Build_a_BERT_Text_classification_using_Amazon_forum_data.ipynb

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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/elateifsara/ML-DS-projects/blob/master/Build_a_BERT_Text_classification_using_Amazon_forum_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orLt3iGHdjjQ"
      },
      "source": [
        "# Tutorial\n",
        "\n",
        "We are going to use [Simple Transformers](https://github.com/ThilinaRajapakse/simpletransformers) - an NLP library based on the [Transformers](https://github.com/huggingface/transformers) library by HuggingFace. Simple Transformers allows us to fine-tune Transformer models in a few lines of code.  \n",
        "\n",
        "For the dataset, we are going to use the scraped Amazon e-commerce Forum data.\n",
        "\n",
        "### We are going to\n",
        "\n",
        "- install Simple Transformers library\n",
        "- select a pre-trained monolingual model\n",
        "- load the dataset\n",
        "- train/fine-tune our model\n",
        "- evaluate the results of it\n",
        "- save and load the model\n",
        "- test the loaded model on a real example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_OTuVwGdlZb"
      },
      "source": [
        "# Install Simple Transformers library "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svum1ZL4dlHn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dd805495-36c0-4082-a149-6d011c5adfb3"
      },
      "source": [
        "# install simpletransformers\n",
        "!pip install simpletransformers\n",
        "\n",
        "# check installed version\n",
        "!pip freeze | grep simpletransformers\n",
        "# simpletransformers==0.28.2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting simpletransformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2e/9d/e8fb8b01d1ac7763d43c4968e1a91336814a5b8af079c06dcd0781e6000e/simpletransformers-0.51.13-py3-none-any.whl (202kB)\n",
            "\r\u001b[K     |█▋                              | 10kB 25.8MB/s eta 0:00:01\r\u001b[K     |███▎                            | 20kB 31.2MB/s eta 0:00:01\r\u001b[K     |████▉                           | 30kB 29.2MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 40kB 33.5MB/s eta 0:00:01\r\u001b[K     |████████                        | 51kB 28.9MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 61kB 31.8MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 71kB 19.9MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 81kB 20.3MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 92kB 18.8MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 102kB 19.9MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 112kB 19.9MB/s eta 0:00:01\r\u001b[K     |███████████████████▌            | 122kB 19.9MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 133kB 19.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 143kB 19.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 153kB 19.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 163kB 19.9MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 174kB 19.9MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 184kB 19.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▉ | 194kB 19.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 204kB 19.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (2.23.0)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (2019.12.20)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (1.4.1)\n",
            "Collecting tokenizers\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/9d/8fbf1f56cc5891e6c3295bf94fc176e9ab0a3ffdd090cc8b354ac2640f9a/pydeck-0.5.0-py2.py3-none-any.whl (4.5MB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/e7/7f/470d6fcdf23f9f3518f6b0b76be9df16dcc8630ad409947f8be2eb0ed13a/pathtools-0.1.2.tar.gz\n",
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            "  Stored in directory: /root/.cache/pip/wheels/9e/11/04/5160b8815b0cc7cf574bdc6d053e510169ec264c8791b4ec3a\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp36-none-any.whl size=6490 sha256=c3a4159d7afcbe35503702be09e191c042870b4d45241234a32cdc5ea505cc18\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=e39b0d6d8251ca908827e6f01f242eefe7607b4b1a245cb6db11468161166fce\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for blinker: filename=blinker-1.4-cp36-none-any.whl size=13450 sha256=45e45c427e8252a36b9079706dfb302a812fe89e007c9cd00a34ec8b701c7590\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/a0/00/8690a57883956a301d91cf4ec999cc0b258b01e3f548f86e89\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-cp36-none-any.whl size=8785 sha256=b79cc6151c02218f80c43a8c835d6263edd96a4f8fac0e604ce6321e2f0356b4\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
            "Successfully built seqeval watchdog subprocess32 sacremoses blinker pathtools\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.4.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: transformers 4.2.2 has requirement tokenizers==0.9.4, but you'll have tokenizers 0.10.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tokenizers, configparser, smmap, gitdb, GitPython, shortuuid, docker-pycreds, pathtools, watchdog, subprocess32, sentry-sdk, wandb, sentencepiece, tqdm, sacremoses, transformers, seqeval, tensorboardx, base58, blinker, ipykernel, pydeck, validators, streamlit, simpletransformers\n",
            "  Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "  Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "Successfully installed GitPython-3.1.12 base58-2.1.0 blinker-1.4 configparser-5.0.1 docker-pycreds-0.4.0 gitdb-4.0.5 ipykernel-5.4.3 pathtools-0.1.2 pydeck-0.5.0 sacremoses-0.0.43 sentencepiece-0.1.95 sentry-sdk-0.19.5 seqeval-1.2.2 shortuuid-1.0.1 simpletransformers-0.51.13 smmap-3.0.5 streamlit-0.75.0 subprocess32-3.5.4 tensorboardx-2.1 tokenizers-0.10.0 tqdm-4.56.0 transformers-4.2.2 validators-0.18.2 wandb-0.10.14 watchdog-0.10.4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "ipykernel"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "simpletransformers==0.51.13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xe4crcqaMYbT",
        "outputId": "c8120671-0f2f-4a99-9a03-fd0fc00101c6"
      },
      "source": [
        "!pip install tokenizers==0.9.4"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tokenizers==0.9.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 15.5MB/s \n",
            "\u001b[?25hInstalling collected packages: tokenizers\n",
            "  Found existing installation: tokenizers 0.10.0\n",
            "    Uninstalling tokenizers-0.10.0:\n",
            "      Successfully uninstalled tokenizers-0.10.0\n",
            "Successfully installed tokenizers-0.9.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBVjDmlETQp_",
        "outputId": "fd3f44e1-37c9-4c0c-b187-42fe4c74c05a"
      },
      "source": [
        "!wget https://github.com/mentorchains/level1_post_recommender_20/raw/md3/resources/amazon_ecom_forum.csv"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-01-23 12:52:00--  https://github.com/mentorchains/level1_post_recommender_20/raw/md3/resources/amazon_ecom_forum.csv\n",
            "Resolving github.com (github.com)... 140.82.112.4\n",
            "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/mentorchains/level1_post_recommender_20/md3/resources/amazon_ecom_forum.csv [following]\n",
            "--2021-01-23 12:52:00--  https://raw.githubusercontent.com/mentorchains/level1_post_recommender_20/md3/resources/amazon_ecom_forum.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 32502914 (31M) [text/plain]\n",
            "Saving to: ‘amazon_ecom_forum.csv’\n",
            "\n",
            "amazon_ecom_forum.c 100%[===================>]  31.00M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2021-01-23 12:52:01 (293 MB/s) - ‘amazon_ecom_forum.csv’ saved [32502914/32502914]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAoybE_yhpxi"
      },
      "source": [
        "# Select a pre-trained monolingual model\n",
        "\n",
        "As mentioned above the Simple Transformers library is based on the Transformers library from HuggingFace. This enables us to use every pre-trained model provided in the [Transformers library](https://huggingface.co/transformers/pretrained_models.html) and all community-uploaded models. For a list that includes community-uploaded models, refer to [https://huggingface.co/models](https://huggingface.co/models).\n",
        "\n",
        "We are going to use the `distilbert-base-german-cased` model. [DistilBERT is a small, fast, cheaper version of BERT](https://huggingface.co/transformers/model_doc/distilbert.html). It has 40% less parameters than `bert-base-uncased` and runs 60% faster while preserving over 95% of Bert’s performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WsJ66z-hpl0"
      },
      "source": [
        "# Load the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uknEcA8Wi5vM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "outputId": "b0922e0e-23d0-4be6-ddfc-2d9a3ea17867"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/amazon_ecom_forum.csv')\n",
        "\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7882, 10)\n"
          ],
          "name": "stdout"
        },
        {
          "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>Unnamed: 0</th>\n",
              "      <th>Link</th>\n",
              "      <th>Title</th>\n",
              "      <th>Category</th>\n",
              "      <th>Post Author</th>\n",
              "      <th>Leading Comment</th>\n",
              "      <th>Publish Time</th>\n",
              "      <th>Reply Authors</th>\n",
              "      <th>Reply Comments</th>\n",
              "      <th>Reply Times</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>http://sellercentral.amazon.com/forums/t/2-box...</td>\n",
              "      <td>2 boxes of books</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>Earth_Light_Books</td>\n",
              "      <td>I need to know how to start</td>\n",
              "      <td>2020-05-26 17:33:26+00:00</td>\n",
              "      <td>&lt;function get_reply_authors at 0x00000119FBB60...</td>\n",
              "      <td>['\\nI need to know how to start\\n', ' \\nAmazon...</td>\n",
              "      <td>[Timestamp('2020-05-26 17:48:57+0000', tz='UTC...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>http://sellercentral.amazon.com/forums/t/fba-s...</td>\n",
              "      <td>FBA Shipment Delivered Delayed Check in</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>HOUSE_OF_LORDS_RODEO</td>\n",
              "      <td>Hi are there any other seller experiencing del...</td>\n",
              "      <td>2020-05-26 15:28:17+00:00</td>\n",
              "      <td>&lt;function get_reply_authors at 0x00000119FBB60...</td>\n",
              "      <td>['\\nHi are there any other seller experiencing...</td>\n",
              "      <td>[Timestamp('2020-05-26 16:03:50+0000', tz='UTC')]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>http://sellercentral.amazon.com/forums/t/fba-r...</td>\n",
              "      <td>FBA removal/disposal fees</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>Texastoys</td>\n",
              "      <td>Hi all\\nSo back when the new removaldisposal f...</td>\n",
              "      <td>2020-05-26 05:43:46+00:00</td>\n",
              "      <td>&lt;function get_reply_authors at 0x00000119FBB60...</td>\n",
              "      <td>['\\nHi all\\nSo back when the new removaldispos...</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>http://sellercentral.amazon.com/forums/t/am-i-...</td>\n",
              "      <td>Am i able to send in FBA inventory now?</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>Assuranceproducts</td>\n",
              "      <td>Im using inventorylab to upload my inventory t...</td>\n",
              "      <td>2020-05-25 16:53:40+00:00</td>\n",
              "      <td>&lt;function get_reply_authors at 0x00000119FBB60...</td>\n",
              "      <td>['\\nIm using inventorylab to upload my invento...</td>\n",
              "      <td>[Timestamp('2020-05-25 17:14:44+0000', tz='UTC...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>http://sellercentral.amazon.com/forums/t/stran...</td>\n",
              "      <td>Stranded Inventory</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>BillsBuys</td>\n",
              "      <td>I recently listed 100 books for sale on Amazon...</td>\n",
              "      <td>2020-05-24 02:14:21+00:00</td>\n",
              "      <td>&lt;function get_reply_authors at 0x00000119FBB60...</td>\n",
              "      <td>['\\nI recently listed 100 books for sale on Am...</td>\n",
              "      <td>[Timestamp('2020-05-25 01:42:41+0000', tz='UTC...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...                                        Reply Times\n",
              "0           0  ...  [Timestamp('2020-05-26 17:48:57+0000', tz='UTC...\n",
              "1           1  ...  [Timestamp('2020-05-26 16:03:50+0000', tz='UTC')]\n",
              "2           2  ...                                                 []\n",
              "3           3  ...  [Timestamp('2020-05-25 17:14:44+0000', tz='UTC...\n",
              "4           4  ...  [Timestamp('2020-05-25 01:42:41+0000', tz='UTC...\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxOcGos8LRqr",
        "outputId": "c7d8638f-60d5-4fab-e345-52b38a8d8a13"
      },
      "source": [
        "df['Category'].unique()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Fulfillment By Amazon', 'Selling on Amazon',\n",
              "       'Amazon Marketplace Web Service (MWS)',\n",
              "       'Amazon Sponsored Products', 'Account Health', 'Global Selling',\n",
              "       'Amazon Pay', 'Groups',\n",
              "       'Health,Safety,Sustainability,Security & Compliance',\n",
              "       'Login With Amazon', 'Amazon Custom'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFDAF8USTyhj"
      },
      "source": [
        "df.drop(columns= ['Unnamed: 0', 'Link', 'Publish Time', 'Reply Times', 'Reply Authors'], inplace=True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "QAVkxGQYUBCw",
        "outputId": "b3931f02-b4bc-499b-e29b-1c24cafde470"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "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>Title</th>\n",
              "      <th>Category</th>\n",
              "      <th>Post Author</th>\n",
              "      <th>Leading Comment</th>\n",
              "      <th>Reply Comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2 boxes of books</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>Earth_Light_Books</td>\n",
              "      <td>I need to know how to start</td>\n",
              "      <td>['\\nI need to know how to start\\n', ' \\nAmazon...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FBA Shipment Delivered Delayed Check in</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>HOUSE_OF_LORDS_RODEO</td>\n",
              "      <td>Hi are there any other seller experiencing del...</td>\n",
              "      <td>['\\nHi are there any other seller experiencing...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FBA removal/disposal fees</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>Texastoys</td>\n",
              "      <td>Hi all\\nSo back when the new removaldisposal f...</td>\n",
              "      <td>['\\nHi all\\nSo back when the new removaldispos...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Am i able to send in FBA inventory now?</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>Assuranceproducts</td>\n",
              "      <td>Im using inventorylab to upload my inventory t...</td>\n",
              "      <td>['\\nIm using inventorylab to upload my invento...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Stranded Inventory</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "      <td>BillsBuys</td>\n",
              "      <td>I recently listed 100 books for sale on Amazon...</td>\n",
              "      <td>['\\nI recently listed 100 books for sale on Am...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     Title  ...                                     Reply Comments\n",
              "0                         2 boxes of books  ...  ['\\nI need to know how to start\\n', ' \\nAmazon...\n",
              "1  FBA Shipment Delivered Delayed Check in  ...  ['\\nHi are there any other seller experiencing...\n",
              "2                FBA removal/disposal fees  ...  ['\\nHi all\\nSo back when the new removaldispos...\n",
              "3  Am i able to send in FBA inventory now?  ...  ['\\nIm using inventorylab to upload my invento...\n",
              "4                       Stranded Inventory  ...  ['\\nI recently listed 100 books for sale on Am...\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-v7NKjgVn9n"
      },
      "source": [
        "df['post'] = df['Title'] + ' ' + df['Leading Comment'] + ' ' + df['Reply Comments']"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4w6fJQBVx0N"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import random\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "%matplotlib inline"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "vFDp-VBWVuni",
        "outputId": "8b0be876-ec7c-4cec-dbf6-c76747129174"
      },
      "source": [
        "my_categories = ['Fulfillment By Amazon', 'Selling on Amazon',\n",
        "       'Amazon Marketplace Web Service (MWS)',\n",
        "       'Amazon Sponsored Products', 'Account Health', 'Global Selling',\n",
        "       'Amazon Pay', 'Groups',\n",
        "       'Health,Safety,Sustainability,Security & Compliance',\n",
        "       'Login With Amazon', 'Amazon Custom']\n",
        "plt.figure(figsize=(10,4))\n",
        "df.Category.value_counts().plot(kind='bar');"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3QuWx08WDqo",
        "outputId": "4b8089dc-0193-47c7-a12b-a69b5e6e8c20"
      },
      "source": [
        "df[df['Category'] == 'Health,Safety,Sustainability,Security & Compliance'].shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(59, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "uIhl91RcWXdb",
        "outputId": "bceddc7e-485a-42d8-c386-3cd84719569f"
      },
      "source": [
        "df[df['Category'] == 'Health,Safety,Sustainability,Security & Compliance'].head()"
      ],
      "execution_count": 23,
      "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>Title</th>\n",
              "      <th>Category</th>\n",
              "      <th>Post Author</th>\n",
              "      <th>Leading Comment</th>\n",
              "      <th>Reply Comments</th>\n",
              "      <th>post</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5495</th>\n",
              "      <td>Selling Pesticide Products NOT</td>\n",
              "      <td>Health,Safety,Sustainability,Security &amp; Compli...</td>\n",
              "      <td>PondExpo</td>\n",
              "      <td>HI…amazon classified about 17 of my listings a...</td>\n",
              "      <td>['\\nHI…amazon classified about 17 of my listin...</td>\n",
              "      <td>Selling Pesticide Products NOT HI…amazon class...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5496</th>\n",
              "      <td>Account suspended because of restricted brand</td>\n",
              "      <td>Health,Safety,Sustainability,Security &amp; Compli...</td>\n",
              "      <td>EMSHOP</td>\n",
              "      <td>Hello there Amazon suspended my account becaus...</td>\n",
              "      <td>['\\nHello there Amazon suspended my account be...</td>\n",
              "      <td>Account suspended because of restricted brand ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5497</th>\n",
              "      <td>Product loading</td>\n",
              "      <td>Health,Safety,Sustainability,Security &amp; Compli...</td>\n",
              "      <td>Ekrem_Arslanalp_Stor</td>\n",
              "      <td>Hello you asked me why I installed suspected f...</td>\n",
              "      <td>['\\nHello you asked me why I installed suspect...</td>\n",
              "      <td>Product loading Hello you asked me why I insta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5498</th>\n",
              "      <td>Automotive Approval</td>\n",
              "      <td>Health,Safety,Sustainability,Security &amp; Compli...</td>\n",
              "      <td>Solution_Concepts1</td>\n",
              "      <td>Hello everyone Could somebody tell me about ow...</td>\n",
              "      <td>['\\nHello everyone Could somebody tell me abou...</td>\n",
              "      <td>Automotive Approval Hello everyone Could someb...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5499</th>\n",
              "      <td>Intellectual property complaints associated wi...</td>\n",
              "      <td>Health,Safety,Sustainability,Security &amp; Compli...</td>\n",
              "      <td>grandmatabby1</td>\n",
              "      <td>The Book I listed is Called The below is a rec...</td>\n",
              "      <td>['\\nThe Book I listed is Called The below is a...</td>\n",
              "      <td>Intellectual property complaints associated wi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  Title  ...                                               post\n",
              "5495                     Selling Pesticide Products NOT  ...  Selling Pesticide Products NOT HI…amazon class...\n",
              "5496      Account suspended because of restricted brand  ...  Account suspended because of restricted brand ...\n",
              "5497                                    Product loading  ...  Product loading Hello you asked me why I insta...\n",
              "5498                                Automotive Approval  ...  Automotive Approval Hello everyone Could someb...\n",
              "5499  Intellectual property complaints associated wi...  ...  Intellectual property complaints associated wi...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBQsYAT1Vgu2"
      },
      "source": [
        "REPLACE_BY_SPACE_RE = re.compile('[/(){}\\[\\]\\'\\“\\”\\’\\|@,;]')\n",
        "\n",
        "def clean_text(text):\n",
        "    \"\"\"\n",
        "        text: a string\n",
        "        return: modified initial string\n",
        "    \"\"\"\n",
        "    text = BeautifulSoup(text, \"lxml\").text # HTML decoding\n",
        "    text = text.lower() # lowercase text\n",
        "    text = REPLACE_BY_SPACE_RE.sub(' ', text) # replace REPLACE_BY_SPACE_RE symbols by space in text\n",
        "    return text\n",
        "    \n",
        "df['post'] = df['post'].apply(clean_text)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-mwDspYKagM"
      },
      "source": [
        "class_list = ['Fulfillment By Amazon', 'Selling on Amazon',\n",
        "       'Amazon Marketplace Web Service (MWS)',\n",
        "       'Amazon Sponsored Products', 'Account Health', 'Global Selling',\n",
        "       'Amazon Pay', 'Groups',\n",
        "       'Health,Safety,Sustainability,Security & Compliance',\n",
        "       'Login With Amazon', 'Amazon Custom']\n",
        "new_df = pd.DataFrame()\n",
        "new_df['post'] = df['post']\n",
        "new_df['category'] = df['Category']\n",
        "new_df['post'] = new_df['post'].apply(lambda x: str(x))\n",
        "new_df['category'] = new_df['category'].apply(lambda x: str(x))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PzxTrc2SM7UL",
        "outputId": "7114f23d-f4d9-49fe-976e-247e6b900a6e"
      },
      "source": [
        "len(class_list)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "PBjFbifHK5yh",
        "outputId": "37940c8f-dd73-437d-ac4a-1674e0a20667"
      },
      "source": [
        "new_df.head()"
      ],
      "execution_count": 27,
      "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>post</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2 boxes of books i need to know how to start  ...</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fba shipment delivered delayed check in hi are...</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fba removal disposal fees hi all\\nso back when...</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>am i able to send in fba inventory now? im usi...</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>stranded inventory i recently listed 100 books...</td>\n",
              "      <td>Fulfillment By Amazon</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                post               category\n",
              "0  2 boxes of books i need to know how to start  ...  Fulfillment By Amazon\n",
              "1  fba shipment delivered delayed check in hi are...  Fulfillment By Amazon\n",
              "2  fba removal disposal fees hi all\\nso back when...  Fulfillment By Amazon\n",
              "3  am i able to send in fba inventory now? im usi...  Fulfillment By Amazon\n",
              "4  stranded inventory i recently listed 100 books...  Fulfillment By Amazon"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AoJoM0XLLMCL"
      },
      "source": [
        "# changing class names (text) to numbers (a necessity for simpletransformers library)\n",
        "new_df['category'] = new_df.apply(lambda x:  class_list.index(x['category']),axis=1)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "Kia6-wX-L9Hc",
        "outputId": "8230f77f-0502-476a-d3c6-72ccd0e96a2f"
      },
      "source": [
        "new_df.head()"
      ],
      "execution_count": 29,
      "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>post</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2 boxes of books i need to know how to start  ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>fba shipment delivered delayed check in hi are...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fba removal disposal fees hi all\\nso back when...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>am i able to send in fba inventory now? im usi...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>stranded inventory i recently listed 100 books...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                post  category\n",
              "0  2 boxes of books i need to know how to start  ...         0\n",
              "1  fba shipment delivered delayed check in hi are...         0\n",
              "2  fba removal disposal fees hi all\\nso back when...         0\n",
              "3  am i able to send in fba inventory now? im usi...         0\n",
              "4  stranded inventory i recently listed 100 books...         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VNlC8i4MASR",
        "outputId": "015df6bf-143c-465f-b7a4-0e70d63301ec"
      },
      "source": [
        "new_df['category'].unique()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8g-l5Erkb0b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b54231aa-cd4f-43ba-c2a6-fbdd62b01abe"
      },
      "source": [
        "# Split the data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_df, test_df = train_test_split(new_df, test_size=0.10)\n",
        "\n",
        "print('train shape: ',train_df.shape)\n",
        "print('test shape: ',test_df.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train shape:  (7093, 2)\n",
            "test shape:  (789, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9-vuoxVoprb"
      },
      "source": [
        "# Load pre-trained model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ei4vAFg9COhb"
      },
      "source": [
        "🚨 **If it doesn't work restart runtime**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPYK6TCvlbK0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0,
          "referenced_widgets": [
            "bf6ea5156738406ca391ffb5ea32578e",
            "472f51a73c604d62af77c8bf8444b6c6",
            "d4ba06563a9e45c9a72d8eeffe071996",
            "1ba29672d2b240d49709e51bfaccf82a",
            "6e9962a8d3a646f0a381b14088ec2290",
            "67eb77987b204f5da790242a4a68009f",
            "22abe6f83aad40418312d6f90dd6b725",
            "67d95a35975a40218fcf72f2a1144094",
            "80007e848dc64856be0fc37ecdf045af",
            "361c6b26fdf448b086731e7438664243",
            "d53bd8a39a244758aee24e2a1d81b190",
            "5403bf7781c541acb4fde31b34ebbbc3",
            "3078bb89e9ae4dd49fb42f600a32e9ee",
            "940ea11ff4fb449c8d2cdea0bc2e9b98",
            "e5fef45bac3248d6be26bfae060b4204",
            "5944122a89064cb0a48135ba0243ae52",
            "26612c618f634177a8ba2cba05a57644",
            "8f385e6798c24c71971d41868c1e2df9",
            "0d139206c28949fcb2baa56f8689c432",
            "3ef06067ae9e4fbfa4aeca2283177fc0",
            "656bf353257147c89b5a726933f020ae",
            "96a8b042b625497f93810903798ba530"
          ]
        },
        "outputId": "e39d8ffc-7973-47bf-b46e-8cf592057a62"
      },
      "source": [
        "from simpletransformers.classification import ClassificationModel\n",
        "\n",
        "# define hyperparameter\n",
        "train_args ={\"reprocess_input_data\": True,\n",
        "             \"overwrite_output_dir\": True,\n",
        "             \"fp16\":False,\n",
        "             \"num_train_epochs\": 4}\n",
        "\n",
        "# Create a ClassificationModel\n",
        "model = ClassificationModel(\n",
        "    \"bert\", \"bert-base-uncased\",\n",
        "    num_labels=11,\n",
        "    args=train_args\n",
        ")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf6ea5156738406ca391ffb5ea32578e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5403bf7781c541acb4fde31b34ebbbc3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEPi3LtPovLR"
      },
      "source": [
        "# Train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9A8ATV2oueD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422,
          "referenced_widgets": [
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            "18e0e2bb20b54f3ead3014efa2ed5c77",
            "7eae65a53f9c425596d90a010c5af504",
            "326e8b95d34c49218760d7d5cff27bc1",
            "768b6ea0bb274599b8e7104f56d4af5b",
            "b8fc5ffae6d145918548601296930b57",
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            "f605b7ae72904bc2a92b070dc3797b8e",
            "7c1b1a5569c54040b38d5c54900dcf99",
            "6e8ac59b3034445ea30ec33fbe0f462b",
            "d6a0e6f1a85c4869b851f31faea9c1f5",
            "f671630fd97a4c3eb25c7f579b0672fe",
            "d9bb10b88e43465f98ee2251c9c63e85",
            "aab68979d1884a499b875ef91c0862ea",
            "40ea2538f3874e3893f619669e367ced",
            "ec2fd798607d442abf10b7cc53d7b967",
            "f9e00704172b44fe98a28925f8a33edf",
            "68b9f06b066348ff8fb910c07648633e",
            "bce546f5290f4f529c1baa17aa198150",
            "b5816e36564a4058a34ead1960f5ed26",
            "5d4f520d5e774bacb3a3b3fe2cf0b408",
            "2d42e20eeaee40ceb98258fa0a3a7899",
            "41841c62a819409db6f779f1ca78d8de",
            "9f8a5ae414fa44aa9202fc51ed57241c",
            "50b0a606d7b641a7af7e2cd206f6045d",
            "562daa0672d443a18bbf7636353569d7",
            "14c63560d8c744bda5faf00f3515b17b",
            "fae0d76ea0364c58ab947993fc4a0d1c"
          ]
        },
        "outputId": "13c8b3da-929f-4f6a-c1d7-31e5ee1a7242"
      },
      "source": [
        "# Train the model\n",
        "model.train_model(train_df)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:395: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7bdbaba53ed2473b82c8ec996c948fb9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/7093 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0ea6e6bf8a84cc387d22d1bd925efa7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Epoch:   0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c092763fd4d7458d9216e65ff90d6934",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 0 of 4:   0%|          | 0/887 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:216: UserWarning: Please also save or load the state of the optimizer when saving or loading the scheduler.\n",
            "  warnings.warn(SAVE_STATE_WARNING, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d9a3067645414aa780c616c0874cbc94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 1 of 4:   0%|          | 0/887 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88c35c0d4b8e430e95a4e6461647cf3a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 2 of 4:   0%|          | 0/887 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "68b9f06b066348ff8fb910c07648633e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Epoch 3 of 4:   0%|          | 0/887 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3548, 0.7146341757604217)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtOZc62to69z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
            "8bfa122f4bbb4ff99a4298c9022a06b1",
            "b5cef832742643c2b210b1032709d544",
            "44082a7e91134380b4936e18da9e87f9",
            "2fb7ff9866874909923d33ba8d274e43",
            "10d8c558b80844a69d96be6f1a6b1f17",
            "e96e2a4524e04b8f8275da50acbdee9d",
            "ec666ebfaf624c77a30a56e5f2212f7a",
            "13cb5166f760475f9478c95851e55b30",
            "c6ca38c187964e4aa087a48f04320342",
            "1b660a743dcc42a9956d99342559e8a9",
            "4eae727f8a5c4764bf90c4e7e643d9fb",
            "51e277b9cee94b09adeb1ea3d04e72bc",
            "d3c4e5785e594fa5a5397c42130a8619",
            "e2b629dec8a647cdadea220c8ad8b591",
            "8cd05f824aca42a1913f7bb2d79cfc94",
            "3dfab2354b4d45d1b55a8a8bcc3432dd",
            "fcbb260fc8fc42d6aeb701372a7eaec0",
            "1bb5f3a5a2d040c0bc7b5e013a70dbf6",
            "1cd6185a4b5f43c8a0a705b746b08ea9",
            "e988eea3887d4c5b8d6483f1026bacc4",
            "38aabf997d2942efaa04935eb83599a7",
            "e7c8746943064e1ebdb5612fe2449902"
          ]
        },
        "outputId": "14c5007c-9440-4dc6-832e-89954ffcb1c2"
      },
      "source": [
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "\n",
        "def f1_multiclass(labels, preds):\n",
        "    return f1_score(labels, preds, average='micro')\n",
        "    \n",
        "result, model_outputs, wrong_predictions = model.eval_model(test_df, f1=f1_multiclass, acc=accuracy_score)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:975: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8bfa122f4bbb4ff99a4298c9022a06b1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/789 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51e277b9cee94b09adeb1ea3d04e72bc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Running Evaluation:   0%|          | 0/99 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEgxAkW8CfIs",
        "outputId": "a32394b0-2509-45ec-e7cc-2cdfb9222825"
      },
      "source": [
        "result"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'acc': 0.7414448669201521,\n",
              " 'eval_loss': 1.1554073952514716,\n",
              " 'f1': 0.7414448669201521,\n",
              " 'mcc': 0.6904852881187264}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CxtD_UvwQDE"
      },
      "source": [
        "# save and load the model\n",
        "\n",
        "save files without outputs/ \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2qUK2A_pmxS"
      },
      "source": [
        "import os\n",
        "import tarfile\n",
        "\n",
        "def save_model(model_path='',file_name=''):\n",
        "  files = [files for root, dirs, files in os.walk(model_path)][0]\n",
        "  with tarfile.open(file_name+ '.tar.gz', 'w:gz') as f:\n",
        "    for file in files:\n",
        "      f.add(f'{model_path}/{file}')"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRe0BChIpKqN"
      },
      "source": [
        "save_model('outputs','amazonforum-bert-categclass')"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gzuem0ydxFho",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e194d9b0-c333-4a48-bb81-29747a5cdf11"
      },
      "source": [
        "!tar -zxvf ./amazonforum-bert-categclass.tar.gz"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "outputs/pytorch_model.bin\n",
            "outputs/special_tokens_map.json\n",
            "outputs/config.json\n",
            "outputs/model_args.json\n",
            "outputs/training_args.bin\n",
            "outputs/tokenizer_config.json\n",
            "outputs/vocab.txt\n",
            "outputs/eval_results.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZQGxYxFLR_A"
      },
      "source": [
        "!rm -rf outputs"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzhrQy5Fqxw_"
      },
      "source": [
        "# Test the loaded model on a real example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Co2-jt-roO5"
      },
      "source": [
        "import os\n",
        "import tarfile\n",
        "\n",
        "def unpack_model(model_name=''): \n",
        "  tar = tarfile.open(f\"{model_name}.tar.gz\", \"r:gz\")\n",
        "  tar.extractall()\n",
        "  tar.close()\n",
        "\n",
        "unpack_model('amazonforum-bert-categclass')"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BwrLjU6RjtD"
      },
      "source": [
        "from simpletransformers.classification import ClassificationModel\n",
        "\n",
        "# define hyperparameter\n",
        "train_args ={\"reprocess_input_data\": True,\n",
        "             \"overwrite_output_dir\": True,\n",
        "             \"fp16\":False,\n",
        "             \"num_train_epochs\": 4}\n",
        "\n",
        "# Create a ClassificationModel\n",
        "model = ClassificationModel(\n",
        "    \"bert\", \"outputs/\",\n",
        "    num_labels=11,\n",
        "    args=train_args\n",
        ")"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmUtXhljXsWJ"
      },
      "source": [
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "REPLACE_BY_SPACE_RE = re.compile('[/(){}\\[\\]\\'\\“\\”\\’\\|@,;]')\n",
        "\n",
        "def clean_text(text):\n",
        "    \"\"\"\n",
        "        text: a string\n",
        "        return: modified initial string\n",
        "    \"\"\"\n",
        "    text = BeautifulSoup(text, \"lxml\").text # HTML decoding\n",
        "    text = text.lower() # lowercase text\n",
        "    text = REPLACE_BY_SPACE_RE.sub(' ', text) # replace REPLACE_BY_SPACE_RE symbols by space in text\n",
        "    return text"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JG2gN7KUqyjY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "12b39da031424ee698b805d10ffb26bd",
            "d2c2bd1622ee459eb091601827dafa3c",
            "5eea818d7675489cb905f38acf94b501",
            "01a60e50a70444949f97866778e3f5bc",
            "734109e44d7b47c3bee7d5ffd96e41cf",
            "55a9be472f074f25b2315024670ace21",
            "612e6a99f0a449c1a25f5ed53b06f056",
            "97518927e2bf4835afa4ca02371fea71",
            "1a73d6c8fbfa444c8b7c14ec80a099eb",
            "896d3b4990004b64a585c1845f20311a",
            "3b450778ae594b4b9b4b60b0618f9455",
            "17319d0944114c688844228e8f7ef9f8",
            "dd260dab4b594e9a84cd9f782a4b0d07",
            "3ce7ec6ccef74745ab661a8d3605fbb3",
            "4e02b7ea7680498882df04e307f3c4f5",
            "a6ea9b703ad24750bfc83991057bca19",
            "b50442d32db846258a4cc7266e5fa16f",
            "52c2bcad0e9e484caf2114589b8a1f11",
            "1e7cecc09a69441ebc287e30d8985a8d",
            "fb15a90af3bc4712971639ece9ba280c",
            "8ee5280af1b34a15ae4011c28ec2b070",
            "20dbfb48c68a48b892c4723ad66dc235"
          ]
        },
        "outputId": "e67a3ce6-5bb6-4947-91bd-e089358b67a9"
      },
      "source": [
        "class_list = ['Fulfillment By Amazon', 'Selling on Amazon',\n",
        "       'Amazon Marketplace Web Service (MWS)',\n",
        "       'Amazon Sponsored Products', 'Account Health', 'Global Selling',\n",
        "       'Amazon Pay', 'Groups',\n",
        "       'Health,Safety,Sustainability,Security & Compliance',\n",
        "       'Login With Amazon', 'Amazon Custom']\n",
        "\n",
        "post = \"As far as overall strategy, think of the Master Import Spreadsheet as your directory for imports. You will only have one copy of this. You will have many copies of the Product Extraction Sheet. One for each extraction you do and each one will be recorded in the Master Import Spreadsheet.\"\n",
        "\n",
        "predictions, raw_outputs = model.predict([clean_text(post)])\n",
        "\n",
        "print(class_list[predictions[0]])\n",
        "# Label : Flowster-specific"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "12b39da031424ee698b805d10ffb26bd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17319d0944114c688844228e8f7ef9f8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Selling on Amazon\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8230lHqdHe_",
        "outputId": "e3732609-30ce-47fb-b5bc-32d8fde2e963"
      },
      "source": [
        "post2 = input('Your forum post: ')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your forum post: I had mis-entered a price of less than $2.00, on a book that was selling for $80.00 (approx). Someone purchased the book, but I contacted them about the error, offering a discount off of what ‘should’ have been the actual price. I don’t intend to sell this for the erroneous price.  I have not heard back from the buyer; what’s the best approach here?  Hoping to hear from an Amazon rep.  Thanks Dave S\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "zbbXVnUwdZ57",
        "outputId": "221f1682-adf4-4681-a967-ed445c6a99c9"
      },
      "source": [
        "post2"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'I had mis-entered a price of less than $2.00, on a book that was selling for $80.00 (approx). Someone purchased the book, but I contacted them about the error, offering a discount off of what ‘should’ have been the actual price. I don’t intend to sell this for the erroneous price.  I have not heard back from the buyer; what’s the best approach here?  Hoping to hear from an Amazon rep.  Thanks Dave S'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pxJGjXRRn_T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
            "1906edcedf42481d8ac0560e719fb65d",
            "38cb0592cb6f4ff28883bcafe8429cf6",
            "31893ee301744557af07be011f078a93",
            "cc9defd855c444a19a8c1fafb50ad609",
            "43fb1bff77624068bc8a2a7224f2a834",
            "542f7037bcd143b19a49ecefe0d9867d",
            "b3f86cde04374cc2865fc76007a0edf1",
            "beb4def529974ccb883208f8bfd0beae",
            "8f4a9b44753540ac9c4e38fbc8dc0d59",
            "b946612788e34e42aa3e40e03271b1bf",
            "3d1e4d94a2964b8f8c2d63399a276200",
            "ca982d8b1fa14a69888b0963436d8e0a",
            "a3808b1e0daf40958fed31f8ce0adb15",
            "d93be28e4b054cdc9f2b4835b4e0ce70",
            "62b79dc7f2e740d88a274e1cb8282293",
            "8bca1d3f7e1c485a83e0c4fbec3c9c12",
            "5cb85cf2bb394f41b2c39e30ec28d04e",
            "c583443732834526bf6d0970363ece22",
            "f08cc060f31c4b5d8d71246eab2a30a0",
            "b272ffad6db746afac188295593da86c",
            "c46c407031fa4aef849ea488ce9ec75e",
            "26ce6d2bca4d4c9dae6cf0bb16a52504"
          ]
        },
        "outputId": "2f2d1a13-3c15-4b42-9799-3aac21a3d203"
      },
      "source": [
        "print(clean_text(post2))\n",
        "\n",
        "predictions, raw_outputs = model.predict([clean_text(post2)])\n",
        "\n",
        "print(class_list[predictions[0]])\n",
        "# Label : Account Health"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "i had mis-entered a price of less than $2.00  on a book that was selling for $80.00  approx . someone purchased the book  but i contacted them about the error  offering a discount off of what ‘should  have been the actual price. i don t intend to sell this for the erroneous price.  i have not heard back from the buyer  what s the best approach here?  hoping to hear from an amazon rep.  thanks dave s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1906edcedf42481d8ac0560e719fb65d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca982d8b1fa14a69888b0963436d8e0a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Selling on Amazon\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wHGCCVueznA"
      },
      "source": [
        "For post 2 our model could have misclassified the post because I only provided it as input the forum post without a title while I trained the model on a concatenation of both the `title` and `post body`. While for post 1 it's actually a post from a different e-commerce forum called Flowster 😅."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efHeHPbedwS3",
        "outputId": "13ad7245-64c4-477a-9744-fc6369e4e4ee"
      },
      "source": [
        "post3_title = input('Your forum post title: ')"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your forum post: Do I dare send a LARGE amount of inventory to any FC?\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7ApZ72sd3ZV",
        "outputId": "09a1dd49-500a-4548-8cad-91615421f669"
      },
      "source": [
        "post3_body = input('Your forum post body: ')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your forum post body: I have a particular ASIN that has been performing well and growing at a very good velocity rate. It’s my only ASIN without a tight sending limit, however, I am out of FBA inventory and have a back-up FBM buy box for now. Manufacture (U.S.) notified me that my late order is about to be ready to be shipped. I can create a shipping plan which will allow up to 85% of the units I have available to send from the manufacture. What I currently have and the other 15% will cover the gap under normal circumstances. My concern is this: Can I trust sending 85% of that inventory to Amazon? I currently have a straggler container that arrived back on November 7th, that didn’t get checked in until January 3rd and won’t be available STILL until January 25th. I hate seeing the slow increase in my BSR by just having FBM the last few days but what would be worst would be to become completely out of inventory due to 3 month delays that I’ve already experienced with my last FBA send.  Any opinions? I’m also concerned that if I only sent 50% of the inventory now to be safe, FBM still has slowed down my sales velocity because of the GIANT shipping estimate padding that is placed on my listing right now. Seriously feeling super damned if I do, damned if I don’t right now as I know most others are as well. But I’d love some opinions before I create my shipping plan! Thanks y’all.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EauvWXBZGWgB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
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            "e6bd8311cd6c4aadb1b13ac006c9c51d",
            "47c7c00e36e747ceb6233e6713c64e94",
            "48a46c55c48546539630be1c7ffc33e8",
            "b62a4d0f456c4b099c288211f518adb9",
            "2d76e8655b164c93a150718011d65106",
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            "75fe9a5f1ace4550a46e3865c5f774e6",
            "1580ec5b25b1422baebe037b51f9b118",
            "9f409be762cd45128fa60ad4e8ef3c4c",
            "4c447dcb5b624530bbdbfa7ccf57203f",
            "10659a576c3746efa2f3ee9599ed6c13",
            "fc3e45886eb84500bf231ef50955229f"
          ]
        },
        "outputId": "8d30993c-23de-42b5-aea8-59c67a0db7d9"
      },
      "source": [
        "post3 = post3_title + ' ' + post3_body\n",
        "predictions, raw_outputs = model.predict([post3])\n",
        "\n",
        "print(class_list[predictions[0]])\n",
        "# Label : Fulfillment By Amazon"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b35765b056be496c92d6b4fad24138c5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47c7c00e36e747ceb6233e6713c64e94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Fulfillment By Amazon\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kzdGvG8oebmO",
        "outputId": "9719bdb7-c7ca-4cd5-a0b3-982086f86640"
      },
      "source": [
        "post4_title = input('Your forum post title: ')\n",
        "post4_body = input('Your forum post body: ')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your forum post title: Reactivate Account\n",
            "Your forum post body: Dear Amazon Seller Performance Team,  As requested I provide below the plan of action - which shows the root causes and corrective & preventive measures to avoid the same in the future. Upon a long phone conversation with the Amazon support specialist who gave me several useful advice now everything is compliant with the standards of Amazon. Problem Identified: Seller Identity Verification Issues The root cause(s) of the issue: I was negligent while gathering all the documents needed and while previously submitting them I missed a couple of documents that were important. There was a discrepancy between data in my utility bill information and the information given in the Account Info of my Seller Account. It happened due to the abbreviation that I used in the translation. As matter of fact in one place, there was the word “street”, while in another place its abbreviation “st.” It occurred due to my inexperience and lack of knowledge. But I am ready to cooperate and hereby I provide all the necessary details. I tried in good faith to gather and submit all the missing documents but just did not understand precisely what I should provide. The actions you have taken to resolve the issues: I have consulted several times over the phone with the Amazon Team Specialist – we together went through all the missing information and hereby I am submitting all the requested documents to unmistakably prove my identity. I have corrected all the discrepancies in the information provided due to the translation. Additionally, I have prepared all the documents of the highest quality. I have hired an experienced SPN manager who helped me in checking the completeness and the validity of each document I’m providing now to exclude any problems. Please check the copies of the documents attached in the attached files.  The steps you have taken to prevent the recurrence of the problem: I will be closely monitoring our account’s metrics to ensure that we meet all the performance targets – I will be acting immediately in case of any red flag or request from Amazon. I will ensure that all the data I provide to Amazon has no meaningful discrepancies and is 100% compliant and is of the highest quality and is convenient and comprehensible for the investigators. I will continuously upgrade my knowledge of Amazon policies to ensure that I will adhere to them.  Please let me know if you need further information in your review of my account. Please kindly check the information and reinstate my account. Warmest regards.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "7a5649662e0943988f6d709ec6ee5440",
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            "8db0e03aa5d5414b8eaec34497a2f4c1",
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            "6b34dc7b5abc46579adb7adab2709c32",
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            "0e3390b07ff5417892cad5b828c6983a",
            "c069fc4d05ec4ad38f223bf6381154f7",
            "8d84f1b5bbd84d44a18179c89fab514b",
            "3a3e9c03e5a64838a0eff29ffa2ffabc",
            "ab8bbad25593406dafd59cba1544406a",
            "60faae03dd2243e0ad7d03f748a715ac",
            "5f5e12ac91db48a1b8754428942846ae",
            "ec9ae61e5f304f029212b1e627750baf",
            "1f2c9b240931423e922ce91ffa66076d",
            "dfa7f0cc37dc46bd842c7a8396d5330a"
          ]
        },
        "id": "Jdmr28jxRKxX",
        "outputId": "cae05265-7b32-4a85-8f0c-226e02ec6300"
      },
      "source": [
        "post4 = post4_title + ' ' + post4_body\n",
        "\n",
        "predictions, raw_outputs = model.predict([post4])\n",
        "\n",
        "print(class_list[predictions[0]])\n",
        "# Label : Account Health"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7a5649662e0943988f6d709ec6ee5440",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c149703d017f4b90910ca1a53c49e607",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Account Health\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0FvKZZ8aRhIP"
      },
      "source": [
        "The model still needs some fine tuning to be able to perform better.  \n",
        "- **Library resources:** \n",
        "https://simpletransformers.ai/docs/multi-class-classification/  \n",
        "- Check out the other [colab notebook](https://colab.research.google.com/drive/1TT6jWJRv5eynmCGEt8yvdmY5GZP75Eb6?usp=sharing) as well.\n",
        "- This notebook is taken and adjusted from this blog post : https://www.philschmid.de/bert-text-classification-in-a-different-language "
      ]
    }
  ]
}