T5_wikiSQL_with_HF_transformers.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/T5_wikiSQL_with_HF_transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Tutorial to fine-tune Google's text2text/seq2seq T5 model on WikiSQL dataset to translate from SQL to Natural Language and vice versa (multitask).\n",
        "\n",
        "Author: **Manuel Romero** [mrm8488](https://twitter.com/mrm8488). NLP/NLG Senior Engineer at [Narrativa](https://narrativa.com/) and Head collaborator at [HuggingFace](https://huggingface.co/mrm8488)\n",
        "\n",
        "> Note: This is still WIP so you may find errors"
      ],
      "metadata": {
        "id": "RXr_ss_50qPf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PART 1\n",
        "## Fine-tune T5 (small) to translate form Natural Language to SQL"
      ],
      "metadata": {
        "id": "HfPmlkfMSsSu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The model\n",
        "## [Exploring Transfer Learning with **T5**: the Text-To-Text Transfer Transformer](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)"
      ],
      "metadata": {
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)\n",
        "\n"
      ],
      "metadata": {
        "id": "omPVRYUmUxup"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The dataset\n",
        "## [WikiSQL](https://github.com/salesforce/WikiSQL)\n",
        "### A large crowd-sourced dataset for developing natural language interfaces for relational databases. WikiSQL is the dataset released along with Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning."
      ],
      "metadata": {
        "id": "ae9yi7fiVl6b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training with 🤗 ecosystem"
      ],
      "metadata": {
        "id": "4EDo89yfWVEn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "21hGe7Mr0gos",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "314c5079-a527-458c-fb20-f274ec69bb80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Apr  3 20:59:28 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   43C    P0    25W / 300W |      0MiB / 16160MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -q transformers datasets"
      ],
      "metadata": {
        "id": "28K5CDBE08jh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HF auth"
      ],
      "metadata": {
        "id": "agUs01Ms12b_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KE_RTo78sqb-",
        "outputId": "8e5bc8f1-87d7-4ddf-d5eb-5bcae3db0f90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
            "        (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "IADibkaEs79C"
      },
      "outputs": [],
      "source": [
        "! git config --global credential.helper store"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Rv5ejYCus9TD"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "! sudo apt-get install git-lfs"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model and tokenizer"
      ],
      "metadata": {
        "id": "qZQ0lpecFdiF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CKPT = 't5-small'\n",
        "from transformers import AutoTokenizer, T5ForConditionalGeneration\n",
        "tokenizer = AutoTokenizer.from_pretrained(CKPT)\n",
        "model = T5ForConditionalGeneration.from_pretrained(CKPT)"
      ],
      "metadata": {
        "id": "XBTV-c1nFiAX"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset preparation"
      ],
      "metadata": {
        "id": "OcC0F2ED2CKJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's load the dataset"
      ],
      "metadata": {
        "id": "1UxMVyz5294I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "train_data = load_dataset('wikisql', split='train+validation')\n",
        "test_data = load_dataset('wikisql', split='test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hM3s5q981Ga6",
        "outputId": "1299c801-ba7f-46ba-a422-e052272ed9bc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n",
            "Reusing dataset wiki_sql (/root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d)\n",
            "Using custom data configuration default\n",
            "Reusing dataset wiki_sql (/root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's explore the dataset"
      ],
      "metadata": {
        "id": "ZewvnLHJ3CQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4b8roCmG1tTA",
        "outputId": "005aae69-e4bb-4e43-e59d-48e42200b60e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['phase', 'question', 'table', 'sql'],\n",
              "    num_rows: 64776\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5V6i4yG2omO",
        "outputId": "90297a95-3942-4d14-d201-75105a41ae3c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'phase': 1,\n",
              " 'question': 'Tell me what the notes are for South Australia ',\n",
              " 'sql': {'agg': 0,\n",
              "  'conds': {'column_index': [3],\n",
              "   'condition': ['SOUTH AUSTRALIA'],\n",
              "   'operator_index': [0]},\n",
              "  'human_readable': 'SELECT Notes FROM table WHERE Current slogan = SOUTH AUSTRALIA',\n",
              "  'sel': 5},\n",
              " 'table': {'caption': '',\n",
              "  'header': ['State/territory',\n",
              "   'Text/background colour',\n",
              "   'Format',\n",
              "   'Current slogan',\n",
              "   'Current series',\n",
              "   'Notes'],\n",
              "  'id': '1-1000181-1',\n",
              "  'name': 'table_1000181_1',\n",
              "  'page_id': '',\n",
              "  'page_title': '',\n",
              "  'rows': [['Australian Capital Territory',\n",
              "    'blue/white',\n",
              "    'Yaa·nna',\n",
              "    'ACT · CELEBRATION OF A CENTURY 2013',\n",
              "    'YIL·00A',\n",
              "    'Slogan screenprinted on plate'],\n",
              "   ['New South Wales',\n",
              "    'black/yellow',\n",
              "    'aa·nn·aa',\n",
              "    'NEW SOUTH WALES',\n",
              "    'BX·99·HI',\n",
              "    'No slogan on current series'],\n",
              "   ['New South Wales',\n",
              "    'black/white',\n",
              "    'aaa·nna',\n",
              "    'NSW',\n",
              "    'CPX·12A',\n",
              "    'Optional white slimline series'],\n",
              "   ['Northern Territory',\n",
              "    'ochre/white',\n",
              "    'Ca·nn·aa',\n",
              "    'NT · OUTBACK AUSTRALIA',\n",
              "    'CB·06·ZZ',\n",
              "    'New series began in June 2011'],\n",
              "   ['Queensland',\n",
              "    'maroon/white',\n",
              "    'nnn·aaa',\n",
              "    'QUEENSLAND · SUNSHINE STATE',\n",
              "    '999·TLG',\n",
              "    'Slogan embossed on plate'],\n",
              "   ['South Australia',\n",
              "    'black/white',\n",
              "    'Snnn·aaa',\n",
              "    'SOUTH AUSTRALIA',\n",
              "    'S000·AZD',\n",
              "    'No slogan on current series'],\n",
              "   ['Victoria',\n",
              "    'blue/white',\n",
              "    'aaa·nnn',\n",
              "    'VICTORIA - THE PLACE TO BE',\n",
              "    'ZZZ·562',\n",
              "    'Current series will be exhausted this year']],\n",
              "  'section_title': '',\n",
              "  'types': ['text', 'text', 'text', 'text', 'text', 'text']}}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's format the dataset\n"
      ],
      "metadata": {
        "id": "x7L8T_VN3LYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def format_dataset(example):\n",
        " return {'input': 'translate to SQL: ' + example['question'], 'target': example['sql']['human_readable']}"
      ],
      "metadata": {
        "id": "gaTsiEd0D-hU"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = train_data.map(format_dataset, remove_columns=train_data.column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mt-c0E0GEPI7",
        "outputId": "082775a9-734f-4813-f4b2-3f2ccafb80fb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-f6eaa945d21cf0a1.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = test_data.map(format_dataset, remove_columns=test_data.column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg0pAzLQE68T",
        "outputId": "78cceed5-4aaf-4a4e-c63a-1ed70ebf743d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-e2cff5b3a1b5b6d3.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgWNGEJPFIdA",
        "outputId": "c3af8cfe-d4a8-463c-be25-26e35f4a0dec"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input', 'target'],\n",
              "    num_rows: 64776\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's calculate the inputs and targets lengths"
      ],
      "metadata": {
        "id": "Apw09O1-M07K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# map article and summary len to dict as well as if sample is longer than 512 tokens\n",
        "def map_to_length(x):\n",
        "  x[\"input_len\"] = len(tokenizer(x[\"input\"]).input_ids)\n",
        "  x[\"input_longer_256\"] = int(x[\"input_len\"] > 256)\n",
        "  x[\"input_longer_128\"] = int(x[\"input_len\"] > 128)\n",
        "  x[\"input_longer_64\"] = int(x[\"input_len\"] > 64)\n",
        "  x[\"out_len\"] = len(tokenizer(x[\"target\"]).input_ids)\n",
        "  x[\"out_longer_256\"] = int(x[\"out_len\"] > 256)\n",
        "  x[\"out_longer_128\"] = int(x[\"out_len\"] > 128)\n",
        "  x[\"out_longer_64\"] = int(x[\"out_len\"] > 64)\n",
        "  return x\n",
        "\n",
        "sample_size = 10000\n",
        "data_stats = train_data.select(range(sample_size)).map(map_to_length, num_proc=4)\n",
        "\n",
        "def compute_and_print_stats(x):\n",
        "  if len(x[\"input_len\"]) == sample_size:\n",
        "    print(\n",
        "        \"Input Mean: {}, %-Input > 256:{},  %-Input > 128:{}, %-Input > 64:{} Output Mean:{}, %-Output > 256:{}, %-Output > 128:{}, %-Output > 64:{}\".format(\n",
        "            sum(x[\"input_len\"]) / sample_size,\n",
        "            sum(x[\"input_longer_256\"]) / sample_size,\n",
        "            sum(x[\"input_longer_128\"]) / sample_size,\n",
        "            sum(x[\"input_longer_64\"]) / sample_size,   \n",
        "            sum(x[\"out_len\"]) / sample_size,\n",
        "            sum(x[\"out_longer_256\"]) / sample_size,\n",
        "            sum(x[\"out_longer_128\"]) / sample_size,\n",
        "            sum(x[\"out_longer_64\"]) / sample_size,\n",
        "        )\n",
        "    )\n",
        "\n",
        "output = data_stats.map(\n",
        "  compute_and_print_stats, \n",
        "  batched=True,\n",
        "  batch_size=-1,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "dab3b41563e2413493b796a22776a6d4",
            "a821ed3f960d44b99aefa77814fa23f1",
            "49e47d1b79cf46a3881843ff210016bb",
            "c740bd8b679448d9ae4badc67c36400f",
            "cc7ec75bd9d14f4caa3ce9f87ff7bfbb",
            "cb4cf1ebfc514891a253d9cd6a874ffc",
            "03772b2541dd43c5a614829c7155cdc3",
            "a2d910b8525c40879a4a31d805e384ab",
            "72ce6fb8cb244ced88abc340f5bf1258",
            "96f366309e644e0cbe7411f4c49f2c10",
            "8a378b1afb7a407f93dacd7b6f10a7a7"
          ]
        },
        "id": "LT7ovuoJFLFC",
        "outputId": "6c0f72bd-4e10-46d2-be64-e02826a2591e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-1863bb36f979efb5.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-5950cb3095aabe7d.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-91b402e4a19939bd.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-83eac86c86834f97.arrow\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dab3b41563e2413493b796a22776a6d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input Mean: 19.8508, %-Input > 256:0.0,  %-Input > 128:0.0, %-Input > 64:0.0002 Output Mean:20.0403, %-Output > 256:0.0, %-Output > 128:0.0002, %-Output > 64:0.0005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see input and output mean are around 20 tokens. So we should not use less than 20 token in both econder and decoder to keep all the info there. For this example we will use 64, but we could use 32."
      ],
      "metadata": {
        "id": "48n7qnn9NAES"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's tokenize our data"
      ],
      "metadata": {
        "id": "ruygyHEAR-BL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenize the examples\n",
        "def convert_to_features(example_batch):\n",
        "    input_encodings = tokenizer.batch_encode_plus(example_batch['input'], pad_to_max_length=True, max_length=64)\n",
        "    target_encodings = tokenizer.batch_encode_plus(example_batch['target'], pad_to_max_length=True, max_length=64)\n",
        "\n",
        "    encodings = {\n",
        "        'input_ids': input_encodings['input_ids'], \n",
        "        'attention_mask': input_encodings['attention_mask'],\n",
        "        'labels': target_encodings['input_ids'],\n",
        "        'decoder_attention_mask': target_encodings['attention_mask']\n",
        "    }\n",
        "\n",
        "    return encodings"
      ],
      "metadata": {
        "id": "ppkB_UwQH7a6"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = train_data.map(convert_to_features, batched=True, remove_columns=train_data.column_names)\n",
        "test_data = test_data.map(convert_to_features, batched=True, emove_columns=test_data.column_names)\n",
        "\n",
        "columns = ['input_ids', 'attention_mask', 'labels', 'decoder_attention_mask']\n",
        "\n",
        "train_data.set_format(type='torch', columns=columns)\n",
        "test_data.set_format(type='torch', columns=columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxPcQH1aIEj_",
        "outputId": "a808392d-d5be-4ee4-b2d3-cfc997f07e74"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-650d53a36abe5b77.arrow\n",
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d/cache-74e255cfb147a24f.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "from transformers import Seq2SeqTrainingArguments"
      ],
      "metadata": {
        "id": "wRNxG52qIYMf"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set the training arguments"
      ],
      "metadata": {
        "id": "A2cbQnUJSG4i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set training arguments - Feel free to adapt it\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"/content/t5-small-finetuned-wikisql\",\n",
        "    per_device_train_batch_size=16,\n",
        "    num_train_epochs=5,\n",
        "    per_device_eval_batch_size=16,\n",
        "    predict_with_generate=True,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "    logging_steps=500,\n",
        "    save_strategy=\"epoch\",\n",
        "    #save_steps=1000,\n",
        "    #eval_steps=1000,\n",
        "    overwrite_output_dir=True,\n",
        "    save_total_limit=3,\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=True\n",
        "    #fp16=True, \n",
        ")"
      ],
      "metadata": {
        "id": "ZH-HHDI0IWrX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's define a metric to track our training progress"
      ],
      "metadata": {
        "id": "0FrvqN66N20E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -q rouge_score"
      ],
      "metadata": {
        "id": "r5_gHE2wJeE0"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "rouge = load_metric(\"rouge\")\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    labels_ids = pred.label_ids\n",
        "    pred_ids = pred.predictions\n",
        "\n",
        "    # all unnecessary tokens are removed\n",
        "    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n",
        "    label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n",
        "\n",
        "    rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n",
        "\n",
        "    return {\n",
        "        \"rouge2_precision\": round(rouge_output.precision, 4),\n",
        "        \"rouge2_recall\": round(rouge_output.recall, 4),\n",
        "        \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n",
        "    }"
      ],
      "metadata": {
        "id": "SqWbevh5Kelo"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's create a `Trainer` instance"
      ],
      "metadata": {
        "id": "y-Q4lxu8SMGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiate trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=test_data,\n",
        ")"
      ],
      "metadata": {
        "id": "IAbtaix-KnXC"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's run a sanity check"
      ],
      "metadata": {
        "id": "UjNABeOSN-dT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "id": "c-MSUMJqKrqb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "3cf2a23e-af5b-45c1-9f74-8b8814dd13b7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: input, target. If input, target are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15878\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='993' max='993' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [993/993 03:49]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 8.005945205688477,\n",
              " 'eval_rouge2_fmeasure': 0.0778,\n",
              " 'eval_rouge2_precision': 0.0785,\n",
              " 'eval_rouge2_recall': 0.0796,\n",
              " 'eval_runtime': 232.7062,\n",
              " 'eval_samples_per_second': 68.232,\n",
              " 'eval_steps_per_second': 4.267}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Track the training in real time using `tensorboard`"
      ],
      "metadata": {
        "id": "z1pKhH-YRQQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir /content/t5-small-finetuned-wikisql/runs"
      ],
      "metadata": {
        "id": "2uiF7tA8KvxQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "qZR_ASsEK5Lp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7d3b37cc-29a5-4bfe-ee49-38bdad08f864"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: input, target. If input, target are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 64776\n",
            "  Num Epochs = 5\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 20245\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='20245' max='20245' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [20245/20245 47:11, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge2 Precision</th>\n",
              "      <th>Rouge2 Recall</th>\n",
              "      <th>Rouge2 Fmeasure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.191800</td>\n",
              "      <td>0.156427</td>\n",
              "      <td>0.792600</td>\n",
              "      <td>0.702900</td>\n",
              "      <td>0.738100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.165500</td>\n",
              "      <td>0.138190</td>\n",
              "      <td>0.809200</td>\n",
              "      <td>0.717100</td>\n",
              "      <td>0.753400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.152500</td>\n",
              "      <td>0.129753</td>\n",
              "      <td>0.815400</td>\n",
              "      <td>0.723700</td>\n",
              "      <td>0.759700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.144300</td>\n",
              "      <td>0.126204</td>\n",
              "      <td>0.816300</td>\n",
              "      <td>0.724100</td>\n",
              "      <td>0.760300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.141400</td>\n",
              "      <td>0.124436</td>\n",
              "      <td>0.817500</td>\n",
              "      <td>0.725800</td>\n",
              "      <td>0.761800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: input, target. If input, target are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15878\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql/checkpoint-4049\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql/checkpoint-4049/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql/checkpoint-4049/pytorch_model.bin\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: input, target. If input, target are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15878\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql/checkpoint-8098\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql/checkpoint-8098/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql/checkpoint-8098/pytorch_model.bin\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: input, target. If input, target are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15878\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql/checkpoint-12147\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql/checkpoint-12147/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql/checkpoint-12147/pytorch_model.bin\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: input, target. If input, target are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15878\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql/checkpoint-16196\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql/checkpoint-16196/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql/checkpoint-16196/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/t5-small-finetuned-wikisql/checkpoint-4049] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: input, target. If input, target are not expected by `T5ForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 15878\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql/checkpoint-20245\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql/checkpoint-20245/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql/checkpoint-20245/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/t5-small-finetuned-wikisql/checkpoint-8098] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from /content/t5-small-finetuned-wikisql/checkpoint-20245 (score: 0.12443569302558899).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=20245, training_loss=0.17887339593157647, metrics={'train_runtime': 2831.3401, 'train_samples_per_second': 114.391, 'train_steps_per_second': 7.15, 'total_flos': 5479312832593920.0, 'train_loss': 0.17887339593157647, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4f_eBTCeTHA",
        "outputId": "6c0ea618-bb69-43b2-a999-24f0f941b30e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql/pytorch_model.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.save_pretrained('/content/t5-small-finetuned-wikisql')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5d-QcwKnebYY",
        "outputId": "059c235f-8bc0-43ac-96de-4d60d865a3a9"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "tokenizer config file saved in /content/t5-small-finetuned-wikisql/tokenizer_config.json\n",
            "Special tokens file saved in /content/t5-small-finetuned-wikisql/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/t5-small-finetuned-wikisql/tokenizer_config.json',\n",
              " '/content/t5-small-finetuned-wikisql/special_tokens_map.json',\n",
              " '/content/t5-small-finetuned-wikisql/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.create_model_card()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrBFmJbdW9XV",
        "outputId": "7a1d0db3-2913-4d5c-94a4-491266246e57"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}, 'dataset': {'name': 'wiki_sql', 'type': 'wiki_sql', 'args': 'default'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "id": "nTn9FjGKo0S7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test the model"
      ],
      "metadata": {
        "id": "wsJum5nEcHWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CKPT = '/content/t5-small-finetuned-wikisql'\n",
        "from transformers import AutoTokenizer, T5ForConditionalGeneration\n",
        "tokenizer = AutoTokenizer.from_pretrained(CKPT)\n",
        "model = T5ForConditionalGeneration.from_pretrained(CKPT)"
      ],
      "metadata": {
        "id": "HeaD6sejb7hg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = load_dataset('wikisql', split='test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGCO9LvFdAG6",
        "outputId": "9b6607ce-a283-4c56-9c5c-900e7b5c8ea6"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n",
            "Reusing dataset wiki_sql (/root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def translate_to_sql(text):\n",
        "    inputs = tokenizer(text, padding='longest', max_length=64, return_tensors='pt')\n",
        "    input_ids = inputs.input_ids\n",
        "    attention_mask = inputs.attention_mask\n",
        "    output = model.generate(input_ids, attention_mask=attention_mask, max_length=64)\n",
        "\n",
        "    return tokenizer.decode(output[0], skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "mAMMJJJvcQ38"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,100,10):\n",
        "  print('translate to SQL: ' + test_data[i]['question'])\n",
        "  print('Predict. :' + translate_to_sql('translate to SQL: ' + test_data[i]['question']))\n",
        "  print('Expected: ' + test_data[i]['sql']['human_readable'])\n",
        "  print('=================================\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbuoLD-hcpPF",
        "outputId": "d90012df-9258-4a35-e356-91e6ed499f6b"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "translate to SQL: What is terrence ross' nationality\n",
            "Predict. :SELECT Nationality FROM table WHERE Player = Terrence Ross\n",
            "Expected: SELECT Nationality FROM table WHERE Player = Terrence Ross\n",
            "=================================\n",
            "\n",
            "translate to SQL: How many different nationalities do the players of New Jersey Devils come from?\n",
            "Predict. :SELECT COUNT Nationality FROM table WHERE Team = New Jersey Devils\n",
            "Expected: SELECT COUNT Nationality FROM table WHERE NHL team = New Jersey Devils\n",
            "=================================\n",
            "\n",
            "translate to SQL: If you are a pilot officer in the commonwealth then what will you called as in the US air force?\n",
            "Predict. :SELECT US air force FROM table WHERE Position = pilot officer AND Country = commonwealth\n",
            "Expected: SELECT US Air Force equivalent FROM table WHERE Commonwealth equivalent = Pilot Officer\n",
            "=================================\n",
            "\n",
            "translate to SQL: what is the total number of total w–l where doubles w–l is 11–11\n",
            "Predict. :SELECT COUNT Total W–L FROM table WHERE Doubles W–L = 11–11\n",
            "Expected: SELECT COUNT Total W–L FROM table WHERE Doubles W–L = 11–11\n",
            "=================================\n",
            "\n",
            "translate to SQL: Which Frequency is used for WEGP calls?\n",
            "Predict. :SELECT Frequency FROM table WHERE Calls = WEGP\n",
            "Expected: SELECT Frequency FROM table WHERE Calls = WEGP\n",
            "=================================\n",
            "\n",
            "translate to SQL: If a radius is 10, what  is the lowest possible mass?\n",
            "Predict. :SELECT MIN Mass FROM table WHERE Radius = 10\n",
            "Expected: SELECT MIN Mass (M ☉ ) FROM table WHERE Radius (R ☉ ) = 10\n",
            "=================================\n",
            "\n",
            "translate to SQL: what's the new south wales with crop (kilotonnes) being canola\n",
            "Predict. :SELECT New South Wales FROM table WHERE Crop (Kilotonnes) = Canola\n",
            "Expected: SELECT New South Wales FROM table WHERE Crop (kilotonnes) = Canola\n",
            "=================================\n",
            "\n",
            "translate to SQL: If % lunsford is 51.82% what is the % mcconnell in Letcher?\n",
            "Predict. :SELECT % McConnell FROM table WHERE % Lunsford = 51.82%\n",
            "Expected: SELECT % McConnell FROM table WHERE % Lunsford = 51.82%\n",
            "=================================\n",
            "\n",
            "translate to SQL: What is the percentage of the Shivalik Zone where the percentage of the Mid-Hill Zone is 10%?\n",
            "Predict. :SELECT Shivalik Zone FROM table WHERE Mid-Hill Zone = 10%\n",
            "Expected: SELECT Shivalik Zone FROM table WHERE Mid-Hill Zone = 10%\n",
            "=================================\n",
            "\n",
            "translate to SQL: How many episodes in season 6 titles \"Poppin' Tags\"?\n",
            "Predict. :SELECT COUNT Season 6 FROM table WHERE Title = \"Poppin' Tags\"\n",
            "Expected: SELECT COUNT No. in season FROM table WHERE Title = \"Poppin' Tags\"\n",
            "=================================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Custom example"
      ],
      "metadata": {
        "id": "7iZtACF4g1Hq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"translate to SQL: How many model with BERT architecture are in the HuggingFace Hub?\"\n",
        "translate_to_sql(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "PO1OvqSedZhI",
        "outputId": "1cea30ac-e49d-4fc1-8332-338053bc6646"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'SELECT COUNT Model FROM table WHERE Architecture = BERT AND Hub = HuggingFace'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-V7bmzeQgqtA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Acknowledgments\n",
        "\n",
        "- [Narrativa](https://narrativa.com)\n",
        "- [HuggingFace](https://huggingface.co/mrm8488)\n",
        "- [QBLOCKS](https://www.qblocks.cloud/)"
      ],
      "metadata": {
        "id": "MOx78lCspgyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "61dslGdDqHfA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}