digi_nlp.ipynb

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  "metadata": {
    "colab": {
      "name": "digi_nlp.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMpnOB2byDu7n7nywfrtK2M",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amirhoseinRj/Digikala-Data-Cup/blob/master/digi_nlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PhXp2oSdJqO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "outputId": "83de602c-41f2-4f79-adcb-ac18b0725d0b"
      },
      "source": [
        "pip install fastai --upgrade"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fastai\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/cf/9140964d3544d904cc718e519e1632a8a42e1eaaf2fafdaaa14716c42336/fastai-2.0.10-py3-none-any.whl (354kB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from fastai) (1.4.1)\n",
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            "Collecting fastcore>=1.0.5\n",
            "  Downloading https://files.pythonhosted.org/packages/ca/f6/fe20bfa7d818104f940da81a60e7532090fd87c31f90d4c3b8ae2823d28a/fastcore-1.0.9-py3-none-any.whl\n",
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            "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy->fastai) (3.1.0)\n",
            "Installing collected packages: fastcore, fastai\n",
            "  Found existing installation: fastai 1.0.61\n",
            "    Uninstalling fastai-1.0.61:\n",
            "      Successfully uninstalled fastai-1.0.61\n",
            "Successfully installed fastai-2.0.10 fastcore-1.0.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eogFUR72dBAT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.text.all import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afBW7jyhdE_g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAJrlo0xdpcI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "60c9c35e-c61e-4035-88c3-054a298f296a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "root_dir = \"/content/gdrive/My Drive/\"\n",
        "base_dir = root_dir + 'digi_sale/'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULDa6foldsIm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "c6591499-eddf-4a2a-9092-14f6a750d9ea"
      },
      "source": [
        "train_df = pd.read_csv('/content/gdrive/My Drive/digikala sales prediction/nlp/train_users.csv')\n",
        "train_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>comment</th>\n",
              "      <th>advantages</th>\n",
              "      <th>disadvantages</th>\n",
              "      <th>title_fa_product</th>\n",
              "      <th>title_fa_category</th>\n",
              "      <th>is_buyer</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>مثل بقیه محصولات الکل دار پوست رو خشک نمیکنه</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ژل ضد عفونی کننده دست شون مدل Aloevera حجم 300 میلی لیتر</td>\n",
              "      <td>ضدعفونی کننده</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Redmi 8</td>\n",
              "      <td>با این مبلغ اگه امکانات و ارزش خرید واستون مهمه این گوشی عالیه و  بیخودی پول برند نمیدی.</td>\n",
              "      <td>[\"رم4\\r\",\"قیمت نسبت به سایر برندها\\r\",\"طراحی و زیبایی\\r\",\"باتری 5000 و فست شارژ\\r\",\"صدای با حجم بالا و کیفیت مناسب\\r\",\"حافظه داخلی 64\\r\",\"دوربین \"]</td>\n",
              "      <td>[\"نداشتن هندزفری\"]</td>\n",
              "      <td>گوشی موبایل شیائومی مدل Redmi 8 M1908C3IG دو سیم‌ کارت ظرفیت 64 گیگابایت</td>\n",
              "      <td>گوشی موبایل</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>عالی</td>\n",
              "      <td>خوبه فقط کج و کوله بدستم رسید ولی پسرم خیلی خوشحال شد لطفا در بسته بندی بیشتر توجه بشه</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ماسک کودک طرح سگ نگهبان مدل رابل</td>\n",
              "      <td>ایفای نقش</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>در کل خوب بود ولی متاسفانه درب محصول شکسته بود و مقداریش داخل بسته بندی خالی شده بود.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>مایع ضدعفونی کننده میوه و سبزیجات من مدل WT حجم 500 میلی لیتر</td>\n",
              "      <td>ضدعفونی کننده سطوح</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>من که خیلی باهاش حال کردم فقط من که همیشه L میپوشیدم اینبار M گرفتم چون لانگه و اندازه ش بزرگتره</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>تی شرت آستین کوتاه مردانه باینت کد 323-1 btt</td>\n",
              "      <td>تی شرت و پولوشرت مردانه</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   id    title  ... verification_status   rate\n",
              "0   0      NaN  ...                   1  100.0\n",
              "1   1  Redmi 8  ...                   1   80.0\n",
              "2   2     عالی  ...                   1  100.0\n",
              "3   3      NaN  ...                   1   70.0\n",
              "4   4      NaN  ...                   1   72.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGjdkD4zeCi2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "36d0add4-6238-4041-c71a-d374f5a6c703"
      },
      "source": [
        "test_df = pd.read_csv('/content/gdrive/My Drive/digikala sales prediction/nlp/test_users.csv')\n",
        "test_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>comment</th>\n",
              "      <th>advantages</th>\n",
              "      <th>disadvantages</th>\n",
              "      <th>title_fa_product</th>\n",
              "      <th>title_fa_category</th>\n",
              "      <th>is_buyer</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>با کیفیت</td>\n",
              "      <td>بسته بندی بهداشتی و کیفیت عالی</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>گوش پاک کن کتابی پنبه ریز بسته 200 عددی</td>\n",
              "      <td>گوش پاک کن</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>گوشی موبایل شیائومی</td>\n",
              "      <td>این گوشی فوق‌العاده است</td>\n",
              "      <td>[\"این گوشی همه چیش فوق‌العاده است از دوربین تا نرم‌افزار هایی که داره واقعا عالیه \"]</td>\n",
              "      <td>[\"اصلا هیچ نقاط ضعفی نداره \"]</td>\n",
              "      <td>گوشی موبایل شیائومی مدل Redmi Note 8 M1908C3JG دو سیم‌ کارت ظرفیت 128 گیگابایت - طرح قیمت شگفت انگیز</td>\n",
              "      <td>گوشی موبایل</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>صداش قطع میشه باتریشم خیییلی ضعیفه</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>هدفون بی سیم مدل inpods 12</td>\n",
              "      <td>هدفون، هدست و هندزفری</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>بنظرم‌ خوبه</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>خمیر دندان نسیم مدل Feluoride Protection مقدار 80 گرم</td>\n",
              "      <td>خمیر دندان</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>عااالیه</td>\n",
              "      <td>عااالیه</td>\n",
              "      <td>[\"عاااالیه\"]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>کرم موبر صورت و بدن سینره حجم 100 میلی لیتر</td>\n",
              "      <td>کرم موبر</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id                title  ...      title_fa_category is_buyer\n",
              "0   0             با کیفیت  ...             گوش پاک کن        1\n",
              "1   1  گوشی موبایل شیائومی  ...            گوشی موبایل        1\n",
              "2   2                  NaN  ...  هدفون، هدست و هندزفری        1\n",
              "3   3                  NaN  ...             خمیر دندان        1\n",
              "4   4              عااالیه  ...               کرم موبر        1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H76qfxS1eJg-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "352926f6-2437-49c1-de01-3f3809b933d0"
      },
      "source": [
        "dls = TextDataLoaders.from_df(train_df, path='.', text_col=['title','comment', 'advantages' ,'disadvantages'],label_col = 'verification_status' , valid_pct=0.2, bs = 128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2WI9-HifrlH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "0edf7df7-f4ee-4391-fea2-8cc31ec14709"
      },
      "source": [
        "learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5 ,metrics=accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAnSaUCug_fo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "73b7fe27-08d6-4036-bb44-f814f154b9b0"
      },
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.025118863582611083, lr_steep=0.0831763744354248)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m1pZloVoRGY/eeJKX9nfysRyebXSorAWBc+6aMR53wMezdfzJluxP8eL+Tvri/RjggJ0t3Ty39wjP7+2gL9HP8jnVLJ9TQ2VJiJ+/fJANu9txDsKhAO9ZOYcPXdTEohHmK3fOsbO1h3U72tiwq53XDnezq7WbaCJ1zH7BgPHHKxv5i4uXMKemdMy6d7X2sLuth55YMnPrpy/RT288/fXhSJRDXTFau2OUFgWpryimtjxMcSiAA1LOURwKUFsepq68mOrSoqPbDVg2u5qTZ1aO68rKI71xth7oojuaJJpMEUv0EwwYxaEg4VCA/pSjK5ogEk2S7E9RWx6mobKY+opiSoqCFIcCFBcFKAuHKCsKjnrM/pQj/SMGAbOcv/JT/OP1I330pxxLZuTm2gPDya0VlqeYc47n9h5h7eb9/HTLAVq742/ap76imJXzaigLB9myv5P/fuUwzsHi6RV88o+Wcm5TLWtfeJ1HNjXz0IZ9NE4rpaGimOmVJYRDASKZD759Hb0c6ooBMLOqhFNmVXLBojqaGiqoLQ9TXhykpCjI41sO8N31e/nh8/t5+7IZVJYUEQoY4VCAhspiZlaVMK08zMbd7fz8pYO8drh72O8tYFAeDtFQlX7OisYaeuP9tPfE2NfRSzyZImDpD89YMkVHb5z+lBv2taaVFXFeUx3TK4uJ9zsS/SlSzlEUCBAKGr3xfl7Yd4SdrT2T9D+TVh4OUlESoqqkiOrSIoIBo7U7RkskRlc0+aYa50wrZU5NKfUV6UCrKi2iLBxkICJCwQDzastYWF/OzKoShYcMqy3zOVBfcfytfL9SEIziS7/cxld/vZ1wKMDFp0zn8uWzqa8I4wDnoHFaKY3TSjF74wMjEk1wpDdxzPbzmur4q4uX8r0Ne9l+uJvDkRjbW7pJ9KeoKimisiTEeU11nLOwlgsW1TO/ruyY1xzs7AW1fPiiJu741Ws88WoLiZQj2Z8ilkzRG+8/ul/A0mc0XHvuqZzeWE1FcREVJSHKw0FKw0HCwcCIxxhOKvPXemdfAsMwg0R/iuf3HmHdjjbW72yjJ56kKBggnDmvOplKkex3FAUDLG+s5j1nNbKisYaasiJKigIUh4KknCOWTBFLpAgEoKok/QEdChht3XFauqO0dseJJvqJJ1NEkyn64km6Y/30xJJEogm6+pJ0RRMkU46TZ1Zx4eIw08rDBM1wpFsHLd0x9nf0saOlhw27O+jqS+8/kpKiANPKwpQXh6goDlEcClAUDBAMGOXFwXSYV5VQVx6muChAOJhuscysLqFxWinVpUXH9f5K7mjrSQdBbXnY40omj4JgBA9v3MdXf72d957VyD++81QqS4rG9bzKkqJh922oLOYT/2vJpNQ2u6aUf7369Ddtj0QTHOqK0hKJs3RGBXWT+BdLIGDUlIWpKTv2h7+poYL3nNU4accZrLw4xLy67IyHOOcyXWRvhGcsmWJPaw87W3vY3dpDZ1+CnniSSDRJPJmiL9FPMuVo7ujlqUgrkSGtjsEqS0LMqSllZnUJs6pLqSsPEw4FCIcCFIcClIdDlBUHqSwpYvH0CmZXlyg4csRAENQpCPLbuu2t/N2jW7hoST3/cvXynLlycCCEFk/3uhL/M7P0WEP42F+BOTWlnL+4flyv0Rfvp703TjyZItGfoi/ez4HOKM0dvexr72X/kSgHOvt4sbmTjt44buQGCNPKijh1dhXzasuoKy+mriLMnJpSTppZydxpZeqm8pH2TNfQNAVB/nrtUISP/McmmhrKufO6lTkTAjL1SsNB5oSPHbBfMXeEnUmfcBDvTxFNpOiNJ+mN93OkN8GrB7t4+fX07b9+f5j2nmPHY0qKAsyvLU937RWHqMy0lBY1VNDUUM6ihgqqS8fXYpWJa+uJUV1alFefDQqCQTbt6eDD922kOBTkWx88m6pxdgeJjEcoGCAUDFAWPrZ/eejVqamUo6M3zr6OPrYdjPDqoQh723vpjSfp6kuwr72XX7x88JgxjvqKYhY1lLN0RiXLZlexbHY1S2dWUBzKj/Pc/aStJ55X3UKgIDjq8S0H+OT3NjOruoR7bzhH5+qLZwIBo66imLqKYs4Y4crVRH+Kfe297GjpYWdLNztautl+uJtHn2vm/vXpcY9wKMDKeTXpExEW1DJnWvqMqfJi/dpPRHt3nLoKBUHe+dZvd/H5x7aycl4N3/jAqkkdZBXJhqJggKaGCpoaKoAZR7enUo497b28/Honz+89wvqdbfz7f792zPhEWTjI/LpylkyvYPH0CubXlTG9siQzsF2SN1fLZktbT4yF9eVelzGpCj4IHnhmL59/bCvvOG0mX3nfGfolkJwWCBgL68tZWF/OFafPBqCzN8ELzUc4HElfOHioK8ru1h6e29vB2hdeP+b5ZunTohc3VLBkRiVLZ1Ry8sxKFk+v0O9GRntPnLPm58dkcwMKOgh+tuUA//dHW/jDkxq4/Zoz82rwR2RAdVkRb106/GSNvfEkrx/p41BXjIOdUfZ19LL9cLqb6Xc72ogn01e2BwPGkukVLJ9TzemN1ayYW8Mps6oK7ncmlXK098SpV9dQfli3vZW/fGgzZ86bxl3XnVVwP9AiAGXhEIunV7J4euWbHkv2p9jd1surByO8crCLLfs7+dUrh/n+pmYgfTbT6XNqOL2xmqUzKlkyI92KqMjjMYgjfQlSLr8uJoMCDYKeWJKP3L+JBfVlfOuDqygNq8krMlQoGGBxZhzh8tPTiwc659h/pI/N+47w3J4jPLe3g/vX7yGWfGNOrNryMHOnlTK3toym+nKaGipY1FDBSTMrCYdy+w+u9p70NDAKgjyws6WHSCzJbW9b+qYrZUVkZGZG47QyGqeVHR2D6E859rb3su1QhB0t3exr76O5o5ct+zt5fMsBBs5yrSoJccmymVx++iwuWFyfk63wgXmG6srz64SSggyCfR29ADpFVGQSBAcNUA8VS/azt62XbYe6+e9XDvHzlw7y/U3NTK8s5s8vXMi1587Lqet1jk4voTGC3LevXUEgMhWKQ0GWzKhkyYxKLj99FtFEP09sa+G+p3fzxZ+9wp2/2s4Hz1/ALX+0OCcufsvHeYagUIOgozc9DXEO/SUikg9KioJcsmwmlyybyZbmTu5+cgdf/fV2frejlbv/9CxmVJV4XeKo8nGeIciRxesn2772PubWjr2oi4hkz/LGau68diV3XbeSVw9GuOKO37Jxd7vXZY0qH+cZgkINgo5e5k5Tt5CIH1y2fBY/+vgFlIeDXPON9fzw+WavSxpRPs4zBAUYBKmUo7mjT+MDIj6ydEYlP/74hayaX8tffe8FvvXbXV6XNKz27njenToKBRgELd0x4skUc6epa0jET6rLivj2DWdz6bKZfP6xrdz2i1eOrjvtF+09+TfhHBRgEAycMdSoFoGI75QUBbnzupVcc85c7vz1Dj7zgy0k+1NjP3GKtPXEqM2zawigAM8aOnoNgVoEIr4UDBj/8u7l1FcUc8evtnM4EuXO61a+aTW5qZZeJyKhMYJ80NzeB0CjBotFfMvM+Ju3n8QX3n0aT2xr4Zo162ntjnlaU2dfgv6UU9dQPtjX0UtDZbGm1BXJAdedO5+7//QsXjkY4dpvrKcrmvCslrY8nWcICjEI2vvULSSSQ96+bCb3XH82O1t6+Ph3n/NszCBf5xmCQgyCjl6dOiqSYy5YXM8/X3UaT73Wyud+stWTs4naM9NL5GOLoKAGi5P9KQ50RnUxmUgOev8589jV2sPXn9zJwvpy/vzChVN6/NZMEOTbojRQYEFwoDNKf8ppegmRHPXpS09mV2sP//TYVg5Fonzq7ScRmqLpHvJ1niEosK6ho7OOqkUgkpMCAeOOa8/kunPn8fUndnLtN5/hcFd0So7d3hOjqiSUd/MMQaEFgdYhEMl5xaEgX3j3cr7yvhVsae7kstt/y7odrVk/bmtPnPqK/BsohkILgvY+ggFjVrW/p7oVkbG9+8xGfvyJC6gqDfGn33yGO3+9nVQqe4PI+TrPEBRaEHT0Mqu6ZMr6FEUku5bOqGTtJy7k8tNnc9svXuVD922kIzOoO9naexQEeWFfu6afFsk3FcUhbn//GfzTlct46rUWLv7yE/x48/5JP8W0rSdGnbqGct++Di1II5KPzIwPvGUBaz9xIY21ZfzlQ5u54d4NNGfGBScqn+cZggIKgmiin5ZITC0CkTx2yqwqHv3Y+dz6zlN5dlc7V371d2xp7pzw6w7MM6SuoRzXrDOGRApCMGDccMFCHrvlQkqKgrx/zdOs2z6xs4oG5hnKxwnnoICCYN/RWUfVNSRSCJoaKnj05vNpnFbG9d/ewONbDpzwa+XzPENQQFcWV5SEeNupM5hfV+51KSIyRWZUlfDwR97Cjd/ZwC0PPk9pOMgfnjR9zOelUo5NeztYv6ONXW09vLy/C8jPeYYALJuTN5nZpcC/A0Hgm865Lw55fB7wHaAms89nnHOPj/aaq1atchs3bsxSxSKSj7pjSd739afZ1drD9256C8sbq9+0j3OOVw9F+OmLB/jh8/tp7kj3IsysKmFBfRmnzqrm7y47OWdPPzezTc65VcM+lq0gMLMgsA14G9AMbACucc5tHbTPGuB559zXzOxU4HHn3ILRXldBICIn4nBXlHfftY5YMsUPbz6fxmml7Gzt4Zmd7azb0cr6nW20dscJWHq206tXzuHiU2ZQWVLkdemTYrQgyGbX0DnAdufczkwRDwFXAlsH7eOAqszX1cDrWaxHRArY9KoS7r3hbN7ztXW852vrSDlHa6bvf3plMRctaeAti+pYvbSB6VWFNftANoNgDrBv0P1m4Nwh+3wW+KWZ3QKUAxcP90JmdhNwE8C8efMmvVARKQxLZlTyjQ+s4nM/2crJMys5Z2EtZy+spam+HDPzujzPeD1YfA1wr3PuS2b2FuB+MzvNOXfMEkTOuTXAGkh3DXlQp4jkiXOb6nj8Ly/yugxfyeaox35g7qD7jZltg90IPAzgnHsaKAHqs1iTiIgMkc0g2AAsMbOFZhYG3g+sHbLPXuCPAMzsFNJB0JLFmkREZIisBYFzLgl8AvgF8HvgYefcy2b2T2b2rsxufwN82MxeAB4ErndeLEYqIlLAsjpGkLkm4PEh2/5x0NdbgQuyWYOIiIwuN6+MEBGRSaMgEBEpcAoCEZECpyAQESlwWZ10LhvMrAXYk7lbDXSO8vXQbUXA8U5MPvg1xvPY0G3jrXHg3/rjrHGq6hvYpvfQX/XlQo1+r28iNY62zW/v4XznXMOwr+6cy9kbsGa0r4duAzZO5BjjeWzotvHWOOjf46pxqurTe+jP+nKhRr/XN5Eax6jVV+/haLdc7xr6yRhfj/T4iR5jPI8N3TbeGv1e31jHGo3ew7GPM5qxnuf3Gv1e30iPj6fGsbYdj2y/hyPKua6hiTCzjW6EaVj9wu81+r0+8H+Nfq8P/F+j3+uD3KhxQK63CI7XGq8LGAe/1+j3+sD/Nfq9PvB/jX6vD3KjRqDAWgQiIvJmhdYiEBGRIRQEIiIFTkEgIlLgFAQZZnaRmd1tZt80s3Ve1zMcMwuY2RfM7A4z+6DX9QxlZqvN7KnM+7ja63qGY2blZrbRzK7wupbhmNkpmffvETP7mNf1DMfMrjKzb5jZ98zs7V7XM5SZNZnZt8zsEa9rGZD5uftO5n27zut6hsqLIDCze8zssJm9NGT7pWb2qpltN7PPjPYazrmnnHMfBR4DvuPHGoErSa/0liC9BrTf6nNAN+kFhvxYH8CnyayKN9km6efw95mfwz8hC1O0T1KNP3LOfRj4KPA+H9a30zl342TWNZzjrPVq4JHM+/auN72Y147nyje/3oC3AiuBlwZtCwI7gCYgDLwAnAosJ/1hP/g2fdDzHgYq/Vgj8BngI5nnPuLD+gKZ580AvuvD+t5GeqW864Er/Ph/nHnOu4CfAdf6tcbM874ErPRxfZP6OzLBWv8WOCOzzwPZrOtEbl4vXj8pnHNPmtmCIZvPAbY753YCmNlDwJXOuX8Fhu0WMLN5QKdzLuLHGs2sGYhn7vb7rb5BOoBiv9WX6a4qJ/2L2WdmjzvnUn6qMfM6a4G1ZvZT4IHJqm+yajQzAzwx/6MAAAQ+SURBVL4I/Mw595zf6psqx1Mr6RZyI7AZH/bE5EUQjGAOsG/Q/Wbg3DGecyPw7axV9GbHW+OjwB1mdhHwZDYLyziu+szsauASoAb4anZLA46zPufc3wOY2fVA62SGwCiO9z1cTboboZghq/tl0fH+HN4CXAxUm9li59zd2SyO438P64AvAGea2d9mAmOqjFTr7cBXzexyTnwKiqzJ5yA4bs65W72uYTTOuV7SYeVLzrlHSYeVrznn7vW6hpE4534D/MbjMkblnLud9AebLznn2kiPX/iGc64HuMHrOkbiuybKJNoPzB10vzGzzU/8XqPqmzjVOHF+r2+wXKr1qHwOgg3AEjNbaGZh0oOEaz2uaSi/16j6Jk41Tpzf6xssl2p9g9ej1ZM0ev8gcIA3Tqu8MbP9MmAb6VH8v1eNqk81+rtGv9eXq7WOddOkcyIiBS6fu4ZERGQcFAQiIgVOQSAiUuAUBCIiBU5BICJS4BQEIiIFTkEgecHMuqf4eJOyZoWl13DoNLPNZvaKmf3/cTznKjM7dTKOLwIKApFhmdmo83A5586fxMM95Zw7AzgTuMLMxlqH4CrSM6iKTAoFgeQtM1tkZj83s02WXjnt5Mz2d5rZM2b2vJn9l5nNyGz/rJndb2a/A+7P3L/HzH5jZjvN7C8GvXZ35t/VmccfyfxF/93MNM2Y2WWZbZvM7HYze2y0ep1zfaSnKZ6Tef6HzWyDmb1gZj8wszIzO5/0egW3ZVoRi0b6PkXGS0Eg+WwNcItz7izgfwN3Zbb/FjjPOXcm8BDwfwY951TgYufcNZn7J5OeWvsc4FYzKxrmOGcCn8w8twm4wMxKgK8D78gcv2GsYs1sGrCEN6YYf9Q5d7ZzbgXwe9JTGKwjPXfNp5xzZzjndozyfYqMi6ahlrxkZhXA+cD3M3+gwxuL5TQC3zOzWaRXkdo16KlrM3+ZD/ipcy4GxMzsMOnV14Yuw/msc645c9zNwALSS3budM4NvPaDwE0jlHuRmb1AOgT+zTl3MLP9NDP7Z9LrO1QAvzjO71NkXBQEkq8CwJFM3/tQdwBfds6tzSwE89lBj/UM2Tc26Ot+hv+dGc8+o3nKOXeFmS0E1pvZw865zcC9wFXOuRcyi+msHua5o32fIuOiriHJS865LmCXmb0X0ssrmtmKzMPVvDFH/AezVMKrQNOgpQzHXOQ903r4IvDpzKZK4ECmO+q6QbtGMo+N9X2KjIuCQPJFmZk1D7r9NekPzxsz3S4vk147FtItgO+b2SagNRvFZLqXbgZ+njlOBOgcx1PvBt6aCZB/AJ4Bfge8Mmifh4BPZQa7FzHy9ykyLpqGWiRLzKzCOdedOYvoTuA159xXvK5LZCi1CESy58OZweOXSXdHfd3jekSGpRaBiEiBU4tARKTAKQhERAqcgkBEpMApCERECpyCQESkwCkIREQK3P8A0PdLb+/RSPkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eE2O4hc_keJ6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "ac8094a8-6721-4052-f73a-b4e5dd88f387"
      },
      "source": [
        "learn.fine_tune(4, 1e-1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.383154</td>\n",
              "      <td>0.162582</td>\n",
              "      <td>0.962291</td>\n",
              "      <td>05:40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.140476</td>\n",
              "      <td>0.169549</td>\n",
              "      <td>0.962613</td>\n",
              "      <td>06:52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.099853</td>\n",
              "      <td>0.082435</td>\n",
              "      <td>0.977023</td>\n",
              "      <td>06:55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.062564</td>\n",
              "      <td>0.323584</td>\n",
              "      <td>0.978025</td>\n",
              "      <td>06:56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.064286</td>\n",
              "      <td>6.003785</td>\n",
              "      <td>0.972087</td>\n",
              "      <td>06:53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtfD-fWXkrJD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nlp_predictions = pd.DataFrame(columns= ['id','verification_status'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvnGhD3avdBy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "1f8e93c1-3d4b-453d-d435-60bf79c2f1ba"
      },
      "source": [
        "test_df = test_df.fillna(' ')\n",
        "test_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>comment</th>\n",
              "      <th>advantages</th>\n",
              "      <th>disadvantages</th>\n",
              "      <th>title_fa_product</th>\n",
              "      <th>title_fa_category</th>\n",
              "      <th>is_buyer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>با کیفیت</td>\n",
              "      <td>بسته بندی بهداشتی و کیفیت عالی</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>گوش پاک کن کتابی پنبه ریز بسته 200 عددی</td>\n",
              "      <td>گوش پاک کن</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>گوشی موبایل شیائومی</td>\n",
              "      <td>این گوشی فوق‌العاده است</td>\n",
              "      <td>[\"این گوشی همه چیش فوق‌العاده است از دوربین تا نرم‌افزار هایی که داره واقعا عالیه \"]</td>\n",
              "      <td>[\"اصلا هیچ نقاط ضعفی نداره \"]</td>\n",
              "      <td>گوشی موبایل شیائومی مدل Redmi Note 8 M1908C3JG دو سیم‌ کارت ظرفیت 128 گیگابایت - طرح قیمت شگفت انگیز</td>\n",
              "      <td>گوشی موبایل</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td></td>\n",
              "      <td>صداش قطع میشه باتریشم خیییلی ضعیفه</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>هدفون بی سیم مدل inpods 12</td>\n",
              "      <td>هدفون، هدست و هندزفری</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td></td>\n",
              "      <td>بنظرم‌ خوبه</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>خمیر دندان نسیم مدل Feluoride Protection مقدار 80 گرم</td>\n",
              "      <td>خمیر دندان</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>عااالیه</td>\n",
              "      <td>عااالیه</td>\n",
              "      <td>[\"عاااالیه\"]</td>\n",
              "      <td></td>\n",
              "      <td>کرم موبر صورت و بدن سینره حجم 100 میلی لیتر</td>\n",
              "      <td>کرم موبر</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19394</th>\n",
              "      <td>19394</td>\n",
              "      <td></td>\n",
              "      <td>خوشمزه مثل همیشه</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>چاشنی خورش قرمه سبزی هاتی کارا مقدار 750 گرم</td>\n",
              "      <td>ادویه و چاشنی</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19395</th>\n",
              "      <td>19395</td>\n",
              "      <td>عالی</td>\n",
              "      <td>عالی\\r\\nبوش مثله خود ادکلنشه</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>اسپری خوشبو کننده بدن زنانه پرستیژ مدل Tresor Midnight Rose حجم 150 میلی لیتر</td>\n",
              "      <td>اسپری</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19396</th>\n",
              "      <td>19396</td>\n",
              "      <td></td>\n",
              "      <td>نازکه. نسبت به کیفیتش قیمتش بالاست تازه من داخل شگفت انگیز خریدم</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>دستکش زنانه دالیا مدل viscose2</td>\n",
              "      <td>دستکش زنانه</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19397</th>\n",
              "      <td>19397</td>\n",
              "      <td>چای ایرانی</td>\n",
              "      <td>چای ایرانی بنوش هموطن</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>چای شکسته ایرانی مهمان دوست مقدار 500 گرم</td>\n",
              "      <td>چای</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19398</th>\n",
              "      <td>19398</td>\n",
              "      <td>کیفیت</td>\n",
              "      <td>شامپوی مناسبی است و با موهایم سازگار است اما قیمتش خیلی افزایش یافته.</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>شامپو سدر صحت مقدار 1لیتر</td>\n",
              "      <td>شامپوی مو</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19399 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id                title  ...      title_fa_category is_buyer\n",
              "0          0             با کیفیت  ...             گوش پاک کن        1\n",
              "1          1  گوشی موبایل شیائومی  ...            گوشی موبایل        1\n",
              "2          2                       ...  هدفون، هدست و هندزفری        1\n",
              "3          3                       ...             خمیر دندان        1\n",
              "4          4              عااالیه  ...               کرم موبر        1\n",
              "...      ...                  ...  ...                    ...      ...\n",
              "19394  19394                       ...          ادویه و چاشنی        1\n",
              "19395  19395                 عالی  ...                  اسپری        1\n",
              "19396  19396                       ...            دستکش زنانه        1\n",
              "19397  19397           چای ایرانی  ...                    چای        1\n",
              "19398  19398                کیفیت  ...              شامپوی مو        1\n",
              "\n",
              "[19399 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbQ9TEt1tnUy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "32b18c0c-0458-4198-964c-c69166f2f776"
      },
      "source": [
        "test_df.iloc[0]['comment'] + ' ' + test_df.iloc[0]['title'] + ' ' + test_df.iloc[0]['advantages'] + ' ' + test_df.iloc[1]['disadvantages']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'بسته بندی بهداشتی و کیفیت عالی با کیفیت   [\"اصلا هیچ نقاط ضعفی نداره \"]'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L11gNU5UsUdI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(len(test_df)):\n",
        "  verified,_,_ = learn.predict(test_df.iloc[i]['comment'] + ' ' + test_df.iloc[i]['title'] + ' ' + test_df.iloc[i]['advantages'] + ' ' + test_df.iloc[i]['disadvantages'] )\n",
        "  nlp_predictions = nlp_predictions.append({'id': i, 'verification_status': verified }, ignore_index=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ai3ohZpzswvu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e814b0fa-738d-4689-aee6-311c37681940"
      },
      "source": [
        "nlp_predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>verification_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19394</th>\n",
              "      <td>19394</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19395</th>\n",
              "      <td>19395</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19396</th>\n",
              "      <td>19396</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19397</th>\n",
              "      <td>19397</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19398</th>\n",
              "      <td>19398</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19399 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id verification_status\n",
              "0          0                   1\n",
              "1          1                   1\n",
              "2          2                   1\n",
              "3          3                   1\n",
              "4          4                   1\n",
              "...      ...                 ...\n",
              "19394  19394                   1\n",
              "19395  19395                   1\n",
              "19396  19396                   1\n",
              "19397  19397                   1\n",
              "19398  19398                   1\n",
              "\n",
              "[19399 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVmO3S8VxMII",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nlp_predictions.to_csv('/content/gdrive/My Drive/digikala sales prediction/nlp/nlp_preds.csv' , index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fz0BdagxdN9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}