{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -U -q transformers datasets accelerate\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "Qs5rHc3PtyT_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
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            "0d6228dc68474f54b738b1865e925172"
          ]
        },
        "id": "lIYdn1woOS1n",
        "outputId": "8d68f865-6faa-4436-aa0d-63415665a1ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/6.25k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2bd20ccf5e743978927dc977c98c4e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Available configurations for dataset 'BEE-spoke-data/govdocs1-by-extension':\n",
            "['doc', 'docx', 'logs', 'ppt', 'pptx', 'rtf', 'txt']\n"
          ]
        }
      ],
      "source": [
        "from pprint import pprint\n",
        "from datasets import get_dataset_config_names\n",
        "\n",
        "def fetch_and_print_configs(dataset_name):\n",
        "    \"\"\"helper fn to print configs\"\"\"\n",
        "    configs = get_dataset_config_names(dataset_name)\n",
        "\n",
        "    print(f\"Available configurations for dataset '{dataset_name}':\")\n",
        "    pprint(configs, width=80, compact=True)\n",
        "\n",
        "    return configs\n",
        "\n",
        "ds_name = \"BEE-spoke-data/govdocs1-by-extension\"\n",
        "configs = fetch_and_print_configs(ds_name)\n",
        "\n",
        "# Available configurations for dataset 'BEE-spoke-data/knowledge-inoc-concat-v1':\n",
        "# ['default', 'default-0919', 'smorgasbord', 'tb-interleaved-deduped',\n",
        "# 'tb-interleaved-deduped-0919', 'tiny-tb-interleaved']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, concatenate_datasets, DatasetDict\n",
        "\n",
        "def concatenate_and_shuffle_multiple_datasets(datasets_with_configs):\n",
        "    # Load all datasets with their respective config names\n",
        "    datasets = [load_dataset(name, config) for name, config in datasets_with_configs]\n",
        "\n",
        "    # Get all unique splits across the datasets\n",
        "    all_splits = set(split for ds in datasets for split in ds.keys())\n",
        "\n",
        "    # Initialize an empty DatasetDict\n",
        "    combined_dataset = DatasetDict()\n",
        "\n",
        "    for split in all_splits:\n",
        "        # Concatenate the same split across all datasets\n",
        "        datasets_with_split = [ds[split] for ds in datasets if split in ds]\n",
        "        concatenated = concatenate_datasets(datasets_with_split)\n",
        "\n",
        "        # Shuffle the concatenated dataset\n",
        "        shuffled = concatenated.shuffle(seed=80085)\n",
        "\n",
        "        # Add the shuffled dataset to the combined dataset\n",
        "        combined_dataset[split] = shuffled\n",
        "\n",
        "    return combined_dataset\n",
        "\n",
        "\n",
        "buildlist = [(ds_name, cfg) for cfg in configs]\n",
        "\n",
        "\n",
        "# Get the combined and shuffled dataset\n",
        "combined_dataset = concatenate_and_shuffle_multiple_datasets(buildlist)\n",
        "\n",
        "# Now you can use combined_dataset as needed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226,
          "referenced_widgets": [
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        "id": "ca79QxfGuD0W",
        "outputId": "acae41ae-5935-400c-fea9-3d925706fd3b"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/289M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "85ee3f9a6f0b49ccabe68ce077e5172b"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5706d9fc7b7c4b7f9059fd56e3330c81"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/15.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "178a012b7a0c408f9c8fe4f7ad05b26d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af92dcab70004d67b398d8ba27ae6dd9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/41393 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "076812fab7bf47a8ab2aeb5f0fb42b9b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/1089 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3917d84cf29742b7973696d07a54ed6d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1090 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f89d4556b6f4695bf0903f43a2fae60"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtsV4Taoueln",
        "outputId": "0d6210b1-7e66-4186-c32f-4a83f84fe353"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['section', 'filename', 'text'],\n",
              "        num_rows: 4334\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['section', 'filename', 'text'],\n",
              "        num_rows: 4328\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['section', 'filename', 'text'],\n",
              "        num_rows: 159189\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def word_count(text):\n",
        "    words = re.findall(r'\\b\\w+\\b', str(text))\n",
        "    return len(words)\n",
        "\n",
        "# Example usage\n",
        "text = \"This is a sample text. This text is just an example.\"\n",
        "result = word_count(text)\n",
        "print(result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNpc3f_CwZds",
        "outputId": "1227450b-21d5-4eb9-f49c-16b08adf5faa"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_filtered = combined_dataset.filter(lambda x: word_count(str(x['text'])) > 8,\n",
        "                                      num_proc=6)\n",
        "ds_filtered"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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        },
        "id": "omFjEkYkvtrP",
        "outputId": "6873eb91-05a3-4b9a-dcb0-d10ee89f14e8"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter (num_proc=6):   0%|          | 0/4334 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "531aeb40b95644b8b8ed0e7ca16f540a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter (num_proc=6):   0%|          | 0/4328 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1ab726802004678a5ff2d6292a248cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter (num_proc=6):   0%|          | 0/159189 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f21c8a969ddd4b13b7ec4418631d06f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['section', 'filename', 'text'],\n",
              "        num_rows: 4334\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['section', 'filename', 'text'],\n",
              "        num_rows: 4328\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['section', 'filename', 'text'],\n",
              "        num_rows: 159185\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langdetect -q\n",
        "!pip install fasttext-langdetect -q\n"
      ],
      "metadata": {
        "id": "rnplFC9MxwxL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ftlangdetect import detect\n",
        "\n",
        "result = detect(text=\"Bugün hava çok güzel\", low_memory=False)\n",
        "print(result)\n",
        "\n",
        "result = detect(text=\"Bugün hava çok güzel\", low_memory=True)\n",
        "print(result)\n",
        "\n",
        "result = detect(text=\"ayy u want sum fuk?\", low_memory=True)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8xlf5VLyEPf",
        "outputId": "2f467126-597b-48ba-c504-2de7fbe2ccc5"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'lang': 'tr', 'score': 1.0}\n",
            "{'lang': 'tr', 'score': 0.9982126951217651}\n",
            "{'lang': 'en', 'score': 0.9140127301216125}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def normalize_text(text):\n",
        "    # Remove newlines\n",
        "    text = text.replace('\\n', ' ')\n",
        "\n",
        "    # Normalize whitespace\n",
        "    text = re.sub(r'\\s+', ' ', text)\n",
        "\n",
        "    return text\n",
        "\n",
        "# Example usage\n",
        "original_text = \"This is a\\nsample   text with\\n\\nmultiple   spaces.\"\n",
        "normalized_text = normalize_text(original_text)\n",
        "print(normalized_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RvIvzrNzEYk",
        "outputId": "b9dddb18-3fe4-4dcc-e99d-a0a786425f41"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a sample text with multiple spaces.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_lang(example):\n",
        "\n",
        "    return {\"lang\": detect(text=normalize_text(example['text'])[:6000], low_memory=False)}\n",
        "\n",
        "ds_filtered = ds_filtered.map(get_lang, num_proc=6)\n",
        "ds_filtered"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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        },
        "id": "7OUtANSEyQyV",
        "outputId": "96129109-912c-4d39-ccda-c528b7e93214"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=6):   0%|          | 0/4334 [00:00<?, ? examples/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": [
              "Map (num_proc=6):   0%|          | 0/4328 [00:00<?, ? examples/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=6):   0%|          | 0/159185 [00:00<?, ? examples/s]"
            ],
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              "model_id": "5f6c134524464bc1afd794e5ed5cafd6"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang'],\n",
              "        num_rows: 4334\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang'],\n",
              "        num_rows: 4328\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang'],\n",
              "        num_rows: 159185\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "_df = pd.DataFrame(ds_filtered[\"train\"][\"lang\"], columns=[\"lang\"])\n",
        "print(_df.lang.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBwFXiJMzghS",
        "outputId": "71e1b329-66ab-430a-f716-38ab74aa3e3a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "en     156342\n",
            "fr        533\n",
            "es        520\n",
            "eu        463\n",
            "vi        278\n",
            "ja        172\n",
            "zh        149\n",
            "de        141\n",
            "it        134\n",
            "ru         95\n",
            "ceb        90\n",
            "el         67\n",
            "pt         42\n",
            "la         15\n",
            "oc         15\n",
            "ko         11\n",
            "pl         10\n",
            "th         10\n",
            "ca          9\n",
            "sv          8\n",
            "nl          7\n",
            "cs          6\n",
            "sr          6\n",
            "az          6\n",
            "no          6\n",
            "tl          6\n",
            "ar          5\n",
            "ro          5\n",
            "uk          4\n",
            "tr          3\n",
            "sk          3\n",
            "sl          3\n",
            "id          3\n",
            "hu          3\n",
            "ht          2\n",
            "da          2\n",
            "fa          2\n",
            "ms          2\n",
            "sq          1\n",
            "ur          1\n",
            "so          1\n",
            "bg          1\n",
            "ml          1\n",
            "fi          1\n",
            "lt          1\n",
            "Name: lang, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import pprint as pp\n",
        "pp.pprint(random.choice(ds_filtered['train']['text']))"
      ],
      "metadata": {
        "id": "NqgRHYz6v4xL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds_filtered = ds_filtered.map(lambda x: {'lang': x['lang']['lang']}, num_proc=6)\n"
      ],
      "metadata": {
        "id": "hpEYX6vM4brB",
        "outputId": "e7b38495-e2e0-4539-99ee-155241db78a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=6):   0%|          | 0/4334 [00:00<?, ? examples/s]"
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      "execution_count": 57,
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          "data": {
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              "version_minor": 0,
              "model_id": "fdafa36ae3ae420d9d19d8cc0fa0036d"
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        {
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          "data": {
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              "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
            ],
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/6.91k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3rAQbHz-xnbB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "P_3jgaVj3TE3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install textstat -q\n",
        "import textstat"
      ],
      "metadata": {
        "id": "9GNbUPOW3TKf",
        "outputId": "4d2c5da8-8353-490b-e2c3-e5701b65ab4e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/105.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m81.9/105.1 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.1/105.1 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.7/2.0 MB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m29.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_filtered['train'][0]"
      ],
      "metadata": {
        "id": "edwxDzT84Nyt",
        "outputId": "f387ada1-c845-48c3-d758-1b2ef4cb86ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'section': 'converted_docs',\n",
              " 'filename': '356139',\n",
              " 'text': \"**DiamondMEMS\\\\***\\n\\nJohn A. Carlisle\\n\\nMaterials Science Division & Center for Nanoscale Materials\\n\\nArgonne National Laboratory\\n\\nArgonne, Illinois 60439 USA\\n\\nDiamond MEMS may offer many potential performance and reliability\\nadvantages over traditional MEMS materials. For instance, its high\\nYoung's modulus may enable diamond RF MEMS devices to operate at\\nunprecedented high frequencies, whereas the surface chemistry of\\ndiamond, which gives rise to it's low friction and stiction properties,\\nmay greatly enhance the reliability of these devices, simplify the\\ncomplexity of their design and also possibility greatly reduce packaging\\ncosts (e.g. the need for anti-stiction coatings and hermetic packaging).\\nHowever, there are many challenges to be overcome if the dream is to\\nbecome a reality. Like any other thin film technology, diamond films\\nmust integrate with many other different materials (semiconductors,\\nmetals, oxides) in order to realize real MEMS devices. Over the past ten\\nyears at ANL we have studied the growth of ultrananocrystalline diamond\\n(UNCD) using argon-rich Ar/CH~4~ plasmas with C~2~-dimers as the\\nprincipal growth species. These films consist of diamond grains 2-5 nm\\nin size and high-energy grain boundaries that are only 0.5 nm wide\\nconsisting of a mixture of sp^3^ and sp^2^ bonding. UNCD possesses many\\nof the extreme materials properties of natural diamond (hardness,\\nmodulus, etc.), and yet the growth chemistry may overcome many of the\\nmaterials integration issues mentioned above. In this talk I will focus\\non the material integration and surface micromachining of UNCD films,\\nwith particular emphasis on the low-temperature growth of UNCD and the\\nnanotribology of the UNCD surfaces. I will also discuss our work in\\nprogress on the development of UNCD films for artificial retinas and\\nRF-MEMS. \\\\*This work was supported by the US Department of Energy,\\nBES-Materials Sciences, under Contract W-13-109-ENG-38\\n\",\n",
              " 'lang': 'en'}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_f2 = ds_filtered.filter(lambda x: x['lang']== 'en', num_proc=6)\n",
        "ds_f2"
      ],
      "metadata": {
        "id": "-3t2Y55B33lN",
        "outputId": "550664fa-5f21-4009-a0ba-94523cd149eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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            "c8412e2c1c0b411aad74c5eac5e6990e",
            "171ceed494d84d91a5e069edc23c9d1f",
            "a4acba8ff8c44955956d2f486e87d0d0",
            "9d2efe053c0645ab91fd7099049eb9ac"
          ]
        }
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter (num_proc=6):   0%|          | 0/4334 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e18b26600cec4709a0170f26bbfb4622"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter (num_proc=6):   0%|          | 0/4328 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15f35993f01344d6b8d460fa2e074c49"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter (num_proc=6):   0%|          | 0/159185 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b1285b6acc9464c9ed329cc426cc47d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang'],\n",
              "        num_rows: 4258\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang'],\n",
              "        num_rows: 4263\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang'],\n",
              "        num_rows: 156342\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import textstat\n",
        "\n",
        "def get_ease(example):\n",
        "\n",
        "    return {\"flesch_reading_ease\": textstat.flesch_reading_ease(example['text'])}\n",
        "\n",
        "ds_f2  = ds_f2.map(get_ease, num_proc=7)\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "_df = pd.DataFrame(ds_f2[\"train\"][\"flesch_reading_ease\"], columns=[\"flesch_reading_ease\"])\n",
        "print(_df.describe())"
      ],
      "metadata": {
        "id": "u1e8rjyg3W40",
        "outputId": "54525145-ed91-41bb-ce85-9b33490ef92e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
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            "df8a8840d13947fc80d00af9e915d1d1",
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            "c0616a56beee4e80bb52ec591a7e1067",
            "89140c8263ed4180a1af3654540e97e8"
          ]
        }
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=7):   0%|          | 0/4258 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7e76feda4034367b22bf0dfbc86b2d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=7):   0%|          | 0/4263 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "16eb5209f3ed4807a19b7f319f43f708"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=7):   0%|          | 0/156342 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e654abbf31b141b7bff709df0df7ee5d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       flesch_reading_ease\n",
            "count        156342.000000\n",
            "mean             24.976902\n",
            "std             716.462062\n",
            "min         -220621.670000\n",
            "25%              28.910000\n",
            "50%              43.430000\n",
            "75%              55.470000\n",
            "max             120.210000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_f2 = ds_f2.filter(lambda x: x['flesch_reading_ease'] > 20)\n",
        "ds_f2"
      ],
      "metadata": {
        "id": "SjfxMluE5oFP",
        "outputId": "25202245-cef2-4b07-ef41-e162be39f27f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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        }
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/4258 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13a22c35840e47cbb06b0b718e1e420b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/4263 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f4518b26a754d9aa304f4ff7474d5a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/156342 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "14975c22cf674a5fa376eaa480b68b7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang', 'flesch_reading_ease'],\n",
              "        num_rows: 3549\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang', 'flesch_reading_ease'],\n",
              "        num_rows: 3541\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['section', 'filename', 'text', 'lang', 'flesch_reading_ease'],\n",
              "        num_rows: 129653\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_f2 = ds_f2.remove_columns(['lang',])\n",
        "ds_f2"
      ],
      "metadata": {
        "id": "jlXLt6YoGfgg",
        "outputId": "4724ee2f-22b0-41e8-d2c4-e68a2ff8352c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    test: Dataset({\n",
              "        features: ['filename', 'text', 'flesch_reading_ease'],\n",
              "        num_rows: 3549\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['filename', 'text', 'flesch_reading_ease'],\n",
              "        num_rows: 3541\n",
              "    })\n",
              "    train: Dataset({\n",
              "        features: ['filename', 'text', 'flesch_reading_ease'],\n",
              "        num_rows: 129653\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_f2.push_to_hub(ds_name, \"all-filtered\")"
      ],
      "metadata": {
        "id": "DtFdM-ZNHNLU",
        "outputId": "d3c21b5d-2201-4f92-ba11-5e2bfc0cb227",
        "colab": {
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          "height": 721,
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