Word2Vec.ipynb

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    "colab": {
      "name": "Task1_NLP.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nthanhkhang/ChatBots-to-Question-Answer-systems/blob/main/Task1_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "b9463e685f375b3bcb254927c459fc71fd51d45f",
        "id": "hHBmXtqa7X63"
      },
      "source": [
        "# Task1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "d6NJGBOK7X7L",
        "outputId": "1d498f97-833a-4ddc-fec8-4141b1e00634"
      },
      "source": [
        "!wget https://github.com/nthanhkhang/Natural-Language-Processing/raw/main/Data/DataChatbot.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-10 16:18:09--  https://github.com/nthanhkhang/Natural-Language-Processing/raw/main/Data/DataChatbot.zip\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/nthanhkhang/Natural-Language-Processing/main/Data/DataChatbot.zip [following]\n",
            "--2021-05-10 16:18:09--  https://raw.githubusercontent.com/nthanhkhang/Natural-Language-Processing/main/Data/DataChatbot.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 164974 (161K) [application/zip]\n",
            "Saving to: ‘DataChatbot.zip.1’\n",
            "\n",
            "DataChatbot.zip.1   100%[=====================>] 161.11K  --.-KB/s   in 0.04s  \n",
            "\n",
            "2021-05-10 16:18:09 (3.74 MB/s) - ‘DataChatbot.zip.1’ saved [164974/164974]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "yObsUMXf7X7P"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import string\n",
        "import gensim\n",
        "from nltk.corpus import stopwords\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn import svm\n",
        "from sklearn import tree\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.feature_extraction.text import TfidfTransformer,TfidfVectorizer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import classification_report, accuracy_score, precision_score, f1_score, recall_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "R1WSnZJu7X7Q",
        "outputId": "6dec4d6e-f89e-476b-b8d4-b0c5a13fbfce"
      },
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile(\"DataChatbot.zip\",\"r\") as zf:\n",
        "    zf.extractall('DataChatbot')\n",
        "print(zf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<zipfile.ZipFile [closed]>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Tl3EnNt77X7R",
        "outputId": "7245bfd9-fe28-4a9f-c942-7249ec22f8e0"
      },
      "source": [
        "chatbot = pd.read_csv(\"DataChatbot/bạn bè.txt\",names=[\"question\",\"answer\",\"category\"],sep='__eou__')\n",
        "for i in range(chatbot.shape[0]):\n",
        "  chatbot.loc[i, 'category'] = 'bạn bè'\n",
        "chatbot.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 question                 answer category\n",
              "0  Thích đánh lộn không?           Ngon nhào vô    bạn bè\n",
              "1       Solo yasua không    Chấp lun 2 mạng đầu    bạn bè\n",
              "2   Mai đi picnic không?        Mai bận học rồi    bạn bè\n",
              "3    Mai học ca mấy vậy?           Mai học ca 3    bạn bè\n",
              "4        Còn tiền không?          Còn chết liền    bạn bè"
            ],
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thích đánh lộn không?</td>\n",
              "      <td>Ngon nhào vô</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Solo yasua không</td>\n",
              "      <td>Chấp lun 2 mạng đầu</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mai đi picnic không?</td>\n",
              "      <td>Mai bận học rồi</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mai học ca mấy vậy?</td>\n",
              "      <td>Mai học ca 3</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Còn tiền không?</td>\n",
              "      <td>Còn chết liền</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RwuCkmPL7X7T",
        "outputId": "951eaf78-0115-4b3c-9984-a8782f130d5f"
      },
      "source": [
        "allFiles = ['các câu hỏi phức tạp','du lịch','gia đình','giải trí','học tập','nghề nghiệp','nghỉ lễ','người yêu','robot','shoping','sở thích','tdtu','thông tin cá nhân','trò chuyện về đi ăn','tán gẫu','đất nước','địa chỉ']\n",
        "for i in allFiles:\n",
        "  df = pd.read_csv('DataChatbot/'+i+'.txt',names=[\"question\",\"answer\",\"category\"],sep='__eou__')\n",
        "  for j in range(df.shape[0]):\n",
        "    df.loc[j, 'category'] = i\n",
        "  chatbot=chatbot.append(df,ignore_index=True)\n",
        "chatbot.head()\n",
        "##df2.head()\n",
        "##df.drop('type',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 question                 answer category\n",
              "0  Thích đánh lộn không?           Ngon nhào vô    bạn bè\n",
              "1       Solo yasua không    Chấp lun 2 mạng đầu    bạn bè\n",
              "2   Mai đi picnic không?        Mai bận học rồi    bạn bè\n",
              "3    Mai học ca mấy vậy?           Mai học ca 3    bạn bè\n",
              "4        Còn tiền không?          Còn chết liền    bạn bè"
            ],
            "text/html": [
              "<div>\n",
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              "    }\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>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thích đánh lộn không?</td>\n",
              "      <td>Ngon nhào vô</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Solo yasua không</td>\n",
              "      <td>Chấp lun 2 mạng đầu</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mai đi picnic không?</td>\n",
              "      <td>Mai bận học rồi</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mai học ca mấy vậy?</td>\n",
              "      <td>Mai học ca 3</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Còn tiền không?</td>\n",
              "      <td>Còn chết liền</td>\n",
              "      <td>bạn bè</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "-eloLGMY7X7V",
        "outputId": "da9a49e0-8d12-4be6-e42d-dc29c908ea9c"
      },
      "source": [
        "chatbot.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5855 entries, 0 to 5854\n",
            "Data columns (total 3 columns):\n",
            "question    5852 non-null object\n",
            "answer      5855 non-null object\n",
            "category    5855 non-null object\n",
            "dtypes: object(3)\n",
            "memory usage: 137.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "rwlHspHp7X7V"
      },
      "source": [
        "def cleaner(x):\n",
        "    return [a for a in (''.join([a for a in x if a not in string.punctuation])).lower().split()]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XOGSk2Rm7X7W"
      },
      "source": [
        "Pipe = Pipeline([\n",
        "    ('bow',CountVectorizer(analyzer=cleaner)),\n",
        "    ('tfidf',TfidfTransformer()),\n",
        "    ('classifier',DecisionTreeClassifier())\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "j6fq7mb37X7X",
        "outputId": "765b7748-f911-4420-d504-a8593ca74574"
      },
      "source": [
        "Pipe.fit(chatbot['question'].values.astype('U'),chatbot['answer'].values.astype('U'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "     steps=[('bow', CountVectorizer(analyzer=<function cleaner at 0x7f853b4bfa60>, binary=False,\n",
              "        decode_error='strict', dtype=<class 'numpy.int64'>,\n",
              "        encoding='utf-8', input='content', lowercase=True, max_df=1.0,\n",
              "        max_features=None, min_df=1, ngram_range=(1, 1), preprocessor=None,\n",
              " ...      min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "            splitter='best'))])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Jvz48k5T7X7Y",
        "outputId": "5693bbe6-41cb-4f9f-ff1c-adea0daf53d7"
      },
      "source": [
        "Pipe.predict(['Còn tiền không?'])[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Còn chết liền '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "syzlQHrX7X7Z",
        "outputId": "b3c41f94-4b53-48f1-88e8-745404f64556"
      },
      "source": [
        "Pipe.predict(['Solo yasua không '])[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Chấp lun 2 mạng đầu '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-so__AlP7X7a"
      },
      "source": [
        "# Use Word2Vec"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YVS6Xxlo7X7a",
        "outputId": "3fffe1be-7a96-4551-c921-7dbbcd44dbca"
      },
      "source": [
        "!pip install srt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting srt\n",
            "  Downloading https://files.pythonhosted.org/packages/3b/dd/0629a23b11da7696ce69e4432a2de6a1d1277bf2ba5cda624009069d4bb3/srt-3.4.1.tar.gz\n",
            "Building wheels for collected packages: srt\n",
            "  Running setup.py bdist_wheel for srt ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/85/9e/29/17bfa5b6d3ce3c026b6bdf49557a269e0ef869492a1bc700b2\n",
            "Successfully built srt\n",
            "\u001b[31mmxnet 1.3.0.post0 has requirement numpy<1.15.0,>=1.8.2, but you'll have numpy 1.15.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mkmeans-smote 0.1.0 has requirement imbalanced-learn<0.4,>=0.3.1, but you'll have imbalanced-learn 0.5.0.dev0 which is incompatible.\u001b[0m\n",
            "\u001b[31mkmeans-smote 0.1.0 has requirement numpy<1.15,>=1.13, but you'll have numpy 1.15.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mfastai 0.7.0 has requirement torch<0.4, but you'll have torch 0.4.1.post2 which is incompatible.\u001b[0m\n",
            "\u001b[31manaconda-client 1.7.2 has requirement python-dateutil>=2.6.1, but you'll have python-dateutil 2.6.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mimbalanced-learn 0.5.0.dev0 has requirement scikit-learn>=0.20, but you'll have scikit-learn 0.19.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: srt\n",
            "Successfully installed srt-3.4.1\n",
            "\u001b[33mYou are using pip version 10.0.1, however version 21.1.1 is available.\n",
            "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "42Xf9C2t7X7b"
      },
      "source": [
        "import re\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd \n",
        "import os\n",
        "import tensorflow as tf\n",
        "import gensim\n",
        "from nltk import ngrams\n",
        "import srt "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "EDtgdYcA7X7c",
        "outputId": "bddf8b6d-d5a6-4ca5-b1ee-b4ac8194822d"
      },
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile(\"DataChatbot.zip\",\"r\") as zf:\n",
        "    zf.extractall('DataChatbot')\n",
        "print(zf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<zipfile.ZipFile [closed]>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Q0xGEvIY7X7c",
        "outputId": "acf04e54-23fe-4023-b1a4-26aeacf133dd"
      },
      "source": [
        "questions=[]\n",
        "answers=[]\n",
        "error=0\n",
        "import glob\n",
        "import os\n",
        "path = 'DataChatbot'\n",
        "for filename in glob.glob(os.path.join(path, '*.txt')):\n",
        "   with open(os.path.join(os.getcwd(), filename), 'r') as f: # open in readonly mode\n",
        "      for line in f:\n",
        "        part = line.strip().strip('__eou_').split('__eou__')\n",
        "        questions.append(\", \".join(part[:-1]))\n",
        "        answers.append(part[-1])\n",
        "print('Question',questions[:5])\n",
        "print('Answers',answers[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Question ['bạn đã có việc làm chưa ? ', 'bạn đã có việc làm chưa ? ', 'bạn đã đi làm hay chưa? ', 'hiện tại bạn làm ở đâu? ', 'địa chỉ nơi làm việc của bạn ở đâu? ']\n",
            "Answers [' chưa, mình đang thất nghiệp ', ' rồi, mình đang làm việc cho một công ty ', ' mình đã đi làm rồi ', ' hiện tại mình làm ở FPT software ', ' ở khu công nghệ cao ']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "32mRll4d7X7d",
        "outputId": "feb8a669-d2a5-406d-e9ba-68155e2aa16c"
      },
      "source": [
        "!wget https://thiaisotajppub.s3-ap-northeast-1.amazonaws.com/publicfiles/baomoi.model.bin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-10 16:20:33--  https://thiaisotajppub.s3-ap-northeast-1.amazonaws.com/publicfiles/baomoi.model.bin\n",
            "Resolving thiaisotajppub.s3-ap-northeast-1.amazonaws.com (thiaisotajppub.s3-ap-northeast-1.amazonaws.com)... 52.219.0.163\n",
            "Connecting to thiaisotajppub.s3-ap-northeast-1.amazonaws.com (thiaisotajppub.s3-ap-northeast-1.amazonaws.com)|52.219.0.163|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 708212586 (675M) [application/macbinary]\n",
            "Saving to: ‘baomoi.model.bin’\n",
            "\n",
            "baomoi.model.bin    100%[=====================>] 675.40M  16.2MB/s   in 52s    \n",
            "\n",
            "2021-05-10 16:21:26 (13.0 MB/s) - ‘baomoi.model.bin’ saved [708212586/708212586]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "9ec94ef740da26a5f673cba9b1f5e051b7715854",
        "trusted": true,
        "id": "eKWk1twT7X7e",
        "outputId": "29f1a6c7-5c75-46b6-a00c-7acf39efd19b"
      },
      "source": [
        "from gensim.models import KeyedVectors\n",
        "model_path = './baomoi.model.bin'\n",
        "w2v = KeyedVectors.load_word2vec_format(model_path, fvocab=None, binary=True, encoding='utf8')\n",
        "print(w2v.similar_by_word('anh'))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('cậu', 0.6509122848510742), ('chị', 0.6356398463249207), ('tôi', 0.6172564029693604), ('cô', 0.5971271991729736), ('ông', 0.5594496726989746), ('hắn', 0.5548809170722961), ('người_yêu', 0.5479433536529541), ('em', 0.5338587760925293), ('anh_trai', 0.5123729109764099), ('vợ', 0.5085247755050659)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "a3cb809b66c0369a3c405bd0451020ded395906f",
        "trusted": true,
        "id": "QCMS_wne7X7f",
        "outputId": "0b33eeb0-0bd3-4ee7-d64d-118d35472a9c"
      },
      "source": [
        "keys = list(w2v.vocab.keys())\n",
        "print(keys[0:400])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['<punct>', '<number>', 'của', 'và', 'là', 'các', 'trong', 'có', 'được', 'cho', 'đã', 'với', 'không', 'một', 'những', 'người', 'khi', 'này', 'để', ':', 'ở', 'về', 'năm', 'cũng', 'sẽ', 'đến', 'từ', 'vào', 'nhiều', 'tại', 'trên', '”', '“', 'đó', 'ra', 'phải', 'ngày', 'theo', 'nhưng', 'như', 'lại', 'sau', 'còn', 'hơn', 'bị', 'tôi', 'làm', 'anh', 'việc', 'đang', 'thì', 'ông', 'rất', 'việt_nam', 'trước', 'biết', 'chỉ', 'đi', 'có_thể', 'mới', 'sự', 'nhất', 'mình', 'đồng', 'mà', 'bạn', 'vẫn', 'lên', '%', 'nước', 'hai', 'nhà', 'tới', 'nên', 'cùng', 'tháng', ';', 'con', 'vì', 'giá', 'khác', 'qua', 'tăng', 'cả', 'họ', 'bộ', 'rằng', 'cao', 'do', 'cô', 'chưa', 'nói', 'lần', 'nếu', 'số', 'triệu', 'đây', 'lớn', 'thấy', 'công_ty', 'tiền', 'hàng', 'gần', 'cần', 'hay', 'bằng', 'đưa', 'thị_trường', 'bên', 'cuộc', 'đầu', 'giảm', 'mức', 'xe', 'nay', 'thứ', 'thời_gian', 'trận', 'thêm', 'tỷ', 'mua', 'bán', 'tuổi', 'hà_nội', 'đường', 'tổ_chức', 'nào', 'cách', 'mỹ', 'đều', 'khiến', 'chiếc', 'khoảng', 'tuy_nhiên', 'em', 'chính', 'thực_hiện', 'trung_quốc', 'đầu_tư', 'gì', 'nhau', 'bà', 'điểm', 'nhận', 'hoặc', 'tp', 'tỉnh', 'sử_dụng', 'chị', 'usd', 'doanh_nghiệp', 'hoạt_động', 'phát_triển', 'ngân_hàng', 'một_số', 'xem', 'giữa', 'kinh_tế', 'lúc', 'vừa', 'chúng_tôi', 'xây_dựng', 'gia_đình', 'tốt', 'từng', 'thế_giới', 'dự_án', 'vụ', 'mang', 'gây', 'trẻ', 'quá', 'tin', 'ngay', 'tiếp_tục', 'giúp', 'đội', 'rồi', 'điều', 'so', 'vốn', 'mọi', 'muốn', '–', 'mỗi', 'vàng', 'trường', 'đạt', 'thông_tin', 'khu_vực', 'ngoài', 'h', 'bóng', 'dùng', 'ấy', 'Ông', 'loại', 'mùa', 'cái', 'bởi', 'tạo', 'đối_với', 'sáng', 'phía', 'm', 'nơi', 'giờ', 'chương_trình', 'mẹ', 'khá', 'gái', 'nó', 'tham_gia', 'vấn_đề', 'thi', 'phần', 'luôn', 'tìm', 'tên', 'gặp', 'người_dân', 'huyện', 'vn', 'giải', 'ăn', 'diễn', 'khả_năng', 'tính', 'kinh_doanh', 'lấy', 'phim', 'chính_phủ', 'học', 'bé', 'ta', 'tuần', 'quyết_định', 'cơ_quan', 'đầu_tiên', 'không_thể', 'xuống', 'mất', 'hiện', 'sản_phẩm', 'nhỏ', 'ngành', 'thành_phố', 'nguồn', 'chuyện', 'công_an', 'lời', 'cuối', 'giữ', 'phòng', 'đặc_biệt', 'tàu', 'sản_xuất', 'đất', 'cổ_phiếu', 'vòng', 'ai', 'dưới', 'quản_lý', 'nhóm', 'xã', 'thuộc', 'sống', 'nga', 'sang', 'quy_định', 'mạnh', 'biển', 'chủ_tịch', 'cấp', 'dù', 'chủ', 'ba', 'giao_dịch', 'bài', 'kể', 'xảy', 'nữa', 'thế', 'quốc_tế', 'hcm', 'đẹp', 'khu', 'yêu_cầu', 'nhằm', 'nhà_nước', 'hỗ_trợ', 'thành', 'quốc_gia', 'phút', 'khó_khăn', 'đúng', 'Đây', 'hệ_thống', 'tay', 'tất_cả', 'đấu', 'đủ', 'cầu_thủ', 'chiều', 'hiện_nay', 'Ảnh', 'chồng', 'tài_chính', 'nam', 'hết', 'kết_quả', 'bệnh', 'chơi', 'khó', 'vợ', 'nhìn', 'thường', 'rõ', 'đặt', 'vùng', 'liên_quan', 'ít', 'bỏ', 'quan_trọng', 'xã_hội', 'trở_thành', 'quận', 'kế_hoạch', 'sao', 'dành', 'công_tác', 'nữ', 'trung_tâm', 'chia_sẻ', 'thời_điểm', 'ảnh', 'thấp', 'làm_việc', 'khách', 'gửi', 'phó', 'thành_công', 'hlv', 'lượng', 'khỏi', 'riêng', 'yêu', 'phát_hiện', 'cơ_hội', 'chết', 'tiếng', 'thủ_tướng', 'khẳng_định', 'máy_bay', 'nằm', 'sân', 'lực_lượng', 'hình_ảnh', 'đơn_vị', 'phiên', 'việt', 'như_vậy', 'bắt_đầu', 'bảo_vệ', 'màu', 'vay', 'xuất_hiện', 'xin', 'ban', 'đêm', 'buổi', 'hỏi', 'thật', 'mặt', 'nước_ngoài', 'cạnh', 'đứng', 'chung', 'báo', 'thay_đổi', 'tự', 'trường_hợp', 'chạy', 'khách_hàng', 'bước', 'hãng', 'điều_tra', 'tết', 'đôi', 'đáng', 'dẫn', 'chúng_ta', 'phụ_nữ', 'nghĩ', 'chọn', 'gọi', 'viết', 'độ', 'xử_lý', 'dịch_vụ']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "6f659114bf7f9d3fcafbc4d5fd93f0dbc8ff4074",
        "trusted": true,
        "id": "7bnlpdXs7X7g",
        "outputId": "3245081c-7ed0-4d5b-8425-7f75d56f4edc"
      },
      "source": [
        "vectors = w2v.vectors\n",
        "print(vectors[0:10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.29991007 -0.48487732  0.25084263 ... -0.06391991  0.1296555\n",
            "  -1.0371715 ]\n",
            " [ 2.4754343   0.45296562  2.2419798  ... -2.4558136   0.02929016\n",
            "  -2.2507153 ]\n",
            " [-2.640626   -1.7568986   0.17347822 ... -0.98626775  0.4481276\n",
            "  -1.4362826 ]\n",
            " ...\n",
            " [-3.1944964  -1.9406408  -2.2700322  ... -0.04263039  0.26658046\n",
            "   0.49616346]\n",
            " [-1.1480718   2.8294067  -1.4234537  ...  0.8699555   2.3107367\n",
            "   1.3925029 ]\n",
            " [-3.1502907  -1.4287512  -2.6768777  ...  0.11743276 -0.30074748\n",
            "   0.20755762]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "25489c648aa156ca72db6d77e57d3d7fc15c717e",
        "trusted": true,
        "id": "H4vG7s7Z7X7g"
      },
      "source": [
        "# print(w2v.vocab.keys())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3590e8ee1881d535b5f85dff448801b6000cf53c",
        "trusted": true,
        "id": "f4XSKVB97X7h",
        "outputId": "62ad0185-529f-4d30-a345-81eeb0fe800a"
      },
      "source": [
        "#TODO Tokenize with keys\n",
        "def tokenize(s):\n",
        "    result = []\n",
        "    arr = s.split()\n",
        "    for item in arr:\n",
        "        if len(result) > 0:\n",
        "            tmp = result[-1] + '_' + item\n",
        "            if tmp in keys:\n",
        "                result[-1] = tmp\n",
        "            else:\n",
        "                result.append(item)\n",
        "        else:\n",
        "            result.append(item)\n",
        "            \n",
        "    return result\n",
        "print(tokenize('Nhân viên trong không gian thật thân thiện'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Nhân', 'viên', 'trong', 'không_gian', 'thật', 'thân_thiện']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "07fbf3469ddf33051f0d31174c7e53222bbc0228",
        "trusted": true,
        "id": "apFb9qBm7X7i"
      },
      "source": [
        "#Create models\n",
        "x_tok = [tokenize(item) for item in questions[:10]]\n",
        "y_tok = [tokenize(item) for item in answers[:10]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "13cf367b0e62c43a5e6bb76ef12ace3a0570cc3a",
        "scrolled": true,
        "trusted": true,
        "id": "b43FXPhT7X7i",
        "outputId": "c4549432-d88c-4e58-ec6c-d18926fc6b0e"
      },
      "source": [
        "print(x_tok[0:10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['bạn', 'đã', 'có', 'việc_làm', 'chưa', '?'], ['bạn', 'đã', 'có', 'việc_làm', 'chưa', '?'], ['bạn', 'đã', 'đi_làm', 'hay', 'chưa?'], ['hiện_tại', 'bạn', 'làm', 'ở', 'đâu?'], ['địa_chỉ', 'nơi', 'làm_việc', 'của', 'bạn', 'ở', 'đâu?'], ['bạn', 'làm_công', 'việc', 'gì?'], ['cảm_xúc', 'của', 'bạn', 'như_thế_nào', 'khi', 'được', 'nhận', 'vào', 'làm', 'việc?'], ['đồng_nghiệp', 'của', 'bạn', 'ở', 'công_ty', 'như_thế', 'nào?'], ['sếp', 'của', 'bạn', 'như_thế', 'nào?'], ['bạn', 'có', 'thích', 'môi_trường', 'làm_việc', 'này', 'không?']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "5f6610116b44815cae47744b0cab129eb36b0237",
        "trusted": true,
        "id": "B_BBfun97X7j",
        "outputId": "03996efb-459e-4d2e-decb-fee03506133f"
      },
      "source": [
        "max_len = 0\n",
        "for item in y_tok:\n",
        "    if len(item) > max_len:\n",
        "        max_len = len(item)\n",
        "print(max_len)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "7cb47ec1b6f6356b9c09d80c06bf1fd3438f8aaa",
        "trusted": true,
        "id": "Aerfjdue7X7k",
        "outputId": "404677aa-d5e3-41f3-c223-4be2c954c301"
      },
      "source": [
        "vectors = w2v.vectors\n",
        "print(vectors.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(439056, 400)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "437365770b421b0b0980938fc6d21c62745190e0",
        "trusted": true,
        "id": "kB_hUomq7X7k"
      },
      "source": [
        "keys = list(w2v.vocab.keys())\n",
        "vectors = w2v.vectors\n",
        "PAD = np.ones((1,400))\n",
        "UNK = np.zeros((1,400))\n",
        "# print(PAD)\n",
        "keys.append('<PAD>')\n",
        "vectors = np.append(vectors, PAD, axis=0)\n",
        "keys.append('<UNK>')\n",
        "vectors = np.append(vectors, UNK, axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "76dafe40af29ea307838c52107d25baa5efed93a",
        "trusted": true,
        "id": "6EJTg5cX7X7l",
        "outputId": "cfc33ad6-4fc1-42fe-cdb8-94bbc904ed00"
      },
      "source": [
        "def get_vector_by_key(key):\n",
        "    if key not in keys:\n",
        "        key = '<UNK>'\n",
        "    return vectors[keys.index(key)]\n",
        "get_vector_by_key('người_yêu')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-2.74268329e-01,  1.91656783e-01, -7.21289873e-01,  2.53674030e-01,\n",
              "       -1.43376306e-01,  1.57112694e+00,  2.65404272e+00, -1.38281539e-01,\n",
              "       -1.41057864e-01, -1.43045509e+00,  2.39217002e-02,  1.43004760e-01,\n",
              "        2.14619294e-01,  1.41312277e+00,  2.51563698e-01,  1.43755943e-01,\n",
              "        1.90474525e-01, -1.41985446e-01, -9.45661128e-01,  1.82852685e+00,\n",
              "        7.01824307e-01,  1.71576118e+00,  1.30878377e+00,  1.80517483e+00,\n",
              "        2.27643579e-01, -2.51354480e+00, -5.95910013e-01,  6.03104949e-01,\n",
              "        1.94675970e+00, -2.64325619e+00, -1.83519334e-01, -4.08825397e+00,\n",
              "        4.30967093e-01,  8.30371618e-01, -1.08892155e+00,  5.36354482e-01,\n",
              "       -1.41104043e+00, -1.28516901e+00, -4.58042949e-01,  2.84221739e-01,\n",
              "        2.69456238e-01, -2.98275501e-01, -1.72609150e+00,  1.05817512e-01,\n",
              "        1.41816521e+00, -5.25495291e-01,  2.84548879e+00,  1.58646420e-01,\n",
              "       -1.33273518e+00, -1.50343144e+00,  1.54525769e+00,  2.08628488e+00,\n",
              "       -2.30622506e+00, -2.44498467e+00, -4.43750679e-01,  5.14842212e-01,\n",
              "       -4.41178948e-01,  8.32913637e-01, -2.78173065e+00,  5.15340447e-01,\n",
              "       -2.48830295e+00, -1.79855013e+00, -1.41795027e+00,  1.19051356e-02,\n",
              "        3.23852420e-01,  4.44463193e-01, -2.98894823e-01,  4.05403405e-01,\n",
              "       -1.79459059e+00, -1.36085224e+00, -3.61206442e-01,  1.88793874e+00,\n",
              "        3.04196358e+00, -4.45803702e-01, -2.22984838e+00,  2.63550520e+00,\n",
              "        2.84311342e+00,  2.38060927e+00,  1.07598674e+00, -1.96371377e+00,\n",
              "        2.58405519e+00,  1.07273173e+00, -1.71634436e+00,  8.30364764e-01,\n",
              "        5.02959728e-01, -7.81490803e-01,  3.86181569e+00, -5.60243368e-01,\n",
              "       -1.49683154e+00, -1.48258781e+00, -7.39754558e-01,  2.06634879e+00,\n",
              "        1.80035740e-01, -2.67130399e+00,  6.14204109e-01, -1.04389238e+00,\n",
              "        3.23596716e+00, -3.73601604e+00, -9.87999499e-01, -2.09397745e+00,\n",
              "       -1.25532484e+00,  1.41048348e+00,  2.45269045e-01,  2.53157568e+00,\n",
              "       -1.09688914e+00,  1.04946220e+00, -3.06211352e-01, -7.83465445e-01,\n",
              "       -1.14406919e+00,  4.01006937e-01, -6.06472604e-02, -7.04188272e-02,\n",
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              "       -3.22831941e+00,  2.91969150e-01, -9.13970888e-01, -1.36047781e-01,\n",
              "        1.60572088e+00,  2.12378264e-03, -9.64220583e-01,  2.88031387e+00,\n",
              "       -8.22779953e-01,  3.35745037e-01, -8.94802332e-01, -6.61950052e-01,\n",
              "       -2.34553671e+00,  5.35220742e-01,  4.77456719e-01,  1.23458132e-01])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "65bcc4bf17dbb9f7e54e25eaa0f9bce76a1066f9",
        "trusted": true,
        "id": "SNgQjSxy7X7l"
      },
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "SENTENCE_LEN = 25\n",
        "for toks in x_tok:\n",
        "    tmp = [get_vector_by_key(words) for words in toks]\n",
        "    tmp += [get_vector_by_key('<PAD>')] * (25 - len(tmp))\n",
        "    if len(tmp) == 25:\n",
        "        x_train.append(tmp)\n",
        "for toks in y_tok:\n",
        "    toks = ['<PAD>'] + toks\n",
        "    tmp = [get_vector_by_key(words) for words in toks]\n",
        "    tmp += [get_vector_by_key('<PAD>')] * (25 - len(tmp))\n",
        "    if len(tmp) == 25:\n",
        "        y_train.append(tmp)\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "94c2572a8d9045168fd269bb50e5888f80f012fb",
        "trusted": true,
        "id": "7IklxXfo7X7n",
        "outputId": "3aa61590-cf72-4dfe-c451-e96a41b059b3"
      },
      "source": [
        "x_train = np.array(x_train).reshape(-1, 25, 400)\n",
        "y_train = np.array(y_train).reshape(-1, 25, 400)\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10, 25, 400)\n",
            "(10, 25, 400)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "4b512080c49a9fbf48cb7cb0e9535e4c991a5e17",
        "trusted": true,
        "id": "VodLM7L17X7o"
      },
      "source": [
        "lstm = tf.nn.rnn_cell.LSTMCell(128)\n",
        "dense = tf.layers.Dense(400)\n",
        "init_state = state = lstm.zero_state(x_train.shape[0], dtype=tf.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c8605a035c6c034a37e2c0f1598d67a2c491c747",
        "trusted": true,
        "id": "HlYcE-Al7X7p"
      },
      "source": [
        "x = x_train\n",
        "y = y_train\n",
        "state = init_state\n",
        "outputs = []\n",
        "# x_arr = \n",
        "for i in range(x.shape[1]):\n",
        "    xi = x[:, i, :]\n",
        "    xi = tf.convert_to_tensor(xi, dtype=tf.float32)\n",
        "    output, state = lstm(xi, state)\n",
        "    logits = dense(output)\n",
        "    outputs.append(logits)\n",
        "outputs = tf.transpose(tf.convert_to_tensor(outputs), perm=[1, 0, 2])\n",
        "loss_op = tf.losses.mean_squared_error(\n",
        "    predictions=outputs, labels=y)\n",
        "optimizer = tf.train.AdamOptimizer()\n",
        "train_op = optimizer.minimize(loss_op)\n",
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "cdbdde8fab47437c460fa0d363c8faac3f6eb374",
        "scrolled": true,
        "trusted": true,
        "id": "XwbgleSU7X7q",
        "outputId": "de269415-45c9-4a1e-8482-612fe14d3e26"
      },
      "source": [
        "for epoch in range(10000):\n",
        "    sess.run(train_op)\n",
        "    loss = sess.run(loss_op)\n",
        "    print(f'epoch: {epoch}      loss: {loss}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "epoch: 1      loss: 1.1915382146835327\n",
            "epoch: 2      loss: 1.1265525817871094\n",
            "epoch: 3      loss: 1.0665947198867798\n",
            "epoch: 4      loss: 1.0072236061096191\n",
            "epoch: 5      loss: 0.9499765634536743\n",
            "epoch: 6      loss: 0.8969860672950745\n",
            "epoch: 7      loss: 0.8499751091003418\n",
            "epoch: 8      loss: 0.8090766072273254\n",
            "epoch: 9      loss: 0.7736595273017883\n",
            "epoch: 10      loss: 0.7432765364646912\n",
            "epoch: 11      loss: 0.7175235748291016\n",
            "epoch: 12      loss: 0.6960096955299377\n",
            "epoch: 13      loss: 0.6780731081962585\n",
            "epoch: 14      loss: 0.6632001996040344\n",
            "epoch: 15      loss: 0.6508896350860596\n",
            "epoch: 16      loss: 0.6407032012939453\n",
            "epoch: 17      loss: 0.6326209902763367\n",
            "epoch: 18      loss: 0.6265178918838501\n",
            "epoch: 19      loss: 0.6209399104118347\n",
            "epoch: 20      loss: 0.6161812543869019\n",
            "epoch: 21      loss: 0.611876368522644\n",
            "epoch: 22      loss: 0.6076192855834961\n",
            "epoch: 23      loss: 0.6031888723373413\n",
            "epoch: 24      loss: 0.5985581874847412\n",
            "epoch: 25      loss: 0.5937994122505188\n",
            "epoch: 26      loss: 0.5890784859657288\n",
            "epoch: 27      loss: 0.5841289162635803\n",
            "epoch: 28      loss: 0.5791857838630676\n",
            "epoch: 29      loss: 0.574357271194458\n",
            "epoch: 30      loss: 0.5695945024490356\n",
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            "epoch: 33      loss: 0.5558800101280212\n",
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            "epoch: 9999      loss: 0.0416155606508255\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d71a83724eadcdfa8cc1eae767e9673a9c5e87b3",
        "trusted": true,
        "id": "DjmhNfNA7X7r",
        "outputId": "c646ef32-91f3-436f-e0b4-926a0aab0bcb"
      },
      "source": [
        "print(x_tok[0])\n",
        "print(x_tok[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['bạn', 'đã', 'có', 'việc_làm', 'chưa', '?']\n",
            "['bạn', 'đã', 'có', 'việc_làm', 'chưa', '?']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "ceef669279fdfce7928407c955382a302f8513c1",
        "trusted": true,
        "id": "W5Ptr4a07X7s",
        "outputId": "266fe8ed-0c35-4795-b8d9-95adb23d54c1"
      },
      "source": [
        "print(x_tok[0][0])\n",
        "# print(x_tok[0][0])\n",
        "test = get_vector_by_key(x_tok[0][0])\n",
        "# print(keys.index(x_tok[0][0]))\n",
        "# print(test)\n",
        "# print(vectors[7258])\n",
        "# print((test - x[0][0]) ** 2)\n",
        "su = np.sum((vectors - x[0][0]) ** 2, axis=1)\n",
        "# print(np.argmin(su))\n",
        "# print()\n",
        "# print(su[np.argmin(su)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bạn\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c08e50a77473867bc77cde2a99a85f280695cdac",
        "trusted": true,
        "id": "axxwsqOI7X7s",
        "outputId": "0a0459a7-3ea4-4c53-90f6-b858ecacb0d3"
      },
      "source": [
        "output = sess.run(outputs)\n",
        "print(output.shape)\n",
        "\n",
        "for j in range(10):\n",
        "    print(y_tok[j])\n",
        "    for i in range(25):\n",
        "        t = np.argmin(np.sum((vectors - output[j][i]) ** 2, axis=1))\n",
        "        if keys[t] != '<PAD>':\n",
        "            print(keys[t], end=' ')\n",
        "    print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10, 25, 400)\n",
            "['chưa,', 'mình', 'đang', 'thất_nghiệp']\n",
            "ve_vuốt mình đang thất_nghiệp tiết_chế_bản_thân gian_hùng công_ti \n",
            "['rồi,', 'mình', 'đang', 'làm_việc', 'cho', 'một', 'công_ty']\n",
            "ve_vuốt mình đang thất_nghiệp tiết_chế_bản_thân gian_hùng công_ti \n",
            "['mình', 'đã', 'đi_làm', 'rồi']\n",
            "ve_vuốt đã đi_làm rồi \n",
            "['hiện_tại', 'mình', 'làm', 'ở', 'FPT', 'software']\n",
            "hiện_tại mình làm ở <UNK> software \n",
            "['ở', 'khu', 'công_nghệ_cao']\n",
            "ở khu công_nghệ_cao \n",
            "['mình', 'đang', 'là', 'kĩ_sư', 'phần', 'mêm']\n",
            "mình đang là kĩ_sư phần mêm \n",
            "['mình', 'rất', 'vui', 'và', 'phấn_chấn', 'khi', 'được', 'làm_việc', 'ở', 'đây']\n",
            "mình rất vui và phấn_chấn khi được làm_việc ở đây \n",
            "['đồng_nghiệp', 'rất', 'hòa_đồng', 'và', 'tốt_bụng']\n",
            "đồng_nghiệp rất hòa_đồng và tốt_bụng \n",
            "['sếp', 'làm_việc', 'rất', 'chuyên_nghiệp', 'và', 'rất', 'vui_tính']\n",
            "sếp làm_việc rất chuyên_nghiệp và rất vui_tính \n",
            "['tôi', 'rất', 'thích', 'môi_trường', 'làm_việc', 'này']\n",
            "tôi rất thích môi_trường làm_việc này \n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}