Named-Entity Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q4gSqDnOaJ7W"
   },
   "source": [
    "<h2 align=center> Named Entity Recognition (NER) using LSTMs with Keras</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fdo8oAodagwo"
   },
   "source": [
    "### Task 1: Project Overview and Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "oLK7Y1jiNXDa",
    "outputId": "0f319464-ee49-4035-f6fb-8396f488c41f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.3.0\n",
      "GPU detected: []\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "import tensorflow as tf\n",
    "print('Tensorflow version:', tf.__version__)\n",
    "print('GPU detected:', tf.config.list_physical_devices('GPU'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4N_AW6lMbB5N"
   },
   "source": [
    "### Task 2: Load and Explore the NER Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Essential info about tagged entities*:\n",
    "- geo = Geographical Entity\n",
    "- org = Organization\n",
    "- per = Person\n",
    "- gpe = Geopolitical Entity\n",
    "- tim = Time indicator\n",
    "- art = Artifact\n",
    "- eve = Event\n",
    "- nat = Natural Phenomenon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ner dataset source: https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence #</th>\n",
       "      <th>Word</th>\n",
       "      <th>POS</th>\n",
       "      <th>Tag</th>\n",
       "    </tr>\n",
       "  </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>Sentence: 1</td>\n",
       "      <td>of</td>\n",
       "      <td>IN</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>demonstrators</td>\n",
       "      <td>NNS</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>have</td>\n",
       "      <td>VBP</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>marched</td>\n",
       "      <td>VBN</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>through</td>\n",
       "      <td>IN</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>London</td>\n",
       "      <td>NNP</td>\n",
       "      <td>B-geo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>to</td>\n",
       "      <td>TO</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>protest</td>\n",
       "      <td>VB</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>the</td>\n",
       "      <td>DT</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>war</td>\n",
       "      <td>NN</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>in</td>\n",
       "      <td>IN</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>NNP</td>\n",
       "      <td>B-geo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>and</td>\n",
       "      <td>CC</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>demand</td>\n",
       "      <td>VB</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>the</td>\n",
       "      <td>DT</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>withdrawal</td>\n",
       "      <td>NN</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>of</td>\n",
       "      <td>IN</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>British</td>\n",
       "      <td>JJ</td>\n",
       "      <td>B-gpe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sentence: 1</td>\n",
       "      <td>troops</td>\n",
       "      <td>NNS</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sentence #           Word  POS    Tag\n",
       "0   Sentence: 1      Thousands  NNS      O\n",
       "1   Sentence: 1             of   IN      O\n",
       "2   Sentence: 1  demonstrators  NNS      O\n",
       "3   Sentence: 1           have  VBP      O\n",
       "4   Sentence: 1        marched  VBN      O\n",
       "5   Sentence: 1        through   IN      O\n",
       "6   Sentence: 1         London  NNP  B-geo\n",
       "7   Sentence: 1             to   TO      O\n",
       "8   Sentence: 1        protest   VB      O\n",
       "9   Sentence: 1            the   DT      O\n",
       "10  Sentence: 1            war   NN      O\n",
       "11  Sentence: 1             in   IN      O\n",
       "12  Sentence: 1           Iraq  NNP  B-geo\n",
       "13  Sentence: 1            and   CC      O\n",
       "14  Sentence: 1         demand   VB      O\n",
       "15  Sentence: 1            the   DT      O\n",
       "16  Sentence: 1     withdrawal   NN      O\n",
       "17  Sentence: 1             of   IN      O\n",
       "18  Sentence: 1        British   JJ  B-gpe\n",
       "19  Sentence: 1         troops  NNS      O"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/ner_dataset.csv', encoding='latin1')\n",
    "data = data.fillna(method='ffill')\n",
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "colab_type": "code",
    "id": "mCKmz4SAbI_m",
    "outputId": "a03b1aed-dc60-4f03-cb06-19e440fa6367"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique words in corpus:  35178\n",
      "Unique tags in corpus:  17\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique words in corpus: \", data['Word'].nunique())\n",
    "print(\"Unique tags in corpus: \", data['Tag'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='Tag'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import tkinter\n",
    "import matplotlib\n",
    "matplotlib.use('agg')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "plt.yticks(rotation=10, horizontalalignment='right')\n",
    "sns.countplot(y='Tag', data=data, dodge=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "riOztP-8NXHT",
    "outputId": "200d251b-17c9-451b-9e96-523f458be47e"
   },
   "outputs": [],
   "source": [
    "words = list(set(data[\"Word\"].values))\n",
    "words.append(\"ENDPAD\")\n",
    "num_words = len(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = list(set(data[\"Tag\"].values))\n",
    "num_tags = len(tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35179, 17)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_words, num_tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M9D9JEzUbdnS"
   },
   "source": [
    "### Task 3: Retrieve Sentences and Corresponsing Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VdJst_g5NYY_"
   },
   "outputs": [],
   "source": [
    "class SentenceGetter(object):\n",
    "    def __init__(self, data):\n",
    "        self.n_sent = 1\n",
    "        self.data = data\n",
    "        agg_func = lambda s: [(w, p, t) for w, p, t in zip(s[\"Word\"].values.tolist(),\n",
    "                                                          s[\"POS\"].values.tolist(),\n",
    "                                                          s[\"Tag\"].values.tolist())]\n",
    "        self.grouped = self.data.groupby(\"Sentence #\").apply(agg_func)\n",
    "        self.sentences = [s for s in self.grouped]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nMUQLppspkPj"
   },
   "outputs": [],
   "source": [
    "getter = SentenceGetter(data)\n",
    "sentences = getter.sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 434
    },
    "colab_type": "code",
    "id": "GhiSTt2UdzYC",
    "outputId": "95d275f6-f386-46d8-a583-2861d64c6b72"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Thousands', 'NNS', 'O'),\n",
       " ('of', 'IN', 'O'),\n",
       " ('demonstrators', 'NNS', 'O'),\n",
       " ('have', 'VBP', 'O'),\n",
       " ('marched', 'VBN', 'O'),\n",
       " ('through', 'IN', 'O'),\n",
       " ('London', 'NNP', 'B-geo'),\n",
       " ('to', 'TO', 'O'),\n",
       " ('protest', 'VB', 'O'),\n",
       " ('the', 'DT', 'O'),\n",
       " ('war', 'NN', 'O'),\n",
       " ('in', 'IN', 'O'),\n",
       " ('Iraq', 'NNP', 'B-geo'),\n",
       " ('and', 'CC', 'O'),\n",
       " ('demand', 'VB', 'O'),\n",
       " ('the', 'DT', 'O'),\n",
       " ('withdrawal', 'NN', 'O'),\n",
       " ('of', 'IN', 'O'),\n",
       " ('British', 'JJ', 'B-gpe'),\n",
       " ('troops', 'NNS', 'O'),\n",
       " ('from', 'IN', 'O'),\n",
       " ('that', 'DT', 'O'),\n",
       " ('country', 'NN', 'O'),\n",
       " ('.', '.', 'O')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ldhuogX4eHE4"
   },
   "source": [
    "### Task 4: Define Mappings between Sentences and Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SvENHO18pkaQ"
   },
   "outputs": [],
   "source": [
    "# enumerate() -> add a counter to an iterable and returns it\n",
    "word2idx = {w: i+1 for i, w in enumerate(words)}\n",
    "tag2idx = {t: i for i, t in enumerate(tags)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B-eve': 0,\n",
       " 'I-geo': 1,\n",
       " 'B-org': 2,\n",
       " 'I-per': 3,\n",
       " 'I-gpe': 4,\n",
       " 'I-art': 5,\n",
       " 'I-eve': 6,\n",
       " 'I-nat': 7,\n",
       " 'B-tim': 8,\n",
       " 'I-tim': 9,\n",
       " 'O': 10,\n",
       " 'B-nat': 11,\n",
       " 'B-per': 12,\n",
       " 'B-art': 13,\n",
       " 'B-gpe': 14,\n",
       " 'I-org': 15,\n",
       " 'B-geo': 16}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag2idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zXzE0MdsemCH"
   },
   "source": [
    "### Task 5: Padding Input Sentences and Creating Train/Test Splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "R44g5T7NYp_H",
    "outputId": "135a85a6-7a85-4b16-a8eb-3ad7bd4da5b6"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([len(s) for s in sentences], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FS4u3CRkpkc1"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "max_len = 50\n",
    "\n",
    "X = [[word2idx[w[0]] for w in s] for s in sentences]\n",
    "X = pad_sequences(maxlen=max_len, sequences=X, padding='post', value=num_words-1)\n",
    "\n",
    "y = [[tag2idx[w[2]] for w in s] for s in sentences]\n",
    "y = pad_sequences(maxlen=max_len, sequences=y, padding='post', value=tag2idx[\"O\"])\n",
    "y = [to_categorical(i, num_classes=num_tags) for i in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23546, 21601, 15631,  3083, 34367,  1579, 19918, 18497, 33501,\n",
       "        6465,  8030,  5525, 14139, 14313, 34615,  6465,  8587, 21601,\n",
       "       15224, 11209, 31290, 15992,  9560, 26470, 35178, 35178, 35178,\n",
       "       35178, 35178, 35178, 35178, 35178, 35178, 35178, 35178, 35178,\n",
       "       35178, 35178, 35178, 35178, 35178, 35178, 35178, 35178, 35178,\n",
       "       35178, 35178, 35178, 35178, 35178], dtype=int32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q7VfnnkXpkfS"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P-r4PR85hpoF"
   },
   "source": [
    "### Task 6: Build and Compile a Bidirectional LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y2vM7IkXpkiH"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import Model, Input\n",
    "from tensorflow.keras.layers import LSTM, GRU, Embedding, Dense\n",
    "from tensorflow.keras.layers import TimeDistributed, SpatialDropout1D, Bidirectional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 330
    },
    "colab_type": "code",
    "id": "Aee3mCZ3pkkv",
    "outputId": "b7fb911b-21d1-43e6-adc9-bb2d8bdfb921"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 50)]              0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 50, 50)            1758950   \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d (SpatialDr (None, 50, 50)            0         \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 50, 200)           120800    \n",
      "_________________________________________________________________\n",
      "time_distributed (TimeDistri (None, 50, 17)            3417      \n",
      "=================================================================\n",
      "Total params: 1,883,167\n",
      "Trainable params: 1,883,167\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_word = Input(shape=(max_len,))\n",
    "model = Embedding(input_dim=num_words, output_dim=max_len, input_length=max_len)(input_word)\n",
    "# SpatialDropout1D -> rather than dropping dropping individual nodes, it drops the entire 1D feature map\n",
    "model = SpatialDropout1D(0.1)(model)\n",
    "# Rather than RNN, we are using LSTM to avoid Vanishing Gradient Problem\n",
    "# using Bi-directional LSTM because, our model can learn from the entire input, rather than just the previous timestamps\n",
    "model = Bidirectional(LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)\n",
    "out = TimeDistributed(Dense(num_tags, activation='softmax'))(model)\n",
    "model = Model(input_word, out)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kOBpQg26pkqh"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss='categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "My0tL0cciMXQ"
   },
   "source": [
    "### Task 7: Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from livelossplot.tf_keras import PlotLossesCallback\n",
    "from livelossplot import PlotLossesKeras\n",
    "import keras\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 536
    },
    "colab_type": "code",
    "id": "Q9HWH06Ypkxh",
    "outputId": "e83ba281-0c14-4bca-dacb-cb708edacba5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.955, max:    0.988, cur:    0.988)\n",
      "\tvalidation       \t (min:    0.980, max:    0.985, cur:    0.985)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.039, max:    0.195, cur:    0.039)\n",
      "\tvalidation       \t (min:    0.048, max:    0.072, cur:    0.048)\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.05166 to 0.04800, saving model to model_ner.h5\n",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1080/1080 [==============================] - 96s 89ms/step - loss: 0.0393 - accuracy: 0.9880 - val_loss: 0.0480 - val_accuracy: 0.9854\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_accuracy', patience=1, verbose=0, mode='max', restore_best_weights=False)\n",
    "\n",
    "filename = 'model_ner.h5'\n",
    "checkpoint = ModelCheckpoint(filename,\n",
    "                             monitor='val_loss',\n",
    "                             verbose=1,\n",
    "                             save_best_only=True,\n",
    "                             mode='min')\n",
    "\n",
    "callbacks = [PlotLossesKeras(), early_stopping, checkpoint]\n",
    "\n",
    "history = model.fit(\n",
    "    x_train, np.array(y_train),\n",
    "    validation_split=0.2,\n",
    "    batch_size=32,\n",
    "    epochs=3,\n",
    "    verbose=1,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 8: Build and Compile a Bidirectional GRU Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 50)]              0         \n",
      "_________________________________________________________________\n",
      "embedding_1 (Embedding)      (None, 50, 50)            1758950   \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_1 (Spatial (None, 50, 50)            0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 50, 200)           91200     \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 50, 17)            3417      \n",
      "=================================================================\n",
      "Total params: 1,853,567\n",
      "Trainable params: 1,853,567\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_word = Input(shape=(max_len,))\n",
    "model_gru = Embedding(input_dim=num_words, output_dim=max_len, input_length=max_len)(input_word)\n",
    "model_gru = SpatialDropout1D(0.1)(model_gru)\n",
    "model_gru = Bidirectional(GRU(units=100, return_sequences=True, recurrent_dropout=0.1))(model_gru)\n",
    "out = TimeDistributed(Dense(num_tags, activation='softmax'))(model_gru)\n",
    "model_gru = Model(input_word, out)\n",
    "\n",
    "model_gru.compile(optimizer='adam',\n",
    "             loss='categorical_crossentropy',\n",
    "             metrics=['accuracy'])\n",
    "\n",
    "model_gru.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.964, max:    0.989, cur:    0.989)\n",
      "\tvalidation       \t (min:    0.983, max:    0.986, cur:    0.986)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.034, max:    0.167, cur:    0.034)\n",
      "\tvalidation       \t (min:    0.045, max:    0.057, cur:    0.045)\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.04623 to 0.04516, saving model to model_ner_gru.h5\n",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1080/1080 [==============================] - 85s 79ms/step - loss: 0.0343 - accuracy: 0.9894 - val_loss: 0.0452 - val_accuracy: 0.9861\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_accuracy', patience=1, verbose=0, mode='max', restore_best_weights=False)\n",
    "\n",
    "filename = 'model_ner_gru.h5'\n",
    "checkpoint = ModelCheckpoint(filename,\n",
    "                             monitor='val_loss',\n",
    "                             verbose=1,\n",
    "                             save_best_only=True,\n",
    "                             mode='min')\n",
    "\n",
    "callbacks = [PlotLossesKeras(), early_stopping, checkpoint]\n",
    "\n",
    "history = model_gru.fit(\n",
    "    x_train, np.array(y_train),\n",
    "    validation_split=0.2,\n",
    "    batch_size=32,\n",
    "    epochs=3,\n",
    "    verbose=1,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2nwnnF0ziU3B"
   },
   "source": [
    "### Task 9: Evaluate Named Entity Recognition Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ner = load_model(\"model_ner.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "6euqX7UHplG7",
    "outputId": "7222c24c-52c5-454b-a5d4-03d6df4173f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150/150 [==============================] - 2s 12ms/step - loss: 0.0498 - accuracy: 0.9851\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.049847036600112915, 0.9850583672523499]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, np.array(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 920
    },
    "colab_type": "code",
    "id": "Tyg4mKOVplJ-",
    "outputId": "59e897c3-cb77-4dc2-a239-dea2c94eec42",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word           True \t Pred\n",
      "\n",
      "------------------------------\n",
      "A              O\tO\n",
      "condominum     O\tO\n",
      "complex        O\tO\n",
      "called         O\tO\n",
      "The            O\tO\n",
      "Soprano        B-art\tI-org\n",
      "will           O\tO\n",
      "be             O\tO\n",
      "built          O\tO\n",
      "on             O\tO\n",
      "the            O\tO\n",
      "site           O\tO\n",
      ".              O\tO\n",
      "Workers        O\tO\n",
      "Workers        O\tO\n"
     ]
    }
   ],
   "source": [
    "i = np.random.randint(0, x_test.shape[0])\n",
    "p = model.predict(np.array([x_test[i]]))\n",
    "p = np.argmax(p, axis=-1)\n",
    "\n",
    "y_true = np.argmax(np.array(y_test), axis=-1)[i]\n",
    "print(\"{:15}{:5}\\t {}\\n\".format(\"word\", \"True\", \"Pred\"))\n",
    "print(\"-\"*30)\n",
    "\n",
    "idx = 0\n",
    "for w, true, pred in zip(x_test[i], y_true, p[0]):\n",
    "    print(\"{:15}{}\\t{}\".format(words[w-1], tags[true], tags[pred]))\n",
    "    idx += 1\n",
    "    if idx == 15:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 10: Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_process(text):\n",
    "    seq = []\n",
    "    for w in text.split():\n",
    "        try:\n",
    "            seq.append(word2idx[w])\n",
    "        except:\n",
    "            seq.append(num_words-1)\n",
    "\n",
    "    sz = len(seq)\n",
    "    for _ in range(sz, 50):\n",
    "        seq.append(num_words-1)\n",
    "    seq = np.array(seq, dtype=int)\n",
    "    return seq, sz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ner(text):\n",
    "    seq, sz = text_process(text)\n",
    "    p = model_ner.predict(np.array([seq]))\n",
    "    p = np.argmax(p, axis=-1)\n",
    "\n",
    "    idx = 0\n",
    "    for w, pred in zip(seq, p[0]):\n",
    "        if tags[pred] != 'O':\n",
    "            print(\"{:15}\\t{}\".format(words[w-1], tags[pred]))\n",
    "        idx += 1\n",
    "        if idx == sz:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunday         \tB-tim\n",
      "London         \tB-geo\n"
     ]
    }
   ],
   "source": [
    "get_ner('Sunday is close day for London .')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "London         \tB-geo\n",
      "Indonesia      \tB-geo\n",
      "Sunday         \tB-tim\n",
      "Morning        \tI-tim\n"
     ]
    }
   ],
   "source": [
    "get_ner('I will go to London from Indonesia Sunday Morning .')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U.S.           \tB-org\n",
      "President      \tB-per\n",
      "George         \tI-per\n",
      "Bush           \tI-per\n",
      "Tomorrow       \tB-geo\n",
      "Morning        \tI-geo\n"
     ]
    }
   ],
   "source": [
    "get_ner('U.S. President George Bush will visit the Vatican city Tomorrow Morning .')"
   ]
  }
 ],
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