SA_Digikala.ipynb

{
  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ZahraArshia/sentiment_analysis/blob/main/SA_Digikala.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Digikala Sentiment Analysis"
      ],
      "metadata": {
        "id": "vHKoPBJFiF_P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## handling data"
      ],
      "metadata": {
        "id": "1omyse69iDMM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "j1oeUsc7IuNC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/drive/MyDrive/Dataset/digikala/data.csv', encoding='utf-8')"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "opIxdUUoJgui",
        "outputId": "16d1f635-960b-4852-aae0-a61cd18d2732"
      },
      "outputs": [
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              "      <td>این اولین تجربه من برای خرید ایفون هست   امروز...</td>\n",
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            "text/plain": [
              "                                                Text  Score  Suggestion\n",
              "0  این اولین تجربه من برای خرید ایفون هست   امروز...    100           1\n",
              "1                     خرید این محصول رو توصیه میکنم      84           1\n",
              "2  1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...     60           1\n",
              "3  سلام خدمت دوستان این گوشی از همه نظر عالی  کیف...     96           1\n",
              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...     92           1\n",
              "5  من پکیج کاملش روخریدم   ازهرنظرعالیه   هیچ نقد...     60           1\n",
              "6  اول از همه خوشحالم که بالاخره نسخه های 128 گیگ...     96           1\n",
              "7  من دو ماهه خریدم واقعا راضیم  دوربینشم عالیه  ...    100           1\n",
              "8  بنظر من گوشی خوب و مقرون به صرفه ای بود توی ای...    100           1\n",
              "9  از خریدم راضی ام و گوشی بینظیریه  منتهی یه ایر...     92           1"
            ]
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          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "df.head(10)"
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              "      <th>mean</th>\n",
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              "      <th>std</th>\n",
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              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-16f9926d-91ef-44fb-81e3-854f3e72d26d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "             Score  Suggestion\n",
              "count  3261.000000  3261.00000\n",
              "mean     74.719411     1.41061\n",
              "std      21.514015     0.72408\n",
              "min       0.000000     1.00000\n",
              "25%      60.000000     1.00000\n",
              "50%      80.000000     1.00000\n",
              "75%      92.000000     2.00000\n",
              "max     100.000000     3.00000"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0zG427gJ5f1",
        "outputId": "7b9b7715-f911-4b74-ae36-651d9fc89920"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text          0\n",
              "Score         0\n",
              "Suggestion    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "iird_o76xMNd",
        "outputId": "94c5bddb-f72e-4828-8555-2af77cf45936"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Suggestion'].value_counts().plot(kind='barh')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "zCp_-1hXXzrD",
        "outputId": "8801dc3f-4430-40ce-d841-2a95596660ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Score'].value_counts().plot(kind='barh')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "OC8a0xAnYJVV"
      },
      "outputs": [],
      "source": [
        "data = df[['Text', 'Score']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "qxJE7WYAYNdM"
      },
      "outputs": [],
      "source": [
        "data = data.dropna(subset=['Score'])\n",
        "data = data.dropna(subset=['Text'])\n",
        "data = data.drop_duplicates(subset=['Text'], keep='first')\n",
        "data = data.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5xASEkOfYymv"
      },
      "outputs": [],
      "source": [
        "!pip install hazm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yupnds3GaG4y"
      },
      "outputs": [],
      "source": [
        "import hazm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tsbGWXf3aI9N"
      },
      "outputs": [],
      "source": [
        "# calculate the length of comments based on their words\n",
        "data['comment_len_by_words'] = data['Text'].apply(lambda t: len(hazm.word_tokenize(t)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2SaYiAeOaPUd"
      },
      "outputs": [],
      "source": [
        "min_max_len = data[\"comment_len_by_words\"].min(), data[\"comment_len_by_words\"].max()\n",
        "print(f'Min: {min_max_len[0]} \\tMax: {min_max_len[1]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YXCF6M_4aTWP"
      },
      "outputs": [],
      "source": [
        "def data_gl_than(data, less_than=100.0, greater_than=0.0, col='comment_len_by_words'):\n",
        "    data_length = data[col].values\n",
        "\n",
        "    data_glt = sum([1 for length in data_length if greater_than < length <= less_than])\n",
        "\n",
        "    data_glt_rate = (data_glt / len(data_length)) * 100\n",
        "\n",
        "    print(f'Texts with word length of greater than {greater_than} and less than {less_than} includes {data_glt_rate:.2f}% of the whole!')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQV1qPKYaYwA",
        "outputId": "d6e78b34-a65c-48a8-e429-3937d8ffbefc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Texts with word length of greater than 3 and less than 256 includes 95.47% of the whole!\n"
          ]
        }
      ],
      "source": [
        "data_gl_than(data, 256, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3YRCyhiyacmj"
      },
      "outputs": [],
      "source": [
        "minlim, maxlim = 3, 256"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RIn2dpkqafvh"
      },
      "outputs": [],
      "source": [
        "# remove comments with the length of fewer than three words\n",
        "data['comment_len_by_words'] = data['comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else None)\n",
        "data = data.dropna(subset=['comment_len_by_words'])\n",
        "data = data.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "qTtMOZNxajHG",
        "outputId": "dc5351b0-d88a-486a-af9d-12cf4a6dcc23"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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of word counts within comments\"},\"xaxis\":{\"title\":{\"text\":\"Word Count\"}},\"yaxis\":{\"title\":{\"text\":\"Frequency\"}},\"bargap\":0.2,\"bargroupgap\":0.2},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('c6b693df-f23d-491c-925e-8b7253908504');\n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Histogram(\n",
        "    x=data['comment_len_by_words']\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text='Distribution of word counts within comments',\n",
        "    xaxis_title_text='Word Count',\n",
        "    yaxis_title_text='Frequency',\n",
        "    bargap=0.2,\n",
        "    bargroupgap=0.2)\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9E3huVlmaxuH",
        "outputId": "307ec6bd-83d1-494d-b0e4-204e4f754215"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "We have #31: [0, 4, 8, 10, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 82, 84, 88, 90, 92, 94, 96, 98, 100]\n"
          ]
        }
      ],
      "source": [
        "unique_rates = list(sorted(data['Score'].unique()))\n",
        "print(f'We have #{len(unique_rates)}: {unique_rates}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Wc0tdDO3a66O",
        "outputId": "960e3846-18e4-4d3c-e4a2-e3dd292719ec"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<html>\n",
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      ],
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "groupby_rate = data.groupby('Score')['Score'].count()\n",
        "\n",
        "fig.add_trace(go.Bar(\n",
        "    x=list(sorted(groupby_rate.index)),\n",
        "    y=groupby_rate.tolist(),\n",
        "    text=groupby_rate.tolist(),\n",
        "    textposition='auto'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text='Distribution of rate within comments',\n",
        "    xaxis_title_text='Score',\n",
        "    yaxis_title_text='Frequency',\n",
        "    bargap=0.2,\n",
        "    bargroupgap=0.2)\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rate_to_label(rate):\n",
        "    if rate <= 25:\n",
        "        return 'bad'\n",
        "    if 25 < rate <= 50:\n",
        "        return 'not good'\n",
        "    if 50 < rate <= 75:\n",
        "        return 'good'\n",
        "    else:\n",
        "        return 'perfect'\n",
        "\n",
        "\n",
        "data['label'] = data['Score'].apply(lambda t: rate_to_label(t))\n",
        "labels = list(sorted(data['label'].unique()))\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Zqv4OcqcjNvO",
        "outputId": "222d3b53-954a-4675-f4aa-f8ed54ed9640"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Text  Score    label\n",
              "0  این اولین تجربه من برای خرید ایفون هست   امروز...    100  perfect\n",
              "1                     خرید این محصول رو توصیه میکنم      84  perfect\n",
              "2  1 ساله این گوشی رو دارم   هیچ نقطه ضعفی ازش ند...     60     good\n",
              "3  سلام خدمت دوستان این گوشی از همه نظر عالی  کیف...     96  perfect\n",
              "4  سلام دوستانی که نگران شکستن صفحه نمایش هستند ا...     92  perfect"
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              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-77f3882b-53e2-4d3e-8976-e3158cf03dc8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data cleaning"
      ],
      "metadata": {
        "id": "vc46Y4E1sdz-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "om-JxVp_bcC-"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "\n",
        "def cleanhtml(raw_html):\n",
        "    cleanr = re.compile('<.*?>')\n",
        "    cleantext = re.sub(cleanr, '', raw_html)\n",
        "    return cleantext\n",
        "\n",
        "\n",
        "def cleaning(text):\n",
        "    text = text.strip()\n",
        "\n",
        "    # regular cleaning\n",
        "    text = clean(text,\n",
        "        fix_unicode=True,\n",
        "        to_ascii=False,\n",
        "        lower=True,\n",
        "        no_line_breaks=True,\n",
        "        no_urls=True,\n",
        "        no_emails=True,\n",
        "        no_phone_numbers=True,\n",
        "        no_numbers=False,\n",
        "        no_digits=False,\n",
        "        no_currency_symbols=True,\n",
        "        no_punct=False,\n",
        "        replace_with_url=\"\",\n",
        "        replace_with_email=\"\",\n",
        "        replace_with_phone_number=\"\",\n",
        "        replace_with_number=\"\",\n",
        "        replace_with_digit=\"0\",\n",
        "        replace_with_currency_symbol=\"\",\n",
        "    )\n",
        "\n",
        "    # cleaning htmls\n",
        "    text = cleanhtml(text)\n",
        "\n",
        "    # normalizing\n",
        "    normalizer = hazm.Normalizer()\n",
        "    text = normalizer.normalize(text)\n",
        "\n",
        "    # removing wierd patterns\n",
        "    wierd_pattern = re.compile(\"[\"\n",
        "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "        u\"\\U00002702-\\U000027B0\"\n",
        "        u\"\\U000024C2-\\U0001F251\"\n",
        "        u\"\\U0001f926-\\U0001f937\"\n",
        "        u'\\U00010000-\\U0010ffff'\n",
        "        u\"\\u200d\"\n",
        "        u\"\\u2640-\\u2642\"\n",
        "        u\"\\u2600-\\u2B55\"\n",
        "        u\"\\u23cf\"\n",
        "        u\"\\u23e9\"\n",
        "        u\"\\u231a\"\n",
        "        u\"\\u3030\"\n",
        "        u\"\\ufe0f\"\n",
        "        u\"\\u2069\"\n",
        "        u\"\\u2066\"\n",
        "        # u\"\\u200c\"\n",
        "        u\"\\u2068\"\n",
        "        u\"\\u2067\"\n",
        "        \"]+\", flags=re.UNICODE)\n",
        "\n",
        "    text = wierd_pattern.sub(r'', text)\n",
        "\n",
        "    # removing extra spaces, hashtags\n",
        "    text = re.sub(\"#\", \"\", text)\n",
        "    text = re.sub(\"\\s+\", \" \", text)\n",
        "\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POf9rWS0cC6I",
        "outputId": "01c2c27d-4995-4641-b493-9ea3a8a782ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.4/175.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.9/235.9 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -qU clean-text[gpl]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MYHIFl0EbVDf"
      },
      "outputs": [],
      "source": [
        "from cleantext import clean\n",
        "\n",
        "# cleaning comments\n",
        "data['cleaned_comment'] = data['Text'].apply(cleaning)\n",
        "\n",
        "\n",
        "# calculate the length of comments based on their words\n",
        "data['cleaned_comment_len_by_words'] = data['cleaned_comment'].apply(lambda t: len(hazm.word_tokenize(t)))\n",
        "\n",
        "# remove comments with the length of fewer than three words\n",
        "data['cleaned_comment_len_by_words'] = data['cleaned_comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else len_t)\n",
        "data = data.dropna(subset=['cleaned_comment_len_by_words'])\n",
        "data = data.reset_index(drop=True)\n",
        "\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YeJINTzIoLBT"
      },
      "outputs": [],
      "source": [
        "# saving the dataframe\n",
        "data.to_csv('/content/drive/MyDrive/Dataset/digikala/data_clean.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "Rg-E8B-GSI8q"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('/content/drive/MyDrive/Dataset/digikala/data_clean.csv', encoding='utf-8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Btmc-pnscXI3"
      },
      "outputs": [],
      "source": [
        "data = data[['cleaned_comment', 'label']]\n",
        "data.columns = ['Text', 'label']\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Frequent Words"
      ],
      "metadata": {
        "id": "tZeuaahio2eF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oc5ymWSdo7Ha",
        "outputId": "edacfa50-d1d9-4d71-f28a-b470e7bed687"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import word_tokenize, sent_tokenize\n",
        "from nltk.tokenize import RegexpTokenizer"
      ],
      "metadata": {
        "id": "cIUoF2xtp2Iy"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def normalizer(txt):\n",
        "    file = nltk.word_tokenize(txt)\n",
        "    newfile=''\n",
        "    for word in file:\n",
        "        word = word.replace(\" \", \"\").replace(\"\\u200c\", \"\").replace(\".\", \". \").upper().replace(\"\\ufeff\\n\",\"\").lower().replace(\n",
        "            \"،\", \"، \").upper().replace('ة' , 'ه').replace('ي', 'ی').replace(\"؛\" , '؛ ').upper().replace(\"؛\" , ' ؛').lower().replace(\".\" , ' .').lower().replace(\n",
        "            \"،\", \" ،\").lower().replace('\\xa0','').replace('ک' , 'ك').replace('پ' , 'ب').replace('گ' ,'ك' )\n",
        "        newfile = newfile + ' ' + word\n",
        "\n",
        "    return newfile"
      ],
      "metadata": {
        "id": "38S5bDFyp6cO"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def delete_Stopword(txt):\n",
        "    file = nltk.word_tokenize(txt)\n",
        "    newFile=''\n",
        "    stopWord=[]\n",
        "    fin=open('/content/drive/MyDrive/stopword.txt',encoding='utf8')\n",
        "\n",
        "    for word in fin.readlines():\n",
        "        stopWord.append(word.replace('\\n', '').lower().replace('\\ufeff', '').lower().replace('\\ufeff', '').upper().replace('ك' , 'ک').replace(\" \" ,\"\"))\n",
        "\n",
        "\n",
        "\n",
        "    for word in file:\n",
        "        word=word.replace(' ', '')\n",
        "        if word in stopWord:\n",
        "            continue\n",
        "        else:\n",
        "            newFile = newFile + ' ' + word\n",
        "\n",
        "\n",
        "    return newFile"
      ],
      "metadata": {
        "id": "8qgWaSLHp9PW"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.read_csv('/content/drive/MyDrive/Dataset/digikala/data.csv', encoding='utf-8')"
      ],
      "metadata": {
        "id": "KcdCuKClqLDE"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "one_string = ''.join(df2['Text'].tolist())"
      ],
      "metadata": {
        "id": "yjzOVdrnq6nY"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc = word_tokenize(delete_Stopword(one_string))"
      ],
      "metadata": {
        "id": "8U5_LHvBrZ6O"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "freq = {}\n",
        "for word in doc:\n",
        "  if word not in freq.keys():\n",
        "    freq[word] = 1\n",
        "  else:\n",
        "    freq[word] += 1"
      ],
      "metadata": {
        "id": "TllFjoIRrQjI"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max(freq.values())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PX79Ip5Crtta",
        "outputId": "8d4dc30f-cd76-439e-96f6-da5a18464d4c"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1203"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyU7u0Mzrwsz",
        "outputId": "5962f58a-fcf3-4259-abcb-a81e546ec1c4"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'اولین': 74,\n",
              " 'تجربه': 57,\n",
              " 'خرید': 467,\n",
              " 'ایفون': 3,\n",
              " 'دستم': 161,\n",
              " 'رسیده': 32,\n",
              " 'اسم': 16,\n",
              " 'طلایی': 6,\n",
              " 'دراصل': 1,\n",
              " 'رزگلده': 1,\n",
              " 'خوش': 76,\n",
              " 'رنگ': 213,\n",
              " 'محصول': 330,\n",
              " 'توصیه': 86,\n",
              " 'میکنم': 493,\n",
              " '1': 123,\n",
              " 'ساله': 60,\n",
              " 'گوشی': 248,\n",
              " 'نقطه': 61,\n",
              " 'ضعفی': 14,\n",
              " 'ازش': 343,\n",
              " 'ندیدم': 49,\n",
              " 'سلام': 329,\n",
              " 'خدمت': 40,\n",
              " 'دوستان': 227,\n",
              " 'نظر': 413,\n",
              " 'عالی': 521,\n",
              " 'کیفیت': 1137,\n",
              " 'ساخت': 255,\n",
              " 'سرعت': 169,\n",
              " 'اجرا': 98,\n",
              " 'صدا': 294,\n",
              " 'تصویر': 144,\n",
              " 'حافظه': 50,\n",
              " '5': 118,\n",
              " 'گیگ': 32,\n",
              " 'دسترس': 6,\n",
              " 'قرار': 142,\n",
              " 'میگیره': 41,\n",
              " 'دوستانی': 53,\n",
              " 'نگران': 25,\n",
              " 'شکستن': 4,\n",
              " 'صفحه': 319,\n",
              " 'نمایش': 158,\n",
              " 'بدونند': 1,\n",
              " 'گوریلا': 1,\n",
              " 'گلس': 4,\n",
              " 'محافظت': 5,\n",
              " 'میشه': 724,\n",
              " 'سامسونگ': 88,\n",
              " 'شرایط': 14,\n",
              " 'دچار': 28,\n",
              " 'ایراد': 42,\n",
              " 'بشه': 193,\n",
              " '200': 11,\n",
              " 'تومان': 12,\n",
              " 'براتون': 60,\n",
              " 'تعویض': 55,\n",
              " 'میکنند': 12,\n",
              " 'لحاظ': 107,\n",
              " 'امکانات': 86,\n",
              " 'عالیه': 590,\n",
              " 'خودم': 118,\n",
              " 'راضیم': 236,\n",
              " 'پکیج': 4,\n",
              " 'کاملش': 4,\n",
              " 'روخریدم': 2,\n",
              " 'ازهرنظرعالیه': 1,\n",
              " 'نقدوایرادی': 1,\n",
              " 'بهش': 92,\n",
              " 'وارد': 39,\n",
              " 'یه': 752,\n",
              " 'لوکسه': 2,\n",
              " 'مواظب': 3,\n",
              " 'ضربه': 30,\n",
              " 'خوردن': 9,\n",
              " 'وشکستنش': 1,\n",
              " 'باشین': 31,\n",
              " 'خوشحالم': 8,\n",
              " 'بالاخره': 16,\n",
              " 'نسخه': 31,\n",
              " '128': 8,\n",
              " 'گیگابایتی': 4,\n",
              " 'فروش': 47,\n",
              " 'همیشه': 65,\n",
              " 'اندرویدی': 6,\n",
              " 'مشکل': 261,\n",
              " 'قبلی': 56,\n",
              " 's7': 3,\n",
              " '2': 159,\n",
              " 'سال': 206,\n",
              " 'راضی': 407,\n",
              " 'بودم': 133,\n",
              " 'دلیل': 56,\n",
              " 'تغییرات': 5,\n",
              " 'مثبتی': 4,\n",
              " 'ایجاد': 39,\n",
              " 'تصمیم': 23,\n",
              " 'عوض': 61,\n",
              " 'واقعا': 601,\n",
              " 'زیباست': 40,\n",
              " 'طراحی': 137,\n",
              " 'منحصر': 7,\n",
              " 'فردی': 2,\n",
              " 'داره': 1203,\n",
              " 'دست': 132,\n",
              " 'میگیرید': 5,\n",
              " 'حس': 59,\n",
              " 'خوبی': 500,\n",
              " 'دلیلی': 4,\n",
              " 'پلاس': 14,\n",
              " 'انتخاب': 144,\n",
              " 'نکردم': 69,\n",
              " 'بزرگی': 14,\n",
              " 'حد': 101,\n",
              " 'وزن': 58,\n",
              " 'اندازه': 98,\n",
              " 'العادست': 15,\n",
              " 'بلندی': 39,\n",
              " 'صدای': 318,\n",
              " 'اسپیکر': 23,\n",
              " 'تست': 71,\n",
              " 'تفاوت': 24,\n",
              " 'زیادی': 94,\n",
              " 'میشد': 51,\n",
              " 'HTC': 4,\n",
              " 'U11': 1,\n",
              " 'مقایسه': 103,\n",
              " 'باز': 173,\n",
              " 'اس': 101,\n",
              " '9': 20,\n",
              " 'کمی': 166,\n",
              " 'u11': 1,\n",
              " 'بهتر': 192,\n",
              " 'نگرانی': 14,\n",
              " 'سنسور': 17,\n",
              " 'اثر': 43,\n",
              " 'انگشت': 39,\n",
              " 'اس8': 1,\n",
              " 'کار': 601,\n",
              " 'مناسبی': 91,\n",
              " 'نداشت': 72,\n",
              " 'تغییر': 41,\n",
              " 'بنظرم': 56,\n",
              " 'حل': 42,\n",
              " 'Intelligent': 1,\n",
              " 'scan': 3,\n",
              " 'ترکیب': 9,\n",
              " 'عنبیه': 2,\n",
              " 'تشخیص': 7,\n",
              " 'چهره': 6,\n",
              " 'نیازی': 35,\n",
              " 'احساس': 45,\n",
              " 'نمیشه': 204,\n",
              " 'توی': 288,\n",
              " 'تاریکی': 7,\n",
              " 'مطلق': 2,\n",
              " 'امنیتی': 2,\n",
              " 'دیدم': 98,\n",
              " 'چشمای': 1,\n",
              " 'بسته': 117,\n",
              " 'باشه': 373,\n",
              " 'روش': 138,\n",
              " 'intelligent': 1,\n",
              " 'سریعا': 8,\n",
              " 'آیفون': 18,\n",
              " 'X': 5,\n",
              " 'ظاهرا': 18,\n",
              " 'چشمان': 1,\n",
              " 'امنیت': 6,\n",
              " 'بهتره': 96,\n",
              " 'دوربین': 208,\n",
              " 'سلفی': 4,\n",
              " 'اصلی': 60,\n",
              " 'درکل': 38,\n",
              " 'نسبت': 402,\n",
              " 'نسل': 28,\n",
              " 'زوم': 19,\n",
              " 'آخر': 28,\n",
              " 'نویز': 16,\n",
              " 'کمتری': 11,\n",
              " 'دیده': 31,\n",
              " 'قابلیت': 97,\n",
              " 'میتونه': 34,\n",
              " 'عکس': 122,\n",
              " 'بوکه': 4,\n",
              " 'بگیره': 13,\n",
              " 'قابلیتی': 3,\n",
              " 'selective': 1,\n",
              " 'focus': 1,\n",
              " 'جسم': 1,\n",
              " 'فاصله': 26,\n",
              " 'نیم': 40,\n",
              " 'متری': 15,\n",
              " 'دقیقا': 29,\n",
              " 'همون': 84,\n",
              " 'افکت': 8,\n",
              " 'اعمال': 5,\n",
              " 'کنه': 223,\n",
              " 'عملکرد': 80,\n",
              " 'خوب': 668,\n",
              " 'نهایت': 42,\n",
              " 'بهترین': 146,\n",
              " 'نقاط': 63,\n",
              " 'ضعف': 68,\n",
              " 'بگم': 217,\n",
              " 'باتری': 100,\n",
              " 'متاسفانه': 122,\n",
              " 'زیاد': 272,\n",
              " 'رضایت': 26,\n",
              " 'زور': 9,\n",
              " 'صبح': 10,\n",
              " 'ساعت': 138,\n",
              " '7': 57,\n",
              " '8': 84,\n",
              " '12': 23,\n",
              " 'شب': 22,\n",
              " 'برسونه': 1,\n",
              " 'اینترنت': 38,\n",
              " 'روشن': 139,\n",
              " 'مخصوصا': 71,\n",
              " '4G': 4,\n",
              " 'انتظار': 114,\n",
              " 'بیشتری': 50,\n",
              " 'میرفت': 5,\n",
              " 'AR': 4,\n",
              " 'ایموجی': 3,\n",
              " 'ضعیف': 77,\n",
              " 'کلا': 120,\n",
              " 'یکبار': 17,\n",
              " 'تستش': 7,\n",
              " 'ناامید': 7,\n",
              " 'شدم': 112,\n",
              " 'نتیجه': 43,\n",
              " 'جالبی': 18,\n",
              " 'همرو': 3,\n",
              " 'تقریبا': 108,\n",
              " 'یکسان': 5,\n",
              " 'نشون': 69,\n",
              " 'میده': 186,\n",
              " 'نمیدونم': 54,\n",
              " 'تبلیغ': 2,\n",
              " 'بلوغ': 1,\n",
              " 'نرسیده': 10,\n",
              " 'کاش': 41,\n",
              " '6': 69,\n",
              " 'گیگابایت': 6,\n",
              " 'رم': 64,\n",
              " 'اپل': 26,\n",
              " 'معمولی': 96,\n",
              " 'فرق': 23,\n",
              " 'بزاره': 3,\n",
              " 'همش': 18,\n",
              " 'تقصیر': 2,\n",
              " 'اینارو': 2,\n",
              " 'باب': 1,\n",
              " 'میکنه': 492,\n",
              " 'D': 3,\n",
              " '14': 10,\n",
              " '15': 27,\n",
              " 'اپ': 7,\n",
              " 'دیگه': 351,\n",
              " 'هارو': 28,\n",
              " 'میبنده': 2,\n",
              " 'شروع': 26,\n",
              " 'ریلود': 2,\n",
              " 'برنامه': 130,\n",
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              " 'سنگین': 136,\n",
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              " 'بدتر': 7,\n",
              " 'میخوره': 35,\n",
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              " 'اجرای': 33,\n",
              " 'دوباره': 52,\n",
              " 'خریدم': 706,\n",
              " 'نمیتونستم': 1,\n",
              " 'بیام': 4,\n",
              " 'اسکن': 9,\n",
              " 'سه': 165,\n",
              " 'بعدی': 47,\n",
              " 'اضافه': 57,\n",
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              " 'ماهه': 71,\n",
              " 'دوربینشم': 1,\n",
              " 'رنج': 126,\n",
              " 'قیمت': 558,\n",
              " 'خودشه': 2,\n",
              " 'بنظر': 34,\n",
              " 'مقرون': 19,\n",
              " 'صرفه': 36,\n",
              " 'دوسالی': 2,\n",
              " 'داشتمش': 2,\n",
              " 'جور': 23,\n",
              " 'نسبتا': 69,\n",
              " 'دیتا': 3,\n",
              " 'العاده': 174,\n",
              " 'قوتش': 10,\n",
              " 'باعث': 113,\n",
              " 'بگیرمش': 1,\n",
              " 'لذت': 61,\n",
              " 'باهاش': 195,\n",
              " 'عمر': 34,\n",
              " 'خودشو': 15,\n",
              " 'انتظاری': 10,\n",
              " 'نداشتم': 53,\n",
              " 'هزینه': 102,\n",
              " 'اینم': 28,\n",
              " 'چندباری': 3,\n",
              " 'زمین': 8,\n",
              " 'خورده': 25,\n",
              " 'مناسب': 263,\n",
              " 'استحکام': 6,\n",
              " 'مشکلی': 169,\n",
              " 'براش': 54,\n",
              " 'نیومد': 15,\n",
              " 'بینظیریه': 1,\n",
              " 'منتهی': 3,\n",
              " 'ایرادی': 32,\n",
              " 'ضبط': 20,\n",
              " 'مکالمات': 2,\n",
              " 'امتحان': 42,\n",
              " 'سایتهای': 5,\n",
              " 'خارجی': 32,\n",
              " 'گشتم': 5,\n",
              " 'امیدوارم': 64,\n",
              " 'آپدیت': 34,\n",
              " 'رفع': 12,\n",
              " 'بیکسبی': 2,\n",
              " 'ویژن': 24,\n",
              " 'ایران': 55,\n",
              " 'نمیکنه': 122,\n",
              " 'ویدئو': 4,\n",
              " 'آهسته': 1,\n",
              " 'اگه': 345,\n",
              " 'نور': 75,\n",
              " 'کافی': 29,\n",
              " 'نباشه': 39,\n",
              " 'کیفیتش': 86,\n",
              " 'خراب': 64,\n",
              " 'فیلمبرداری': 8,\n",
              " 'حالت': 142,\n",
              " 'عادی': 32,\n",
              " 'خوبه': 519,\n",
              " 'بنده': 75,\n",
              " 'حدود': 168,\n",
              " 'عدد': 31,\n",
              " 'یکیش': 12,\n",
              " 'الان': 187,\n",
              " 'خاصی': 34,\n",
              " 'برخورد': 13,\n",
              " 'ظرفیت': 21,\n",
              " 'کمه': 66,\n",
              " 'ذوق': 16,\n",
              " 'میرنه': 1,\n",
              " 'موبایل': 39,\n",
              " 'نداشتن': 40,\n",
              " 'اتصال': 48,\n",
              " '4g': 1,\n",
              " 'هستش': 172,\n",
              " 'میتونید': 83,\n",
              " 'مدل': 362,\n",
              " 'پرو': 8,\n",
              " 'حلش': 3,\n",
              " 'عمومی': 12,\n",
              " 'اشکالی': 3,\n",
              " 'نداره': 446,\n",
              " 'یادم': 6,\n",
              " 'نرفته': 1,\n",
              " 'یکسال': 23,\n",
              " 'افت': 19,\n",
              " 'مگاپیکسل': 6,\n",
              " 'جمله': 19,\n",
              " 'میتونم': 48,\n",
              " 'مطلوب': 13,\n",
              " 'ماه': 163,\n",
              " 'سخت': 126,\n",
              " 'افزار': 105,\n",
              " 'کاربری': 42,\n",
              " 'نهایتا': 7,\n",
              " 'تولید': 75,\n",
              " 'استاندارد': 15,\n",
              " 'جهانی': 11,\n",
              " 'اشاره': 27,\n",
              " 'توجه': 199,\n",
              " 'دیجی': 607,\n",
              " 'کالا': 554,\n",
              " 'خاطر': 47,\n",
              " 'خوبش': 14,\n",
              " 'ممنونم': 29,\n",
              " 'ممنون': 101,\n",
              " 'جون': 18,\n",
              " 'نظرتومهمه': 13,\n",
              " 'بجز': 15,\n",
              " 'بخاطر': 83,\n",
              " 'گرون': 25,\n",
              " 'دلاره': 2,\n",
              " 'گلکسی': 3,\n",
              " 'جزئی': 3,\n",
              " 'ترغیب': 1,\n",
              " 'کوچیکِ': 1,\n",
              " 'مشتریان': 5,\n",
              " 'جدیده': 2,\n",
              " 'تغییراتی': 1,\n",
              " 'جابجایی': 7,\n",
              " 'مثبت': 23,\n",
              " 'حسگر': 14,\n",
              " 'ارتقا': 22,\n",
              " 'نرمافزاری': 1,\n",
              " 'عکسای': 8,\n",
              " 'باکیفیت': 24,\n",
              " 'جذاب': 5,\n",
              " 'شکلک': 1,\n",
              " 'سلیقگی': 2,\n",
              " 'هدست': 30,\n",
              " 'level': 3,\n",
              " 'pro': 6,\n",
              " 'هدیه': 22,\n",
              " 'خریداری': 89,\n",
              " 'همخوانی': 4,\n",
              " 'امکان': 53,\n",
              " 'تعیین': 5,\n",
              " 'حداقل': 66,\n",
              " 'مشابه': 30,\n",
              " 'تحویل': 53,\n",
              " 'خانواده': 19,\n",
              " 'متفاوتی': 6,\n",
              " 'note': 2,\n",
              " '3': 146,\n",
              " 'مدلهای': 13,\n",
              " 'همگی': 7,\n",
              " 'کرات': 1,\n",
              " 'بارها': 4,\n",
              " 'افتادن': 10,\n",
              " 'اکثرا': 7,\n",
              " 'ایستاده': 2,\n",
              " 'سفت': 9,\n",
              " 'نا': 15,\n",
              " 'هموار': 1,\n",
              " 'آسفالت': 2,\n",
              " 'سنگ': 6,\n",
              " 'سرامیک': 9,\n",
              " 'مقاوم': 14,\n",
              " 'موردی': 7,\n",
              " 'شکستگی': 5,\n",
              " 'خش': 31,\n",
              " 'زننده': 4,\n",
              " 'برایشان': 1,\n",
              " 'حاصل': 1,\n",
              " 'نشدهاست': 1,\n",
              " 'حالی': 7,\n",
              " 'اخیرا': 4,\n",
              " 'آنهم': 1,\n",
              " 'ارتفاع': 16,\n",
              " '40': 14,\n",
              " 'سانتیمتری': 1,\n",
              " 'گوشه': 14,\n",
              " 'ترک': 10,\n",
              " 'ناجوری': 2,\n",
              " 'برداشته': 3,\n",
              " 'لبه': 17,\n",
              " 'تیز': 13,\n",
              " 'پذیر': 6,\n",
              " 'دوام': 30,\n",
              " 'آنست': 1,\n",
              " 'مجموع': 38,\n",
              " 'کاربر': 20,\n",
              " 'حرفه': 95,\n",
              " 'قلمش': 6,\n",
              " 'امکاناتش': 22,\n",
              " 'آشنا': 4,\n",
              " 'احتمالا': 25,\n",
              " 'حرف': 73,\n",
              " 'منو': 37,\n",
              " 'تأیید': 1,\n",
              " 'میکنین': 15,\n",
              " 'کابردیه': 1,\n",
              " 'ظاهر': 62,\n",
              " 'زیبایی': 38,\n",
              " 'کم': 292,\n",
              " 'نمیاره': 14,\n",
              " 'عادت': 18,\n",
              " 'دستیار': 1,\n",
              " 'صوتی': 37,\n",
              " 'اسامی': 1,\n",
              " 'فارسی': 24,\n",
              " 'دستورات': 1,\n",
              " 'انگلیسی': 11,\n",
              " 'بزرگترین': 9,\n",
              " 'ضعفش': 15,\n",
              " 'باتریشه': 2,\n",
              " 'معنا': 5,\n",
              " 'گوشیهای': 3,\n",
              " 'هوآوی': 1,\n",
              " 'میاره': 40,\n",
              " 'رضایتبخشه': 1,\n",
              " 'عمرش': 2,\n",
              " 'کاهش': 14,\n",
              " 'نکنه': 14,\n",
              " 'سیمکارت': 3,\n",
              " 'جانبی': 15,\n",
              " 'زودتر': 10,\n",
              " 'موعد': 1,\n",
              " 'هماهنگ': 6,\n",
              " 'رسید': 153,\n",
              " 'حدیثی': 1,\n",
              " 'باقی': 16,\n",
              " 'نمیگداره': 1,\n",
              " 'نوت': 9,\n",
              " 'زیباییه': 4,\n",
              " 'میکنید': 44,\n",
              " 'کشیدست': 1,\n",
              " 'اون': 297,\n",
              " 'طبیعی': 38,\n",
              " 'دستی': 29,\n",
              " 'راست': 25,\n",
              " 'دستا': 1,\n",
              " 'چپ': 24,\n",
              " 'میگیرن': 4,\n",
              " 'لمس': 5,\n",
              " 'میکنن': 37,\n",
              " 'اینا': 23,\n",
              " 'بدیاش': 2,\n",
              " 'قبلیم': 19,\n",
              " 'انصافا': 14,\n",
              " 'هیچی': 16,\n",
              " 'کسری': 3,\n",
              " 'نمایشش': 28,\n",
              " 'روون': 7,\n",
              " 'سریعه': 4,\n",
              " 'دورینش': 1,\n",
              " 'امکاناتی': 6,\n",
              " 'سلامتی': 5,\n",
              " 'سیستم': 162,\n",
              " 'عامل': 38,\n",
              " 'اندروید': 23,\n",
              " 'هاست': 13,\n",
              " 'مشکیش': 3,\n",
              " 'غوغا': 1,\n",
              " 'اکثر': 23,\n",
              " 'همکارام': 2,\n",
              " 'متوجه': 69,\n",
              " 'گوشیم': 9,\n",
              " 'چشم': 45,\n",
              " 'کاربدیه': 1,\n",
              " 'بعضی': 75,\n",
              " 'البیته': 1,\n",
              " 'گیر': 37,\n",
              " 'دستگاه': 520,\n",
              " 'اعتماد': 16,\n",
              " 'شناسوندم': 1,\n",
              " 'راحت': 186,\n",
              " 'الکی': 14,\n",
              " 'قفل': 30,\n",
              " 'تونید': 22,\n",
              " 'گزینه': 94,\n",
              " 'هوشمند': 25,\n",
              " 'رجوع': 1,\n",
              " 'ایتم': 1,\n",
              " 'محل': 18,\n",
              " 'خونتون': 2,\n",
              " 'نشه': 33,\n",
              " 'داخل': 107,\n",
              " 'همراه': 77,\n",
              " 'هدفون': 342,\n",
              " 'بیسیم': 12,\n",
              " 'یو': 51,\n",
              " 'بخرند': 3,\n",
              " 'گوشیشون': 1,\n",
              " 'خیابون': 4,\n",
              " 'نگیرند': 1,\n",
              " 'کیف': 51,\n",
              " 'لباسشون': 1,\n",
              " 'بزارند': 1,\n",
              " 'اسمارت': 3,\n",
              " 'سوییج': 1,\n",
              " 'کلیه': 4,\n",
              " 'اطلاعات': 19,\n",
              " 'منتقل': 11,\n",
              " 'تم': 2,\n",
              " 'چیدمان': 2,\n",
              " 'ایکون': 1,\n",
              " 'پسورد': 1,\n",
              " 'وایرلس': 11,\n",
              " 'خونه': 37,\n",
              " 'شرکت': 95,\n",
              " 'جالب': 34,\n",
              " 'شماره': 44,\n",
              " 'هاو': 1,\n",
              " 'اطلاهات': 1,\n",
              " 'تماس': 34,\n",
              " 'براحتی': 27,\n",
              " 'اب': 25,\n",
              " 'پروسه': 2,\n",
              " 'کابل': 63,\n",
              " 'طول': 68,\n",
              " 'کشید': 11,\n",
              " 'اعتقاد': 1,\n",
              " 'پرچم': 1,\n",
              " 'افرادی': 20,\n",
              " 'زندگیشون': 1,\n",
              " 'گوشیه': 4,\n",
              " 'هنور': 2,\n",
              " 'دکس': 1,\n",
              " 'فعلا': 21,\n",
              " 'فیدیبو': 1,\n",
              " 'هاش': 80,\n",
              " 'کاربریه': 1,\n",
              " 'ارزشمند': 1,\n",
              " 'بدوستان': 1,\n",
              " 'فلش': 37,\n",
              " 'OTG': 3,\n",
              " 'تایپ': 43,\n",
              " 'B': 6,\n",
              " 'رابط': 24,\n",
              " 'بایستی': 2,\n",
              " 'C': 4,\n",
              " 'بخریم': 8,\n",
              " 'موجوده': 12,\n",
              " 'کاور': 15,\n",
              " 'ساده': 69,\n",
              " 'پشت': 53,\n",
              " 'جعبه': 57,\n",
              " 'اولیه': 13,\n",
              " 'ننداختم': 2,\n",
              " 'اینطوری': 5,\n",
              " 'عیب': 27,\n",
              " 'جووون': 1,\n",
              " 'بابت': 31,\n",
              " 'کاملترین': 2,\n",
              " 'قصد': 39,\n",
              " 'صبر': 12,\n",
              " 'x': 3,\n",
              " 'نظرم': 153,\n",
              " 'اینو': 126,\n",
              " 'قلم': 27,\n",
              " 's': 6,\n",
              " 'pen': 2,\n",
              " 'خریدمش': 52,\n",
              " 'هفته': 110,\n",
              " 'اسپیکرهاش': 1,\n",
              " 'استریو': 3,\n",
              " 'میکرد': 30,\n",
              " 'گرچه': 10,\n",
              " 'الانم': 15,\n",
              " 'راحتی': 101,\n",
              " 'سنگینی': 15,\n",
              " 'بیاد': 29,\n",
              " 'کاغذ': 42,\n",
              " 'کنین': 87,\n",
              " 'تفاوتش': 4,\n",
              " 'بقیه': 67,\n",
              " 'رده': 59,\n",
              " 'بحث': 2,\n",
              " 'میشین': 11,\n",
              " 'موعدش': 1,\n",
              " 'باسلام': 25,\n",
              " 'گوشیرو': 2,\n",
              " 'باگارانتی': 1,\n",
              " 'مایکروتل': 1,\n",
              " 'روزه': 38,\n",
              " 'تقریباً': 3,\n",
              " 'چیش': 13,\n",
              " 'میخوای': 3,\n",
              " 'بنویسی': 1,\n",
              " 'حتماً': 9,\n",
              " 'فشاربدی': 1,\n",
              " 'وگرنه': 47,\n",
              " 'حرفها': 2,\n",
              " 'بریده': 5,\n",
              " 'مینویسه': 1,\n",
              " 'خودکاری': 1,\n",
              " 'رنگش': 56,\n",
              " 'تموم': 39,\n",
              " 'حالم': 3,\n",
              " 'برام': 58,\n",
              " 'باتشکر': 9,\n",
              " 'مشکلاتی': 10,\n",
              " 'واضح': 13,\n",
              " 'دگمه': 4,\n",
              " 'نرم': 143,\n",
              " 'یکم': 117,\n",
              " 'خشک': 38,\n",
              " 'ظاهرش': 20,\n",
              " 'فولدر': 2,\n",
              " 'سازی': 13,\n",
              " 'قسمت': 74,\n",
              " 'پیام': 5,\n",
              " 'نوشتن': 15,\n",
              " 'اذیت': 83,\n",
              " 'هکش': 2,\n",
              " 'پایینه': 32,\n",
              " 'تعداد': 23,\n",
              " 'لیست': 13,\n",
              " 'بلک': 16,\n",
              " 'رد': 10,\n",
              " 'تماسش': 1,\n",
              " 'هک': 6,\n",
              " 'زجر': 1,\n",
              " 'آوره': 1,\n",
              " 'پیامش': 1,\n",
              " 'موبایلم': 2,\n",
              " 'کانتکت': 3,\n",
              " 'میز': 11,\n",
              " 'هکر': 1,\n",
              " 'زنگ': 37,\n",
              " 'زده': 29,\n",
              " 'قطع': 53,\n",
              " 'نصفه': 6,\n",
              " 'لطفا': 49,\n",
              " 'موبایلی': 5,\n",
              " 'بیارین': 5,\n",
              " 'بلا': 7,\n",
              " 'اچ': 76,\n",
              " 'تی': 40,\n",
              " 'سی': 95,\n",
              " 'چاچا': 1,\n",
              " 'برایم': 7,\n",
              " 'اومده': 25,\n",
              " 'ذهنم': 3,\n",
              " 'پشیمونم': 29,\n",
              " '5سال': 3,\n",
              " 'مث': 5,\n",
              " 'گوشیای': 1,\n",
              " 'بازار': 117,\n",
              " 'میاد': 92,\n",
              " 'قوی': 94,\n",
              " 'دوساله': 7,\n",
              " 'دارمش': 49,\n",
              " 'یبار': 5,\n",
              " 'هنگ': 42,\n",
              " 'چندبارم': 1,\n",
              " 'افتاده': 10,\n",
              " 'اخ': 3,\n",
              " 'نگفته': 11,\n",
              " 'گلسی': 1,\n",
              " 'قابی': 1,\n",
              " 'نداختم': 1,\n",
              " 'مزیت': 23,\n",
              " 'عالیشم': 1,\n",
              " 'اینه': 137,\n",
              " 'سیم': 124,\n",
              " 'شارژش': 20,\n",
              " 'بلنده': 3,\n",
              " 'برخلاف': 12,\n",
              " 'گوشیا': 3,\n",
              " 'حافظش': 2,\n",
              " 'ضعیفه': 50,\n",
              " '4جی': 1,\n",
              " 'خریدش': 127,\n",
              " 'شک': 115,\n",
              " 'نکنید': 119,\n",
              " 'داغ': 82,\n",
              " 'دوربینش': 9,\n",
              " 'هندزفری': 33,\n",
              " 'همراش': 3,\n",
              " 'بدیشه': 1,\n",
              " 'عالییییه': 1,\n",
              " '2ساله': 1,\n",
              " 'بعنوان': 9,\n",
              " 'بصرفه': 4,\n",
              " 'ست': 39,\n",
              " 'نوشتم': 10,\n",
              " 'مرگ': 3,\n",
              " 'دلیلش': 7,\n",
              " 'دیروز': 28,\n",
              " 'فراموش': 9,\n",
              " 'جیب': 8,\n",
              " 'شلوار': 5,\n",
              " 'بمدت': 1,\n",
              " '2ساعت': 4,\n",
              " 'لباسشویی': 13,\n",
              " 'اتوماتیک': 20,\n",
              " 'شسته': 9,\n",
              " 'گذاشتم': 14,\n",
              " 'کوچکترین': 15,\n",
              " 'وقت': 56,\n",
              " 'کلش': 2,\n",
              " 'زود': 68,\n",
              " 'خرابش': 1,\n",
              " 'ایرادتشم': 1,\n",
              " 'شدنش': 11,\n",
              " 'اندازیه': 1,\n",
              " 'باطریش': 24,\n",
              " 'خیلییییی': 2,\n",
              " 'عوضش': 12,\n",
              " 'اندرویدش': 2,\n",
              " 'دونه': 17,\n",
              " 'بستگانم': 1,\n",
              " 'سفارش': 87,\n",
              " 'ماهی': 64,\n",
              " 'خدا': 27,\n",
              " 'شکر': 10,\n",
              " 'توجهی': 5,\n",
              " 'نداشته': 54,\n",
              " 'بخواد': 3,\n",
              " 'ظاهری': 29,\n",
              " 'قشنگه': 17,\n",
              " 'چشمه': 1,\n",
              " 'میبینیش': 1,\n",
              " 'نمایی': 1,\n",
              " 'قدرت': 113,\n",
              " 'عملکردش': 10,\n",
              " 'نباشید': 20,\n",
              " 'مختلفی': 5,\n",
              " 'Mortal': 1,\n",
              " 'kombat': 1,\n",
              " 'واقها': 1,\n",
              " 'سنگینیه': 1,\n",
              " 'اجراش': 3,\n",
              " 'Android': 3,\n",
              " 'EMUI': 2,\n",
              " 'اپدیت': 11,\n",
              " 'Game': 5,\n",
              " 'suit': 1,\n",
              " 'کنترل': 67,\n",
              " 'Gaming': 1,\n",
              " 'mode': 5,\n",
              " 'بزارید': 13,\n",
              " 'asfalt': 1,\n",
              " 'نگه': 53,\n",
              " 'میداره': 18,\n",
              " 'خبر': 6,\n",
              " 'خوبیه': 208,\n",
              " 'گیمرا': 1,\n",
              " 'هواوی': 4,\n",
              " 'نزاشته': 1,\n",
              " 'محیط': 39,\n",
              " 'Night': 1,\n",
              " 'ثبت': 16,\n",
              " 'نیاز': 112,\n",
              " 'پایه': 27,\n",
              " 'خودتون': 45,\n",
              " 'میدونید': 5,\n",
              " 'بحبود': 1,\n",
              " 'Face': 1,\n",
              " 'unlock': 1,\n",
              " 'باطری': 53,\n",
              " 'بد': 89,\n",
              " 'دقیقه': 58,\n",
              " 'شارژ': 114,\n",
              " 'ترکیبی': 1,\n",
              " 'انلاین': 4,\n",
              " 'Clash': 1,\n",
              " 'of': 11,\n",
              " 'Clans': 1,\n",
              " 'نت': 7,\n",
              " 'درصد': 41,\n",
              " 'Asfalt': 1,\n",
              " '11': 12,\n",
              " 'چیزش': 18,\n",
              " 'بخریدش': 11,\n",
              " 'اهمیت': 20,\n",
              " 'میدیید': 1,\n",
              " 'پیشنهاد': 413,\n",
              " 'Huawei': 1,\n",
              " 'P10': 1,\n",
              " 'درضمن': 17,\n",
              " 'دیجیکالا': 90,\n",
              " 'وتو': 1,\n",
              " 'بدستم': 38,\n",
              " 'میخواید': 26,\n",
              " 'بخرید': 140,\n",
              " 'توپه': 1,\n",
              " 'خواندید': 1,\n",
              " 'حتما': 236,\n",
              " 'سرعتشم': 1,\n",
              " 'هنوزم': 7,\n",
              " 'ارزش': 140,\n",
              " 'جنس': 162,\n",
              " 'بدنه': 130,\n",
              " 'ضمخت': 1,\n",
              " 'هیچگونه': 4,\n",
              " 'هیچوقت': 6,\n",
              " 'داخلی': 56,\n",
              " 'درست': 92,\n",
              " 'نکرد': 23,\n",
              " 'کلام': 30,\n",
              " 'سالی': 16,\n",
              " 'باتریش': 36,\n",
              " 'استفادتون': 1,\n",
              " 'مثه': 9,\n",
              " 'دستتون': 9,\n",
              " 'قوت': 32,\n",
              " 'بودنش': 31,\n",
              " 'استفادست': 1,\n",
              " 'اشتباه': 38,\n",
              " 'میگیرنش': 1,\n",
              " 'مرده': 4,\n",
              " 'دوتا': 38,\n",
              " 'اتفاقی': 9,\n",
              " 'براشون': 11,\n",
              " 'نیوفتاده': 1,\n",
              " 'بعیده': 3,\n",
              " 'قاچاق': 4,\n",
              " 'بیاره': 9,\n",
              " 'دوتایی': 2,\n",
              " 'هستن': 72,\n",
              " '4': 119,\n",
              " 'دیتای': 1,\n",
              " 'یخورده': 9,\n",
              " 'اسپیکرش': 1,\n",
              " 'کیفیتی': 23,\n",
              " 'گزگز': 1,\n",
              " 'چیزاش': 3,\n",
              " 'کلی': 78,\n",
              " 'کارمو': 3,\n",
              " 'انداخته': 4,\n",
              " 'شخصه': 8,\n",
              " 'حسابی': 7,\n",
              " 'کشیدم': 9,\n",
              " 'نیاورد': 6,\n",
              " 'شیش': 3,\n",
              " 'گرفتمش': 10,\n",
              " 'دائم': 4,\n",
              " 'اینستا': 1,\n",
              " 'خالی': 25,\n",
              " 'غیره': 9,\n",
              " 'جواب': 73,\n",
              " 'گیری': 28,\n",
              " 'فیلم': 113,\n",
              " 'دیدن': 39,\n",
              " 'مطالعه': 10,\n",
              " 'فیلمتون': 1,\n",
              " 'FullHD': 1,\n",
              " 'بزرگتر': 16,\n",
              " 'شیکه': 13,\n",
              " 'باریکه': 4,\n",
              " 'فریم': 41,\n",
              " 'پایین': 139,\n",
              " 'نازکه': 7,\n",
              " 'گوگل': 7,\n",
              " 'دنبال': 84,\n",
              " 'بگردید': 1,\n",
              " 'گوشیتون': 4,\n",
              " 'بگید': 4,\n",
              " 'ایکس': 37,\n",
              " 'ایگرگ': 1,\n",
              " 'تره': 40,\n",
              " 'تواناییهاش': 1,\n",
              " 'سرتره': 4,\n",
              " 'کلمه': 7,\n",
              " 'میسوزونه': 4,\n",
              " 'عملا': 18,\n",
              " 'تومن': 25,\n",
              " 'پول': 57,\n",
              " 'دور': 71,\n",
              " 'ریختم': 24,\n",
              " 'ال': 82,\n",
              " 'جی': 123,\n",
              " 'الجی': 20,\n",
              " 'آرنا': 1,\n",
              " 'جاوا': 1,\n",
              " 'g2': 1,\n",
              " 'g6': 1,\n",
              " 'دوست': 37,\n",
              " 'نو': 20,\n",
              " 'کاری': 38,\n",
              " 'برندها': 7,\n",
              " 'بلاخره': 7,\n",
              " 'میدن': 25,\n",
              " 'دسته': 91,\n",
              " 'میگیری': 3,\n",
              " 'حرفم': 1,\n",
              " 'میشید': 18,\n",
              " 'واید': 2,\n",
              " 'عقب': 8,\n",
              " 'جوری': 23,\n",
              " 'جورایی': 7,\n",
              " 'میرسه': 27,\n",
              " 'سلفیش': 2,\n",
              " 'سامسوگ': 1,\n",
              " 'هوواوی': 1,\n",
              " 'نگیره': 1,\n",
              " 'بازم': 43,\n",
              " 'لنز': 39,\n",
              " 'وایدش': 1,\n",
              " 'کاربردیه': 16,\n",
              " 'آیفون7': 2,\n",
              " 'وی20': 3,\n",
              " 'پردازنده': 35,\n",
              " 'مرکزیشون': 1,\n",
              " 'بالاتر': 47,\n",
              " 'نکته': 107,\n",
              " 'LG6': 1,\n",
              " 'lg': 4,\n",
              " 'v20': 1,\n",
              " 'amp': 1,\n",
              " 'g5': 2,\n",
              " 'معرفی': 15,\n",
              " 'شدت': 54,\n",
              " 'پشتش': 8,\n",
              " 'V20': 1,\n",
              " 'حالا': 82,\n",
              " 'مموری': 6,\n",
              " 'اکانت': 2,\n",
              " 'اسپاتیفای': 2,\n",
              " 'Hi': 1,\n",
              " 'fi': 1,\n",
              " 'Quad': 1,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnxOyNkxckzP"
      },
      "source": [
        "## Unbalanced Data !"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "groupby_label = data.groupby('label')['label'].count()\n",
        "\n",
        "fig.add_trace(go.Bar(\n",
        "    x=list(sorted(groupby_label.index)),\n",
        "    y=groupby_label.tolist(),\n",
        "    text=groupby_label.tolist(),\n",
        "    textposition='auto'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text='Distribution of label within comments [DATA]',\n",
        "    xaxis_title_text='Label',\n",
        "    yaxis_title_text='Frequency',\n",
        "    bargap=0.2,\n",
        "    bargroupgap=0.2)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "GlFGD6S6mhKY",
        "outputId": "526bfdc2-d695-4a96-92d5-2c57203d6d9e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQR2GR8_c-oO"
      },
      "source": [
        "## Train,Validation,Test split\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "1oC6S7PcpuVu"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['label_id'] = data['label'].apply(lambda t: labels.index(t))\n",
        "\n",
        "train, test = train_test_split(data, test_size=0.1, random_state=1, stratify=data['label'])\n",
        "train, valid = train_test_split(train, test_size=0.1, random_state=1, stratify=train['label'])\n",
        "\n",
        "train = train.reset_index(drop=True)\n",
        "valid = valid.reset_index(drop=True)\n",
        "test = test.reset_index(drop=True)\n",
        "\n",
        "x_train, y_train = train['Text'].values.tolist(), train['label_id'].values.tolist()\n",
        "x_valid, y_valid = valid['Text'].values.tolist(), valid['label_id'].values.tolist()\n",
        "x_test, y_test = test['Text'].values.tolist(), test['label_id'].values.tolist()\n",
        "\n",
        "print(train.shape)\n",
        "print(valid.shape)\n",
        "print(test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6PLl54Fm67y",
        "outputId": "1b76d398-ab64-4587-a4c6-a1e607263406"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2446, 4)\n",
            "(272, 4)\n",
            "(303, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Train sample:', x_train[0], y_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kle-p57InBsf",
        "outputId": "3a70f03a-72a7-4651-d172-6a8890a608f7"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train sample: درکل همه چیش خوبه  3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjgwX9aHey8S"
      },
      "source": [
        "## PyTorch Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqEcyTDPp4o8",
        "outputId": "6d63d35b-33f8-4c08-c961-f98b2c9a7db4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m30.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m37.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.32.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "PNe9WPvmehp7"
      },
      "outputs": [],
      "source": [
        "from transformers import BertConfig, BertTokenizer\n",
        "from transformers import BertModel\n",
        "\n",
        "from transformers import AdamW\n",
        "from transformers import get_linear_schedule_with_warmup\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vb5y51xJe4Cc",
        "outputId": "3454bb49-37cd-4e78-fa0c-1c699a7626d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OUTPUT_MODEL_PATH ./bert-fa-base-uncased-sentiment-digikala-v1/pytorch_model.bin\n"
          ]
        }
      ],
      "source": [
        "# general config\n",
        "MAX_LEN = 128\n",
        "TRAIN_BATCH_SIZE = 16\n",
        "VALID_BATCH_SIZE = 16\n",
        "TEST_BATCH_SIZE = 16\n",
        "\n",
        "EPOCHS = 3\n",
        "EEVERY_EPOCH = 1000\n",
        "LEARNING_RATE = 2e-5\n",
        "CLIP = 0.0\n",
        "\n",
        "MODEL_NAME_OR_PATH = 'HooshvareLab/bert-fa-base-uncased'\n",
        "OUTPUT_PATH = './bert-fa-base-uncased-sentiment-digikala-v1'\n",
        "OUTPUT_MODEL_PATH = f'{OUTPUT_PATH}/pytorch_model.bin'\n",
        "\n",
        "print('OUTPUT_MODEL_PATH', OUTPUT_MODEL_PATH)\n",
        "import os\n",
        "os.makedirs(OUTPUT_PATH, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a key finder based on label 2 id and id to label\n",
        "\n",
        "label2id = {label: i for i, label in enumerate(labels)}\n",
        "id2label = {v: k for k, v in label2id.items()}\n",
        "\n",
        "print(f'label2id: {label2id}')\n",
        "print(f'id2label: {id2label}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSxx9D_OnJkj",
        "outputId": "a49b4cbd-bb06-4e23-ad05-3332dee850dd"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label2id: {'bad': 0, 'good': 1, 'not good': 2, 'perfect': 3}\n",
            "id2label: {0: 'bad', 1: 'good', 2: 'not good', 3: 'perfect'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# setup the tokenizer and configuration\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME_OR_PATH)\n",
        "config = BertConfig.from_pretrained(\n",
        "    MODEL_NAME_OR_PATH, **{\n",
        "        'label2id': label2id,\n",
        "        'id2label': id2label,\n",
        "    })\n",
        "\n",
        "print(config.to_json_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHVz0cadnTBF",
        "outputId": "a931b534-5d4a-4d74-9fb4-e6ae02d59644"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"bad\",\n",
            "    \"1\": \"good\",\n",
            "    \"2\": \"not good\",\n",
            "    \"3\": \"perfect\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"bad\": 0,\n",
            "    \"good\": 1,\n",
            "    \"not good\": 2,\n",
            "    \"perfect\": 3\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.32.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 100000\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hb8_oqdsfLF9"
      },
      "source": [
        "## Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gxkkdBPJfMQ3"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = np.random.randint(0, len(train))\n",
        "sample_comment = train.iloc[idx]['Text']\n",
        "sample_label = train.iloc[idx]['label']\n",
        "\n",
        "print(f'Sample: \\n{sample_comment}\\n{sample_label}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nl8S_OIynYqN",
        "outputId": "983352d6-7358-4de1-8bd3-253333533454"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample: \n",
            "من از این پرینتر هم برای کار سابلیمیشن هم برای چاپ عکس اتلیه استفاده میکنم دوتا مخزن مجزا برای هر دو نوع کارم گرفتم و واقعا کیفیتش خیلی بیشتر از اونیه که فکر میکردم خیلی عاااالیه شک نکنید  خطاهایی هم که میده رابطه مستقیمی با نابلدی استفاده کننده داره   \n",
            "good\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHlH5ulOfO-R",
        "outputId": "dc8aea9e-4026-442f-e315-04cb9abacdda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Comment: قبل این رونیکس داشتم که چکشی نبود و زود خراب شد اما این دریل واقعا عالیه تو شگفت‌انگیز خریدم و باید بگم واقعا می‌ارزه\n",
            "   Tokens: قبل این رونیکس داشتم که چکشی نبود و زود خراب شد اما این دریل واقعا عالیه تو شگفتانگیز خریدم و باید بگم واقعا میارزه\n",
            "Token IDs: [3556, 2802, 28025, 3233, 8065, 2800, 47215, 3827, 1379, 5505, 8415, 2817, 2949, 2802, 47840, 5668, 19142, 2861, 12197, 38993, 1379, 3013, 18757, 5668, 24003, 3223]\n"
          ]
        }
      ],
      "source": [
        "tokens = tokenizer.tokenize(sample_comment)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(f'  Comment: {sample_comment}')\n",
        "print(f'   Tokens: {tokenizer.convert_tokens_to_string(tokens)}')\n",
        "print(f'Token IDs: {token_ids}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioFykponfKIK",
        "outputId": "f0935fc4-256a-436f-d5d7-15ac30ce6bca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys: dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])\n",
            "\n",
            "input_ids:\n",
            "tensor([[    2,  3556,  2802, 28025,  3233,  8065,  2800, 47215,  3827,  1379,\n",
            "          5505,  8415,  2817,  2949,  2802, 47840,  5668, 19142,  2861, 12197,\n",
            "         38993,  1379,  3013, 18757,  5668, 24003,  3223,     4,     0,     0,\n",
            "             0,     0]])\n",
            "token_type_ids:\n",
            "tensor([[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",
            "         0, 0, 0, 0, 0, 0, 0, 0]])\n",
            "attention_mask:\n",
            "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "         1, 1, 1, 1, 0, 0, 0, 0]])\n"
          ]
        }
      ],
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "    sample_comment,\n",
        "    max_length=32,\n",
        "    truncation=True,\n",
        "    add_special_tokens=True, # Add '[CLS]' and '[SEP]'\n",
        "    return_token_type_ids=True,\n",
        "    return_attention_mask=True,\n",
        "    padding='max_length',\n",
        "    return_tensors='pt',  # Return PyTorch tensors\n",
        ")\n",
        "\n",
        "print(f'Keys: {encoding.keys()}\\n')\n",
        "for k in encoding.keys():\n",
        "    print(f'{k}:\\n{encoding[k]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxseSOzbfcAy"
      },
      "source": [
        "## Input Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "dvwjDmYNfeP7"
      },
      "outputs": [],
      "source": [
        "class TaaghcheDataset(torch.utils.data.Dataset):\n",
        "    \"\"\" Create a PyTorch dataset for digikala. \"\"\"\n",
        "\n",
        "    def __init__(self, tokenizer, comments, targets=None, label_list=None, max_len=128):\n",
        "        self.comments = comments\n",
        "        self.targets = targets\n",
        "        self.has_target = isinstance(targets, list) or isinstance(targets, np.ndarray)\n",
        "\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_len = max_len\n",
        "\n",
        "\n",
        "        self.label_map = {label: i for i, label in enumerate(label_list)} if isinstance(label_list, list) else {}\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.comments)\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        comment = str(self.comments[item])\n",
        "\n",
        "        if self.has_target:\n",
        "            target = self.label_map.get(str(self.targets[item]), str(self.targets[item]))\n",
        "\n",
        "        encoding = self.tokenizer.encode_plus(\n",
        "            comment,\n",
        "            add_special_tokens=True,\n",
        "            truncation=True,\n",
        "            max_length=self.max_len,\n",
        "            return_token_type_ids=True,\n",
        "            padding='max_length',\n",
        "            return_attention_mask=True,\n",
        "            return_tensors='pt')\n",
        "\n",
        "        inputs = {\n",
        "            'comment': comment,\n",
        "            'input_ids': encoding['input_ids'].flatten(),\n",
        "            'attention_mask': encoding['attention_mask'].flatten(),\n",
        "            'token_type_ids': encoding['token_type_ids'].flatten(),\n",
        "        }\n",
        "\n",
        "        if self.has_target:\n",
        "            inputs['targets'] = torch.tensor(target, dtype=torch.long)\n",
        "\n",
        "        return inputs\n",
        "\n",
        "\n",
        "def create_data_loader(x, y, tokenizer, max_len, batch_size, label_list):\n",
        "    dataset = TaaghcheDataset(\n",
        "        comments=x,\n",
        "        targets=y,\n",
        "        tokenizer=tokenizer,\n",
        "        max_len=max_len,\n",
        "        label_list=label_list)\n",
        "\n",
        "    return torch.utils.data.DataLoader(dataset, batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_list = ['bad', 'not good', 'good', 'perfect']\n",
        "train_data_loader = create_data_loader(train['Text'].to_numpy(), train['label'].to_numpy(), tokenizer, MAX_LEN, TRAIN_BATCH_SIZE, label_list)\n",
        "valid_data_loader = create_data_loader(valid['Text'].to_numpy(), valid['label'].to_numpy(), tokenizer, MAX_LEN, VALID_BATCH_SIZE, label_list)\n",
        "test_data_loader = create_data_loader(test['Text'].to_numpy(), None, tokenizer, MAX_LEN, TEST_BATCH_SIZE, label_list)"
      ],
      "metadata": {
        "id": "rLSmzbBqnlDK"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jONBXdtnflXE",
        "outputId": "0f829db5-1490-4edb-e04c-25c893199357"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['comment', 'input_ids', 'attention_mask', 'token_type_ids', 'targets'])\n",
            "['ساخت مجارستان کیفیت ساخت و کارایی عالی تنها نکته منفی نداشتن باتری اضافه ست', 'به نظر میرسه دستگاه خوبی باشه قبل از این دستگاه بخار پز فلر داشتم و استفاده زیادی داشتیم ازش مخصوصا برای پخت برنج و بخاطر ظرفیت بیشتر اینو خریدم در نگاه اول با کیفیت به نظر میرسه مخصوصا محصولات پارس خزر که تونسته در تمامی این سال\\u200cها اصالت و کیفیت خودش رو حفظ کنه ایراد خاصی نداره و میشه بهش اعتماد کرد', 'بوش یعنی کیفیت ایمنی تو شگفت\\u200cانگیز خریدم و ازش راضیم واسه کارای سبک و خانگی عالیه به شرط اینکه از منه بوش استفاده بشه', 'واقعا فوق\\u200cالعاده است کار باهاش راحته در خریدش شک نکنید با اتو\\u200cهای معمولی قابل\\u200cمقایسه نیست', 'این لپتاپ عالیه کافیه روش یه اس اس دی بندارین تا بشه تمام دنیاتون', 'شیک و باکیفیت می\\u200cباشد', 'بازی injustice ۲ بسیار بهتر از نسخه قبلی هست و حتما پیشنهاد می\\u200cکنم امتحان کنید با داستان بسیار محیج خود کنسول هم خیلی خوب کار میکنه ولی اگر نزدیک ۱ ساعت بازی کنید کمی داغ میکنه تنها مشکل این هست که برای اینکه دو دسته داشته باری نمی\\u200cتوانید با هم دسته\\u200cها را با usb شارژ کنید به همین دلیل باید شارژر دو تایی دسته را تهیه کنید که در خود دیجیکالا هم موجود هست ممنون بابت خواندن نظر', 'واقعا دستگاه خیلی خوبیه چند تا رنده مختلف داره قدرتش خوبه صداش کمه و لرزش نداره برای سالاد هم عالی خورد میکنه واقعا از خریدش راضیم', 'روی بدنه نوشته ساخت چین ایرانی نیست ولی از نمونه\\u200cهای ترکیه که دیدم خیلی بهتره مخصوصا سیستم باز و بستش که هم قفل ایمن داره تا بخارش کامل تخلیه نشه ضامنش آزاد نمیشه بنظرم ارزش خرید داره', 'عالی است خوشم آمد', 'کلا چیز خوبیه برای کار\\u200cهای اداری برای کار\\u200cهای دیگه لپ\\u200cتاپ\\u200cهای asua رو پیش نهاد می\\u200cکنم', 'بیاس بسیار زیبا و صدای شفاف درکل تو رنج خودش بسیار با کیفیت', 'با سلام خدمت دوستان من این لپتاپ رو یک هفته هست که از دیجیکالا خریدم اول تصمیم داشتم یک لپتاپ کور ۷ با رم و گرافیک بالا بگیرم اما تصمیم عوض شد چون دیدم نه قراره باهاش بازی کنم نه طراحی یا کار سنگین دیگه\\u200cای برای همین تصمیم گرفتم تو لپتاپ\\u200cهای کور ۵ یک لپتاپ انتخاب کنم لپتاپ با این مشخصات زیاد هست با برند\\u200cهای مختلف ولی تصمیم داشتم هزینه کمی بکنم برای همین این مدل رو خریداری کردم اگر برای کار\\u200cهای عادی نیاز ب لپتاپ دارید تموم نیاز\\u200cهای\\u200cهای شما رو پاسخ میده و بازی\\u200cهای سنگین رو هم با رزولوشن پایین بالا میاره ب نظرم صفحش اگر اچ دی بود بهتر بود بلند گوهای خوبی داره و کیبوردش هم کیبور ساده و مناسبی هست اگر تو این رنج قیمت دنبال کور ۵ هستید انتخاب خوبی هست ب نظر مه برای کسی که فقط کارای روزمره قرار انجام بده همین رنج قیمت و امکانات تا ۱۰ سال دیگه هم کافی خواهد بود', 'از خریدم راضی هستم چون خیلی با کیفیت', 'درود من خیلی وقته دارمش اصلا خراب نشده که با توجه به ظاهر ضریفش خیلی عجیبه دوباره موجود بشه ۱۰ تا می\\u200cخرم ولی متاسفانه خیلی وقته ناموجوده وما همچنان منتظر', 'وضوح صدا مهمترین ویژگی این هدفونه بیسش عالیه که برندشم اینو میگه همیشه سنهایزر به وضوح صدا و بیس مناسبش معروفه حجم صداشم بسیار بالاست ولی صدای بیرون رو خیلی ایزوله نمیکنه از لحاظ کیفیت ساخت و ارگونومی هم بسیار عاایه']\n",
            "torch.Size([16, 128])\n",
            "tensor([    2,  3043, 10368,  4589,  3043,  1379,  7863,  4392,  3327,  4909,\n",
            "         5133, 10254,  7097,  4241,  3705,     4,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0])\n",
            "torch.Size([16, 128])\n",
            "tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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,\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,\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,\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,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0])\n",
            "torch.Size([16, 128])\n",
            "tensor([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",
            "        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",
            "        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",
            "        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",
            "        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",
            "        0, 0, 0, 0, 0, 0, 0, 0])\n",
            "torch.Size([16])\n",
            "tensor(1)\n"
          ]
        }
      ],
      "source": [
        "sample_data = next(iter(train_data_loader))\n",
        "\n",
        "print(sample_data.keys())\n",
        "\n",
        "print(sample_data['comment'])\n",
        "print(sample_data['input_ids'].shape)\n",
        "print(sample_data['input_ids'][0, :])\n",
        "print(sample_data['attention_mask'].shape)\n",
        "print(sample_data['attention_mask'][0, :])\n",
        "print(sample_data['token_type_ids'].shape)\n",
        "print(sample_data['token_type_ids'][0, :])\n",
        "print(sample_data['targets'].shape)\n",
        "print(sample_data['targets'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5g7TxELfoWJ",
        "outputId": "25c03a1d-7a76-4771-8e2d-bea8dbe23215"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['comment', 'input_ids', 'attention_mask', 'token_type_ids'])\n"
          ]
        }
      ],
      "source": [
        "sample_test = next(iter(test_data_loader))\n",
        "print(sample_test.keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVHes97bfr8F"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "2LIPrXR7ftCR"
      },
      "outputs": [],
      "source": [
        "class SentimentModel(nn.Module):\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super(SentimentModel, self).__init__()\n",
        "\n",
        "        self.bert = BertModel.from_pretrained(MODEL_NAME_OR_PATH)\n",
        "        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
        "        self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask, token_type_ids):\n",
        "        _, pooled_output = self.bert(\n",
        "            input_ids=input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            token_type_ids=token_type_ids,\n",
        "            return_dict=False)\n",
        "\n",
        "        pooled_output = self.dropout(pooled_output)\n",
        "        logits = self.classifier(pooled_output)\n",
        "        return logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "73cd91fa81a44673a5aab3422f05ae91",
            "d8189d8995b04c11bdac0048c6b69b25",
            "19644bf12b6f4fe992a78a02861e280c",
            "eb13f07395eb4d4a9ec04f09fe9a3941",
            "ad54d81ef91a4d1eb5932b4dc1dce95c",
            "c539fab4116443adb1c5bfe2ecf78190",
            "058770ea79d34d0fb9b6a55661be749b",
            "aa6e0b1365cf4a99aecbe6afaad15ac7",
            "87a9f1f161474f0bb538a0dc62974a11",
            "17a086f2ecc74c59a2bd0fe548efcf96",
            "e6bf59ed014e4d719509581865bf4b4c"
          ]
        },
        "id": "2YAMeag3r9C-",
        "outputId": "3415bac0-9680-4631-a2ae-c4ee599a658e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/654M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73cd91fa81a44673a5aab3422f05ae91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pt_model <class '__main__.SentimentModel'>\n"
          ]
        }
      ],
      "source": [
        "pt_model = SentimentModel(config=config)\n",
        "pt_model = pt_model.to(device)\n",
        "\n",
        "print('pt_model', type(pt_model))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxfCO1mmsKGc"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "dg0iprHAXAnY"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.utils import shuffle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "BVfMGP2lsMAS"
      },
      "outputs": [],
      "source": [
        "def simple_accuracy(y_true, y_pred):\n",
        "    return (y_true == y_pred).mean()\n",
        "\n",
        "def acc_and_f1(y_true, y_pred, average='weighted'):\n",
        "    acc = simple_accuracy(y_true, y_pred)\n",
        "    f1 = f1_score(y_true=y_true, y_pred=y_pred, average=average)\n",
        "    return {\n",
        "        \"acc\": acc,\n",
        "        \"f1\": f1,\n",
        "    }\n",
        "\n",
        "def y_loss(y_true, y_pred, losses):\n",
        "    y_true = torch.stack(y_true).cpu().detach().numpy()\n",
        "    y_pred = torch.stack(y_pred).cpu().detach().numpy()\n",
        "    y = [y_true, y_pred]\n",
        "    loss = np.mean(losses)\n",
        "\n",
        "    return y, loss\n",
        "\n",
        "\n",
        "def eval_op(model, data_loader, loss_fn):\n",
        "    model.eval()\n",
        "\n",
        "    losses = []\n",
        "    y_pred = []\n",
        "    y_true = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for dl in tqdm(data_loader, total=len(data_loader), desc=\"Evaluation... \"):\n",
        "\n",
        "            input_ids = dl['input_ids']\n",
        "            attention_mask = dl['attention_mask']\n",
        "            token_type_ids = dl['token_type_ids']\n",
        "            targets = dl['targets']\n",
        "\n",
        "            # compute predicted outputs by passing inputs to the model\n",
        "            outputs = model(\n",
        "                input_ids=input_ids,\n",
        "                attention_mask=attention_mask,\n",
        "                token_type_ids=token_type_ids)\n",
        "\n",
        "            # convert output probabilities to predicted class\n",
        "            _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "            # calculate the batch loss\n",
        "            loss = loss_fn(outputs, targets)\n",
        "\n",
        "            # accumulate all the losses\n",
        "            losses.append(loss.item())\n",
        "\n",
        "            y_pred.extend(preds)\n",
        "            y_true.extend(targets)\n",
        "\n",
        "    eval_y, eval_loss = y_loss(y_true, y_pred, losses)\n",
        "    return eval_y, eval_loss\n",
        "\n",
        "\n",
        "def train_op(model,\n",
        "             data_loader,\n",
        "             loss_fn,\n",
        "             optimizer,\n",
        "             scheduler,\n",
        "             step=0,\n",
        "             print_every_step=100,\n",
        "             eval=False,\n",
        "             eval_cb=None,\n",
        "             eval_loss_min=np.Inf,\n",
        "             eval_data_loader=None,\n",
        "             clip=0.0):\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    losses = []\n",
        "    y_pred = []\n",
        "    y_true = []\n",
        "\n",
        "    for dl in tqdm(data_loader, total=len(data_loader), desc=\"Training... \"):\n",
        "        step += 1\n",
        "\n",
        "        input_ids = dl['input_ids']\n",
        "        attention_mask = dl['attention_mask']\n",
        "        token_type_ids = dl['token_type_ids']\n",
        "        targets = dl['targets']\n",
        "\n",
        "        # clear the gradients of all optimized variables\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # compute predicted outputs by passing inputs to the model\n",
        "        outputs = model(\n",
        "            input_ids=input_ids,\n",
        "            attention_mask=attention_mask,\n",
        "            token_type_ids=token_type_ids)\n",
        "\n",
        "        # convert output probabilities to predicted class\n",
        "        _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "        # calculate the batch loss\n",
        "        loss = loss_fn(outputs, targets)\n",
        "\n",
        "        # accumulate all the losses\n",
        "        losses.append(loss.item())\n",
        "\n",
        "        # compute gradient of the loss with respect to model parameters\n",
        "        loss.backward()\n",
        "\n",
        "        # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n",
        "        if clip > 0.0:\n",
        "            nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip)\n",
        "\n",
        "        # perform optimization step\n",
        "        optimizer.step()\n",
        "\n",
        "        # perform scheduler step\n",
        "        scheduler.step()\n",
        "\n",
        "        y_pred.extend(preds)\n",
        "        y_true.extend(targets)\n",
        "\n",
        "        if eval:\n",
        "            train_y, train_loss = y_loss(y_true, y_pred, losses)\n",
        "            train_score = acc_and_f1(train_y[0], train_y[1], average='weighted')\n",
        "\n",
        "            if step % print_every_step == 0:\n",
        "                eval_y, eval_loss = eval_op(model, eval_data_loader, loss_fn)\n",
        "                eval_score = acc_and_f1(eval_y[0], eval_y[1], average='weighted')\n",
        "\n",
        "                if hasattr(eval_cb, '__call__'):\n",
        "                    eval_loss_min = eval_cb(model, step, train_score, train_loss, eval_score, eval_loss, eval_loss_min)\n",
        "\n",
        "    train_y, train_loss = y_loss(y_true, y_pred, losses)\n",
        "\n",
        "    return train_y, train_loss, step, eval_loss_min"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "sD7YOI-FstX7"
      },
      "outputs": [],
      "source": [
        "import collections\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330,
          "referenced_widgets": [
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        "id": "90n50y4usXCs",
        "outputId": "af0cd3c2-cdd0-44ce-8159-a022466ab599"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning:\n",
            "\n",
            "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "\n"
          ]
        },
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            "text/plain": [
              "Epochs... :   0%|          | 0/3 [00:00<?, ?it/s]"
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          },
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              "Training... :   0%|          | 0/146 [00:00<?, ?it/s]"
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              "Evaluation... :   0%|          | 0/17 [00:00<?, ?it/s]"
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          },
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              "Training... :   0%|          | 0/146 [00:00<?, ?it/s]"
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            "text/plain": [
              "Training... :   0%|          | 0/146 [00:00<?, ?it/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluation... :   0%|          | 0/17 [00:00<?, ?it/s]"
            ],
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      ],
      "source": [
        "optimizer = AdamW(pt_model.parameters(), lr=LEARNING_RATE, correct_bias=False)\n",
        "total_steps = len(train_data_loader) * EPOCHS\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "    optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=total_steps\n",
        ")\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "step = 0\n",
        "eval_loss_min = np.Inf\n",
        "history = collections.defaultdict(list)\n",
        "\n",
        "\n",
        "def eval_callback(epoch, epochs, output_path):\n",
        "    def eval_cb(model, step, train_score, train_loss, eval_score, eval_loss, eval_loss_min):\n",
        "        statement = ''\n",
        "        statement += 'Epoch: {}/{}...'.format(epoch, epochs)\n",
        "        statement += 'Step: {}...'.format(step)\n",
        "\n",
        "        statement += 'Train Loss: {:.6f}...'.format(train_loss)\n",
        "        statement += 'Train Acc: {:.3f}...'.format(train_score['acc'])\n",
        "\n",
        "        statement += 'Valid Loss: {:.6f}...'.format(eval_loss)\n",
        "        statement += 'Valid Acc: {:.3f}...'.format(eval_score['acc'])\n",
        "\n",
        "        print(statement)\n",
        "\n",
        "        if eval_loss <= eval_loss_min:\n",
        "            print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
        "                eval_loss_min,\n",
        "                eval_loss))\n",
        "\n",
        "            torch.save(model.state_dict(), output_path)\n",
        "            eval_loss_min = eval_loss\n",
        "\n",
        "        return eval_loss_min\n",
        "\n",
        "\n",
        "    return eval_cb\n",
        "\n",
        "\n",
        "for epoch in tqdm(range(1, EPOCHS + 1), desc=\"Epochs... \"):\n",
        "    train_y, train_loss, step, eval_loss_min = train_op(\n",
        "        model=pt_model,\n",
        "        data_loader=train_data_loader,\n",
        "        loss_fn=loss_fn,\n",
        "        optimizer=optimizer,\n",
        "        scheduler=scheduler,\n",
        "        step=step,\n",
        "        print_every_step=EEVERY_EPOCH,\n",
        "        eval=True,\n",
        "        eval_cb=eval_callback(epoch, EPOCHS, OUTPUT_MODEL_PATH),\n",
        "        eval_loss_min=eval_loss_min,\n",
        "        eval_data_loader=valid_data_loader,\n",
        "        clip=CLIP)\n",
        "\n",
        "    train_score = acc_and_f1(train_y[0], train_y[1], average='weighted')\n",
        "\n",
        "    eval_y, eval_loss = eval_op(\n",
        "        model=pt_model,\n",
        "        data_loader=valid_data_loader,\n",
        "        loss_fn=loss_fn)\n",
        "\n",
        "    eval_score = acc_and_f1(eval_y[0], eval_y[1], average='weighted')\n",
        "\n",
        "    history['train_acc'].append(train_score['acc'])\n",
        "    history['train_loss'].append(train_loss)\n",
        "    history['val_acc'].append(eval_score['acc'])\n",
        "    history['val_loss'].append(eval_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "DOV4UPoocYyL"
      },
      "outputs": [],
      "source": [
        "!ls {OUTPUT_PATH}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrKPinCNcc81"
      },
      "source": [
        "## Prediction/Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "too_vM5acZyn"
      },
      "outputs": [],
      "source": [
        "def predict(model, comments, tokenizer, max_len=128, batch_size=32):\n",
        "    data_loader = create_data_loader(comments, None, tokenizer, max_len, batch_size, None)\n",
        "\n",
        "    predictions = []\n",
        "    prediction_probs = []\n",
        "\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for dl in tqdm(data_loader, position=0):\n",
        "            input_ids = dl['input_ids']\n",
        "            attention_mask = dl['attention_mask']\n",
        "            token_type_ids = dl['token_type_ids']\n",
        "\n",
        "            # compute predicted outputs by passing inputs to the model\n",
        "            outputs = model(\n",
        "                input_ids=input_ids,\n",
        "                attention_mask=attention_mask,\n",
        "                token_type_ids=token_type_ids)\n",
        "\n",
        "            # convert output probabilities to predicted class\n",
        "            _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "            predictions.extend(preds)\n",
        "            prediction_probs.extend(F.softmax(outputs, dim=1))\n",
        "\n",
        "    predictions = torch.stack(predictions).cpu().detach().numpy()\n",
        "    prediction_probs = torch.stack(prediction_probs).cpu().detach().numpy()\n",
        "\n",
        "    return predictions, prediction_probs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
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        "id": "fcDre2CzckRJ",
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      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
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          "text": [
            "(289,) (289, 2)\n"
          ]
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      ],
      "source": [
        "test_comments = test['Text'].to_numpy()\n",
        "preds, probs = predict(pt_model, test_comments, tokenizer, max_len=128)\n",
        "\n",
        "print(preds.shape, probs.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EfUSwSq4crEe"
      },
      "outputs": [],
      "source": [
        "y_test, y_pred = [label_list.index(label) for label in test['label'].values], preds\n",
        "\n",
        "print(f'F1: {f1_score(y_test, y_pred, average=\"weighted\")}')\n",
        "print()\n",
        "print(classification_report(y_test, y_pred, target_names=label_list))"
      ]
    }
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