NER_using_Bilstm.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Name Entity Recognition using BiLstm "
      ],
      "metadata": {
        "id": "M8P-Rn7cHZZ8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "wDLBRim92h8l",
        "outputId": "43ca1717-7f9a-4f10-9ce1-654da339fa0d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-59061f28-4c09-47d2-8073-8d2efd6bc07e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-59061f28-4c09-47d2-8073-8d2efd6bc07e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving ner_datasetreference.csv to ner_datasetreference.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "anUwu92G3BKj"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "g0yr1eXv3HvV",
        "outputId": "de9a6fa1-6109-4d81-d9e8-3006a7c5569b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Sentence #           Word  POS Tag\n",
              "0  Sentence: 1      Thousands  NNS   O\n",
              "1          NaN             of   IN   O\n",
              "2          NaN  demonstrators  NNS   O\n",
              "3          NaN           have  VBP   O\n",
              "4          NaN        marched  VBN   O"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentence #</th>\n",
              "      <th>Word</th>\n",
              "      <th>POS</th>\n",
              "      <th>Tag</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sentence: 1</td>\n",
              "      <td>Thousands</td>\n",
              "      <td>NNS</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>of</td>\n",
              "      <td>IN</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>demonstrators</td>\n",
              "      <td>NNS</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>have</td>\n",
              "      <td>VBP</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>marched</td>\n",
              "      <td>VBN</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                     [key], {});\n",
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              "            + ' 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": 3
        }
      ],
      "source": [
        "data=pd.read_csv('ner_datasetreference.csv',encoding='unicode_escape')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "3pE4Mmnp3r9T"
      },
      "outputs": [],
      "source": [
        "from itertools import chain\n",
        "def get_dict_map(data, token_or_tag):\n",
        "    tok2idx = {}\n",
        "    idx2tok = {}\n",
        "    \n",
        "    if token_or_tag == 'token':\n",
        "        vocab = list(set(data['Word'].to_list()))\n",
        "    else:\n",
        "        vocab = list(set(data['Tag'].to_list()))\n",
        "    \n",
        "    idx2tok = {idx:tok for  idx, tok in enumerate(vocab)}\n",
        "    tok2idx = {tok:idx for  idx, tok in enumerate(vocab)}\n",
        "    return tok2idx, idx2tok\n",
        "\n",
        "\n",
        "token2idx, idx2token = get_dict_map(data, 'token')\n",
        "tag2idx, idx2tag = get_dict_map(data, 'tag')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "F3bRhNnQ77Jh",
        "outputId": "b777c415-d3b2-43b6-d9b5-66594a83c270"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          Sentence #           Word  POS Tag  Word_idx  Tag_idx\n",
              "0        Sentence: 1      Thousands  NNS   O     25314       16\n",
              "1                NaN             of   IN   O     21115       16\n",
              "2                NaN  demonstrators  NNS   O     30777       16\n",
              "3                NaN           have  VBP   O     20896       16\n",
              "4                NaN        marched  VBN   O     23024       16\n",
              "...              ...            ...  ...  ..       ...      ...\n",
              "1048570          NaN           they  PRP   O      3274       16\n",
              "1048571          NaN      responded  VBD   O      4186       16\n",
              "1048572          NaN             to   TO   O      4158       16\n",
              "1048573          NaN            the   DT   O      3568       16\n",
              "1048574          NaN         attack   NN   O     29489       16\n",
              "\n",
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              "      <th>1048570</th>\n",
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              "      <th>1048573</th>\n",
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              "      <td>16</td>\n",
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              "      <th>1048574</th>\n",
              "      <td>NaN</td>\n",
              "      <td>attack</td>\n",
              "      <td>NN</td>\n",
              "      <td>O</td>\n",
              "      <td>29489</td>\n",
              "      <td>16</td>\n",
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              "<p>1048575 rows × 6 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-799a2345-20e5-47da-b5d3-dec32f411b91');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data['Word_idx'] = data['Word'].map(token2idx)\n",
        "data['Tag_idx'] = data['Tag'].map(tag2idx)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "C_xBRTve79AA"
      },
      "outputs": [],
      "source": [
        "data_fillna = data.fillna(method='ffill', axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "PkwlXRqX8joJ"
      },
      "outputs": [],
      "source": [
        "data_group=data_fillna.groupby(['Sentence #'],as_index=False)[['Word', 'POS', 'Tag', 'Word_idx', 'Tag_idx']].agg(lambda x: list(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "B57zIUiu9Fph",
        "outputId": "be48ea31-59ab-4b8f-f314-3f3ac732028a"
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        {
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          "data": {
            "text/plain": [
              "        Sentence #                                               Word  \\\n",
              "0      Sentence: 1  [Thousands, of, demonstrators, have, marched, ...   \n",
              "1     Sentence: 10  [Iranian, officials, say, they, expect, to, ge...   \n",
              "2    Sentence: 100  [Helicopter, gunships, Saturday, pounded, mili...   \n",
              "3   Sentence: 1000  [They, left, after, a, tense, hour-long, stand...   \n",
              "4  Sentence: 10000  [U.N., relief, coordinator, Jan, Egeland, said...   \n",
              "\n",
              "                                                 POS  \\\n",
              "0  [NNS, IN, NNS, VBP, VBN, IN, NNP, TO, VB, DT, ...   \n",
              "1  [JJ, NNS, VBP, PRP, VBP, TO, VB, NN, TO, JJ, J...   \n",
              "2  [NN, NNS, NNP, VBD, JJ, NNS, IN, DT, NNP, JJ, ...   \n",
              "3     [PRP, VBD, IN, DT, NN, JJ, NN, IN, NN, NNS, .]   \n",
              "4  [NNP, NN, NN, NNP, NNP, VBD, NNP, ,, NNP, ,, J...   \n",
              "\n",
              "                                                 Tag  \\\n",
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              "2  [O, O, B-tim, O, O, O, O, O, B-geo, O, O, O, O...   \n",
              "3                  [O, O, O, O, O, O, O, O, O, O, O]   \n",
              "4  [B-geo, O, O, B-per, I-per, O, B-tim, O, B-geo...   \n",
              "\n",
              "                                            Word_idx  \\\n",
              "0  [25314, 21115, 30777, 20896, 23024, 26050, 139...   \n",
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              "\n",
              "                                             Tag_idx  \n",
              "0  [16, 16, 16, 16, 16, 16, 1, 16, 16, 16, 16, 16...  \n",
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              "      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-2766cde7-a3b4-40dd-926e-b626bdaa4bc5 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-2766cde7-a3b4-40dd-926e-b626bdaa4bc5');\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": 8
        }
      ],
      "source": [
        "data_group.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "WQZtIU_U-REc"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0_4g01p-cpI",
        "outputId": "b958a329-a70f-4d7b-be6a-442e5cc1a772"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25314 21115 30777 ... 35177 35177 35177]\n",
            " [16559  9333 13572 ... 35177 35177 35177]\n",
            " [25034 11753   157 ... 35177 35177 35177]\n",
            " ...\n",
            " [26595 23895 27403 ... 35177 35177 35177]\n",
            " [ 4456  3218 27055 ... 35177 35177 35177]\n",
            " [27639 10475 27098 ... 35177 35177 35177]]\n",
            "[[16 16 16 ... 16 16 16]\n",
            " [11 16 16 ... 16 16 16]\n",
            " [16 16  9 ... 16 16 16]\n",
            " ...\n",
            " [16 16 16 ... 16 16 16]\n",
            " [16 16 16 ... 16 16 16]\n",
            " [11 16 16 ... 16 16 16]]\n",
            "train_tokens length: 32372 \n",
            "train_tokens length: 32372 \n",
            "test_tokens length: 4796 \n",
            "test_tags: 4796 \n",
            "val_tokens: 10791 \n",
            "val_tags: 10791\n"
          ]
        }
      ],
      "source": [
        "def get_pad_train_test_val(data_group, data):\n",
        "\n",
        "    #get max token and tag length\n",
        "    n_token = len(list(set(data['Word'].to_list())))\n",
        "    n_tag = len(list(set(data['Tag'].to_list())))\n",
        "\n",
        "    #Pad tokens (X var)    \n",
        "    tokens = data_group['Word_idx'].tolist()\n",
        "    maxlen = max([len(s) for s in tokens])\n",
        "    pad_tokens = pad_sequences(tokens, maxlen=maxlen, dtype='int32', padding='post', value= n_token - 1)\n",
        "    print(pad_tokens[:10])\n",
        "    #Pad Tags (y var) and convert it into one hot encoding\n",
        "    tags = data_group['Tag_idx'].tolist()\n",
        "    pad_tags = pad_sequences(tags, maxlen=maxlen, dtype='int32', padding='post', value= tag2idx[\"O\"])\n",
        "    print(pad_tags[:10])\n",
        "    n_tags = len(tag2idx)\n",
        "    pad_tags = [to_categorical(i, num_classes=n_tags) for i in pad_tags]\n",
        "    \n",
        "    #Split train, test and validation set\n",
        "    tokens_, test_tokens, tags_, test_tags = train_test_split(pad_tokens, pad_tags, test_size=0.1, train_size=0.9, random_state=2020)\n",
        "    train_tokens, val_tokens, train_tags, val_tags = train_test_split(tokens_,tags_,test_size = 0.25,train_size =0.75, random_state=2020)\n",
        "\n",
        "    print(\n",
        "        'train_tokens length:', len(train_tokens),\n",
        "        '\\ntrain_tokens length:', len(train_tokens),\n",
        "        '\\ntest_tokens length:', len(test_tokens),\n",
        "        '\\ntest_tags:', len(test_tags),\n",
        "        '\\nval_tokens:', len(val_tokens),\n",
        "        '\\nval_tags:', len(val_tags),\n",
        "    )\n",
        "    \n",
        "    return train_tokens, val_tokens, test_tokens, train_tags, val_tags, test_tags\n",
        "\n",
        "train_tokens, val_tokens, test_tokens, train_tags, val_tags, test_tags = get_pad_train_test_val(data_group, data)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zPqCyjMDFtGy",
        "outputId": "98b4b4f7-e0ab-4e1f-b7e2-9fb8a4db7ecf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "104"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "len(train_tokens[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "abrvivuLBRuK"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow\n",
        "from tensorflow.keras import Sequential, Model, Input\n",
        "from tensorflow.keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional\n",
        "from tensorflow.keras.utils import plot_model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "9WyXu8yDBRIX"
      },
      "outputs": [],
      "source": [
        "from numpy.random import seed\n",
        "seed(1)\n",
        "tensorflow.random.set_seed(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "lFVOr6IYFg5B"
      },
      "outputs": [],
      "source": [
        "input_dim = len(list(set(data['Word'].to_list())))+1\n",
        "output_dim = 64\n",
        "input_length = max([len(s) for s in data_group['Word_idx'].tolist()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__IyGUZOHNTz",
        "outputId": "486efeda-3f0e-4a87-b1c1-45dec7b21cfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35179"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "input_dim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "edQa4XNPHRQa"
      },
      "outputs": [],
      "source": [
        "n_tags = len(tag2idx)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "2oigkj4wMivY"
      },
      "outputs": [],
      "source": [
        "output_dim=64\n",
        "input_dim=35179\n",
        "input_length=104\n",
        "n_tags=17"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLY8an05KWgO",
        "outputId": "68ccbf4f-2ee9-4e0e-8147-5f23e41dc030"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17\n"
          ]
        }
      ],
      "source": [
        "print(n_tags)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "L0TcovRvKYgy"
      },
      "outputs": [],
      "source": [
        "def get_billstm_model():\n",
        "  model=Sequential()\n",
        "  model.add(Embedding(input_dim=input_dim,output_dim=output_dim,input_length=input_length))\n",
        "  #add bidirectional Lstm\n",
        "  model.add(Bidirectional(LSTM(units=output_dim,return_sequences=True,dropout=.2,recurrent_dropout=.5)))\n",
        "  # add Lstm\n",
        "  \n",
        "  model.add(LSTM(units=output_dim, return_sequences=True, dropout=0.5, recurrent_dropout=0.5))\n",
        "  #add time distributed layer\n",
        "  model.add(TimeDistributed(Dense(n_tags, activation=\"relu\")))\n",
        "\n",
        "  adam = tensorflow.keras.optimizers.Adam(lr=0.0005, beta_1=0.9, beta_2=0.999)\n",
        "  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "  model.summary()\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "byi5uCgjLhQu"
      },
      "outputs": [],
      "source": [
        "def train_model(X, y, model):\n",
        "    loss = list()\n",
        "    for i in range(10):\n",
        "        # fit model for one epoch on this sequence\n",
        "        hist = model.fit(X, y, batch_size=1000, verbose=1, epochs=1, validation_split=0.2)\n",
        "        loss.append(hist.history['loss'][0])\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        },
        "id": "xdGcbE5QMGc4",
        "outputId": "305ceb62-ab6c-46f9-aa87-d41f39376f60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 104, 64)           2251456   \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 104, 128)         66048     \n",
            " l)                                                              \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 104, 64)           49408     \n",
            "                                                                 \n",
            " time_distributed (TimeDistr  (None, 104, 17)          1105      \n",
            " ibuted)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,368,017\n",
            "Trainable params: 2,368,017\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "results = pd.DataFrame()\n",
        "model_bilstm_lstm = get_billstm_model()\n",
        "plot_model(model_bilstm_lstm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WFJY9Wg_MJ1k",
        "outputId": "0c8fe32d-00cf-41ca-dd8a-b68d7ece96ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - 44s 1s/step - loss: 0.7547 - accuracy: 0.9298 - val_loss: 0.3765 - val_accuracy: 0.9681\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.3302 - accuracy: 0.9677 - val_loss: 0.2786 - val_accuracy: 0.9681\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.2732 - accuracy: 0.9677 - val_loss: 0.2274 - val_accuracy: 0.9681\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.2355 - accuracy: 0.9677 - val_loss: 0.2114 - val_accuracy: 0.9681\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.2544 - accuracy: 0.9678 - val_loss: 0.2173 - val_accuracy: 0.9681\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.2139 - accuracy: 0.9678 - val_loss: 0.2005 - val_accuracy: 0.9681\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.1991 - accuracy: 0.9678 - val_loss: 0.1979 - val_accuracy: 0.9681\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.1918 - accuracy: 0.9678 - val_loss: 0.1886 - val_accuracy: 0.9682\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.1902 - accuracy: 0.9678 - val_loss: 0.1876 - val_accuracy: 0.9682\n",
            "26/26 [==============================] - 33s 1s/step - loss: 0.1873 - accuracy: 0.9678 - val_loss: 0.1832 - val_accuracy: 0.9682\n"
          ]
        }
      ],
      "source": [
        "results['with_add_lstm'] = train_model(train_tokens, np.array(train_tags), model_bilstm_lstm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "0lXVa1h1NFC9"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=results.values"
      ],
      "metadata": {
        "id": "821xBT25GCqW"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot([1,2,3,4,5,6,7,8,9,10],data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-Wa7_vtAGPW-",
        "outputId": "17ea7b40-69d0-4005-89be-d18bb7c7fb6f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe88c200150>]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "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": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "EBUqacK8Go1h"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "NER using Bilstm.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}