainBertEmbeddings_sentence, trainBertEmbeddings_T1, axis=1), trainBertEmbeddings_T2, axis=1), \n",
        "    trainBertY, \n",
        "    test_size=0.15, random_state=42)"
      ],
      "metadata": {
        "id": "LNMBLXMQ8Dar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bertModel = Sequential()\n",
        "input_dim, output_dim = trainBertEmbeddings.shape\n",
        "\n",
        "# Keras Embedding layer with Word2Vec weights initialization\n",
        "bertModel.add(Embedding(input_dim=input_dim,\n",
        "                    output_dim=output_dim,\n",
        "                    input_length=3 * 768,\n",
        "                    mask_zero=True,\n",
        "                    trainable=False))\n",
        "\n",
        "bertModel.add(Bidirectional(LSTM(100)))\n",
        "bertModel.add(Dense(n_categories, activation='softmax'))\n",
        "\n",
        "bertModel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = bertModel.fit(train_x, train_y, epochs=10, batch_size=128,\n",
        "                    validation_data=(val_x, val_y), verbose=1)"
      ],
      "metadata": {
        "id": "SXG3HO8f_R6G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b771038b-83ab-4482-8c63-071c2dfaf954"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 776s 97s/step - loss: 0.7052 - accuracy: 0.4920 - val_loss: 0.6891 - val_accuracy: 0.5449\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 755s 96s/step - loss: 0.6962 - accuracy: 0.4980 - val_loss: 0.7014 - val_accuracy: 0.4551\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 706s 89s/step - loss: 0.6941 - accuracy: 0.5080 - val_loss: 0.6947 - val_accuracy: 0.4551\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 710s 90s/step - loss: 0.6944 - accuracy: 0.4980 - val_loss: 0.6908 - val_accuracy: 0.5449\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 810s 104s/step - loss: 0.6933 - accuracy: 0.5119 - val_loss: 0.7026 - val_accuracy: 0.4551\n",
            "Epoch 6/10\n",
            "5/8 [=================>............] - ETA: 4:25 - loss: 0.6961 - accuracy: 0.5031"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_probs = bertModel.predict(np.append(np.append(testBertEmbeddings_sentence, testBertEmbeddings_T1, axis=1), testBertEmbeddings_T2, axis=1))\n",
        "preds = np.argmax(predicted_probs, axis=-1)"
      ],
      "metadata": {
        "id": "yQx4f2Ql_pJ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(testBertY, preds))"
      ],
      "metadata": {
        "id": "8ht-J8wN_pJ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-uncased',\n",
        "                                                       do_lower_case=True, is_split_into_words = False)\n",
        "\n",
        "def getFeatureEmbeddingBert(corpus):\n",
        "  maxlen = 50\n",
        "\n",
        "  maxqnans = np.int((maxlen-20)/2)\n",
        "  corpus_tokenized = [\"[CLS] \"+\n",
        "              \" \".join(tokenizer.tokenize(re.sub(r'[^\\w\\s]+|\\n', '', \n",
        "              str(txt).lower().strip()))[:maxqnans])+\n",
        "              \" [SEP]\" for txt in corpus]\n",
        "\n",
        "  masks = [[1]*len(txt.split()) + [0]*(maxlen - len(\n",
        "            txt.split())) for txt in corpus_tokenized]\n",
        "\n",
        "  \n",
        "  txt2seq = [txt + \" [PAD]\"*(maxlen-len(txt.split())) if len(txt.split()) != maxlen\n",
        "             else txt for txt in corpus_tokenized]\n",
        "      \n",
        "  idx = tokenizer(txt2seq, truncation=True, padding=True, max_length = maxlen)\n",
        "  idx = idx['input_ids']\n",
        "\n",
        "  segments = [] \n",
        "  for seq in txt2seq:\n",
        "      temp, i = [], 0\n",
        "      for token in seq.split():\n",
        "          temp.append(i)\n",
        "          if token == \"[SEP]\":\n",
        "              i += 1\n",
        "      segments.append(temp)\n",
        "  return [np.asarray(idx, dtype='int32'), \n",
        "            np.asarray(masks, dtype='int32'), \n",
        "            np.asarray(segments, dtype='int32')]\n",
        "\n",
        "X_train_Bert = getFeatureEmbeddingBert(twoClassCleanTrainDF['sentence'].dropna())\n",
        "X_test_Bert = getFeatureEmbeddingBert(twoClassCleanDevDF['sentence'].dropna())"
      ],
      "metadata": {
        "id": "-Ovol8NVCM8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
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        "outputId": "6d26cc89-7353-48cb-ef13-28504369502b"
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            "text/plain": [
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  import sys\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## inputs\n",
        "idx = layers.Input((50), dtype=\"int32\", name=\"input_idx\")\n",
        "masks = layers.Input((50), dtype=\"int32\", name=\"input_masks\")\n",
        "segments = layers.Input((50), dtype=\"int32\", name=\"input_segments\")\n",
        "## pre-trained bert\n",
        "nlp = transformers.TFBertModel.from_pretrained(\"bert-base-uncased\")\n",
        "bert_out = nlp([idx, masks, segments])\n",
        "print(bert_out.keys())\n",
        "## fine-tuning\n",
        "x = layers.GlobalAveragePooling1D()(bert_out['last_hidden_state'])\n",
        "x = layers.Dense(64, activation=\"relu\")(x)\n",
        "y_out = layers.Dense(len(np.unique(trainBertY)), \n",
        "                     activation='softmax')(x)\n",
        "## compile\n",
        "model = models.Model([idx, masks, segments], y_out)\n",
        "for layer in model.layers[:4]:\n",
        "    layer.trainable = False\n",
        "model.compile(loss='sparse_categorical_crossentropy', \n",
        "              optimizer='adam', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "W5uDXfxeKHnH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798,
          "referenced_widgets": [
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        "outputId": "5b8c4441-d87c-46fd-e4c9-4bbdabb11417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/511M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']\n",
            "- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFBertModel were initialized from the model checkpoint at bert-base-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "odict_keys(['last_hidden_state', 'pooler_output'])\n",
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_idx (InputLayer)         [(None, 50)]         0           []                               \n",
            "                                                                                                  \n",
            " input_masks (InputLayer)       [(None, 50)]         0           []                               \n",
            "                                                                                                  \n",
            " input_segments (InputLayer)    [(None, 50)]         0           []                               \n",
            "                                                                                                  \n",
            " tf_bert_model (TFBertModel)    TFBaseModelOutputWi  109482240   ['input_idx[0][0]',              \n",
            "                                thPoolingAndCrossAt               'input_masks[0][0]',            \n",
            "                                tentions(last_hidde               'input_segments[0][0]']         \n",
            "                                n_state=(None, 50,                                                \n",
            "                                768),                                                             \n",
            "                                 pooler_output=(Non                                               \n",
            "                                e, 768),                                                          \n",
            "                                 past_key_values=No                                               \n",
            "                                ne, hidden_states=N                                               \n",
            "                                one, attentions=Non                                               \n",
            "                                e, cross_attentions                                               \n",
            "                                =None)                                                            \n",
            "                                                                                                  \n",
            " global_average_pooling1d (Glob  (None, 768)         0           ['tf_bert_model[0][0]']          \n",
            " alAveragePooling1D)                                                                              \n",
            "                                                                                                  \n",
            " dense_9 (Dense)                (None, 64)           49216       ['global_average_pooling1d[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " dense_10 (Dense)               (None, 2)            130         ['dense_9[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 109,531,586\n",
            "Trainable params: 49,346\n",
            "Non-trainable params: 109,482,240\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## train\n",
        "training = model.fit(x=X_train_Bert, y=trainBertY, batch_size=64, \n",
        "                     epochs=1, shuffle=True, verbose=1, \n",
        "                     validation_split=0.1)\n",
        "\n",
        "## plot loss and accuracy\n",
        "metrics = [k for k in training.history.keys() if (\"loss\" not in k) and (\"val\" not in k)]\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True)\n",
        "ax[0].set(title=\"Training\")\n",
        "ax11 = ax[0].twinx()\n",
        "ax[0].plot(training.history['loss'], color='black')\n",
        "ax[0].set_xlabel('Epochs')\n",
        "ax[0].set_ylabel('Loss', color='black')\n",
        "for metric in metrics:\n",
        "    ax11.plot(training.history[metric], label=metric)\n",
        "ax11.set_ylabel(\"Score\", color='steelblue')\n",
        "ax11.legend()\n",
        "ax[1].set(title=\"Validation\")\n",
        "ax22 = ax[1].twinx()\n",
        "ax[1].plot(training.history['val_loss'], color='black')\n",
        "ax[1].set_xlabel('Epochs')\n",
        "ax[1].set_ylabel('Loss', color='black')\n",
        "for metric in metrics:\n",
        "     ax22.plot(training.history['val_'+metric], label=metric)\n",
        "ax22.set_ylabel(\"Score\", color=\"steelblue\")\n",
        "plt.show()\n",
        "## test\n",
        "predicted_prob = model.predict(X_test_Bert)\n",
        "predicted = [np.argmax(pred) for pred in \n",
        "             predicted_prob]"
      ],
      "metadata": {
        "id": "VMyxBt8fKk_U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "3db2437f-ade4-461b-e3d3-528bf1ea30c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/ops/structured_function.py:265: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  \"Even though the `tf.config.experimental_run_functions_eagerly` \"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17/17 [==============================] - 718s 42s/step - loss: 0.7114 - accuracy: 0.5408 - val_loss: 0.7502 - val_accuracy: 0.4370\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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u/XrguxGxN/CfwK/drs2q011uPBkPzIiINWn5bOD2iGgsk/eUiNgPODy9PlOuwIiYFhH1EVFfV+fJFqxLtbVXY50UDOsi4k6AiLgiIg5wuzarTpFB7iVgp9zysJRWznhyXZXAocA5kp4HvgecKunbABHxUvr5JnAj2T8Qs65UV+o1SK8zm62vtlfjREmPSZohqfTd2RNYJunXkh6R9N10ZmhmVSjyUHAusIekEWTBbTxwcvNMkvYCBgNzSmkRcUpu/USgPiImS6oDtoqIv0nqA3wMuKvAfTCrRlNE1HewjNuAmyJitaTPA9cBR5J9Rw8nuynrf4CbgYnAzzr4eWabhMLO5CKiCTgHmAUsAH4VEU9KuljScbms44HpERFVFLsZMEvSY8B8suD5006uullnq9irERFLImJ1WrwaODC9bwTmp67OJuA3wAEF19esZqi62NKzDRgwIN56662urobVKEkrI2JAK+vrgEXAh8mC21zg5Ih4MpdnaES8kt5/HDg/Ig5JXZN/Bv4xIl6TdC0wLyKudLu2olVq2z2Br1ybFSwimiSVejV6A9eUejXIAtZM4Muph6MJWErWJUlErJF0HnB3upP4Ydx7YVY1n8mZdVBXHe26XVvRauFMrrs8QmBmZtbpHOTMzKxmOciZmVm7VBquLpfvREkhqT4tD5f0dm4Yu58UVUffeGJmZm1WzSD8Kd8g4CvAg82KeCYiRhddT5/JmZlZe1Q7XN1U4FJg1casXImDnJmZtaS1IesqDlcn6QBgp4hoKFP2iDRU3R8lHd75Vc+4u9LMzFrS7iHr0mwyPyA989nMK8DOEbEkTav2G0n7RMQb7a9qeT6TMzOz9qg0XN0gYF9gdhps/xBgpqT6iFgdEUsAIuJh4Bmywcg7nYOcmZm1x7pB+CX1JRuHeGZpZUQsj4htImJ4RAwHHgCOi4h5krYtzaYhaVdgD+DZIirp7kozM2uzKoera8kHgYslvQusBc6KiKVF1NPDepl1kIf1slrlYb3MzMy6MQc5MzOrWQ5yZmZWswoNcpXGNZN0WW7sskWSljVbv4WkRkk/zqUdKOnxVOblaY4tMzOzDRQW5HLjmh0DjAQmSBqZzxMR50bE6DR+2RXAr5sVMxW4r1nafwKfI7vldA/g6AKqb2ZmNaDIM7lqxzUrmQDcVFpIT8FvB/whlzYU2CIiHojsttDrgROKqLyZmfV8RQa5iuOalUjaBRgB3JOWewHfB84rU2ZjlWWeWRpvrampqV07YNbduF2btU13ufFkPDAjItak5bOB2yOisZVtWhUR0yKiPiLq6+r8zLvVBrdrs7Yp8ltSaVyzvPHAF3PLhwKHSzobGAj0lbQC+FEqp5oyzcxsE1dkkFs3rhlZIBoPnNw8k6S9gMHAnFJaRJySWz8RqI+IyWn5DUmHkE3AdyrZDStmZmYbKKy7MiKagNK4ZguAX5XGNZN0XC7reGB6VD++2NnA1cBispGrf9+J1TYzsxrisSvNOshjV1qt8tiVZmZm3ZiDnJmZ1SwHOTMzq1kOcmZmVrMc5Mw2gioGK58o6bXcgOVn5NatyaW3NtuymTXjIRPMCpYbrPwosqHo5kqaGRFPNct6c0ScU6aIt9Mg5mbWRj6TMyteWwcrN7NO4iBnVrxqBys/UdJjkmZIyg+J1y8NyvyAJM+6YdYGDnJmHVdXmhkgvc5sRxm3AcMjYhRwJ3Bdbt0uEVFPNizeDyXt1gl1Ntsk+JqcWcc1pSDUkoqDlUfEktzi1cB3cuteSj+flTQbGEM2pJ2ZVeAzObPirRusXFJfsvFa17tLMk0IXHIc2XivSBosabP0fhvg74HmN6yYWQt8JmdWsIhoklQarLw3cE1psHJgXkTMBL6cBi5vApYCE9PmewNXSVpLdlD67TJ3ZZpZCzxAs1kHeYBmq1UeoNnMzKwbc5AzM7Oa5SBnZmY1q9AgV8V4fZflxuRbJGlZSt9F0p9T+pOSzsptMzuVWdrufUXug5mZ9VyF3V1ZzXh9EXFuLv+XyJ7/AXgFODQiVksaCDyRtn05rT8lIuYVVXczM6sNRZ7JtXW8vgnATQAR8U5ErE7pmxVcTzMzq1FFBo9qx+tD0i7ACOCeXNpOkh5LZVyaO4sDuDZ1VU6RpM6vupmZ1YLucoY0HpgREWtKCRHxYhrHb3fgNEnbpVWnRMR+wOHp9ZlyBUo6szSWYFNTU8HVN9s43K7N2qbIIFdxvL6c8aSuyubSGdwTZAEtP47fm8CNZN2i5babFhH1EVFfV+eBXaw2uF2btU2RQa7ieH0AkvYCBgNzcmnDJPVP7wcDHwAWSqpL4/chqQ/wMbIAaGZmtoHCDgWrHK8PsuA3PdYfX2xv4PuSAhDwvYh4XNIAYFYKcL2Bu4CfFrUPZmbWs3nsSrMO8tiVVqsqtW1JRwM/IjvpuDoivt1CvhOBGcD7849/SdqZbFaNiyLie51a+aS73HhiZmY9SO5Z6GOAkcAESSPL5BsEfAV4sEwxPwB+X2Q9HeTMzKw9qn0WeipwKbAqnyjpBOA54MkiK+kgZ2ZmLakrPbKSXmfm1lV8FlrSAcBOEdHQLH0gcD7wrwXVex3fg2xmZi1pioj69mwoqRdZd+TEMqsvAi6LiBVFj+fhIGdmZu1R6VnoQcC+wOwUyLYHZko6DjgY+KSk7wBbAWslrYqIH3d2JR3kzGrM6nfX8Nflb7PTNgO7uipW29Y9C00W3MYDJ5dWRsRyYJvSsqTZwHnp7srDc+kXASsqBbixUxv6AzvPmjJuYVsq6WtyZjXkgUX/y9nT7ucbNz0EwDOvLueb0+d2ca2sFkVEE1B6FnoB8KvSs9DpbK3TjJ3acCwwH7gjLY8eO7Vhg8FFynGQM6shv/jjIi4//e8ZuFkfAHbbfkteXfZ2F9fKalVE3B4Re0bEbhHxrZR2YW6wj3zeI8pNkRYR1TwjdxHZ3ZzLAGZNGTefbFD/iqoKcpIGpIuISNpT0nFp1BEz6ySd8T2r692LAf3W38TzdFgNeHfWlHHLm6VVNZJJtdfk7gMOT+NI/oGsL/Yk4JSqq2hmlXT4e7bLtgO55/GXWBvBS0ve4jdzn2PvYYMLqq7ZRvPk2KkNJwO9x05t2AP4MvCnajastrtSEbES+ATwHxHxKWCfdlXVzFrS4e/Z2UfvywuvvUmf3r349q2PMGCzPnxh7AaDUJj1NF8i+y6sJpt9Zjnwf6rZsNozOUk6lOyI8vSU1ruNlTSz1nXoe7ZmbTDlpof47qmHMunIQupnttGNndrQG2iYNWXch4BvtHX7as/k/g9wAXBruntmV+Detn6YmbWqQ9+z3r1EL4m3Vr1bWAXNNrZZU8atAdaOndqwZXu2b/MsBOnC+MCIeKM9H9gVPFq7FamIWQiq+Z6Va9ffvHkez7y6nAN23YZ+fd7rqDn7aF9dsLbrqhk2mhs7teG3wBjgTmBdo581ZdyXK21bVXelpBuBs4A1ZBfDt5D0o4j4brtqbGYb6Izv2Qf22p4P7LV9UVU06yq/Tq82q+pMTtL8iBgt6RTgAGAy8HBEjGrPh25sPpOzInXW0W5bv2cttet316zlpSVZ+rCtB1DX24/DWvt0lzM5gLFTG/oCe6bFhbOmjKuqX77a1t8nPa9zAjAzIt6lymcUzCybXFLSQkmLJU0us34isK+k+WRzdK3Jf88kbSGpUVKrQx89+vwSPnvlbH78+ye44vdP8Nn/mM3jLywpYI/MNp6xUxuOAJ4m+278B7Bo7NSGD1azbbV3V14FPA88CtwnaReg4jW5SrPGSroM+FBa3Bx4X0Rslcq/lSwI9wGuiIifpG0OBH4O9AduB74Sm8L05tZj5SaXPIpsOpK5kmZGxFPNsv4J2C39PL/Z92wq2XN0rZp251P828kHrRu3snHJCv79149w5ecOr7ClWbf2feAjpXErx05t2BO4CTiw0oZVnclFxOURsWNEfDQyL/BecCqrmlljI+LciBgdEaOBK3ivz/UV4NCUfjAwWdIOad1/Ap8D9kivo6vZB7MuVO3kko+V+56lA7vtyB4Qb9WatbHewMzDth7ImrU+BrQer09+YOZZU8YtIjsBqqjaG0+2BL4JlE4P/whcTPZAXkvWfbFTGaUvdvOj15IJ6TNI/whKNiMFY0lDgS0i4oG0fD1ZF2qh06ebVVAnKT8m37SImJZbLje55MFlyvmkpJPIej5eBO4iO4P7PvBPwD9WqsgeQ7fkstse48j9srkr73niJfYY2q47r826k3ljpzZcDdyQlk8BNhgHs5xquyuvAZ4APp2WPwNcSzYyQ0uq/WKTumVGAPfk0nYCGoDdga9GxMuS6lM5+TLXm4k2t/2ZwJkAffv2baWaZh3W7oklc24jOwh8DHgHGEfWVTkbuDEiGtOcXPuUAmq5dv2lj+7LbfNe4LcPPQfAvjsP4WP1u3SwamZd7gvAF8mG8wK4n+zaXEXVBrndIuLE3PK/pgvknWU8MCMi1pQSIuJFYFTqpvyNpBltKTAdSU+D7C60TqyrWVtVmlySiFgiaUREfDx19X8tIg6T9BXgHElnAwOBvmRDfk0u167Xrg1OOGgEJx6yK5B1X767Zm1Bu2W20dQBP5o1ZdwPYN0oKJtVs2G1d1e+LekDpQVJfw9Umr+j4hc7ZzzZRcQNRMTLZGeRh6fth1VZpll3sW5ySUl9ydr7elORpK740vfsOGBB+p4tjIidI2I4cB5wfURscHdmyfk3PMg7TeuOFXmnaQ2Tb3ig03fIbCO7m+xmw5L+ZN35FVUb5M4CrpT0vKTngR8Dn6+wTcUvNoCkvYDBwJxc2jBJ/dP7wcAHyL7srwBvSDpEWd/NqcBvq9wHsy5R5eSSXwa2JRvR4UayA8RqvmfreadpLf37vtdB079vHavf9Zmc9Xj9Zk0Zt6K0kN5vXs2G1d5d+WhE7A+MAkZFxBig1SFg2zBr7HhgerPHAPYGHpT0KNlNLt+LiMfTurOBq4HFwDP4phPrASpNLhkRF0TE7hHRn+xOyr2bf88i4ucRcU5rn9OvT2+efuW9+8EWvbyMzer8MLj1eG+NndpwQGlh7NSGeir3JgLtGLty3YbS/0TEzu3aeCPziCdWpCJHhWjte1auXS98eRn//utHGDIwu1yxdMVqvnHiAb7D0tqlu4x4MnZqw/vJHr15OSUNBU6aNWXcw5W27cghnucbNiteVd+zhS8vY+mKVfzdDltx9Rf+gX/YZwfqeveifrdt2W6r/pULMOuGxk5teP/YqQ3bz5oybi6wF3Az8C5wB/BcNWV0JMj5jkWz4lX1Pbu84fF1Y1QuaHyd6f+1mGPrd2FQvz786HePV9jarNu6iuyRGoBDga+TDTLyOunu+UpafYRA0puU/5KJ9e90MbN26ozv2Zq1wRb9s+fm/vjUK3z0gJ05fO+hHL73UL4w7f7Oq6zZxtV71pRxS9P7k4Bps6aMuwW4ZezUhqoeY2v1TC4iBkXEFmVegyKi2mfszKwVnfE9WxvBmrXZXZSPPPc3Rg/fet26UrpZD9R77NSG0nfgw+QGDKHK57wdqMxqwBH77MB51z3AFpv3ZbO63uy78xAAXlr6FgM2q2qIP7Pu6Cbgj2OnNvyN7G7K+wHGTm3YndaHlVzHQc6sBpx8+B6MGbENS1es5sBdtyENAUZEeFZw67FmTRn3rbFTG+4mu5vyD7OmjCt16/cCvlRNGe1+hKAn8SMEVqSuus3a7dqK1l0eIegIPyVqZmY1y0HOzMxqloOcmZnVLAc5MzOrWQ5yZmZWsxzkzMysZjnImZlZzXKQMzOzmuUgZ2ZmNavQICfpaEkLJS2WNLnM+sskzU+vRZKWpfTRkuZIelLSY5JOym3zc0nP5bYbXeQ+mJlZz1XY2JWSepPN+3MU0AjMlTQzIp4q5YmIc3P5vwSMSYsrgVMj4mlJOwAPS5oVEcvS+q9GxIyi6m5mZrWhyDO5g4DFEfFsRLxDNnX58a3kn0A24jQRsSgink7vXwb+CmxbYF3NzKyNKvXW5fKdKCkk1aflg3K9cY9K+nhRdSwyyO0IvJhbbkxpG5C0CzCC9ecKKq07COgLPJNL/lbqxrxM0madV2UzM6tGrrfuGGAkMEHSyDL5BgFfAR7MJelKZyoAABDdSURBVD8B1EfEaOBo4CpJhfQsdpcbT8YDMyJiTT5R0lDgF8CkiCjN/HgBsBfwfmAIcH65AiWdKWmepHlNTU3F1dxsI3K7tm6k2t66qcClwKpSQkSsjIhSA+4HFDYdTpFB7iVgp9zysJRWznhSV2WJpC2ABuAbEfFAKT0iXonMauBasl/0BiJiWkTUR0R9XZ2nzbPa4HZt3UjF3jpJBwA7RURD840lHSzpSeBx4Kxc0OtURQa5ucAekkZI6ksWyGY2zyRpL2AwMCeX1he4Fbi++Q0m6ewOZbNCnkB22mtmZp2vrtRzkF5nVruhpF7AD4B/Lrc+Ih6MiH3IeuUukNSvc6q8vsIOBSOiSdI5wCygN3BNRDwp6WJgXkSUAt54YHqsP3vrp4EPAltLmpjSJkbEfOCXkrYFBMwHzipqH8zMNnFNEVHfwrpKvXWDgH2B2Wmm+u2BmZKOi4h5pUwRsUDSipR3Hp3MM4ObdZBnBrda1VrbTjeKLAI+TBbc5gInR8STLeSfDZwXEfMkjQBeTCdDu5D15I2KiL919j64U9/MzNqsDb115XwAmCzpXWAtcHYRAQ58JmfWYT6Ts1rVVW27M3WXRwjMaloVQ9xNlPRa7gHZM1L6LpL+nNKelORr0GZt4O5Ks4JVM8RdcnNEnNMs7RXg0IhYLWkg8ETa9uXia27W8/lMzqx4bR3ibp2IeCc9EwqwGf7OmrWJvzBmHVfpWaJqh7g7MQ1XN0PSuluzJe0k6bFUxqU+izOrnrsrzTqutWeJqnUbcFPqlvw8cB1wJEBEvAiMSjNy/EbSjIj43w5+ntkmwWdyZsWrOMRdRCzJdUteDRzYvJB0BvcEcHhB9TSrOQ5yZsWrOMRdabi65DhgQUofJql/ej+Y7PmihRul1mY1wN2VZgWr8qHZL0s6DmgClgIT0+Z7A9+XFGRD2X0vIh7f6Dth1kP5YXCzDvLD4Far/DC4mZlZN+YgZ2ZmNctBzszMapaDnJmZ1SwHOTMzq1kOcmZmVrMKDXJVTC9yWW5qkUWSlqX00ZLmpKlFHpN0Um6bEZIeTGXenB6uNTMz20BhQS43vcgxwEhggqSR+TwRcW5EjI6I0cAVwK/TqpXAqRGxD3A08ENJW6V1lwKXRcTuwOvA6UXtg5mZ9WxFnsm1dXqRCcBNABGxKCKeTu9fBv4KbCtJZIPWzkjbXAecUFD9zcyshysyyFU7vQiSdgFGAPeUWXcQ0Bd4BtgaWBYRTVWUeWZp6pOmpqZyWcx6HLdrs7bpLjeejAdmRMSafGIatPYXwKSIWNuWAiNiWkTUR0R9XZ2H6LTa4HZt1jZFBrmK04vkjCd1VZZI2gJoAL4REQ+k5CXAVpJK3+7WyjQzs01ckUGu4vQiAJL2AgYDc3JpfYFbgesjonT9jchGk74X+GRKOg34bWF7YGZmPVphQS5dNytNL7IA+FVpepE0pUjJeGB6rD8dwqeBDwITc48YjE7rzgf+r6TFZNfoflbUPpiZWc/mqXbMOshT7Vit8lQ7ZmZm3ZiDnJmZ1SwHOTMzq1kOcmZmVrMc5MzMrGY5yJmZWc1ykDMzs5rlIGdmZjXLQc7MzGqWg5yZmdUsBzkzM6tZDnJmZlazHOTMNgJJR0taKGmxpMll1k+U9Fpu1o0zUvpoSXMkPSnpMUknbfzam/VcDnJmBZPUG7gSOAYYCUyQNLJM1psjYnR6XZ3SVgKnRsQ+wNHADyVttVEqblZBpYO3XL4TJYWk+rR8lKSHJT2efh5ZVB3rKmcxsw46CFgcEc8CSJoOHA88VWnDiFiUe/+ypL8C2wLLCqqrWVVyB29HAY3AXEkzI+KpZvkGAV8BHswl/w04NrXpfcnmHd2xiHr6TM6s4+okzcu9zmy2fkfgxdxyI+W/0CemLskZknZqvlLSQUBf4JlOq7lZ+607eIuId4DSwVtzU4FLgVWlhIh4JCJeTotPAv0lbVZEJQsNclVch7gsdw1ikaRluXV3SFom6XfNtvm5pOfKzBhu1lWaIqI+95rWjjJuA4ZHxCjgTuC6/EpJQ4FfAJMiYm3Hq2xWldYO4CoevEk6ANgpIhpa+YwTgT9HxOpOq3VOYd2V1ZzKRsS5ufxfAsbkivgusDnw+TLFfzUiZhRScbPO9xKQPzMbltLWiYglucWrge+UFiRtATQA34iIBwqsp1lzTRFR354NJfUCfgBMbCXPPmRneR9pV+2qUOSZXLWnsiUTgJtKCxFxN/BmgfUz21jmAntIGiGpLzAemJnPkM7USo4DFqT0vsCtwPU+sLNuptLB2yBgX2C2pOeBQ4CZuZtPhpG17VMjorAu+CJvPCl3KntwuYySdgFGAPdUWfa3JF0I3A1MLneam06rzwTo27dvG6pt1rkioknSOWQX13sD10TEk5IuBuZFxEzgy5KOA5qApbx39Ptp4IPA1pImAlsDbwBvu11bF1t38EYW3MYDJ5dWRsRyYJvSsqTZwHkRMS/dIdxA9v/7v4usZHe5u3I8MCMi1lSR9wLgVbIL8NOA84GLm2dK10WmAQwYMCA6r6pmbRcRtwO3N0u7MPf+ArK23Xy7G4AbypXpdm1dqcqDt5acA+wOXJhOWAA+EhF/7ex6FhnkKl6HyBkPfLGaQiPilfR2taRrgfPaXUMzM2u3SgdvzdKPyL2/BLik0MolRV6Tq3gdAkDSXsBgYE41hZauXUgScALwRKfV2MzMakphZ3JtOJUdD0yPiPW6XiTdD+wFDJTUCJweEbOAX0raFhAwHzirqH0wM7OeTc1iS00aMGBAvPXWW11dDatRklZGxICN/blu11a0rmrbnckjnpiZWc1ykDMzs5rlIGdmZjXLQc7MzGqWg5yZmdUsBzkzM6tZDnJmZlazHOTMzKxmOciZmVnNcpAzM7Oa5SBnZmY1y0HOzMxqloOcmZnVLAc5MzOrWQ5yZmZWsxzkzMysZjnImZlZzSo0yEk6WtJCSYslTS6z/jJJ89NrkaRluXV3SFom6XfNthkh6cFU5s2S+ha5D2Zm1nMVFuQk9QauBI4BRgITJI3M54mIcyNidESMBq4Afp1b/V3gM2WKvhS4LCJ2B14HTi+i/mZm1vMVeSZ3ELA4Ip6NiHeA6cDxreSfANxUWoiIu4E38xkkCTgSmJGSrgNO6MxKm5lZ7SgyyO0IvJhbbkxpG5C0CzACuKdCmVsDyyKiqYoyz5Q0T9K8pqamclnMehy3a7O26S43nowHZkTEms4qMCKmRUR9RNTX1dV1VrFmXcrt2qxtigxyLwE75ZaHpbRyxpPrqmzFEmArSaVvd2tlmpnZJq7IQ8G5wB6SRpAFovHAyc0zSdoLGAzMqVRgRISke4FPkl3jOw34baXtVq5cGZLebmF1HdAd+n26Sz3AdSmntXr035gVKekh7Rpcl3K6Sz2gG7btzqSIKK5w6aPAD4HewDUR8S1JFwPzImJmynMR0C8iJjfb9n5gL2Ag2Rnc6RExS9KuZAFuCPAI8E8RsboDdZwXEfXt3b6zdJd6gOvSnetRre5UX9el+9YDulddilBop35E3A7c3iztwmbLF7Ww7eEtpD9LduemmZlZq7rLjSdmZmadzkEOpnV1BZLuUg9wXcrpLvWoVneqr+uyoe5SD+hedel0hV6TMzMz60o+kzMzs5rlIGdmZjWrJoOcpCGS7pT0dPo5uIV8p6U8T0s6LZc+O82eUJoh4X0pfbM088HiNBPC8CLrImlzSQ2S/iLpSUnfzuWfKOm1XB3PaKHcSjNBtLhPki5I6Qslja22zFZ+F+2qi6SjJD0s6fH088jcNmX/VgXWZbikt3Of95PcNgemOi6WdHkaa7VTuW2vV263aNtu191cRNTcC/gOMDm9nwxcWibPEODZ9HNwej84rZsN1JfZ5mzgJ+n9eODmIusCbA58KOXpC9wPHJOWJwI/rvDZvYFngF3T9o8CI6vZJ7KZIx4FNiMbV/SZVF7FMguoyxhgh/R+X+Cl3DZl/1YF1mU48EQL5T4EHAII+H3pb+W2Xbtt2+26+79q8kyObLaD69L7lmYqGAvcGRFLI+J14E7g6DaUOwP4cBVHNe2uS0SsjIh7ASKbyeHPZEOZVauamSBa2qfjgekRsToingMWp/LaOrtEh+sSEY9ExMsp/Umgv6TNqvoNdHJdWipQ0lBgi4h4ILL/DNdTzAwZbtuZ7tK23a67uVoNcttFxCvp/avAdmXyVJol4dp02j4l1wjWbRPZTAjLyWZGKLouSNoKOBa4O5d8oqTHJM2QlB8ntOpyaXmfWtq26tklOrEueScCf471R7kp97cqsi4jJD0i6Y+SDs/lb6xQZmdw266yXDZO23a77uZ67DDmku4Cti+z6hv5hYgISW19TuKUiHhJ0iDgFrLJW6/vorqgbEDqm4DLIxvxBeA24KaIWC3p82RHZ0e2VEYtkLQP2aS5H8klt+lv1QleAXaOiCWSDgR+k+rVady2N622vam0667SY4NcRPxjS+sk/a+koRHxSjrd/muZbC8BR+SWh5H1gxMRL6Wfb0q6kawb4Hrem1mhMX05twSWFFmXZBrwdET8sJQQEUty668muz5SrtxKM0GU3acK21Y7u0Rn1QVJw4BbgVMj4pnSBq38rQqpS+qyWZ0+82FJzwB7pvz57rZ2z5Dhtt2j2rbbdXdX9EW/rngB32X9C+LfKZNnCPAc2UXwwen9ELLAv03K04es3/qstPxF1r9o+6si65LWXUJ2JNer2TZDc+8/DjxQptw6sgv9I3jvQvQ+zfKU3SdgH9a/OP8s2YXtimW28HvoSF22Svk/UabMsn+rAuuyLdA7vd+V7Atf+ls1v0D/Ubft2m7bbtfd/9XlFShkp7I+5ruBp4G7cn+seuDqXL7Pkl10XgxMSmkDgIeBx8guBv8o98fvB/y/lP8hYNeC6zIMCGABMD+9zkjr/j3V71HgXmCvFj7/o8AisruuvpHSLgaOq7RPZF1SzwALyd1RVa7MKv8u7aoL8C/AW7nfwXzgfa39rQqsy4nps+aT3SxxbK7MeuCJVOaPSSMKuW3XdtvuQFtyu94ILw/rZWZmNatW7640MzNzkDMzs9rlIGdmZjXLQc7MzGqWg5yZmdUsB7kaI2lNbiTx+eVGIu9A2cMlPdFZ5Zm1hdu2tUePHfHEWvR2RIzu6kqYFcBt29rMZ3KbCEnPS/pOmhfqIUm7p/Thku5Jg+HeLWnnlL6dpFslPZpeh6Wiekv6qbI5wP4gqX/K/2VJT6VypnfRbtomyG3bWuMgV3v6N+vSOSm3bnlE7Ec2akFprMArgOsiYhTwS+DylH458MeI2B84gGw0BIA9gCsjYh9gGdlICZAN6zQmlXNWUTtnmzS3bWszj3hSYyStiIiBZdKfB46MiGcl9QFejYitJf2NbKzAd1P6KxGxjaTXgGGRm/pD2SzCd0bEHmn5fKBPRFwi6Q5gBfAb4DcRsaLgXbVNjNu2tYfP5DYt0cL7tsjPd7WG967rjgOuJDsynptGODfbWNy2rSwHuU3LSbmfc9L7P5GNRg5wCnB/en838AUASb0lbdlSoZJ6ATtFNtPz+WTTd2xwxG1WILdtK8tHJLWnv6T5ueU7IqJ0q/VgSY+RHbFOSGlfIpuB+KvAa8CklP4VYJqk08mOar9ANrFiOb2BG9I/C5FNgLms0/bILOO2bW3ma3KbiHTdoj4i/tbVdTHrTG7b1hp3V5qZWc3ymZyZmdUsn8mZmVnNcpAzM7Oa5SBnZmY1y0HOzMxqloOcmZnVrP8PsSH7Q78RxXsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "classes = np.unique(testBertY)\n",
        "y_test_array = pd.get_dummies(testBertY, drop_first=False).values\n",
        "    \n",
        "## Accuracy, Precision, Recall\n",
        "accuracy = metrics.accuracy_score(testBertY, predicted)\n",
        "# auc = metrics.roc_auc_score(y_test_liar, predicted_prob, \n",
        "#                             multi_class=\"ovr\")\n",
        "print(\"Accuracy:\",  round(accuracy,2))\n",
        "# print(\"Auc:\", round(auc,2))\n",
        "print(\"Detail:\")\n",
        "print(metrics.classification_report(testBertY, predicted))\n",
        "    \n",
        "## Plot confusion matrix\n",
        "cm = metrics.confusion_matrix(testBertY, predicted)\n",
        "fig, ax = plt.subplots()\n",
        "sns.heatmap(cm, annot=True, fmt='d', ax=ax, cmap=plt.cm.Blues, \n",
        "            cbar=False)\n",
        "ax.set(xlabel=\"Pred\", ylabel=\"True\", xticklabels=classes, \n",
        "       yticklabels=classes, title=\"Confusion matrix\")\n",
        "plt.yticks(rotation=0)\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
        "## Plot roc\n",
        "for i in range(len(classes)):\n",
        "    fpr, tpr, thresholds = metrics.roc_curve(y_test_array[:,i],  \n",
        "                           predicted_prob[:,i])\n",
        "    ax[0].plot(fpr, tpr, lw=3, \n",
        "              label='{0} (area={1:0.2f})'.format(classes[i], \n",
        "                              metrics.auc(fpr, tpr))\n",
        "               )\n",
        "ax[0].plot([0,1], [0,1], color='navy', lw=3, linestyle='--')\n",
        "ax[0].set(xlim=[-0.05,1.0], ylim=[0.0,1.05], \n",
        "          xlabel='False Positive Rate', \n",
        "          ylabel=\"True Positive Rate (Recall)\", \n",
        "          title=\"Receiver operating characteristic\")\n",
        "ax[0].legend(loc=\"lower right\")\n",
        "ax[0].grid(True)\n",
        "    \n",
        "## Plot precision-recall curve\n",
        "for i in range(len(classes)):\n",
        "    precision, recall, thresholds = metrics.precision_recall_curve(\n",
        "                 y_test_array[:,i], predicted_prob[:,i])\n",
        "    ax[1].plot(recall, precision, lw=3, \n",
        "               label='{0} (area={1:0.2f})'.format(classes[i], \n",
        "                                  metrics.auc(recall, precision))\n",
        "              )\n",
        "ax[1].set(xlim=[0.0,1.05], ylim=[0.0,1.05], xlabel='Recall', \n",
        "          ylabel=\"Precision\", title=\"Precision-Recall curve\")\n",
        "ax[1].legend(loc=\"best\")\n",
        "ax[1].grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kvto2fpbKXrW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "outputId": "6aa88e34-4225-40e7-eae8-3418ecd07b54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.55\n",
            "Detail:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.77      0.63        78\n",
            "           1       0.59      0.33      0.43        78\n",
            "\n",
            "    accuracy                           0.55       156\n",
            "   macro avg       0.56      0.55      0.53       156\n",
            "weighted avg       0.56      0.55      0.53       156\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}