{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
            "bf4bba951e214693be2c7f0b5b4ad673",
            "73f9a2d12d4c43d58e814745680c9919",
            "3248fcf1bf4e452c9f9e666b3c203ed9",
            "2416fd4fc017438a9d018aba74141705",
            "5b597e99e54c4fc8872e9051f0ce781f",
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            "6837139b13404701aabde4fbc3858240",
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            "e701aa55e8104afa85fe72c611bd37d8",
            "fae089ae724945c38d5a7d02d1628e86",
            "720a06f38d4743a4972c320ec05a61bf",
            "c022c2c428734e07b9f5a67b55d3cce6",
            "d02d44c969a84f53ab64764b068fb79d",
            "5410d4432f7a4932a3d203d2ae3c6d1c",
            "b53b0ed8dda8492d844094af11e92be9",
            "7a9f5a34f3bc401698f75fb643892774"
          ]
        },
        "id": "lIYdn1woOS1n",
        "outputId": "5be5051d-aa2f-4103-e4f3-21436e605bf7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf4bba951e214693be2c7f0b5b4ad673"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install -U -q transformers datasets accelerate sentencepiece\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install optimum[onnxruntime,exporters] onnx onnxruntime onnxoptimizer -q"
      ],
      "metadata": {
        "id": "uEcVApg6vLXd"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip freeze | grep cupy\n",
        "!pip uninstall -y cupy-cuda12x"
      ],
      "metadata": {
        "id": "WfiDFm4SvPBY",
        "outputId": "043e8f0c-e436-4470-e556-12a68460a795",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cupy-cuda12x==12.2.0\n",
            "Found existing installation: cupy-cuda12x 12.2.0\n",
            "Uninstalling cupy-cuda12x-12.2.0:\n",
            "  Successfully uninstalled cupy-cuda12x-12.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# If the dataset is gated/private, make sure you have run huggingface-cli login\n",
        "dataset = load_dataset(\"BEE-spoke-data/rp_books-en\", 'clean')\n",
        "dataset"
      ],
      "metadata": {
        "id": "UEXZLdhmvV7x",
        "outputId": "d42b4d18-3ddd-4993-a797-4fc92b70ec33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['meta', 'text'],\n",
              "        num_rows: 26372\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.map(lambda x: {\"first_25k\": x['text'][:25000]}, num_proc=6)\n",
        "dataset"
      ],
      "metadata": {
        "id": "tOqYTEdTvtuU",
        "outputId": "9d859756-2f3d-4050-91df-9b3428eba9a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['meta', 'text', 'first_25k'],\n",
              "        num_rows: 26372\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from optimum.pipelines import pipeline\n",
        "\n",
        "pipe = pipeline(\"text-classification\", model=\"/content/quantized_model\", accelerator=\"ort\")\n",
        "question = \"What's my name?\"\n",
        "context = \"My name is Philipp and I live in Nuremberg.\"\n",
        "\n",
        "pipe(context)\n"
      ],
      "metadata": {
        "id": "S1rpxVWdwD7Z",
        "outputId": "72098cec-31e7-475d-d169-2a77774873cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'clean', 'score': 0.9644750952720642}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_graded = dataset.map(lambda x:\n",
        "                        pipe(x['first_25k'], truncation=True)[0],\n",
        "                        batched=False)"
      ],
      "metadata": {
        "id": "RTi1dzjuydXF",
        "outputId": "7e81870e-106c-4373-f940-70de2b642cb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "223b6be9fc5c48308462b46d9a5ec670"
          ]
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "daeb895c577f40db9c12cc683e00ae42",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/26372 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_graded"
      ],
      "metadata": {
        "id": "OA5svyARfJo2",
        "outputId": "09a09f75-6ab3-4f56-98a9-7bf6a0eee8f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['meta', 'text', 'first_25k', 'label', 'score'],\n",
              "        num_rows: 26372\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_graded.remove_columns('first_25k').push_to_hub(\"BEE-spoke-data/rp_books-en\",config_name=\"graded\")"
      ],
      "metadata": {
        "id": "QLyuQPnMy-fL",
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        "df = ds_graded['train'].remove_columns(['text']).to_pandas()\n",
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      "execution_count": 15,
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              "              score\n",
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              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --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-a6fc3c44-593e-447c-9e34-529488231ab1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9323.67899430167,\n        \"min\": 0.1366471351440814,\n        \"max\": 26372.0,\n        \"samples\": [\n          0.748563445686399,\n          0.7575096189975739,\n          26372.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.label.value_counts()"
      ],
      "metadata": {
        "id": "bGyNruj3iKoC",
        "outputId": "4b88fb66-b282-43f0-fe94-3f2a9eaaf883",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mild gibberish    21286\n",
              "clean              5084\n",
              "word salad            2\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_ds = ds_graded.filter(lambda x: x['label']=='clean' and x['score']>= 0.6)\n",
        "print(clean_ds)\n",
        "\n",
        "mild_ds = ds_graded.filter(lambda x: x['label']=='mild gibberish' and x['score']>= 0.75)\n",
        "print(mild_ds)\n"
      ],
      "metadata": {
        "id": "7gxQRAnSjQuL",
        "outputId": "339bc459-b7f8-4b4f-f3c8-69d72834f0c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257,
          "referenced_widgets": [
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            "25d607ef4be94747a4409de780335807",
            "4569c05c471a48bcaaf7a12c0358c8e1",
            "22e65077c9a8467c9cfccde437df4ab0",
            "e700073cdc6d475ba3045f8f8a708e31",
            "6a51778aab7e4e6c9ea08f4953ae955b",
            "0fb44f79dea14292bb5a7475ae5e7ae8",
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            "119091983e264452878041c2ee1030b9",
            "0b7134609681494f9646633dfb523d9d"
          ]
        }
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['meta', 'text', 'first_25k', 'label', 'score'],\n",
            "        num_rows: 2923\n",
            "    })\n",
            "})\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/26372 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1001e7d5a48842edb831b40568117174"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['meta', 'text', 'first_25k', 'label', 'score'],\n",
            "        num_rows: 12711\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "clean_ds.remove_columns('first_25k').push_to_hub(\"BEE-spoke-data/rp_books-en\",config_name=\"filtered-clean_grade\")"
      ],
      "metadata": {
        "id": "0VU5SsSEqf7n",
        "outputId": "0ca6df64-afa4-4c75-ea57-0d91be7bbd38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229,
          "referenced_widgets": [
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            "385e7533f3444bb687f410fda1aa8743"
          ]
        }
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Uploading the dataset shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/2.50k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/datasets/BEE-spoke-data/rp_books-en/commit/88b10d748959d25bf470c3b501ea53299c200d8c', commit_message='Upload dataset', commit_description='', oid='88b10d748959d25bf470c3b501ea53299c200d8c', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mild_ds.remove_columns('first_25k').push_to_hub(\"BEE-spoke-data/rp_books-en\",config_name=\"filtered-mild_grade\")"
      ],
      "metadata": {
        "id": "d6yYzcpMqpKA",
        "outputId": "cc675a70-8e2b-4f39-8ebf-7a2b8accaa4f",
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              "Uploading the dataset shards:   0%|          | 0/11 [00:00<?, ?it/s]"
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          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/datasets/BEE-spoke-data/rp_books-en/commit/0ad811e6954489b5329690ab93044b8dda0bf7b0', commit_message='Upload dataset', commit_description='', oid='0ad811e6954489b5329690ab93044b8dda0bf7b0', pr_url=None, pr_revision=None, pr_num=None)"
            ],
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              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "def basic_distribution_stats(numbers):\n",
        "    if not numbers:\n",
        "        return None\n",
        "\n",
        "    # Calculate the mean\n",
        "    mean = sum(numbers) / len(numbers)\n",
        "\n",
        "    # Calculate the median\n",
        "    sorted_numbers = sorted(numbers)\n",
        "    mid = len(sorted_numbers) // 2\n",
        "    median = (sorted_numbers[mid - 1] + sorted_numbers[mid]) / 2 if len(sorted_numbers) % 2 == 0 else sorted_numbers[mid]\n",
        "\n",
        "    # Calculate the variance and standard deviation\n",
        "    variance = 0\n",
        "    for num in numbers:\n",
        "        variance += (num - mean) ** 2\n",
        "    variance /= len(numbers)\n",
        "    standard_deviation = math.sqrt(variance)\n",
        "\n",
        "    return {\n",
        "        'mean': mean,\n",
        "        'median': median,\n",
        "        \"min\": min(numbers),\n",
        "        \"max\": max(numbers),\n",
        "        'variance': variance,\n",
        "        'standard_deviation': standard_deviation\n",
        "    }\n",
        "#@markdown basic_distribution_stats\n",
        "# Example usage:\n",
        "numbers = [1.2, 3.4, 2.5, 5.6, 4.7]\n",
        "stats = basic_distribution_stats(numbers)\n",
        "print(\"Mean:\", stats['mean'])\n",
        "print(\"Median:\", stats['median'])\n",
        "print(\"Variance:\", stats['variance'])\n",
        "print(\"Standard Deviation:\", stats['standard_deviation'])"
      ],
      "metadata": {
        "cellView": "form",
        "id": "qjMqw_I1kc1A",
        "outputId": "70883833-f8ee-4e38-c0e6-8f88946f54d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 3.4799999999999995\n",
            "Median: 3.4\n",
            "Variance: 2.4295999999999998\n",
            "Standard Deviation: 1.558717421471897\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "basic_distribution_stats(clean_ds['train']['score'])"
      ],
      "metadata": {
        "id": "jtwwnNy7kiCd",
        "outputId": "06c59ee5-bc06-465e-bb7a-9548a8392bc9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean': 0.642014354467392,\n",
              " 'median': 0.6205157935619354,\n",
              " 'min': 0.4895378053188324,\n",
              " 'max': 0.9694902896881104,\n",
              " 'variance': 0.010988485032607827,\n",
              " 'standard_deviation': 0.10482597499001775}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "basic_distribution_stats(mild_ds['train']['score'])\n"
      ],
      "metadata": {
        "id": "HfAvLMnak1bg",
        "outputId": "431d8c6a-e2e1-4f14-b3b6-7772aeaa146d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean': 0.7740383158512025,\n",
              " 'median': 0.7920204699039459,\n",
              " 'min': 0.4629112780094147,\n",
              " 'max': 0.994440495967865,\n",
              " 'variance': 0.017140581955241218,\n",
              " 'standard_deviation': 0.13092204533706772}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import json\n",
        "xx = random.choice(clean_ds['train'])\n",
        "print(json.dumps(xx['meta'], indent=2))\n",
        "print(f\"score is: \", xx['score'])\n",
        "print(xx['first_25k'][:1000])"
      ],
      "metadata": {
        "id": "LpySECRIiPLk",
        "outputId": "5e91d19c-dc05-4de3-ce18-54facaa9eb17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"publication_date\": 1881,\n",
            "  \"short_book_title\": \"Harper's Young People August 2 1881 by Various\",\n",
            "  \"url\": \"http://www.gutenberg.org/ebooks/48597\"\n",
            "}\n",
            "score is:  0.6047353744506836\n",
            "Produced by Annie R. McGuire\n",
            "\n",
            "[Illustration: HARPER'S YOUNG PEOPLE]\n",
            "\n",
            " * * * * *\n",
            "\n",
            "VOL. II.--NO. 92. PUBLISHED BY HARPER & BROTHERS, NEW YORK. PRICE FOUR\n",
            "CENTS.\n",
            "\n",
            "Tuesday, August 2, 1881. Copyright, 1881, by HARPER & BROTHERS. $1.50\n",
            "per Year, In Advance.\n",
            "\n",
            " * * * * *\n",
            "\n",
            "[Illustration: TIM SHOWS THE MARKS OF CAPTAIN BABBIGE'S WHIP.]\n",
            "\n",
            "TIM AND TIP;\n",
            "\n",
            "OR, THE ADVENTURES OF A BOY AND A DOG.\n",
            "\n",
            "BY JAMES OTIS, AUTHOR OF \"TOBY TYLER,\" ETC.\n",
            "\n",
            "CHAPTER I.\n",
            "\n",
            "TIM'S FLIGHT.\n",
            "\n",
            " \"'STRAYED.--A boy from the home of the subscriber; and any one\n",
            " returning him will be suitably rewarded. Said boy is about eleven\n",
            " years old, has short light hair, a turned-up nose, and face very\n",
            " much tanned. When last seen he had on a suit of blue clothes\n",
            " considerably faded and worn, and had with him a yellow dog with a\n",
            " long body, short legs, and a short tail. The boy answers to the\n",
            " name of Tim, and the dog to that of Tip. Any information regarding\n",
            " the runaway will be liberally paid for. Address Captain Rufus\n",
            " Babbige, in care of th\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "xx = random.choice(mild_ds['train'])\n",
        "print(xx['meta'])\n",
        "print(f\"score is: \", xx['score'])\n",
        "print(xx['first_25k'][:2000])"
      ],
      "metadata": {
        "id": "bGiM3Y7linEd",
        "outputId": "e97e9450-808b-49d7-e5fd-d99dc4a2d938",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'publication_date': 1895, 'short_book_title': 'The Story of the Other Wise Man by Henry van Dyke', 'url': 'http://www.gutenberg.org/ebooks/10679'}\n",
            "score is:  0.8565642833709717\n",
            "E-text prepared by Margaret Macaskill, David Garcia, and the Project\n",
            "Gutenberg Online Distributed Proofreading Team\n",
            "\n",
            "THE STORY OF THE OTHER WISE MAN\n",
            "\n",
            "BY\n",
            "\n",
            "HENRY VAN <DW18>\n",
            "\n",
            "[Illustration: \"IT IS THE SIGN\" HE SAID]\n",
            "\n",
            "CONTENTS\n",
            "\n",
            "INTRODUCTION\n",
            "\n",
            "THE SIGN IN THE SKY\n",
            "\n",
            "BY THE WATERS OF BABYLON\n",
            "\n",
            "FOR THE SAKE OF A LITTLE CHILD\n",
            "\n",
            "IN THE HIDDEN WAY OF SORROW\n",
            "\n",
            "A PEARL OF GREAT PRICE\n",
            "\n",
            "ILLUSTRATIONS\n",
            "\n",
            "\"'IT IS THE SIGN,' HE SAID\"\n",
            "\n",
            "\"HE CAUGHT IT UP AND READ\"\n",
            "\n",
            "\"'THERE IS NONE HERE SAVE ME\"'\n",
            "\n",
            "\"HE HEALED THE SICK\"\n",
            "\n",
            "\"THE OLD MAN FOLLOWED THE MULTITUDE\"\n",
            "\n",
            "\"THE OTHER WISE MAN HAD FOUND THE KING\"\n",
            "\n",
            " _Who seeks for heaven alone to save his soul,\n",
            " May keep the path, but will not reach the goal;\n",
            " While he who walks in love may wander far,\n",
            " Yet God will bring him where the blessed are._\n",
            "\n",
            "You know the story of the Three Wise Men of the East, and how they\n",
            "travelled from far away to offer their gifts at the manger-cradle in\n",
            "Bethlehem. But have you ever heard the story of the Other Wise Man, who\n",
            "also saw the star in its rising, and set out to follow it, yet did not\n",
            "arrive with his brethren in the presence of the young child Jesus? Of\n",
            "the great desire of this fourth pilgrim, and how it was denied, yet\n",
            "accomplished in the denial; of his many wanderings and the probations of\n",
            "his soul; of the long way of his seeking, and the strange way of his\n",
            "finding, the One whom he sought--I would tell the tale as I have heard\n",
            "fragments of it in the Hall of Dreams, in the palace of the Heart of\n",
            "Man.\n",
            "\n",
            "THE SIGN IN THE SKY\n",
            "\n",
            "In the days when Augustus Caesar was master of many kings and Herod\n",
            "reigned in Jerusalem, there lived in the city of Ecbatana, among the\n",
            "mountains of Persia, a certain man named Artaban, the Median. His house\n",
            "stood close to the outermost of the seven walls which encircled the\n",
            "royal treasury. From his roof he could look over the rising battlements\n",
            "of black and white and crimson and blue and red and silver and gold, to\n",
            "the hill where the summer palace of the Parthian emperors glittered like\n",
            "a jewel in a sev\n"
          ]
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