HASOC_2023_Bodo_Code_Fellas.ipynb

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        "# Hate Speech Detection on Bodo - HASOC 2023\n",
        "\n",
        "## Team: Code Fellas\n",
        "- Members: Abhinav, Adarsh, Ananya, Dinesh\n",
        "\n",
        "In the HASOC 2023 competition, our team \"Code Fellas\" took on the challenge of hate speech detection. We employed a variety of approaches, ranging from basic machine learning models to more advanced deep learning techniques.\n",
        "\n",
        "### Approaches Explored:\n",
        "\n",
        "1. **Traditional Models:**\n",
        "   - Logistic Regression\n",
        "   - Support Vector Machine (SVM)\n",
        "   - XGBoost\n",
        "   - Decision Trees\n",
        "\n",
        "2. **Deep Learning Models:**\n",
        "   - LSTM (Long Short-Term Memory)\n",
        "   - BiLSTM (Bidirectional LSTM)\n",
        "   - LSTM with CNN 1D\n",
        "   - BiLSTM with CNN 1D\n",
        "   - XLM Roberta\n",
        "   - M-Bert (Cased and Uncased)\n",
        "   - M-Roberta\n",
        "   - Distilled Bert\n",
        "   - Indic Bert\n",
        "\n",
        "### Results:\n",
        "After rigorous experimentation, we found that the BiLSTM model yielded the best accuracy for hate speech detection in Bodo, based on our research. The model achieved an impressive F1 Score of 0.83513, showcasing its effectiveness in handling the nuances of the Bodo language and detecting hate speech accurately.\n",
        "As bodo is a low resource language unlike assamese and bengali, bert based models may not give better results as compared to that of BiLSTM.\n",
        "\n",
        "Our journey in this competition allowed us to delve into the complexities of hate speech detection, explore a wide range of models, and understand their strengths and weaknesses in the context of Bodo text.\n",
        "\n",
        "We're proud of our team's collaborative efforts and the achievements we've made in advancing the field of hate speech detection for the Bodo language. We look forward to future opportunities to contribute to such meaningful tasks"
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        "# Importing dataset\n",
        "data1 = pd.read_csv(\"/content/train_BO_AH_HASOC2023.csv\")\n",
        "test_data = pd.read_csv(\"/content/test_BO_AH_HASOC2023.csv\")"
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              "   S. No.                                               text task_1\n",
              "0       1       गोदाव खामानि मावओ बोला नो सानसे देरहा थारगोन    NOT\n",
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              "3       4            थोद जामबा सैमा साला मा मिसेस जाखो बेलाय    HOF\n",
              "4       5  माखौ बकिबाय थादों नों बोरमा फानथा दम दंब्ला खा...    HOF"
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              "      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-93e07c45-f55e-4066-913c-842a19fd4927 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-93e07c45-f55e-4066-913c-842a19fd4927');\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",
              "\n",
              "\n",
              "<div id=\"df-4f8deeda-d7c0-4529-a741-52b48dce097e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4f8deeda-d7c0-4529-a741-52b48dce097e')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4f8deeda-d7c0-4529-a741-52b48dce097e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data Preprocessing and Cleaning"
      ],
      "metadata": {
        "id": "WPu5FRRwWIfX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import nltk\n",
        "import string"
      ],
      "metadata": {
        "id": "xOckEHcX_DWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1 = data1[[\"text\", \"task_1\"]]  # task_1 denotes the labels"
      ],
      "metadata": {
        "id": "A2stEXKFVz2D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing usernames from the text and replacing them with an empty string\n",
        "def username_remover(text):\n",
        "  text = re.sub(r'@[^ ]+',\"\", text)\n",
        "  return text"
      ],
      "metadata": {
        "id": "yOIj6cDNYd0X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1[\"text\"] = data1[\"text\"].apply(username_remover)"
      ],
      "metadata": {
        "id": "qnvw0RmJYqJR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0b47480-24bb-43f2-e5f9-d810a9e1c0f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-60-0f3e037ba30c>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data1[\"text\"] = data1[\"text\"].apply(username_remover)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cleaning the text by removing urls, newlines, puncuations, brackets etc. Then the clean text is split into words and then reduced. The reduced words are then joined."
      ],
      "metadata": {
        "id": "pcFy6P8BxKNf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that cleans the text by removing the unnecessary parts mentioned above\n",
        "def clean(text):\n",
        "  text = re.sub(r'#\\w+', '', text)\n",
        "  text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "  text = re.sub('\\n', '', text)\n",
        "  text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "  pattern = re.compile(r\"(.)\\1{2,}\")\n",
        "  words = text.split()\n",
        "  reduced_words = []\n",
        "  for word in words:\n",
        "      reduced_word = pattern.sub(r\"\\1\\1\", word)\n",
        "      reduced_words.append(reduced_word)\n",
        "  text = ' '.join(reduced_words)\n",
        "  return text"
      ],
      "metadata": {
        "id": "my4c5Y4nWiXA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing the function. You can observe that the 2nd word (මමමමමමම) in the sentence got reduced to (මම).\n",
        "print(clean(\"සුමිත් මමමමමමම අවස්ථාවේ සිටියාවූ උපදේශයේ උපකාරයක් පවතී.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQD9gjdYahge",
        "outputId": "de6aef9e-609f-4715-cb1d-d41724b00e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "සුමිත් මම අවස්ථාවේ සිටියාවූ උපදේශයේ උපකාරයක් පවතී\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Text is cleaned by applying the clean function\n",
        "data1[\"text\"] = data1[\"text\"].apply(clean)"
      ],
      "metadata": {
        "id": "s-po_eiVajde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "767efb7c-3f93-44f4-d14d-1833e8004e08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-63-c276db5b44b7>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data1[\"text\"] = data1[\"text\"].apply(clean)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing duplicates from the text\n",
        "data1 = data1.drop_duplicates('text')"
      ],
      "metadata": {
        "id": "tKuav3NwbyE4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GNiDVTkzb_Xx",
        "outputId": "d6e54047-2010-4978-8fcc-df5af13db638"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text      0\n",
              "task_1    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1['task_1'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4Pdt29xcbJd",
        "outputId": "169311e6-06ac-4893-a852-522c0215d3d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    998\n",
              "0    681\n",
              "Name: task_1, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "LR4yQ0U1aL4s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting data into text and label\n",
        "x = data1['text']\n",
        "y = data1['task_1']"
      ],
      "metadata": {
        "id": "ZWPDwiCIaTh9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x\n",
        "y_train = y"
      ],
      "metadata": {
        "id": "1f_9E8Pwu4Yn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Tokenization to convert text to keras tensors"
      ],
      "metadata": {
        "id": "dEg1EECPA4YL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras-preprocessing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6423257b-3ca9-4ecc-b3e2-a9fd7f449839",
        "id": "_JXCcrnhtVO4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras-preprocessing in /usr/local/lib/python3.10/dist-packages (1.1.2)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from keras-preprocessing) (1.23.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from keras-preprocessing) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv1D, Embedding, Dense,MaxPool1D, LSTM, Dropout\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "# from keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from wordcloud import WordCloud"
      ],
      "metadata": {
        "id": "HzQ_4ikZy1VW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the tokenizer\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(x_train.values)\n",
        "word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens' % len(word_index))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KTtHUFAyF8c",
        "outputId": "9fdb6bc0-8844-4146-91c7-628fdc8ddb45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4806 unique tokens\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the maximum length of text in the dataset\n",
        "lengths = []\n",
        "for i in range(0,len(x_train.values)):\n",
        "  lengths.append(len(x_train.values[i]))\n",
        "max(lengths)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KMAP9sUkyIyB",
        "outputId": "b3f877dc-bd4c-4f24-b5f1-9efb3b5fa23f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "394"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting text to tensors by tokenization\n",
        "X = tokenizer.texts_to_sequences(x_train.values)\n",
        "X[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjbmVojvyNMw",
        "outputId": "9acb6c7d-3bd2-4cec-ecbf-e686fefadfe3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[252, 20, 1502, 1503, 172, 456, 457, 869]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequence padding\n",
        "X = pad_sequences(X, maxlen=130)"
      ],
      "metadata": {
        "id": "5tcANP0LyN0R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbVflrDjyP64",
        "outputId": "b15328df-e22e-419e-9dce-235038b56a23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1679, 130)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Shape of data tensor:', X.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wNjUrOBVzpKF",
        "outputId": "6c101d9d-2a72-49d9-f672-a5e51f15ca37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data tensor: (1679, 130)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Appyling our models, LSTM, BiLSTM and CNN"
      ],
      "metadata": {
        "id": "cFpLzchKA2Xu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.regularizers import l2\n",
        "from keras.layers import Conv1D, Embedding, Dense, MaxPool1D, Bidirectional, LSTM, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "metadata": {
        "id": "J55b32KyCqPm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### i. Using Bidirectional LSTM layer"
      ],
      "metadata": {
        "id": "cjfEV_AKFMXk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(len(word_index) + 1, 128, input_length=X.shape[1]))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Bidirectional(LSTM(128, dropout=0.2, recurrent_dropout=0.2, kernel_regularizer=l2(1e-4))))  # BiLSTM layer\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(256,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "metadata": {
        "id": "NxkgStItrzV3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a6262d3-29b0-445b-be88-5283d5ec1120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_2 (Embedding)     (None, 130, 128)          615296    \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 130, 128)          0         \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirectio  (None, 256)              263168    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 256)               65792     \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 257       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 944,513\n",
            "Trainable params: 944,513\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### After experimentation we arrived at fitting the model using 3 epochs and a batch size of 64, which yielded the best results mentioned in the beginning."
      ],
      "metadata": {
        "id": "P6Hc8bmLuA-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# history = model.fit(X, yc_train, epochs=20, batch_size=32, validation_split=0.2, callbacks=[early_stopping])\n",
        "history = model.fit(X, y_train, epochs=3, batch_size=64, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kir8SJDszvGp",
        "outputId": "96eecf72-09bf-4a7d-b702-1f43b8c10790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "21/21 [==============================] - 50s 2s/step - loss: 0.7104 - accuracy: 0.5957 - val_loss: 0.6964 - val_accuracy: 0.5774\n",
            "Epoch 2/3\n",
            "21/21 [==============================] - 26s 1s/step - loss: 0.6179 - accuracy: 0.6478 - val_loss: 0.5553 - val_accuracy: 0.7679\n",
            "Epoch 3/3\n",
            "21/21 [==============================] - 27s 1s/step - loss: 0.2768 - accuracy: 0.9114 - val_loss: 0.5735 - val_accuracy: 0.7857\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the text from test data\n",
        "x_test = test_data[\"text\"]"
      ],
      "metadata": {
        "id": "NqWdrnM802_K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhNZBix81Dfh",
        "outputId": "1cfe6cad-0273-4b6a-9edb-a0f17b1c35ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              BPF बानाय लांनाय लामाया 5 बोसोरानो जोरासै\n",
              "1      बै समाव माबेयाव हाबसोनानै दंमोन नोंलाय, दाना ब...\n",
              "2      बे थांखिखौ मिनिग्रापोरा हारिखौ लेवारपोरबायदि थ...\n",
              "3                   मोसौ खुगायाव एमफौ नांबाय नोंनाव सैमा\n",
              "4      2003आव BTC गोरोबथा जादों बेनि थाखाय बो थोजासे ...\n",
              "                             ...                        \n",
              "415                                आं आनो खाजा होआखै मोन\n",
              "416    बियो आंखौ बिनि बिमा बिफा बुथारनायनि थाखाय दायन...\n",
              "417                     राहुलआ गावनि फोरोंगिरिखौ मान होआ\n",
              "418                     राकेशआ गावनि फोरोंगिरिखौ मान होआ\n",
              "419                      रमेशआ निखावरि मानसिखौ रायज्लाया\n",
              "Name: text, Length: 420, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting the test texts to tensors\n",
        "x_test = tokenizer.texts_to_sequences(x_test.ravel())\n",
        "x_test = pad_sequences(x_test, maxlen=130)\n",
        "test_prediction = (model.predict(x_test) > 0.5).astype(\"int32\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ay1diJiZ1EWw",
        "outputId": "078933c6-97e4-420a-9af6-26dba4ea3691"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 2s 108ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting the test values\n",
        "test_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhcNqqSQ1UXB",
        "outputId": "ce8235fc-91eb-42c9-d09e-21b84c05b8f2"
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        "fin_data = pd.DataFrame()"
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        "id": "-in-fGKPqwnT",
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              "Empty DataFrame\n",
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      "cell_type": "code",
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        "# Creating list of Sr. No.s\n",
        "lst = []\n",
        "for i in range(len(test_prediction)):\n",
        "  lst.append(i+1)"
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        "fin_data[\"S. No.\"] = pd.DataFrame(lst)\n",
        "fin_data[\"task_1\"] = pd.DataFrame(test_prediction)"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mapping \"NOT\" to 0 and \"HOF\" to 1\n",
        "fin_data[\"task_1\"] = fin_data[\"task_1\"].map({1 : \"HOF\", 0 : \"NOT\"})"
      ],
      "metadata": {
        "id": "hrdPz82gq5S0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing our predictions to csv\n",
        "fin_data.to_csv(\"bodo_BiLSTM_without_earlystopping.csv\")"
      ],
      "metadata": {
        "id": "tiMUskxAq8ua"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### ii. Using Conv1D layer with BiLSTM Layer using early stopping\n",
        "#### Early stopping is used to prevent overfitting"
      ],
      "metadata": {
        "id": "8OLOzsk-L31N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = Sequential()\n",
        "model2.add(Embedding(len(word_index) + 1, 128, input_length = X.shape[1]))\n",
        "model2.add(Dropout(0.2))\n",
        "model2.add(Conv1D(filters=128, kernel_size=3, padding='same', activation='relu'))   # Conv1D layer\n",
        "model2.add(MaxPool1D(pool_size = 2))\n",
        "# model2.add(LSTM(200,dropout=0.2, recurrent_dropout=0.2))\n",
        "model2.add(Bidirectional(LSTM(128, dropout=0.2, recurrent_dropout=0.2, kernel_regularizer=l2(1e-4))))  # BiLSTM layer\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(256,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model2.add(Dense(1,activation='sigmoid'))\n",
        "model2.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "print(model2.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5yjhUQvI2wu",
        "outputId": "5ff0e36c-e2e4-4016-a04e-4931435393e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_3 (Embedding)     (None, 130, 128)          615296    \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 130, 128)          0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 130, 128)          49280     \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 65, 128)          0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " bidirectional_3 (Bidirectio  (None, 256)              263168    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 257       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 928,001\n",
            "Trainable params: 928,001\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using early to stopping using 3 patience epochs\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)"
      ],
      "metadata": {
        "id": "5G1u5vgaymeS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### After experimentation we arrived at fitting the model using 3 epochs and a batch size of 64."
      ],
      "metadata": {
        "id": "NbYwGEchudIS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model2.fit(X, yc_train,epochs=20,batch_size = 32, validation_split=0.2, callbacks=[early_stopping])\n",
        "model2.fit(X, y_train,epochs=3,batch_size = 64, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKk-4hseI3eY",
        "outputId": "f2d431fd-a7ee-485a-bef1-1c6b7068eaaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "21/21 [==============================] - 32s 1s/step - loss: 0.7025 - accuracy: 0.5987 - val_loss: 0.6972 - val_accuracy: 0.5774\n",
            "Epoch 2/3\n",
            "21/21 [==============================] - 13s 640ms/step - loss: 0.5904 - accuracy: 0.6813 - val_loss: 0.5974 - val_accuracy: 0.7708\n",
            "Epoch 3/3\n",
            "21/21 [==============================] - 13s 633ms/step - loss: 0.2621 - accuracy: 0.9181 - val_loss: 0.6294 - val_accuracy: 0.8065\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7a39bf7d97e0>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting the test labels\n",
        "test_prediction2 = (model2.predict(x_test) > 0.5).astype(\"int32\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-w0acQ6SJDDY",
        "outputId": "dc84886b-c9ba-44a6-f8a9-4f37dc420d47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 1s 56ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting final data to DataFrame\n",
        "fin_data = pd.DataFrame()"
      ],
      "metadata": {
        "id": "8wS1abvcJg6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Final data\n",
        "fin_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "H7ft7QhvMPpU",
        "outputId": "2d3589a6-d9e8-43b1-a0bb-8f9d16cd1778"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
            ],
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating list of Sr. No.s\n",
        "lst = []\n",
        "for i in range(len(test_prediction)):\n",
        "  lst.append(i+1)"
      ],
      "metadata": {
        "id": "me3TqARNJn9i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding column headers to file\n",
        "fin_data[\"S. No.\"] = pd.DataFrame(lst)\n",
        "fin_data[\"task_1\"] = pd.DataFrame(test_prediction2)"
      ],
      "metadata": {
        "id": "8Bmv3Hz-JqSK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mapping \"NOT\" to 0 and \"HOF\" to 1\n",
        "fin_data[\"task_1\"] = fin_data[\"task_1\"].map({1 : \"HOF\", 0 : \"NOT\"})"
      ],
      "metadata": {
        "id": "sxnLVwsLJtiR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing our predictions to csv\n",
        "fin_data.to_csv(\"bodo_BiLSTM_CNN1D_without_earlystopping.csv\")"
      ],
      "metadata": {
        "id": "cr7-egT5Jw3F"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}