Binoculars_eval.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jmccrosky/playing/blob/main/Binoculars_eval.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6S1Z_NXgjOGw",
        "outputId": "82d37ffc-d24d-4e45-fbbd-d4824395647e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Binoculars'...\n",
            "remote: Enumerating objects: 197, done.\u001b[K\n",
            "remote: Counting objects: 100% (49/49), done.\u001b[K\n",
            "remote: Compressing objects: 100% (6/6), done.\u001b[K\n",
            "remote: Total 197 (delta 46), reused 43 (delta 43), pack-reused 148\u001b[K\n",
            "Receiving objects: 100% (197/197), 54.08 MiB | 15.42 MiB/s, done.\n",
            "Resolving deltas: 100% (96/96), done.\n",
            "Updating files: 100% (28/28), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ahans30/Binoculars.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yacz8Zj6kHHT",
        "outputId": "5bec97c0-7c72-4f39-df49-f4d5237efa19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Binoculars\n",
            "Obtaining file:///content/Binoculars\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: ffmpy, transformers\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=00ca7db0b7ac0d61b440fed3dcc2288f36d9d31cb20875d7583786fa56836b3b\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.31.0-py3-none-any.whl size=7386692 sha256=8756594f8aad1c8fd7b2afec7a082fb0cb3a0a38e637ac39d6cadf98a5bc76b9\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-zudoe34m/wheels/65/c2/80/8b510d2bb50a7091ba8ede5ff3dd575972fdcce0c17c50080c\n",
            "Successfully built ffmpy transformers\n",
            "Installing collected packages: tokenizers, pydub, ffmpy, xxhash, websockets, tomlkit, shellingham, semantic-version, ruff, python-multipart, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, h11, dill, colorama, aiofiles, uvicorn, starlette, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, httpcore, transformers, nvidia-cusolver-cu12, httpx, fastapi, gradio_client, datasets, gradio, accelerate, Binoculars\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.15.2\n",
            "    Uninstalling tokenizers-0.15.2:\n",
            "      Successfully uninstalled tokenizers-0.15.2\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.38.2\n",
            "    Uninstalling transformers-4.38.2:\n",
            "      Successfully uninstalled transformers-4.38.2\n",
            "  Running setup.py develop for Binoculars\n",
            "Successfully installed Binoculars-0.0.10 accelerate-0.28.0 aiofiles-23.2.1 colorama-0.4.6 datasets-2.18.0 dill-0.3.8 fastapi-0.110.0 ffmpy-0.3.2 gradio-4.22.0 gradio_client-0.13.0 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 orjson-3.9.15 pydub-0.25.1 python-multipart-0.0.9 ruff-0.3.4 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.36.3 tokenizers-0.13.3 tomlkit-0.12.0 transformers-4.31.0 uvicorn-0.29.0 websockets-11.0.3 xxhash-3.4.1\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd Binoculars\n",
        "!pip install -e .\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cRNnNmqAkVii"
      },
      "outputs": [],
      "source": [
        "from binoculars import Binoculars\n",
        "import pyarrow.parquet as pq\n",
        "import pandas as pd\n",
        "import random\n",
        "from google.colab import data_table\n",
        "data_table.enable_dataframe_formatter()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assumes you have downloaded the data from https://huggingface.co/datasets/artem9k/ai-text-detection-pile to your Google Drive\n",
        "\n",
        "filenames = [\n",
        "    \"/content/drive/MyDrive/ai-text-detection-pile/data/train-00000-of-00007-bc5952582e004d67.parquet\",\n",
        "    \"/content/drive/MyDrive/ai-text-detection-pile/data/train-00001-of-00007-71c80017bc45f30d.parquet\",\n",
        "    \"/content/drive/MyDrive/ai-text-detection-pile/data/train-00002-of-00007-ee2d43f396e78fbc.parquet\",\n",
        "    \"/content/drive/MyDrive/ai-text-detection-pile/data/train-00003-of-00007-529931154b42b51d.parquet\",\n",
        "    \"/content/drive/MyDrive/ai-text-detection-pile/data/train-00004-of-00007-b269dc49374a2c0b.parquet\",\n",
        "    \"/content/drive/MyDrive/ai-text-detection-pile/data/train-00005-of-00007-3dce5e05ddbad789.parquet\",\n",
        "    \"/content/drive/MyDrive/ai-text-detection-pile/data/train-00006-of-00007-3d8a471ba0cf1c8d.parquet\",\n",
        "]"
      ],
      "metadata": {
        "id": "iwTrP7LgV4qN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bino = Binoculars()"
      ],
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        "outputId": "60cea11c-a62e-49e4-e312-deff49251aa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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": [
              "tokenizer_config.json:   0%|          | 0.00/287 [00:00<?, ?B/s]"
            ],
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        {
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              "special_tokens_map.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
            ],
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        {
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          "data": {
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              "tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
            ],
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        {
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              "special_tokens_map.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "configuration_falcon.py:   0%|          | 0.00/7.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
            "- configuration_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "WARNING:transformers_modules.tiiuae.falcon-7b.898df1396f35e447d5fe44e0a3ccaaaa69f30d36.configuration_falcon:\n",
            "WARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model without the trust_remote_code=True argument.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
            "- modeling_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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          "output_type": "display_data",
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b-instruct:\n",
            "- configuration_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "WARNING:transformers_modules.tiiuae.falcon-7b-instruct.cf4b3c42ce2fdfe24f753f0f0d179202fea59c99.configuration_falcon:\n",
            "WARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model without the trust_remote_code=True argument.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
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          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b-instruct:\n",
            "- modeling_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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          "output_type": "display_data",
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          "name": "stderr",
          "text": [
            "Using pad_token, but it is not set yet.\n"
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x0nC7zY8z7jv"
      },
      "outputs": [],
      "source": [
        "def run_eval(sample_rate, length_requirement=None):\n",
        "    true_positives = []\n",
        "    false_positives = []\n",
        "    true_negatives = []\n",
        "    false_negatives = []\n",
        "    error_count = 0\n",
        "\n",
        "    for filename in filenames:\n",
        "        parquet_file = pq.ParquetFile(filename)\n",
        "        print(f\"Loaded {filename}\")\n",
        "\n",
        "        for batch in parquet_file.iter_batches(batch_size=1000):\n",
        "            batch_pandas = batch.to_pandas()\n",
        "\n",
        "            for index, row in batch_pandas.iterrows():\n",
        "                if random.random() < sample_rate:\n",
        "                    if length_requirement and len(row.text) <= length_requirement:\n",
        "                        continue\n",
        "                    try:\n",
        "                        if length_requirement:\n",
        "                            predicted_ai = (bino.predict(row.text[:length_requirement]) == \"Most likely AI-generated\")\n",
        "                        else:\n",
        "                            predicted_ai = (bino.predict(row.text) == \"Most likely AI-generated\")\n",
        "                    except Exception as e:\n",
        "                        print(f\"Error predicting for text: {row.text[:length_requirement] if length_requirement else row.text}, Error: {e}\")\n",
        "                        error_count += 1\n",
        "                        continue  # Skip to the next iteration if there's an error as happens with a very small number of texts which are empty\n",
        "\n",
        "                    actually_ai = (row.source == \"ai\")\n",
        "\n",
        "                    if predicted_ai and actually_ai:\n",
        "                        true_positives.append(row.text)\n",
        "                    elif predicted_ai and not actually_ai:\n",
        "                        false_positives.append(row.text)\n",
        "                    elif not predicted_ai and actually_ai:\n",
        "                        false_negatives.append(row.text)\n",
        "                    elif not predicted_ai and not actually_ai:\n",
        "                        true_negatives.append(row.text)\n",
        "\n",
        "    return {\n",
        "        'true_positives': true_positives,\n",
        "        'false_positives': false_positives,\n",
        "        'true_negatives': true_negatives,\n",
        "        'false_negatives': false_negatives,\n",
        "        'error_count': error_count\n",
        "    }\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DADeysU3qrUW",
        "outputId": "adf2d4ef-a071-4603-8258-1372b18b9b1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00000-of-00007-bc5952582e004d67.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00001-of-00007-71c80017bc45f30d.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00002-of-00007-ee2d43f396e78fbc.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00003-of-00007-529931154b42b51d.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00004-of-00007-b269dc49374a2c0b.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00005-of-00007-3dce5e05ddbad789.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00006-of-00007-3d8a471ba0cf1c8d.parquet\n",
            "Error predicting for text: , Error: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)\n",
            "Error predicting for text: , Error: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)\n",
            "Error predicting for text: , Error: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)\n",
            "Error predicting for text: , Error: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)\n",
            "Error predicting for text: , Error: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)\n",
            "Error predicting for text: , Error: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)\n",
            "Error predicting for text: , Error: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)\n",
            "TPR is 0.4313138189244384\n",
            "FPR is 0.0065321527072144\n",
            "Error rate is 0.00024989290304155363\n"
          ]
        }
      ],
      "source": [
        "res = run_eval(1.0 / 50)\n",
        "\n",
        "true_positives_count = len(res['true_positives'])\n",
        "false_positives_count = len(res['false_positives'])\n",
        "true_negatives_count = len(res['true_negatives'])\n",
        "false_negatives_count = len(res['false_negatives'])\n",
        "error_count = res['error_count']\n",
        "\n",
        "TPR = true_positives_count / (true_positives_count + false_negatives_count) if (true_positives_count + false_negatives_count) > 0 else 0\n",
        "FPR = false_positives_count / (false_positives_count + true_negatives_count) if (false_positives_count + true_negatives_count) > 0 else 0\n",
        "Error_rate = error_count / (false_positives_count + true_negatives_count + true_positives_count + false_negatives_count) if (false_positives_count + true_negatives_count + true_positives_count + false_negatives_count) > 0 else 0\n",
        "\n",
        "print(f\"TPR is {TPR}\")\n",
        "print(f\"FPR is {FPR}\")\n",
        "print(f\"Error rate is {Error_rate}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res2 = run_eval(1.0 / 50, 1024)\n",
        "\n",
        "true_positives_count = len(res2['true_positives'])\n",
        "false_positives_count = len(res2['false_positives'])\n",
        "true_negatives_count = len(res2['true_negatives'])\n",
        "false_negatives_count = len(res2['false_negatives'])\n",
        "error_count = res2['error_count']\n",
        "\n",
        "TPR = true_positives_count / (true_positives_count + false_negatives_count) if (true_positives_count + false_negatives_count) > 0 else 0\n",
        "FPR = false_positives_count / (false_positives_count + true_negatives_count) if (false_positives_count + true_negatives_count) > 0 else 0\n",
        "Error_rate = error_count / (false_positives_count + true_negatives_count + true_positives_count + false_negatives_count) if (false_positives_count + true_negatives_count + true_positives_count + false_negatives_count) > 0 else 0\n",
        "\n",
        "print(f\"TPR is {TPR}\")\n",
        "print(f\"FPR is {FPR}\")\n",
        "print(f\"Error rate is {Error_rate}\")"
      ],
      "metadata": {
        "id": "Lq4bS94T0KcF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "626f6a82-8c83-476e-dda7-a3d2eeed872b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00000-of-00007-bc5952582e004d67.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00001-of-00007-71c80017bc45f30d.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00002-of-00007-ee2d43f396e78fbc.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00003-of-00007-529931154b42b51d.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00004-of-00007-b269dc49374a2c0b.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00005-of-00007-3dce5e05ddbad789.parquet\n",
            "Loaded /content/drive/MyDrive/ai-text-detection-pile/data/train-00006-of-00007-3d8a471ba0cf1c8d.parquet\n",
            "TPR is 0.4265898420827998\n",
            "FPR is 0.004435682602267127\n",
            "Error rate is 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(res2['false_positives'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 12063
        },
        "id": "Y8qbs2Xrry7s",
        "outputId": "5502f7ac-3997-4bc9-8c82-f7123eeb3a63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dubai Airport Free Zone: Business Models Essay...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Exhibition in the Museum of Fine Arts in Bosto...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Strategic Management: the Hyatt Kathmandu Repo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Marketing Plan for Galaxy S6 Evaluation Essay\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Interaction between People of Different Backgr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Analysis of the Leader-Member Exchange Theory ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Introduction to Clinical Trial Research Resear...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Reaction Paper for the Article “Thanks for the...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>The original three Ace Attorney games are head...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Save Capture and Monitor Metro apps using the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>The 'Hubble flow' describes the motion of gala...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Over the end credits of the new HBO documentar...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>I wanna be the very best\\n\\nLike no one ever w...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Mercury\\n\\n\\nMercury is the innermost planet i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>CLEVELAND, Ohio -- With the Detroit Tigers in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>\"The Healthy Geezer\" answers questions about h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Parts of the country are under water and a cle...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Overview\\n\\nPoison ivy rash Poison ivy rash\\n\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>On a hunch, I did a search of Supreme Court ca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Ruby on Rails is a very popular open source we...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Features\\n\\nThe first major feature introduced...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Independent news is more important than ever. ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Today's birthday, December 13: US singer-songw...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>11/21/2017\\n\\nFriday, Nov 24 at 9:00am PST\\n\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Flood Warning issued September 29 at 7:57PM CD...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>R&amp;B sensation Brandy was involved in a fatal c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Edible wild mushrooms in Utah? We're too dry f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Looking for news you can trust?\\n\\nSubscribe t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>The best way to think about it is in terms of ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>In computer science, the computational complex...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem.\\nThe study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory. Both areas are highly related, as the complexity of an algorithm is always an upper bound on the complexity of the problem solved by this algorithm. Moreover, for designing efficient algorithms, it is often fundamental to compare the complexity of a specific algorithm to the complexity of the problem to be solved. Also, in most cases, the only thing that is known about the complexity of a problem is that it is lower than the complexity of the most efficient known algorithms. Therefore, there is a large overlap between analysis of algorithms and complexity theory. \\nAs the amount of resources required to run an algorithm generally varies with the size of the input, the complexity is typically expressed as a function n \\u2192 f(n), where n is the size of the input and f(n) is either the worst-case complexity (the maximum of the amount of resources that are needed over all inputs of size n) or the average-case complexity (the average of the amount of resources over all inputs of size n). Time complexity is generally expressed as the number of required elementary operations on an input of size n, where elementary operations are assumed to take a constant amount of time on a given computer and change only by a constant factor when run on a different computer. Space complexity is generally expressed as the amount of memory required by an algorithm on an input of size n.\",\n          \"Reaction Paper for the Article \\u201cThanks for the Memories\\u201d Essay\\n\\nThis article discussed the work of cognitive psychologist Elizabeth Loftus from the University of Washington. She contended that when we recall a memory the event is not accurately recreated, but instead we recall a reconstruction of the actual event. She stated that this is most common in criminal investigation cases. Loftus argues that changing minor details in a question can cause a person to recall the event differently or even add in details that were not there to begin with.\\n\\nShe used four experiments to prove her hypothesis. Loftus\\u2019 work has been challenged, with some psychologists stating that the memory is not lost, but can be obtained with proper questioning. Recently, Loftus\\u2019 research has been applied to repressed memories of childhood sexual abuse that is recalled through the assistance of a therapist (Loftus, 560-572).\\n\\nUpon reading the article, I found that many of the points made by Loftus make sense. I can easily compare and apply her research to real life situations. If you want current crime dramas, such as Law and Order, you can see the questions the lawyers use to trigger memories with the eyewitnesses.\\n\\nIt was very interesting however that changing simple key words in the question can almost create a memory in the person\\u2019s head. For example, Loftus compares the usage of \\u201ca\\u201d as opposed to \\u201cthe.\\u201d I could ask an eyewitness, \\u201cDid you see a knife?\\u201d This question evokes a different answer than, for example, \\u201cDid you see the knife?\\u201d The second question implies that there was a knife at the scene.\\n\\nI feel that it is up to the lawyers to ask the questions properly so the eyewitness can accurately recall the events, to the best of their ability. Of course, what I find fascinating is that a lawyer could use this method to elicit the responses they need from an eyewitness. By using this research, the prosecuting attorney could, in effect, make an eyewitness recreate an inaccurate event. I can understand where some of her research could be called into question as well.\\n\\nThe end of the article states that the memory is still there, but it is up to the investigators to focus on the right wording, as I stated above. In order to obtain accurate eyewitness accounts, an investigator must be made aware of how a witness will later recall the event in question.\\n\\nI find it interesting and apt that Loftus\\u2019 research is being used to call into question reports of repressed child abuse. While I am sure that the complaining person is not automatically being doubted, a therapist could conceivably manipulate a person into believing that they were abused because it fits the diagnosis.\\n\\nFor example, a therapist could ask a client, \\u201cDid he ever touch you?\\u201d This could mean a variety of things. The therapist would have to define the type of touch. I think it is important for therapists not to put their own personal spin on things.\\n\\nThey are there to assist their clients in unraveling their problems and offering plausible explanations and solutions. While it is important to question these repressed allegations, it should not be forgotten that if the therapist is at fault, their client is still a victim of their manipulative, whether intentional or not, tactics.\\n\\nIn conclusion, it is my belief that eyewitness testimony should never solely be used to convict someone of a crime. I am thankful that the judicial system does not rely on just one aspect of evidence in order to obtain a conviction. However, Loftus\\u2019 research could be used by a defense team to discredit an eyewitness. This must be taken into consideration as well as not all event recall is recreated memory.\\n\\nI have to agree with Loftus\\u2019 research on some level in that I could see how different questions might lead someone to think that there was something there that maybe they had forgotten before that moment. However, I do not think there is enough information on how the brain stores and recalls information at this point in time to make Loftus\\u2019 research the tell all and end all to cognitive memory studies.\\n\\nReference\\n\\nLoftus, E.F (1975). Leading Questions and the Eyewitness Report. Cognitive Psychology, 7, 560-572.\\n\",\n          \"Features\\n\\nThe first major feature introduced is Virtual Super Resolution (VSR). This is really a \\\"me-too\\\" of NVIDIA's Dynamic Super Resolution (DSR) feature introduced with its GeForce GTX 900 series GPUs. It lets you simulate higher resolutions than what your display can handle. The 3D app's output is of a higher resolution than what your display supports, while the driver intelligently scales it down to your display's resolution, giving you a sense of detail. VSR is essentially a glorified super-sampling feature.AMD's implementation works much like NVIDIA's, with one difference: VSR is a global setting in Catalyst Control Center, while NVIDIA GeForce Experience offers it as an app-specific setting. With VSR enabled, you'll need to launch your 3D app to set its output resolution above what your display supports. VSR acts as a layer between the app and your physical display, which prevents your monitor from going out of sync. VSR is supported on the Radeon R9 290X, R9 290, R9 295X2, and R9 285. AMD also introduced new frame-pacing enhancements, which give you a more fluid display output with 3D apps and eliminate the micro-stutter AMD GPUs earned some flack for.The second major feature introduced is AMD Fluid Motion Video. This is a highly specific feature that works to smooth out Blu-ray playback with Cyberlink PowerDVD 14. At this point, no other software we know of can take advantage of this feature. It uses GPU compute to interpolate \\\"inferred\\\" frames with real frames in a Blu-ray playback, increasing frame-rates and, in turn, overall smoothness. It can be enabled in Catalyst Control Center and comes with a slider.The third notable feature is video Contour Removal. Designed for compressed video, it appears to be a pixel shader code that works to remove compression artifacts in, particularly, \\\"repetitive\\\" portions of the frame (eg: clear blue sky) without affecting details in non-repetitive portions. Unlike Fluid Motion Video, it is designed for compressed video and works with any video player that can output through DXVA. VideoLAN Client, a popular open-source player, works with this feature.1080p Detail Enhancement is the fourth notable feature, and it is comprised of a set of video image quality enhancements, such as edge-enhancement, mosquito-noise reduction, and de-blocking. FullHD to UltraHD is another neat feature as it improves the quality of 1080p videos that have been upscaled to 2160p. It's essentially an upscaling pixel-shader algorithm, like the ones MadVR gives you, only this one isn't as taxing. Subjectively, the upscale quality is comparable to some of the basic levels of the \\\"Jinc\\\" algorithm. To use it, simply play 1080p videos on a 4K Ultra HD display via a DXVA-enabled player, such as VLC or MPC-Home Cinema.Catalyst 14.12 Omega introduces 5K (5120 x 2880 pixels) display support for Windows. AMD GPUs already support this resolution on Apple's Retina iMac. This driver preps your Windows PC up for aftermarket 5K displays, such as the Dell UltraSharp UP2715K. Just don't expect to play AAA games using your R9 285 or any single AMD GPU. And no, you cannot VSR 5K onto a lower-resolution 4K display. We tried.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>On Monday, the Supreme Court gave the final sa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[NOTE: I had not done any real analysis on thi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cape Town - The EFF has made progress with its...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A B.C. police commander says a group of men he...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[Updated at 11 a.m. ET] A major US television ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2682</th>\n",
              "      <td>I have an extensive database of laptops that h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2683</th>\n",
              "      <td>(also known as the “Chinese seasons”) is the r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2684</th>\n",
              "      <td>. In order to achieve goals, such as personal ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2685</th>\n",
              "      <td>Automatic windshield wipers use sensors to det...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2686</th>\n",
              "      <td>Le message décodé en utilisant la table ASCII ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2687 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2db0587-4043-4d45-a9fd-595efd2afa6a')\"\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-c2db0587-4043-4d45-a9fd-595efd2afa6a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  @keyframes spin {\n",
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As usual, the players will wear patriotic patriotic clothes that are available in our shop, and if you purchase any of the products for proceeds to help benefit cancer research for the American Institute of Cancer Research, you'll also receive a free t-shirt!\\n\\nThis year, we're also happy to introduce the following new products at this year's Golf Tournament.\\n\\n* We'll have 1 player wearing Red and Blue clothing.\\n\\n* One player will be wearing a Patriot Jersey from the Nike collection.\\n\\n* All other players in the field will be wearing our new Patriot hats, available in several colors.\\n\\n* The tournament proceeds will be used to benefit American Institute of Cancer Research.\\n\\n* Prizes for several of the top scoring players will be awarded.\\n\\n* Each of the 18 holes has a different Patriot theme, and each hole will be highlighted in patriotic red and blue on the course backdrop.\\n\\n* The day of the event, we will have our own Patriot-themed party for the fans!\\n\\n* Each of the 18 holes will feature a flag backdrop as a backdrop at the 18th and will have \\\"Welcome to the U.S.A.\\\" on it.\\n\\n* Each hole will be painted red and blue and will include the flag of the United States.\\n\\n* Each tee shot will be a custom patriotic tee.\\n\\n* All of our Patriot players will be wearing our new Patriot jerseys, available in several colors, that include the following patches:\\n\\n* The first 18 holes have our patriotic \\\"America-First\\\" logo.\\n\\n* The second 18 holes have our patriotic \\\"One Nation At War\\\" logo.\\n\\nOur tournament starts at 9am on Thursday, June 5th. $500 pot bonus will be awarded in addition to the tournament entries.\\n\\nAll proceeds from our Charity Golf Tournament will go to American Institute of Cancer Research.\",\n          \"Tens of thousands of protesters took to the streets of several Ukrainian cities Saturday in support of a wave of protests against ousted President Viktor Yanukovych.\\n\\nThe demonstrations have been triggered by a crackdown on a pro-EU protest camp in Independence Square in central Kyiv on February 20, in which dozens were killed in confrontations with police.\\n\\nIn central Kyiv the authorities have placed a total of six people under house arrest in relation to the incident, including ousted Prime Minister Viktor Yanukovych.\\n\\nMeanwhile, Ukrainian politicians have agreed to hold early parliamentary elections in early 2015.\\n\\nDemonstrators rallied in support of anti-government demonstrators on Kiev's Independence Square in December after Ukraine's opposition ousted Yanukovych.\\n\\nAt least 33 people were killed in the violent crackdown.\\n\\nMany Ukrainians blame Yanukovych's ouster last month of a Russian-leaning political system and an EU trade agreement favored by Europe for the political crisis gripping their country.\\n\\nRussia has denounced the coup as illegitimate.\",\n          \"PITTSBURGH -- Pittsburgh Steelers running back Marshawn Lynch, who has been fined nearly $200,000 for his involvement in a hit on a defenseless player last season, was not fined for the hit in Sunday's 34-31 loss to Kansas City. Lynch's hit caused a fumble that Chiefs safety Husain Abdullah recovered.\\n\\nIt marked Lynch's ninth fines this season. His latest fine came during a game at Denver in September, when he was fined $50,000 for a hit on defensive end Antonio Smith. He has previously been fined $10,000 for unsportsmanlike conduct against the Dallas Cowboys, $21,000 for unsportsmanlike conduct against the Pittsburgh Steelers and $7,500 for unsportsmanlike conduct against the Baltimore Ravens.\\n\\nCoach Mike Tomlin said he believed the play occurred in a \\\"haymaker\\\" and that the helmet came off instead of hitting the ground. He said he was not aware of the fine during the game and didn't understand why Lynch was fined.\\n\\n\\\"I have no clue about it,\\\" Tomlin said.\\n\\nLynch said earlier this month that \\\"it's a dirty play,\\\" but said he wasn't aware of a penalty on the play. He said he was looking to make a play for the win and had not heard from his coach about the fine.\\n\\nMarshawn Lynch is out of the NFL indefinitely due to a hit he made last season. Mark Duncan/AP Images\\n\\nAfter Sunday, Lynch addressed the play and the fine.\\n\\n\\\"I'll be honest with you, it was a mistake,\\\" Lynch said. \\\"It wasn't a dirty play. It came as a result of having all the momentum, as we were on the right side, and I was looking downfield at the ball coming my way to break that tackle. At the time, I didn't know Husain was defenseless so the hit I did was on purpose to try to break that defensive back outside. As I'm going to break through and try to get through in a flash, I felt the ground, and the helmet came off.\\n\\n\\\"That's never happened to me in my whole life. I don't think we really talked about it. I think it's just kind of just a play. If you look up in the replay, you'll be able to see Husain's face is completely stunned. I felt so bad about it after the game, and I felt it after the fine; it was very unnecessary.\\\"\\n\\nIn addition to the $200,000 fine, Lynch also received a $13,000 fine for leaving the field illegally, a rule violation.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "Retired Military Veteran, John: \"Honor and support for our troops stand at the heart of the Biden administration. With a proven track record of enhancing veterans' healthcare and increasing defense budgets, we're committed to keeping America safe and treating our heroes with the dignity they deserve.\"\n",
        "\n",
        "Young Urban Professional, Sofia: \"Join Team Biden in building a sustainable future with cutting-edge clean energy initiatives and policies that foster innovation. Together, we can tackle climate change, reduce student debt, and create a dynamic job market that's ready for tomorrow's challenges.\"\n",
        "\n",
        "Rural Small Business Owner, Emma: \"President Biden understands the backbone of America's economy lies in its small businesses and farms. We're working to implement fair trade deals, improve rural infrastructure, and provide tax relief to help your business thrive in a global market.\"\n",
        "\n",
        "Suburban Middle-Class Parent, Michael: \"Every child deserves a first-rate education, and under the Biden administration, we're investing more in our public schools and educators than ever before. With policies aimed at making healthcare affordable and our neighborhoods safe, we're building stronger communities for all our families.\"\n",
        "'''\n",
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        "text='''\n",
        "Retired Military Veteran, John: \"The heart of the Biden administration? It's all about honor and support for our troops. They've really stepped up, improving veterans' healthcare, bumping up defense budgets... It's about keeping us safe and treating our heroes right.\"\n",
        "\n",
        "Young Urban Professional, Sofia: \"Let's get on board with Team Biden for a greener future. They're rolling out some pretty cool clean energy plans and innovation policies. It’s about fighting climate change, cutting down student loans, and sparking a job market ready for what's coming.\"\n",
        "\n",
        "Rural Small Business Owner, Emma: \"Biden gets it – small businesses and farms are the real deal in our economy. Fair trade, better rural internet and roads, tax breaks... he’s pushing for changes that matter to us out here, making sure we can stand tall in the global market.\"\n",
        "\n",
        "Suburban Middle-Class Parent, Michael: \"Every kid should get top-notch schooling, right? Biden's team is pouring more into public schools and teachers than before. They're all about affordable healthcare and safe neighborhoods, really making a difference for families like ours.\"\n",
        "'''\n",
        "bino.predict(text)"
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