src/lc-quad/finetune-t5-base-lcquad.ipynb

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            "application/javascript": [
              "function ClickConnect(){\n",
              "console.log(\"Working\");\n",
              "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
              "}setInterval(ClickConnect,60000)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "566GHCfaILnG",
        "outputId": "b03a7edb-fc89-477f-b4d6-94bd0300a1d0"
      },
      "source": [
        "!pip install wandb datasets transformers==4.10.0 bert-score sacrebleu==1.5.1 -qqq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.7 MB 4.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 287 kB 41.8 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 59 kB 6.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 54 kB 2.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 36.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 895 kB 26.9 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 596 kB 42.7 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 180 kB 48.5 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 1.3 MB 38.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 243 kB 44.1 MB/s \n",
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            "\u001b[K     |████████████████████████████████| 160 kB 43.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 45.6 MB/s \n",
            "\u001b[?25h  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXgnU7gQOGGS",
        "outputId": "bcfea62b-5c0f-4d72-b82d-567f99ed6834"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Oct 18 16:14:45 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.74       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 K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P8    27W / 149W |      0MiB / 11441MiB |      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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezmwUFhcYOcs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "192bddda-2e83-4615-d78f-59fe7a8c7c13"
      },
      "source": [
        "import tokenizers\n",
        "import transformers\n",
        "from tqdm import tqdm\n",
        "import datetime\n",
        "\n",
        "print(transformers.__version__)\n",
        "print(tokenizers.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.10.0\n",
            "0.10.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9TjriZbNwxy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0da289a-9808-44dc-adb2-fd45523d71d0"
      },
      "source": [
        "model_checkpoint = 't5-base'\n",
        "fp16 = True\n",
        "todaydate = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
        "print(todaydate)\n",
        "gdir = f'drive/My Drive/Colab Notebooks/{todaydate}/'\n",
        "model_name=f'text-to-sparql-{model_checkpoint}-{todaydate}'\n",
        "model_path='./models/'+model_name\n",
        "ds_path= 'lc-quad-wikidata-2021-07-09'\n",
        "ds_name = 'yazdipour/text-to-sparql-t5-lc-quad-v2'\n",
        "print(model_name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-10-18_16-15\n",
            "text-to-sparql-t5-base-2021-10-18_16-15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUM2HkRlKpUE",
        "outputId": "a49af8f5-f82a-464f-eefa-a64ef85ac0de"
      },
      "source": [
        "!unzip {ds_path}.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  lc-quad-wikidata-2021-07-09.zip\n",
            " extracting: lc-quad-wikidata-2021-07-09/dataset_dict.json  \n",
            "   creating: lc-quad-wikidata-2021-07-09/test/\n",
            "  inflating: lc-quad-wikidata-2021-07-09/test/dataset.arrow  \n",
            "  inflating: lc-quad-wikidata-2021-07-09/test/dataset_info.json  \n",
            "  inflating: lc-quad-wikidata-2021-07-09/test/state.json  \n",
            "   creating: lc-quad-wikidata-2021-07-09/train/\n",
            "  inflating: lc-quad-wikidata-2021-07-09/train/dataset.arrow  \n",
            "  inflating: lc-quad-wikidata-2021-07-09/train/dataset_info.json  \n",
            "  inflating: lc-quad-wikidata-2021-07-09/train/state.json  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "id": "ueKQ2exH7rX-",
        "outputId": "17976016-95f2-4a1c-8edb-7b2e7655c603"
      },
      "source": [
        "# Flexible integration for any Python script\n",
        "import wandb\n",
        "\n",
        "# 1. Start a W&B run\n",
        "wandb.init(project='text-to-sparql', entity='shahriar', name= model_name)\n",
        "\n",
        "# 2. Save model inputs and hyperparameters\n",
        "config = wandb.config\n",
        "config.learning_rate = 0.01"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "wandb: Paste an API key from your profile and hit enter: ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/shahriar/text-to-sparql/runs/1pscgmzn\" target=\"_blank\">text-to-sparql-t5-base-2021-10-18_16-15</a></strong> to <a href=\"https://wandb.ai/shahriar/text-to-sparql\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PkWdvEuaJmv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283,
          "referenced_widgets": [
            "10bebbbb62c44370a553fc4ddcf17071",
            "cb1336a49dd6499b9b6c52d4c6dd9a03",
            "f2c29f52aba74552a6f1873fcecb3997",
            "fb9947e7fcf1460ca4e4607075a37be0",
            "1eaf95c06bcd41a19697783def1562cf",
            "75a42b6933854a668b24ce3c34d2e781",
            "02af202549f2464187956ccfbb5815a8",
            "edeb7dc7074b4a2bb854d222c7e40959",
            "af052590d46740f78837a05d6dd7917e",
            "1871b139694b4533bfae753e2e8ad21a",
            "bcaaf9b5533341fb8568ba8f6aac7876",
            "47ef3bf742ca4903a66153cb3b61b5f8",
            "5b20fc439183406597caec53730fcde0",
            "8189d2dd331845de8e79e3f42e582b4a",
            "173bdc726ec7495084b3e4dcb1c0a6a6",
            "d18fd556ddc54fe2894ed64ba203d83e"
          ]
        },
        "outputId": "3890d05e-89e2-4228-e038-f219e5f4632f"
      },
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-crendential store but this isn't the helper defined on your machine.\n",
            "You will have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal to set it as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFRrHXOne4IG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bacd054d-d68c-4adb-9091-a3bb5e6cd46e"
      },
      "source": [
        "!apt install git-lfs -qqq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting previously unselected package git-lfs.\n",
            "(Reading database ... 155047 files and directories currently installed.)\n",
            "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n",
            "Unpacking git-lfs (2.3.4-1) ...\n",
            "Setting up git-lfs (2.3.4-1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VU2GxDI-aQjR"
      },
      "source": [
        "!git config --global credential.helper store"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Acx8YqC8Ok-H"
      },
      "source": [
        "!mkdir models"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0gq9_ijtbHt"
      },
      "source": [
        "from datasets import load_dataset, load_metric, Dataset, load_from_disk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGFsMmwlNwxz"
      },
      "source": [
        "raw_datasets = load_from_disk(ds_path)\n",
        "# raw_datasets = load_dataset(\"yazdipour/text-to-sparql-t5-lc-quad-v2\", data_files={\"train\": \"train.csv\", \"test\": \"test.csv\"})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGDdBpGfNwxz"
      },
      "source": [
        "# Preprocessing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "07c2fbe0f373447bb2e2d287ce1d82ca",
            "8a0119c3243945c8ab78eee662cbec53",
            "573c853dcebd407b8bec90b6fd1d0ddc",
            "665441c9db574da0a3f8d509e136f54a",
            "c00158a59bec4dde95744a80393925c7",
            "d34edc790c07417b87b447f7e6161edf",
            "8da841d248bf459b896898ad611df88a",
            "279714eedfb8491c87e125290505a7ff",
            "63aaa92081a4407682a7f0c27039cc7a",
            "480c1b7b33f54037b27c6978c471b185",
            "f98f6f8beed14b88a70ad99fcea9d305",
            "79381a925a614ef9878198d7d6631364",
            "b096eda6a0ca4784b1fee2a55787ec23",
            "798d6ba362e74e12ac42163bd98618e9",
            "854d2e37d5c04ae6810c6ed4aef8646b",
            "a9f0c135639945e79e925a15e402ca43",
            "9c4811589e8e407792f8b7ed7ed7df8a",
            "1602229502574a38aa26dd5c9aa957e0",
            "40fb06b8affe45079849ded9910c0ee9",
            "b4afb9d8c6374b7aa1874232abb29a5f",
            "64e36e1e2bab4fed91757e1a8d9cff79",
            "63fb843404654cfa85b50e1e0ea86b9a",
            "14f9eb8026174e1f90dac80d476415ab",
            "8c9481f91b0d4c75bb9cd617ece680e4",
            "91c748ab84764d358778e0a677661fd6",
            "2ec3ab1c0fcb4acf8f2e6737d9309b63",
            "13077292dd9b4f46bced28a53bc5167a",
            "c963439694d9498784a506abc1cd36fd",
            "b5bb0dc5f0cd413789639bdb0d393433",
            "3dca2b5497f54c14ad07cb5f41a6f826",
            "3fab053fd8d740eaa87fc0ebeaeede6d",
            "584cd23563d6410fb90d568ea4529828",
            "2a148ef5790d44728a2fcff8d7ea7a04"
          ]
        },
        "id": "UnY4aKkPNwx0",
        "outputId": "9ac7ee94-620a-4b74-cfcf-d110e526d272"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "07c2fbe0f373447bb2e2d287ce1d82ca",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "79381a925a614ef9878198d7d6631364",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "14f9eb8026174e1f90dac80d476415ab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZdjRilTNwx1",
        "outputId": "aa5d2414-f477-4d6a-b513-aa4a006fb159"
      },
      "source": [
        "q = raw_datasets['test']['translation'][10]['sparql']\n",
        "enc = tokenizer(q)\n",
        "dec = tokenizer.decode(enc['input_ids'])\n",
        "print(q,'\\n',\n",
        "      raw_datasets['test']['translation'][10]['en'],'\\n',\n",
        "      enc,'\\n',\n",
        "      dec,'\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "select ?ent where [ ?ent wdt:instance_of wd:open_cluster . ?ent wdt:radius ?obj ] ORDER BY DESC(?obj)LIMIT 5 \n",
            " What open cluster has the largest radius? \n",
            " {'input_ids': [1738, 3, 58, 295, 213, 784, 3, 58, 295, 3, 210, 26, 17, 10, 77, 8389, 834, 858, 3, 210, 26, 10, 8751, 834, 75, 26156, 3, 5, 3, 58, 295, 3, 210, 26, 17, 10, 5883, 302, 3, 58, 32, 115, 354, 3, 908, 4674, 11300, 272, 476, 309, 25067, 599, 58, 32, 115, 354, 61, 8159, 12604, 305, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} \n",
            " select?ent where [?ent wdt:instance_of wd:open_cluster.?ent wdt:radius?obj ] ORDER BY DESC(?obj)LIMIT 5</s> \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkidcZ_hNwx2",
        "outputId": "76dc14b8-ffed-49fb-a377-99670511c4c9"
      },
      "source": [
        "max_input_length = 0 \n",
        "max_target_length = 0\n",
        "for d in tqdm(raw_datasets['train']['translation']):\n",
        "    len_en = len(d['en'])\n",
        "    len_qry = len(d['sparql'])\n",
        "    if len_en > max_input_length: max_input_length=len_en\n",
        "    if len_qry > max_target_length: max_target_length=len_qry\n",
        "print('\\n',max_input_length, max_target_length)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 38175/38175 [00:00<00:00, 979210.45it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 248 349\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXiURhR-Nwx3"
      },
      "source": [
        "source_lang = \"en\"\n",
        "target_lang = \"sparql\"\n",
        "prefix = \"translate english to sparql:\"\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = []\n",
        "    targets= []\n",
        "    for ex in examples[\"translation\"]:\n",
        "      inputs.append(prefix + ex[source_lang])\n",
        "      targets.append(ex[target_lang])\n",
        "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
        "\n",
        "    # Setup the tokenizer for targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "      labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
        "      \n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "0a13ab6d283a487b9a579010b04c1176",
            "1fb8d1d338d44bf49fccaedf1ed9d857",
            "bbbe22c92ca0417c8d5bd7834311d633",
            "5ac33238db9745af9282d4eb7c4bc350",
            "8bb5aec575fc491f80b8c560ed05bb06",
            "6b14550ac349473ca4f69b10b4a62f06",
            "28207e64cab3471ea895285bb38751e8",
            "ab8127b23691483797f7ca19613b8f8b",
            "563ccc91e27b44e0ab28e6be5c0bc7ff",
            "5f678724b7454f248519abc5d6296e79",
            "d96f55b4e71448ec9a9d418e172575a8",
            "b8beca84be3147888a81f14dec20902f",
            "2a820a9a5ed64ba2a13da7747c2462ff",
            "e585b26869c84d1b941fa261590ecd20",
            "54fc49e32eb946fe856f1e980f4f1522",
            "102a97b4ede54e10899caf529335099c",
            "19890c3de27c47ff876585bcef123459",
            "6969dfc09ed54105b738c12ed405c071",
            "d6cd3058897549b5bdd5b4d1dec005d1",
            "c8bd680459e44adca0ca283ad6f6482a",
            "9545b27ab2104711b2a840707cc3cca4",
            "3e71e1ab90e645b2ad1bbcd69621ba53"
          ]
        },
        "id": "qmaK1UX0Nwx3",
        "outputId": "de8e20cd-16e2-431e-b09c-62281a6bbbb7"
      },
      "source": [
        "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a13ab6d283a487b9a579010b04c1176",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/39 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8beca84be3147888a81f14dec20902f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knRoh_8wNwx4",
        "outputId": "f1612a1a-4a56-4b76-82bc-fc6c614f5e7f"
      },
      "source": [
        "tokenized_datasets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'labels', 'translation'],\n",
              "        num_rows: 38175\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'labels', 'translation'],\n",
              "        num_rows: 9485\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUiaZ1Y0Nwx4"
      },
      "source": [
        "# Fine-tuning the model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUJNfPjvNwx5"
      },
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "643a0231b89245c1b1c7ac4225241e9d",
            "39fa8e4bc80f4bcdb5098d95e5a70d5c",
            "cedd60c029284cd591cb8f86060ebc79",
            "2c23c3d417a14c92b4b793d37458008e",
            "3214795b7ef34e308933abed04f82703",
            "a311cb3fccae43c68c796264fdb39235",
            "1fd27cdefae24916a4d8dd1ed77e3405",
            "c9d250a6d5f04ea6807824917ec10f8b",
            "c038af83a28b4181bd7a041423157e61",
            "7d63cb211961462e88ee934a2eed93fa",
            "e105814e11564a4097d7ac219d2ac12f"
          ]
        },
        "id": "sxaVAoZowzBD",
        "outputId": "9ba2ad13-19ba-41ae-dc19-530ce3cd49bb"
      },
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "643a0231b89245c1b1c7ac4225241e9d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/892M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qt8rECNwNwx5"
      },
      "source": [
        "batch_size = 8\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    'yazdipour/'+ model_name,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=3e-4,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=1,\n",
        "    predict_with_generate=True,\n",
        "    fp16=fp16,\n",
        "    push_to_hub=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHZPXO3lNwx5"
      },
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E_b1Wc4gbMf"
      },
      "source": [
        "import numpy as np\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.replace('?',' ?').replace('.', ' .').strip() for pred in preds]\n",
        "    labels = [[label.replace('?',' ?').replace('.', ' .').strip()] for label in labels]\n",
        "    return preds, labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQyXIlPMgqzO"
      },
      "source": [
        "# The last thing to define for our Seq2SeqTrainer is how to compute \n",
        "# the metrics from the predictions. We need to define \n",
        "# a function for this, which will just use the metric we loaded earlier, \n",
        "# and we have to do a bit of pre-processing to decode the predictions into texts:"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279,
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            "09e3f5e11677432cbb69073eb56f5655",
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            "fed70a16d3284609ad07b9a498ba1e80",
            "629fa729b8cf45298645d75d6049ce72",
            "e3734fd280d34e28b246db891af5517a",
            "9b2019f1c9e942b982d4360afe856adb",
            "f36bfe82e2454ab49e143d983af7bdea",
            "ef39d88d729748d5b0aac604f3882755",
            "7bf7c67bb0674d938d7289ce4bce080c",
            "72ece793d353423d911d6919193b2323",
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            "d9ae695c5ec349e09c302681ff57599d",
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            "5bfc1f17522346328f379844356c3a11",
            "cc5eaaf5f9884fd9bf99d8f6d6508e68",
            "7e502078ae9e4d4f8d19d1ed47c20760",
            "a205d94fbab84dfb828434fe49b1d050",
            "575630f3d4fb46b0a637efe6365288da",
            "c74becb856754199b86016c144eda71e",
            "12d981f43038441eb7d6710f1ac78e64",
            "b5360c45a8f44cd1bc15e05c1fb04434",
            "670e632b81f34d8487878eee3b9a9cd9",
            "4b313976a12e45c5bc62c36afba346ab",
            "5cb99feccb5947df8fb74c56eac75abd",
            "e537acb88b0d4a7693948a5d9d6f717f",
            "0c0c8bde4208424b9c36ceaaaa122d35",
            "d794eb5db5a3492fb61bfe7903a37695",
            "0528091572ad4843ad9a549f5846ba46",
            "054a81be28bd4005b4674c944003edac",
            "ad588d3242c34c3fb99f19bc73c3b7fb",
            "1567ac75555d4fdb9e8ae89a13886480",
            "4059ac63f5704619adef50cf0a61b426",
            "e8e5c5ac34e44493ae765fe9d6d74cd8",
            "dfba12e7edb04d14adff6557a1da9371",
            "db2d7f2f693f4870b1ae392ce6282fe3",
            "f853cf8b6f604b11af20510fa1a6ae5d",
            "4b0602fe0d7f417ba6fdcf28927c3cff",
            "30021a320a794f18a623addb1b26f4e7",
            "3566f24f80724101bef1ba24e7b052c2",
            "b1efa0a7d11d49e7b4b9fb69267171a4",
            "ae7e0eecf2584a4c8addcedbcfcb8539",
            "1ee9b81320a94317b98880a2889e1c3d",
            "eec7245120314a42b4d856312427d214",
            "440e2952ed864833baa0aab2a0a9142a",
            "419f24698f374527b5474a911029ec92",
            "8d2ce9faad944ad68f30fd05b886e065",
            "e06c584dc5d041c38099ac11aabbf04d",
            "f247a2e273b242ae8c12e23acf8bec88",
            "f0e48882599d46bd8f2b2ac6d6637811",
            "a2045dafa3bd4a42b4f7782c9e006cd1",
            "0097c2d170e043528cb9ed5749a7403c",
            "cb32f19ee6554c3fa4a920af9f9c2814",
            "f91e4e95de64433db24c0264da120a65",
            "c0d530be7a10466781a9de472866f29a",
            "1e39256de49b4447ac2f989a7aa973c7",
            "d29c25418ec2456d81d79efc90250b96"
          ]
        },
        "id": "6e8FOhSBjZKM",
        "outputId": "77d3ca88-9b74-4e28-924e-758f5a003eed"
      },
      "source": [
        "from bert_score import BERTScorer\n",
        "scorer = BERTScorer(lang=\"en\", rescale_with_baseline=True)\n",
        "metric = load_metric(\"sacrebleu\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b3d4a42ef2f6429cb077b5720cd0654d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/482 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fed70a16d3284609ad07b9a498ba1e80",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5244287bbde469fb5ab012caf4f4497",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5cb99feccb5947df8fb74c56eac75abd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db2d7f2f693f4870b1ae392ce6282fe3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.43G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8d2ce9faad944ad68f30fd05b886e065",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.37k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxB0Sih5fsYd"
      },
      "source": [
        "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",
        "\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",
        "\n",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    P, R, F1 = scorer.score(decoded_preds, decoded_labels)\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "    \n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    gen_len = np.mean(prediction_lens)\n",
        "    \n",
        "    return {\"gen_len\":gen_len, 'BERTScorer-P':P.mean(), 'BERTScorer-R':R.mean(), 'BERTScorer-F1':F1.mean(), \"sacrebleu-score\": result[\"score\"], \"sacrebleu-precisions\": result[\"precisions\"], \"bleu-bp\": result[\"bp\"]}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZhWPx09Nwx6",
        "outputId": "b5b2ebd8-fefe-49ef-9f78-91ac287da317"
      },
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/yazdipour/text-to-sparql-t5-base-2021-10-18_16-15 into local empty directory.\n",
            "Using amp fp16 backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qS5Q9hT2Nwx6",
        "outputId": "962f50ce-671a-422f-e58e-d12c521cda7a"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: translation.\n",
            "***** Running training *****\n",
            "  Num examples = 38175\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 4772\n",
            "Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1314: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n",
            "  args.max_grad_norm,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='4772' max='4772' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [4772/4772 2:37:40, Epoch 1/1]\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>Gen Len</th>\n",
              "      <th>Bertscorer-p</th>\n",
              "      <th>Bertscorer-r</th>\n",
              "      <th>Bertscorer-f1</th>\n",
              "      <th>Sacrebleu-score</th>\n",
              "      <th>Sacrebleu-precisions</th>\n",
              "      <th>Bleu-bp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>nan</td>\n",
              "      <td>0.129441</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.582710</td>\n",
              "      <td>0.081231</td>\n",
              "      <td>0.320176</td>\n",
              "      <td>5.940951</td>\n",
              "      <td>[92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601]</td>\n",
              "      <td>0.072066</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-500\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-500/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-500/special_tokens_map.json\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1000\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1000/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1000/special_tokens_map.json\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1500\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1500/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1500/special_tokens_map.json\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2000\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2000/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2000/special_tokens_map.json\n",
            "Deleting older checkpoint [yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-500] due to args.save_total_limit\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2500\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2500/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2500/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2500/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2500/special_tokens_map.json\n",
            "Deleting older checkpoint [yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1000] due to args.save_total_limit\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3000\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3000/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3000/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3000/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3000/special_tokens_map.json\n",
            "Deleting older checkpoint [yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-1500] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1314: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n",
            "  args.max_grad_norm,\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3500\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3500/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3500/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3500/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3500/special_tokens_map.json\n",
            "Deleting older checkpoint [yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1314: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n",
            "  args.max_grad_norm,\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4000\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4000/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4000/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4000/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4000/special_tokens_map.json\n",
            "Deleting older checkpoint [yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-2500] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:1314: FutureWarning: Non-finite norm encountered in torch.nn.utils.clip_grad_norm_; continuing anyway. Note that the default behavior will change in a future release to error out if a non-finite total norm is encountered. At that point, setting error_if_nonfinite=false will be required to retain the old behavior.\n",
            "  args.max_grad_norm,\n",
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4500\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4500/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4500/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4500/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-4500/special_tokens_map.json\n",
            "Deleting older checkpoint [yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/checkpoint-3000] due to args.save_total_limit\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `T5ForConditionalGeneration.forward` and have been ignored: translation.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 9485\n",
            "  Batch size = 8\n",
            "Trainer is attempting to log a value of \"[92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601]\" of type <class 'list'> for key \"eval/sacrebleu-precisions\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=4772, training_loss=nan, metrics={'train_runtime': 9462.6913, 'train_samples_per_second': 4.034, 'train_steps_per_second': 0.504, 'total_flos': 6850430161873920.0, 'train_loss': nan, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdHQ-R8XNwx6",
        "outputId": "317895bd-aaac-45a3-b7bc-ec62ff3b3d08"
      },
      "source": [
        "trainer.save_model(model_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to ./models/text-to-sparql-t5-base-2021-10-18_16-15\n",
            "Configuration saved in ./models/text-to-sparql-t5-base-2021-10-18_16-15/config.json\n",
            "Model weights saved in ./models/text-to-sparql-t5-base-2021-10-18_16-15/pytorch_model.bin\n",
            "tokenizer config file saved in ./models/text-to-sparql-t5-base-2021-10-18_16-15/tokenizer_config.json\n",
            "Special tokens file saved in ./models/text-to-sparql-t5-base-2021-10-18_16-15/special_tokens_map.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzmEW98cZ81E"
      },
      "source": [
        "!git config --global credential.helper store"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      ],
      "execution_count": null,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "Saving model checkpoint to yazdipour/text-to-sparql-t5-base-2021-10-18_16-15\n",
            "Configuration saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/config.json\n",
            "Model weights saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/pytorch_model.bin\n",
            "tokenizer config file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/tokenizer_config.json\n",
            "Special tokens file saved in yazdipour/text-to-sparql-t5-base-2021-10-18_16-15/special_tokens_map.json\n"
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              "Upload file runs/Oct18_16-18-04_90f41ff2b9f8/1634573982.9499002/events.out.tfevents.1634573982.90f41ff2b9f8.77…"
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              "Upload file runs/Oct18_16-18-04_90f41ff2b9f8/events.out.tfevents.1634573982.90f41ff2b9f8.77.0:  54%|#####4    …"
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