NER_trivia_BiLSTM.ipynb

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      "name": "NER_trivia_BiLSTM.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YsSB3CDt2NxF"
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      "source": [
        "## Outline\n",
        "- [Introduction](#0)\n",
        "    - [Import libraries](#0.1)\n",
        "- [Part 1:  Explore the data](#1)\n",
        "    - [1.1  Import the datasets](#1.1)\n",
        "    - [1.2  Exploratory Analysis](#1.2)\n",
        "    - [Conclusion after analysis](#1.3)\n",
        "    \n",
        "- [Part 2:  Pre-process the data](#2)\n",
        "    - [2.1  Stemming](#2.1)\n",
        "    - [2.2  Lemmatization](#2.2)\n",
        "    - [2.3  Replacement](#2.3)\n",
        "    - [2.4  Pre-processing pipeline](#2.4)\n",
        "    - [2.5  Split to train/val datasets](#2.5)\n",
        "    - [2.6  Tokenization and Padding](#2.6)\n",
        "    - [2.7  Check the Imbalance in train dataset](#2.7)\n",
        "    - [2.8  One-hot encoding](#2.8)\n",
        "\n",
        "- [Part 3:  Build the model](#3)\n",
        "    - [3.1  Glove Embedding](#3.1)\n",
        "    - [3.2  Define the model](#3.2)\n",
        "    - [3.3  Callbacks](#3.3)\n",
        "    \n",
        "\n",
        "- [Part 4:  Train the model](#4)\n",
        "- [Part 5:  Test the model](#5)\n",
        "- [Part 6:  Test with your own sentence](#6)\n",
        "\n",
        "- [Part 7:  Analyse the incorrect predictions](#7)\n",
        "    - [Potential improvement](#7.1)\n",
        "\n",
        "- [Export result to .tsv file](#8)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uj9t5Iav2nzR"
      },
      "source": [
        "<a name=\"0\"></a>\n",
        "# Introduction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZDTBRDfi6u7"
      },
      "source": [
        "<a name=\"0.1\"></a>\n",
        "## Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ntqkdg4N3HW4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3960f95-492d-40c7-f57a-1d0e39c11fcd"
      },
      "source": [
        "!python --version\n",
        "import os\n",
        "\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "# build the tokenized sentences and tags\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.initializers import Constant\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, \\\n",
        "TimeDistributed, Dense, Dropout\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, \\\n",
        "LearningRateScheduler\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "from collections import Counter\n",
        "import random as rnd\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.stem import WordNetLemmatizer \n",
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "!pip install sklearn_crfsuite\n",
        "from sklearn_crfsuite.metrics import flat_classification_report\n",
        "!pip install seqeval\n",
        "from seqeval.metrics import precision_score, recall_score, f1_score, classification_report\n",
        "import csv\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.7.10\n",
            "2.5.0\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "Collecting sklearn_crfsuite\n",
            "  Downloading https://files.pythonhosted.org/packages/25/74/5b7befa513482e6dee1f3dd68171a6c9dfc14c0eaa00f885ffeba54fe9b0/sklearn_crfsuite-0.3.6-py2.py3-none-any.whl\n",
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            "Collecting python-crfsuite>=0.8.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/79/47/58f16c46506139f17de4630dbcfb877ce41a6355a1bbf3c443edb9708429/python_crfsuite-0.9.7-cp37-cp37m-manylinux1_x86_64.whl (743kB)\n",
            "\u001b[K     |████████████████████████████████| 747kB 8.7MB/s \n",
            "\u001b[?25hInstalling collected packages: python-crfsuite, sklearn-crfsuite\n",
            "Successfully installed python-crfsuite-0.9.7 sklearn-crfsuite-0.3.6\n",
            "Collecting seqeval\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz (43kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from seqeval) (1.19.5)\n",
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            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.0.1)\n",
            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-cp37-none-any.whl size=16184 sha256=9799e8a9d85ef24cf7d61633bec3a02b9574dda85b1785d301d2f0d8a3bbc28d\n",
            "  Stored in directory: /root/.cache/pip/wheels/52/df/1b/45d75646c37428f7e626214704a0e35bd3cfc32eda37e59e5f\n",
            "Successfully built seqeval\n",
            "Installing collected packages: seqeval\n",
            "Successfully installed seqeval-1.2.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T58oiLSU25Za"
      },
      "source": [
        "<a name=\"1\"></a>\n",
        "# Part 1:  Explore the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WP5M3GnHxY-9"
      },
      "source": [
        "<a name=\"1.1\"></a>\n",
        "## 1.1 Import the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHhxVylamxCG",
        "outputId": "61a675f5-464b-4cf3-caf9-1f7ece62d62a"
      },
      "source": [
        "# Create new directories\n",
        "!mkdir -p /data/eng\n",
        "!mkdir -p /data/trivia10k13\n",
        "!mkdir -p /model\n",
        "\n",
        "# Download data\n",
        "!wget --no-check-certificate \\\n",
        "https://groups.csail.mit.edu/sls/downloads/movie/engtrain.bio \\\n",
        "-O /data/eng/train.tsv\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "https://groups.csail.mit.edu/sls/downloads/movie/engtest.bio \\\n",
        "-O /data/eng/test.tsv\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "https://groups.csail.mit.edu/sls/downloads/movie/trivia10k13train.bio \\\n",
        "-O /data/trivia10k13/train.tsv\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "https://groups.csail.mit.edu/sls/downloads/movie/trivia10k13test.bio \\\n",
        "-O /data/trivia10k13/test.tsv"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "--2021-06-02 17:10:01--  https://groups.csail.mit.edu/sls/downloads/movie/engtrain.bio\n",
            "Resolving groups.csail.mit.edu (groups.csail.mit.edu)... 128.30.2.44\n",
            "Connecting to groups.csail.mit.edu (groups.csail.mit.edu)|128.30.2.44|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1013492 (990K)\n",
            "Saving to: ‘/data/eng/train.tsv’\n",
            "\n",
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            "\n",
            "2021-06-02 17:10:02 (2.23 MB/s) - ‘/data/eng/train.tsv’ saved [1013492/1013492]\n",
            "\n",
            "--2021-06-02 17:10:02--  https://groups.csail.mit.edu/sls/downloads/movie/engtest.bio\n",
            "Resolving groups.csail.mit.edu (groups.csail.mit.edu)... 128.30.2.44\n",
            "Connecting to groups.csail.mit.edu (groups.csail.mit.edu)|128.30.2.44|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 252636 (247K)\n",
            "Saving to: ‘/data/eng/test.tsv’\n",
            "\n",
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            "\n",
            "2021-06-02 17:10:03 (826 KB/s) - ‘/data/eng/test.tsv’ saved [252636/252636]\n",
            "\n",
            "--2021-06-02 17:10:03--  https://groups.csail.mit.edu/sls/downloads/movie/trivia10k13train.bio\n",
            "Resolving groups.csail.mit.edu (groups.csail.mit.edu)... 128.30.2.44\n",
            "Connecting to groups.csail.mit.edu (groups.csail.mit.edu)|128.30.2.44|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1785558 (1.7M)\n",
            "Saving to: ‘/data/trivia10k13/train.tsv’\n",
            "\n",
            "/data/trivia10k13/t 100%[===================>]   1.70M  3.35MB/s    in 0.5s    \n",
            "\n",
            "2021-06-02 17:10:04 (3.35 MB/s) - ‘/data/trivia10k13/train.tsv’ saved [1785558/1785558]\n",
            "\n",
            "--2021-06-02 17:10:04--  https://groups.csail.mit.edu/sls/downloads/movie/trivia10k13test.bio\n",
            "Resolving groups.csail.mit.edu (groups.csail.mit.edu)... 128.30.2.44\n",
            "Connecting to groups.csail.mit.edu (groups.csail.mit.edu)|128.30.2.44|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 438729 (428K)\n",
            "Saving to: ‘/data/trivia10k13/test.tsv’\n",
            "\n",
            "/data/trivia10k13/t 100%[===================>] 428.45K  1.13MB/s    in 0.4s    \n",
            "\n",
            "2021-06-02 17:10:04 (1.13 MB/s) - ‘/data/trivia10k13/test.tsv’ saved [438729/438729]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-D8_xEVDN-dj",
        "outputId": "97f1f98b-0097-46e2-fabd-800939a857d0"
      },
      "source": [
        "def get_sentence(file_path):\n",
        "    '''\n",
        "        Input:\n",
        "            file_path - path to the tsv file\n",
        "        Output:\n",
        "            sentences - list of sentences in string format\n",
        "            tags - list associated tags in string format\n",
        "    '''\n",
        "    sentences = []\n",
        "    tags = []\n",
        "    with open(file_path) as f:\n",
        "        contents = f.read()\n",
        "        sens_tags = contents.split(\"\\n\\n\")\n",
        "        for sen_tag in sens_tags:\n",
        "            words_tags = sen_tag.split(\"\\n\")\n",
        "            while (\"\" in words_tags):\n",
        "                words_tags.remove(\"\")\n",
        "            sen = ' '.join([word_tag.split(\"\\t\")[1] for word_tag in words_tags])\n",
        "            tag = ' '.join([word_tag.split(\"\\t\")[0] for word_tag in words_tags])\n",
        "            sentences.append(sen)\n",
        "            tags.append(tag)\n",
        "\n",
        "    return sentences, tags\n",
        "\n",
        "\n",
        "train_path = \"/data/trivia10k13/train.tsv\"\n",
        "test_path = \"/data/trivia10k13/test.tsv\"\n",
        "\n",
        "sentences, tags = get_sentence(train_path)\n",
        "test_sentences, test_tags = get_sentence(test_path)\n",
        "\n",
        "print(\"The train dataset has {} sentences.\".format(len(sentences)))\n",
        "print(\"The test dataset has {} sentences.\".format(len(test_sentences)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The train dataset has 7817 sentences.\n",
            "The test dataset has 1954 sentences.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJyYqFkQNCoW"
      },
      "source": [
        "<a name=\"1.2\"></a>\n",
        "## 1.2 Exploratory Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "Q6KzO6mq8326",
        "outputId": "1e12ffca-e377-4ad9-a60c-a5aa2df4a1bb"
      },
      "source": [
        "# Take a look at the data\n",
        "df = pd.read_csv(train_path, delimiter=\"\\t\", names=[\"Tag\", \"Word\"])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Tag</th>\n",
              "      <th>Word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>B-Actor</td>\n",
              "      <td>steve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I-Actor</td>\n",
              "      <td>mcqueen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>O</td>\n",
              "      <td>provided</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>O</td>\n",
              "      <td>a</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B-Plot</td>\n",
              "      <td>thrilling</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Tag       Word\n",
              "0  B-Actor      steve\n",
              "1  I-Actor    mcqueen\n",
              "2        O   provided\n",
              "3        O          a\n",
              "4   B-Plot  thrilling"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBVUmUQtFRJ1"
      },
      "source": [
        "<a name=\"1.2.1\"></a>\n",
        "### 1.2.1 Sentence Length \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "h2J38zVa-wkD",
        "outputId": "c4f9ef1d-a3ab-46e5-9ba3-380b9f3f4495"
      },
      "source": [
        "plt.style.use(\"dark_background\")\n",
        "# How long are the sentences?\n",
        "def plot_sentence_length_histogram(list_sentences):\n",
        "    '''\n",
        "      Input:\n",
        "        list_sentences - a list of sentences\n",
        "      Output:\n",
        "        [print] - Min, Max, Median and Average value of sentence length\n",
        "        [plot] - Histogram plot of sentence length\n",
        "    '''\n",
        "    lengths = [len(sen.split(' ')) for sen in list_sentences]\n",
        "    a4_dims = (11.7, 8.27)\n",
        "    fig, ax = plt.subplots(figsize=a4_dims)\n",
        "    sns.histplot(lengths)\n",
        "    plt.xlabel(\"Number of tokens in a sentence\")\n",
        "    plt.ylabel(\"Number of occurrences\")\n",
        "    print(\"Min: \",np.min(lengths))\n",
        "    print(\"Max: \",np.max(lengths))\n",
        "    \n",
        "    print(\"Median: \",np.median(lengths))\n",
        "    print(\"Average: \",round(np.mean(lengths),2))\n",
        "\n",
        "plot_sentence_length_histogram(sentences)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min:  1\n",
            "Max:  71\n",
            "Median:  19.0\n",
            "Average:  20.32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qY8VCLDQhCbJ"
      },
      "source": [
        "### 1.2.2 Entity Length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "XQrFzlrkhCm3",
        "outputId": "592c48e6-e95d-40d0-ee98-5f0b3fd25b3a"
      },
      "source": [
        "# How long are the entities?\n",
        "def plot_entity_length_histogram(series):\n",
        "    '''\n",
        "      Input:\n",
        "        series - a pandas series of the tags\n",
        "      Output:\n",
        "        [print] - Min, Max, Median and Average value of entity length\n",
        "        [plot] - Histogram plot of entity length\n",
        "    '''\n",
        "    tags_list=[tag for tag in series]\n",
        "    tag_length = []\n",
        "    current_length = 0\n",
        "    for tag in tags_list:\n",
        "        if tag.startswith(\"B\"):\n",
        "            tag_length.append(current_length)\n",
        "            current_length = 1\n",
        "        elif tag.startswith(\"I\"):\n",
        "            current_length += 1\n",
        "    tag_length = tag_length[1:]\n",
        "    \n",
        "    a4_dims = (11.7, 8.27)\n",
        "    fig, ax = plt.subplots(figsize=a4_dims)\n",
        "    sns.histplot(tag_length)\n",
        "    plt.xlabel(\"Number of tokens in a tag\")\n",
        "    plt.ylabel(\"Number of occurrences\")\n",
        "    print(\"Min: \",np.min(tag_length))\n",
        "    print(\"Max: \",np.max(tag_length))\n",
        "    print(\"Median: \",np.median(tag_length))\n",
        "    print(\"Average: \",round(np.mean(tag_length),2))\n",
        "\n",
        "plot_entity_length_histogram(df[\"Tag\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min:  1\n",
            "Max:  44\n",
            "Median:  2.0\n",
            "Average:  4.47\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pNDRUoaGheu2117R+/frWyAYAAAC4hMOSPHnyZB06dEiffvqpPv30UzU0NGjixImtkQ0AAABwCYfzJg4ePKhnn31Wzz77bGvkAQAAAFzO4Z1kq9Wqd999V1lZWcrNzbUvzbFjxw5t3LhRhYWFstlskiQfHx9lZ2erpKRE2dnZslgs9vXnzZun0tJSFRUVKSwszD4+btw4lZSUqKSkROPGjTvTcwQAAADOiMM7yYsWLdI777yj999/X42NjWd8gNtuu63Jc5UTEhKUm5urpKQkzZgxQwkJCUpISNCIESNktVpltVoVGRmp+fPnq1+/fvLx8VFiYqJuuOEGGYah9evXKzMzU/v27TvjLAAAAEBzOCzJR44c0TvvvNNiB4yOjtatt94qSUpLS9NXX32lhIQERUdHa8GCBZKkvLw8WSwWBQQE6NZbb1VOTo7q6uokSTk5ORo+fLjS09NbLBMAAABwPIfTLb744gs98cQTCggIkI+Pj31pDsMwlJ2dre+//17x8fGSJH9/f1VXV0uSqqur5e/vL0kKDAxURUWFfdvKykoFBgaectwsPj5eNptNNptNfn5+zcoHAAAAnIzDO8mxsbGSpKeffto+ZhiGLrnkEoc7v+mmm/Tzzz/rP/7jP5STk6OtW7eesI5hGGeS95SSk5OVnJwsSfb5zwAAAMDZcFiSQ0NDz3rnP//8syRpz549Wrp0qSIiIlRTU6OAgABVV1crICBAu3fvliRVVVUpODjYvm1QUJCqqqpUVVVln57x5/hXX3111pkAAAAARxxOt+jatauef/55vfvuu5KkSy+9VKNGjXK4427duum8886z/x0VFaXNmzcrMzPTfnc6NjZWy5YtkyRlZmban1wRGRmp+vp6VVdXKysrS1FRUbJYLLJYLIqKilJWVtbZnS0AAADQDA7vJKempmr9+vW68cYbJR2747to0SItX778tNv5+/tr6dKlxw7i6amPP/5YWVlZstlsysjIUFxcnMrLyzV27FhJ0ooVKzRy5EiVlZXp4MGDGj9+vCSprq5Os2bNsk+heOmll+w/4gMAAACcwWFJvuSSSxQTE6P7779fkvT777/Lzc3N4Y537Niha6+99oTxvXv3asiQISfdZtKkSScdT01NVWpqqsNjAgAAAC3B4XSLQ4cOqUuXLvYf2IWGhqqhocHpwQAAAABXcXgnOTExUStXrlRwcLA++ugjDRgwQA8//HArRAMAAABc47Ql2c3NTT4+Prr77rvVr18/ubm5aerUqU3eoAcAAAC0N6ctyYZh6JlnntGiRYu0YsWK1soEAAAAuJTDOcmrVq3S9OnTFRQUdMZv3AMAAADORQ7nJN93332SpIkTJ9rHmvvGPQAAAOBc5HBOckJCgjIyMlorDwAAAOByp51uYRiGnn766dbKAgAAALQJzEkGAAAATJiTDAAAAJg4LMmhoaGtkQMAAABoMxyW5L/97W8nHf/www9bPAwAAADQFjgsyeHh4fa/u3TposGDB6ugoICSDAAAgHbLYUmeMmVKk8/e3t5KT093WiAAAADA1Rw+3cLswIED6t27tzOyAAAAAG2CwzvJmZmZMgxDkuTu7q4rrriCl4sAAACgXXNYkl999VX730eOHFF5ebmqqqqcGgoAAABwJYcl+aefftKuXbvU0NAg6diP93r16qXy8nKnhwMAAABcweGc5EWLFuno0aP2z42NjVq0aJFTQwEAAACu5LAke3p66vDhw/bPhw8flpeXl1NDAQAAAK7ksCTv2bNHd9xxh/3znXfeqdraWqeGAgAAAFzJ4Zzkxx9/XAsXLtRbb70lSaqsrNS4ceOcHgwAAABwFYcl+ccff1T//v3VvXt3SceekwwAAAC0Zw6nW/zzn/+Ut7e3Dhw4oAMHDshisWjWrFmtkQ0AAABwCYclecSIEaqvr7d/3rdvn0aOHOnUUAAAAIArOSzJHh4eTZ5m0aVLF3Xu3NmpoQAAAABXcjgneeHChcrNzVVqaqokafz48UpLS3N6MAAAAMBVHJbkV155RUVFRRoyZIgkadasWcrOznZ6MAAAAMBVHJZkSSosLFSnTp1kGIYKCwudnQkAAABwKYdzku+9917l5+drzJgxGjt2rPLy8nTPPfe0RjYAAADAJRzeSX7++ecVHh6uPXv2SJL8/Py0atUqLVmyxOnhAAAAAFdweCfZ3d3dXpAl6ZdffpG7u8PNAAAAgHOWwzvJK1eu1MqVK/XJJ59Iku677z6tWLHC6cEAAAAAV3FYkp955hnddddduummmyRJ7733nj7//HOnBwMAAABcpVlPt1i6dKmWLl3q7CwAAABAm8DkYgAAAMCEkgwAAACYnLIkr1q1SpI0Z86cVgsDAAAAtAWnnJN84YUXqn///rrzzjuVnp4uNze3Jt/z5j0AAAC0V6csyX//+981c+ZMBQUF6fXXX2/ynWEYGjx4sNPDAQAAAK5wypK8ZMkSLVmyRC+88IJmz57dmpkAAAAAl3L4CLjZs2frjjvu0C233CJJ+uqrr7R8+XKnBwMAAABcxeHTLV5++WVNnTpVxcXFKi4u1tSpU/XPf/6zNbIBAAAALuGwJI8aNUpDhw5VamqqUlNTNXz4cN1+++3NP4C7uwoKCvTFF19IkkJCQrRu3TqVlpYqPT1dnTp1kiR5eXkpPT1dpaWlWrdunXr16mXfR0JCgkpLS7V161ZFRUWd6TkCAAAAZ6RZz0m2WCz2v729vc/oAFOnTtWWLVvsn5OSkjR37lxZrVbV1dUpLi5OkhQXF6e6ujpZrVbNnTtXSUlJkqS+ffsqJiZGV155pYYPH663335b7u483hkAAADO47Bt/utf/1JhYaFSU1P1wQcfaP369c2ebhEYGKhRo0bp/ffft48NGjRIixcvliSlpaVp9OjRkqTo6GilpaVJkhYvXmx/ekZ0dLTS09N16NAh7dy5U2VlZYqIiDizswQAAADOgMMf7qWnp+urr75SeHi4JGnGjBmqqalp1s7feOMNPfPMM+rRo4ckydfXV/v27VNjY6MkqbKyUoGBgZKOFeqKigpJUmNjo+rr6+Xr66vAwECtW7fOvs/jtwEAAACcwWFJlqTq6mr7nOLmGjVqlHbv3q2CggINHDjwrMKdifj4eE2YMEGS5Ofn5/TjAQAAoP1qVkk+GwMGDNCdd96pkSNHqkuXLurZs6fmzZsni8UiDw8PNTY2KigoSFVVVZKkqqoqBQcHq6qqSh4eHvL29tYvv/xiH//T8dscLzk5WcnJyZIkm83mrNMCAABAB+C0X8A999xzCg4OVu/evRUTE6PVq1froYce0po1azRmzBhJUmxsrJYtWyZJyszMVGxsrCRpzJgxWr16tX08JiZGXl5eCgkJkdVqVX5+vrNiAwAAAKcvye7u7k2eTNESZsyYoaeeekqlpaXy9fVVSkqKJCklJUW+vr4qLS3VU089pYSEBElScXGxMjIyVFxcrJUrV2rixIk6evRoi2YCAAAAjnfa6RZHjx7Vtm3bFBwcbP9R3dn4+uuv9fXXX0uSduzYocjIyBPWaWho0NixY0+6/csvv6yXX375rI8PAAAAnAmHc5J9fHz0ww8/KD8/XwcOHLCPR0dHOzUYAAAA4CoOS/LMmTNbIwcAAADQZjgsyd98840uvvhiWa1W5ebmqmvXrvLw8GiNbAAAAIBLOHy6xaOPPqrFixfr3XfflXTspR+ff/6504MBAAAAruKwJE+cOFEDBgzQr7/+KkkqKyvTBRdc4PRgAAAAgKs4LMkNDQ06fPiw/bOHh4cMw3BqKAAAAMCVHJbkr7/+Ws8++6y6du2qIUOGaNGiRWf8imoAAADgXOKwJCckJGjPnj3atGmTHnvsMa1YsUIvvPBCa2QDAAAAXMLh0y0Mw1BaWpry8vJkGIa2bdvWGrkAAAAAl3FYkkeOHKl33nlH27dvl5ubm3r37q3HHntMK1eubI18AAAAQKtzWJJfe+013Xbbbdq+fbskKTQ0VMuXL6ckAwAAoN1yOCd5//799oIsST/++KP279/v1FAAAACAK53yTvJdd90lSfr++++1fPlyZWRkyDAM3XvvvbLZbK0WEAAAAGhtpyzJd9xxh/3vmpoaDRw4UJK0Z88ede3a1fnJAAAAABc5ZUl+5JFHWjMHAAAA0GY4/OFeSEiIJk+erJCQEHl6/v/Vo6OjnRoMAAAAcBWHJfnzzz9XSkqKvvjiCx09erQ1MgEAAAAu5bAk//HHH3rzzTdbIwsAAADQJjgsyfPmzdPf//53ZWdnq6GhwT5eWFjo1GAAAACAqzgsyVdddZX+9re/adCgQfbpFoZhaPDgwU4PBwAAALiCw5J87733KjQ0VIcPH26NPAAAAIDLOXzj3ubNm2WxWFojCwAAANAmOLyTbLFYtHXrVtlstiZzknkEHAAAANorhyU5MTGxNXIAAAAAbYbDkvzNN9+0Rg4AAACgzXBYkn/99VcZhiFJ8vLyUqdOnXTgwAF5e3s7PRwAAADgCg5Lcs+ePZt8jo6OVr9+/ZwWCAAAAHA1h0+3MFu2bJmGDRvmjCwAAABAm+DwTvJdd91l/9vd3V033HCD/vjjD6eGAgAAAFzJYUm+44477H8fOXJEO3fu5PFvAAAAaNccluRHHnmkNXIAAAAAbcYpS/LMmTNPuZFhGJo9e7ZTAgEAAACudsqSfODAgRPGunfvrri4OPn6+lKSAQAA0G6dsiS//vrr9r/PO+88TZ06VePHj1d6erpee+21VgkHAAAAuMJpHwHn4+OjWbNmaePGjfL09NR1112nhIQE7dmzp7XyAQAAAK3ulHeSX3nlFd1999167733dNVVV510+gUAAADQHp3yTvL06dN10UUX6YUXXtDPP/+s+vp61dfX69dff1V9fX1rZgQAAABa1SnvJHt4eLRmDgAAAKDNOOPXUgMAAADtHSUZAAAAMKEkAwAAACaUZAAAAMCEkgwAAACYUJIBAAAAE6eV5M6dOysvL08bNmzQ5s2b9eKLL0qSQkJCtG7dOpWWlio9PV2dOnWSJHl5eSk9PV2lpaVat26devXqZd9XQkKCSktLtXXrVkVFRTkrMgAAACDJiSW5oaFBgwYN0rXXXqtrr71Ww4cPV2RkpJKSkjR37lxZrVbV1dUpLi5OkhQXF6e6ujpZrVbNnTtXSUlJkqS+ffsqJiZGV155pYYPH663335b7u7cAAcAAIDzOLVt/vkq606dOqlTp04yDEODBg3S4sWLJUlpaWkaPXq0JCk6OlppaWmSpMWLF2vw4MH28fT0dB06dEg7d+5UWVmZIiIinBkbAAAAHZxTS7K7u7sKCwu1e/du5eTkaPv27dq3b58aGxslSZWVlQoMDJQkBQYGqqKiQpLU2Nio+vp6+fr6Nhk3b3O8+Ph42Ww22Ww2+fn5OfO0AAAA0M45tSQfPXpUYWFhCgoKUkREhC6//HKnHSs5OVnh4eEKDw9XbW2t044DAACA9q9VJvfW19drzZo16t+/vywWizw8PCRJQUFBqqqqkiRVVVUpODhYkuTh4SFvb2/98ssvTcbN2wAAAADO4LSS7OfnJ29vb0lSly5dNHToUG3ZskVr1qzRmDFjJEmxsbFatmyZJCkzM1OxsbGSpDFjxmj16tX28ZiYGHl5eSkkJERWq1X5+fnOig0AAADI01k7vvDCC5WWliYPDw+5u7srIyNDy5cvV3FxsdLT0zV79mwVFhYqJSVFkpSSkqIPP/xQpaWl2rt3r2JiYiRJxcXFysjIUHFxsY4cOaKJEyfq6NGjzooNAAAAOK8kb9q0Sdddd90J4zt27FBkZOQJ4w0NDRo7duxJ9/Xyyy/r5ZdfbvGMAAAAwMnwwGEAAADAhJIMAAAAmFCSAQAAABNKMgAAAGBCSQYAAABMKMkAAACACSUZAAAAMKEkAwAAACaUZAAAAMCEkgwAAACYUJIBAAAAE0oyAAAAYEJJBgAAAEwoyQAAAIAJJRkAAAAwoSQDAAAAJpRkAAAAwISSDAAAAJhQkgEAAAATSjIAAABgQkkGAAAATCjJAAAAgAklGQAAADChJAMAAAAmlGQAAADAhJIMAAAAmFCSAQAAABNKMgAAAGBCSQYAAABMKMkAAACACSUZAG0m09AAABXNSURBVAAAMKEkAwAAACaUZAAAAMCEkgwAAACYUJIBAAAAE0oyAAAAYEJJBgAAAEwoyQAAAIAJJRkAAAAwoSQDAAAAJpRkAAAAwISSDAAAAJhQkgEAAAATp5XkoKAgrV69Wj/88IM2b96sKVOmSJJ8fHyUnZ2tkpISZWdny2Kx2LeZN2+eSktLVVRUpLCwMPv4uHHjVFJSopKSEo0bN85ZkQEAAABJTizJR44c0fTp03XllVeqX79+mjhxovr27auEhATl5uaqT58+ys3NVUJCgiRpxIgRslqtslqtmjBhgubPny/pWKlOTExUZGSkIiIilJiY2KRYAwAAAC3NaSW5urpahYWFkqTffvtNW7ZsUWBgoKKjo5WWliZJSktL0+jRoyVJ0dHRWrBggSQpLy9PFotFAQEBGjZsmHJyclRXV6d9+/YpJydHw4cPd1ZsAAAAQJ6tcZBevXopLCxMeXl58vf3V3V1taRjRdrf31+SFBgYqIqKCvs2lZWVCgwMPOW4WXx8vCZMmCBJ8vPzc+bpAAAAoJ1z+g/3unfvriVLlmjatGnav3//Cd8bhtEix0lOTlZ4eLjCw8NVW1vbIvsEAABAx+TUkuzp6aklS5Zo4cKFWrp0qSSppqZGAQEBkqSAgADt3r1bklRVVaXg4GD7tkFBQaqqqjrlOAAAAOAsTi3JKSkp2rJli+bOnWsfy8zMVGxsrCQpNjZWy5Yts4//+eSKyMhI1dfXq7q6WllZWYqKipLFYpHFYlFUVJSysrKcGRsAAAAdnNPmJA8YMEDjxo3Txo0b7T/ge+655zRnzhxlZGQoLi5O5eXlGjt2rCRpxYoVGjlypMrKynTw4EGNHz9eklRXV6dZs2bJZrNJkl566SXV1dU5KzYAAADgvJL87bffys3N7aTfDRky5KTjkyZNOul4amqqUlNTWywbAAAAcDq8cQ8AAAAwoSQDAAAAJpRkAAAAwISSDAAAAJhQkgEAAAATSjIAAABgQkkGAAAATCjJAAAAgAklGQAAADChJAMAAAAmlGQAAADAhJIMAAAAmFCSAQAAABNKMgAAAGBCSQYAAABMKMkAAACACSUZAAAAMKEkAwAAACaUZAAAAMCEkgwAAACYUJIBAAAAE0oyAAAAYEJJBgAAAEwoyQAAAICJp6sDoHk6d+6siIiIE8bz8/PV0NDggkQAAADtFyX5HBEREaFJSbNVubfWPhZ0vp/emvGC1q5d68JkAAAA7Q8l+RxSubdWZTW7XB0DAACg3WNOMgAAAGBCSQYAAABMKMkAAACACSUZAAAAMKEkAwAAACaUZAAAAMCEkgwAAACYUJIBAAAAE0oyAAAAYEJJBgAAAEwoyQAAAIAJJRkAAAAwoSQDAAAAJpRkAAAAwISSDAAAAJhQkgEAAAATp5XklJQU1dTUaNOmTfYxHx8fZWdnq6SkRNnZ2bJYLPbv5s2bp9LSUhUVFSksLMw+Pm7cOJWUlKikpETjxo1zVlwAAADAzmkl+YMPPtDw4cObjCUkJCg3N1d9+vRRbm6uEhISJEkjRoyQ1WqV1WrVhAkTNH/+fEnHSnViYqIiIyMVERGhxMTEJsUaAAAAcAanleS1a9dq7969Tcaio6OVlpYmSUpLS9Po0aPt4wsWLJAk5eXlyWKxKCAgQMOGDVNOTo7q6uq0b98+5eTknFC8AQAAgJbm2ZoH8/f3V3V1tSSpurpa/v7+kqTAwEBVVFTY16usrFRgYOApx08mPj5eEyZMkCT5+fk56xQAAADQAbj0h3uGYbTYvpKTkxUeHq7w8HDV1ta22H4BAADQ8bRqSa6pqVFAQIAkKSAgQLt375YkVVVVKTg42L5eUFCQqqqqTjkOAAAAOFOrluTMzEzFxsZKkmJjY7Vs2TL7+J9ProiMjFR9fb2qq6uVlZWlqKgoWSwWWSwWRUVFKSsrqzUjAwAAoANy2pzkjz/+WLfeeqv8/PxUUVGhxMREzZkzRxkZGYqLi1N5ebnGjh0rSVqxYoVGjhypsrIyHTx4UOPHj5ck1dXVadasWbLZbJKkl156SXV1dc6KDAAAAEhyYkl+4IEHTjo+ZMiQk45PmjTppOOpqalKTU1tsVwAAACAI7xxDwAAADChJAMAAAAmlGQAAADApFVfJgLX6Ny5syIiIpqM5efnq6GhwUWJAAAA2jZKcgcQERGhSUmzVbn32EtWgs7301szXtDatWtdnAwAAKBtoiR3EJV7a1VWs8vVMQAAAM4JzEkGAAAATCjJAAAAgAklGQAAADChJAMAAAAmlGQAAADAhKdb4JRO9nxliWcsAwCA9o+SjFMyP19Zav4zlnmBCQAAOJdRknFaZ/t8ZV5gAgAAzmWUZDgNLzABAADnKkoy2hTmQQMAgLaAkow25a/MgwYAAGgplGS0OWc7TYMfCwIAgJZCSUa70ZwfC/6V6RyUcAAAOg5KMtoVR3eh/8p0Dp7YAQBAx0FJRofzV566wRM7AADoGCjJgJPxxA4AAM49lGTAyXhiBwAA5x5KMtAKWvuJHdy9BgDgr6EkA23Y2f5YkLvXAAD8NZRkoI0727vQ/MgQAICz5+7qAAAAAEBbw51kAE3w0hQAACjJAEx4aQoAAJRkACfBfGYAQEfHnGQAAADAhDvJAFoEz2YGALQnlGQALYJnMwMA2hNKMoAW05y5zDw9AwBwLqAkA2hVPD0DAHAuoCQDaHVn8/QM5jwDAFoTJRnAOYE5zwCA1kRJBnDOONvnNzMPGgBwpijJANo95kEDAM4UJRlAh9Da86C5ew0A5zZKMgCcwl+ZB83dawA4t1GSAeA0znYe9Nluy1M8AKBtoCQDQBvyV+5eN2eKByUcAJrnnCnJw4YN07x58+Th4aH3339fSUlJro4EAE5xtnevmzPFw9klvLnb/ZVtnX1MAJDOkZLs7u6u//mf/9HQoUNVWVkpm82mzMxMbdmyxdXRAKBNaU7BdmYJb852f2Xb1jims4t5Ryn+HeU80X6dEyU5IiJCZWVl2rFjhyQpPT1d0dHRba4kB53vd8Lna665psnYNddc02S9k61zMubt/sq2HfmY7fGczrVjtsdzOpeO+Vf335yx5q7Tlo8ZM+kJ/fLbr5Ik3/N6Kv2t+SoqKjqj7U61rbP331Z0lPNEy2lrP2x2k2S4OoQj99xzj4YPH674+HhJ0kMPPaTIyEhNnjzZvk58fLwmTJggSbrsssu0bdu2sz6en5+famtrHa+IDo3rBM3BdYLm4DpBc3CdtLxevXrpggsuOOl358Sd5OZITk5WcnJyi+zLZrMpPDy8RfaF9ovrBM3BdYLm4DpBc3CdtC53VwdojqqqKgUHB9s/BwUFqaqqyoWJAAAA0J6dEyXZZrPJarUqJCREnTp1UkxMjDIzM10dCwAAAO2Uh6QXXR3CEcMwVFpaqoULF2ry5Mn66KOP9Nlnnzn1mAUFBU7dP9oHrhM0B9cJmoPrBM3BddJ6zokf7gEAAACt6ZyYbgEAAAC0JkoyAAAAYEJJNhk2bJi2bt2q0tJSzZgxw9Vx0EakpKSopqZGmzZtso/5+PgoOztbJSUlys7OlsVicWFCtAVBQUFavXq1fvjhB23evFlTpkyRxLWCpjp37qy8vDxt2LBBmzdv1osvvihJCgkJ0bp161RaWqr09HR16tTJtUHhcu7u7iooKNAXX3whiWvEFQyWY4u7u7tRVlZm9O7d2+jUqZOxYcMGo2/fvi7PxeL65eabbzbCwsKMTZs22ceSkpKMGTNmGJKMGTNmGHPmzHF5ThbXLgEBAUZYWJghyTjvvPOMbdu2GX379uVaYTlh6d69uyHJ8PT0NNatW2dERkYan376qXHfffcZkoz58+cbjz/+uMtzsrh2efLJJ42FCxcaX3zxhSGJa6T1F5cHaDNLv379jJUrV9o/JyQkGAkJCS7PxdI2ll69ejUpyVu3bjUCAgIM6Vg52rp1q8szsrSt5fPPPzeGDBnCtcJyyqVr167G+vXrjYiICGPPnj2Gh4eHIZ343yOWjrcEBgYaq1atMm677TZ7SeYaad2F6RbHCQwMVEVFhf1zZWWlAgMDXZgIbZm/v7+qq6slSdXV1fL393dxIrQlvXr1UlhYmPLy8rhWcAJ3d3cVFhZq9+7dysnJ0fbt27Vv3z41NjZK4r8/kN544w0988wzOnr0qCTJ19eXa6SVUZKBFmIYhqsjoI3o3r27lixZomnTpmn//v0nfM+1gqNHjyosLExBQUGKiIjQ5Zdf7upIaENGjRql3bt380xkF/N0dYC2hNdf40zU1NQoICBA1dXVCggI0O7du10dCW2Ap6enlixZooULF2rp0qWSuFZwavX19VqzZo369+8vi8UiDw8PNTY28t+fDm7AgAG68847NXLkSHXp0kU9e/bUvHnzuEZaGXeSj8Prr3EmMjMzFRsbK0mKjY3VsmXLXJwIbUFKSoq2bNmiuXPn2se4VnA8Pz8/eXt7S5K6dOmioUOHasuWLVqzZo3GjBkjieuko3vuuecUHBys3r17KyYmRqtXr9ZDDz3ENeICLp8Y3ZaWESNGGNu2bTPKysqM5557zuV5WNrG8vHHHxs///yzcejQIaOiosJ45JFHjPPPP99YtWqVUVJSYuTk5Bg+Pj4uz8ni2mXAgAGGYRhGUVGRUVhYaBQWFhojRozgWmFpslx11VVGQUGBUVRUZGzatMmYOXOmIcno3bu3kZeXZ5SWlhoZGRmGl5eXy7OyuH4ZOHCg/Yd7XCOtu/BaagAAAMCE6RYAAACACSUZAAAAMKEkAwAAACaUZAAAAMCEkgwAAACYUJIBdBiGYejVV1+1f54+fboSExNbZN+pqam65557WmRfpzNmzBgVFxdr9erVTcZ79eql+++/3+H2sbGxevPNN50Vr4lvv/22xfc5cOBA9e/fv8X3CwBmlGQAHcYff/yhu+++W76+vq6O0oSHh0ez142Li1N8fLwGDRrUZDwkJEQPPPBAS0f7SwYMGNDi+7z11lt14403tvh+AcCMkgygwzhy5Ijee+89Pfnkkyd8Z74TvH//fknH7lx+9dVX+vzzz7V9+3b961//0gMPPKC8vDxt3LhRoaGh9m2GDBkim82mbdu2adSoUZIkd3d3vfLKK8rPz1dRUZEmTJhg3+8333yjZcuWqbi4+IQ8MTEx2rhxozZt2qQ5c+ZIkmbOnKmbbrpJKSkpeuWVV5qsP2fOHN18880qLCzUtGnT1LlzZ/3v//6vNm7cqIKCAt16660nHGPkyJH67rvv5Ovrq6FDh+q7777T+vXrlZGRoe7du0uSduzYoRdffFHr16/Xxo0bddlll0mSbrnlFhUWFqqwsFAFBQU677zzTtj/8f+Ga9as0aJFi7RlyxZ99NFHJ/2/z6OPPqr8/Hxt2LBBixcvVteuXZt836tXLz3++ON68sknVVhYqJtuukm333671q1bp4KCAuXk5OiCCy6QdOytdtnZ2dq8ebOSk5O1c+fONvf/HAFo+1z+RhMWFhaW1lj2799v9OjRw9ixY4fRs2dPY/r06UZiYqIhyUhNTTXuueeeJutKx952VVdXZwQEBBheXl5GZWWl8eKLLxqSjClTphhz5861b//ll18abm5uxqWXXmpUVFQYnTt3NuLj443nn3/ekGR4eXkZNpvNCAkJMQYOHGj89ttvRkhIyAk5L7zwQqO8vNzw8/MzPDw8jNzcXCM6OtqQZKxZs8a4/vrrT9jm+LdySTKeeuopIyUlxZBkXHbZZUZ5ebnRuXNnIzY21njzzTeN0aNHG998841hsVgMX19f4+uvvza6detmSDKeeeYZ+1vgduzYYUyaNMmQZDzxxBNGcnKyIcnIzMw0brzxRkOS0b17d8PDw+Ok/95/Ztu3b58RGBhouLm5Gd99950xYMCAE9Y///zz7X/PmjXLftzjl8TERGP69On2zxaLxf53XFyc8eqrrxqSjDfffNNISEgwJBnDhg0zDMMwfH19XX4NsrCwnDuLpwCgA9m/f78WLFigKVOm6Pfff2/WNjabTdXV1ZKk7du3Kzs7W5K0adMm3Xbbbfb1MjIyZBiGysrK9OOPP+ryyy9XVFSUrr76ao0ZM0aS5O3tLavVqkOHDik/P187d+484Xjh4eH66quvVFtbK0lauHChbrnlFi1btqzZ53nTTTfZ5x5v27ZN5eXl6tOnjyRp0KBBuuGGGxQVFaX9+/dr1KhRuuKKK+xziL28vPTvf//bvq/PPvtMkrR+/Xrdfffdko7NN3799de1cOFCffbZZ6qqqjptnvz8fPs6GzZsUEhIyAlzlv/zP/9Ts2fPlsVi0XnnnaesrCyH5xkUFKRPP/1UF154oby8vLRjxw77+d91112SpKysLO3du9fhvgDgeEy3ANDhvPHGG4qLi7NPKZCOTcVwdz/2P4lubm7y8vKyf9fQ0GD/++jRo/bPR48elafn/7/XYBhGk+MYhiE3NzdNnjxZYWFhCgsLU2hoqHJyciRJBw4caPmTa4bt27erR48e9tLs5uamnJwce8Yrr7xSjz76qH39P8+3sbHRfr5JSUl69NFH1bVrV3377bf2aRincvy/4fH7Od4HH3ygSZMm6eqrr9Y//vEPdenSxeG5vPnmm3rrrbd09dVX67HHHmvWNgDQHJRkAB1OXV2dMjIyFBcXZx/buXOnrr/+eknSnXfe2aQkN9e9994rNzc3hYaGKjQ0VNu2bVNWVpaeeOIJeym0Wq3q1q3bafeTn5+vgQMHytfXV+7u7rr//vv19ddfn3ab/fv3q0ePHvbPa9eu1YMPPmg/5sUXX6xt27ZJksrLy3XPPfdowYIFuuKKK7Ru3ToNGDBAl1xyiSSpW7duslqtpz1eaGioNm/erFdeeUU2m02XX3756f9xmqFHjx7atWuXPD097dkdnae3t7f9DnVsbKx9/Ntvv9XYsWMlSUOHDtX555//l/MB6FgoyQA6pNdee01+fn72z8nJyRo4cKA2bNig/v3767fffjvjff7000/Kz8/Xl19+qccff1wNDQ16//33VVxcrIKCAm3atEnvvvvuSe+iHq+6uloJCQlas2aNioqKtH79emVmZp52m40bN6qxsVEbNmzQtGnT9Pbbb8vd3V0bN27Up59+qocffliHDh2yr79t2zY9+OCDWrRokXr27KmHH35Yn3zyiYqKivTvf//bYemdNm2aNm3apKKiIh0+fFhffvll8/+hTmHmzJnKy8vTt99+q61bt550nS+++EJ33XWX/Yd7L774ohYtWqTvv//ePj1Fkv7xj38oKipKmzZt0r333qtdu3bZf0gIAM3hpmOTkwEAaDe8vLzU2NioxsZG9evXT/Pnz1dYWJirYwE4h/DDPQBAu3PxxRcrIyND7u7uOnTokOLj410dCcA5hjvJAAAAgAlzkgEAAAATSjIAAABgQkkGAAAATCjJAAAAgAklGQAAADD5f2n6iw5Sl+zfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcoUQIfwfOAe"
      },
      "source": [
        "### 1.2.3 Token frequency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2kvMJJ3KvXu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "e4a479bf-ef36-40d7-8a47-d2003e0eae9c"
      },
      "source": [
        "def plot_top_non_stopwords_barchart(series, top=20, word=True):\n",
        "    '''\n",
        "      Input:\n",
        "        series - a pd.Series of words or tags\n",
        "        top - number of most common words to plot\n",
        "      Output:\n",
        "        [print] - No of distinct words in train dataset\n",
        "        [plot] - Barchart of most common words' occurrence\n",
        "    '''\n",
        "    stop=set(stopwords.words('english'))\n",
        "    value = 'words' if word == True else 'tags'\n",
        "    corpus=[word for word in series]\n",
        "    counter=Counter(corpus)\n",
        "    print(\"There are {} distinct {} in dataset\".format(len(dict(counter)), value))\n",
        "    print(dict(counter))\n",
        "\n",
        "    most=counter.most_common()\n",
        "    x, y=[], []\n",
        "    for word,count in most:\n",
        "        if (word not in stop):\n",
        "            x.append(count)\n",
        "            y.append(word)\n",
        "        if len(x) == top:\n",
        "          break\n",
        "    a4_dims = (11.7, 8.27)\n",
        "    fig, ax = plt.subplots(figsize=a4_dims)\n",
        "    sns.barplot(x=x,y=y)\n",
        "    plt.xlabel(\"Number of {} occurrences in a sentence\".format(value))\n",
        "    plt.ylabel(\"Most common {}s\".format(value))\n",
        "    return dict(counter)\n",
        "\n",
        "word_counter = plot_top_non_stopwords_barchart(df[\"Word\"], top=40)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 10987 distinct words in dataset\n",
            "{'steve': 85, 'mcqueen': 12, 'provided': 6, 'a': 7798, 'thrilling': 7, 'motorcycle': 8, 'chase': 19, 'in': 3435, 'this': 1962, 'greatest': 25, 'of': 4255, 'all': 177, 'ww': 5, '2': 43, 'prison': 52, 'escape': 35, 'movies': 80, 'liza': 3, 'minnelli': 7, 'and': 4049, 'joel': 16, 'gray': 5, 'won': 100, 'oscars': 22, 'for': 781, 'their': 346, 'roles': 25, '1972': 23, 'movie': 3924, 'that': 2194, 'follows': 64, 'nightclub': 2, 'entertainers': 1, 'berlin': 8, 'as': 1373, 'the': 8372, 'nazis': 16, 'come': 49, 'to': 2536, 'power': 25, 'what': 4273, 'is': 3785, 'tom': 147, 'hanks': 81, 'julia': 30, 'roberts': 26, 'about': 1849, 'who': 1716, 'plays': 175, 'down': 87, 'on': 1130, 'his': 1169, 'luck': 5, 'average': 9, 'guy': 118, 'goes': 117, 'back': 137, 'college': 50, 'gets': 125, 'taught': 7, 'by': 1411, 'making': 22, 'fun': 21, 'macgyver': 2, 're': 20, 'enacting': 1, 'scenes': 14, 'similar': 5, 'i': 706, 'am': 259, 'thinking': 595, 'an': 981, 'animated': 459, 'film': 2298, 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          "name": "stdout"
        },
        {
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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7pIJmZCgRMA"
      },
      "source": [
        "### 1.2.4 Cumulative token frequency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1mi4Vw5ABFXg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "outputId": "0e421a91-75a4-4b2d-b5cb-8fb1bd2bac98"
      },
      "source": [
        "# Plot the cumulative distribution of token frequency\n",
        "def cumulative_token_frequency(series, limit=20):\n",
        "    '''\n",
        "      Input:\n",
        "        series - pd.Series of words\n",
        "      Output:\n",
        "        [plot] - cumulative distribution of token frequency\n",
        "    '''\n",
        "    corpus=[word for word in series]\n",
        "    counter=Counter(corpus)\n",
        "    tokens_count = dict(counter).items()\n",
        "\n",
        "    prop_list = []\n",
        "    print(\"Vocabulary Size: \", len(tokens_count))\n",
        "    for i in range(limit):\n",
        "        tokens_filtered = len(list(filter(lambda x: x[1]<=i, tokens_count)))\n",
        "        prop_list.append(round(tokens_filtered*100/len(tokens_count),2))\n",
        "    a4_dims = (11.7, 8.27)\n",
        "    fig, ax = plt.subplots(figsize=a4_dims)\n",
        "    plt.plot(prop_list)\n",
        "    plt.grid()\n",
        "    plt.xlabel(\"Counts\")\n",
        "    plt.ylabel(\"Proportion of Vocabulary (%)\")\n",
        "    # print(\"Proportion of unique words less than\",limit,\": \", round(tokens_filtered*100/len(tokens_dict),2),\"%\")\n",
        "\n",
        "cumulative_token_frequency(df[\"Word\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocabulary Size:  10987\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE4VV7X4fUwj"
      },
      "source": [
        "### 1.2.5 Entity Frequency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dB_9MvjOAoqq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "4f8faa61-0504-4701-de7f-d825b78dec00"
      },
      "source": [
        "tag_counter = plot_top_non_stopwords_barchart(df[\"Tag\"], top=25, word=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 25 distinct tags in dataset\n",
            "{'B-Actor': 5010, 'I-Actor': 6121, 'O': 55895, 'B-Plot': 6468, 'I-Plot': 62107, 'B-Opinion': 810, 'I-Opinion': 539, 'B-Award': 309, 'I-Award': 719, 'B-Year': 2702, 'B-Genre': 3384, 'B-Origin': 779, 'I-Origin': 3340, 'B-Director': 1787, 'I-Director': 1653, 'I-Genre': 2283, 'I-Year': 195, 'B-Soundtrack': 50, 'I-Soundtrack': 158, 'B-Relationship': 580, 'I-Relationship': 1206, 'B-Character_Name': 1025, 'I-Character_Name': 760, 'B-Quote': 126, 'I-Quote': 817}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPCRkkm768sx"
      },
      "source": [
        "<a name=\"1.3\"></a>\n",
        "## Conclusion after analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhDBB8847Dev"
      },
      "source": [
        "### Vocab\n",
        "1. The vocab size is 10987 (excluding stopwords). This is quite a large dataset.\n",
        "2. Our dataset it comprised of long sentences, with average length of 20 and median length of 19, and the length ranges from 1 to 71. Only a small amount of sentences have the length greater than 40 => set the max length equal to 40 => need a lot of padding tokens.\n",
        "3. 45% of the vocabulary only occur once but they could be person's names so let's keep them.\n",
        "4. Year: can be replaced by a common\n",
        "5. Number in text: can be replaced by a common\n",
        "6. Lemmatization: *films* to *film*\n",
        "7. All words are in lowercase.\n",
        "8. No punctuations.\n",
        "9. No informal text.\n",
        "10. Lots of abbreviation (like *i'm, i'll, can't, 2morrow*)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRaEnZIe8UWy"
      },
      "source": [
        "### Tags\n",
        "1. Most of tags are short, with average length of 2 words and median length of 4 words. There're some long tag and the longest tag has 40 words. This could be a plot description.\n",
        "2. Most sentences are about plots.\n",
        "3. There are 25 classes of entities, divided to 3 categories: B Tags (Beginning of an entity), I Tag (Intermediate Entity), Or None Tag (O). Proportions of B,I,O are about 14.50%, 50.31%, 35.19% respectively (section 2.7 Check the dataset imbalance)\n",
        "4. Most of the tags are in the minority and O is the most common entity => need to over-sample the tags from the minority groups.\n",
        "5. As under the section 2.7 Check the dataset imbalance, the percentage of sentences that only contain O tags -> 0.02% => It's small amount so we don't need to delete those sentences.\n",
        "6. ALso as under the section 2.7 Check the dataset imbalance, the percentage of OOV tokens in test set -> 3.34%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dtr2P3ZE3sE6"
      },
      "source": [
        "<a name=\"2\"></a>\n",
        "# Part 2:  Pre-process the data\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRofcZfAUd9g"
      },
      "source": [
        "<a name=\"2.1\"></a>\n",
        "## 2.1 Stemming"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1J6BsedjUeOf"
      },
      "source": [
        "def stem_sentence(sentence):\n",
        "    sentence = sentence.split(' ')\n",
        "    stemmer = PorterStemmer()\n",
        "    result = [stemmer.stem(word) for word in sentence]\n",
        "    stemmed_sentence = ' '.join(result)\n",
        "    return stemmed_sentence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4p6nLSAePE9U"
      },
      "source": [
        "<a name=\"2.2\"></a>\n",
        "## 2.2 Lemmatization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reh2ym9rO2PM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8aeeca0a-9089-4146-db88-b7ba43d9f6b5"
      },
      "source": [
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "def lemmatize_sentence(sentence):\n",
        "    tokenization = nltk.word_tokenize(sentence)\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    result = [lemmatizer.lemmatize(word) for word in tokenization]\n",
        "    lemmatized_sentence = ' '.join(result)\n",
        "    return lemmatized_sentence"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hm9UnKBqUeZA"
      },
      "source": [
        "<a name=\"2.3\"></a>\n",
        "## 2.3 Replacement"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZzhc1zBUeiv"
      },
      "source": [
        "import re\n",
        "\n",
        "def replace(sentence, to_replace, replace_by):\n",
        "    replaced = sentence.replace(to_replace, replace_by)\n",
        "    return replaced\n",
        "\n",
        "def replace_num(sentence):\n",
        "    replaced = re.sub(r'^\\d{1,2}$', \"NUM\", sentence) # replace 1, 2 digits\n",
        "    replaced = re.sub(r'^\\d{4} s$', \"YEAR\", replaced) # replace year\n",
        "    replaced = re.sub(r'^\\d{4}$', \"YEAR\", replaced) # replace year\n",
        "    return replaced\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfswr-bAXwl5"
      },
      "source": [
        "<a name=\"2.4\"></a>\n",
        "## 2.4 Pre-processing pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvGcbsdOXw3N"
      },
      "source": [
        "def apply_preproc(data_generator):\n",
        "    data_generator = list(map(lambda x: replace(x, \"ca n t\",\"cannot\"), data_generator))\n",
        "    data_generator = list(map(lambda x: replace(x, \"ll\",\"will\"), data_generator))\n",
        "    data_generator = list(map(lambda x: replace_num(x), data_generator))\n",
        "    data_generator = list(map(lambda x: lemmatize_sentence(x), data_generator))\n",
        "    return data_generator\n",
        "\n",
        "processed_sentences = apply_preproc(sentences)\n",
        "processed_test_sentences = apply_preproc(test_sentences)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMK0gaxoOi97"
      },
      "source": [
        "<a name=\"2.5\"></a>\n",
        "## 2.5 Split to train/val datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjPJLq1I0_CX"
      },
      "source": [
        "split_ratio = 0.8\n",
        "\n",
        "def train_val_split(data, label, ratio, shuffle=True, random_seed=33):\n",
        "    length = len(data)\n",
        "    lines_index = [*range(length)]  \n",
        "    # shuffle the indexes if shuffle is set to True\n",
        "    rnd.seed(random_seed)\n",
        "    if shuffle:\n",
        "        rnd.shuffle(lines_index)\n",
        "    split_point = int(length * ratio)\n",
        "\n",
        "    train_data = []\n",
        "    train_label = []\n",
        "    val_data = []\n",
        "    val_label = []\n",
        "    for i in range(length):\n",
        "        if i <= split_point:\n",
        "            train_data.append(data[lines_index[i]])\n",
        "            train_label.append(label[lines_index[i]])\n",
        "        else:\n",
        "            val_data.append(data[lines_index[i]])\n",
        "            val_label.append(label[lines_index[i]])\n",
        "    return train_data, train_label, val_data, val_label\n",
        "\n",
        "\n",
        "train_sentences, train_tags, val_sentences, val_tags = \\\n",
        "      train_val_split(processed_sentences, tags, split_ratio)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Sot8kCJltNu"
      },
      "source": [
        "<a name=\"2.6\"></a>\n",
        "## 2.6 Tokenization and Padding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GDfsVDq9KxgV"
      },
      "source": [
        "oov_tok = \"<OOV>\"\n",
        "trunc_type='post'\n",
        "pad_type='post'\n",
        "max_length = 71\n",
        "\n",
        "vocab_tokenizer = Tokenizer(oov_token=oov_tok)\n",
        "vocab_tokenizer.fit_on_texts(train_sentences)\n",
        "\n",
        "vocab = vocab_tokenizer.word_index\n",
        "reverse_vocab = dict([(value, key) for (key, value) in vocab.items()])\n",
        "vocab_size = len(vocab)\n",
        "\n",
        "\n",
        "train_sequences = vocab_tokenizer.texts_to_sequences(train_sentences)\n",
        "val_sequences = vocab_tokenizer.texts_to_sequences(val_sentences)\n",
        "test_sequences = vocab_tokenizer.texts_to_sequences(processed_test_sentences)\n",
        "\n",
        "train_padded_sequences = pad_sequences(train_sequences,\n",
        "                                        maxlen=max_length, \n",
        "                                        truncating=trunc_type, \n",
        "                                        padding=pad_type)\n",
        "\n",
        "val_padded_sequences = pad_sequences(val_sequences,\n",
        "                                      maxlen=max_length, \n",
        "                                      truncating=trunc_type, \n",
        "                                      padding=pad_type)\n",
        "\n",
        "test_padded_sequences = pad_sequences(test_sequences,\n",
        "                                      maxlen=max_length, \n",
        "                                      truncating=trunc_type, \n",
        "                                      padding=pad_type)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CP_DmX44K24X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d2cf840-66e6-495e-f3b6-ccd62dcbd3f9"
      },
      "source": [
        "tag_tokenizer = Tokenizer(filters=\".\", lower=False, oov_token=oov_tok)\n",
        "tag_tokenizer.fit_on_texts(train_tags)\n",
        "\n",
        "tag_map = tag_tokenizer.word_index\n",
        "reverse_tag_map = dict([(value, key) for (key, value) in tag_map.items()])\n",
        "tag_size = len(tag_map)\n",
        "\n",
        "train_tag_sequences = tag_tokenizer.texts_to_sequences(train_tags)\n",
        "val_tag_sequences = tag_tokenizer.texts_to_sequences(val_tags)\n",
        "test_tag_sequences = tag_tokenizer.texts_to_sequences(test_tags)\n",
        "\n",
        "\n",
        "train_padded_tags = pad_sequences(train_tag_sequences,\n",
        "                                        maxlen=max_length, \n",
        "                                        truncating=trunc_type, \n",
        "                                        padding=pad_type)\n",
        "\n",
        "val_padded_tags = pad_sequences(val_tag_sequences,\n",
        "                                      maxlen=max_length, \n",
        "                                      truncating=trunc_type, \n",
        "                                      padding=pad_type)\n",
        "\n",
        "test_padded_tags = pad_sequences(test_tag_sequences,\n",
        "                                      maxlen=max_length, \n",
        "                                      truncating=trunc_type, \n",
        "                                      padding=pad_type)\n",
        "\n",
        "print(\"\\nExample of a a sentence and its tokenized, padded version\")\n",
        "print(train_sentences[0])\n",
        "print(train_padded_sequences[0])\n",
        "print(\"\\nExample of a list of tags in a sentence and its tokenized, padded version\")\n",
        "print(train_tags[0])\n",
        "print(train_padded_tags[0])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Example of a a sentence and its tokenized, padded version\n",
            "what 2011 animated movie feature the voice of seth green joan cusack and dan fogler\n",
            "[   4   52   40    7   37    3  200    5  627  628 3033 1735    6 2199\n",
            " 3760    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0]\n",
            "\n",
            "Example of a list of tags in a sentence and its tokenized, padded version\n",
            "O B-Year B-Genre O O O O O B-Actor I-Actor I-Actor I-Actor O B-Actor I-Actor\n",
            "[3 9 7 3 3 3 3 3 6 5 5 5 3 6 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmNBER6vh8fZ"
      },
      "source": [
        "<a name=\"2.7\"></a>\n",
        "## 2.7 Check the Imbalance in train/test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JkHts8fDElj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21199e96-faf4-4ff5-b376-683f6f91db13"
      },
      "source": [
        "#Percentage of B, I and O Tags in train dataset\n",
        "def get_tag_proportion(series_tags):\n",
        "    '''\n",
        "      Input:\n",
        "        series_tags - pd.Series of tags\n",
        "      Output:\n",
        "        [print] - B, I and O tags' proportion\n",
        "    '''\n",
        "    tags_list=[tag for tag in series_tags]\n",
        "    counter=dict(Counter(tags_list))\n",
        "    beg = 0\n",
        "    inter = 0\n",
        "    out = 0\n",
        "    for key, value in counter.items():\n",
        "        if key.startswith(\"B\"):\n",
        "            beg += value\n",
        "        elif key.startswith(\"I\"):\n",
        "            inter += value\n",
        "        else:\n",
        "            out += value\n",
        "    total = len(tags_list)\n",
        "    print(\"B tags proportion = {0:.2%}\".format(round(beg/total,4)))\n",
        "    print(\"I tags proportion = {0:.2%}\".format(round(inter/total,4)))\n",
        "    print(\"O tags proportion = {0:.2%}\".format(round(out/total,4)))\n",
        "\n",
        "get_tag_proportion(df[\"Tag\"])\n",
        "\n",
        "# Percentage of sentences that only contain O tags\n",
        "# If this percentage > 50% => the dataset is imbalanced => drop empty sentences\n",
        "def get_empty_tag_sentence_proportion(list_tag_sequence):\n",
        "    '''\n",
        "      Input:\n",
        "        list_tag_sequence - list of tag sequences in train/test set\n",
        "      Output:\n",
        "        [print] - Percentage of sentences that only contain O tags\n",
        "    '''\n",
        "    count = 0\n",
        "    for seq in list_tag_sequence:\n",
        "        if sum(seq) == 2 * len(seq): # if seq contains only 2 (token for O tag)\n",
        "            count += 1\n",
        "\n",
        "    \n",
        "    print(\"\\nPercentage of sentences that only contain O tags -> {0:.2%}\".\\\n",
        "          format(round(count/len(list_tag_sequence),4)))\n",
        "    \n",
        "get_empty_tag_sentence_proportion(train_tag_sequences)\n",
        "\n",
        "def get_OOV_density(list_token_sequence):\n",
        "    '''\n",
        "      Input:\n",
        "        list_token_sequence - list of token sequences in test set\n",
        "      Output:\n",
        "        [print] - Percentage of OOV token in the test set\n",
        "    '''\n",
        "    list_token_sequence = [token for seq in list_token_sequence for token in seq]\n",
        "    counter=dict(Counter(list_token_sequence))\n",
        "    print(\"\\nPercentage of OOV tokens in test set -> {0:.2%}\".\\\n",
        "          format(round(counter[1]/len(list_token_sequence),4)))\n",
        "\n",
        "get_OOV_density(test_sequences)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "B tags proportion = 14.50%\n",
            "I tags proportion = 50.31%\n",
            "O tags proportion = 35.19%\n",
            "\n",
            "Percentage of sentences that only contain O tags -> 0.02%\n",
            "\n",
            "Percentage of OOV tokens in test set -> 3.36%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0OZsbuw2iDAp"
      },
      "source": [
        "<a name=\"2.8\"></a>\n",
        "## 2.8 One-hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdsnDiIZwGaC"
      },
      "source": [
        "# Ont hot encoding\n",
        "train_padded_tags = np.array([to_categorical(tags, num_classes = tag_size+1) \\\n",
        "                              for tags in train_padded_tags])\n",
        "val_padded_tags = np.array([to_categorical(tags, num_classes = tag_size+1) \\\n",
        "                            for tags in val_padded_tags])\n",
        "test_padded_tags = np.array([to_categorical(tags, num_classes = tag_size+1) \\\n",
        "                             for tags in test_padded_tags])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84RwGepu37jO"
      },
      "source": [
        "<a name=\"3\"></a>\n",
        "# Part 3:  Building the model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yX9PMBUtuJmJ"
      },
      "source": [
        "<a name=\"3.1\"></a>\n",
        "## 3.1 Glove Embedding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QAO1_GehxQ1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e91e9cf9-a8b1-42d6-b836-5ec6a2a9ae74"
      },
      "source": [
        "!mkdir -p /glove_embedding\n",
        "# Download data\n",
        "!wget --no-check-certificate \\\n",
        "http://nlp.stanford.edu/data/glove.6B.zip -O /glove_embedding/glove.6B.zip\n",
        "!unzip /glove_embedding/glove.6B.zip -d /glove_embedding\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-02 17:10:13--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2021-06-02 17:10:13--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2021-06-02 17:10:13--  http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘/glove_embedding/glove.6B.zip’\n",
            "\n",
            "/glove_embedding/gl 100%[===================>] 822.24M  5.08MB/s    in 2m 40s  \n",
            "\n",
            "2021-06-02 17:12:53 (5.15 MB/s) - ‘/glove_embedding/glove.6B.zip’ saved [862182613/862182613]\n",
            "\n",
            "Archive:  /glove_embedding/glove.6B.zip\n",
            "  inflating: /glove_embedding/glove.6B.50d.txt  \n",
            "  inflating: /glove_embedding/glove.6B.100d.txt  \n",
            "  inflating: /glove_embedding/glove.6B.200d.txt  \n",
            "  inflating: /glove_embedding/glove.6B.300d.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2SIlF-NeR_o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b6137ac-2988-4387-dc70-41fb7b13f552"
      },
      "source": [
        "GLOVE_DIR = \"/glove_embedding\"\n",
        "embedding_dim = 300\n",
        "hits = 0\n",
        "misses = 0\n",
        "embeddings_index = {}\n",
        "\n",
        "with open(os.path.join(GLOVE_DIR, 'glove.6B.300d.txt')) as f:\n",
        "    for line in f:\n",
        "        values = line.split()\n",
        "        word = values[0]\n",
        "        coefs = np.asarray(values[1:], dtype='float32')\n",
        "        embeddings_index[word] = coefs\n",
        "\n",
        "print('Found %s word vectors.' % len(embeddings_index))\n",
        "\n",
        "embedding_matrix = np.zeros((len(vocab) + 1, embedding_dim))\n",
        "for word, i in vocab.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        # words not found in embedding index will be all-zeros.\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "        hits += 1\n",
        "    else:\n",
        "        misses += 1\n",
        "print(\"Converted %d words (%d misses)\" % (hits, misses))\n",
        "\n",
        "        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors.\n",
            "Converted 8448 words (664 misses)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3h-sT21kjV2X"
      },
      "source": [
        "<a name=\"3.2\"></a>\n",
        "## 3.2 Define the model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tshH4jK03oWM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6018b1a-5900-48f7-e5c0-5d21647288a9"
      },
      "source": [
        "!pip install tensorflow_addons\n",
        "import tensorflow_addons as tfa\n",
        "\n",
        "\n",
        "# Model architecture\n",
        "batch_size = 32\n",
        "embedding_dim = 300\n",
        "max_length = 71\n",
        "\n",
        "def BiLSTM(vocab_size=vocab_size, tag_size=tag_size, hidden_size = 32, \n",
        "                embedding_dim=embedding_dim):\n",
        "    sequence_input = Input(shape = (max_length,))\n",
        "\n",
        "    model = Embedding(input_dim = vocab_size+1, \n",
        "                      output_dim = embedding_dim, \n",
        "                      input_length = max_length, \n",
        "                      embeddings_initializer=Constant(embedding_matrix),\n",
        "                      trainable=False,\n",
        "                      mask_zero = False)(sequence_input)\n",
        "                            \n",
        "    model = Bidirectional(LSTM(units = hidden_size,return_sequences=True,\n",
        "                              recurrent_dropout=0.1))(model)\n",
        "\n",
        "    model = TimeDistributed(Dense(hidden_size, activation=\"relu\"))(model)\n",
        "    outputs = Dense(tag_size+1, activation='softmax')(model)\n",
        "    #crf = tfa.layers.CRF(tag_size+1) # CRF layer\n",
        "    #outputs = crf(model)\n",
        "\n",
        "    model = Model(inputs=sequence_input, outputs=outputs)\n",
        "\n",
        "    model.compile(optimizer=\"RMSprop\", \n",
        "                  loss = tf.keras.losses.categorical_crossentropy, \n",
        "                  metrics=['accuracy'])\n",
        "                  #loss=crf.loss_function, metrics=[crf.accuracy])\n",
        "    return model\n",
        "\n",
        "\n",
        "model = BiLSTM(vocab_size=vocab_size, tag_size=tag_size, hidden_size = 32, \\\n",
        "                embedding_dim=embedding_dim)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow_addons in /usr/local/lib/python3.7/dist-packages (0.13.0)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "WARNING:tensorflow:Layer lstm_3 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_3 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_3 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXWo98J3jgLQ"
      },
      "source": [
        "<a name=\"3.3\"></a>\n",
        "## 3.3 Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2hDC-w7DGvu"
      },
      "source": [
        "# Callback\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('val_accuracy')>0.95):\n",
        "      print(\"\\nReached 95% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "checkpointer = ModelCheckpoint(filepath = 'NER_BiLSTM.h5',\n",
        "                       verbose = 0,\n",
        "                       mode = 'auto',\n",
        "                       save_best_only = True,\n",
        "                       monitor='val_loss')\n",
        "\n",
        "earlystopper = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, \n",
        "                             verbose=0, mode='auto', \n",
        "                             baseline=None, restore_best_weights=True)\n",
        "\n",
        "initial_learning_rate = 0.001\n",
        "epochs = 15\n",
        "decay = initial_learning_rate / epochs\n",
        "def lr_time_based_decay(epoch, lr):\n",
        "    return lr * 1 / (1 + decay * epoch)\n",
        "\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lr_time_based_decay, verbose=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKnkkiSS4EwI"
      },
      "source": [
        "<a name=\"4\"></a>\n",
        "# Part 4:  Train the Model \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQ7uIYmgX3SC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "faa0e4c9-2a76-4ada-eab4-8f2f85460b38"
      },
      "source": [
        "num_epochs = 15\n",
        "history = model.fit(train_padded_sequences, train_padded_tags, \n",
        "          batch_size=batch_size, epochs=num_epochs, \n",
        "          validation_data= (val_padded_sequences, val_padded_tags),\n",
        "          callbacks=[checkpointer, earlystopper, lr_scheduler])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "\n",
            "Epoch 00001: LearningRateScheduler reducing learning rate to 0.0010000000474974513.\n",
            "196/196 [==============================] - 88s 428ms/step - loss: 0.4487 - accuracy: 0.9060 - val_loss: 0.2207 - val_accuracy: 0.9352\n",
            "Epoch 2/15\n",
            "\n",
            "Epoch 00002: LearningRateScheduler reducing learning rate to 0.0009999333852717665.\n",
            "196/196 [==============================] - 81s 415ms/step - loss: 0.1669 - accuracy: 0.9501 - val_loss: 0.1606 - val_accuracy: 0.9510\n",
            "Epoch 3/15\n",
            "\n",
            "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0009998000348467315.\n",
            "196/196 [==============================] - 81s 414ms/step - loss: 0.1343 - accuracy: 0.9582 - val_loss: 0.1579 - val_accuracy: 0.9514\n",
            "Epoch 4/15\n",
            "\n",
            "Epoch 00004: LearningRateScheduler reducing learning rate to 0.0009996001259493627.\n",
            "196/196 [==============================] - 80s 410ms/step - loss: 0.1163 - accuracy: 0.9629 - val_loss: 0.1405 - val_accuracy: 0.9560\n",
            "Epoch 5/15\n",
            "\n",
            "Epoch 00005: LearningRateScheduler reducing learning rate to 0.0009993336718878056.\n",
            "196/196 [==============================] - 81s 413ms/step - loss: 0.1035 - accuracy: 0.9662 - val_loss: 0.1327 - val_accuracy: 0.9579\n",
            "Epoch 6/15\n",
            "\n",
            "Epoch 00006: LearningRateScheduler reducing learning rate to 0.0009990006859666584.\n",
            "196/196 [==============================] - 81s 414ms/step - loss: 0.0936 - accuracy: 0.9692 - val_loss: 0.1316 - val_accuracy: 0.9579\n",
            "Epoch 7/15\n",
            "\n",
            "Epoch 00007: LearningRateScheduler reducing learning rate to 0.0009986012978557466.\n",
            "196/196 [==============================] - 80s 410ms/step - loss: 0.0856 - accuracy: 0.9716 - val_loss: 0.1377 - val_accuracy: 0.9581\n",
            "Epoch 8/15\n",
            "\n",
            "Epoch 00008: LearningRateScheduler reducing learning rate to 0.0009981355208293137.\n",
            "196/196 [==============================] - 81s 414ms/step - loss: 0.0780 - accuracy: 0.9744 - val_loss: 0.1378 - val_accuracy: 0.9582\n",
            "Epoch 9/15\n",
            "\n",
            "Epoch 00009: LearningRateScheduler reducing learning rate to 0.0009976034845113318.\n",
            "196/196 [==============================] - 81s 415ms/step - loss: 0.0709 - accuracy: 0.9762 - val_loss: 0.1359 - val_accuracy: 0.9582\n",
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 71)]              0         \n",
            "_________________________________________________________________\n",
            "embedding_3 (Embedding)      (None, 71, 300)           2733900   \n",
            "_________________________________________________________________\n",
            "bidirectional_3 (Bidirection (None, 71, 64)            85248     \n",
            "_________________________________________________________________\n",
            "time_distributed_3 (TimeDist (None, 71, 32)            2080      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 71, 27)            891       \n",
            "=================================================================\n",
            "Total params: 2,822,119\n",
            "Trainable params: 2,822,119\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3vHe1vaC-fG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b226742-9c0d-4451-e357-e8816e877ab9"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-LuVEjYZ1qg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "74ec1ae3-0c31-486f-804c-4037a65ca371"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "plt.figure(figsize = (8,8))\n",
        "epochs = range(1, len(acc) + 1)\n",
        "plt.plot(epochs, acc, 'wo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'w', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff45f481910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_orawE5havtv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "0a67400a-eaea-4a26-823d-2a3b71e80816"
      },
      "source": [
        "plt.figure(figsize = (8,8))\n",
        "plt.plot(epochs, loss, 'wo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'w', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ygvHcHU4Nkj"
      },
      "source": [
        "<a name=\"5\"></a>\n",
        "# Part 5:  Test the model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYIAQDjhsHNQ"
      },
      "source": [
        "# Convert from one-hot encoding (3D array) to 2D array \n",
        "test_padded_tags_pred = model.predict(test_padded_sequences)\n",
        "test_padded_tags_pred = np.argmax(test_padded_tags_pred, axis=-1)\n",
        "test_padded_tags_true = np.argmax(test_padded_tags, axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aS4oaqkBE4XX"
      },
      "source": [
        "# Convert the index to tag\n",
        "test_tags_pred =[0]*len(test_padded_tags_pred)\n",
        "for idx, row in enumerate(test_padded_tags_pred):\n",
        "    add = []\n",
        "    for i in row:\n",
        "        add.append(reverse_tag_map[i]) if i != 0 else add.append(\"PAD\")\n",
        "    test_tags_pred[idx] = add\n",
        "\n",
        "test_tags_true =[0]*len(test_padded_tags_true)\n",
        "for idx, row in enumerate(test_padded_tags_true):\n",
        "    add = []\n",
        "    for i in row:\n",
        "        add.append(reverse_tag_map[i]) if i != 0 else add.append(\"PAD\")\n",
        "    test_tags_true[idx] = add"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OO5Pg9r2MmV8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96156078-c726-4d4d-8f4e-43886bdc7f1c"
      },
      "source": [
        "print(\"Micro F1-score is : {:.1%}\".format(f1_score(test_tags_true, test_tags_pred)))\n",
        "print(\"Micro Precision-score is : {:.1%}\".format(precision_score(test_tags_true, test_tags_pred)))\n",
        "print(\"Micro Recall-score is : {:.1%}\".format(recall_score(test_tags_true, test_tags_pred)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PAD seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Micro F1-score is : 69.6%\n",
            "Micro Precision-score is : 66.4%\n",
            "Micro Recall-score is : 73.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znKBJcadMr9l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef4323f4-fa78-45b7-c69f-bb68f54bdfb0"
      },
      "source": [
        "report = flat_classification_report(y_pred=test_tags_pred, y_true=test_tags_true)\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "                  precision    recall  f1-score   support\n",
            "\n",
            "         B-Actor       0.91      0.95      0.93      1274\n",
            "         B-Award       0.61      0.52      0.56        66\n",
            "B-Character_Name       0.65      0.48      0.55       283\n",
            "      B-Director       0.87      0.87      0.87       425\n",
            "         B-Genre       0.78      0.83      0.81       789\n",
            "       B-Opinion       0.47      0.53      0.50       195\n",
            "        B-Origin       0.54      0.27      0.36       190\n",
            "          B-Plot       0.55      0.43      0.48      1577\n",
            "         B-Quote       0.00      0.00      0.00        47\n",
            "  B-Relationship       0.80      0.62      0.70       171\n",
            "    B-Soundtrack       0.00      0.00      0.00         8\n",
            "          B-Year       0.94      0.97      0.96       661\n",
            "         I-Actor       0.91      0.95      0.93      1553\n",
            "         I-Award       0.64      0.74      0.69       147\n",
            "I-Character_Name       0.64      0.41      0.50       227\n",
            "      I-Director       0.91      0.90      0.91       411\n",
            "         I-Genre       0.84      0.62      0.71       544\n",
            "       I-Opinion       0.50      0.02      0.04       143\n",
            "        I-Origin       0.68      0.69      0.69       808\n",
            "          I-Plot       0.90      0.92      0.91     14661\n",
            "         I-Quote       0.60      0.62      0.61       344\n",
            "  I-Relationship       0.59      0.36      0.44       289\n",
            "    I-Soundtrack       0.00      0.00      0.00        30\n",
            "          I-Year       0.71      0.57      0.63        44\n",
            "               O       0.85      0.89      0.86     14143\n",
            "             PAD       1.00      1.00      1.00     99704\n",
            "\n",
            "        accuracy                           0.96    138734\n",
            "       macro avg       0.65      0.58      0.60    138734\n",
            "    weighted avg       0.96      0.96      0.96    138734\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HE7Q2oD2M9rL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df0d97c9-fa16-4333-876c-915231440dc6"
      },
      "source": [
        "# At every execution model picks some random test sample from test set.\n",
        "i = np.random.randint(0,test_padded_sequences.shape[0]) # choose a random number between 0 and len(X_te)b\n",
        "p = model.predict(np.array([test_padded_sequences[i]]))\n",
        "p = np.argmax(p, axis=-1)\n",
        "true = np.argmax(test_padded_tags[i], -1)\n",
        "\n",
        "print(\"Sample number {} of {} (Test Set)\".format(i, test_padded_sequences.shape[0]))\n",
        "# Visualization\n",
        "print(\"{:20}||{:20}||{}\".format(\"Word\", \"True\", \"Pred\"))\n",
        "print(60 * \"=\")\n",
        "for word, tag, pred in zip(test_padded_sequences[i], true, p[0]):\n",
        "    if word != 0:\n",
        "        print(\"{:20}: {:20} {}\".format(reverse_vocab[word], reverse_tag_map[tag], reverse_tag_map[pred]))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample number 1382 of 1954 (Test Set)\n",
            "Word                ||True                ||Pred\n",
            "============================================================\n",
            "a                   : O                    O\n",
            "animated            : B-Genre              B-Genre\n",
            "movie               : O                    O\n",
            "about               : O                    O\n",
            "a                   : B-Plot               B-Plot\n",
            "father              : I-Plot               I-Plot\n",
            "clown               : I-Plot               I-Plot\n",
            "fish                : I-Plot               I-Plot\n",
            "that                : I-Plot               I-Plot\n",
            "ha                  : I-Plot               I-Plot\n",
            "lost                : I-Plot               I-Plot\n",
            "his                 : I-Plot               I-Plot\n",
            "son                 : I-Plot               I-Plot\n",
            "in                  : I-Plot               I-Plot\n",
            "the                 : I-Plot               I-Plot\n",
            "deep                : I-Plot               I-Plot\n",
            "blue                : I-Plot               I-Plot\n",
            "ocean               : I-Plot               I-Plot\n",
            "and                 : I-Plot               I-Plot\n",
            "wiwill              : I-Plot               I-Plot\n",
            "stop                : I-Plot               I-Plot\n",
            "at                  : I-Plot               I-Plot\n",
            "nothing             : I-Plot               I-Plot\n",
            "to                  : I-Plot               I-Plot\n",
            "find                : I-Plot               I-Plot\n",
            "him                 : I-Plot               I-Plot\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNtJLetx4Sb_"
      },
      "source": [
        "<a name=\"6\"></a>\n",
        "# Part 6:  Test with your own sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0niUrOnVskX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "outputId": "77c0aa7b-7836-4909-edda-72903c0e893f"
      },
      "source": [
        "# if this cell fails => run the 2nd time, it will work\n",
        "\n",
        "original_to_test = [\"is michael scofield the protagonist in prison break\", \n",
        "                    \"what is the highest rated romantic movie in all time\"]\n",
        "\n",
        "to_test = apply_preproc(original_to_test)\n",
        "\n",
        "vocab_tokenizer.fit_on_texts(to_test)\n",
        "to_test = vocab_tokenizer.texts_to_sequences(to_test)\n",
        "\n",
        "to_test = pad_sequences(to_test,\n",
        "                        maxlen=max_length, \n",
        "                        truncating=trunc_type, \n",
        "                        padding=pad_type)\n",
        "\n",
        "to_test_tag_pred = model.predict(to_test)\n",
        "to_test_tag_pred = np.argmax(to_test_tag_pred, axis=-1)\n",
        "\n",
        "for i, row in enumerate(to_test_tag_pred):\n",
        "    print(\"\\n{:20}||{}\".format(\"Word\", \"Pred\"))\n",
        "    print(40 * \"=\")\n",
        "    for j, pred in enumerate(row):\n",
        "        words = original_to_test[i].split(' ')\n",
        "        length = len(words)\n",
        "        if pred != 0 and j < length:\n",
        "            print(\"{:20}: {}\".format(words[j], reverse_tag_map[pred]))\n",
        "            "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "InvalidArgumentError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-c4e2d3b9d23e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m                         padding=pad_type)\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mto_test_tag_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mto_test_tag_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_test_tag_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1725\u001b[0m           \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1726\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_predict_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1727\u001b[0;31m             \u001b[0mtmp_batch_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1728\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1729\u001b[0m               \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    922\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3023\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3024\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1959\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1961\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1963\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: 2 root error(s) found.\n  (0) Invalid argument:  indices[0,2] = 9113 is not in [0, 9113)\n\t [[node model/embedding/embedding_lookup (defined at <ipython-input-33-c4e2d3b9d23e>:16) ]]\n  (1) Invalid argument:  indices[0,2] = 9113 is not in [0, 9113)\n\t [[node model/embedding/embedding_lookup (defined at <ipython-input-33-c4e2d3b9d23e>:16) ]]\n\t [[model/embedding/embedding_lookup/_21]]\n0 successful operations.\n0 derived errors ignored. [Op:__inference_predict_function_14054]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node model/embedding/embedding_lookup:\n model/embedding/embedding_lookup/13534 (defined at /usr/lib/python3.7/contextlib.py:112)\n\nInput Source operations connected to node model/embedding/embedding_lookup:\n model/embedding/embedding_lookup/13534 (defined at /usr/lib/python3.7/contextlib.py:112)\n\nFunction call stack:\npredict_function -> predict_function\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZJApvzvaJcy"
      },
      "source": [
        "<a name=\"7\"></a>\n",
        "# Part 7: Analyse the incorrect predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fb8q_rD4aNRH"
      },
      "source": [
        "def get_incorrect(y_pred, y_true, X_test):\n",
        "    y_pred.flatten()\n",
        "    y_true.flatten()\n",
        "    X_test.flatten()\n",
        "    where_incorrect = y_true != y_pred\n",
        "    incorrect_idxes = np.where(where_incorrect==1)[0]\n",
        "    incorrect_tokens = X_test[incorrect_idxes]\n",
        "    incorrect_tokens = dict(Counter(incorrect_tokens.flatten()))\n",
        "    incorrect_tags = y_true[incorrect_idxes]\n",
        "    incorrect_tags = dict(Counter(incorrect_tags.flatten()))\n",
        "    return incorrect_tokens, incorrect_tags\n",
        "\n",
        "\n",
        "incorrect_tokens, incorrect_tags = get_incorrect(test_padded_tags_pred, \n",
        "                                                 test_padded_tags_true, \n",
        "                                                 test_padded_sequences) \n",
        "\n",
        "incorrect_tokens = sorted(incorrect_tokens.items(), key=lambda x:x[1], reverse=True)\n",
        "incorrect_tags = sorted(incorrect_tags.items(), key=lambda x:x[1], reverse=True)\n",
        "\n",
        "print(\"{:^20}||{:^15}\".format(\"Incorrect word\", \"Frequency\"))\n",
        "print(37 * \"=\")\n",
        "for idx, count in incorrect_tokens[:20]:\n",
        "    if idx != 0:\n",
        "        print(\"{:20}: {:15}\".format(reverse_vocab[idx], count))\n",
        "\n",
        "print(\"\\n{:^20}||{:^15}\".format(\"Incorrect tag\", \"Frequency\"))\n",
        "print(37 * \"=\")\n",
        "for idx, count in incorrect_tags[:20]:\n",
        "    if idx != 0:\n",
        "        print(\"{:20}: {:15}\".format(reverse_tag_map[idx], count))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5rOw_59Ztzy7"
      },
      "source": [
        "## Conclusion after analysis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiAN2Zy--j-_"
      },
      "source": [
        "1. A lot of incorrect words are stopwords, due to their occurence in the plot. This is the problem of token label consistency.\n",
        "2. The values of macro-average are much lower than micro-average of Precision, Recall and F1-score, as a result of imbalanced classes.\n",
        "3. The number of incorrect Plot entities are so high, as Plot entities are the longest ones.\n",
        "4. Lots of abbreviations that need to be cleaned. For example: country names (US -> u, s) or person name (J.K.Rowling => j, k, rowling)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2J2LDiBRkyq"
      },
      "source": [
        "## Potential improvements"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aBUF7SIHVhd"
      },
      "source": [
        "1. To tackle imbalanced classes problem, use over-sample to gain more examples of tags from the minority groups.\n",
        "2. To tackle label inconsistency, there're 3 solutions:\n",
        "\n",
        "  *   Use larger context. For example, use longer sentences, or combine 2 or more sentences that have similar/corelated meaning.\n",
        "  *   Use CRF decoder layer.\n",
        "  *   Use Character/Subword-level encoders like ELMO, Flair, CNN and BERT. \n",
        "\n",
        "3. To regconize long entity better:\n",
        "  *   Incorporate POS tagging beside BIO tagging. \n",
        "  *   Use ensemble to combine multiple models.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkJ8Go7BVVgz"
      },
      "source": [
        "<a name=\"8\"></a>\n",
        "# Export result to .tsv file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EQxLZgzRdL9"
      },
      "source": [
        "# write result to a new txt file\n",
        "with open('/tmp/pred.tsv', 'wt') as out_file:\n",
        "    tsv_writer = csv.writer(out_file, delimiter='\\t')\n",
        "    test_size = len(test_padded_sequences)\n",
        "    for i in range(test_size):\n",
        "        for pred, word in zip(test_padded_tags_pred[i], test_padded_sequences[i]):\n",
        "            if pred != 0 and word != 0:\n",
        "                tsv_writer.writerow([reverse_tag_map[pred], reverse_vocab[word]])\n",
        "        tsv_writer.writerow([])"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}