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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xeh1s45iU1OG"
      },
      "source": [
        "**BERT-Finetuning with Triplet Loss**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPs-h3JbUBQq",
        "scrolled": true,
        "outputId": "8cba6fe8-eca3-4d8d-d416-c7bdef393fa4"
      },
      "source": [
        "# install required dependencies\n",
        "!pip3 install sentence-transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting sentence-transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6e/26/8203e3c1945be1b838c8fba6cbbee31a15a4e00a8b9ee5a70ddbaa31cf94/sentence-transformers-1.0.2.tar.gz (74kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 5.5MB/s \n",
            "\u001b[?25hCollecting transformers<5.0.0,>=3.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/d5/f4157a376b8a79489a76ce6cfe147f4f3be1e029b7144fa7b8432e8acb26/transformers-4.4.2-py3-none-any.whl (2.0MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0MB 7.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.41.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.8.0+cu101)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.4.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (3.2.5)\n",
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/99/e0808cb947ba10f575839c43e8fafc9cc44e4a7a2c8f79c60db48220a577/sentencepiece-0.1.95-cp37-cp37m-manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 37.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=3.1.0->sentence-transformers) (20.9)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=3.1.0->sentence-transformers) (2019.12.20)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=3.1.0->sentence-transformers) (3.7.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=3.1.0->sentence-transformers) (3.0.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=3.1.0->sentence-transformers) (2.23.0)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 53.2MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/23/2ddc317b2121117bf34dd00f5b0de194158f2a44ee2bf5e47c7166878a97/tokenizers-0.10.1-cp37-cp37m-manylinux2010_x86_64.whl (3.2MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 55.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->sentence-transformers) (3.7.4.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence-transformers) (1.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (1.15.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers<5.0.0,>=3.1.0->sentence-transformers) (2.4.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers<5.0.0,>=3.1.0->sentence-transformers) (3.4.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=3.1.0->sentence-transformers) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=3.1.0->sentence-transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=3.1.0->sentence-transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=3.1.0->sentence-transformers) (1.24.3)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=3.1.0->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers, sacremoses\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-1.0.2-cp37-none-any.whl size=114270 sha256=80a111d3a0af63a58d1ca0c78aff6d095dcbd877cb6c5c2613bc4ef816069c72\n",
            "  Stored in directory: /root/.cache/pip/wheels/c7/45/c2/1e55040d016e678dd9378d96b86b7632a4d820f66c60566c05\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp37-none-any.whl size=893262 sha256=70d7b39fe511b1c2a294874ab231f89ea1f4c53d47734ad71a955877ddd51005\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sentence-transformers sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers, sentencepiece, sentence-transformers\n",
            "Successfully installed sacremoses-0.0.43 sentence-transformers-1.0.2 sentencepiece-0.1.95 tokenizers-0.10.1 transformers-4.4.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eiAH18NXUWNz"
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ImPKk7r3UWLh",
        "outputId": "dc33c195-e1ed-458d-90d6-478d589e9c7a"
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4b6Tz2eUWEm",
        "outputId": "209eb21e-31de-41af-c670-95a723a54a67"
      },
      "source": [
        "%cd /content/drive/MyDrive/BERT-FAQ"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/BERT-FAQ\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4By5FO-UWCX"
      },
      "source": [
        "# import required libraries\n",
        "import pandas as pd\n",
        "from faq_bert_finetuning import FAQ_BERT_Finetuning\n",
        "from shared.utils import make_dirs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeZi2LnRzfe2"
      },
      "source": [
        "# define output path to save model & evaluation files\n",
        "output_path = 'output/FAQIR'\n",
        "make_dirs(output_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FMxUD7UxQOI"
      },
      "source": [
        "**query_type=faq, neg_type=\"hard\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "RgUwGks_UWAJ",
        "outputId": "ee5c6230-7626-48e4-cc41-898b38528d18"
      },
      "source": [
        "# load hard_faq_dataset.csv\n",
        "hard_faq_dataset = pd.read_csv('data/FAQIR/dataset/triplet/faq/hard_faq_dataset.csv')\n",
        "hard_faq_dataset"
      ],
      "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>question</th>\n",
              "      <th>positive</th>\n",
              "      <th>negative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>wash it off and buy a car cover</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>Mayonaise works, some dealers use this trick t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>just use cold water with a soft rag/kitchen ro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>Use a small amount of Goo Gone to remove the s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>Tree sap is basically sugar water with traces ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103507</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>I am not a mechanic, but I did change an o2 se...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103508</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>I mean no harm.  If you are on here asking how...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103509</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>I do not think you can.  Cell phones do keep l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103510</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>Coolant Pressure tester is the best way. PM de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103511</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>You can't, there is no provision made by the m...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>103512 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 question  ...                                           negative\n",
              "0       How to remove tree sap from car? Hi,I have som...  ...                    wash it off and buy a car cover\n",
              "1       How to remove tree sap from car? Hi,I have som...  ...  Mayonaise works, some dealers use this trick t...\n",
              "2       How to remove tree sap from car? Hi,I have som...  ...  just use cold water with a soft rag/kitchen ro...\n",
              "3       How to remove tree sap from car? Hi,I have som...  ...  Use a small amount of Goo Gone to remove the s...\n",
              "4       How to remove tree sap from car? Hi,I have som...  ...  Tree sap is basically sugar water with traces ...\n",
              "...                                                   ...  ...                                                ...\n",
              "103507                     how can i change  my birthday?  ...  I am not a mechanic, but I did change an o2 se...\n",
              "103508                     how can i change  my birthday?  ...  I mean no harm.  If you are on here asking how...\n",
              "103509                     how can i change  my birthday?  ...  I do not think you can.  Cell phones do keep l...\n",
              "103510                     how can i change  my birthday?  ...  Coolant Pressure tester is the best way. PM de...\n",
              "103511                     how can i change  my birthday?  ...  You can't, there is no provision made by the m...\n",
              "\n",
              "[103512 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_0DQgqiVqGU",
        "outputId": "e2d1177e-eb88-4264-a479-1aee1bccde2c"
      },
      "source": [
        "# check dataset shape\n",
        "hard_faq_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(103512, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
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        "id": "fekkpRFSVunj",
        "outputId": "32382054-0655-442f-8f45-0a28117d966b"
      },
      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_hard_faq = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"triplet\", query_type='faq', neg_type='hard', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_hard_faq.create_model(hard_faq_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-16 09:30:59 - Lock 140360852784144 acquired on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n"
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            "\n",
            "2021-03-16 09:31:00 - Lock 140360852784144 released on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n",
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            "\n",
            "2021-03-16 09:31:05 - Lock 140359054784592 released on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n",
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          "text": [
            "\n",
            "2021-03-16 09:31:06 - Lock 140359041613392 released on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n",
            "2021-03-16 09:31:06 - Lock 140359054968592 acquired on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n"
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          "output_type": "stream",
          "text": [
            "\n",
            "2021-03-16 09:31:06 - Lock 140359054968592 released on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
            "2021-03-16 09:31:06 - Use pytorch device: cuda\n",
            "2021-03-16 09:31:06 - Generating directory structure\n",
            "2021-03-16 09:31:06 - Generating triplets for training\n",
            "2021-03-16 09:31:14 - Training model\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "display_data",
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          "output_type": "stream",
          "text": [
            "2021-03-16 09:40:22 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 1000 steps:\n",
            "2021-03-16 09:41:37 - Accuracy Cosine Distance:   \t81.11\n",
            "2021-03-16 09:41:37 - Accuracy Manhatten Distance:\t81.93\n",
            "2021-03-16 09:41:37 - Accuracy Euclidean Distance:\t82.00\n",
            "\n",
            "2021-03-16 09:41:37 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 09:50:26 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 2000 steps:\n",
            "2021-03-16 09:51:41 - Accuracy Cosine Distance:   \t88.09\n",
            "2021-03-16 09:51:41 - Accuracy Manhatten Distance:\t88.96\n",
            "2021-03-16 09:51:41 - Accuracy Euclidean Distance:\t88.83\n",
            "\n",
            "2021-03-16 09:51:41 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 10:00:26 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 3000 steps:\n",
            "2021-03-16 10:01:40 - Accuracy Cosine Distance:   \t93.75\n",
            "2021-03-16 10:01:40 - Accuracy Manhatten Distance:\t94.16\n",
            "2021-03-16 10:01:40 - Accuracy Euclidean Distance:\t94.21\n",
            "\n",
            "2021-03-16 10:01:40 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 10:10:21 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 4000 steps:\n",
            "2021-03-16 10:11:36 - Accuracy Cosine Distance:   \t96.16\n",
            "2021-03-16 10:11:36 - Accuracy Manhatten Distance:\t96.39\n",
            "2021-03-16 10:11:36 - Accuracy Euclidean Distance:\t96.40\n",
            "\n",
            "2021-03-16 10:11:36 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 10:20:13 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 5000 steps:\n",
            "2021-03-16 10:21:27 - Accuracy Cosine Distance:   \t97.71\n",
            "2021-03-16 10:21:27 - Accuracy Manhatten Distance:\t97.81\n",
            "2021-03-16 10:21:27 - Accuracy Euclidean Distance:\t97.81\n",
            "\n",
            "2021-03-16 10:21:27 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "\n",
            "2021-03-16 10:22:59 - TripletEvaluator: Evaluating the model on val dataset after epoch 0:\n",
            "2021-03-16 10:24:14 - Accuracy Cosine Distance:   \t97.53\n",
            "2021-03-16 10:24:14 - Accuracy Manhatten Distance:\t97.73\n",
            "2021-03-16 10:24:14 - Accuracy Euclidean Distance:\t97.72\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "output_type": "stream",
          "text": [
            "2021-03-16 10:32:48 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 1000 steps:\n",
            "2021-03-16 10:34:03 - Accuracy Cosine Distance:   \t98.38\n",
            "2021-03-16 10:34:03 - Accuracy Manhatten Distance:\t98.38\n",
            "2021-03-16 10:34:03 - Accuracy Euclidean Distance:\t98.41\n",
            "\n",
            "2021-03-16 10:34:03 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 10:42:37 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 2000 steps:\n",
            "2021-03-16 10:43:52 - Accuracy Cosine Distance:   \t98.52\n",
            "2021-03-16 10:43:52 - Accuracy Manhatten Distance:\t98.50\n",
            "2021-03-16 10:43:52 - Accuracy Euclidean Distance:\t98.52\n",
            "\n",
            "2021-03-16 10:43:52 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 10:52:25 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 3000 steps:\n",
            "2021-03-16 10:53:39 - Accuracy Cosine Distance:   \t99.04\n",
            "2021-03-16 10:53:39 - Accuracy Manhatten Distance:\t99.05\n",
            "2021-03-16 10:53:39 - Accuracy Euclidean Distance:\t99.10\n",
            "\n",
            "2021-03-16 10:53:39 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 11:02:10 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 4000 steps:\n",
            "2021-03-16 11:03:25 - Accuracy Cosine Distance:   \t99.12\n",
            "2021-03-16 11:03:25 - Accuracy Manhatten Distance:\t99.15\n",
            "2021-03-16 11:03:25 - Accuracy Euclidean Distance:\t99.17\n",
            "\n",
            "2021-03-16 11:03:25 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 11:11:54 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 5000 steps:\n",
            "2021-03-16 11:13:09 - Accuracy Cosine Distance:   \t99.29\n",
            "2021-03-16 11:13:09 - Accuracy Manhatten Distance:\t99.31\n",
            "2021-03-16 11:13:09 - Accuracy Euclidean Distance:\t99.34\n",
            "\n",
            "2021-03-16 11:13:09 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "\n",
            "2021-03-16 11:14:41 - TripletEvaluator: Evaluating the model on val dataset after epoch 1:\n",
            "2021-03-16 11:15:56 - Accuracy Cosine Distance:   \t99.29\n",
            "2021-03-16 11:15:56 - Accuracy Manhatten Distance:\t99.31\n",
            "2021-03-16 11:15:56 - Accuracy Euclidean Distance:\t99.29\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          "output_type": "stream",
          "text": [
            "2021-03-16 11:24:25 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 1000 steps:\n",
            "2021-03-16 11:25:39 - Accuracy Cosine Distance:   \t99.26\n",
            "2021-03-16 11:25:39 - Accuracy Manhatten Distance:\t99.29\n",
            "2021-03-16 11:25:39 - Accuracy Euclidean Distance:\t99.29\n",
            "\n",
            "2021-03-16 11:34:05 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 2000 steps:\n",
            "2021-03-16 11:35:20 - Accuracy Cosine Distance:   \t99.49\n",
            "2021-03-16 11:35:20 - Accuracy Manhatten Distance:\t99.49\n",
            "2021-03-16 11:35:20 - Accuracy Euclidean Distance:\t99.49\n",
            "\n",
            "2021-03-16 11:35:20 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 11:43:50 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 3000 steps:\n",
            "2021-03-16 11:45:06 - Accuracy Cosine Distance:   \t99.53\n",
            "2021-03-16 11:45:06 - Accuracy Manhatten Distance:\t99.53\n",
            "2021-03-16 11:45:06 - Accuracy Euclidean Distance:\t99.54\n",
            "\n",
            "2021-03-16 11:45:06 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 11:53:34 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 4000 steps:\n",
            "2021-03-16 11:54:48 - Accuracy Cosine Distance:   \t99.64\n",
            "2021-03-16 11:54:48 - Accuracy Manhatten Distance:\t99.66\n",
            "2021-03-16 11:54:48 - Accuracy Euclidean Distance:\t99.67\n",
            "\n",
            "2021-03-16 11:54:48 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 12:03:18 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 5000 steps:\n",
            "2021-03-16 12:04:33 - Accuracy Cosine Distance:   \t99.63\n",
            "2021-03-16 12:04:33 - Accuracy Manhatten Distance:\t99.64\n",
            "2021-03-16 12:04:33 - Accuracy Euclidean Distance:\t99.64\n",
            "\n",
            "\n",
            "2021-03-16 12:06:01 - TripletEvaluator: Evaluating the model on val dataset after epoch 2:\n",
            "2021-03-16 12:07:16 - Accuracy Cosine Distance:   \t99.67\n",
            "2021-03-16 12:07:16 - Accuracy Manhatten Distance:\t99.67\n",
            "2021-03-16 12:07:16 - Accuracy Euclidean Distance:\t99.68\n",
            "\n",
            "2021-03-16 12:07:16 - Save model to output/FAQIR/models/triplet_hard_faq_1.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "2021-03-16 12:15:43 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 1000 steps:\n",
            "2021-03-16 12:16:57 - Accuracy Cosine Distance:   \t99.59\n",
            "2021-03-16 12:16:57 - Accuracy Manhatten Distance:\t99.58\n",
            "2021-03-16 12:16:57 - Accuracy Euclidean Distance:\t99.59\n",
            "\n",
            "2021-03-16 12:25:23 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 2000 steps:\n",
            "2021-03-16 12:26:38 - Accuracy Cosine Distance:   \t99.64\n",
            "2021-03-16 12:26:38 - Accuracy Manhatten Distance:\t99.59\n",
            "2021-03-16 12:26:38 - Accuracy Euclidean Distance:\t99.64\n",
            "\n",
            "2021-03-16 12:35:02 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 3000 steps:\n",
            "2021-03-16 12:36:17 - Accuracy Cosine Distance:   \t99.65\n",
            "2021-03-16 12:36:17 - Accuracy Manhatten Distance:\t99.64\n",
            "2021-03-16 12:36:17 - Accuracy Euclidean Distance:\t99.65\n",
            "\n",
            "2021-03-16 12:44:43 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 4000 steps:\n",
            "2021-03-16 12:45:57 - Accuracy Cosine Distance:   \t99.67\n",
            "2021-03-16 12:45:57 - Accuracy Manhatten Distance:\t99.66\n",
            "2021-03-16 12:45:57 - Accuracy Euclidean Distance:\t99.66\n",
            "\n",
            "2021-03-16 12:54:22 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 5000 steps:\n",
            "2021-03-16 12:55:37 - Accuracy Cosine Distance:   \t99.65\n",
            "2021-03-16 12:55:37 - Accuracy Manhatten Distance:\t99.66\n",
            "2021-03-16 12:55:37 - Accuracy Euclidean Distance:\t99.66\n",
            "\n",
            "\n",
            "2021-03-16 12:57:06 - TripletEvaluator: Evaluating the model on val dataset after epoch 3:\n",
            "2021-03-16 12:58:21 - Accuracy Cosine Distance:   \t99.65\n",
            "2021-03-16 12:58:21 - Accuracy Manhatten Distance:\t99.66\n",
            "2021-03-16 12:58:21 - Accuracy Euclidean Distance:\t99.66\n",
            "\n",
            "\n",
            "2021-03-16 12:58:21 - Evaluating model\n",
            "2021-03-16 12:58:21 - Load pretrained SentenceTransformer: output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 12:58:21 - Load SentenceTransformer from folder: output/FAQIR/models/triplet_hard_faq_1.1\n",
            "2021-03-16 12:58:22 - Use pytorch device: cuda\n",
            "2021-03-16 12:58:22 - TripletEvaluator: Evaluating the model on test dataset:\n",
            "2021-03-16 12:59:37 - Accuracy Cosine Distance:   \t99.69\n",
            "2021-03-16 12:59:37 - Accuracy Manhatten Distance:\t99.68\n",
            "2021-03-16 12:59:37 - Accuracy Euclidean Distance:\t99.71\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDtMChx8zIOZ"
      },
      "source": [
        "**query_type=faq, neg_type=\"simple\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "nNZ_-22CysCE",
        "outputId": "6dbef132-3460-4a42-911a-e15ba0d01056"
      },
      "source": [
        "# load simple_faq_dataset.csv\n",
        "simple_faq_dataset = pd.read_csv('data/FAQIR/dataset/triplet/faq/simple_faq_dataset.csv')\n",
        "simple_faq_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>question</th>\n",
              "      <th>positive</th>\n",
              "      <th>negative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>yes. simply dont hook up the ground. first, ma...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>You didnt mention if it was a stick or an auto...</td>\n",
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              "      <th>2</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>I could'nt find a diagram or picture of where ...</td>\n",
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              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>There are bolts under the back of the seat tha...</td>\n",
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              "      <th>103507</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
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              "      <td>And after draining the radiator, I recommend f...</td>\n",
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              "    <tr>\n",
              "      <th>103508</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>Is the house batteries on 12v or 24volt ? I wo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103509</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>Cyanuric acid has no place in a spa. It is a c...</td>\n",
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              "    <tr>\n",
              "      <th>103510</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>Each manufacturer has different setup / system...</td>\n",
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              "    <tr>\n",
              "      <th>103511</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Time travel back and induce labor on your moth...</td>\n",
              "      <td>An environmentally friendly solution that has ...</td>\n",
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              "                                                 question  ...                                           negative\n",
              "0       How to remove tree sap from car? Hi,I have som...  ...  yes. simply dont hook up the ground. first, ma...\n",
              "1       How to remove tree sap from car? Hi,I have som...  ...  You didnt mention if it was a stick or an auto...\n",
              "2       How to remove tree sap from car? Hi,I have som...  ...  I could'nt find a diagram or picture of where ...\n",
              "3       How to remove tree sap from car? Hi,I have som...  ...  Your truck has torsion bars for the front. The...\n",
              "4       How to remove tree sap from car? Hi,I have som...  ...  There are bolts under the back of the seat tha...\n",
              "...                                                   ...  ...                                                ...\n",
              "103507                     how can i change  my birthday?  ...  And after draining the radiator, I recommend f...\n",
              "103508                     how can i change  my birthday?  ...  Is the house batteries on 12v or 24volt ? I wo...\n",
              "103509                     how can i change  my birthday?  ...  Cyanuric acid has no place in a spa. It is a c...\n",
              "103510                     how can i change  my birthday?  ...  Each manufacturer has different setup / system...\n",
              "103511                     how can i change  my birthday?  ...  An environmentally friendly solution that has ...\n",
              "\n",
              "[103512 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 7
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    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "EU8uiY-pzU0S",
        "outputId": "669b09ce-8f01-44e3-ca06-d3f66d9d53ab"
      },
      "source": [
        "simple_faq_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "(103512, 3)"
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        "id": "VDF9IV84yhSq",
        "outputId": "151f90a7-825c-442a-b8bd-9ae76d394d95"
      },
      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_simple_faq = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"triplet\", query_type='faq', neg_type='simple', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_simple_faq.create_model(simple_faq_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "2021-03-16 16:16:33 - Lock 140339823334032 acquired on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n"
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            "\n",
            "2021-03-16 16:16:34 - Lock 140339823334032 released on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n",
            "2021-03-16 16:16:34 - Lock 140339823334864 acquired on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n"
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            "\n",
            "2021-03-16 16:16:42 - Lock 140339823334864 released on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n",
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            "2021-03-16 16:16:46 - Lock 140342379198160 released on /root/.cache/huggingface/transformers/0e1bbfda7f63a99bb52e3915dcf10c3c92122b827d92eb2d34ce94ee79ba486c.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n",
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            "\n",
            "2021-03-16 16:16:48 - Lock 140342379198160 released on /root/.cache/huggingface/transformers/75abb59d7a06f4f640158a9bfcde005264e59e8d566781ab1415b139d2e4c603.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
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          "text": [
            "\n",
            "2021-03-16 16:16:50 - Lock 140339810192272 released on /root/.cache/huggingface/transformers/8c8624b8ac8aa99c60c912161f8332de003484428c47906d7ff7eb7f73eecdbb.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79.lock\n",
            "2021-03-16 16:16:50 - Use pytorch device: cuda\n",
            "2021-03-16 16:16:50 - Generating directory structure\n",
            "2021-03-16 16:16:50 - Generating triplets for training\n",
            "2021-03-16 16:17:03 - Training model\n"
          ],
          "name": "stdout"
        },
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          "text": [
            "2021-03-16 16:32:48 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 1000 steps:\n",
            "2021-03-16 16:35:04 - Accuracy Cosine Distance:   \t95.32\n",
            "2021-03-16 16:35:04 - Accuracy Manhatten Distance:\t95.79\n",
            "2021-03-16 16:35:04 - Accuracy Euclidean Distance:\t95.80\n",
            "\n",
            "2021-03-16 16:35:04 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 16:50:38 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 2000 steps:\n",
            "2021-03-16 16:52:53 - Accuracy Cosine Distance:   \t97.78\n",
            "2021-03-16 16:52:53 - Accuracy Manhatten Distance:\t98.01\n",
            "2021-03-16 16:52:53 - Accuracy Euclidean Distance:\t98.02\n",
            "\n",
            "2021-03-16 16:52:53 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 17:08:23 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 3000 steps:\n",
            "2021-03-16 17:10:38 - Accuracy Cosine Distance:   \t98.77\n",
            "2021-03-16 17:10:38 - Accuracy Manhatten Distance:\t98.92\n",
            "2021-03-16 17:10:38 - Accuracy Euclidean Distance:\t98.90\n",
            "\n",
            "2021-03-16 17:10:38 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 17:26:05 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 4000 steps:\n",
            "2021-03-16 17:28:20 - Accuracy Cosine Distance:   \t99.21\n",
            "2021-03-16 17:28:20 - Accuracy Manhatten Distance:\t99.26\n",
            "2021-03-16 17:28:20 - Accuracy Euclidean Distance:\t99.27\n",
            "\n",
            "2021-03-16 17:28:20 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 17:43:48 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 5000 steps:\n",
            "2021-03-16 17:46:04 - Accuracy Cosine Distance:   \t99.37\n",
            "2021-03-16 17:46:04 - Accuracy Manhatten Distance:\t99.38\n",
            "2021-03-16 17:46:04 - Accuracy Euclidean Distance:\t99.37\n",
            "\n",
            "2021-03-16 17:46:04 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "\n",
            "2021-03-16 17:48:46 - TripletEvaluator: Evaluating the model on val dataset after epoch 0:\n",
            "2021-03-16 17:51:02 - Accuracy Cosine Distance:   \t99.35\n",
            "2021-03-16 17:51:02 - Accuracy Manhatten Distance:\t99.36\n",
            "2021-03-16 17:51:02 - Accuracy Euclidean Distance:\t99.36\n",
            "\n"
          ],
          "name": "stdout"
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          "text": [
            "2021-03-16 18:06:24 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 1000 steps:\n",
            "2021-03-16 18:08:40 - Accuracy Cosine Distance:   \t99.58\n",
            "2021-03-16 18:08:40 - Accuracy Manhatten Distance:\t99.59\n",
            "2021-03-16 18:08:40 - Accuracy Euclidean Distance:\t99.59\n",
            "\n",
            "2021-03-16 18:08:40 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 18:24:06 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 2000 steps:\n",
            "2021-03-16 18:26:21 - Accuracy Cosine Distance:   \t99.41\n",
            "2021-03-16 18:26:21 - Accuracy Manhatten Distance:\t99.37\n",
            "2021-03-16 18:26:21 - Accuracy Euclidean Distance:\t99.41\n",
            "\n",
            "2021-03-16 18:41:45 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 3000 steps:\n",
            "2021-03-16 18:44:00 - Accuracy Cosine Distance:   \t99.60\n",
            "2021-03-16 18:44:00 - Accuracy Manhatten Distance:\t99.62\n",
            "2021-03-16 18:44:00 - Accuracy Euclidean Distance:\t99.64\n",
            "\n",
            "2021-03-16 18:44:00 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 18:59:22 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 4000 steps:\n",
            "2021-03-16 19:01:38 - Accuracy Cosine Distance:   \t99.64\n",
            "2021-03-16 19:01:38 - Accuracy Manhatten Distance:\t99.63\n",
            "2021-03-16 19:01:38 - Accuracy Euclidean Distance:\t99.63\n",
            "\n",
            "2021-03-16 19:17:01 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 5000 steps:\n",
            "2021-03-16 19:19:17 - Accuracy Cosine Distance:   \t99.69\n",
            "2021-03-16 19:19:17 - Accuracy Manhatten Distance:\t99.68\n",
            "2021-03-16 19:19:17 - Accuracy Euclidean Distance:\t99.70\n",
            "\n",
            "2021-03-16 19:19:17 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "\n",
            "2021-03-16 19:22:01 - TripletEvaluator: Evaluating the model on val dataset after epoch 1:\n",
            "2021-03-16 19:24:17 - Accuracy Cosine Distance:   \t99.72\n",
            "2021-03-16 19:24:17 - Accuracy Manhatten Distance:\t99.71\n",
            "2021-03-16 19:24:17 - Accuracy Euclidean Distance:\t99.73\n",
            "\n",
            "2021-03-16 19:24:17 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n"
          ],
          "name": "stdout"
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          "text": [
            "2021-03-16 19:39:41 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 1000 steps:\n",
            "2021-03-16 19:41:57 - Accuracy Cosine Distance:   \t99.77\n",
            "2021-03-16 19:41:57 - Accuracy Manhatten Distance:\t99.80\n",
            "2021-03-16 19:41:57 - Accuracy Euclidean Distance:\t99.77\n",
            "\n",
            "2021-03-16 19:41:57 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 19:57:25 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 2000 steps:\n",
            "2021-03-16 19:59:40 - Accuracy Cosine Distance:   \t99.75\n",
            "2021-03-16 19:59:40 - Accuracy Manhatten Distance:\t99.73\n",
            "2021-03-16 19:59:40 - Accuracy Euclidean Distance:\t99.75\n",
            "\n",
            "2021-03-16 20:15:05 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 3000 steps:\n",
            "2021-03-16 20:17:21 - Accuracy Cosine Distance:   \t99.80\n",
            "2021-03-16 20:17:21 - Accuracy Manhatten Distance:\t99.80\n",
            "2021-03-16 20:17:21 - Accuracy Euclidean Distance:\t99.80\n",
            "\n",
            "2021-03-16 20:32:49 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 4000 steps:\n",
            "2021-03-16 20:35:05 - Accuracy Cosine Distance:   \t99.74\n",
            "2021-03-16 20:35:05 - Accuracy Manhatten Distance:\t99.75\n",
            "2021-03-16 20:35:05 - Accuracy Euclidean Distance:\t99.74\n",
            "\n",
            "2021-03-16 20:50:29 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 5000 steps:\n",
            "2021-03-16 20:52:46 - Accuracy Cosine Distance:   \t99.83\n",
            "2021-03-16 20:52:46 - Accuracy Manhatten Distance:\t99.83\n",
            "2021-03-16 20:52:46 - Accuracy Euclidean Distance:\t99.82\n",
            "\n",
            "2021-03-16 20:52:46 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "\n",
            "2021-03-16 20:55:30 - TripletEvaluator: Evaluating the model on val dataset after epoch 2:\n",
            "2021-03-16 20:57:46 - Accuracy Cosine Distance:   \t99.77\n",
            "2021-03-16 20:57:46 - Accuracy Manhatten Distance:\t99.78\n",
            "2021-03-16 20:57:46 - Accuracy Euclidean Distance:\t99.79\n",
            "\n"
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          "name": "stdout"
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          "text": [
            "2021-03-16 21:13:11 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 1000 steps:\n",
            "2021-03-16 21:15:27 - Accuracy Cosine Distance:   \t99.81\n",
            "2021-03-16 21:15:27 - Accuracy Manhatten Distance:\t99.83\n",
            "2021-03-16 21:15:27 - Accuracy Euclidean Distance:\t99.82\n",
            "\n",
            "2021-03-16 21:30:52 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 2000 steps:\n",
            "2021-03-16 21:33:08 - Accuracy Cosine Distance:   \t99.83\n",
            "2021-03-16 21:33:08 - Accuracy Manhatten Distance:\t99.84\n",
            "2021-03-16 21:33:08 - Accuracy Euclidean Distance:\t99.84\n",
            "\n",
            "2021-03-16 21:33:08 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 21:48:34 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 3000 steps:\n",
            "2021-03-16 21:50:50 - Accuracy Cosine Distance:   \t99.84\n",
            "2021-03-16 21:50:50 - Accuracy Manhatten Distance:\t99.83\n",
            "2021-03-16 21:50:50 - Accuracy Euclidean Distance:\t99.83\n",
            "\n",
            "2021-03-16 22:06:12 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 4000 steps:\n",
            "2021-03-16 22:08:28 - Accuracy Cosine Distance:   \t99.86\n",
            "2021-03-16 22:08:28 - Accuracy Manhatten Distance:\t99.86\n",
            "2021-03-16 22:08:28 - Accuracy Euclidean Distance:\t99.86\n",
            "\n",
            "2021-03-16 22:08:28 - Save model to output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 22:23:52 - TripletEvaluator: Evaluating the model on val dataset in epoch 3 after 5000 steps:\n",
            "2021-03-16 22:26:07 - Accuracy Cosine Distance:   \t99.86\n",
            "2021-03-16 22:26:07 - Accuracy Manhatten Distance:\t99.86\n",
            "2021-03-16 22:26:07 - Accuracy Euclidean Distance:\t99.86\n",
            "\n",
            "\n",
            "2021-03-16 22:28:49 - TripletEvaluator: Evaluating the model on val dataset after epoch 3:\n",
            "2021-03-16 22:31:04 - Accuracy Cosine Distance:   \t99.86\n",
            "2021-03-16 22:31:04 - Accuracy Manhatten Distance:\t99.86\n",
            "2021-03-16 22:31:04 - Accuracy Euclidean Distance:\t99.86\n",
            "\n",
            "\n",
            "2021-03-16 22:31:04 - Evaluating model\n",
            "2021-03-16 22:31:04 - Load pretrained SentenceTransformer: output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 22:31:04 - Load SentenceTransformer from folder: output/FAQIR/models/triplet_simple_faq_1.1\n",
            "2021-03-16 22:31:05 - Use pytorch device: cuda\n",
            "2021-03-16 22:31:05 - TripletEvaluator: Evaluating the model on test dataset:\n",
            "2021-03-16 22:33:21 - Accuracy Cosine Distance:   \t99.83\n",
            "2021-03-16 22:33:21 - Accuracy Manhatten Distance:\t99.85\n",
            "2021-03-16 22:33:21 - Accuracy Euclidean Distance:\t99.85\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ds_0l1n1hIK"
      },
      "source": [
        "**query_type=user_query, neg_type=\"hard\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "6t2mL9fQ03EP",
        "outputId": "aee72ba4-d302-497b-fe34-3eb0e8c38c1a"
      },
      "source": [
        "# load hard_user_query_dataset.csv\n",
        "hard_user_query_dataset = pd.read_csv('data/FAQIR/dataset/triplet/user_query/hard_user_query_dataset.csv')\n",
        "hard_user_query_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <th>question</th>\n",
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              "0       How to remove tree sap from car? Hi,I have som...  ...                    wash it off and buy a car cover\n",
              "1       How to remove tree sap from car? Hi,I have som...  ...  Mayonaise works, some dealers use this trick t...\n",
              "2       How to remove tree sap from car? Hi,I have som...  ...  just use cold water with a soft rag/kitchen ro...\n",
              "3       How to remove tree sap from car? Hi,I have som...  ...  Use a small amount of Goo Gone to remove the s...\n",
              "4       How to remove tree sap from car? Hi,I have som...  ...  Tree sap is basically sugar water with traces ...\n",
              "...                                                   ...  ...                                                ...\n",
              "391771  My roommate started smoking marijuana every th...  ...  the best way to clean concrete is one half gal...\n",
              "391772  My roommate started smoking marijuana every th...  ...  Okay.  First of all you say that the building ...\n",
              "391773  My roommate started smoking marijuana every th...  ...  the way i clean dryer ducts is with a drain sn...\n",
              "391774  My roommate started smoking marijuana every th...  ...  Well since you live in a Apartment building Th...\n",
              "391775  My roommate started smoking marijuana every th...  ...  I had a cigarette burn in my old apartment car...\n",
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              "[391776 rows x 3 columns]"
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      "source": [
        "hard_user_query_dataset.shape"
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      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_hard_user_query = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"triplet\", query_type='user_query', neg_type='hard', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_hard_user_query.create_model(hard_user_query_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
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            "\n",
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            "\n",
            "2021-03-17 05:38:54 - Lock 140463926899600 released on /root/.cache/huggingface/transformers/75abb59d7a06f4f640158a9bfcde005264e59e8d566781ab1415b139d2e4c603.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
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            "2021-03-17 05:38:55 - Lock 140463926637008 released on /root/.cache/huggingface/transformers/8c8624b8ac8aa99c60c912161f8332de003484428c47906d7ff7eb7f73eecdbb.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79.lock\n",
            "2021-03-17 05:38:55 - Use pytorch device: cuda\n",
            "2021-03-17 05:38:55 - Generating directory structure\n",
            "2021-03-17 05:38:55 - Generating triplets for training\n",
            "2021-03-17 05:39:24 - Training model\n"
          ],
          "name": "stdout"
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          "text": [
            "2021-03-17 05:44:06 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 1000 steps:\n",
            "2021-03-17 05:46:43 - Accuracy Cosine Distance:   \t81.81\n",
            "2021-03-17 05:46:43 - Accuracy Manhatten Distance:\t82.48\n",
            "2021-03-17 05:46:43 - Accuracy Euclidean Distance:\t82.49\n",
            "\n",
            "2021-03-17 05:46:43 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 05:51:17 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 2000 steps:\n",
            "2021-03-17 05:53:54 - Accuracy Cosine Distance:   \t89.99\n",
            "2021-03-17 05:53:54 - Accuracy Manhatten Distance:\t90.30\n",
            "2021-03-17 05:53:54 - Accuracy Euclidean Distance:\t90.28\n",
            "\n",
            "2021-03-17 05:53:54 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 05:58:27 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 3000 steps:\n",
            "2021-03-17 06:01:04 - Accuracy Cosine Distance:   \t92.68\n",
            "2021-03-17 06:01:04 - Accuracy Manhatten Distance:\t93.05\n",
            "2021-03-17 06:01:04 - Accuracy Euclidean Distance:\t93.02\n",
            "\n",
            "2021-03-17 06:01:04 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:05:37 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 4000 steps:\n",
            "2021-03-17 06:08:14 - Accuracy Cosine Distance:   \t93.97\n",
            "2021-03-17 06:08:14 - Accuracy Manhatten Distance:\t94.24\n",
            "2021-03-17 06:08:14 - Accuracy Euclidean Distance:\t94.24\n",
            "\n",
            "2021-03-17 06:08:14 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:12:46 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 5000 steps:\n",
            "2021-03-17 06:15:23 - Accuracy Cosine Distance:   \t94.78\n",
            "2021-03-17 06:15:23 - Accuracy Manhatten Distance:\t94.98\n",
            "2021-03-17 06:15:23 - Accuracy Euclidean Distance:\t94.99\n",
            "\n",
            "2021-03-17 06:15:23 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:19:54 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 6000 steps:\n",
            "2021-03-17 06:22:32 - Accuracy Cosine Distance:   \t95.79\n",
            "2021-03-17 06:22:32 - Accuracy Manhatten Distance:\t95.90\n",
            "2021-03-17 06:22:32 - Accuracy Euclidean Distance:\t95.90\n",
            "\n",
            "2021-03-17 06:22:32 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:27:04 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 7000 steps:\n",
            "2021-03-17 06:29:41 - Accuracy Cosine Distance:   \t96.37\n",
            "2021-03-17 06:29:41 - Accuracy Manhatten Distance:\t96.58\n",
            "2021-03-17 06:29:41 - Accuracy Euclidean Distance:\t96.59\n",
            "\n",
            "2021-03-17 06:29:41 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:34:14 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 8000 steps:\n",
            "2021-03-17 06:36:51 - Accuracy Cosine Distance:   \t96.81\n",
            "2021-03-17 06:36:51 - Accuracy Manhatten Distance:\t96.94\n",
            "2021-03-17 06:36:51 - Accuracy Euclidean Distance:\t96.97\n",
            "\n",
            "2021-03-17 06:36:51 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:41:23 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 9000 steps:\n",
            "2021-03-17 06:44:00 - Accuracy Cosine Distance:   \t97.17\n",
            "2021-03-17 06:44:00 - Accuracy Manhatten Distance:\t97.26\n",
            "2021-03-17 06:44:00 - Accuracy Euclidean Distance:\t97.26\n",
            "\n",
            "2021-03-17 06:44:00 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:48:33 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 10000 steps:\n",
            "2021-03-17 06:51:10 - Accuracy Cosine Distance:   \t97.40\n",
            "2021-03-17 06:51:10 - Accuracy Manhatten Distance:\t97.63\n",
            "2021-03-17 06:51:10 - Accuracy Euclidean Distance:\t97.61\n",
            "\n",
            "2021-03-17 06:51:10 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 06:55:43 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 11000 steps:\n",
            "2021-03-17 06:58:20 - Accuracy Cosine Distance:   \t97.72\n",
            "2021-03-17 06:58:20 - Accuracy Manhatten Distance:\t97.90\n",
            "2021-03-17 06:58:20 - Accuracy Euclidean Distance:\t97.90\n",
            "\n",
            "2021-03-17 06:58:20 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:02:54 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 12000 steps:\n",
            "2021-03-17 07:05:32 - Accuracy Cosine Distance:   \t98.08\n",
            "2021-03-17 07:05:32 - Accuracy Manhatten Distance:\t98.18\n",
            "2021-03-17 07:05:32 - Accuracy Euclidean Distance:\t98.19\n",
            "\n",
            "2021-03-17 07:05:32 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:10:06 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 13000 steps:\n",
            "2021-03-17 07:12:43 - Accuracy Cosine Distance:   \t98.41\n",
            "2021-03-17 07:12:43 - Accuracy Manhatten Distance:\t98.52\n",
            "2021-03-17 07:12:43 - Accuracy Euclidean Distance:\t98.51\n",
            "\n",
            "2021-03-17 07:12:43 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:17:15 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 14000 steps:\n",
            "2021-03-17 07:19:53 - Accuracy Cosine Distance:   \t98.45\n",
            "2021-03-17 07:19:53 - Accuracy Manhatten Distance:\t98.55\n",
            "2021-03-17 07:19:53 - Accuracy Euclidean Distance:\t98.54\n",
            "\n",
            "2021-03-17 07:19:53 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:24:28 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 15000 steps:\n",
            "2021-03-17 07:27:06 - Accuracy Cosine Distance:   \t98.67\n",
            "2021-03-17 07:27:06 - Accuracy Manhatten Distance:\t98.71\n",
            "2021-03-17 07:27:06 - Accuracy Euclidean Distance:\t98.72\n",
            "\n",
            "2021-03-17 07:27:06 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:31:39 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 16000 steps:\n",
            "2021-03-17 07:34:17 - Accuracy Cosine Distance:   \t98.72\n",
            "2021-03-17 07:34:17 - Accuracy Manhatten Distance:\t98.78\n",
            "2021-03-17 07:34:17 - Accuracy Euclidean Distance:\t98.77\n",
            "\n",
            "2021-03-17 07:34:17 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:38:51 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 17000 steps:\n",
            "2021-03-17 07:41:28 - Accuracy Cosine Distance:   \t99.06\n",
            "2021-03-17 07:41:28 - Accuracy Manhatten Distance:\t99.09\n",
            "2021-03-17 07:41:28 - Accuracy Euclidean Distance:\t99.09\n",
            "\n",
            "2021-03-17 07:41:28 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:46:02 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 18000 steps:\n",
            "2021-03-17 07:48:39 - Accuracy Cosine Distance:   \t99.08\n",
            "2021-03-17 07:48:39 - Accuracy Manhatten Distance:\t99.13\n",
            "2021-03-17 07:48:39 - Accuracy Euclidean Distance:\t99.13\n",
            "\n",
            "2021-03-17 07:48:39 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 07:53:13 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 19000 steps:\n",
            "2021-03-17 07:55:50 - Accuracy Cosine Distance:   \t99.06\n",
            "2021-03-17 07:55:50 - Accuracy Manhatten Distance:\t99.10\n",
            "2021-03-17 07:55:50 - Accuracy Euclidean Distance:\t99.09\n",
            "\n",
            "\n",
            "2021-03-17 07:58:31 - TripletEvaluator: Evaluating the model on val dataset after epoch 0:\n",
            "2021-03-17 08:01:08 - Accuracy Cosine Distance:   \t99.23\n",
            "2021-03-17 08:01:08 - Accuracy Manhatten Distance:\t99.26\n",
            "2021-03-17 08:01:08 - Accuracy Euclidean Distance:\t99.25\n",
            "\n",
            "2021-03-17 08:01:08 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "2021-03-17 08:05:41 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 1000 steps:\n",
            "2021-03-17 08:08:18 - Accuracy Cosine Distance:   \t99.22\n",
            "2021-03-17 08:08:18 - Accuracy Manhatten Distance:\t99.25\n",
            "2021-03-17 08:08:18 - Accuracy Euclidean Distance:\t99.24\n",
            "\n",
            "2021-03-17 08:12:49 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 2000 steps:\n",
            "2021-03-17 08:15:27 - Accuracy Cosine Distance:   \t99.27\n",
            "2021-03-17 08:15:27 - Accuracy Manhatten Distance:\t99.28\n",
            "2021-03-17 08:15:27 - Accuracy Euclidean Distance:\t99.29\n",
            "\n",
            "2021-03-17 08:15:27 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 08:19:58 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 3000 steps:\n",
            "2021-03-17 08:22:36 - Accuracy Cosine Distance:   \t99.33\n",
            "2021-03-17 08:22:36 - Accuracy Manhatten Distance:\t99.36\n",
            "2021-03-17 08:22:36 - Accuracy Euclidean Distance:\t99.35\n",
            "\n",
            "2021-03-17 08:22:36 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 08:27:07 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 4000 steps:\n",
            "2021-03-17 08:29:44 - Accuracy Cosine Distance:   \t99.41\n",
            "2021-03-17 08:29:44 - Accuracy Manhatten Distance:\t99.43\n",
            "2021-03-17 08:29:44 - Accuracy Euclidean Distance:\t99.44\n",
            "\n",
            "2021-03-17 08:29:44 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 08:34:16 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 5000 steps:\n",
            "2021-03-17 08:36:53 - Accuracy Cosine Distance:   \t99.47\n",
            "2021-03-17 08:36:53 - Accuracy Manhatten Distance:\t99.47\n",
            "2021-03-17 08:36:53 - Accuracy Euclidean Distance:\t99.49\n",
            "\n",
            "2021-03-17 08:36:53 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 08:41:25 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 6000 steps:\n",
            "2021-03-17 08:44:02 - Accuracy Cosine Distance:   \t99.49\n",
            "2021-03-17 08:44:02 - Accuracy Manhatten Distance:\t99.52\n",
            "2021-03-17 08:44:02 - Accuracy Euclidean Distance:\t99.52\n",
            "\n",
            "2021-03-17 08:44:02 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 08:48:34 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 7000 steps:\n",
            "2021-03-17 08:51:11 - Accuracy Cosine Distance:   \t99.49\n",
            "2021-03-17 08:51:11 - Accuracy Manhatten Distance:\t99.49\n",
            "2021-03-17 08:51:11 - Accuracy Euclidean Distance:\t99.50\n",
            "\n",
            "2021-03-17 08:55:41 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 8000 steps:\n",
            "2021-03-17 08:58:18 - Accuracy Cosine Distance:   \t99.48\n",
            "2021-03-17 08:58:18 - Accuracy Manhatten Distance:\t99.52\n",
            "2021-03-17 08:58:18 - Accuracy Euclidean Distance:\t99.52\n",
            "\n",
            "2021-03-17 09:02:49 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 9000 steps:\n",
            "2021-03-17 09:05:27 - Accuracy Cosine Distance:   \t99.59\n",
            "2021-03-17 09:05:27 - Accuracy Manhatten Distance:\t99.60\n",
            "2021-03-17 09:05:27 - Accuracy Euclidean Distance:\t99.60\n",
            "\n",
            "2021-03-17 09:05:27 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 09:09:59 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 10000 steps:\n",
            "2021-03-17 09:12:36 - Accuracy Cosine Distance:   \t99.58\n",
            "2021-03-17 09:12:36 - Accuracy Manhatten Distance:\t99.59\n",
            "2021-03-17 09:12:36 - Accuracy Euclidean Distance:\t99.59\n",
            "\n",
            "2021-03-17 09:17:06 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 11000 steps:\n",
            "2021-03-17 09:19:43 - Accuracy Cosine Distance:   \t99.63\n",
            "2021-03-17 09:19:43 - Accuracy Manhatten Distance:\t99.65\n",
            "2021-03-17 09:19:43 - Accuracy Euclidean Distance:\t99.64\n",
            "\n",
            "2021-03-17 09:19:43 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 09:24:14 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 12000 steps:\n",
            "2021-03-17 09:26:51 - Accuracy Cosine Distance:   \t99.62\n",
            "2021-03-17 09:26:51 - Accuracy Manhatten Distance:\t99.65\n",
            "2021-03-17 09:26:51 - Accuracy Euclidean Distance:\t99.65\n",
            "\n",
            "2021-03-17 09:26:51 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 09:31:22 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 13000 steps:\n",
            "2021-03-17 09:33:59 - Accuracy Cosine Distance:   \t99.63\n",
            "2021-03-17 09:33:59 - Accuracy Manhatten Distance:\t99.64\n",
            "2021-03-17 09:33:59 - Accuracy Euclidean Distance:\t99.65\n",
            "\n",
            "2021-03-17 09:38:29 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 14000 steps:\n",
            "2021-03-17 09:41:06 - Accuracy Cosine Distance:   \t99.68\n",
            "2021-03-17 09:41:06 - Accuracy Manhatten Distance:\t99.69\n",
            "2021-03-17 09:41:06 - Accuracy Euclidean Distance:\t99.69\n",
            "\n",
            "2021-03-17 09:41:06 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 09:45:37 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 15000 steps:\n",
            "2021-03-17 09:48:14 - Accuracy Cosine Distance:   \t99.74\n",
            "2021-03-17 09:48:14 - Accuracy Manhatten Distance:\t99.74\n",
            "2021-03-17 09:48:14 - Accuracy Euclidean Distance:\t99.75\n",
            "\n",
            "2021-03-17 09:48:14 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 09:52:46 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 16000 steps:\n",
            "2021-03-17 09:55:24 - Accuracy Cosine Distance:   \t99.68\n",
            "2021-03-17 09:55:24 - Accuracy Manhatten Distance:\t99.68\n",
            "2021-03-17 09:55:24 - Accuracy Euclidean Distance:\t99.68\n",
            "\n",
            "2021-03-17 09:59:54 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 17000 steps:\n",
            "2021-03-17 10:02:32 - Accuracy Cosine Distance:   \t99.69\n",
            "2021-03-17 10:02:32 - Accuracy Manhatten Distance:\t99.70\n",
            "2021-03-17 10:02:32 - Accuracy Euclidean Distance:\t99.71\n",
            "\n",
            "2021-03-17 10:07:04 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 18000 steps:\n",
            "2021-03-17 10:09:41 - Accuracy Cosine Distance:   \t99.68\n",
            "2021-03-17 10:09:41 - Accuracy Manhatten Distance:\t99.70\n",
            "2021-03-17 10:09:41 - Accuracy Euclidean Distance:\t99.69\n",
            "\n",
            "2021-03-17 10:14:12 - TripletEvaluator: Evaluating the model on val dataset in epoch 1 after 19000 steps:\n",
            "2021-03-17 10:16:49 - Accuracy Cosine Distance:   \t99.77\n",
            "2021-03-17 10:16:49 - Accuracy Manhatten Distance:\t99.78\n",
            "2021-03-17 10:16:49 - Accuracy Euclidean Distance:\t99.77\n",
            "\n",
            "2021-03-17 10:16:49 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "\n",
            "2021-03-17 10:19:30 - TripletEvaluator: Evaluating the model on val dataset after epoch 1:\n",
            "2021-03-17 10:22:08 - Accuracy Cosine Distance:   \t99.75\n",
            "2021-03-17 10:22:08 - Accuracy Manhatten Distance:\t99.76\n",
            "2021-03-17 10:22:08 - Accuracy Euclidean Distance:\t99.75\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        {
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          "text": [
            "2021-03-17 10:26:38 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 1000 steps:\n",
            "2021-03-17 10:29:15 - Accuracy Cosine Distance:   \t99.73\n",
            "2021-03-17 10:29:15 - Accuracy Manhatten Distance:\t99.75\n",
            "2021-03-17 10:29:15 - Accuracy Euclidean Distance:\t99.73\n",
            "\n",
            "2021-03-17 10:33:45 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 2000 steps:\n",
            "2021-03-17 10:36:22 - Accuracy Cosine Distance:   \t99.77\n",
            "2021-03-17 10:36:22 - Accuracy Manhatten Distance:\t99.79\n",
            "2021-03-17 10:36:22 - Accuracy Euclidean Distance:\t99.77\n",
            "\n",
            "2021-03-17 10:36:22 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 10:40:55 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 3000 steps:\n",
            "2021-03-17 10:43:33 - Accuracy Cosine Distance:   \t99.75\n",
            "2021-03-17 10:43:33 - Accuracy Manhatten Distance:\t99.76\n",
            "2021-03-17 10:43:33 - Accuracy Euclidean Distance:\t99.76\n",
            "\n",
            "2021-03-17 10:48:03 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 4000 steps:\n",
            "2021-03-17 10:50:41 - Accuracy Cosine Distance:   \t99.75\n",
            "2021-03-17 10:50:41 - Accuracy Manhatten Distance:\t99.75\n",
            "2021-03-17 10:50:41 - Accuracy Euclidean Distance:\t99.76\n",
            "\n",
            "2021-03-17 10:55:12 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 5000 steps:\n",
            "2021-03-17 10:57:50 - Accuracy Cosine Distance:   \t99.79\n",
            "2021-03-17 10:57:50 - Accuracy Manhatten Distance:\t99.80\n",
            "2021-03-17 10:57:50 - Accuracy Euclidean Distance:\t99.80\n",
            "\n",
            "2021-03-17 10:57:50 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 11:02:23 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 6000 steps:\n",
            "2021-03-17 11:05:01 - Accuracy Cosine Distance:   \t99.77\n",
            "2021-03-17 11:05:01 - Accuracy Manhatten Distance:\t99.76\n",
            "2021-03-17 11:05:01 - Accuracy Euclidean Distance:\t99.78\n",
            "\n",
            "2021-03-17 11:09:33 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 7000 steps:\n",
            "2021-03-17 11:12:12 - Accuracy Cosine Distance:   \t99.81\n",
            "2021-03-17 11:12:12 - Accuracy Manhatten Distance:\t99.83\n",
            "2021-03-17 11:12:12 - Accuracy Euclidean Distance:\t99.81\n",
            "\n",
            "2021-03-17 11:12:12 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 11:16:47 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 8000 steps:\n",
            "2021-03-17 11:19:25 - Accuracy Cosine Distance:   \t99.81\n",
            "2021-03-17 11:19:25 - Accuracy Manhatten Distance:\t99.82\n",
            "2021-03-17 11:19:25 - Accuracy Euclidean Distance:\t99.82\n",
            "\n",
            "2021-03-17 11:23:57 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 9000 steps:\n",
            "2021-03-17 11:26:35 - Accuracy Cosine Distance:   \t99.81\n",
            "2021-03-17 11:26:35 - Accuracy Manhatten Distance:\t99.81\n",
            "2021-03-17 11:26:35 - Accuracy Euclidean Distance:\t99.80\n",
            "\n",
            "2021-03-17 11:31:06 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 10000 steps:\n",
            "2021-03-17 11:33:44 - Accuracy Cosine Distance:   \t99.83\n",
            "2021-03-17 11:33:44 - Accuracy Manhatten Distance:\t99.83\n",
            "2021-03-17 11:33:44 - Accuracy Euclidean Distance:\t99.83\n",
            "\n",
            "2021-03-17 11:33:44 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 11:38:17 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 11000 steps:\n",
            "2021-03-17 11:40:55 - Accuracy Cosine Distance:   \t99.85\n",
            "2021-03-17 11:40:55 - Accuracy Manhatten Distance:\t99.86\n",
            "2021-03-17 11:40:55 - Accuracy Euclidean Distance:\t99.86\n",
            "\n",
            "2021-03-17 11:40:55 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 11:45:28 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 12000 steps:\n",
            "2021-03-17 11:48:06 - Accuracy Cosine Distance:   \t99.85\n",
            "2021-03-17 11:48:06 - Accuracy Manhatten Distance:\t99.85\n",
            "2021-03-17 11:48:06 - Accuracy Euclidean Distance:\t99.86\n",
            "\n",
            "2021-03-17 11:52:38 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 13000 steps:\n",
            "2021-03-17 11:55:16 - Accuracy Cosine Distance:   \t99.84\n",
            "2021-03-17 11:55:16 - Accuracy Manhatten Distance:\t99.85\n",
            "2021-03-17 11:55:16 - Accuracy Euclidean Distance:\t99.85\n",
            "\n",
            "2021-03-17 11:59:47 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 14000 steps:\n",
            "2021-03-17 12:02:25 - Accuracy Cosine Distance:   \t99.85\n",
            "2021-03-17 12:02:25 - Accuracy Manhatten Distance:\t99.84\n",
            "2021-03-17 12:02:25 - Accuracy Euclidean Distance:\t99.85\n",
            "\n",
            "2021-03-17 12:06:56 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 15000 steps:\n",
            "2021-03-17 12:09:33 - Accuracy Cosine Distance:   \t99.88\n",
            "2021-03-17 12:09:33 - Accuracy Manhatten Distance:\t99.87\n",
            "2021-03-17 12:09:33 - Accuracy Euclidean Distance:\t99.87\n",
            "\n",
            "2021-03-17 12:09:33 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 12:14:06 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 16000 steps:\n",
            "2021-03-17 12:16:43 - Accuracy Cosine Distance:   \t99.91\n",
            "2021-03-17 12:16:43 - Accuracy Manhatten Distance:\t99.90\n",
            "2021-03-17 12:16:43 - Accuracy Euclidean Distance:\t99.90\n",
            "\n",
            "2021-03-17 12:16:43 - Save model to output/FAQIR/models/triplet_hard_user_query_1.1\n",
            "2021-03-17 12:21:16 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 17000 steps:\n",
            "2021-03-17 12:23:54 - Accuracy Cosine Distance:   \t99.84\n",
            "2021-03-17 12:23:54 - Accuracy Manhatten Distance:\t99.84\n",
            "2021-03-17 12:23:54 - Accuracy Euclidean Distance:\t99.84\n",
            "\n",
            "2021-03-17 12:28:26 - TripletEvaluator: Evaluating the model on val dataset in epoch 2 after 18000 steps:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kh6_c9uM1ji_"
      },
      "source": [
        "**query_type=user_query, neg_type=\"simple\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "-2U7l78x0QWJ",
        "outputId": "f808d8ef-ccdc-41aa-aa5f-99e9ae6acef4"
      },
      "source": [
        "# load simple_user_query_dataset.csv\n",
        "simple_user_query_dataset = pd.read_csv('data/FAQIR/dataset/triplet/user_query/simple_user_query_dataset.csv')\n",
        "simple_user_query_dataset"
      ],
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      "outputs": [
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              "2       How to increase the efficiency of my car with ...  ...  You might check your wiring, the spot lights s...\n",
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      "source": [
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      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_simple_user_query = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"triplet\", query_type='user_query', neg_type='simple', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_simple_user_query.create_model(simple_user_query_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=28.0, style=ProgressStyle(description_w…"
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          "output_type": "stream",
          "text": [
            "\n",
            "2021-03-20 08:29:41 - Lock 140099213246416 released on /root/.cache/huggingface/transformers/8c8624b8ac8aa99c60c912161f8332de003484428c47906d7ff7eb7f73eecdbb.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79.lock\n",
            "2021-03-20 08:29:41 - Use pytorch device: cuda\n",
            "2021-03-20 08:29:41 - Generating directory structure\n",
            "2021-03-20 08:29:41 - Generating triplets for training\n",
            "2021-03-20 08:30:03 - Training model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "7eeaff1c6ff14f05a872d31f80d8c05c",
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              "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=4.0, style=ProgressStyle(description_width='i…"
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        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "2021-03-20 08:46:55 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 1000 steps:\n",
            "2021-03-20 08:50:40 - Accuracy Cosine Distance:   \t93.78\n",
            "2021-03-20 08:50:40 - Accuracy Manhatten Distance:\t94.04\n",
            "2021-03-20 08:50:40 - Accuracy Euclidean Distance:\t94.01\n",
            "\n",
            "2021-03-20 08:50:40 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 09:07:19 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 2000 steps:\n",
            "2021-03-20 09:11:04 - Accuracy Cosine Distance:   \t96.36\n",
            "2021-03-20 09:11:04 - Accuracy Manhatten Distance:\t96.50\n",
            "2021-03-20 09:11:04 - Accuracy Euclidean Distance:\t96.48\n",
            "\n",
            "2021-03-20 09:11:04 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 09:27:35 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 3000 steps:\n",
            "2021-03-20 09:31:20 - Accuracy Cosine Distance:   \t96.94\n",
            "2021-03-20 09:31:20 - Accuracy Manhatten Distance:\t96.94\n",
            "2021-03-20 09:31:20 - Accuracy Euclidean Distance:\t96.95\n",
            "\n",
            "2021-03-20 09:31:20 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 09:47:38 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 4000 steps:\n",
            "2021-03-20 09:51:23 - Accuracy Cosine Distance:   \t97.01\n",
            "2021-03-20 09:51:23 - Accuracy Manhatten Distance:\t97.02\n",
            "2021-03-20 09:51:23 - Accuracy Euclidean Distance:\t97.01\n",
            "\n",
            "2021-03-20 09:51:23 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 10:07:50 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 5000 steps:\n",
            "2021-03-20 10:11:35 - Accuracy Cosine Distance:   \t97.13\n",
            "2021-03-20 10:11:35 - Accuracy Manhatten Distance:\t97.15\n",
            "2021-03-20 10:11:35 - Accuracy Euclidean Distance:\t97.15\n",
            "\n",
            "2021-03-20 10:11:35 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 10:27:48 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 6000 steps:\n",
            "2021-03-20 10:31:32 - Accuracy Cosine Distance:   \t97.18\n",
            "2021-03-20 10:31:32 - Accuracy Manhatten Distance:\t97.16\n",
            "2021-03-20 10:31:32 - Accuracy Euclidean Distance:\t97.17\n",
            "\n",
            "2021-03-20 10:31:32 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 10:48:15 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 7000 steps:\n",
            "2021-03-20 10:51:59 - Accuracy Cosine Distance:   \t97.19\n",
            "2021-03-20 10:51:59 - Accuracy Manhatten Distance:\t97.21\n",
            "2021-03-20 10:51:59 - Accuracy Euclidean Distance:\t97.21\n",
            "\n",
            "2021-03-20 10:51:59 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 11:08:27 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 8000 steps:\n",
            "2021-03-20 11:12:12 - Accuracy Cosine Distance:   \t97.16\n",
            "2021-03-20 11:12:12 - Accuracy Manhatten Distance:\t97.13\n",
            "2021-03-20 11:12:12 - Accuracy Euclidean Distance:\t97.16\n",
            "\n",
            "2021-03-20 11:28:34 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 9000 steps:\n",
            "2021-03-20 11:32:19 - Accuracy Cosine Distance:   \t97.24\n",
            "2021-03-20 11:32:19 - Accuracy Manhatten Distance:\t97.22\n",
            "2021-03-20 11:32:19 - Accuracy Euclidean Distance:\t97.24\n",
            "\n",
            "2021-03-20 11:32:19 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 11:48:48 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 10000 steps:\n",
            "2021-03-20 11:52:33 - Accuracy Cosine Distance:   \t97.22\n",
            "2021-03-20 11:52:33 - Accuracy Manhatten Distance:\t97.23\n",
            "2021-03-20 11:52:33 - Accuracy Euclidean Distance:\t97.22\n",
            "\n",
            "2021-03-20 12:08:57 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 11000 steps:\n",
            "2021-03-20 12:12:42 - Accuracy Cosine Distance:   \t97.33\n",
            "2021-03-20 12:12:42 - Accuracy Manhatten Distance:\t97.34\n",
            "2021-03-20 12:12:42 - Accuracy Euclidean Distance:\t97.32\n",
            "\n",
            "2021-03-20 12:12:42 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 12:29:13 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 12000 steps:\n",
            "2021-03-20 12:32:58 - Accuracy Cosine Distance:   \t97.37\n",
            "2021-03-20 12:32:58 - Accuracy Manhatten Distance:\t97.37\n",
            "2021-03-20 12:32:58 - Accuracy Euclidean Distance:\t97.36\n",
            "\n",
            "2021-03-20 12:32:58 - Save model to output/FAQIR/models/triplet_simple_user_query_1.1\n",
            "2021-03-20 12:49:11 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 13000 steps:\n",
            "2021-03-20 12:52:56 - Accuracy Cosine Distance:   \t97.35\n",
            "2021-03-20 12:52:56 - Accuracy Manhatten Distance:\t97.34\n",
            "2021-03-20 12:52:56 - Accuracy Euclidean Distance:\t97.35\n",
            "\n",
            "2021-03-20 13:09:25 - TripletEvaluator: Evaluating the model on val dataset in epoch 0 after 14000 steps:\n",
            "2021-03-20 13:13:11 - Accuracy Cosine Distance:   \t97.33\n",
            "2021-03-20 13:13:11 - Accuracy Manhatten Distance:\t97.32\n",
            "2021-03-20 13:13:11 - Accuracy Euclidean Distance:\t97.34\n",
            "\n",
            "\n",
            "2021-03-20 13:19:51 - TripletEvaluator: Evaluating the model on val dataset after epoch 0:\n",
            "2021-03-20 13:23:36 - Accuracy Cosine Distance:   \t97.33\n",
            "2021-03-20 13:23:36 - Accuracy Manhatten Distance:\t97.33\n",
            "2021-03-20 13:23:36 - Accuracy Euclidean Distance:\t97.34\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "output_type": "stream",
          "text": [
            "2021-03-20 13:23:45 - error occured\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/MyDrive/BERT-FAQ/faq_bert_finetuning.py\", line 135, in create_model\n",
            "    self.model = self.train(self.bert_model, train_samples, val_samples, models_path)\n",
            "  File \"/content/drive/MyDrive/BERT-FAQ/faq_bert_finetuning.py\", line 171, in train\n",
            "    warmup_steps=warmup_steps, output_path=output_path\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sentence_transformers/SentenceTransformer.py\", line 567, in fit\n",
            "    loss_value = loss_model(features, labels)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sentence_transformers/losses/TripletLoss.py\", line 52, in forward\n",
            "    reps = [self.model(sentence_feature)['sentence_embedding'] for sentence_feature in sentence_features]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sentence_transformers/losses/TripletLoss.py\", line 52, in <listcomp>\n",
            "    reps = [self.model(sentence_feature)['sentence_embedding'] for sentence_feature in sentence_features]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py\", line 119, in forward\n",
            "    input = module(input)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sentence_transformers/models/Transformer.py\", line 38, in forward\n",
            "    output_states = self.auto_model(**trans_features, return_dict=False)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/transformers/models/distilbert/modeling_distilbert.py\", line 489, in forward\n",
            "    return_dict=return_dict,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/transformers/models/distilbert/modeling_distilbert.py\", line 309, in forward\n",
            "    x=hidden_state, attn_mask=attn_mask, head_mask=head_mask[i], output_attentions=output_attentions\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/transformers/models/distilbert/modeling_distilbert.py\", line 267, in forward\n",
            "    ffn_output = self.output_layer_norm(ffn_output + sa_output)  # (bs, seq_length, dim)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/normalization.py\", line 171, in forward\n",
            "    input, self.normalized_shape, self.weight, self.bias, self.eps)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py\", line 2202, in layer_norm\n",
            "    return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)\n",
            "RuntimeError: CUDA out of memory. Tried to allocate 24.00 MiB (GPU 0; 14.76 GiB total capacity; 13.27 GiB already allocated; 23.75 MiB free; 13.71 GiB reserved in total by PyTorch)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHIGgc062BDM"
      },
      "source": [
        "**BERT-Finetuning with Softmax Loss**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuVJzSOKtjhy"
      },
      "source": [
        "**query_type=faq, neg_type=\"hard\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "He-npwIUtjhy",
        "outputId": "1728f3c6-97d6-4bc1-8a4c-7a236449c8ef"
      },
      "source": [
        "# load hard_faq_dataset.csv\n",
        "hard_faq_dataset = pd.read_csv('data/FAQIR/dataset/softmax/faq/hard_faq_dataset.csv')\n",
        "hard_faq_dataset"
      ],
      "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>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>How to remove tree sap from car? Hi,I have som...</td>\n",
              "      <td>I have had good luck with commercial products ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>How do I get a sticker off of a car window  (p...</td>\n",
              "      <td>Goo Gone is a fantastic product (available at ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>How to get rid of garbage disposal odor?</td>\n",
              "      <td>1. Inspect the black rubber cover that fits in...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>How to maintain a lawn mower?</td>\n",
              "      <td>To properly maintain your lawnmover, you'll ne...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>How to fix vertical blinds? I can no longer re...</td>\n",
              "      <td>Click on the link and follow the steps.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119639</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>I am not a mechanic, but I did change an o2 se...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119640</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>I mean no harm.  If you are on here asking how...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119641</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>I do not think you can.  Cell phones do keep l...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119642</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>Coolant Pressure tester is the best way. PM de...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119643</th>\n",
              "      <td>how can i change  my birthday?</td>\n",
              "      <td>You can't, there is no provision made by the m...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119644 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 question  ... label\n",
              "0       How to remove tree sap from car? Hi,I have som...  ...     1\n",
              "1       How do I get a sticker off of a car window  (p...  ...     1\n",
              "2                How to get rid of garbage disposal odor?  ...     1\n",
              "3                           How to maintain a lawn mower?  ...     1\n",
              "4       How to fix vertical blinds? I can no longer re...  ...     1\n",
              "...                                                   ...  ...   ...\n",
              "119639                     how can i change  my birthday?  ...     0\n",
              "119640                     how can i change  my birthday?  ...     0\n",
              "119641                     how can i change  my birthday?  ...     0\n",
              "119642                     how can i change  my birthday?  ...     0\n",
              "119643                     how can i change  my birthday?  ...     0\n",
              "\n",
              "[119644 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G92BHllItjhz",
        "outputId": "49a6698e-6171-4abc-f38a-6102bd1294db"
      },
      "source": [
        "# check dataset shape\n",
        "hard_faq_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(119644, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
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        "id": "i7-ktid7tjhz",
        "outputId": "95ab3fd3-199f-4925-b96a-116b56be3421"
      },
      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_hard_faq = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"softmax\", query_type='faq', neg_type='hard', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_hard_faq.create_model(hard_faq_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-09 16:55:45 - Lock 140104693481296 acquired on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n"
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          "output_type": "stream",
          "text": [
            "\n",
            "2021-03-09 16:55:45 - Lock 140104693481296 released on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n",
            "2021-03-09 16:55:45 - Lock 140105127605264 acquired on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "2021-03-09 16:55:50 - Lock 140105127605264 released on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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": "stream",
          "text": [
            "2021-03-09 16:55:52 - Lock 140104679901136 acquired on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n"
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          "text": [
            "\n",
            "2021-03-09 16:55:52 - Lock 140104679901136 released on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n",
            "2021-03-09 16:55:52 - Lock 140104693506128 acquired on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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          "output_type": "stream",
          "text": [
            "\n",
            "2021-03-09 16:55:52 - Lock 140104693506128 released on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
            "2021-03-09 16:55:52 - Use pytorch device: cuda\n",
            "2021-03-09 16:55:52 - Generating directory structure\n",
            "2021-03-09 16:55:52 - Generating triplets for training\n",
            "2021-03-09 16:56:01 - Training model\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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          "text": [
            "2021-03-09 17:06:46 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 1000 steps:\n",
            "2021-03-09 17:10:05 - Accuracy:           95.48\t(Threshold: 0.0767)\n",
            "2021-03-09 17:10:05 - F1:                 80.38\t(Threshold: 0.0767)\n",
            "2021-03-09 17:10:05 - Precision:          98.14\n",
            "2021-03-09 17:10:05 - Recall:             68.06\n",
            "2021-03-09 17:10:05 - Average Precision:  79.11\n",
            "\n",
            "2021-03-09 17:10:05 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n",
            "2021-03-09 17:20:49 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 2000 steps:\n",
            "2021-03-09 17:24:08 - Accuracy:           95.90\t(Threshold: 0.9862)\n",
            "2021-03-09 17:24:08 - F1:                 82.36\t(Threshold: 0.9862)\n",
            "2021-03-09 17:24:08 - Precision:          99.48\n",
            "2021-03-09 17:24:08 - Recall:             70.27\n",
            "2021-03-09 17:24:08 - Average Precision:  80.65\n",
            "\n",
            "2021-03-09 17:24:08 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n",
            "2021-03-09 17:34:41 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 3000 steps:\n",
            "2021-03-09 17:38:00 - Accuracy:           96.01\t(Threshold: 0.9763)\n",
            "2021-03-09 17:38:00 - F1:                 82.88\t(Threshold: 0.9763)\n",
            "2021-03-09 17:38:00 - Precision:          99.65\n",
            "2021-03-09 17:38:00 - Recall:             70.95\n",
            "2021-03-09 17:38:00 - Average Precision:  81.24\n",
            "\n",
            "2021-03-09 17:38:00 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n",
            "2021-03-09 17:48:39 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 4000 steps:\n",
            "2021-03-09 17:51:58 - Accuracy:           96.06\t(Threshold: 0.9923)\n",
            "2021-03-09 17:51:58 - F1:                 83.06\t(Threshold: 0.9923)\n",
            "2021-03-09 17:51:58 - Precision:          99.91\n",
            "2021-03-09 17:51:58 - Recall:             71.07\n",
            "2021-03-09 17:51:58 - Average Precision:  81.84\n",
            "\n",
            "2021-03-09 17:51:58 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n",
            "2021-03-09 18:02:33 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 5000 steps:\n",
            "2021-03-09 18:05:52 - Accuracy:           96.10\t(Threshold: 0.9959)\n",
            "2021-03-09 18:05:52 - F1:                 83.26\t(Threshold: 0.9959)\n",
            "2021-03-09 18:05:52 - Precision:          100.00\n",
            "2021-03-09 18:05:52 - Recall:             71.31\n",
            "2021-03-09 18:05:52 - Average Precision:  82.02\n",
            "\n",
            "2021-03-09 18:05:52 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n",
            "\n",
            "2021-03-09 18:16:19 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 0:\n",
            "2021-03-09 18:19:37 - Accuracy:           96.12\t(Threshold: 0.9982)\n",
            "2021-03-09 18:19:37 - F1:                 83.42\t(Threshold: 0.9982)\n",
            "2021-03-09 18:19:37 - Precision:          99.74\n",
            "2021-03-09 18:19:37 - Recall:             71.68\n",
            "2021-03-09 18:19:37 - Average Precision:  81.80\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db0217ee956c41ec8954c58111cfde59",
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          "text": [
            "2021-03-09 18:30:10 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 1000 steps:\n",
            "2021-03-09 18:33:28 - Accuracy:           96.14\t(Threshold: 0.9960)\n",
            "2021-03-09 18:33:28 - F1:                 83.46\t(Threshold: 0.9960)\n",
            "2021-03-09 18:33:28 - Precision:          100.00\n",
            "2021-03-09 18:33:28 - Recall:             71.62\n",
            "2021-03-09 18:33:28 - Average Precision:  81.97\n",
            "\n",
            "2021-03-09 18:44:12 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 2000 steps:\n",
            "2021-03-09 18:47:31 - Accuracy:           96.15\t(Threshold: 0.1812)\n",
            "2021-03-09 18:47:31 - F1:                 83.52\t(Threshold: 0.1812)\n",
            "2021-03-09 18:47:31 - Precision:          99.91\n",
            "2021-03-09 18:47:31 - Recall:             71.74\n",
            "2021-03-09 18:47:31 - Average Precision:  81.88\n",
            "\n",
            "2021-03-09 18:58:15 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 3000 steps:\n",
            "2021-03-09 19:01:33 - Accuracy:           96.16\t(Threshold: 0.9991)\n",
            "2021-03-09 19:01:33 - F1:                 83.55\t(Threshold: 0.9991)\n",
            "2021-03-09 19:01:33 - Precision:          100.00\n",
            "2021-03-09 19:01:33 - Recall:             71.74\n",
            "2021-03-09 19:01:33 - Average Precision:  82.58\n",
            "\n",
            "2021-03-09 19:01:33 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n",
            "2021-03-09 19:12:14 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 4000 steps:\n",
            "2021-03-09 19:15:32 - Accuracy:           96.14\t(Threshold: 0.2382)\n",
            "2021-03-09 19:15:32 - F1:                 83.50\t(Threshold: 0.2382)\n",
            "2021-03-09 19:15:32 - Precision:          99.74\n",
            "2021-03-09 19:15:32 - Recall:             71.81\n",
            "2021-03-09 19:15:32 - Average Precision:  82.39\n",
            "\n",
            "2021-03-09 19:26:05 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 5000 steps:\n",
            "2021-03-09 19:29:24 - Accuracy:           96.16\t(Threshold: 0.9995)\n",
            "2021-03-09 19:29:24 - F1:                 83.59\t(Threshold: 0.9843)\n",
            "2021-03-09 19:29:24 - Precision:          99.66\n",
            "2021-03-09 19:29:24 - Recall:             71.99\n",
            "2021-03-09 19:29:24 - Average Precision:  82.70\n",
            "\n",
            "2021-03-09 19:29:24 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n",
            "\n",
            "2021-03-09 19:39:50 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 1:\n",
            "2021-03-09 19:43:08 - Accuracy:           96.16\t(Threshold: 0.9980)\n",
            "2021-03-09 19:43:08 - F1:                 83.55\t(Threshold: 0.9980)\n",
            "2021-03-09 19:43:08 - Precision:          100.00\n",
            "2021-03-09 19:43:08 - Recall:             71.74\n",
            "2021-03-09 19:43:08 - Average Precision:  82.70\n",
            "\n",
            "2021-03-09 19:43:08 - Save model to output/FAQIR/models/softmax_hard_faq_1.1\n"
          ],
          "name": "stdout"
        },
        {
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              "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=5983.0, style=ProgressStyle(description_w…"
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          "text": [
            "2021-03-09 19:53:39 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 1000 steps:\n",
            "2021-03-09 19:56:58 - Accuracy:           96.15\t(Threshold: 0.9911)\n",
            "2021-03-09 19:56:58 - F1:                 83.51\t(Threshold: 0.9911)\n",
            "2021-03-09 19:56:58 - Precision:          100.00\n",
            "2021-03-09 19:56:58 - Recall:             71.68\n",
            "2021-03-09 19:56:58 - Average Precision:  82.26\n",
            "\n",
            "2021-03-09 20:07:35 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 2000 steps:\n",
            "2021-03-09 20:10:54 - Accuracy:           96.16\t(Threshold: 0.9943)\n",
            "2021-03-09 20:10:54 - F1:                 83.60\t(Threshold: 0.9943)\n",
            "2021-03-09 20:10:54 - Precision:          99.91\n",
            "2021-03-09 20:10:54 - Recall:             71.87\n",
            "2021-03-09 20:10:54 - Average Precision:  82.20\n",
            "\n",
            "2021-03-09 20:21:31 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 3000 steps:\n",
            "2021-03-09 20:24:49 - Accuracy:           96.16\t(Threshold: 0.9581)\n",
            "2021-03-09 20:24:49 - F1:                 83.59\t(Threshold: 0.9581)\n",
            "2021-03-09 20:24:49 - Precision:          100.00\n",
            "2021-03-09 20:24:49 - Recall:             71.81\n",
            "2021-03-09 20:24:49 - Average Precision:  82.36\n",
            "\n",
            "2021-03-09 20:35:30 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 4000 steps:\n",
            "2021-03-09 20:38:49 - Accuracy:           96.18\t(Threshold: 0.9529)\n",
            "2021-03-09 20:38:49 - F1:                 83.75\t(Threshold: 0.4384)\n",
            "2021-03-09 20:38:49 - Precision:          99.16\n",
            "2021-03-09 20:38:49 - Recall:             72.48\n",
            "2021-03-09 20:38:49 - Average Precision:  82.62\n",
            "\n",
            "2021-03-09 20:49:29 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 5000 steps:\n",
            "2021-03-09 20:52:47 - Accuracy:           96.20\t(Threshold: 0.9208)\n",
            "2021-03-09 20:52:47 - F1:                 83.81\t(Threshold: 0.9208)\n",
            "2021-03-09 20:52:47 - Precision:          99.58\n",
            "2021-03-09 20:52:47 - Recall:             72.36\n",
            "2021-03-09 20:52:47 - Average Precision:  82.39\n",
            "\n",
            "\n",
            "2021-03-09 21:03:14 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 2:\n",
            "2021-03-09 21:06:32 - Accuracy:           96.19\t(Threshold: 0.9922)\n",
            "2021-03-09 21:06:32 - F1:                 83.71\t(Threshold: 0.9922)\n",
            "2021-03-09 21:06:32 - Precision:          100.00\n",
            "2021-03-09 21:06:32 - Recall:             71.99\n",
            "2021-03-09 21:06:32 - Average Precision:  82.47\n",
            "\n"
          ],
          "name": "stdout"
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          "text": [
            "2021-03-09 21:17:15 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 1000 steps:\n",
            "2021-03-09 21:20:33 - Accuracy:           96.20\t(Threshold: 0.9882)\n",
            "2021-03-09 21:20:33 - F1:                 83.76\t(Threshold: 0.9882)\n",
            "2021-03-09 21:20:33 - Precision:          100.00\n",
            "2021-03-09 21:20:33 - Recall:             72.05\n",
            "2021-03-09 21:20:33 - Average Precision:  82.09\n",
            "\n",
            "2021-03-09 21:31:16 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 2000 steps:\n",
            "2021-03-09 21:34:35 - Accuracy:           96.18\t(Threshold: 0.9892)\n",
            "2021-03-09 21:34:35 - F1:                 83.68\t(Threshold: 0.9790)\n",
            "2021-03-09 21:34:35 - Precision:          99.91\n",
            "2021-03-09 21:34:35 - Recall:             71.99\n",
            "2021-03-09 21:34:35 - Average Precision:  82.15\n",
            "\n",
            "2021-03-09 21:45:14 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 3000 steps:\n",
            "2021-03-09 21:48:32 - Accuracy:           96.21\t(Threshold: 0.9770)\n",
            "2021-03-09 21:48:32 - F1:                 83.84\t(Threshold: 0.9757)\n",
            "2021-03-09 21:48:32 - Precision:          99.66\n",
            "2021-03-09 21:48:32 - Recall:             72.36\n",
            "2021-03-09 21:48:32 - Average Precision:  82.08\n",
            "\n",
            "2021-03-09 21:59:11 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 4000 steps:\n",
            "2021-03-09 22:02:30 - Accuracy:           96.20\t(Threshold: 0.9863)\n",
            "2021-03-09 22:02:30 - F1:                 83.79\t(Threshold: 0.9852)\n",
            "2021-03-09 22:02:30 - Precision:          99.75\n",
            "2021-03-09 22:02:30 - Recall:             72.24\n",
            "2021-03-09 22:02:30 - Average Precision:  82.04\n",
            "\n",
            "2021-03-09 22:13:01 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 5000 steps:\n",
            "2021-03-09 22:16:20 - Accuracy:           96.19\t(Threshold: 0.9944)\n",
            "2021-03-09 22:16:20 - F1:                 83.74\t(Threshold: 0.9826)\n",
            "2021-03-09 22:16:20 - Precision:          99.83\n",
            "2021-03-09 22:16:20 - Recall:             72.11\n",
            "2021-03-09 22:16:20 - Average Precision:  82.05\n",
            "\n",
            "\n",
            "2021-03-09 22:26:50 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 3:\n",
            "2021-03-09 22:30:09 - Accuracy:           96.21\t(Threshold: 0.9810)\n",
            "2021-03-09 22:30:09 - F1:                 83.82\t(Threshold: 0.9810)\n",
            "2021-03-09 22:30:09 - Precision:          99.83\n",
            "2021-03-09 22:30:09 - Recall:             72.24\n",
            "2021-03-09 22:30:09 - Average Precision:  82.04\n",
            "\n",
            "\n",
            "2021-03-09 22:30:09 - Evaluating model\n",
            "2021-03-09 22:30:11 - Use pytorch device: cuda\n",
            "2021-03-09 22:30:11 - CEBinaryClassificationEvaluator: Evaluating the model on test dataset:\n",
            "2021-03-09 22:33:30 - Accuracy:           96.30\t(Threshold: 0.2624)\n",
            "2021-03-09 22:33:30 - F1:                 83.80\t(Threshold: 0.2266)\n",
            "2021-03-09 22:33:30 - Precision:          99.31\n",
            "2021-03-09 22:33:30 - Recall:             72.49\n",
            "2021-03-09 22:33:30 - Average Precision:  83.13\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyZRI2KZtjhz"
      },
      "source": [
        "**query_type=faq, neg_type=\"simple\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "FjRD16Zrtjhz",
        "outputId": "f3fcf5c2-f68a-419f-9236-d404adc8cfdc"
      },
      "source": [
        "# load simple_faq_dataset.csv\n",
        "simple_faq_dataset = pd.read_csv('data/FAQIR/dataset/softmax/faq/simple_faq_dataset.csv')\n",
        "simple_faq_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>how can i change  my birthday?</td>\n",
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              "        label  ...                                             answer\n",
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              "3           1  ...  To properly maintain your lawnmover, you'll ne...\n",
              "4           1  ...            Click on the link and follow the steps.\n",
              "...       ...  ...                                                ...\n",
              "107820      0  ...  And after draining the radiator, I recommend f...\n",
              "107821      0  ...  Is the house batteries on 12v or 24volt ? I wo...\n",
              "107822      0  ...  Cyanuric acid has no place in a spa. It is a c...\n",
              "107823      0  ...  Each manufacturer has different setup / system...\n",
              "107824      0  ...  An environmentally friendly solution that has ...\n",
              "\n",
              "[107825 rows x 3 columns]"
            ]
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          "metadata": {
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          "execution_count": 7
        }
      ]
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      "source": [
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            "text/plain": [
              "(107825, 3)"
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      },
      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_simple_faq = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"softmax\", query_type='faq', neg_type='simple', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_simple_faq.create_model(simple_faq_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-10 03:18:41 - Lock 139690891924496 acquired on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n"
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            "\n",
            "2021-03-10 03:18:41 - Lock 139690891924496 released on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n",
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            "\n",
            "2021-03-10 03:18:45 - Lock 139691038331984 released on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n"
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          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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"
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            "\n",
            "2021-03-10 03:18:47 - Lock 139690878893392 released on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
            "2021-03-10 03:18:47 - Use pytorch device: cuda\n",
            "2021-03-10 03:18:47 - Generating directory structure\n",
            "2021-03-10 03:18:47 - Generating triplets for training\n",
            "2021-03-10 03:18:55 - Training model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=4.0, style=ProgressStyle(description_width='i…"
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          "text": [
            "2021-03-10 03:28:19 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 1000 steps:\n",
            "2021-03-10 03:30:52 - Accuracy:           95.86\t(Threshold: 0.1215)\n",
            "2021-03-10 03:30:52 - F1:                 12.43\t(Threshold: 0.0616)\n",
            "2021-03-10 03:30:52 - Precision:          7.57\n",
            "2021-03-10 03:30:52 - Recall:             34.82\n",
            "2021-03-10 03:30:52 - Average Precision:  7.04\n",
            "\n",
            "2021-03-10 03:30:52 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "2021-03-10 03:40:08 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 2000 steps:\n",
            "2021-03-10 03:42:42 - Accuracy:           96.34\t(Threshold: 0.6041)\n",
            "2021-03-10 03:42:42 - F1:                 44.21\t(Threshold: 0.2121)\n",
            "2021-03-10 03:42:42 - Precision:          46.98\n",
            "2021-03-10 03:42:42 - Recall:             41.74\n",
            "2021-03-10 03:42:42 - Average Precision:  39.25\n",
            "\n",
            "2021-03-10 03:42:42 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "2021-03-10 03:51:57 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 3000 steps:\n",
            "2021-03-10 03:54:30 - Accuracy:           96.74\t(Threshold: 0.4987)\n",
            "2021-03-10 03:54:30 - F1:                 50.13\t(Threshold: 0.2589)\n",
            "2021-03-10 03:54:30 - Precision:          59.94\n",
            "2021-03-10 03:54:30 - Recall:             43.08\n",
            "2021-03-10 03:54:30 - Average Precision:  47.43\n",
            "\n",
            "2021-03-10 03:54:30 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "2021-03-10 04:03:45 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 4000 steps:\n",
            "2021-03-10 04:06:18 - Accuracy:           96.86\t(Threshold: 0.2913)\n",
            "2021-03-10 04:06:18 - F1:                 55.16\t(Threshold: 0.0370)\n",
            "2021-03-10 04:06:18 - Precision:          63.29\n",
            "2021-03-10 04:06:18 - Recall:             48.88\n",
            "2021-03-10 04:06:18 - Average Precision:  50.47\n",
            "\n",
            "2021-03-10 04:06:18 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "2021-03-10 04:15:21 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 5000 steps:\n",
            "2021-03-10 04:17:55 - Accuracy:           96.92\t(Threshold: 0.3644)\n",
            "2021-03-10 04:17:55 - F1:                 54.50\t(Threshold: 0.1218)\n",
            "2021-03-10 04:17:55 - Precision:          58.96\n",
            "2021-03-10 04:17:55 - Recall:             50.67\n",
            "2021-03-10 04:17:55 - Average Precision:  51.78\n",
            "\n",
            "2021-03-10 04:17:55 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "\n",
            "2021-03-10 04:21:29 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 0:\n",
            "2021-03-10 04:24:02 - Accuracy:           96.98\t(Threshold: 0.1565)\n",
            "2021-03-10 04:24:02 - F1:                 57.98\t(Threshold: 0.0933)\n",
            "2021-03-10 04:24:02 - Precision:          67.76\n",
            "2021-03-10 04:24:02 - Recall:             50.67\n",
            "2021-03-10 04:24:02 - Average Precision:  53.88\n",
            "\n",
            "2021-03-10 04:24:02 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3327a7b4eb94c5a9efbde4d20ed46da",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=5392.0, style=ProgressStyle(description_w…"
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        {
          "output_type": "stream",
          "text": [
            "2021-03-10 04:33:20 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 1000 steps:\n",
            "2021-03-10 04:35:53 - Accuracy:           96.97\t(Threshold: 0.0822)\n",
            "2021-03-10 04:35:53 - F1:                 54.59\t(Threshold: 0.0552)\n",
            "2021-03-10 04:35:53 - Precision:          69.18\n",
            "2021-03-10 04:35:53 - Recall:             45.09\n",
            "2021-03-10 04:35:53 - Average Precision:  53.05\n",
            "\n",
            "2021-03-10 04:44:56 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 2000 steps:\n",
            "2021-03-10 04:47:30 - Accuracy:           97.05\t(Threshold: 0.7980)\n",
            "2021-03-10 04:47:30 - F1:                 56.93\t(Threshold: 0.4828)\n",
            "2021-03-10 04:47:30 - Precision:          64.59\n",
            "2021-03-10 04:47:30 - Recall:             50.89\n",
            "2021-03-10 04:47:30 - Average Precision:  56.50\n",
            "\n",
            "2021-03-10 04:47:30 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "2021-03-10 04:56:46 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 3000 steps:\n",
            "2021-03-10 04:59:19 - Accuracy:           97.06\t(Threshold: 0.0565)\n",
            "2021-03-10 04:59:19 - F1:                 60.26\t(Threshold: 0.0373)\n",
            "2021-03-10 04:59:19 - Precision:          65.19\n",
            "2021-03-10 04:59:19 - Recall:             56.03\n",
            "2021-03-10 04:59:19 - Average Precision:  57.51\n",
            "\n",
            "2021-03-10 04:59:19 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "2021-03-10 05:08:35 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 4000 steps:\n",
            "2021-03-10 05:11:09 - Accuracy:           97.05\t(Threshold: 0.8706)\n",
            "2021-03-10 05:11:09 - F1:                 59.41\t(Threshold: 0.6354)\n",
            "2021-03-10 05:11:09 - Precision:          65.68\n",
            "2021-03-10 05:11:09 - Recall:             54.24\n",
            "2021-03-10 05:11:09 - Average Precision:  55.95\n",
            "\n",
            "2021-03-10 05:20:19 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 5000 steps:\n",
            "2021-03-10 05:22:53 - Accuracy:           97.13\t(Threshold: 0.2551)\n",
            "2021-03-10 05:22:53 - F1:                 59.55\t(Threshold: 0.0872)\n",
            "2021-03-10 05:22:53 - Precision:          67.04\n",
            "2021-03-10 05:22:53 - Recall:             53.57\n",
            "2021-03-10 05:22:53 - Average Precision:  56.78\n",
            "\n",
            "\n",
            "2021-03-10 05:26:34 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 1:\n",
            "2021-03-10 05:29:07 - Accuracy:           97.17\t(Threshold: 0.3196)\n",
            "2021-03-10 05:29:07 - F1:                 60.73\t(Threshold: 0.1076)\n",
            "2021-03-10 05:29:07 - Precision:          66.94\n",
            "2021-03-10 05:29:07 - Recall:             55.58\n",
            "2021-03-10 05:29:07 - Average Precision:  58.75\n",
            "\n",
            "2021-03-10 05:29:07 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a9d28b8ab754dceb201247871b7f444",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=5392.0, style=ProgressStyle(description_w…"
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          }
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        {
          "output_type": "stream",
          "text": [
            "2021-03-10 05:38:18 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 1000 steps:\n",
            "2021-03-10 05:40:51 - Accuracy:           97.23\t(Threshold: 0.7717)\n",
            "2021-03-10 05:40:51 - F1:                 60.08\t(Threshold: 0.7717)\n",
            "2021-03-10 05:40:51 - Precision:          74.75\n",
            "2021-03-10 05:40:51 - Recall:             50.22\n",
            "2021-03-10 05:40:51 - Average Precision:  57.55\n",
            "\n",
            "2021-03-10 05:50:03 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 2000 steps:\n",
            "2021-03-10 05:52:37 - Accuracy:           97.10\t(Threshold: 0.9515)\n",
            "2021-03-10 05:52:37 - F1:                 60.61\t(Threshold: 0.1161)\n",
            "2021-03-10 05:52:37 - Precision:          64.25\n",
            "2021-03-10 05:52:37 - Recall:             57.37\n",
            "2021-03-10 05:52:37 - Average Precision:  59.68\n",
            "\n",
            "2021-03-10 05:52:37 - Save model to output/FAQIR/models/softmax_simple_faq_1.1\n",
            "2021-03-10 06:01:44 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 3000 steps:\n",
            "2021-03-10 06:04:18 - Accuracy:           97.15\t(Threshold: 0.8694)\n",
            "2021-03-10 06:04:18 - F1:                 60.22\t(Threshold: 0.1060)\n",
            "2021-03-10 06:04:18 - Precision:          67.69\n",
            "2021-03-10 06:04:18 - Recall:             54.24\n",
            "2021-03-10 06:04:18 - Average Precision:  57.95\n",
            "\n",
            "2021-03-10 06:13:36 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 4000 steps:\n",
            "2021-03-10 06:16:09 - Accuracy:           97.13\t(Threshold: 0.7243)\n",
            "2021-03-10 06:16:09 - F1:                 60.05\t(Threshold: 0.1564)\n",
            "2021-03-10 06:16:09 - Precision:          67.60\n",
            "2021-03-10 06:16:09 - Recall:             54.02\n",
            "2021-03-10 06:16:09 - Average Precision:  58.00\n",
            "\n",
            "2021-03-10 06:25:25 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 5000 steps:\n",
            "2021-03-10 06:27:58 - Accuracy:           97.15\t(Threshold: 0.9305)\n",
            "2021-03-10 06:27:58 - F1:                 60.80\t(Threshold: 0.3419)\n",
            "2021-03-10 06:27:58 - Precision:          69.54\n",
            "2021-03-10 06:27:58 - Recall:             54.02\n",
            "2021-03-10 06:27:58 - Average Precision:  58.26\n",
            "\n",
            "\n",
            "2021-03-10 06:31:39 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 2:\n",
            "2021-03-10 06:34:13 - Accuracy:           97.16\t(Threshold: 0.8764)\n",
            "2021-03-10 06:34:13 - F1:                 59.51\t(Threshold: 0.1025)\n",
            "2021-03-10 06:34:13 - Precision:          70.77\n",
            "2021-03-10 06:34:13 - Recall:             51.34\n",
            "2021-03-10 06:34:13 - Average Precision:  58.07\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=5392.0, style=ProgressStyle(description_w…"
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        },
        {
          "output_type": "stream",
          "text": [
            "2021-03-10 06:43:34 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 1000 steps:\n",
            "2021-03-10 06:46:08 - Accuracy:           97.16\t(Threshold: 0.9402)\n",
            "2021-03-10 06:46:08 - F1:                 59.18\t(Threshold: 0.3625)\n",
            "2021-03-10 06:46:08 - Precision:          72.49\n",
            "2021-03-10 06:46:08 - Recall:             50.00\n",
            "2021-03-10 06:46:08 - Average Precision:  57.65\n",
            "\n",
            "2021-03-10 06:55:16 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 2000 steps:\n",
            "2021-03-10 06:57:49 - Accuracy:           97.21\t(Threshold: 0.9031)\n",
            "2021-03-10 06:57:49 - F1:                 60.03\t(Threshold: 0.3689)\n",
            "2021-03-10 06:57:49 - Precision:          70.15\n",
            "2021-03-10 06:57:49 - Recall:             52.46\n",
            "2021-03-10 06:57:49 - Average Precision:  58.36\n",
            "\n",
            "2021-03-10 07:06:55 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 3000 steps:\n",
            "2021-03-10 07:09:27 - Accuracy:           97.13\t(Threshold: 0.9672)\n",
            "2021-03-10 07:09:27 - F1:                 60.07\t(Threshold: 0.4723)\n",
            "2021-03-10 07:09:27 - Precision:          66.31\n",
            "2021-03-10 07:09:27 - Recall:             54.91\n",
            "2021-03-10 07:09:27 - Average Precision:  58.60\n",
            "\n",
            "2021-03-10 07:18:36 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 4000 steps:\n",
            "2021-03-10 07:21:09 - Accuracy:           97.14\t(Threshold: 0.9629)\n",
            "2021-03-10 07:21:09 - F1:                 60.02\t(Threshold: 0.1399)\n",
            "2021-03-10 07:21:09 - Precision:          67.89\n",
            "2021-03-10 07:21:09 - Recall:             53.79\n",
            "2021-03-10 07:21:09 - Average Precision:  58.07\n",
            "\n",
            "2021-03-10 07:30:14 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 5000 steps:\n",
            "2021-03-10 07:32:47 - Accuracy:           97.13\t(Threshold: 0.9626)\n",
            "2021-03-10 07:32:47 - F1:                 58.76\t(Threshold: 0.1861)\n",
            "2021-03-10 07:32:47 - Precision:          69.51\n",
            "2021-03-10 07:32:47 - Recall:             50.89\n",
            "2021-03-10 07:32:47 - Average Precision:  57.52\n",
            "\n",
            "\n",
            "2021-03-10 07:36:21 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 3:\n",
            "2021-03-10 07:38:54 - Accuracy:           97.13\t(Threshold: 0.9637)\n",
            "2021-03-10 07:38:54 - F1:                 59.35\t(Threshold: 0.0887)\n",
            "2021-03-10 07:38:54 - Precision:          67.23\n",
            "2021-03-10 07:38:54 - Recall:             53.12\n",
            "2021-03-10 07:38:54 - Average Precision:  57.71\n",
            "\n",
            "\n",
            "2021-03-10 07:38:54 - Evaluating model\n",
            "2021-03-10 07:38:56 - Use pytorch device: cuda\n",
            "2021-03-10 07:38:56 - CEBinaryClassificationEvaluator: Evaluating the model on test dataset:\n",
            "2021-03-10 07:41:33 - Accuracy:           96.89\t(Threshold: 0.9456)\n",
            "2021-03-10 07:41:33 - F1:                 60.87\t(Threshold: 0.1626)\n",
            "2021-03-10 07:41:33 - Precision:          66.50\n",
            "2021-03-10 07:41:33 - Recall:             56.12\n",
            "2021-03-10 07:41:33 - Average Precision:  58.91\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-sitJPBctjhz"
      },
      "source": [
        "**query_type=user_query, neg_type=\"hard\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "ATXM-qX8tjh0",
        "outputId": "81764b3f-bbfd-4a36-d42a-5e4c1e9979a6"
      },
      "source": [
        "# load hard_user_query_dataset.csv\n",
        "hard_user_query_dataset = pd.read_csv('data/FAQIR/dataset/softmax/user_query/hard_user_query_dataset.csv')\n",
        "hard_user_query_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>148035</th>\n",
              "      <td>My roommate started smoking marijuana every th...</td>\n",
              "      <td>I had a cigarette burn in my old apartment car...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>148036 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 question  ... label\n",
              "0       How to remove tree sap from car? Hi,I have som...  ...     1\n",
              "1       How do I get a sticker off of a car window  (p...  ...     1\n",
              "2                How to get rid of garbage disposal odor?  ...     1\n",
              "3                           How to maintain a lawn mower?  ...     1\n",
              "4       How to fix vertical blinds? I can no longer re...  ...     1\n",
              "...                                                   ...  ...   ...\n",
              "148031  My roommate started smoking marijuana every th...  ...     0\n",
              "148032  My roommate started smoking marijuana every th...  ...     0\n",
              "148033  My roommate started smoking marijuana every th...  ...     0\n",
              "148034  My roommate started smoking marijuana every th...  ...     0\n",
              "148035  My roommate started smoking marijuana every th...  ...     0\n",
              "\n",
              "[148036 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "zi0D8HYetjh0",
        "outputId": "1d918cb3-5f28-434d-d0ce-66da85751002"
      },
      "source": [
        "hard_user_query_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(148036, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "4b6248c7-b402-4116-8dd7-6be33254a2e6"
      },
      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_hard_user_query = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"softmax\", query_type='user_query', neg_type='hard', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_hard_user_query.create_model(hard_user_query_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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": "stream",
          "text": [
            "2021-03-10 14:18:45 - Use pytorch device: cuda\n",
            "2021-03-10 14:18:45 - Generating directory structure\n",
            "2021-03-10 14:18:45 - Generating triplets for training\n",
            "2021-03-10 14:18:56 - Training model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db42d85da2e04ebb9c6e43e13639af0f",
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              "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=4.0, style=ProgressStyle(description_width='i…"
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        {
          "output_type": "stream",
          "text": [
            "2021-03-10 14:28:49 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 1000 steps:\n",
            "2021-03-10 14:32:30 - Accuracy:           91.75\t(Threshold: 0.2766)\n",
            "2021-03-10 14:32:30 - F1:                 56.16\t(Threshold: 0.1420)\n",
            "2021-03-10 14:32:30 - Precision:          62.46\n",
            "2021-03-10 14:32:30 - Recall:             51.02\n",
            "2021-03-10 14:32:30 - Average Precision:  55.25\n",
            "\n",
            "2021-03-10 14:32:30 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 14:42:21 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 2000 steps:\n",
            "2021-03-10 14:46:02 - Accuracy:           92.90\t(Threshold: 0.5486)\n",
            "2021-03-10 14:46:02 - F1:                 62.64\t(Threshold: 0.4192)\n",
            "2021-03-10 14:46:02 - Precision:          70.56\n",
            "2021-03-10 14:46:02 - Recall:             56.32\n",
            "2021-03-10 14:46:02 - Average Precision:  62.26\n",
            "\n",
            "2021-03-10 14:46:02 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 14:55:56 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 3000 steps:\n",
            "2021-03-10 14:59:37 - Accuracy:           93.62\t(Threshold: 0.6464)\n",
            "2021-03-10 14:59:37 - F1:                 65.51\t(Threshold: 0.2313)\n",
            "2021-03-10 14:59:37 - Precision:          73.37\n",
            "2021-03-10 14:59:37 - Recall:             59.16\n",
            "2021-03-10 14:59:37 - Average Precision:  66.82\n",
            "\n",
            "2021-03-10 14:59:37 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 15:09:37 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 4000 steps:\n",
            "2021-03-10 15:13:18 - Accuracy:           94.13\t(Threshold: 0.4213)\n",
            "2021-03-10 15:13:18 - F1:                 69.91\t(Threshold: 0.2296)\n",
            "2021-03-10 15:13:18 - Precision:          77.24\n",
            "2021-03-10 15:13:18 - Recall:             63.85\n",
            "2021-03-10 15:13:18 - Average Precision:  70.22\n",
            "\n",
            "2021-03-10 15:13:18 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 15:23:01 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 5000 steps:\n",
            "2021-03-10 15:26:41 - Accuracy:           94.83\t(Threshold: 0.3834)\n",
            "2021-03-10 15:26:41 - F1:                 72.97\t(Threshold: 0.2424)\n",
            "2021-03-10 15:26:41 - Precision:          83.65\n",
            "2021-03-10 15:26:41 - Recall:             64.71\n",
            "2021-03-10 15:26:41 - Average Precision:  73.04\n",
            "\n",
            "2021-03-10 15:26:41 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 15:36:26 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 6000 steps:\n",
            "2021-03-10 15:40:06 - Accuracy:           94.76\t(Threshold: 0.8414)\n",
            "2021-03-10 15:40:06 - F1:                 72.91\t(Threshold: 0.1523)\n",
            "2021-03-10 15:40:06 - Precision:          79.97\n",
            "2021-03-10 15:40:06 - Recall:             67.00\n",
            "2021-03-10 15:40:06 - Average Precision:  74.44\n",
            "\n",
            "2021-03-10 15:40:06 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 15:49:59 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 7000 steps:\n",
            "2021-03-10 15:53:39 - Accuracy:           95.48\t(Threshold: 0.7519)\n",
            "2021-03-10 15:53:39 - F1:                 76.61\t(Threshold: 0.3747)\n",
            "2021-03-10 15:53:39 - Precision:          87.23\n",
            "2021-03-10 15:53:39 - Recall:             68.29\n",
            "2021-03-10 15:53:39 - Average Precision:  77.17\n",
            "\n",
            "2021-03-10 15:53:39 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "\n",
            "2021-03-10 15:57:38 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 0:\n",
            "2021-03-10 16:01:19 - Accuracy:           95.36\t(Threshold: 0.1912)\n",
            "2021-03-10 16:01:19 - F1:                 75.51\t(Threshold: 0.1912)\n",
            "2021-03-10 16:01:19 - Precision:          89.44\n",
            "2021-03-10 16:01:19 - Recall:             65.33\n",
            "2021-03-10 16:01:19 - Average Precision:  76.12\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce72764b263645b381725f11080bfa7a",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=7402.0, style=ProgressStyle(description_w…"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "2021-03-10 16:11:15 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 1000 steps:\n",
            "2021-03-10 16:14:56 - Accuracy:           95.41\t(Threshold: 0.9031)\n",
            "2021-03-10 16:14:56 - F1:                 76.35\t(Threshold: 0.7524)\n",
            "2021-03-10 16:14:56 - Precision:          87.17\n",
            "2021-03-10 16:14:56 - Recall:             67.92\n",
            "2021-03-10 16:14:56 - Average Precision:  77.38\n",
            "\n",
            "2021-03-10 16:14:56 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 16:24:45 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 2000 steps:\n",
            "2021-03-10 16:28:26 - Accuracy:           95.70\t(Threshold: 0.8891)\n",
            "2021-03-10 16:28:26 - F1:                 77.72\t(Threshold: 0.8525)\n",
            "2021-03-10 16:28:26 - Precision:          89.74\n",
            "2021-03-10 16:28:26 - Recall:             68.54\n",
            "2021-03-10 16:28:26 - Average Precision:  78.29\n",
            "\n",
            "2021-03-10 16:28:26 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 16:38:18 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 3000 steps:\n",
            "2021-03-10 16:41:59 - Accuracy:           95.66\t(Threshold: 0.9521)\n",
            "2021-03-10 16:41:59 - F1:                 77.87\t(Threshold: 0.9430)\n",
            "2021-03-10 16:41:59 - Precision:          88.08\n",
            "2021-03-10 16:41:59 - Recall:             69.77\n",
            "2021-03-10 16:41:59 - Average Precision:  78.34\n",
            "\n",
            "2021-03-10 16:41:59 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 16:51:46 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 4000 steps:\n",
            "2021-03-10 16:55:26 - Accuracy:           95.90\t(Threshold: 0.9393)\n",
            "2021-03-10 16:55:26 - F1:                 78.64\t(Threshold: 0.8192)\n",
            "2021-03-10 16:55:26 - Precision:          89.59\n",
            "2021-03-10 16:55:26 - Recall:             70.08\n",
            "2021-03-10 16:55:26 - Average Precision:  79.29\n",
            "\n",
            "2021-03-10 16:55:26 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 17:05:19 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 5000 steps:\n",
            "2021-03-10 17:09:00 - Accuracy:           95.87\t(Threshold: 0.3062)\n",
            "2021-03-10 17:09:00 - F1:                 78.62\t(Threshold: 0.0850)\n",
            "2021-03-10 17:09:00 - Precision:          88.94\n",
            "2021-03-10 17:09:00 - Recall:             70.45\n",
            "2021-03-10 17:09:00 - Average Precision:  79.11\n",
            "\n",
            "2021-03-10 17:18:47 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 6000 steps:\n",
            "2021-03-10 17:22:27 - Accuracy:           96.08\t(Threshold: 0.7562)\n",
            "2021-03-10 17:22:27 - F1:                 79.55\t(Threshold: 0.7562)\n",
            "2021-03-10 17:22:27 - Precision:          92.62\n",
            "2021-03-10 17:22:27 - Recall:             69.71\n",
            "2021-03-10 17:22:27 - Average Precision:  80.50\n",
            "\n",
            "2021-03-10 17:22:27 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 17:32:21 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 7000 steps:\n",
            "2021-03-10 17:36:02 - Accuracy:           96.08\t(Threshold: 0.6508)\n",
            "2021-03-10 17:36:02 - F1:                 79.76\t(Threshold: 0.2227)\n",
            "2021-03-10 17:36:02 - Precision:          90.99\n",
            "2021-03-10 17:36:02 - Recall:             71.01\n",
            "2021-03-10 17:36:02 - Average Precision:  80.40\n",
            "\n",
            "\n",
            "2021-03-10 17:39:57 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 1:\n",
            "2021-03-10 17:43:38 - Accuracy:           96.28\t(Threshold: 0.8073)\n",
            "2021-03-10 17:43:38 - F1:                 80.66\t(Threshold: 0.5600)\n",
            "2021-03-10 17:43:38 - Precision:          92.92\n",
            "2021-03-10 17:43:38 - Recall:             71.25\n",
            "2021-03-10 17:43:38 - Average Precision:  80.62\n",
            "\n",
            "2021-03-10 17:43:38 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "31a8050af75b4d88b7f17b6b8878ea39",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Iteration', max=7402.0, style=ProgressStyle(description_w…"
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        },
        {
          "output_type": "stream",
          "text": [
            "2021-03-10 17:53:32 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 1000 steps:\n",
            "2021-03-10 17:57:14 - Accuracy:           96.17\t(Threshold: 0.7425)\n",
            "2021-03-10 17:57:14 - F1:                 80.10\t(Threshold: 0.7425)\n",
            "2021-03-10 17:57:14 - Precision:          92.92\n",
            "2021-03-10 17:57:14 - Recall:             70.39\n",
            "2021-03-10 17:57:14 - Average Precision:  80.24\n",
            "\n",
            "2021-03-10 18:07:06 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 2000 steps:\n",
            "2021-03-10 18:10:46 - Accuracy:           96.06\t(Threshold: 0.9746)\n",
            "2021-03-10 18:10:46 - F1:                 79.46\t(Threshold: 0.9328)\n",
            "2021-03-10 18:10:46 - Precision:          91.41\n",
            "2021-03-10 18:10:46 - Recall:             70.27\n",
            "2021-03-10 18:10:46 - Average Precision:  80.21\n",
            "\n",
            "2021-03-10 18:20:30 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 3000 steps:\n",
            "2021-03-10 18:24:11 - Accuracy:           96.21\t(Threshold: 0.9387)\n",
            "2021-03-10 18:24:11 - F1:                 80.37\t(Threshold: 0.7344)\n",
            "2021-03-10 18:24:11 - Precision:          91.77\n",
            "2021-03-10 18:24:11 - Recall:             71.50\n",
            "2021-03-10 18:24:11 - Average Precision:  80.45\n",
            "\n",
            "2021-03-10 18:33:57 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 4000 steps:\n",
            "2021-03-10 18:37:38 - Accuracy:           96.22\t(Threshold: 0.7108)\n",
            "2021-03-10 18:37:38 - F1:                 80.23\t(Threshold: 0.5014)\n",
            "2021-03-10 18:37:38 - Precision:          93.37\n",
            "2021-03-10 18:37:38 - Recall:             70.33\n",
            "2021-03-10 18:37:38 - Average Precision:  79.75\n",
            "\n",
            "2021-03-10 18:47:30 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 5000 steps:\n",
            "2021-03-10 18:51:10 - Accuracy:           96.35\t(Threshold: 0.9631)\n",
            "2021-03-10 18:51:10 - F1:                 80.97\t(Threshold: 0.9169)\n",
            "2021-03-10 18:51:10 - Precision:          93.75\n",
            "2021-03-10 18:51:10 - Recall:             71.25\n",
            "2021-03-10 18:51:10 - Average Precision:  81.11\n",
            "\n",
            "2021-03-10 18:51:10 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "2021-03-10 19:01:03 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 6000 steps:\n",
            "2021-03-10 19:04:44 - Accuracy:           96.31\t(Threshold: 0.9175)\n",
            "2021-03-10 19:04:44 - F1:                 80.79\t(Threshold: 0.7616)\n",
            "2021-03-10 19:04:44 - Precision:          92.87\n",
            "2021-03-10 19:04:44 - Recall:             71.50\n",
            "2021-03-10 19:04:44 - Average Precision:  80.62\n",
            "\n",
            "2021-03-10 19:14:37 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 2 after 7000 steps:\n",
            "2021-03-10 19:18:18 - Accuracy:           96.51\t(Threshold: 0.9373)\n",
            "2021-03-10 19:18:18 - F1:                 81.72\t(Threshold: 0.9373)\n",
            "2021-03-10 19:18:18 - Precision:          95.70\n",
            "2021-03-10 19:18:18 - Recall:             71.31\n",
            "2021-03-10 19:18:18 - Average Precision:  81.12\n",
            "\n",
            "2021-03-10 19:18:18 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n",
            "\n",
            "2021-03-10 19:22:17 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 2:\n",
            "2021-03-10 19:25:57 - Accuracy:           96.44\t(Threshold: 0.9059)\n",
            "2021-03-10 19:25:57 - F1:                 81.41\t(Threshold: 0.7650)\n",
            "2021-03-10 19:25:57 - Precision:          95.06\n",
            "2021-03-10 19:25:57 - Recall:             71.19\n",
            "2021-03-10 19:25:57 - Average Precision:  81.26\n",
            "\n",
            "2021-03-10 19:25:57 - Save model to output/FAQIR/models/softmax_hard_user_query_1.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "2021-03-10 19:35:47 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 1000 steps:\n",
            "2021-03-10 19:39:28 - Accuracy:           96.31\t(Threshold: 0.9766)\n",
            "2021-03-10 19:39:28 - F1:                 80.89\t(Threshold: 0.9657)\n",
            "2021-03-10 19:39:28 - Precision:          92.81\n",
            "2021-03-10 19:39:28 - Recall:             71.68\n",
            "2021-03-10 19:39:28 - Average Precision:  81.08\n",
            "\n",
            "2021-03-10 19:49:13 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 2000 steps:\n",
            "2021-03-10 19:52:54 - Accuracy:           96.39\t(Threshold: 0.9740)\n",
            "2021-03-10 19:52:54 - F1:                 81.15\t(Threshold: 0.9178)\n",
            "2021-03-10 19:52:54 - Precision:          94.36\n",
            "2021-03-10 19:52:54 - Recall:             71.19\n",
            "2021-03-10 19:52:54 - Average Precision:  80.75\n",
            "\n",
            "2021-03-10 20:02:45 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 3000 steps:\n",
            "2021-03-10 20:06:26 - Accuracy:           96.27\t(Threshold: 0.9912)\n",
            "2021-03-10 20:06:26 - F1:                 80.79\t(Threshold: 0.9456)\n",
            "2021-03-10 20:06:26 - Precision:          92.24\n",
            "2021-03-10 20:06:26 - Recall:             71.87\n",
            "2021-03-10 20:06:26 - Average Precision:  80.35\n",
            "\n",
            "2021-03-10 20:16:17 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 4000 steps:\n",
            "2021-03-10 20:19:58 - Accuracy:           96.31\t(Threshold: 0.9582)\n",
            "2021-03-10 20:19:58 - F1:                 80.78\t(Threshold: 0.7205)\n",
            "2021-03-10 20:19:58 - Precision:          92.73\n",
            "2021-03-10 20:19:58 - Recall:             71.56\n",
            "2021-03-10 20:19:58 - Average Precision:  80.69\n",
            "\n",
            "2021-03-10 20:29:48 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 3 after 5000 steps:\n",
            "2021-03-10 20:33:29 - Accuracy:           96.35\t(Threshold: 0.9904)\n",
            "2021-03-10 20:33:29 - F1:                 81.16\t(Threshold: 0.8978)\n",
            "2021-03-10 20:33:29 - Precision:          93.20\n",
            "2021-03-10 20:33:29 - Recall:             71.87\n",
            "2021-03-10 20:33:29 - Average Precision:  80.66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8ZXSukatjh2"
      },
      "source": [
        "**query_type=user_query, neg_type=\"simple\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "11jOQA2utjh2",
        "outputId": "f9c5700b-7005-4a79-a092-add257a5e43d"
      },
      "source": [
        "# load simple_user_query_dataset.csv\n",
        "simple_user_query_dataset = pd.read_csv('data/FAQIR/dataset/softmax/user_query/simple_user_query_dataset.csv')\n",
        "simple_user_query_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "        label  ...                                             answer\n",
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              "300271      0  ...  If you have power locks you can use a wire coa...\n",
              "300272      0  ...  Here's what I do. Unscrew the shower head. Tak...\n",
              "300273      0  ...  Easy remove the two metal pims up and pull the...\n",
              "300274      0  ...  Mothballs placed around the outside of the hou...\n",
              "\n",
              "[300275 rows x 3 columns]"
            ]
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          "metadata": {
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      "source": [
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              "(300275, 3)"
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      "source": [
        "# create instance of FAQ_BERT_Finetuning\n",
        "bert_simple_user_query = FAQ_BERT_Finetuning(\n",
        "    loss_type=\"softmax\", query_type='user_query', neg_type='simple', version=\"1.1\", epochs=4, batch_size=16, \n",
        "    pre_trained_name='distilbert-base-uncased', \n",
        "    evaluation_steps=1000, test_size=0.2\n",
        ")\n",
        "\n",
        "# create model and save to output path\n",
        "bert_simple_user_query.create_model(simple_user_query_dataset, output_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-12 06:58:17 - Lock 140507682978384 acquired on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n"
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          "output_type": "stream",
          "text": [
            "\n",
            "2021-03-12 06:58:17 - Lock 140507682978384 released on /root/.cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.d423bdf2f58dc8b77d5f5d18028d7ae4a72dcfd8f468e81fe979ada957a8c361.lock\n",
            "2021-03-12 06:58:17 - Lock 140507929109584 acquired on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n"
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            "\n",
            "2021-03-12 06:58:22 - Lock 140507929109584 released on /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a.lock\n"
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          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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"
          ],
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        {
          "output_type": "stream",
          "text": [
            "2021-03-12 06:58:24 - Lock 140507669783312 acquired on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n"
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          "text": [
            "\n",
            "2021-03-12 06:58:24 - Lock 140507669783312 released on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n",
            "2021-03-12 06:58:24 - Lock 140507682978640 acquired on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n"
          ],
          "name": "stdout"
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          "text": [
            "\n",
            "2021-03-12 06:58:24 - Lock 140507682978640 released on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
            "2021-03-12 06:58:24 - Use pytorch device: cuda\n",
            "2021-03-12 06:58:24 - Generating directory structure\n",
            "2021-03-12 06:58:24 - Generating triplets for training\n",
            "2021-03-12 06:58:47 - Training model\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "text": [
            "2021-03-12 07:08:05 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 1000 steps:\n",
            "2021-03-12 07:15:34 - Accuracy:           96.06\t(Threshold: 0.0383)\n",
            "2021-03-12 07:15:34 - F1:                 10.39\t(Threshold: 0.0356)\n",
            "2021-03-12 07:15:34 - Precision:          6.28\n",
            "2021-03-12 07:15:34 - Recall:             29.95\n",
            "2021-03-12 07:15:34 - Average Precision:  5.52\n",
            "\n",
            "2021-03-12 07:15:34 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 07:24:55 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 2000 steps:\n",
            "2021-03-12 07:32:20 - Accuracy:           96.06\t(Threshold: 0.0329)\n",
            "2021-03-12 07:32:20 - F1:                 8.66\t(Threshold: 0.0324)\n",
            "2021-03-12 07:32:20 - Precision:          6.13\n",
            "2021-03-12 07:32:20 - Recall:             14.72\n",
            "2021-03-12 07:32:20 - Average Precision:  4.99\n",
            "\n",
            "2021-03-12 07:41:43 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 3000 steps:\n",
            "2021-03-12 07:49:13 - Accuracy:           96.08\t(Threshold: 0.2427)\n",
            "2021-03-12 07:49:13 - F1:                 19.25\t(Threshold: 0.0473)\n",
            "2021-03-12 07:49:13 - Precision:          17.96\n",
            "2021-03-12 07:49:13 - Recall:             20.73\n",
            "2021-03-12 07:49:13 - Average Precision:  12.36\n",
            "\n",
            "2021-03-12 07:49:13 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 07:58:36 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 4000 steps:\n",
            "2021-03-12 08:06:04 - Accuracy:           96.08\t(Threshold: 0.4385)\n",
            "2021-03-12 08:06:04 - F1:                 35.27\t(Threshold: 0.2285)\n",
            "2021-03-12 08:06:04 - Precision:          29.29\n",
            "2021-03-12 08:06:04 - Recall:             44.33\n",
            "2021-03-12 08:06:04 - Average Precision:  26.40\n",
            "\n",
            "2021-03-12 08:06:04 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 08:15:27 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 5000 steps:\n",
            "2021-03-12 08:22:56 - Accuracy:           96.19\t(Threshold: 0.3014)\n",
            "2021-03-12 08:22:56 - F1:                 36.30\t(Threshold: 0.0295)\n",
            "2021-03-12 08:22:56 - Precision:          30.42\n",
            "2021-03-12 08:22:56 - Recall:             45.01\n",
            "2021-03-12 08:22:56 - Average Precision:  29.84\n",
            "\n",
            "2021-03-12 08:22:56 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 08:32:37 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 6000 steps:\n",
            "2021-03-12 08:40:05 - Accuracy:           96.12\t(Threshold: 0.5642)\n",
            "2021-03-12 08:40:05 - F1:                 41.61\t(Threshold: 0.1746)\n",
            "2021-03-12 08:40:05 - Precision:          35.99\n",
            "2021-03-12 08:40:05 - Recall:             49.32\n",
            "2021-03-12 08:40:05 - Average Precision:  35.05\n",
            "\n",
            "2021-03-12 08:40:05 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 08:49:26 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 7000 steps:\n",
            "2021-03-12 08:56:53 - Accuracy:           96.07\t(Threshold: 0.4442)\n",
            "2021-03-12 08:56:53 - F1:                 43.82\t(Threshold: 0.1229)\n",
            "2021-03-12 08:56:53 - Precision:          33.73\n",
            "2021-03-12 08:56:53 - Recall:             62.52\n",
            "2021-03-12 08:56:53 - Average Precision:  35.11\n",
            "\n",
            "2021-03-12 08:56:53 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 09:06:12 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 8000 steps:\n",
            "2021-03-12 09:13:41 - Accuracy:           96.18\t(Threshold: 0.5157)\n",
            "2021-03-12 09:13:41 - F1:                 48.59\t(Threshold: 0.0228)\n",
            "2021-03-12 09:13:41 - Precision:          37.44\n",
            "2021-03-12 09:13:41 - Recall:             69.20\n",
            "2021-03-12 09:13:41 - Average Precision:  39.56\n",
            "\n",
            "2021-03-12 09:13:41 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 09:23:09 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 9000 steps:\n",
            "2021-03-12 09:30:38 - Accuracy:           96.35\t(Threshold: 0.4985)\n",
            "2021-03-12 09:30:38 - F1:                 49.28\t(Threshold: 0.1060)\n",
            "2021-03-12 09:30:38 - Precision:          39.63\n",
            "2021-03-12 09:30:38 - Recall:             65.14\n",
            "2021-03-12 09:30:38 - Average Precision:  43.52\n",
            "\n",
            "2021-03-12 09:30:38 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 09:39:53 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 10000 steps:\n",
            "2021-03-12 09:47:22 - Accuracy:           96.33\t(Threshold: 0.5863)\n",
            "2021-03-12 09:47:22 - F1:                 49.89\t(Threshold: 0.0350)\n",
            "2021-03-12 09:47:22 - Precision:          37.32\n",
            "2021-03-12 09:47:22 - Recall:             75.21\n",
            "2021-03-12 09:47:22 - Average Precision:  44.85\n",
            "\n",
            "2021-03-12 09:47:22 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 09:56:34 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 11000 steps:\n",
            "2021-03-12 10:04:03 - Accuracy:           96.36\t(Threshold: 0.5389)\n",
            "2021-03-12 10:04:03 - F1:                 50.73\t(Threshold: 0.0387)\n",
            "2021-03-12 10:04:03 - Precision:          38.06\n",
            "2021-03-12 10:04:03 - Recall:             76.06\n",
            "2021-03-12 10:04:03 - Average Precision:  46.31\n",
            "\n",
            "2021-03-12 10:04:03 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 10:13:26 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 12000 steps:\n",
            "2021-03-12 10:20:55 - Accuracy:           96.46\t(Threshold: 0.5974)\n",
            "2021-03-12 10:20:55 - F1:                 51.98\t(Threshold: 0.1180)\n",
            "2021-03-12 10:20:55 - Precision:          40.65\n",
            "2021-03-12 10:20:55 - Recall:             72.08\n",
            "2021-03-12 10:20:55 - Average Precision:  48.33\n",
            "\n",
            "2021-03-12 10:20:55 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 10:30:15 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 13000 steps:\n",
            "2021-03-12 10:37:43 - Accuracy:           96.45\t(Threshold: 0.5390)\n",
            "2021-03-12 10:37:43 - F1:                 52.93\t(Threshold: 0.1904)\n",
            "2021-03-12 10:37:43 - Precision:          41.87\n",
            "2021-03-12 10:37:43 - Recall:             71.91\n",
            "2021-03-12 10:37:43 - Average Precision:  49.69\n",
            "\n",
            "2021-03-12 10:37:43 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 10:47:10 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 14000 steps:\n",
            "2021-03-12 10:54:37 - Accuracy:           96.42\t(Threshold: 0.6135)\n",
            "2021-03-12 10:54:37 - F1:                 49.98\t(Threshold: 0.0069)\n",
            "2021-03-12 10:54:37 - Precision:          40.45\n",
            "2021-03-12 10:54:37 - Recall:             65.40\n",
            "2021-03-12 10:54:37 - Average Precision:  42.01\n",
            "\n",
            "2021-03-12 11:03:54 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 0 after 15000 steps:\n",
            "2021-03-12 11:11:22 - Accuracy:           96.42\t(Threshold: 0.5791)\n",
            "2021-03-12 11:11:22 - F1:                 52.28\t(Threshold: 0.0159)\n",
            "2021-03-12 11:11:22 - Precision:          40.70\n",
            "2021-03-12 11:11:22 - Recall:             73.10\n",
            "2021-03-12 11:11:22 - Average Precision:  45.14\n",
            "\n",
            "\n",
            "2021-03-12 11:11:30 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset after epoch 0:\n",
            "2021-03-12 11:18:57 - Accuracy:           96.44\t(Threshold: 0.5735)\n",
            "2021-03-12 11:18:57 - F1:                 51.79\t(Threshold: 0.0369)\n",
            "2021-03-12 11:18:57 - Precision:          38.83\n",
            "2021-03-12 11:18:57 - Recall:             77.75\n",
            "2021-03-12 11:18:57 - Average Precision:  49.53\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "text": [
            "2021-03-12 11:28:20 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 1000 steps:\n",
            "2021-03-12 11:35:46 - Accuracy:           96.48\t(Threshold: 0.5647)\n",
            "2021-03-12 11:35:46 - F1:                 52.99\t(Threshold: 0.0085)\n",
            "2021-03-12 11:35:46 - Precision:          39.54\n",
            "2021-03-12 11:35:46 - Recall:             80.29\n",
            "2021-03-12 11:35:46 - Average Precision:  49.28\n",
            "\n",
            "2021-03-12 11:44:49 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 2000 steps:\n",
            "2021-03-12 11:52:15 - Accuracy:           96.53\t(Threshold: 0.5245)\n",
            "2021-03-12 11:52:15 - F1:                 55.16\t(Threshold: 0.1449)\n",
            "2021-03-12 11:52:15 - Precision:          42.62\n",
            "2021-03-12 11:52:15 - Recall:             78.17\n",
            "2021-03-12 11:52:15 - Average Precision:  51.54\n",
            "\n",
            "2021-03-12 11:52:15 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 12:01:37 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 3000 steps:\n",
            "2021-03-12 12:09:03 - Accuracy:           96.52\t(Threshold: 0.5016)\n",
            "2021-03-12 12:09:03 - F1:                 54.76\t(Threshold: 0.0302)\n",
            "2021-03-12 12:09:03 - Precision:          42.28\n",
            "2021-03-12 12:09:03 - Recall:             77.66\n",
            "2021-03-12 12:09:03 - Average Precision:  51.62\n",
            "\n",
            "2021-03-12 12:09:03 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 12:18:23 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 4000 steps:\n",
            "2021-03-12 12:25:48 - Accuracy:           96.50\t(Threshold: 0.5797)\n",
            "2021-03-12 12:25:48 - F1:                 53.93\t(Threshold: 0.0915)\n",
            "2021-03-12 12:25:48 - Precision:          41.28\n",
            "2021-03-12 12:25:48 - Recall:             77.75\n",
            "2021-03-12 12:25:48 - Average Precision:  49.27\n",
            "\n",
            "2021-03-12 12:35:11 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 5000 steps:\n",
            "2021-03-12 12:42:38 - Accuracy:           96.61\t(Threshold: 0.6589)\n",
            "2021-03-12 12:42:38 - F1:                 54.67\t(Threshold: 0.0067)\n",
            "2021-03-12 12:42:38 - Precision:          42.01\n",
            "2021-03-12 12:42:38 - Recall:             78.26\n",
            "2021-03-12 12:42:38 - Average Precision:  52.64\n",
            "\n",
            "2021-03-12 12:42:38 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 12:51:58 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 6000 steps:\n",
            "2021-03-12 12:59:26 - Accuracy:           96.62\t(Threshold: 0.5991)\n",
            "2021-03-12 12:59:26 - F1:                 55.93\t(Threshold: 0.0888)\n",
            "2021-03-12 12:59:26 - Precision:          42.77\n",
            "2021-03-12 12:59:26 - Recall:             80.80\n",
            "2021-03-12 12:59:26 - Average Precision:  55.24\n",
            "\n",
            "2021-03-12 12:59:26 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n",
            "2021-03-12 13:08:56 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 7000 steps:\n",
            "2021-03-12 13:16:23 - Accuracy:           96.63\t(Threshold: 0.5079)\n",
            "2021-03-12 13:16:23 - F1:                 56.29\t(Threshold: 0.0744)\n",
            "2021-03-12 13:16:23 - Precision:          41.42\n",
            "2021-03-12 13:16:23 - Recall:             87.82\n",
            "2021-03-12 13:16:23 - Average Precision:  53.62\n",
            "\n",
            "2021-03-12 13:25:35 - CEBinaryClassificationEvaluator: Evaluating the model on  dataset in epoch 1 after 8000 steps:\n",
            "2021-03-12 13:33:04 - Accuracy:           96.65\t(Threshold: 0.5032)\n",
            "2021-03-12 13:33:04 - F1:                 56.57\t(Threshold: 0.0207)\n",
            "2021-03-12 13:33:04 - Precision:          41.07\n",
            "2021-03-12 13:33:04 - Recall:             90.86\n",
            "2021-03-12 13:33:04 - Average Precision:  55.25\n",
            "\n",
            "2021-03-12 13:33:04 - Save model to output/FAQIR/models/softmax_simple_user_query_1.1\n"
          ],
          "name": "stdout"
        }
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    {
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