Gotcha_GPT_eng.ipynb

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          "text": [
            "Collecting docx2txt\n",
            "  Downloading docx2txt-0.8.tar.gz (2.8 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: docx2txt\n",
            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for docx2txt: filename=docx2txt-0.8-py3-none-any.whl size=3959 sha256=ba86cbf52923d57680dd6f77e5baa749cbb49aae66716321e45e8d15724f4010\n",
            "  Stored in directory: /root/.cache/pip/wheels/22/58/cf/093d0a6c3ecfdfc5f6ddd5524043b88e59a9a199cb02352966\n",
            "Successfully built docx2txt\n",
            "Installing collected packages: docx2txt\n",
            "Successfully installed docx2txt-0.8\n",
            "Collecting transformers==4.23.0\n",
            "  Downloading transformers-4.23.0-py3-none-any.whl.metadata (88 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.7/88.7 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (3.16.0)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (2.32.3)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.23.0)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.23.0) (4.66.5)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.23.0) (2024.8.30)\n",
            "Downloading transformers-4.23.0-py3-none-any.whl (5.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.19.1\n",
            "    Uninstalling tokenizers-0.19.1:\n",
            "      Successfully uninstalled tokenizers-0.19.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.44.2\n",
            "    Uninstalling transformers-4.44.2:\n",
            "      Successfully uninstalled transformers-4.44.2\n",
            "Successfully installed tokenizers-0.13.3 transformers-4.23.0\n"
          ]
        }
      ],
      "source": [
        "# Install required libraries\n",
        "!pip install --upgrade docx2txt\n",
        "!pip install transformers==4.23.0"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import docx2txt\n",
        "import numpy as np\n",
        "import torch\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer"
      ],
      "metadata": {
        "id": "HFH7znOjAZfI"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step #2: Function to upload and read PDF file\n",
        "def upload_and_read_word(uploaded_file):\n",
        "  text = docx2txt.process(uploaded_file)\n",
        "  text = text.replace('\\n', '')\n",
        "  return text\n",
        "\n",
        "# Step 3: Calculate Perplexity\n",
        "def calculate_perplexity(text):\n",
        "    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "    model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "\n",
        "    tokens = tokenizer.encode(text, return_tensors='pt')\n",
        "    max_length = 1024\n",
        "    stride = 512\n",
        "    lls = []\n",
        "\n",
        "    for i in range(0, tokens.size(1), stride):\n",
        "        begin_loc = max(i + stride - max_length, 0)\n",
        "        end_loc = min(i + stride, tokens.size(1))\n",
        "        trg_len = end_loc - i  # may be different from stride on last loop\n",
        "        input_ids = tokens[:, begin_loc:end_loc]\n",
        "        target_ids = input_ids.clone()\n",
        "        target_ids[:, :-trg_len] = -100\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(input_ids, labels=target_ids)\n",
        "            log_likelihood = outputs[0] * trg_len\n",
        "\n",
        "        lls.append(log_likelihood)\n",
        "\n",
        "    perplexity = torch.exp(torch.stack(lls).sum() / end_loc)\n",
        "    return perplexity.item()\n",
        "\n",
        "# Step 4: Analyze Burstiness\n",
        "def calculate_burstiness(text):\n",
        "    sentences = text.split('.')\n",
        "    sentence_lengths = [len(sentence.split()) for sentence in sentences if sentence]\n",
        "\n",
        "    mean_length = np.mean(sentence_lengths)\n",
        "    std_dev = np.std(sentence_lengths)\n",
        "\n",
        "    burstiness = std_dev / mean_length if mean_length else 0\n",
        "    return burstiness"
      ],
      "metadata": {
        "id": "eiR5LwdzAl45"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Upload the PDF file\n",
        "uploaded = files.upload()\n",
        "filenames = uploaded.keys()\n",
        "lista_texts = list(filenames)\n",
        "lista_texts"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
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        "outputId": "782e6c2c-4ef1-4862-aa47-30b253461790"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-71d4ec08-d9a9-47bc-8c85-20ece332ffe0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-71d4ec08-d9a9-47bc-8c85-20ece332ffe0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving discussion_ai_1.docx to discussion_ai_1.docx\n",
            "Saving discussion_ai_2.docx to discussion_ai_2.docx\n",
            "Saving discussion_ai_3.docx to discussion_ai_3.docx\n",
            "Saving discussion_ai_4.docx to discussion_ai_4.docx\n",
            "Saving discussion_ai_5.docx to discussion_ai_5.docx\n",
            "Saving discussion_ai_6.docx to discussion_ai_6.docx\n",
            "Saving discussion_ai_7.docx to discussion_ai_7.docx\n",
            "Saving discussion_ai_8.docx to discussion_ai_8.docx\n",
            "Saving discussion_ai_9.docx to discussion_ai_9.docx\n",
            "Saving discussion_ai_10.docx to discussion_ai_10.docx\n",
            "Saving discussion_ai_humanized_1.docx to discussion_ai_humanized_1.docx\n",
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            "Saving discussion_ai_humanized_7.docx to discussion_ai_humanized_7.docx\n",
            "Saving discussion_ai_humanized_8.docx to discussion_ai_humanized_8.docx\n",
            "Saving discussion_ai_humanized_9.docx to discussion_ai_humanized_9.docx\n",
            "Saving discussion_ai_humanized_10.docx to discussion_ai_humanized_10.docx\n",
            "Saving introduction_ai_1.docx to introduction_ai_1.docx\n",
            "Saving introduction_ai_2.docx to introduction_ai_2.docx\n",
            "Saving introduction_ai_3.docx to introduction_ai_3.docx\n",
            "Saving introduction_ai_4.docx to introduction_ai_4.docx\n",
            "Saving introduction_ai_5.docx to introduction_ai_5.docx\n",
            "Saving introduction_ai_6.docx to introduction_ai_6.docx\n",
            "Saving introduction_ai_7.docx to introduction_ai_7.docx\n",
            "Saving introduction_ai_8.docx to introduction_ai_8.docx\n",
            "Saving introduction_ai_9.docx to introduction_ai_9.docx\n",
            "Saving introduction_ai_10.docx to introduction_ai_10.docx\n",
            "Saving introduction_ai_humanized_1.docx to introduction_ai_humanized_1.docx\n",
            "Saving introduction_ai_humanized_2.docx to introduction_ai_humanized_2.docx\n",
            "Saving introduction_ai_humanized_3.docx to introduction_ai_humanized_3.docx\n",
            "Saving introduction_ai_humanized_4.docx to introduction_ai_humanized_4.docx\n",
            "Saving introduction_ai_humanized_5.docx to introduction_ai_humanized_5.docx\n",
            "Saving introduction_ai_humanized_6.docx to introduction_ai_humanized_6.docx\n",
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            "Saving introduction_ai_humanized_9.docx to introduction_ai_humanized_9.docx\n",
            "Saving introduction_ai_humanized_10.docx to introduction_ai_humanized_10.docx\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['discussion_ai_1.docx',\n",
              " 'discussion_ai_2.docx',\n",
              " 'discussion_ai_3.docx',\n",
              " 'discussion_ai_4.docx',\n",
              " 'discussion_ai_5.docx',\n",
              " 'discussion_ai_6.docx',\n",
              " 'discussion_ai_7.docx',\n",
              " 'discussion_ai_8.docx',\n",
              " 'discussion_ai_9.docx',\n",
              " 'discussion_ai_10.docx',\n",
              " 'discussion_ai_humanized_1.docx',\n",
              " 'discussion_ai_humanized_2.docx',\n",
              " 'discussion_ai_humanized_3.docx',\n",
              " 'discussion_ai_humanized_4.docx',\n",
              " 'discussion_ai_humanized_5.docx',\n",
              " 'discussion_ai_humanized_6.docx',\n",
              " 'discussion_ai_humanized_7.docx',\n",
              " 'discussion_ai_humanized_8.docx',\n",
              " 'discussion_ai_humanized_9.docx',\n",
              " 'discussion_ai_humanized_10.docx',\n",
              " 'introduction_ai_1.docx',\n",
              " 'introduction_ai_2.docx',\n",
              " 'introduction_ai_3.docx',\n",
              " 'introduction_ai_4.docx',\n",
              " 'introduction_ai_5.docx',\n",
              " 'introduction_ai_6.docx',\n",
              " 'introduction_ai_7.docx',\n",
              " 'introduction_ai_8.docx',\n",
              " 'introduction_ai_9.docx',\n",
              " 'introduction_ai_10.docx',\n",
              " 'introduction_ai_humanized_1.docx',\n",
              " 'introduction_ai_humanized_2.docx',\n",
              " 'introduction_ai_humanized_3.docx',\n",
              " 'introduction_ai_humanized_4.docx',\n",
              " 'introduction_ai_humanized_5.docx',\n",
              " 'introduction_ai_humanized_6.docx',\n",
              " 'introduction_ai_humanized_7.docx',\n",
              " 'introduction_ai_humanized_8.docx',\n",
              " 'introduction_ai_humanized_9.docx',\n",
              " 'introduction_ai_humanized_10.docx']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Main execution\n",
        "if __name__ == \"__main__\":\n",
        "    lista_perplexity = []\n",
        "    lista_burstiness = []\n",
        "    lista_token_length = []\n",
        "    for files in lista_texts:\n",
        "        text = upload_and_read_word(files)\n",
        "        perplexity = calculate_perplexity(text)\n",
        "        lista_perplexity.append(perplexity)\n",
        "        burstiness = calculate_burstiness(text)\n",
        "        lista_burstiness.append(burstiness)\n",
        "        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "        token_length = len(tokenizer.encode(text))\n",
        "        lista_token_length.append(token_length)\n",
        "\n",
        "\n",
        "df_texts = pd.DataFrame({'text': lista_texts, 'Token': lista_token_length,\n",
        "                         'Perplexity': lista_perplexity, 'Burstiness': lista_burstiness, 'AI Metric': 0})\n",
        "df_texts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "2a083b1537c34598979d2993dc57676f",
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            "ac5010cb1bf8430da5a88f0f1970bc58",
            "b77ce830c68a4aa7b32b92202bf74965",
            "1e8491e1ad1f4f119ffaf4c0683095b8",
            "b89d91d0b316423593a3ac2cf1c0d3f9",
            "fda42e3216c64406858dc13a6ac89478",
            "a9fb349bffd348439efa6c290a34cba0",
            "1821c48f61474220adcbd8a16917f25c",
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            "0dbfdae9d0d640e698e24f754ee0046c",
            "f230b76f83664846957f3816bb63c574",
            "4501a6a800b54da181ad49c3b23f282b",
            "4c3d8b40ef3b402393de55f6dba4f6b2",
            "43a6517e084e4ab6a0d2fafa78235ded",
            "a834cd9374114f67b257028b51c9e935",
            "f4c444bc4f0b40d58a48151053bdad9a",
            "fae11e4337d748dbbaba5686f9a83e7f",
            "d390acc7959b4346b46be8e413f89bda",
            "96debc5e9d264d1db9ff591373395218",
            "8356f0bf43cc43eab499696ea4450ed4",
            "e924c39646614b4f9214bc832fe20ea0",
            "c0b20c76ca6448cca921b7a9085a6dc5",
            "cf4d2c674d0d4504b26d87f7b7e0a168",
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            "6b3ff8131059423eaf74a615b10b6718",
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            "b867f3b593e64a4ba3f039cf09b3d328",
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            "9c11c37a65b7481e8b93233a1d18c917",
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            "ac170cb712b941bfb735f5928fcfa605",
            "69ac7c423905425a83d791c798d0641e",
            "0d9458bace204dca82d7ad2550fdaf30"
          ]
        },
        "id": "cZOVsNT-A0rW",
        "outputId": "ff112c1d-49ee-4c73-c3d4-0329789b2859"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a083b1537c34598979d2993dc57676f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0dbfdae9d0d640e698e24f754ee0046c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e924c39646614b4f9214bc832fe20ea0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "504fe9b0de87448185e3a5906711ea2e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b867f3b593e64a4ba3f039cf09b3d328"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 text  Token  Perplexity  Burstiness  \\\n",
              "0                discussion_ai_1.docx    430   12.736505    0.181379   \n",
              "1                discussion_ai_2.docx    436    9.090506    0.230476   \n",
              "2                discussion_ai_3.docx    394   10.906150    0.184105   \n",
              "3                discussion_ai_4.docx    399   11.778743    0.254030   \n",
              "4                discussion_ai_5.docx    470   10.470787    0.223905   \n",
              "5                discussion_ai_6.docx    442   11.411176    0.234732   \n",
              "6                discussion_ai_7.docx    599   12.674426    0.132911   \n",
              "7                discussion_ai_8.docx    695   18.993662    0.185130   \n",
              "8                discussion_ai_9.docx    822   18.604256    0.294872   \n",
              "9               discussion_ai_10.docx    398   12.894266    0.191273   \n",
              "10     discussion_ai_humanized_1.docx    476   28.242548    0.228841   \n",
              "11     discussion_ai_humanized_2.docx    473   28.207138    0.288509   \n",
              "12     discussion_ai_humanized_3.docx    510   28.357067    0.493972   \n",
              "13     discussion_ai_humanized_4.docx    494   21.901716    0.288171   \n",
              "14     discussion_ai_humanized_5.docx    565   28.905794    0.322368   \n",
              "15     discussion_ai_humanized_6.docx    510   17.110500    0.295201   \n",
              "16     discussion_ai_humanized_7.docx    511   22.439995    0.327048   \n",
              "17     discussion_ai_humanized_8.docx    553   33.875523    0.348095   \n",
              "18     discussion_ai_humanized_9.docx    591   22.608248    0.257021   \n",
              "19    discussion_ai_humanized_10.docx    398   12.894266    0.191273   \n",
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              "31   introduction_ai_humanized_2.docx    515   21.162598    0.226491   \n",
              "32   introduction_ai_humanized_3.docx    374   26.259531    0.298956   \n",
              "33   introduction_ai_humanized_4.docx    424   26.801723    0.278012   \n",
              "34   introduction_ai_humanized_5.docx    408   23.865345    0.283221   \n",
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              "36   introduction_ai_humanized_7.docx    464   19.326187    0.245176   \n",
              "37   introduction_ai_humanized_8.docx    388   28.251875    0.179323   \n",
              "38   introduction_ai_humanized_9.docx    789   30.893530    0.261470   \n",
              "39  introduction_ai_humanized_10.docx    914   41.944344    0.388241   \n",
              "\n",
              "    AI Metric  \n",
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              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>Token</th>\n",
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              "      <th>Burstiness</th>\n",
              "      <th>AI Metric</th>\n",
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              "      <th>0</th>\n",
              "      <td>discussion_ai_1.docx</td>\n",
              "      <td>430</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>discussion_ai_2.docx</td>\n",
              "      <td>436</td>\n",
              "      <td>9.090506</td>\n",
              "      <td>0.230476</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>discussion_ai_3.docx</td>\n",
              "      <td>394</td>\n",
              "      <td>10.906150</td>\n",
              "      <td>0.184105</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>399</td>\n",
              "      <td>11.778743</td>\n",
              "      <td>0.254030</td>\n",
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              "      <th>4</th>\n",
              "      <td>discussion_ai_5.docx</td>\n",
              "      <td>470</td>\n",
              "      <td>10.470787</td>\n",
              "      <td>0.223905</td>\n",
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              "      <th>5</th>\n",
              "      <td>discussion_ai_6.docx</td>\n",
              "      <td>442</td>\n",
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              "      <th>6</th>\n",
              "      <td>discussion_ai_7.docx</td>\n",
              "      <td>599</td>\n",
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              "      <th>7</th>\n",
              "      <td>discussion_ai_8.docx</td>\n",
              "      <td>695</td>\n",
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              "      <th>8</th>\n",
              "      <td>discussion_ai_9.docx</td>\n",
              "      <td>822</td>\n",
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              "      <td>0.294872</td>\n",
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              "      <th>9</th>\n",
              "      <td>discussion_ai_10.docx</td>\n",
              "      <td>398</td>\n",
              "      <td>12.894266</td>\n",
              "      <td>0.191273</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>discussion_ai_humanized_1.docx</td>\n",
              "      <td>476</td>\n",
              "      <td>28.242548</td>\n",
              "      <td>0.228841</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>discussion_ai_humanized_2.docx</td>\n",
              "      <td>473</td>\n",
              "      <td>28.207138</td>\n",
              "      <td>0.288509</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>discussion_ai_humanized_3.docx</td>\n",
              "      <td>510</td>\n",
              "      <td>28.357067</td>\n",
              "      <td>0.493972</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>discussion_ai_humanized_4.docx</td>\n",
              "      <td>494</td>\n",
              "      <td>21.901716</td>\n",
              "      <td>0.288171</td>\n",
              "      <td>0</td>\n",
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              "      <th>14</th>\n",
              "      <td>discussion_ai_humanized_5.docx</td>\n",
              "      <td>565</td>\n",
              "      <td>28.905794</td>\n",
              "      <td>0.322368</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>discussion_ai_humanized_6.docx</td>\n",
              "      <td>510</td>\n",
              "      <td>17.110500</td>\n",
              "      <td>0.295201</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>discussion_ai_humanized_7.docx</td>\n",
              "      <td>511</td>\n",
              "      <td>22.439995</td>\n",
              "      <td>0.327048</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>discussion_ai_humanized_8.docx</td>\n",
              "      <td>553</td>\n",
              "      <td>33.875523</td>\n",
              "      <td>0.348095</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>discussion_ai_humanized_9.docx</td>\n",
              "      <td>591</td>\n",
              "      <td>22.608248</td>\n",
              "      <td>0.257021</td>\n",
              "      <td>0</td>\n",
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              "      <th>19</th>\n",
              "      <td>discussion_ai_humanized_10.docx</td>\n",
              "      <td>398</td>\n",
              "      <td>12.894266</td>\n",
              "      <td>0.191273</td>\n",
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              "      <th>20</th>\n",
              "      <td>introduction_ai_1.docx</td>\n",
              "      <td>644</td>\n",
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              "      <th>21</th>\n",
              "      <td>introduction_ai_2.docx</td>\n",
              "      <td>474</td>\n",
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              "      <td>introduction_ai_3.docx</td>\n",
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              "      <th>23</th>\n",
              "      <td>introduction_ai_4.docx</td>\n",
              "      <td>199</td>\n",
              "      <td>17.774416</td>\n",
              "      <td>0.406478</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>introduction_ai_5.docx</td>\n",
              "      <td>219</td>\n",
              "      <td>20.459091</td>\n",
              "      <td>0.232452</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>introduction_ai_6.docx</td>\n",
              "      <td>186</td>\n",
              "      <td>20.842690</td>\n",
              "      <td>0.240021</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>introduction_ai_7.docx</td>\n",
              "      <td>232</td>\n",
              "      <td>15.646683</td>\n",
              "      <td>0.101631</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>introduction_ai_8.docx</td>\n",
              "      <td>200</td>\n",
              "      <td>27.306522</td>\n",
              "      <td>0.216270</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>introduction_ai_9.docx</td>\n",
              "      <td>273</td>\n",
              "      <td>23.866323</td>\n",
              "      <td>0.303763</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>introduction_ai_10.docx</td>\n",
              "      <td>232</td>\n",
              "      <td>24.879385</td>\n",
              "      <td>0.276914</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>introduction_ai_humanized_1.docx</td>\n",
              "      <td>671</td>\n",
              "      <td>25.586863</td>\n",
              "      <td>0.313119</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>introduction_ai_humanized_2.docx</td>\n",
              "      <td>515</td>\n",
              "      <td>21.162598</td>\n",
              "      <td>0.226491</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>introduction_ai_humanized_3.docx</td>\n",
              "      <td>374</td>\n",
              "      <td>26.259531</td>\n",
              "      <td>0.298956</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>introduction_ai_humanized_4.docx</td>\n",
              "      <td>424</td>\n",
              "      <td>26.801723</td>\n",
              "      <td>0.278012</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>introduction_ai_humanized_5.docx</td>\n",
              "      <td>408</td>\n",
              "      <td>23.865345</td>\n",
              "      <td>0.283221</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>introduction_ai_humanized_6.docx</td>\n",
              "      <td>509</td>\n",
              "      <td>24.012999</td>\n",
              "      <td>0.287638</td>\n",
              "      <td>0</td>\n",
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              "      <th>36</th>\n",
              "      <td>introduction_ai_humanized_7.docx</td>\n",
              "      <td>464</td>\n",
              "      <td>19.326187</td>\n",
              "      <td>0.245176</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>introduction_ai_humanized_8.docx</td>\n",
              "      <td>388</td>\n",
              "      <td>28.251875</td>\n",
              "      <td>0.179323</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>introduction_ai_humanized_9.docx</td>\n",
              "      <td>789</td>\n",
              "      <td>30.893530</td>\n",
              "      <td>0.261470</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>introduction_ai_humanized_10.docx</td>\n",
              "      <td>914</td>\n",
              "      <td>41.944344</td>\n",
              "      <td>0.388241</td>\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-29b9a3a7-fa24-4a47-a44f-c66c4d9f7533 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6d2e8dd3-ebd2-49a1-b3a0-d2f4ff86c094\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_texts')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6d2e8dd3-ebd2-49a1-b3a0-d2f4ff86c094 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_texts');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_texts",
              "summary": "{\n  \"name\": \"df_texts\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"discussion_ai_humanized_10.docx\",\n          \"discussion_ai_humanized_7.docx\",\n          \"discussion_ai_humanized_6.docx\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Token\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 167,\n        \"min\": 186,\n        \"max\": 914,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          591,\n          494,\n          470\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perplexity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.699608993769556,\n        \"min\": 9.090505599975586,\n        \"max\": 41.94434356689453,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          23.865345001220703,\n          28.251874923706055,\n          10.470787048339844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Burstiness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07350225353066496,\n        \"min\": 0.10163055643383234,\n        \"max\": 0.4939719267962603,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          0.28322125310708446,\n          0.17932278848353858,\n          0.22390474164995552\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AI Metric\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Upload the model file\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "96Ml8wfJBCh5",
        "outputId": "dba3d383-1dd4-4a17-db9c-0437cbaedf34"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f5256748-0ce6-41c0-8bc4-1f89219d485a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-f5256748-0ce6-41c0-8bc4-1f89219d485a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving rf_model_resample.pkl to rf_model_resample.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#save model\n",
        "import pickle\n",
        "\n",
        "#with open(\"rf_model.pkl\", \"wb\") as f:\n",
        "#     pickle.dump(rf_model, f)\n",
        "\n",
        "#load model\n",
        "with open(\"rf_model_resample.pkl\", \"rb\") as f:\n",
        "     best_rf_resample = pickle.load(f) # Use pickle.load to load the model"
      ],
      "metadata": {
        "id": "8l7aJB04G0WK"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_texts.drop(columns=['text', 'AI Metric'])\n",
        "#X = df_texts[['word counts', 'Token', 'Perplexity', 'character counts']]\n",
        "#y = df_texts['AI Metric']"
      ],
      "metadata": {
        "id": "q-UefrEjGy7A"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Predict if the text is AI-generated or human-written\n",
        "Text_predicted = best_rf_resample.predict(X)\n",
        "result = pd.DataFrame({'text': lista_texts, 'AI Predicted': Text_predicted})\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "64yXEQcjCftk",
        "outputId": "0e0f8938-698e-4a11-c3fb-dfea0bf2e665"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 text  AI Predicted\n",
              "0                discussion_ai_1.docx             1\n",
              "1                discussion_ai_2.docx             1\n",
              "2                discussion_ai_3.docx             1\n",
              "3                discussion_ai_4.docx             1\n",
              "4                discussion_ai_5.docx             1\n",
              "5                discussion_ai_6.docx             1\n",
              "6                discussion_ai_7.docx             1\n",
              "7                discussion_ai_8.docx             1\n",
              "8                discussion_ai_9.docx             1\n",
              "9               discussion_ai_10.docx             1\n",
              "10     discussion_ai_humanized_1.docx             1\n",
              "11     discussion_ai_humanized_2.docx             1\n",
              "12     discussion_ai_humanized_3.docx             0\n",
              "13     discussion_ai_humanized_4.docx             1\n",
              "14     discussion_ai_humanized_5.docx             1\n",
              "15     discussion_ai_humanized_6.docx             1\n",
              "16     discussion_ai_humanized_7.docx             1\n",
              "17     discussion_ai_humanized_8.docx             1\n",
              "18     discussion_ai_humanized_9.docx             1\n",
              "19    discussion_ai_humanized_10.docx             1\n",
              "20             introduction_ai_1.docx             1\n",
              "21             introduction_ai_2.docx             1\n",
              "22             introduction_ai_3.docx             1\n",
              "23             introduction_ai_4.docx             1\n",
              "24             introduction_ai_5.docx             1\n",
              "25             introduction_ai_6.docx             1\n",
              "26             introduction_ai_7.docx             1\n",
              "27             introduction_ai_8.docx             1\n",
              "28             introduction_ai_9.docx             1\n",
              "29            introduction_ai_10.docx             1\n",
              "30   introduction_ai_humanized_1.docx             1\n",
              "31   introduction_ai_humanized_2.docx             1\n",
              "32   introduction_ai_humanized_3.docx             1\n",
              "33   introduction_ai_humanized_4.docx             1\n",
              "34   introduction_ai_humanized_5.docx             1\n",
              "35   introduction_ai_humanized_6.docx             1\n",
              "36   introduction_ai_humanized_7.docx             1\n",
              "37   introduction_ai_humanized_8.docx             1\n",
              "38   introduction_ai_humanized_9.docx             1\n",
              "39  introduction_ai_humanized_10.docx             0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5b714086-2e34-4dd2-826b-928070d4e66e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>AI Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>discussion_ai_1.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>discussion_ai_2.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>discussion_ai_3.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>discussion_ai_4.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>discussion_ai_5.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>discussion_ai_6.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>discussion_ai_7.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>discussion_ai_8.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>discussion_ai_9.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>discussion_ai_10.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>discussion_ai_humanized_1.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>discussion_ai_humanized_2.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>discussion_ai_humanized_3.docx</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>discussion_ai_humanized_4.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>discussion_ai_humanized_5.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>discussion_ai_humanized_6.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>discussion_ai_humanized_7.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>discussion_ai_humanized_8.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>discussion_ai_humanized_9.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>discussion_ai_humanized_10.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>introduction_ai_1.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>introduction_ai_2.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>introduction_ai_3.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>introduction_ai_4.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>introduction_ai_5.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>introduction_ai_6.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>introduction_ai_7.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>introduction_ai_8.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>introduction_ai_9.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>introduction_ai_10.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>introduction_ai_humanized_1.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>introduction_ai_humanized_2.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>introduction_ai_humanized_3.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>introduction_ai_humanized_4.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>introduction_ai_humanized_5.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>introduction_ai_humanized_6.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>introduction_ai_humanized_7.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>introduction_ai_humanized_8.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>introduction_ai_humanized_9.docx</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>introduction_ai_humanized_10.docx</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"result\",\n  \"rows\": 40,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"discussion_ai_humanized_10.docx\",\n          \"discussion_ai_humanized_7.docx\",\n          \"discussion_ai_humanized_6.docx\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AI Predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}