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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting simpletransformers\n",
            "  Downloading simpletransformers-0.63.11-py3-none-any.whl (250 kB)\n",
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            "\u001b[?25hCollecting aiosignal>=1.1.2 (from aiohttp->datasets->simpletransformers)\n",
            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
            "Collecting gitdb<5,>=4.0.1 (from GitPython!=3.1.29,>=1.0.0->wandb>=0.10.32->simpletransformers)\n",
            "  Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard->simpletransformers) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard->simpletransformers) (1.3.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=1.4->streamlit->simpletransformers) (3.15.0)\n",
            "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->streamlit->simpletransformers) (2.2.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->streamlit->simpletransformers) (2.14.0)\n",
            "Requirement already satisfied: pytz-deprecation-shim in /usr/local/lib/python3.10/dist-packages (from tzlocal>=1.1->streamlit->simpletransformers) (0.1.0.post0)\n",
            "Requirement already satisfied: decorator>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from validators>=0.2->streamlit->simpletransformers) (4.4.2)\n",
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            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb>=0.10.32->simpletransformers)\n",
            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<5,>=3.2.0->streamlit->simpletransformers) (0.19.3)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py<3.0.0,>=2.2.0->rich>=10.11.0->streamlit->simpletransformers) (0.1.2)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard->simpletransformers) (3.2.2)\n",
            "Requirement already satisfied: tzdata in /usr/local/lib/python3.10/dist-packages (from pytz-deprecation-shim->tzlocal>=1.1->streamlit->simpletransformers) (2023.3)\n",
            "Building wheels for collected packages: seqeval, validators, pathtools\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16165 sha256=be65c51dfe261698f676ecbf29e3eaeb3521d4f66debf74cb1a4a90416773a23\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "  Building wheel for validators (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for validators: filename=validators-0.20.0-py3-none-any.whl size=19579 sha256=fa20a53ffaf0943892a592a98da74c6141ef2d9082c6e84edc2480a390a97056\n",
            "  Stored in directory: /root/.cache/pip/wheels/f2/ed/dd/d3a556ad245ef9dc570c6bcd2f22886d17b0b408dd3bbb9ac3\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8791 sha256=2ad0d8f633f5b6cd0a74d2c3c2b531465656a654f76d6f2a01a517c083c56fc8\n",
            "  Stored in directory: /root/.cache/pip/wheels/e7/f3/22/152153d6eb222ee7a56ff8617d80ee5207207a8c00a7aab794\n",
            "Successfully built seqeval validators pathtools\n",
            "Installing collected packages: tokenizers, sentencepiece, pathtools, xxhash, watchdog, validators, smmap, setproctitle, sentry-sdk, pympler, multidict, importlib-metadata, frozenlist, docker-pycreds, dill, blinker, async-timeout, yarl, responses, pydeck, multiprocess, huggingface-hub, gitdb, aiosignal, transformers, seqeval, GitPython, aiohttp, wandb, streamlit, datasets, simpletransformers\n",
            "Successfully installed GitPython-3.1.31 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 blinker-1.6.2 datasets-2.12.0 dill-0.3.6 docker-pycreds-0.4.0 frozenlist-1.3.3 gitdb-4.0.10 huggingface-hub-0.14.1 importlib-metadata-6.6.0 multidict-6.0.4 multiprocess-0.70.14 pathtools-0.1.2 pydeck-0.8.1b0 pympler-1.0.1 responses-0.18.0 sentencepiece-0.1.99 sentry-sdk-1.24.0 seqeval-1.2.2 setproctitle-1.3.2 simpletransformers-0.63.11 smmap-5.0.0 streamlit-1.22.0 tokenizers-0.13.3 transformers-4.29.2 validators-0.20.0 wandb-0.15.3 watchdog-3.0.0 xxhash-3.2.0 yarl-1.9.2\n"
          ]
        }
      ],
      "source": [
        "!pip install simpletransformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(r\"train.json\", \"r\") as read_file:\n",
        "  train = json.load(read_file)"
      ],
      "metadata": {
        "id": "g0aoaYEBdOEJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nkHXV_qd6y_",
        "outputId": "1d306179-b5c0-44e2-b57b-863273b250e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'context': \"module icons { \\n yang-version 1.1; \\n namespace 'http://anutanetworks.com/icons'; \\n prefix ico; \\n\\n organization \\n 'Anuta Networks'; \\n \\n revision 2021-06-30 { \\n description \\n 'Report Infra Icons'; \\n } \\n\\n container icons { \\n list category { \\n key 'name'; \\n leaf name { \\n type string; \\n } \\n leaf description { \\n type string; \\n } \\n list icon { \\n key 'name'; \\n leaf name { \\n type string; \\n } \\n leaf description { \\n type string; \\n } \\n leaf url { \\n type string; \\n } \\n } \\n } \\n } \\n } \\n. Based on the above YANG model return me RESTCONF-API method and request body for the questions asked, [Method: GET; API: /restconf/data/icons:icons/category]\",\n",
              "  'qas': [{'id': '00001',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Retrieve all the available icons?',\n",
              "    'answers': [{'text': 'Method: GET; API: /restconf/data/icons:icons/category',\n",
              "      'answer_start': 580}]},\n",
              "   {'id': '00002',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Give me all the icons?',\n",
              "    'answers': [{'text': 'Method: GET; API: /restconf/data/icons:icons/category',\n",
              "      'answer_start': 580}]}]},\n",
              " {'context': \"module example-forms {\\n yang-version 1.1; \\n namespace 'http://example.com/example-forms'; \\n prefix js; \\n \\n organization \\n 'Example Networks'; \\n \\n revision 2021-11-19; \\n \\n container forms { \\n list form { \\n key 'name'; \\n leaf name { \\n type string; \\n } \\n leaf description { \\n type string; \\n } \\n leaf-list tags { \\n type string; \\n } \\n leaf form-schema { \\n description \\n 'Json payload.'; \\n type string; \\n } \\n } \\n } \\n }. Based on the above YANG model return me RESTCONF-API method and request body for the questions asked, [Method: GET; API: /example-forms:forms/form]\",\n",
              "  'qas': [{'id': '00003',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Can you list all the available forms?',\n",
              "    'answers': [{'text': 'Method: GET; API: /example-forms:forms/form',\n",
              "      'answer_start': 520}]},\n",
              "   {'id': '00004',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Can you give me all the available forms?',\n",
              "    'answers': [{'text': 'Method: GET; API: /example-forms:forms/form',\n",
              "      'answer_start': 520}]},\n",
              "   {'id': '00005',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Can you show me all the forms?',\n",
              "    'answers': [{'text': 'Method: GET; API: /example-forms:forms/form',\n",
              "      'answer_start': 520}]}]}]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(r\"test.json\", \"r\") as read_file:\n",
        "  test = json.load(read_file)"
      ],
      "metadata": {
        "id": "t3yp9S-Qdkar"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kAlvx2qRd48N",
        "outputId": "1155d77b-6867-4843-c015-bc7cd22ddd19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'context': \"module alert-group { \\n namespace 'http://example.com/alert-group'; \\n prefix alert-group; \\n  \\ncontainer alert-groups { \\n container config { \\n leaf-list name { \\n type string; \\n } \\n } \\n list group-config { \\n key 'tenant'; \\n leaf tenant { \\n type string; \\n } \\n leaf-list name { \\n type string; \\n } \\n } \\n } \\n }. Based on the above YANG model return me RESTCONF-API method and request body for the questions asked, [Method: GET; API: /restconf/data/alert-group:alert-groups]\",\n",
              "  'qas': [{'id': '00001',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Retrieve all the alert groups?',\n",
              "    'answers': [{'text': 'Method: GET; API: /restconf/data/alert-group:alert-groups',\n",
              "      'answer_start': 432}]},\n",
              "   {'id': '00002',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Can you give me all the alert groups?',\n",
              "    'answers': [{'text': 'Method: GET; API: /restconf/data/alert-group:alert-groups',\n",
              "      'answer_start': 432}]}]}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "\n",
        "from simpletransformers.question_answering import QuestionAnsweringModel, QuestionAnsweringArgs"
      ],
      "metadata": {
        "id": "kpDYTcYTd-sj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "T_oIs5_loTIz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_type = \"bert\"\n",
        "model_name = \"bert-base-uncased\""
      ],
      "metadata": {
        "id": "ClmCETvee14m"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
      "metadata": {
        "id": "98ofpVRyhuQU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d602532-0e85-4027-9de3-8dd3552b3796"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.15.3)\n",
            "Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (8.1.3)\n",
            "Requirement already satisfied: GitPython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.1.31)\n",
            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.27.1)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (5.9.5)\n",
            "Requirement already satisfied: sentry-sdk>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (1.24.0)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (0.4.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from wandb) (6.0)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.10/dist-packages (from wandb) (0.1.2)\n",
            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.10/dist-packages (from wandb) (1.3.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from wandb) (67.7.2)\n",
            "Requirement already satisfied: appdirs>=1.4.3 in /usr/local/lib/python3.10/dist-packages (from wandb) (1.4.4)\n",
            "Requirement already satisfied: protobuf!=4.21.0,<5,>=3.19.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.20.3)\n",
            "Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from GitPython!=3.1.29,>=1.0.0->wandb) (4.0.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.4)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb) (5.0.0)\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0mTraceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3108, in _dep_map\n",
            "    return self.__dep_map\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 2901, in __getattr__\n",
            "    raise AttributeError(attr)\n",
            "AttributeError: _DistInfoDistribution__dep_map\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 169, in exc_logging_wrapper\n",
            "    status = run_func(*args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py\", line 242, in wrapper\n",
            "    return func(self, options, args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py\", line 441, in run\n",
            "    conflicts = self._determine_conflicts(to_install)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py\", line 572, in _determine_conflicts\n",
            "    return check_install_conflicts(to_install)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/check.py\", line 101, in check_install_conflicts\n",
            "    package_set, _ = create_package_set_from_installed()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/check.py\", line 42, in create_package_set_from_installed\n",
            "    dependencies = list(dist.iter_dependencies())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/metadata/pkg_resources.py\", line 216, in iter_dependencies\n",
            "    return self._dist.requires(extras)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 2821, in requires\n",
            "    dm = self._dep_map\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3110, in _dep_map\n",
            "    self.__dep_map = self._compute_dependencies()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3120, in _compute_dependencies\n",
            "    reqs.extend(parse_requirements(req))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pkg_resources/__init__.py\", line 3173, in __init__\n",
            "    super(Requirement, self).__init__(requirement_string)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/packaging/requirements.py\", line 102, in __init__\n",
            "    req = REQUIREMENT.parseString(requirement_string)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 1131, in parse_string\n",
            "    loc, tokens = self._parse(instring, 0)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 3886, in parseImpl\n",
            "    loc, exprtokens = e._parse(instring, loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 4114, in parseImpl\n",
            "    return e._parse(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 3886, in parseImpl\n",
            "    loc, exprtokens = e._parse(instring, loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 4959, in parseImpl\n",
            "    loc, tokens = self_expr._parse(instring, loc, doActions, callPreParse=False)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 3886, in parseImpl\n",
            "    loc, exprtokens = e._parse(instring, loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 3886, in parseImpl\n",
            "    loc, exprtokens = e._parse(instring, loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 5226, in parseImpl\n",
            "    return super().parseImpl(instring, loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 4375, in parseImpl\n",
            "    return self.expr._parse(instring, loc, doActions, callPreParse=False)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 3864, in parseImpl\n",
            "    loc, resultlist = self.exprs[0]._parse(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 4114, in parseImpl\n",
            "    return e._parse(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 4375, in parseImpl\n",
            "    return self.expr._parse(instring, loc, doActions, callPreParse=False)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 3886, in parseImpl\n",
            "    loc, exprtokens = e._parse(instring, loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 817, in _parseNoCache\n",
            "    loc, tokens = self.parseImpl(instring, pre_loc, doActions)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 4114, in parseImpl\n",
            "    return e._parse(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 856, in _parseNoCache\n",
            "    tokens = fn(instring, tokens_start, ret_tokens)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/pyparsing/core.py\", line 287, in wrapper\n",
            "    def wrapper(*args):\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/pip3\", line 8, in <module>\n",
            "    sys.exit(main())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/main.py\", line 79, in main\n",
            "    return command.main(cmd_args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 101, in main\n",
            "    return self._main(args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 223, in _main\n",
            "    return run(options, args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 206, in exc_logging_wrapper\n",
            "    logger.critical(\"Operation cancelled by user\")\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1524, in critical\n",
            "    self._log(CRITICAL, msg, args, **kwargs)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1624, in _log\n",
            "    self.handle(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1634, in handle\n",
            "    self.callHandlers(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1696, in callHandlers\n",
            "    hdlr.handle(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 968, in handle\n",
            "    self.emit(record)\n",
            "  File \"/usr/lib/python3.10/logging/handlers.py\", line 73, in emit\n",
            "    if self.shouldRollover(record):\n",
            "  File \"/usr/lib/python3.10/logging/handlers.py\", line 193, in shouldRollover\n",
            "    if self.stream is None:                 # delay was set...\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "args = QuestionAnsweringArgs()\n"
      ],
      "metadata": {
        "id": "lrjgWflyYwpq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_args = {\n",
        "    \"reprocess_input_data\": True,\n",
        "    \"overwrite_output_dir\": True,\n",
        "    \"use_cached_eval_features\": True,\n",
        "    \"output_dir\": f\"outputs/{model_type}\",\n",
        "    \"best_model_dir\": f\"outputs/{model_type}/best_model\",\n",
        "    \"evaluate_during_training\": True,\n",
        "    \"max_seq_length\": 128,\n",
        "    \"num_train_epochs\": 20,\n",
        "    \"evaluate_during_training_steps\": 1000,\n",
        "    # \"wandb_project\": \"Question Answer Application\",\n",
        "    # \"wandb_kwargs\": {\"name\": model_name},\n",
        "    \"save_model_every_epoch\": False,\n",
        "    \"save_eval_checkpoints\": False,\n",
        "    \"n_best_size\": 3,\n",
        "    \"train_batch_size\": 128,\n",
        "    \"eval_batch_size\": 64,\n",
        "}"
      ],
      "metadata": {
        "id": "Wp28CaCzfUoT"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf outputs"
      ],
      "metadata": {
        "id": "Jh65NG75MQDh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = QuestionAnsweringModel(\n",
        "    model_type, model_name, args=train_args\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252,
          "referenced_widgets": [
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            "c6b9aa8bac2149f985a49416103b1e8a"
          ]
        },
        "id": "-_OaywWjhqKx",
        "outputId": "d6cc5e11-ef61-4ca6-f1f6-6fa2ed1183e4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c64caf543c84d799a210fb030b8ef81"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f846a08f4744a13ae9aa7c030a51020"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForQuestionAnswering: ['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertForQuestionAnswering 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 BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForQuestionAnswering were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "237220d5084e4863b2d1955ec2f3abd7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee831568351c47b0bdcfe2194887da7c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.train_model(train, eval_data=test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "13QiM3KwirCp",
        "outputId": "9093f127-e1ff-4913-8241-853f166ad9bf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "convert squad examples to features: 100%|██████████| 5/5 [00:00<00:00, 216.85it/s]\n",
            "add example index and unique id: 100%|██████████| 5/5 [00:00<00:00, 57143.11it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff97072f886c468585225b4287a13261"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c88bec75f294a028a35a224a78d0a28"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
            "\n",
            "convert squad examples to features: 100%|██████████| 2/2 [00:00<00:00, 167.13it/s]\n",
            "\n",
            "add example index and unique id: 100%|██████████| 2/2 [00:00<00:00, 23899.17it/s]\n"
          ]
        },
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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            }
          },
          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
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            }
          },
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            }
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            }
          },
          "metadata": {}
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            }
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            }
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            }
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            }
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            }
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            }
          },
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            }
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            }
          },
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          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
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              "version_major": 2,
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              "model_id": "9b884d5a1b4a4e499ff50cb7aae4f85b"
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b036e05f51e34066a41d5fd91da0ca8c"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20,\n",
              " {'global_step': [1,\n",
              "   2,\n",
              "   3,\n",
              "   4,\n",
              "   5,\n",
              "   6,\n",
              "   7,\n",
              "   8,\n",
              "   9,\n",
              "   10,\n",
              "   11,\n",
              "   12,\n",
              "   13,\n",
              "   14,\n",
              "   15,\n",
              "   16,\n",
              "   17,\n",
              "   18,\n",
              "   19,\n",
              "   20],\n",
              "  'correct': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "  'similar': [0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "  'incorrect': [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "  'train_loss': [4.753125190734863,\n",
              "   4.76953125,\n",
              "   4.714062690734863,\n",
              "   3.9865236282348633,\n",
              "   3.196484327316284,\n",
              "   2.5601563453674316,\n",
              "   1.802343726158142,\n",
              "   1.300146460533142,\n",
              "   0.9734863042831421,\n",
              "   0.672436535358429,\n",
              "   0.48029783368110657,\n",
              "   0.3706299066543579,\n",
              "   0.25574952363967896,\n",
              "   0.17088621854782104,\n",
              "   0.16478270292282104,\n",
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              "   0.092376708984375,\n",
              "   0.07353515923023224,\n",
              "   0.068695068359375],\n",
              "  'eval_loss': [0.282958984375,\n",
              "   0.282958984375,\n",
              "   0.01451873779296875,\n",
              "   -0.2442626953125,\n",
              "   -0.5234375,\n",
              "   -0.80810546875,\n",
              "   -1.0859375,\n",
              "   -1.3486328125,\n",
              "   -1.59765625,\n",
              "   -1.8447265625,\n",
              "   -2.080078125,\n",
              "   -2.296875,\n",
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              "   -2.986328125,\n",
              "   -3.05078125,\n",
              "   -3.095703125,\n",
              "   -3.1171875]})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
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    {
      "cell_type": "code",
      "source": [
        "results, text = model.eval_model(test)"
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          "height": 49,
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        "id": "2RybRlsJqOtU",
        "outputId": "4bc43fd7-80a8-4724-d1c5-741f635b3fb4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afe0c9ec5a974e3b8a05b5aad269c871"
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    {
      "cell_type": "code",
      "source": [
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      "metadata": {
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        "id": "Evdi5ggVqdVO",
        "outputId": "464d2606-e3b0-4129-a8ab-5f6c2d2a3aaa"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'correct': 0, 'similar': 2, 'incorrect': 0, 'eval_loss': -3.1171875}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLmCVk2VqgGG",
        "outputId": "836ab8cd-0a6a-439c-a334-5acad3da045d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'correct_text': {},\n",
              " 'similar_text': {'00001': {'truth': 'Method: GET; API: /restconf/data/alert-group:alert-groups',\n",
              "   'predicted': '',\n",
              "   'question': 'Retrieve all the alert groups?'},\n",
              "  '00002': {'truth': 'Method: GET; API: /restconf/data/alert-group:alert-groups',\n",
              "   'predicted': '',\n",
              "   'question': 'Can you give me all the alert groups?'}},\n",
              " 'incorrect_text': {}}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(r\"predictions.json\", \"r\") as read_file:\n",
        "  to_predict = json.load(read_file)"
      ],
      "metadata": {
        "id": "u0n2J7Fgq3Ru"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "to_predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jM5egt2Uq7xD",
        "outputId": "fa552219-dd6f-4c87-ee28-cefb14ee090f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'context': \"module feature-toggle { \\n namespace 'http://example.com/feature-toggle'; \\n prefix ft; \\n \\n revision 2020-05-14 { \\n description \\n 'Initial revision'; \\n } \\n \\n container feature-toggles { \\n list feature-toggle { \\n 0description \\n 'The feature toggles enables a state information to decide if a feature/functionality should be enabled or disabled.'; \\n key 'name'; \\n leaf name { \\n type string; \\n description \\n 'This is a predefined string value, usually populated by seed data only. This predefined key can be used to identify the features'; \\n mandatory true; \\n } \\n leaf enable { \\n type boolean; \\n description \\n 'This value tells whether to activate the feature'; \\n } \\n leaf description { \\n type string; \\n description \\n 'Brief description about the feature toggle'; \\n } \\n } \\n } \\n }. Based on the above YANG model return me RESTCONF-API method and request body for the questions asked, [Method: GET; API: /restconf/data/feature-toggle:feature-toggles/feature-toggle]\",\n",
              "  'qas': [{'question': 'Can you give me all the feature-toggles?',\n",
              "    'id': '0'}]}]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_answer, probablities = model.predict(to_predict)\n",
        "print(final_answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "ae4eccc8fd94483d87af27e80401ffd1",
            "346bb59bbced4a969173e4a69a0e9c9f",
            "794b4fbe28ab49e5ab954b65c38c4b3d",
            "33dacd50e3fa419280bc0ef03ab9acb5",
            "e6f68b87af08469ba89ad4e473a03897",
            "302801b1137b4589b2ff1139adbba68a",
            "130aef4ad5fc4ac6b3007c263893f3ec",
            "e18d3e52f22746f9a046c8e9e63ac0b8",
            "4a67194004504042ad27514cdc17b80a",
            "0c99cb71800a4b84beba315407ad36e2",
            "57e95b1bf04547feb0ffec0734dd05fa"
          ]
        },
        "id": "ts3D7I_7rDwQ",
        "outputId": "489d5ceb-d8ae-4738-e38e-d01dd3cfbe72"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 168.45it/s]\n",
            "add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 12748.64it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Prediction:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae4eccc8fd94483d87af27e80401ffd1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'id': '0', 'answer': ['empty']}]\n"
          ]
        }
      ]
    }
  ]
}