CCKProject_Unbalanced_08_Tunnel.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ImenMasmoudiEm/CCKInternshipProject/blob/main/CCKProject_Unbalanced_08_Tunnel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Text Classification: Primary work"
      ],
      "metadata": {
        "id": "Iiruy8t57ISt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Connecting to drive & importing data"
      ],
      "metadata": {
        "id": "6A7gIEXP7oqV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KwJf5Jnk9iw8",
        "outputId": "acc2006d-d022-41da-cc6a-9ac1c397f00e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "u6Xmp6Sf7Ay4"
      },
      "outputs": [],
      "source": [
        "import os \n",
        "os.chdir('/content/drive/MyDrive/All/Projects/Ing Internship/Data')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data=pd.read_excel(\"/content/drive/MyDrive/All/Projects/Ing Internship/Data/final-dataset.xlsx\")\n",
        "data"
      ],
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      "execution_count": 3,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Preprocessing"
      ],
      "metadata": {
        "id": "E78jh1K4ATql"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "1/ Changing the Class into bin"
      ],
      "metadata": {
        "id": "4WPH66dgAsdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['classe']=data['classe'].replace(\"hate\", int(2))\n",
        "data['classe']=data['classe'].replace(\"normal\", int(0)) \n",
        "data['classe']=data['classe'].replace(\"abusive\", int(1))"
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      "metadata": {
        "id": "g0G8MT47A3Z_"
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      "outputs": []
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              "                                             commentaire  classe\n",
              "0      مبروك و سامحونا لعجزنا التام. عقبال اللي جوه. ...       0\n",
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              "4      وهكذا رجال الشو اللي محرومين من عمل برنامج الغ...       2\n",
              "...                                                  ...     ...\n",
              "23028    يلا ولا خنزير الله يرحم صدام حسين لي كان داعسكن       2\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.figure(1 , figsize = (15 , 5))\n",
        "sns.countplot(y = 'classe' , data = data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QWAQiWYVAzQN",
        "outputId": "42d06b46-edb9-437b-af6b-2d55096c1072"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.loc[data.classe==0].groupby('classe')[\"classe\"].count())\n",
        "print(data.loc[data.classe==1].groupby('classe')[\"classe\"].count())\n",
        "print(data.loc[data.classe==2].groupby('classe')[\"classe\"].count())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWndFEOiC4pP",
        "outputId": "41b65522-5e61-48c9-842b-ccc6090bb47f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classe\n",
            "0    12353\n",
            "Name: classe, dtype: int64\n",
            "classe\n",
            "1    3850\n",
            "Name: classe, dtype: int64\n",
            "classe\n",
            "2    6830\n",
            "Name: classe, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2/ NLP Data Preparations: Data Balancing"
      ],
      "metadata": {
        "id": "nWDDcwDwA32H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import numpy as np\n",
        "\n",
        "sentences = data['commentaire']\n",
        "labels = data['classe']\n",
        "\n",
        "labels = [int(i) for i in labels]"
      ],
      "metadata": {
        "id": "BgMfbCVpBAOB"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data=[[i,j] for i,j in zip(sentences , labels)]\n",
        "\n",
        "data=pd.DataFrame(data,columns=[\"sentences\",\"classe\"])\n",
        "\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zQgP6WSIbrNm",
        "outputId": "9cabdb05-bd05-4dd1-cd0b-1d9b24cf225d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               sentences  classe\n",
              "0      مبروك و سامحونا لعجزنا التام. عقبال اللي جوه. ...       0\n",
              "1      كلنا بره ومش هنبطل نزايد على العجايز الي جابون...       2\n",
              "2              بدل ما انت قاعد بره كده تعالي ازرع الصحرا       0\n",
              "3          قذر اتفووو ماتيجى مصر وتورينا نفسك كدا ياجبان       2\n",
              "4      وهكذا رجال الشو اللي محرومين من عمل برنامج الغ...       2\n",
              "...                                                  ...     ...\n",
              "23028    يلا ولا خنزير الله يرحم صدام حسين لي كان داعسكن       2\n",
              "23029            وبلدك سوريا لا تبغي سوا القتل والاجرام        2\n",
              "23030  معالي الوزير نؤمن بك دائما لأنك الدرع المنيع ف...       0\n",
              "23031  حياة سندي مستشارة امين عام الامم المتحدة بان ك...       0\n",
              "23032  انت يا جبران باسيل بعقليتك الفجة النتنة تقولرب...       2\n",
              "\n",
              "[23033 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efd0d1a1-06cd-4940-a61f-10ff8d787495\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentences</th>\n",
              "      <th>classe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>مبروك و سامحونا لعجزنا التام. عقبال اللي جوه. ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>كلنا بره ومش هنبطل نزايد على العجايز الي جابون...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>بدل ما انت قاعد بره كده تعالي ازرع الصحرا</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>قذر اتفووو ماتيجى مصر وتورينا نفسك كدا ياجبان</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>وهكذا رجال الشو اللي محرومين من عمل برنامج الغ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23028</th>\n",
              "      <td>يلا ولا خنزير الله يرحم صدام حسين لي كان داعسكن</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23029</th>\n",
              "      <td>وبلدك سوريا لا تبغي سوا القتل والاجرام</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23030</th>\n",
              "      <td>معالي الوزير نؤمن بك دائما لأنك الدرع المنيع ف...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23031</th>\n",
              "      <td>حياة سندي مستشارة امين عام الامم المتحدة بان ك...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23032</th>\n",
              "      <td>انت يا جبران باسيل بعقليتك الفجة النتنة تقولرب...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23033 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efd0d1a1-06cd-4940-a61f-10ff8d787495')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-efd0d1a1-06cd-4940-a61f-10ff8d787495 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-efd0d1a1-06cd-4940-a61f-10ff8d787495');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.figure(1 , figsize = (15 , 5))\n",
        "sns.countplot(y = 'classe' , data = data)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Fz8P60LwbLjI",
        "outputId": "b3b36c65-aa23-42ff-efd9-abf2bebb65f5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Figure out the longest comment\n",
        "\n",
        "def count_words(input):\n",
        "  return len(input.split())\n",
        "max_length=count_words(max(sentences,key=count_words))\n",
        "max_length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A37mvaVcD-ji",
        "outputId": "2482d3a5-ac2a-4ef1-f007-a53fc6ce2e91"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "388"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing extra data"
      ],
      "metadata": {
        "id": "cCJNbJcEzes_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data12=pd.read_excel(\"/content/drive/MyDrive/All/Projects/Ing Internship/Data/dataset1and2.xlsx\")\n",
        "data34=pd.read_excel(\"/content/drive/MyDrive/All/Projects/Ing Internship/Data/dataset3and4.xlsx\")\n",
        "data5=pd.read_excel(\"/content/drive/MyDrive/All/Projects/Ing Internship/Data/D5.xlsx\")\n",
        "\n",
        "data12['classe']=data12['classe'].replace(\"hate\", int(2))\n",
        "data12['classe']=data12['classe'].replace(\"normal\", int(0)) \n",
        "data12['classe']=data12['classe'].replace(\"abusive\", int(1))\n",
        "\n",
        "data34['classe']=data34['classe'].replace(\"hate\", int(2))\n",
        "data34['classe']=data34['classe'].replace(\"normal\", int(0)) \n",
        "data34['classe']=data34['classe'].replace(\"abusive\", int(1))\n",
        "\n",
        "\n",
        "data5['classe']=data5['classe'].replace(\"hate\", int(2))\n",
        "data5['classe']=data5['classe'].replace(\"normal\", int(0)) \n",
        "data5['classe']=data5['classe'].replace(\"abusive\", int(1))\n",
        "\n",
        "\n",
        "C1=data12[\"commentaire\"]\n",
        "C2=data34[\"commentaire\"]\n",
        "C3=data5[\"commentaire\"]\n",
        "L1=data12[\"classe\"]\n",
        "L2=data34[\"classe\"]\n",
        "L3=data5[\"classe\"]\n",
        "\n",
        "\n",
        "data12=[[i,j] for i,j in zip(C1 , L1)]\n",
        "data34=[[i,j] for i,j in zip(C2 , L2)]\n",
        "data5=[[i,j] for i,j in zip(C3 , L3)]\n",
        "\n",
        "data12=pd.DataFrame(data12,columns=[\"sentences\",\"classe\"])\n",
        "data34=pd.DataFrame(data34,columns=[\"sentences\",\"classe\"])\n",
        "data5=pd.DataFrame(data5,columns=[\"sentences\",\"classe\"])"
      ],
      "metadata": {
        "id": "lJ4ViTx6zc-7"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data5[\"sentences\"]=data5[\"sentences\"].str.replace(\"_\",\" \")"
      ],
      "metadata": {
        "id": "YEFxuA6H0gMb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data5[\"sentences\"][1097]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IhOSM1XP17Oy",
        "outputId": "d142efd2-7642-4fc3-cd95-2b4db33ef4d4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\"#أزهى عصور المسخره  هل ضيعت \"شيما\" محافظ بورسعيد؟\"'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(1 , figsize = (15 , 5))\n",
        "sns.countplot(y = 'classe' , data = data12)\n",
        "plt.show()\n",
        "print(data12.loc[data12.classe==0].groupby('classe')[\"classe\"].count())\n",
        "print(data12.loc[data12.classe==1].groupby('classe')[\"classe\"].count())\n",
        "print(data12.loc[data12.classe==2].groupby('classe')[\"classe\"].count())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "15BM6cRP0Wrl",
        "outputId": "242842aa-549c-4999-968c-a1d0fa96ff30"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classe\n",
            "0    3857\n",
            "Name: classe, dtype: int64\n",
            "classe\n",
            "1    1204\n",
            "Name: classe, dtype: int64\n",
            "classe\n",
            "2    1399\n",
            "Name: classe, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(1 , figsize = (15 , 5))\n",
        "sns.countplot(y = 'classe' , data = data34)\n",
        "plt.show()\n",
        "print(data34.loc[data34.classe==0].groupby('classe')[\"classe\"].count())\n",
        "print(data34.loc[data34.classe==1].groupby('classe')[\"classe\"].count())\n",
        "print(data34.loc[data34.classe==2].groupby('classe')[\"classe\"].count())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "OOiCKJ4y1I5T",
        "outputId": "8ccc7d64-40a5-46e1-c052-29e0ade166c3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classe\n",
            "0    4565\n",
            "Name: classe, dtype: int64\n",
            "classe\n",
            "1    1746\n",
            "Name: classe, dtype: int64\n",
            "classe\n",
            "2    2885\n",
            "Name: classe, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##The way that the computers percieve words and phrases is diffrent from the way that we preceve it as human beings. There are a veriety of ways for us that we can utilise to program the data type that we have and to make it numerical so that the model can get some meaning out of it. We call this part encoding text.\n",
        "##we can transfor each alphabet and caracter to it's ASCII Code, but this approach wasn't proven to give great results [To Research] !!!\n",
        "#What I did in the code was: Continus or distributed Embeddings Vectors representation:\n",
        "* First: Build a dictionary of tokens: I defined a dictionnary That has the words as keys and a unique number as a value\n",
        "* I then changed all of the phrases into Lists that has integers that indicate which word is in that particular place\n",
        "* Use One-Hot Vector\n",
        "\n",
        "##After doing this process, we have a numerical data that the model can use to get some meaning out of the labels"
      ],
      "metadata": {
        "id": "qbiHzGSbw8sE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_size=int(len(sentences)*0.8)   #Experimenting setup\n",
        "\n",
        "training_sentences = sentences[0:training_size]\n",
        "testing_sentences = sentences[training_size:]\n",
        "training_labels = labels[0:training_size]\n",
        "testing_labels = labels[training_size:]\n",
        "\n",
        "embedding_dim = 16\n",
        "\n",
        "tokenizer = Tokenizer(num_words=3000, oov_token=\"<OOV>\")\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "\n",
        "vocab_size = len(tokenizer.word_index) + 1 \n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "training_sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "training_padded = pad_sequences(training_sequences, maxlen=max_length, padding='post', truncating='post')\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding='post', truncating='post')"
      ],
      "metadata": {
        "id": "FrNFMsATGjbL"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size"
      ],
      "metadata": {
        "id": "GCD5XHrK8HaL",
        "outputId": "61d682fe-4ed9-4d28-ffb7-ff3b311ac998",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52168"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(word_index)"
      ],
      "metadata": {
        "id": "PB3wZKaR8MXq",
        "outputId": "40a33cfd-163f-4216-c86f-40a2d5d23a72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52167"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Nsentences12=data12[\"sentences\"]\n",
        "Nlabels12=data12[\"classe\"]\n",
        "\n",
        "training_size12=int(len(Nsentences12)*0.8)\n",
        "\n",
        "testing_sentences12 = Nsentences12[training_size12:]\n",
        "testing_labels12 = Nlabels12[training_size12:]\n",
        "\n",
        "##########\n",
        "\n",
        "Nsentences34=data34[\"sentences\"]\n",
        "Nlabels34=data34[\"classe\"]\n",
        "\n",
        "training_size34=int(len(Nsentences34)*0.8)\n",
        "\n",
        "testing_sentences34 = Nsentences34[training_size34:]\n",
        "testing_labels34 = Nlabels34[training_size34:]\n",
        "\n",
        "##########\n",
        "\n",
        "Nsentences5=data5[\"sentences\"]\n",
        "Nlabels5=data5[\"classe\"]\n",
        "\n",
        "training_size5=int(len(Nsentences5)*0.8)\n",
        "\n",
        "testing_sentences5 = Nsentences34[training_size5:]\n",
        "testing_labels5 = Nlabels34[training_size5:]"
      ],
      "metadata": {
        "id": "mkUjHchx4FaT"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length12=count_words(max(testing_sentences12,key=count_words))\n",
        "max_length12"
      ],
      "metadata": {
        "id": "NPn7XcNo5qUy",
        "outputId": "1ddc9c21-04af-4b6d-b89d-6c60193b1135",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "206"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length34=count_words(max(testing_sentences34,key=count_words))\n",
        "max_length34"
      ],
      "metadata": {
        "id": "5FkaUBXI60-_",
        "outputId": "33475a60-1fbc-42e3-9310-cdb41d1b3cb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length5=count_words(max(testing_sentences5,key=count_words))\n",
        "max_length5"
      ],
      "metadata": {
        "id": "BzkNojMI17FG",
        "outputId": "543ace36-4570-40ed-9098-cbc093b6135a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###The Maximum length in the new data is less than 207, so we can padd them without losing information."
      ],
      "metadata": {
        "id": "AFubPmEL7Qrh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testing_sequences12 = tokenizer.texts_to_sequences(testing_sentences12)\n",
        "testing_padded12 = pad_sequences(testing_sequences12, maxlen=max_length, padding='post', truncating='post')\n",
        "\n",
        "###########\n",
        "\n",
        "testing_sequences34 = tokenizer.texts_to_sequences(testing_sentences34)\n",
        "testing_padded34 = pad_sequences(testing_sequences34, maxlen=max_length, padding='post', truncating='post')\n",
        "\n",
        "###########\n",
        "\n",
        "testing_sequences5 = tokenizer.texts_to_sequences(testing_sentences5)\n",
        "testing_padded5 = pad_sequences(testing_sequences5, maxlen=max_length, padding='post', truncating='post')"
      ],
      "metadata": {
        "id": "e0p6r_d_7O58"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Modelling"
      ],
      "metadata": {
        "id": "TzmCjIfYBA8G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##For The First Model"
      ],
      "metadata": {
        "id": "-CRh9Gqc7KxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "model=tf.keras.models.Sequential ([\n",
        "                                   tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "                                   tf.keras.layers.Conv1D(128, 5, activation='relu'),\n",
        "                                   tf.keras.layers.Conv1D(filters=32, kernel_size=1, activation='selu'),\n",
        "                                   tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32,return_sequences=True)),\n",
        "                                   tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32,return_sequences=True)),\n",
        "                                   tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(16,return_sequences=True)),\n",
        "                                   tf.keras.layers.Flatten(),\n",
        "                                   tf.keras.layers.Dense(units=24,activation='relu'),\n",
        "                                   tf.keras.layers.Dense(units=3, activation='softmax')])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "attb9Ze4BGlm",
        "outputId": "86b12094-a982-4df2-ce4c-02b6ea5057c0"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 388, 16)           834688    \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 384, 128)          10368     \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 384, 32)           4128      \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 384, 64)          16640     \n",
            " l)                                                              \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 384, 64)          24832     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirectio  (None, 384, 32)          10368     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 12288)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 24)                294936    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 75        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,196,035\n",
            "Trainable params: 1,196,035\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#For the First Model\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\",optimizer='adam',metrics=['accuracy'],run_eagerly=True)\n",
        "\n",
        "training_padded = np.asarray(training_padded).astype(np.int)\n",
        "training_labels = np.asarray(training_labels).astype(np.int)\n",
        "testing_padded = np.asarray(testing_padded).astype(np.int)\n",
        "testing_labels = np.asarray(testing_labels).astype(np.int)\n",
        "\n",
        "#training_padded = np.expand_dims(training_padded, axis=1)\n",
        "training_labels = np.expand_dims(training_labels, axis=1)\n",
        "#testing_padded = np.expand_dims(testing_padded, axis=1)\n",
        "testing_labels = np.expand_dims(testing_labels, axis=1)\n",
        "\n",
        "history = model.fit(training_padded, training_labels, batch_size=128, epochs=55, validation_data=(testing_padded, testing_labels), verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YK8y9kQFL5T5",
        "outputId": "3d677e7a-1cd2-4c4c-86da-e426ee3368a0"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/55\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: 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",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: 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",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: 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",
            "  \n",
            "/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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "144/144 [==============================] - 24s 150ms/step - loss: 0.9329 - accuracy: 0.5692 - val_loss: 0.6970 - val_accuracy: 0.6942\n",
            "Epoch 2/55\n",
            "144/144 [==============================] - 21s 147ms/step - loss: 0.7236 - accuracy: 0.6872 - val_loss: 0.5636 - val_accuracy: 0.7656\n",
            "Epoch 3/55\n",
            "144/144 [==============================] - 23s 159ms/step - loss: 0.6022 - accuracy: 0.7657 - val_loss: 0.5116 - val_accuracy: 0.8135\n",
            "Epoch 4/55\n",
            "144/144 [==============================] - 21s 147ms/step - loss: 0.5183 - accuracy: 0.8047 - val_loss: 0.4639 - val_accuracy: 0.8194\n",
            "Epoch 5/55\n",
            "144/144 [==============================] - 22s 153ms/step - loss: 0.4633 - accuracy: 0.8246 - val_loss: 0.4303 - val_accuracy: 0.8442\n",
            "Epoch 6/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.4213 - accuracy: 0.8397 - val_loss: 0.3976 - val_accuracy: 0.8520\n",
            "Epoch 7/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.3671 - accuracy: 0.8641 - val_loss: 0.3809 - val_accuracy: 0.8583\n",
            "Epoch 8/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.3261 - accuracy: 0.8801 - val_loss: 0.3756 - val_accuracy: 0.8652\n",
            "Epoch 9/55\n",
            "144/144 [==============================] - 22s 153ms/step - loss: 0.2912 - accuracy: 0.8945 - val_loss: 0.3778 - val_accuracy: 0.8722\n",
            "Epoch 10/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.2634 - accuracy: 0.9046 - val_loss: 0.3695 - val_accuracy: 0.8700\n",
            "Epoch 11/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.2457 - accuracy: 0.9115 - val_loss: 0.3803 - val_accuracy: 0.8758\n",
            "Epoch 12/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.2177 - accuracy: 0.9230 - val_loss: 0.3761 - val_accuracy: 0.8811\n",
            "Epoch 13/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.2027 - accuracy: 0.9281 - val_loss: 0.3942 - val_accuracy: 0.8776\n",
            "Epoch 14/55\n",
            "144/144 [==============================] - 24s 167ms/step - loss: 0.1911 - accuracy: 0.9329 - val_loss: 0.3873 - val_accuracy: 0.8819\n",
            "Epoch 15/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.1816 - accuracy: 0.9367 - val_loss: 0.3925 - val_accuracy: 0.8845\n",
            "Epoch 16/55\n",
            "144/144 [==============================] - 21s 149ms/step - loss: 0.1710 - accuracy: 0.9396 - val_loss: 0.3859 - val_accuracy: 0.8847\n",
            "Epoch 17/55\n",
            "144/144 [==============================] - 22s 150ms/step - loss: 0.1593 - accuracy: 0.9433 - val_loss: 0.3943 - val_accuracy: 0.8841\n",
            "Epoch 18/55\n",
            "144/144 [==============================] - 21s 149ms/step - loss: 0.1574 - accuracy: 0.9424 - val_loss: 0.4054 - val_accuracy: 0.8882\n",
            "Epoch 19/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.1451 - accuracy: 0.9488 - val_loss: 0.4293 - val_accuracy: 0.8852\n",
            "Epoch 20/55\n",
            "144/144 [==============================] - 22s 153ms/step - loss: 0.1371 - accuracy: 0.9491 - val_loss: 0.4461 - val_accuracy: 0.8824\n",
            "Epoch 21/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.1372 - accuracy: 0.9506 - val_loss: 0.4655 - val_accuracy: 0.8797\n",
            "Epoch 22/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.1319 - accuracy: 0.9502 - val_loss: 0.4583 - val_accuracy: 0.8893\n",
            "Epoch 23/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.1286 - accuracy: 0.9517 - val_loss: 0.4606 - val_accuracy: 0.8771\n",
            "Epoch 24/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.1224 - accuracy: 0.9523 - val_loss: 0.5478 - val_accuracy: 0.8873\n",
            "Epoch 25/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.1212 - accuracy: 0.9521 - val_loss: 0.4748 - val_accuracy: 0.8882\n",
            "Epoch 26/55\n",
            "144/144 [==============================] - 22s 155ms/step - loss: 0.1139 - accuracy: 0.9554 - val_loss: 0.4973 - val_accuracy: 0.8904\n",
            "Epoch 27/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.1096 - accuracy: 0.9551 - val_loss: 0.4640 - val_accuracy: 0.8867\n",
            "Epoch 28/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.1135 - accuracy: 0.9545 - val_loss: 0.5457 - val_accuracy: 0.8886\n",
            "Epoch 29/55\n",
            "144/144 [==============================] - 21s 147ms/step - loss: 0.1070 - accuracy: 0.9556 - val_loss: 0.5375 - val_accuracy: 0.8876\n",
            "Epoch 30/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.1098 - accuracy: 0.9545 - val_loss: 0.5242 - val_accuracy: 0.8880\n",
            "Epoch 31/55\n",
            "144/144 [==============================] - 21s 147ms/step - loss: 0.1052 - accuracy: 0.9573 - val_loss: 0.5396 - val_accuracy: 0.8882\n",
            "Epoch 32/55\n",
            "144/144 [==============================] - 22s 155ms/step - loss: 0.1011 - accuracy: 0.9568 - val_loss: 0.5736 - val_accuracy: 0.8828\n",
            "Epoch 33/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.0963 - accuracy: 0.9577 - val_loss: 0.5980 - val_accuracy: 0.8867\n",
            "Epoch 34/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.0930 - accuracy: 0.9576 - val_loss: 0.5677 - val_accuracy: 0.8893\n",
            "Epoch 35/55\n",
            "144/144 [==============================] - 21s 149ms/step - loss: 0.0928 - accuracy: 0.9582 - val_loss: 0.5751 - val_accuracy: 0.8867\n",
            "Epoch 36/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.0911 - accuracy: 0.9596 - val_loss: 0.6264 - val_accuracy: 0.8904\n",
            "Epoch 37/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.0879 - accuracy: 0.9589 - val_loss: 0.5874 - val_accuracy: 0.8891\n",
            "Epoch 38/55\n",
            "144/144 [==============================] - 22s 152ms/step - loss: 0.0931 - accuracy: 0.9582 - val_loss: 0.6631 - val_accuracy: 0.8921\n",
            "Epoch 39/55\n",
            "144/144 [==============================] - 21s 147ms/step - loss: 0.0899 - accuracy: 0.9591 - val_loss: 0.6489 - val_accuracy: 0.8891\n",
            "Epoch 40/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.0930 - accuracy: 0.9581 - val_loss: 0.6322 - val_accuracy: 0.8893\n",
            "Epoch 41/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.0939 - accuracy: 0.9576 - val_loss: 0.6250 - val_accuracy: 0.8873\n",
            "Epoch 42/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.0868 - accuracy: 0.9592 - val_loss: 0.7119 - val_accuracy: 0.8897\n",
            "Epoch 43/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.0880 - accuracy: 0.9599 - val_loss: 0.6578 - val_accuracy: 0.8880\n",
            "Epoch 44/55\n",
            "144/144 [==============================] - 22s 155ms/step - loss: 0.0859 - accuracy: 0.9607 - val_loss: 0.6471 - val_accuracy: 0.8871\n",
            "Epoch 45/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.0860 - accuracy: 0.9606 - val_loss: 0.6155 - val_accuracy: 0.8865\n",
            "Epoch 46/55\n",
            "144/144 [==============================] - 21s 149ms/step - loss: 0.0888 - accuracy: 0.9590 - val_loss: 0.6331 - val_accuracy: 0.8906\n",
            "Epoch 47/55\n",
            "144/144 [==============================] - 21s 146ms/step - loss: 0.0938 - accuracy: 0.9583 - val_loss: 0.6596 - val_accuracy: 0.8895\n",
            "Epoch 48/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.0879 - accuracy: 0.9599 - val_loss: 0.6941 - val_accuracy: 0.8897\n",
            "Epoch 49/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.0786 - accuracy: 0.9613 - val_loss: 0.7930 - val_accuracy: 0.8845\n",
            "Epoch 50/55\n",
            "144/144 [==============================] - 22s 155ms/step - loss: 0.0765 - accuracy: 0.9620 - val_loss: 0.7237 - val_accuracy: 0.8891\n",
            "Epoch 51/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.0759 - accuracy: 0.9627 - val_loss: 0.7411 - val_accuracy: 0.8828\n",
            "Epoch 52/55\n",
            "144/144 [==============================] - 21s 147ms/step - loss: 0.0779 - accuracy: 0.9620 - val_loss: 0.6789 - val_accuracy: 0.8856\n",
            "Epoch 53/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.0867 - accuracy: 0.9589 - val_loss: 0.6838 - val_accuracy: 0.8873\n",
            "Epoch 54/55\n",
            "144/144 [==============================] - 21s 148ms/step - loss: 0.0883 - accuracy: 0.9614 - val_loss: 0.6506 - val_accuracy: 0.8895\n",
            "Epoch 55/55\n",
            "144/144 [==============================] - 21s 145ms/step - loss: 0.0819 - accuracy: 0.9604 - val_loss: 0.7086 - val_accuracy: 0.8906\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Epoch 50/50\n",
        "#129/129 [==============================] - 17s 131ms/step - loss: 0.0800 - accuracy: 0.9551 - val_loss: 0.1488 - val_accuracy: 0.9221\n",
        "#Epoch 2/2\n",
        "#129/129 [==============================] - 14s 111ms/step - loss: 0.0793 - accuracy: 0.9576 - val_loss: 0.1594 - val_accuracy: 0.9425"
      ],
      "metadata": {
        "id": "87O-L-vRBuy-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('Model1Unbalanced08.h5')"
      ],
      "metadata": {
        "id": "vFcGBuccVWcd"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "model=tf.keras.models.load_model('Model1Unbalanced08.h5')"
      ],
      "metadata": {
        "id": "qGUVfM0kUzwD"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(training_padded, training_labels, batch_size=128, epochs=2, validation_data=(testing_padded, testing_labels), verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARtVuoWqY5Fn",
        "outputId": "5f36c2b3-eab5-480e-cdd9-cccc7ff3f935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "129/129 [==============================] - 15s 119ms/step - loss: 0.0821 - accuracy: 0.9550 - val_loss: 0.1520 - val_accuracy: 0.9128\n",
            "Epoch 2/2\n",
            "129/129 [==============================] - 14s 111ms/step - loss: 0.0793 - accuracy: 0.9576 - val_loss: 0.1594 - val_accuracy: 0.9425\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model, show_shapes=True)"
      ],
      "metadata": {
        "id": "fpzHswneMSZv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "631c30a7-1135-4e33-b97b-0a5f27eb6eff"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "\n",
        "pred=model.predict(testing_padded)\n",
        "rounded_labels1=np.argmax(pred, axis=1).astype(int)\n",
        "cm = confusion_matrix(testing_labels, rounded_labels1)\n",
        "print(cm)\n",
        "print(classification_report(testing_labels, rounded_labels1, labels=[0,1,2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctfA3AV-NbFv",
        "outputId": "faff3ffc-5077-41b0-a484-f57491411217"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2573   35   87]\n",
            " [  63  666   69]\n",
            " [ 163   94  857]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.95      0.94      2695\n",
            "           1       0.84      0.83      0.84       798\n",
            "           2       0.85      0.77      0.81      1114\n",
            "\n",
            "    accuracy                           0.89      4607\n",
            "   macro avg       0.87      0.85      0.86      4607\n",
            "weighted avg       0.89      0.89      0.89      4607\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred12=model.predict(testing_padded12)\n",
        "rounded_labels112=np.argmax(pred12, axis=1).astype(int)\n",
        "cm12 = confusion_matrix(testing_labels12, rounded_labels112)\n",
        "print(cm12)\n",
        "print(classification_report(testing_labels12, rounded_labels112, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "XP0QbLyyJiJl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91ed7127-0527-4251-dd2d-de7d25d9c8b3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[507  31  49]\n",
            " [ 51 193   9]\n",
            " [ 80 135 237]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.86      0.83       587\n",
            "           1       0.54      0.76      0.63       253\n",
            "           2       0.80      0.52      0.63       452\n",
            "\n",
            "    accuracy                           0.73      1292\n",
            "   macro avg       0.71      0.72      0.70      1292\n",
            "weighted avg       0.75      0.73      0.72      1292\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred34=model.predict(testing_padded34)\n",
        "rounded_labels134=np.argmax(pred34, axis=1).astype(int)\n",
        "cm34 = confusion_matrix(testing_labels34, rounded_labels134)\n",
        "print(cm34)\n",
        "print(classification_report(testing_labels34, rounded_labels134, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "xKHQnnggJ3PJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58466e0b-86dd-4396-958c-4a0e3f20bbb3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 523    0    3]\n",
            " [   0    7    0]\n",
            " [  22    3 1282]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.98       526\n",
            "           1       0.70      1.00      0.82         7\n",
            "           2       1.00      0.98      0.99      1307\n",
            "\n",
            "    accuracy                           0.98      1840\n",
            "   macro avg       0.89      0.99      0.93      1840\n",
            "weighted avg       0.99      0.98      0.98      1840\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred5=model.predict(testing_padded5)\n",
        "rounded_labels15=np.argmax(pred5, axis=1).astype(int)\n",
        "cm5 = confusion_matrix(testing_labels5, rounded_labels15)\n",
        "print(cm5)\n",
        "print(classification_report(testing_labels5, rounded_labels15, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "8noH9-BkMA7s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7765d1de-fdcc-4b12-ea11-fe7b8d839435"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3762   67  206]\n",
            " [ 356  507  591]\n",
            " [ 138  150 2539]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.93      0.91      4035\n",
            "           1       0.70      0.35      0.47      1454\n",
            "           2       0.76      0.90      0.82      2827\n",
            "\n",
            "    accuracy                           0.82      8316\n",
            "   macro avg       0.78      0.73      0.73      8316\n",
            "weighted avg       0.81      0.82      0.80      8316\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "QXLyKWWv58Qd",
        "outputId": "96635d3f-6afb-42b7-91bf-43a2da2fa0e8"
      },
      "execution_count": 34,
      "outputs": [
        {
          "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 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##For the Second Model"
      ],
      "metadata": {
        "id": "2DGrTNt17GKc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "model2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Conv1D(128, 5, activation='relu'),\n",
        "    tf.keras.layers.GlobalMaxPooling1D(),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(24, activation='relu'),\n",
        "    tf.keras.layers.Dense(units=3, activation='softmax')\n",
        "])\n",
        "model2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vsv2gTUcITR-",
        "outputId": "a4af918f-3067-4466-efaf-fbe784c023ef"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 388, 16)           834688    \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 384, 128)          10368     \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 128)              0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 24)                3096      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 75        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 848,227\n",
            "Trainable params: 848,227\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.compile(loss=\"sparse_categorical_crossentropy\",optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "history = model2.fit(training_padded, training_labels, batch_size=128, epochs=50, validation_data=(testing_padded, testing_labels), verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-edaUQlH57pA",
        "outputId": "d709f1fd-5864-4010-9e44-ff59edffb1bc"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "144/144 [==============================] - 2s 10ms/step - loss: 0.9405 - accuracy: 0.5641 - val_loss: 0.7260 - val_accuracy: 0.6816\n",
            "Epoch 2/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.6497 - accuracy: 0.7384 - val_loss: 0.5000 - val_accuracy: 0.8144\n",
            "Epoch 3/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.5248 - accuracy: 0.7955 - val_loss: 0.4491 - val_accuracy: 0.8359\n",
            "Epoch 4/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.4682 - accuracy: 0.8178 - val_loss: 0.4232 - val_accuracy: 0.8444\n",
            "Epoch 5/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.4269 - accuracy: 0.8359 - val_loss: 0.4043 - val_accuracy: 0.8483\n",
            "Epoch 6/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.3878 - accuracy: 0.8538 - val_loss: 0.3951 - val_accuracy: 0.8550\n",
            "Epoch 7/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.3513 - accuracy: 0.8670 - val_loss: 0.3820 - val_accuracy: 0.8659\n",
            "Epoch 8/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.3138 - accuracy: 0.8838 - val_loss: 0.3687 - val_accuracy: 0.8698\n",
            "Epoch 9/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.2793 - accuracy: 0.8995 - val_loss: 0.3854 - val_accuracy: 0.8674\n",
            "Epoch 10/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.2514 - accuracy: 0.9108 - val_loss: 0.3760 - val_accuracy: 0.8761\n",
            "Epoch 11/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.2254 - accuracy: 0.9209 - val_loss: 0.3710 - val_accuracy: 0.8819\n",
            "Epoch 12/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.2003 - accuracy: 0.9293 - val_loss: 0.3724 - val_accuracy: 0.8821\n",
            "Epoch 13/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1846 - accuracy: 0.9338 - val_loss: 0.3723 - val_accuracy: 0.8845\n",
            "Epoch 14/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1718 - accuracy: 0.9379 - val_loss: 0.3886 - val_accuracy: 0.8841\n",
            "Epoch 15/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1586 - accuracy: 0.9418 - val_loss: 0.3876 - val_accuracy: 0.8845\n",
            "Epoch 16/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1520 - accuracy: 0.9446 - val_loss: 0.3980 - val_accuracy: 0.8852\n",
            "Epoch 17/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1436 - accuracy: 0.9461 - val_loss: 0.4057 - val_accuracy: 0.8880\n",
            "Epoch 18/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1380 - accuracy: 0.9483 - val_loss: 0.4144 - val_accuracy: 0.8843\n",
            "Epoch 19/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1339 - accuracy: 0.9500 - val_loss: 0.4256 - val_accuracy: 0.8826\n",
            "Epoch 20/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1279 - accuracy: 0.9518 - val_loss: 0.4245 - val_accuracy: 0.8824\n",
            "Epoch 21/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1244 - accuracy: 0.9531 - val_loss: 0.4191 - val_accuracy: 0.8915\n",
            "Epoch 22/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1207 - accuracy: 0.9531 - val_loss: 0.4251 - val_accuracy: 0.8895\n",
            "Epoch 23/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1165 - accuracy: 0.9544 - val_loss: 0.4364 - val_accuracy: 0.8908\n",
            "Epoch 24/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1146 - accuracy: 0.9542 - val_loss: 0.4299 - val_accuracy: 0.8886\n",
            "Epoch 25/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1110 - accuracy: 0.9557 - val_loss: 0.4424 - val_accuracy: 0.8856\n",
            "Epoch 26/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1114 - accuracy: 0.9556 - val_loss: 0.4414 - val_accuracy: 0.8867\n",
            "Epoch 27/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1099 - accuracy: 0.9560 - val_loss: 0.4422 - val_accuracy: 0.8873\n",
            "Epoch 28/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1064 - accuracy: 0.9564 - val_loss: 0.4543 - val_accuracy: 0.8860\n",
            "Epoch 29/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1054 - accuracy: 0.9572 - val_loss: 0.4505 - val_accuracy: 0.8895\n",
            "Epoch 30/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1046 - accuracy: 0.9575 - val_loss: 0.4652 - val_accuracy: 0.8819\n",
            "Epoch 31/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1038 - accuracy: 0.9585 - val_loss: 0.4639 - val_accuracy: 0.8867\n",
            "Epoch 32/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1007 - accuracy: 0.9578 - val_loss: 0.4627 - val_accuracy: 0.8867\n",
            "Epoch 33/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.1004 - accuracy: 0.9587 - val_loss: 0.4613 - val_accuracy: 0.8850\n",
            "Epoch 34/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0992 - accuracy: 0.9586 - val_loss: 0.4588 - val_accuracy: 0.8884\n",
            "Epoch 35/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.1000 - accuracy: 0.9577 - val_loss: 0.4703 - val_accuracy: 0.8869\n",
            "Epoch 36/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0972 - accuracy: 0.9591 - val_loss: 0.4615 - val_accuracy: 0.8893\n",
            "Epoch 37/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0957 - accuracy: 0.9597 - val_loss: 0.4637 - val_accuracy: 0.8904\n",
            "Epoch 38/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0939 - accuracy: 0.9599 - val_loss: 0.4690 - val_accuracy: 0.8928\n",
            "Epoch 39/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.0952 - accuracy: 0.9597 - val_loss: 0.4919 - val_accuracy: 0.8880\n",
            "Epoch 40/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0958 - accuracy: 0.9576 - val_loss: 0.4786 - val_accuracy: 0.8884\n",
            "Epoch 41/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.0924 - accuracy: 0.9604 - val_loss: 0.4759 - val_accuracy: 0.8906\n",
            "Epoch 42/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.0921 - accuracy: 0.9597 - val_loss: 0.4763 - val_accuracy: 0.8891\n",
            "Epoch 43/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.0930 - accuracy: 0.9586 - val_loss: 0.4721 - val_accuracy: 0.8889\n",
            "Epoch 44/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.0922 - accuracy: 0.9603 - val_loss: 0.4827 - val_accuracy: 0.8873\n",
            "Epoch 45/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.0915 - accuracy: 0.9592 - val_loss: 0.4850 - val_accuracy: 0.8904\n",
            "Epoch 46/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0906 - accuracy: 0.9598 - val_loss: 0.4821 - val_accuracy: 0.8891\n",
            "Epoch 47/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0902 - accuracy: 0.9602 - val_loss: 0.4982 - val_accuracy: 0.8873\n",
            "Epoch 48/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0902 - accuracy: 0.9599 - val_loss: 0.4921 - val_accuracy: 0.8882\n",
            "Epoch 49/50\n",
            "144/144 [==============================] - 1s 9ms/step - loss: 0.0895 - accuracy: 0.9609 - val_loss: 0.4782 - val_accuracy: 0.8917\n",
            "Epoch 50/50\n",
            "144/144 [==============================] - 1s 8ms/step - loss: 0.0880 - accuracy: 0.9603 - val_loss: 0.4818 - val_accuracy: 0.8936\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Epoch 50/50\n",
        "#129/129 [==============================] - 1s 6ms/step - loss: 0.0928 - accuracy: 0.9549 - val_loss: 0.1237 - val_accuracy: 0.9445"
      ],
      "metadata": {
        "id": "ELYuX4V6V9HC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('Model2Unbalanced08.h5')"
      ],
      "metadata": {
        "id": "qV2xI0z5V6_Q"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model2, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "Djs_EiWq6Sc1",
        "outputId": "ea921f93-3399-435e-c472-acab0de481de"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred1=model2.predict(testing_padded)\n",
        "\n",
        "pred11=[]\n",
        "for i in pred1:\n",
        "  if i[0]==max(i):\n",
        "    pred11.append(0)\n",
        "  elif i[1]==max(i):\n",
        "    pred11.append(1)\n",
        "  else:\n",
        "    pred11.append(2)\n",
        "#rounded_labels11=np.argmax(pred11, axis=1).astype(int)\n",
        "cm = confusion_matrix(testing_labels, pred11)\n",
        "print(cm)\n",
        "print(classification_report(testing_labels, pred11, labels=[0,1,2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RAIZlNi58Ht",
        "outputId": "d8e9288f-6110-457b-8af2-e59d8e0bc34f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2580   32   83]\n",
            " [  64  664   70]\n",
            " [ 147   94  873]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.96      0.94      2695\n",
            "           1       0.84      0.83      0.84       798\n",
            "           2       0.85      0.78      0.82      1114\n",
            "\n",
            "    accuracy                           0.89      4607\n",
            "   macro avg       0.87      0.86      0.86      4607\n",
            "weighted avg       0.89      0.89      0.89      4607\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred12=model2.predict(testing_padded12)\n",
        "rounded_labels212=np.argmax(pred12, axis=1).astype(int)\n",
        "cm12 = confusion_matrix(testing_labels12, rounded_labels212)\n",
        "print(cm12)\n",
        "print(classification_report(testing_labels12, rounded_labels212, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "p7iWnGdoKkHJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50bea440-85d4-496a-ba6c-b3abef066e39"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[519  30  38]\n",
            " [ 51 192  10]\n",
            " [ 85 137 230]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.88      0.84       587\n",
            "           1       0.53      0.76      0.63       253\n",
            "           2       0.83      0.51      0.63       452\n",
            "\n",
            "    accuracy                           0.73      1292\n",
            "   macro avg       0.72      0.72      0.70      1292\n",
            "weighted avg       0.75      0.73      0.72      1292\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred34=model2.predict(testing_padded34)\n",
        "rounded_labels234=np.argmax(pred34, axis=1).astype(int)\n",
        "cm34 = confusion_matrix(testing_labels34, rounded_labels234)\n",
        "print(cm34)\n",
        "print(classification_report(testing_labels34, rounded_labels234, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "t3trQk4OKw0Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed45d3a8-448c-41e6-ba1e-565c5ab6f51d"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 518    0    8]\n",
            " [   0    7    0]\n",
            " [  13    1 1293]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98       526\n",
            "           1       0.88      1.00      0.93         7\n",
            "           2       0.99      0.99      0.99      1307\n",
            "\n",
            "    accuracy                           0.99      1840\n",
            "   macro avg       0.95      0.99      0.97      1840\n",
            "weighted avg       0.99      0.99      0.99      1840\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred5=model2.predict(testing_padded5)\n",
        "rounded_labels25=np.argmax(pred5, axis=1).astype(int)\n",
        "cm5 = confusion_matrix(testing_labels5, rounded_labels25)\n",
        "print(cm5)\n",
        "print(classification_report(testing_labels5, rounded_labels25, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "YMHiBV5cMN_r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4c98996-aa70-448c-c21e-357845709b8f"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3759   64  212]\n",
            " [ 351  494  609]\n",
            " [ 120  141 2566]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.93      0.91      4035\n",
            "           1       0.71      0.34      0.46      1454\n",
            "           2       0.76      0.91      0.83      2827\n",
            "\n",
            "    accuracy                           0.82      8316\n",
            "   macro avg       0.78      0.73      0.73      8316\n",
            "weighted avg       0.81      0.82      0.80      8316\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "R2WgOyeb_PHC",
        "outputId": "926f3dda-b852-4272-f34d-d3efe92b32f4"
      },
      "execution_count": 43,
      "outputs": [
        {
          "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 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##For The Third Model"
      ],
      "metadata": {
        "id": "FRfELBgw7A3Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(24, activation='relu'),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "model3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvX2b2nORvKS",
        "outputId": "6ac1456a-7af9-4638-fe8e-8201f66b5278"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_2 (Embedding)     (None, 388, 16)           834688    \n",
            "                                                                 \n",
            " bidirectional_3 (Bidirectio  (None, 64)               12544     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 24)                1560      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 3)                 75        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 848,867\n",
            "Trainable params: 848,867\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3.compile(loss=\"sparse_categorical_crossentropy\",optimizer='adam',metrics=['accuracy'])\n",
        "history = model3.fit(training_padded, training_labels, batch_size=128, epochs=50, validation_data=(testing_padded, testing_labels), verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "El4d_GRU6x2l",
        "outputId": "53a5bd16-3b40-4a18-f362-fa0ec84b01d1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "144/144 [==============================] - 8s 37ms/step - loss: 0.9547 - accuracy: 0.5582 - val_loss: 0.7261 - val_accuracy: 0.6753\n",
            "Epoch 2/50\n",
            "144/144 [==============================] - 5s 33ms/step - loss: 0.7416 - accuracy: 0.6806 - val_loss: 0.5953 - val_accuracy: 0.7384\n",
            "Epoch 3/50\n",
            "144/144 [==============================] - 5s 35ms/step - loss: 0.6309 - accuracy: 0.7449 - val_loss: 0.5330 - val_accuracy: 0.7899\n",
            "Epoch 4/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.5469 - accuracy: 0.7875 - val_loss: 0.4881 - val_accuracy: 0.8168\n",
            "Epoch 5/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.5198 - accuracy: 0.7981 - val_loss: 0.4703 - val_accuracy: 0.8214\n",
            "Epoch 6/50\n",
            "144/144 [==============================] - 4s 30ms/step - loss: 0.4802 - accuracy: 0.8135 - val_loss: 0.4627 - val_accuracy: 0.8203\n",
            "Epoch 7/50\n",
            "144/144 [==============================] - 4s 30ms/step - loss: 0.4594 - accuracy: 0.8235 - val_loss: 0.4435 - val_accuracy: 0.8277\n",
            "Epoch 8/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.4466 - accuracy: 0.8293 - val_loss: 0.4438 - val_accuracy: 0.8277\n",
            "Epoch 9/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.4360 - accuracy: 0.8301 - val_loss: 0.4419 - val_accuracy: 0.8303\n",
            "Epoch 10/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.4265 - accuracy: 0.8367 - val_loss: 0.4258 - val_accuracy: 0.8359\n",
            "Epoch 11/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.4147 - accuracy: 0.8421 - val_loss: 0.4351 - val_accuracy: 0.8344\n",
            "Epoch 12/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.4040 - accuracy: 0.8468 - val_loss: 0.4260 - val_accuracy: 0.8428\n",
            "Epoch 13/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3912 - accuracy: 0.8545 - val_loss: 0.4167 - val_accuracy: 0.8437\n",
            "Epoch 14/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3789 - accuracy: 0.8578 - val_loss: 0.4148 - val_accuracy: 0.8439\n",
            "Epoch 15/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3659 - accuracy: 0.8645 - val_loss: 0.4107 - val_accuracy: 0.8491\n",
            "Epoch 16/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3619 - accuracy: 0.8657 - val_loss: 0.4097 - val_accuracy: 0.8487\n",
            "Epoch 17/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3418 - accuracy: 0.8748 - val_loss: 0.4001 - val_accuracy: 0.8563\n",
            "Epoch 18/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3342 - accuracy: 0.8792 - val_loss: 0.3986 - val_accuracy: 0.8561\n",
            "Epoch 19/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3203 - accuracy: 0.8842 - val_loss: 0.3984 - val_accuracy: 0.8596\n",
            "Epoch 20/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3134 - accuracy: 0.8845 - val_loss: 0.3984 - val_accuracy: 0.8615\n",
            "Epoch 21/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.3034 - accuracy: 0.8898 - val_loss: 0.3890 - val_accuracy: 0.8622\n",
            "Epoch 22/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2947 - accuracy: 0.8931 - val_loss: 0.4020 - val_accuracy: 0.8604\n",
            "Epoch 23/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2845 - accuracy: 0.8973 - val_loss: 0.3975 - val_accuracy: 0.8669\n",
            "Epoch 24/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2753 - accuracy: 0.9008 - val_loss: 0.4100 - val_accuracy: 0.8689\n",
            "Epoch 25/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2704 - accuracy: 0.9014 - val_loss: 0.4084 - val_accuracy: 0.8685\n",
            "Epoch 26/50\n",
            "144/144 [==============================] - 5s 31ms/step - loss: 0.2637 - accuracy: 0.9058 - val_loss: 0.3984 - val_accuracy: 0.8706\n",
            "Epoch 27/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2566 - accuracy: 0.9078 - val_loss: 0.4125 - val_accuracy: 0.8656\n",
            "Epoch 28/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2533 - accuracy: 0.9083 - val_loss: 0.4084 - val_accuracy: 0.8706\n",
            "Epoch 29/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2465 - accuracy: 0.9109 - val_loss: 0.4051 - val_accuracy: 0.8667\n",
            "Epoch 30/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2390 - accuracy: 0.9145 - val_loss: 0.4168 - val_accuracy: 0.8695\n",
            "Epoch 31/50\n",
            "144/144 [==============================] - 5s 38ms/step - loss: 0.2346 - accuracy: 0.9152 - val_loss: 0.4084 - val_accuracy: 0.8702\n",
            "Epoch 32/50\n",
            "144/144 [==============================] - 5s 31ms/step - loss: 0.2377 - accuracy: 0.9155 - val_loss: 0.4170 - val_accuracy: 0.8698\n",
            "Epoch 33/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2247 - accuracy: 0.9171 - val_loss: 0.4291 - val_accuracy: 0.8665\n",
            "Epoch 34/50\n",
            "144/144 [==============================] - 5s 31ms/step - loss: 0.2252 - accuracy: 0.9191 - val_loss: 0.4126 - val_accuracy: 0.8739\n",
            "Epoch 35/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2189 - accuracy: 0.9217 - val_loss: 0.4305 - val_accuracy: 0.8680\n",
            "Epoch 36/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2154 - accuracy: 0.9226 - val_loss: 0.4360 - val_accuracy: 0.8665\n",
            "Epoch 37/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2102 - accuracy: 0.9248 - val_loss: 0.4452 - val_accuracy: 0.8711\n",
            "Epoch 38/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2031 - accuracy: 0.9264 - val_loss: 0.4508 - val_accuracy: 0.8702\n",
            "Epoch 39/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2031 - accuracy: 0.9267 - val_loss: 0.4605 - val_accuracy: 0.8648\n",
            "Epoch 40/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2104 - accuracy: 0.9239 - val_loss: 0.4335 - val_accuracy: 0.8715\n",
            "Epoch 41/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.2008 - accuracy: 0.9254 - val_loss: 0.4614 - val_accuracy: 0.8713\n",
            "Epoch 42/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.1961 - accuracy: 0.9265 - val_loss: 0.4488 - val_accuracy: 0.8713\n",
            "Epoch 43/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.1933 - accuracy: 0.9285 - val_loss: 0.4309 - val_accuracy: 0.8732\n",
            "Epoch 44/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.1886 - accuracy: 0.9287 - val_loss: 0.4555 - val_accuracy: 0.8719\n",
            "Epoch 45/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.1854 - accuracy: 0.9305 - val_loss: 0.4441 - val_accuracy: 0.8763\n",
            "Epoch 46/50\n",
            "144/144 [==============================] - 5s 31ms/step - loss: 0.1843 - accuracy: 0.9302 - val_loss: 0.4410 - val_accuracy: 0.8708\n",
            "Epoch 47/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.1804 - accuracy: 0.9328 - val_loss: 0.4597 - val_accuracy: 0.8750\n",
            "Epoch 48/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.1750 - accuracy: 0.9341 - val_loss: 0.4415 - val_accuracy: 0.8765\n",
            "Epoch 49/50\n",
            "144/144 [==============================] - 4s 31ms/step - loss: 0.1714 - accuracy: 0.9347 - val_loss: 0.4676 - val_accuracy: 0.8722\n",
            "Epoch 50/50\n",
            "144/144 [==============================] - 5s 31ms/step - loss: 0.1694 - accuracy: 0.9339 - val_loss: 0.4671 - val_accuracy: 0.8745\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Epoch 50/50\n",
        "#129/129 [==============================] - 2s 19ms/step - loss: 0.1259 - accuracy: 0.9401 - val_loss: 0.1715 - val_accuracy: 0.9209"
      ],
      "metadata": {
        "id": "BHRXgvpCoku9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('Model3Unbalanced08.h5')"
      ],
      "metadata": {
        "id": "xac47tKAXYJT"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model3, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "1dVvLzBo61G8",
        "outputId": "f2a52de6-32df-4939-e04c-4a3423e6463e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred3=model3.predict(testing_padded)\n",
        "rounded_labels3=np.argmax(pred3, axis=1).astype(int)\n",
        "cm2 = confusion_matrix(testing_labels, rounded_labels3)\n",
        "print(cm2)\n",
        "print(classification_report(testing_labels, rounded_labels3, labels=[0,1,2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vg0RP9_k63Ri",
        "outputId": "f1b9128b-05e6-4647-c827-2bf6da03b83f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2514   45  136]\n",
            " [  73  633   92]\n",
            " [ 135   97  882]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.93      0.93      2695\n",
            "           1       0.82      0.79      0.80       798\n",
            "           2       0.79      0.79      0.79      1114\n",
            "\n",
            "    accuracy                           0.87      4607\n",
            "   macro avg       0.84      0.84      0.84      4607\n",
            "weighted avg       0.87      0.87      0.87      4607\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred12=model3.predict(testing_padded12)\n",
        "rounded_labels312=np.argmax(pred12, axis=1).astype(int)\n",
        "cm12 = confusion_matrix(testing_labels12, rounded_labels312)\n",
        "print(cm12)\n",
        "print(classification_report(testing_labels12, rounded_labels312, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "MfclteTGK-_x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d36cc02-65b7-4f15-a64a-6d46755c40f7"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[496  38  53]\n",
            " [ 51 183  19]\n",
            " [ 84 130 238]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.84      0.81       587\n",
            "           1       0.52      0.72      0.61       253\n",
            "           2       0.77      0.53      0.62       452\n",
            "\n",
            "    accuracy                           0.71      1292\n",
            "   macro avg       0.69      0.70      0.68      1292\n",
            "weighted avg       0.73      0.71      0.71      1292\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred34=model3.predict(testing_padded34)\n",
        "rounded_labels334=np.argmax(pred34, axis=1).astype(int)\n",
        "cm34 = confusion_matrix(testing_labels34, rounded_labels334)\n",
        "print(cm34)\n",
        "print(classification_report(testing_labels34, rounded_labels334, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "zYydjCkILMG-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1af52ff-4d89-4a38-ebec-052b7553e89e"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 454    0   72]\n",
            " [   0    6    1]\n",
            " [  15    1 1291]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.86      0.91       526\n",
            "           1       0.86      0.86      0.86         7\n",
            "           2       0.95      0.99      0.97      1307\n",
            "\n",
            "    accuracy                           0.95      1840\n",
            "   macro avg       0.92      0.90      0.91      1840\n",
            "weighted avg       0.95      0.95      0.95      1840\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred5=model3.predict(testing_padded5)\n",
        "rounded_labels35=np.argmax(pred5, axis=1).astype(int)\n",
        "cm5 = confusion_matrix(testing_labels5, rounded_labels35)\n",
        "print(cm5)\n",
        "print(classification_report(testing_labels5, rounded_labels35, labels=[0,1,2]))"
      ],
      "metadata": {
        "id": "WEXdC3JtMXBu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1684213-bfbd-4174-ba79-7b2800ee41df"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3562   77  396]\n",
            " [ 317  506  631]\n",
            " [  98  147 2582]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.88      0.89      4035\n",
            "           1       0.69      0.35      0.46      1454\n",
            "           2       0.72      0.91      0.80      2827\n",
            "\n",
            "    accuracy                           0.80      8316\n",
            "   macro avg       0.77      0.71      0.72      8316\n",
            "weighted avg       0.80      0.80      0.79      8316\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "ddbAtRKXatG3",
        "outputId": "8b3af61d-670a-44ca-df8c-f7bac03950ac"
      },
      "execution_count": 52,
      "outputs": [
        {
          "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 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Conclusion\n",
        "###The accuracy for the three models is 58% yet the complexity varies. The first model has 690 579 trainale parameters which makes it the most complex amongs the three of them with the second having 618 083 trainable parameters and the third having 618 739 trainable parameters.\n",
        "###After giving the problem a first look, we can suspect that:\n",
        "* This is a deep Learning Problem where we need more data and a bigger model so we can track the pattern and better the model's accuracy\n",
        "* Te model is not getting the pattern in the data well so this can be due to some data imbalence \n",
        "* We need to further explore our data and to create a bigger model"
      ],
      "metadata": {
        "id": "__2IcBnQa-4Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Terms\n",
        "##BURT: is a transformar model\n",
        "##Sentiment Classification: A prabability distrubution over two classes\n",
        "##Text Encoding: How to encode the texts into a numerical presentation that we can be handled by the machine learning model\n",
        "##Transformers: The State-Of-The-Art architecture for language modeling\n",
        "#Word2vec: 2013, The Model that introduced distributed representations for language modeling"
      ],
      "metadata": {
        "id": "5SNCH0-6Eq4I"
      }
    }
  ]
}