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        "# 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": {
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      }
    },
    {
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        "## PART 2\n",
        "## Fine-tune T5 (small) to translate form SQL to Natural Language and vice versa (multitask)"
      ],
      "metadata": {
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      }
    },
    {
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      "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)"
      ],
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        "\n"
      ],
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    },
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      "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."
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Apr  7 17:25:57 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   33C    P0    23W / 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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dd58cfc-fe33-4a46-de62-2de3789426a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.0 MB 14.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 325 kB 52.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.5 MB 56.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 67.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 895 kB 53.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 77 kB 7.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 46.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 136 kB 73.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 69.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 35.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 51.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 144 kB 13.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 94 kB 849 kB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HF auth"
      ],
      "metadata": {
        "id": "agUs01Ms12b_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KE_RTo78sqb-",
        "outputId": "016043a8-8d89-4445-fac6-b1936fdf08ba"
      },
      "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/tokens.\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",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IADibkaEs79C"
      },
      "outputs": [],
      "source": [
        "! git config --global credential.helper store"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "8c5e238ae33548b480a97151388fb5ba",
            "f18f81c13a764b868bb9f200b60c6f86",
            "9c9caae416f64aa6ae9b5d7f5ee67133",
            "0e91bc53f560459db247f67b944ecceb",
            "687bf5ceeaa64b9aab2ef9cf2f1f1fce",
            "3906fc60e62b4e54a3028576ab08df1f",
            "37bf88f5dc0e4cd096af13168cba5ca1",
            "7d4cb4717e7f4f849c23a95cb2f53a68",
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            "2be1635041194714b787c7a478b0718d",
            "99876d8933224e6a8bcfbe5439c5711e",
            "d44e0f21075c457cb23745d2a9dfb49a",
            "56cf872df766460bac2bf7d8539271ec",
            "a0684021ee884432a327df25ca4af76b",
            "9e5c67c13c904e409c9ab650070d9fd6",
            "b2b7c201551149ab88736e8f34f92071",
            "608a999c15584d4a99139e99a8223533",
            "72fb80b094e24693815026b046a4418d",
            "537d70b4f61a48b7839493eb0d41c8ab",
            "06444928d45d45819e67b4269f6bdadb",
            "af8cca31130d418fabf0235cbf7f2a2b",
            "95d0645680714d46994de846b2258011",
            "32d20697b63f4b38b03576965694138c",
            "507cada48ea04bd79abff809f76f5b08",
            "35f0c4b0ba734bfea2f96523fe1d24c6",
            "0c7220ac0e41429197d81800cb5f4710",
            "e851ec568eef45529d79c9c10f0357d1",
            "bc175cb3deff4280b1a8a799f7ac0726",
            "bf9bd4054609471ca531dac50eb2948e",
            "ad925a3067ed4be0a0e4bccc270bc05c",
            "96430340623941fa833a08f54b2bbcd9",
            "ae72cc0a968742c0adf997b0a876c2ad",
            "4d07750eff5e4bc3be1747c986c30c32",
            "a9ce9a68abc845cc9504c75ae0b80996",
            "fc2d7d0ec7f14e4da6d5ad0e6955a55b",
            "710dd6fa6f1341c9b7f9405aa762fa13",
            "e7e5d113f5b34a3dbdc3d0469f7763c7",
            "5e0094a7108c4ca1a1a727cd5313e26e",
            "6ef76a5167ff472b943621488c7c5a1e",
            "f10ace8303af423da0af70944eb43137",
            "6d705ca1c2f7492fabf5ca2d7b0b8c0e",
            "c333f773bce04a57bc35e7ae490477a6",
            "45315543539445d79aa3cb311e385baa",
            "0786ca3e94634916b6dea19cb5b45a03"
          ]
        },
        "outputId": "48596e7b-8941-43c2-9f5d-505c71a98d23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c5e238ae33548b480a97151388fb5ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/773k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d44e0f21075c457cb23745d2a9dfb49a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.32M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "32d20697b63f4b38b03576965694138c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/231M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9ce9a68abc845cc9504c75ae0b80996"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
        "ds_1 = load_dataset('wikisql')\n",
        "ds_2 = load_dataset('wikisql')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
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            "660890a0821841399ccc807820f644ab",
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            "f3e14cbfa6074be7bfe0e04501e3df56",
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            "3d6bb484b75c4660a726b80b80c42664",
            "6271f26bebe84e66afc64f0da7801817",
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            "5d9998db2fe44bb4b9790877a3eb9c33",
            "fa9be0c090974db98b0cb1a3d803e62d",
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            "06848e3d04e04b4191cd14c6b730ce14",
            "07c50181e14240d3a09ec63b4a856d5b",
            "a01c1a16af7d47f892f1647e2482c6bd",
            "91b659310f7f47fc9298bba9e9837f88",
            "9782b5c446014a61a83d8da019d97a20",
            "966b29971cdf449280a70aab0bcd6ab0",
            "21b6991ee4f143278c00da82aa8748d5",
            "c4bcdc805c524ca5a4fb851b765ef7db",
            "e87b5da3ae774960a31cd94ab6a683dd",
            "2b9adbde2bb14600b343cf7b49b78cfc",
            "f764c29daac945b2a86103c1eed74b8c",
            "d953331707524570ba79434674bc1b9e",
            "844e28d4b91040deb0ff5ec4d7583030",
            "f46dab94698e4afcae4cfc93c41124bb",
            "651235f922254ce0b123c079f3bcce45",
            "bd24b3eef2f242b2a2147ec136d314b2",
            "fbdbc4683f334489bd779162ceac7781",
            "d08d7f83ba0e4493a2e5728b412a6e72",
            "a00f54749e144b4d86805c450c10ea3b",
            "5ddfa190f35f475fb11938db2a49191e",
            "455e9c69030041cbaf34a960b3812222",
            "2a0d53df2b1f4b1181b7db222cde2582"
          ]
        },
        "id": "hM3s5q981Ga6",
        "outputId": "c4831c2f-db07-4e21-9ff5-fcff7aa76182"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.87k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a430654389c4d9590ccf544b6cad9ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/875 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "816a80692c72439b8daf07a49939b219"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset wiki_sql/default (download: 24.95 MiB, generated: 147.57 MiB, post-processed: Unknown size, total: 172.52 MiB) to /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/26.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cdddc75eb4ff47d2b3c502fd47b2a7a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/15878 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d4377edf933438eae255e425758bf4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/8421 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36a8487885df4ad6a992066959ff3961"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/56355 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6271f26bebe84e66afc64f0da7801817"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset wiki_sql downloaded and prepared to /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06848e3d04e04b4191cd14c6b730ce14"
            }
          },
          "metadata": {}
        },
        {
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d953331707524570ba79434674bc1b9e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's explore the dataset"
      ],
      "metadata": {
        "id": "ZewvnLHJ3CQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4b8roCmG1tTA",
        "outputId": "efdabc5e-e288-49aa-afd9-f34dbb3f69a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['phase', 'question', 'table', 'sql'],\n",
              "        num_rows: 15878\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['phase', 'question', 'table', 'sql'],\n",
              "        num_rows: 8421\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['phase', 'question', 'table', 'sql'],\n",
              "        num_rows: 56355\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_1['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5V6i4yG2omO",
        "outputId": "c80d0966-88ba-4595-f11b-0097bdf0ee6f"
      },
      "execution_count": null,
      "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 datasets\n"
      ],
      "metadata": {
        "id": "x7L8T_VN3LYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def format_dataset_1(example):\n",
        " return {'input': 'translate to SQL: ' + example['question'], 'target': example['sql']['human_readable']}"
      ],
      "metadata": {
        "id": "gaTsiEd0D-hU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_dataset_2(example):\n",
        " return {'input': 'translate to English: ' + example['sql']['human_readable'], 'target': example['question'] }"
      ],
      "metadata": {
        "id": "v42UbsbaK-NM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds_1 = ds_1.map(format_dataset_1, remove_columns=ds_1['train'].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "da146937a9a844f69920b39496fd8b20",
            "7db909b15bfd460fb205319c59b86e62",
            "3a72359f322a4505b747aa4929f82fa1",
            "c91238d7d07644ed985c06a9cd12fd00",
            "7ba8e650e1824fabbaf52ef1dcdb2900",
            "0fa2531d2e68406aaf9208226e8364fa",
            "16ed197629424481b1f19db24e52b6b8",
            "b19d2de23efe4558843857f996bfc0c3",
            "d2428edb6c964f779859736f8738524c",
            "e64004b2442e4679bc1eceee4c639a73",
            "445c8f869525499bae1da88964fc0110",
            "db51909020d346b28d9897eaa58074d1",
            "385998a7a3924076965bfa234763a4cd",
            "22d8ae0a9261452b9c15650256ccaee7",
            "8aebd2aecff241949215ee195cb5d48e",
            "fc1a8defab60447fbaca4fd2257cb23a",
            "b1f3ed79971646ab86cd87d981896e12",
            "e0dfdb3161a44e28b2169d7293d6de68",
            "b4733c6d5d4e4142a6d6be92f9f3c64c",
            "bff3ab25251048a4a248e7c9f0e10485",
            "75f9098038fc4416864a9e9d6135026b",
            "4eabe8c7984e4f74ad1ef95310ece633",
            "1023f3658000406b8ee83e2bc56f49a3",
            "f11dad14d15c433abf52d17930fc21ce",
            "30693eb6de1c45ac89b8735c8741b981",
            "416e5ef1acf14a6da489bfc7c2face25",
            "cf28aeb458a44ce2a443d5fb75172d80",
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            "b1f0bd37f74547e688bad3369648a2f6",
            "e90440847c2344c3b0794823fd0e00fb"
          ]
        },
        "id": "mt-c0E0GEPI7",
        "outputId": "816ea47a-1e91-48ed-de4d-8a0c06579e9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15878 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "da146937a9a844f69920b39496fd8b20"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_2 = ds_2.map(format_dataset_2, remove_columns=ds_2['train'].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "78857472d95c444ab8df2534b8790ef1",
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            "711f9e3f636e4eeaad909f47270196c2",
            "3e63ba0595dc4a5aaf73ceec0f4e0094",
            "9f682d71205e42ffa7aac8de91a2750c",
            "cf562c162f77477d98b89aa07cc3008c",
            "c59d666dafad473c944469479de7df23",
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        },
        "id": "SQK3NzyzLd4-",
        "outputId": "a2c29a9c-6688-4fd3-b55e-b095d966e11d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15878 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/8421 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/56355 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7938ffd4397a49a0a9368cd76477cc7c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_1['train'][0], ds_2['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg0pAzLQE68T",
        "outputId": "592a779a-dcaf-4449-a183-e7cc6de257c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'input': 'translate to SQL: Tell me what the notes are for South Australia ',\n",
              "  'target': 'SELECT Notes FROM table WHERE Current slogan = SOUTH AUSTRALIA'},\n",
              " {'input': 'translate to English: SELECT Notes FROM table WHERE Current slogan = SOUTH AUSTRALIA',\n",
              "  'target': 'Tell me what the notes are for South Australia '})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_1, ds_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJxcFckBN5zi",
        "outputId": "71b2385c-5c12-4435-bbc5-8b8b5e5bfe00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['input', 'target'],\n",
              "        num_rows: 15878\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input', 'target'],\n",
              "        num_rows: 8421\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['input', 'target'],\n",
              "        num_rows: 56355\n",
              "    })\n",
              "}),\n",
              " DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['input', 'target'],\n",
              "        num_rows: 15878\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input', 'target'],\n",
              "        num_rows: 8421\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['input', 'target'],\n",
              "        num_rows: 56355\n",
              "    })\n",
              "}))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "train_data = concatenate_datasets([ds_1['train'], ds_2['train'], ds_1['validation'], ds_2['validation']]).shuffle(seed=42)\n",
        "test_data = concatenate_datasets([ds_1['test'], ds_2['test']]).shuffle(seed=42)"
      ],
      "metadata": {
        "id": "bgWNGEJPFIdA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oecuquU-Ore8",
        "outputId": "1b3da0b8-d19c-4dc0-fd74-c527e0bf4449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset({\n",
              "     features: ['input', 'target'],\n",
              "     num_rows: 129552\n",
              " }), Dataset({\n",
              "     features: ['input', 'target'],\n",
              "     num_rows: 31756\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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": 229,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "   "
          ]
        },
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input Mean: 23.6118, %-Input > 256:0.0,  %-Input > 128:0.0, %-Input > 64:0.0005 Output Mean:19.6397, %-Output > 256:0.0, %-Output > 128:0.0, %-Output > 64:0.0004\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": null,
      "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, remove_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/",
          "height": 153,
          "referenced_widgets": [
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        },
        "id": "JxPcQH1aIEj_",
        "outputId": "41908433-3339-4ab5-d09a-5a94a6ee844a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/130 [00:00<?, ?ba/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2269: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?ba/s]"
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              "model_id": "80c6fcc66a774a8ea676f1db6aa230fe"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "from transformers import Seq2SeqTrainingArguments"
      ],
      "metadata": {
        "id": "wRNxG52qIYMf"
      },
      "execution_count": null,
      "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-sql-nl-nl-sql\",\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": null,
      "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 sacrebleu"
      ],
      "metadata": {
        "id": "r5_gHE2wJeE0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afa26af2-c942-4f3f-dd56-4a50c755ebdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |███▋                            | 10 kB 25.9 MB/s eta 0:00:01\r\u001b[K     |███████▏                        | 20 kB 8.7 MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 30 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 40 kB 7.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 51 kB 3.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▋          | 61 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 71 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 81 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 90 kB 3.8 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"sacrebleu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "923e41b2d0eb4df18bfa1aa78657efd8",
            "52654b6e98134d709b142157fdc90b05",
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        },
        "id": "VFGfl47JgKy1",
        "outputId": "1bc6ff38-fdaa-408c-d746-88334080e2d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.36k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [[label.strip()] for label in labels]\n",
        "    return preds, labels\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "    result = {\"bleu\": result[\"score\"]}\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    result = {k: round(v, 4) for k, v in result.items()}\n",
        "    return result"
      ],
      "metadata": {
        "id": "WUNV11YJfg8Q"
      },
      "execution_count": null,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e688ce8-c8f0-4a3e-d535-8900020655b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/mrm8488/t5-small-finetuned-wikisql-sql-nl-nl-sql into local empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's run a sanity check"
      ],
      "metadata": {
        "id": "UjNABeOSN-dT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate(eval_dataset=test_data.select(range(100)))"
      ],
      "metadata": {
        "id": "c-MSUMJqKrqb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "e3436aa4-05ca-408e-c91b-e4d05ea1c696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 100\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='7' max='7' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7/7 00:01]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_bleu': 3.7289,\n",
              " 'eval_gen_len': 17.89,\n",
              " 'eval_loss': 7.404248237609863,\n",
              " 'eval_runtime': 1.5812,\n",
              " 'eval_samples_per_second': 63.243,\n",
              " 'eval_steps_per_second': 4.427}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "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-sql-nl-nl-sql/runs"
      ],
      "metadata": {
        "id": "2uiF7tA8KvxQ",
        "colab": {
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              "headers": [
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            "https://localhost:6006/data/environment": {
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                ]
              ],
              "status": 200,
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            },
            "https://localhost:6006/data/runs": {
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              "headers": [
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              ],
              "status": 200,
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            },
            "https://localhost:6006/data/plugin/scalars/tags": {
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              "headers": [
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                ]
              ],
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              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=eval%2Fbleu": {
              "data": "W1sxNjQ5MzQzMzA3LjM3MzU4ODYsIDAsIDMuNzI4ODk5OTU1NzQ5NTExN10sIFsxNjQ5MzQ0MzgxLjIxOTg1NTUsIDgwOTcsIDM5Ljc5OTkwMDA1NDkzMTY0XSwgWzE2NDkzNDU1MTAuNjg1MDMxMiwgMTYxOTQsIDQwLjk0NTU5ODYwMjI5NDkyXSwgWzE2NDkzNDY2MzIuNjM2NjI2NywgMjQyOTEsIDQxLjM1MDg5ODc0MjY3NTc4XSwgWzE2NDkzNDc3MzguOTA2MTQ4LCAzMjM4OCwgNDEuNjk4Nzk5MTMzMzAwNzhdXQ==",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=eval%2Floss": {
              "data": "W1sxNjQ5MzQzMzA3LjM3MzUxODcsIDAsIDcuNDA0MjQ4MjM3NjA5ODYzXSwgWzE2NDkzNDQzODEuMjE5NzU0MiwgODA5NywgMC4yMjUxNjY5MzE3NDgzOTAyXSwgWzE2NDkzNDU1MTAuNjg0OTM0NiwgMTYxOTQsIDAuMjA2NjAzNTg2NjczNzM2NTddLCBbMTY0OTM0NjYzMi42MzY1MzM1LCAyNDI5MSwgMC4xOTg1MTIwMzI2MjgwNTk0XSwgWzE2NDkzNDc3MzguOTA2MDI4MywgMzIzODgsIDAuMTk0NDE5NDQzNjA3MzMwMzJdXQ==",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=eval%2Fgen_len": {
              "data": "W1sxNjQ5MzQzMzA3LjM3MzYxOCwgMCwgMTcuODg5OTk5Mzg5NjQ4NDM4XSwgWzE2NDkzNDQzODEuMjE5ODg5NCwgODA5NywgMTYuNjg5MzAwNTM3MTA5Mzc1XSwgWzE2NDkzNDU1MTAuNjg1MDY5MywgMTYxOTQsIDE2LjY3MTE5OTc5ODU4Mzk4NF0sIFsxNjQ5MzQ2NjMyLjYzNjY2MDMsIDI0MjkxLCAxNi41ODgzOTk4ODcwODQ5Nl0sIFsxNjQ5MzQ3NzM4LjkwNjE5MjgsIDMyMzg4LCAxNi42MTY1MDA4NTQ0OTIxODhdXQ==",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=eval%2Fsamples_per_second": {
              "data": "W1sxNjQ5MzQzMzA3LjM3MzY1NzcsIDAsIDYzLjI0MzAwMDAzMDUxNzU4XSwgWzE2NDkzNDQzODEuMjE5OTMxNCwgODA5NywgNzcuMzkxOTk4MjkxMDE1NjJdLCBbMTY0OTM0NTUxMC42ODUxMzA4LCAxNjE5NCwgNzcuMDkzMDAyMzE5MzM1OTRdLCBbMTY0OTM0NjYzMi42MzY3MDI1LCAyNDI5MSwgNzUuMjA1MDAxODMxMDU0NjldLCBbMTY0OTM0NzczOC45MDYyNDI2LCAzMjM4OCwgNzUuNDQxMDAxODkyMDg5ODRdXQ==",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=eval%2Fsteps_per_second": {
              "data": "W1sxNjQ5MzQzMzA3LjM3MzY3NiwgMCwgNC40MjcwMDAwNDU3NzYzNjddLCBbMTY0OTM0NDM4MS4yMTk5NTA3LCA4MDk3LCA0LjgzNzk5OTgyMDcwOTIyODVdLCBbMTY0OTM0NTUxMC42ODUxODQsIDE2MTk0LCA0LjgxODk5OTc2NzMwMzQ2N10sIFsxNjQ5MzQ2NjMyLjYzNjcyMywgMjQyOTEsIDQuNzAxMDAwMjEzNjIzMDQ3XSwgWzE2NDkzNDc3MzguOTA2MjY2LCAzMjM4OCwgNC43MTYwMDAwODAxMDg2NDNdXQ==",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=eval%2Fruntime": {
              "data": "W1sxNjQ5MzQzMzA3LjM3MzYzODYsIDAsIDEuNTgxMjAwMDAzNjIzOTYyNF0sIFsxNjQ5MzQ0MzgxLjIxOTkxMTMsIDgwOTcsIDQxMC4zMjgzOTk2NTgyMDMxXSwgWzE2NDkzNDU1MTAuNjg1MDk1LCAxNjE5NCwgNDExLjkxNzI5NzM2MzI4MTI1XSwgWzE2NDkzNDY2MzIuNjM2NjgxOCwgMjQyOTEsIDQyMi4yNTcyMDIxNDg0Mzc1XSwgWzE2NDkzNDc3MzguOTA2MjE5LCAzMjM4OCwgNDIwLjkzNTQ4NTgzOTg0Mzc1XV0=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/font-roboto/Hgo13k-tfSpn0qi1SFdUfZBw1xU1rKptJj_0jans920.woff2": {
              "data": "[encoded data, 14504 characters]",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "font/woff2"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=train%2Fepoch": {
              "data": "[encoded data, 5052 characters]",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=train%2Flearning_rate": {
              "data": "[encoded data, 5264 characters]",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Apr07_14-54-36_a02ef029c5bf&tag=train%2Floss": {
              "data": "[encoded data, 4984 characters]",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {
              "data": "[encoded data, 14352 characters]",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "font/woff2"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "outputId": "52c5d40b-84d7-4da1-aa6a-31493f47151f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "qZR_ASsEK5Lp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0dc8c142-fcd2-4b8c-e682-6e4e6a86dac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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 = 129552\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 = 40485\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='40485' max='40485' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [40485/40485 1:32:16, 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>Bleu</th>\n",
              "      <th>Gen Len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.265500</td>\n",
              "      <td>0.225167</td>\n",
              "      <td>39.799900</td>\n",
              "      <td>16.689300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.240100</td>\n",
              "      <td>0.206604</td>\n",
              "      <td>40.945600</td>\n",
              "      <td>16.671200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.223600</td>\n",
              "      <td>0.198512</td>\n",
              "      <td>41.350900</td>\n",
              "      <td>16.588400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.215800</td>\n",
              "      <td>0.194419</td>\n",
              "      <td>41.698800</td>\n",
              "      <td>16.616500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.212200</td>\n",
              "      <td>0.193180</td>\n",
              "      <td>41.878700</td>\n",
              "      <td>16.625100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 31756\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='1992' max='7' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7/7 17:48]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-8097\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-8097/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-8097/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 31756\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-16194\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-16194/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-16194/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 31756\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-24291\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-24291/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-24291/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 31756\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-32388\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-32388/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-32388/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-8097] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 31756\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-40485\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-40485/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-40485/pytorch_model.bin\n",
            "Deleting older checkpoint [/content/t5-small-finetuned-wikisql-sql-nl-nl-sql/checkpoint-16194] 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-sql-nl-nl-sql/checkpoint-40485 (score: 0.19318026304244995).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=40485, training_loss=0.24889112813864075, metrics={'train_runtime': 5536.4098, 'train_samples_per_second': 117.0, 'train_steps_per_second': 7.313, 'total_flos': 1.095862566518784e+16, 'train_loss': 0.24889112813864075, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
            "5e5aabc1b72f45ebbd6eee17aa42d9a2",
            "36d4e537ed4d452481962cd21f8c708b",
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            "b9bc134b6d3d44ccbf8473cf73567526",
            "5b175a479f0e41f4aa3ed950e3b76761",
            "6435c0c3d9de4bcb9032742320720b44",
            "3a81a4ad54014f20b75922dc0d2697b0",
            "1edb7c0ee97741f386e5bdcd963ee30a",
            "c4f232b3c2b9483e8e4fa034237fb68d",
            "4f347c9e87e8465298287dcabef7af96",
            "7d9f396404cb4b599cb7906944dfb6eb",
            "96ce7b4357444924b252c99bfce1004c",
            "68937866e42f4628b7b6cbbd2cc14267",
            "7a6b534893d34f6bb018b050b63726fe",
            "ede9695efd0a41efbdfbccf281ffc18e",
            "9b24b49c46164384a6a7b14d361ea800",
            "77e5a62f544d4cd495d2e4ab5a900d0f",
            "11e09c4c5a854b82980d43ca0aacaa21",
            "67df1f2eeacb4601879c2b1e50f9a201",
            "ec8217105316448587e96c6c46ea6cc4",
            "96b4b832996c4758a081bb7b947bfc20",
            "d7ec5847ba9d446e8542f425ae0556e8"
          ]
        },
        "id": "M4f_eBTCeTHA",
        "outputId": "b7575b19-b231-4532-8e56-d1eb8a89926e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/pytorch_model.bin\n",
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/pytorch_model.bin\n",
            "Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.34k/231M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e5aabc1b72f45ebbd6eee17aa42d9a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Apr07_14-54-36_a02ef029c5bf/events.out.tfevents.1649343307.a02ef029c5bf.71.0:  18%|#7        …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96ce7b4357444924b252c99bfce1004c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/mrm8488/t5-small-finetuned-wikisql-sql-nl-nl-sql\n",
            "   deb414a..0e8df6b  main -> main\n",
            "\n",
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}, 'metrics': [{'name': 'Bleu', 'type': 'bleu', 'value': 41.8787}]}\n",
            "To https://huggingface.co/mrm8488/t5-small-finetuned-wikisql-sql-nl-nl-sql\n",
            "   0e8df6b..dc5e761  main -> main\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.create_model_card()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrBFmJbdW9XV",
        "outputId": "29a176d5-2cfd-42cd-a112-edf3abee49fd"
      },
      "execution_count": null,
      "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'}, 'metrics': [{'name': 'Bleu', 'type': 'bleu', 'value': 41.8787}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.push_to_hub()"
      ],
      "metadata": {
        "id": "a19cn0m38o7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "id": "nTn9FjGKo0S7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4620308f-f817-49c6-8511-46fcd6a73c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/t5-small-finetuned-wikisql-sql-nl-nl-sql\n",
            "Configuration saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/config.json\n",
            "Model weights saved in /content/t5-small-finetuned-wikisql-sql-nl-nl-sql/pytorch_model.bin\n",
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Sequence-to-sequence Language Modeling', 'type': 'text2text-generation'}, 'metrics': [{'name': 'Bleu', 'type': 'bleu', 'value': 41.8787}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test the model"
      ],
      "metadata": {
        "id": "wsJum5nEcHWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CKPT = 'mrm8488/t5-small-finetuned-wikisql-sql-nl-nl-sql'\n",
        "from transformers import AutoTokenizer, T5ForConditionalGeneration\n",
        "tokenizer = AutoTokenizer.from_pretrained('t5-small')\n",
        "model = T5ForConditionalGeneration.from_pretrained(CKPT)"
      ],
      "metadata": {
        "id": "HeaD6sejb7hg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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        "outputId": "a2435620-439f-470c-f0d1-5fbca2463f95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"
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        {
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        {
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        {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "test_data = load_dataset('wikisql', split='test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185,
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            "5e50dd0bcb2b43a1b8c2474c4beda1ce",
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            "7b687b35a85d486b96327518c8fad4ce",
            "454a86b45f3a4acdab0372952c6e3690",
            "f03821ac74284d2f94277f662ec3786f",
            "546890b9c6e1482191333ef23665f7be",
            "8c3d60ee634048b5ab640fa02c9476b7",
            "b30a515b38b4494786638f0b054f1686",
            "698eba4ddff247e29ca4ec96f3e228a6",
            "299bc6cff6084e749e9ba461024ae03c",
            "30a00783af4342f6a8c233f8a9067b23",
            "3fe3dea682904e8aa1774782b5c7746f",
            "294e626f6b144a2d8663a30b348ff482",
            "2dd7b806440a480496c5827552d2f49a",
            "87f1198c618946c78984413fc09a9e9e",
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            "582b26856e6f4bf88f7ed795b7684b0b",
            "1e2d42c116ef48399d95be842bb12529",
            "8288ab25699f483bb8e152edb78962eb",
            "5b31a5c41dfd4f34a424f38b1f2beba6",
            "14475444a53d4d5f86eaf51342af2fad",
            "72f9b5df19604701be75acc78767e7d7",
            "840e50efa7d14bcf99d7ac43f1df1493",
            "a5bc2829d5fa4f51895e6851b8a49e36",
            "1a62e784737a4caf8573cfb3edf2bda8",
            "3fdab7fcf91f4bae856a5c94f6d78e36",
            "8ae70bcd2fa24186b2e6afec429e66c2",
            "c57ef835425d478a99bccf12bc4d2cec",
            "f01a103e31ff440188994ca964723f6b",
            "50f62ef11b2b4909b55cf0f478d85444",
            "4f157943449c41518eeb793b945c92fc",
            "5069a04fef444356a3ef18419f89f083",
            "016724c63fe14dbb8ab84fcc2801d7fb"
          ]
        },
        "id": "fGCO9LvFdAG6",
        "outputId": "8b039542-bcf9-422b-9a00-198f802f8929"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.87k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "22690169d67544d8bf79a384c981c3d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/875 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f141e3174a943db8c59f870e0147e24"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset wiki_sql/default (download: 24.95 MiB, generated: 147.57 MiB, post-processed: Unknown size, total: 172.52 MiB) to /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/26.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbee5260a3864b67ae779c905bf7c4aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/15878 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8dbe36f4422a42cc9c634805f6a873e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/8421 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "294e626f6b144a2d8663a30b348ff482"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/56355 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "840e50efa7d14bcf99d7ac43f1df1493"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset wiki_sql downloaded and prepared to /root/.cache/huggingface/datasets/wiki_sql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def translate(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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9yuM3Ub9Iv0",
        "outputId": "f7631bca-40a4-4c8d-d1e1-35d1cbdc2631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'phase': 1,\n",
              " 'question': \"What is terrence ross' nationality\",\n",
              " 'sql': {'agg': 0,\n",
              "  'conds': {'column_index': [0],\n",
              "   'condition': ['Terrence Ross'],\n",
              "   'operator_index': [0]},\n",
              "  'human_readable': 'SELECT Nationality FROM table WHERE Player = Terrence Ross',\n",
              "  'sel': 2},\n",
              " 'table': {'caption': 'R',\n",
              "  'header': ['Player',\n",
              "   'No.',\n",
              "   'Nationality',\n",
              "   'Position',\n",
              "   'Years in Toronto',\n",
              "   'School/Club Team'],\n",
              "  'id': '1-10015132-16',\n",
              "  'name': 'table_10015132_16',\n",
              "  'page_id': '',\n",
              "  'page_title': 'Toronto Raptors all-time roster',\n",
              "  'rows': [['Aleksandar Radojević',\n",
              "    '25',\n",
              "    'Serbia',\n",
              "    'Center',\n",
              "    '1999-2000',\n",
              "    'Barton CC (KS)'],\n",
              "   ['Shawn Respert',\n",
              "    '31',\n",
              "    'United States',\n",
              "    'Guard',\n",
              "    '1997-98',\n",
              "    'Michigan State'],\n",
              "   ['Quentin Richardson',\n",
              "    'N/A',\n",
              "    'United States',\n",
              "    'Forward',\n",
              "    '2013-present',\n",
              "    'DePaul'],\n",
              "   ['Alvin Robertson',\n",
              "    '7, 21',\n",
              "    'United States',\n",
              "    'Guard',\n",
              "    '1995-96',\n",
              "    'Arkansas'],\n",
              "   ['Carlos Rogers',\n",
              "    '33, 34',\n",
              "    'United States',\n",
              "    'Forward-Center',\n",
              "    '1995-98',\n",
              "    'Tennessee State'],\n",
              "   ['Roy Rogers', '9', 'United States', 'Forward', '1998', 'Alabama'],\n",
              "   ['Jalen Rose',\n",
              "    '5',\n",
              "    'United States',\n",
              "    'Guard-Forward',\n",
              "    '2003-06',\n",
              "    'Michigan'],\n",
              "   ['Terrence Ross',\n",
              "    '31',\n",
              "    'United States',\n",
              "    'Guard',\n",
              "    '2012-present',\n",
              "    'Washington']],\n",
              "  'section_title': 'R',\n",
              "  'types': ['text', 'text', 'text', 'text', 'text', 'text']}}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,50,1):\n",
        "  if i % 2 == 0:\n",
        "    print('translate to SQL: ' + test_data[i]['question'])\n",
        "    print('Predict.: ' + translate('translate to SQL: ' + test_data[i]['question']))\n",
        "    print('Expected: ' + test_data[i]['sql']['human_readable'])\n",
        "  else:\n",
        "    print('translate to English: ' + test_data[i]['sql']['human_readable'])\n",
        "    print('Predict.: ' + translate('translate to English: ' + test_data[i]['sql']['human_readable']))\n",
        "    print('Expected: ' + test_data[i]['question'])\n",
        "  print('=================================\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbuoLD-hcpPF",
        "outputId": "7a0d9052-a02f-495a-96a2-6d06152524dc"
      },
      "execution_count": null,
      "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 English: SELECT School/Club Team FROM table WHERE Years in Toronto = 1995-96\n",
            "Predict.: What school/club team was in Toronto in 1995-96?\n",
            "Expected: What clu was in toronto 1995-96\n",
            "=================================\n",
            "\n",
            "translate to SQL: which club was in toronto 2003-06\n",
            "Predict.: SELECT Club FROM table WHERE Location = Toronto 2003-06\n",
            "Expected: SELECT School/Club Team FROM table WHERE Years in Toronto = 2003-06\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT COUNT School/Club Team FROM table WHERE Player = Jalen Rose\n",
            "Predict.: How many schools did jalen rose play?\n",
            "Expected: how many schools or teams had jalen rose\n",
            "=================================\n",
            "\n",
            "translate to SQL: Where was Assen held?\n",
            "Predict.: SELECT Venue FROM table WHERE Team = assen\n",
            "Expected: SELECT Round FROM table WHERE Circuit = Assen\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT COUNT No FROM table WHERE Pole Position = Kevin Curtain\n",
            "Predict.: How many times was the pole position Kevin Curtain?\n",
            "Expected: What was the number of race that Kevin Curtain won?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What was the date of the race in Misano?\n",
            "Predict.: SELECT Date FROM table WHERE Location = misano\n",
            "Expected: SELECT Date FROM table WHERE Circuit = Misano\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT COUNT Position FROM table WHERE College/junior/club team = Sherbrooke Faucons (QMJHL)\n",
            "Predict.: How many positions did sherbrooke faucons (qmjhl) have?\n",
            "Expected: How many different positions did Sherbrooke Faucons (qmjhl) provide in the draft?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What are the nationalities of the player picked from Thunder Bay Flyers (ushl)\n",
            "Predict.: SELECT Nationality FROM table WHERE Pick = Thunder Bay Flyers (ushl)\n",
            "Expected: SELECT Nationality FROM table WHERE College/junior/club team = Thunder Bay Flyers (USHL)\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT COUNT College/junior/club team FROM table WHERE NHL team = Washington Capitals\n",
            "Predict.: How many colleges are there for the Washington Capitals?\n",
            "Expected: How many different college/junior/club teams provided a player to the Washington Capitals NHL Team?\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 English: SELECT Pick FROM table WHERE Player = Dorain Anneck\n",
            "Predict.: What pick did dorain anneck have?\n",
            "Expected: What's Dorain Anneck's pick number?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What is the nationality of the player from Vancouver Canucks?\n",
            "Predict.: SELECT Nationality FROM table WHERE College = valencia canucks\n",
            "Expected: SELECT Nationality FROM table WHERE NHL team = Vancouver Canucks\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Pick FROM table WHERE College/junior/club team = Springfield Olympics (NEJHL)\n",
            "Predict.: What pick has Springfield Olympics (NEJHL) as the college/junior/club team?\n",
            "Expected: What's the pick number of the player from Springfield Olympics (Nejhl)?\n",
            "=================================\n",
            "\n",
            "translate to SQL: When were the ships launched that were laid down on september 1, 1964?\n",
            "Predict.: SELECT Launched FROM table WHERE Date of Laydown = september 1, 1964\n",
            "Expected: SELECT Launched FROM table WHERE Laid down = September 1, 1964\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT # FROM table WHERE Commissioned = December 18, 1965\n",
            "Predict.: What is the # of the episode that was Commissioned December 18, 1965?\n",
            "Expected: List the # for ships commissioned on december 18, 1965.\n",
            "=================================\n",
            "\n",
            "translate to SQL: List the # for ships commissioned on september 30, 1967.\n",
            "Predict.: SELECT # FROM table WHERE Date of commissioned = september 30, 1967\n",
            "Expected: SELECT # FROM table WHERE Commissioned = September 30, 1967\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Laid down FROM table WHERE Commissioned = October 29, 1965\n",
            "Predict.: What was the Laid down for the game that was Commissioned on October 29, 1965?\n",
            "Expected: When were ships laid down that were commissioned on october 29, 1965?\n",
            "=================================\n",
            "\n",
            "translate to SQL:  What could a spanish coronel be addressed as in the commonwealth military?\n",
            "Predict.: SELECT Spanish Coronel FROM table WHERE Military = commonwealth\n",
            "Expected: SELECT Commonwealth equivalent FROM table WHERE Rank in Spanish = Coronel\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Rank in English FROM table WHERE Commonwealth equivalent = Group Captain\n",
            "Predict.: What is the rank in English for the Commonwealth equivalent of Group Captain?\n",
            "Expected: Give me a list of all spanish officer titles that could receive recognition as group captain in english\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 English: SELECT Commonwealth equivalent FROM table WHERE US Air Force equivalent = Major General\n",
            "Predict.: What is the Commonwealth equivalent of Major General?\n",
            "Expected: If you're a major general in the US air force then what ranking will you receive in the commonwealth's air force?\n",
            "=================================\n",
            "\n",
            "translate to SQL: If you get a ranking as major in the  english military then what would the spanish military address you as? \n",
            "Predict.: SELECT Spanish Military FROM table WHERE Ranking = major AND Country = english military\n",
            "Expected: SELECT Rank in Spanish FROM table WHERE Rank in English = Major\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Wrestler FROM table WHERE # of reigns = 2\n",
            "Predict.: Who was the wrestler when the # of reigns was 2?\n",
            "Expected: Which wrestlers have had 2 reigns?\n",
            "=================================\n",
            "\n",
            "translate to SQL: In terms of reigns, what is the lowest number listed?\n",
            "Predict.: SELECT MIN Number FROM table WHERE Name = reigns\n",
            "Expected: SELECT MIN # of reigns FROM table\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Rank Each wrestlers total number of days as champion are ranked highest to lowest; wrestlers with the same number mean that they are tied for that certain rank. FROM table WHERE Wrestler = Bryan Danielson\n",
            "Predict.: What is the rank of the wrestlers who are tied for that certain rank?\n",
            "Expected: What rank was Bryan Danielson in this chart?\n",
            "=================================\n",
            "\n",
            "translate to SQL: How many combined days did Go Shiozaki have?\n",
            "Predict.: SELECT COUNT Day FROM table WHERE Name = go shiozaki\n",
            "Expected: SELECT Combined days FROM table WHERE Wrestler = Go Shiozaki\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT MIN Rank Each wrestlers total number of days as champion are ranked highest to lowest; wrestlers with the same number mean that they are tied for that certain rank. FROM table WHERE Wrestler = Go Shiozaki\n",
            "Predict.: What is the lowest rank of the wrestlers who are tied for that certain rank?\n",
            "Expected: What was Go Shiozaki's rank?\n",
            "=================================\n",
            "\n",
            "translate to SQL: Which province is grey and bell electorate in\n",
            "Predict.: SELECT Province FROM table WHERE Electorate = grey AND Name = bell\n",
            "Expected: SELECT Province FROM table WHERE Electorate = Grey and Bell\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Province FROM table WHERE Electorate = Bay of Islands\n",
            "Predict.: What province is the Electorate Bay of Islands?\n",
            "Expected: Which province is bay of islands in\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 English: SELECT COUNT Singles W–L FROM table WHERE Doubles W–L = 11–14\n",
            "Predict.: How many singles w–l have doubles w–l of 11–14?\n",
            "Expected: what is the total number of singles w–l where doubles w–l is 11–14\n",
            "=================================\n",
            "\n",
            "translate to SQL:  what's the total w–l where player is boro jovanović category:articles with hcards\n",
            "Predict.: SELECT COUNT W–L FROM table WHERE Player = Boris Jovanovi Category:Articles with hCards\n",
            "Expected: SELECT Total W–L FROM table WHERE Player = Boro Jovanović Category:Articles with hCards\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT MAX Ties played FROM table WHERE Player = Josip Palada Category:Articles with hCards\n",
            "Predict.: What is the highest number of ties played for Josip Palada Category:Articles with hCards?\n",
            "Expected: what is the maximum ties played where player is josip palada category:articles with hcards\n",
            "=================================\n",
            "\n",
            "translate to SQL: what is the total number of ties played where total w–l is 38–24\n",
            "Predict.: SELECT COUNT Ties played FROM table WHERE Total W–L = 38–24\n",
            "Expected: SELECT COUNT Ties played FROM table WHERE Total W–L = 38–24\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT COUNT Frequency FROM table WHERE Market/Rank = Burlington - Plattsburgh , Vermont - New York /143\n",
            "Predict.: How many frequencies are there for Burlington - Platsburgh, Vermont - New York /143?\n",
            "Expected: What is the Frequency at the Market/Rank of Burlington - Plattsburgh , Vermont - New York /143?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What is the Branding for Group Owner Qantam of Cape Cod, LLC?\n",
            "Predict.: SELECT Branding FROM table WHERE Group Owner = qantam AND Company = cape cod, lcl\n",
            "Expected: SELECT Branding FROM table WHERE Group owner = Qantam of Cape Cod, LLC\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Branding FROM table WHERE Calls = WRKO\n",
            "Predict.: What is the branding of the player with WRKO calls?\n",
            "Expected: What Branding does WRKO calls use?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What is the Format for Branding of 1290 wkbk w281au 104.1?\n",
            "Predict.: SELECT Format FROM table WHERE Branding = 1290 wkbk w281au 104.1\n",
            "Expected: SELECT Format FROM table WHERE Branding = 1290 WKBK W281AU 104.1\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Market/Rank FROM table WHERE Calls = WCRN\n",
            "Predict.: What is the market/ranked for the WCRN call?\n",
            "Expected: Which Market/Rank is associated with WCRN calls?\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 English: SELECT BTs retail price (regulated) FROM table WHERE Tariff code = ff0 PRS\n",
            "Predict.: What is the BTs retail price (regulated) when the tariff code is ff0 PRS?\n",
            "Expected: What is the regulated retail price for the tariff code ff0 prs?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What is the premium associated with tariff code g9?\n",
            "Predict.: SELECT Premium FROM table WHERE Tariff code = g9\n",
            "Expected: SELECT Approx premium FROM table WHERE Tariff code = g9\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT COUNT Tariff code FROM table WHERE BTs retail price (regulated) = 2p/min or inclusive\n",
            "Predict.: How many tariff codes are there for the bts retail price (regulated) of 2p/min or inclusive?\n",
            "Expected: How many tariff codes have a bts retail price of 2p/min or inclusive?\n",
            "=================================\n",
            "\n",
            "translate to SQL: How many tariff codes have a bts retail price of 2.553p/min?\n",
            "Predict.: SELECT COUNT Tariff codes FROM table WHERE BTS Retail price = 2.553p/min\n",
            "Expected: SELECT COUNT Tariff code FROM table WHERE BTs retail price (regulated) = 2.553p/min\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Prefixes FROM table WHERE Scheme = Pence per minute, fixed at all times AND Approx premium = 3p/min\n",
            "Predict.: What is the prefix for the scheme Pence per minute, fixed at all times, and an approx premium of 3p/min?\n",
            "Expected: What prefixes are priced at pence per minute, fixed at all times with a premium of 3p/min?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What is the bts retail price (regulated) for tariff code g10?\n",
            "Predict.: SELECT Retail price (regulated) FROM table WHERE Tariff code = g10\n",
            "Expected: SELECT BTs retail price (regulated) FROM table WHERE Tariff code = g10\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Tone (Latvian notation: /~/ - level, /^/ - broken) FROM table WHERE Translation = Gen.Sing. plague\n",
            "Predict.: What is the tone of the translation Gen.Sing. plague?\n",
            "Expected: What is the tone for gen.sing. plague?\n",
            "=================================\n",
            "\n",
            "translate to SQL: What is the smallest possible radius?\n",
            "Predict.: SELECT MIN Radius FROM table\n",
            "Expected: SELECT MIN Radius (R ☉ ) FROM table\n",
            "=================================\n",
            "\n",
            "translate to English: SELECT Spectral type FROM table WHERE Star (Pismis24-#) = 1SW\n",
            "Predict.: What is the Spectral type of the Star (Pismis24-#) of 1SW?\n",
            "Expected: What are all the spectral types for star mismis24-# is 1sw?\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(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "PO1OvqSedZhI",
        "outputId": "d401b7d7-825b-409b-bfe9-32bce1b5dc9f"
      },
      "execution_count": null,
      "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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"translate to English: SELECT COUNT Model FROM table WHERE Architecture = RoBERTa AND creator = Manuel Romero\"\n",
        "translate(text)"
      ],
      "metadata": {
        "id": "-V7bmzeQgqtA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "237c29c3-bcb2-4b0f-ff02-ebb4b0c2efd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'How many models have a roberta architecture and a creator of manuel romero?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Acknowledgments\n",
        "\n",
        "- [Narrativa](https://narrativa.com)\n",
        "- [HuggingFace](https://huggingface.co/mrm8488)\n",
        "- [Q BLOCKS](https://www.qblocks.cloud/)"
      ],
      "metadata": {
        "id": "MOx78lCspgyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "XzYJZVgmEsB_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}