diff --git "a/cnn_dailymail-cleaned.ipynb" "b/cnn_dailymail-cleaned.ipynb"
new file mode 100644--- /dev/null
+++ "b/cnn_dailymail-cleaned.ipynb"
@@ -0,0 +1,5448 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lIYdn1woOS1n",
+        "outputId": "e8ba8f28-d1a2-4aab-97aa-0240f92cecab"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/175.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m143.4/175.4 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.4/175.4 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.8/44.8 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.5/235.5 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install datasets hf-transfer clean-text[gpl] -U -q"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ['HF_HOME'] = '/workspace/local-HF-cache'"
+      ],
+      "metadata": {
+        "id": "p-0s0S5oldXW"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "ds = load_dataset(\"abisee/cnn_dailymail\", \"3.0.0\", num_proc=8).rename_columns(\n",
+        "    {\"article\": \"text\", \"highlights\": \"summary\"}\n",
+        ")\n",
+        "ds"
+      ],
+      "metadata": {
+        "id": "rEqVvnGelXpD",
+        "outputId": "57af1569-dc2a-4a4b-bf69-ee7d122aee0c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 287113\n",
+              "    })\n",
+              "    validation: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 13368\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 11490\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from cleantext import clean\n",
+        "\n",
+        "ds = ds.map(lambda x: {\"summary\": clean(x[\"summary\"], lower=False, keep_two_line_breaks=True, no_emoji=True)}, num_proc=8)\n",
+        "ds"
+      ],
+      "metadata": {
+        "id": "bOqvQYxylgxT",
+        "outputId": "c0ca11a0-1f68-4b7b-a2db-f6099465397b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356,
+          "referenced_widgets": [
+            "2495b11431534fef9fe179d574a12647",
+            "7d83c59cce2f4643a11a3568e1dee18f",
+            "42db380cdcd4437ea87874d669018645",
+            "7384e64dd0e0478b8110a9a621ba0c56",
+            "f38e353980674ebc9c908b34d3cbfbed",
+            "a8170345ee77490abb2ad2979d460a2f",
+            "f171482cb32e4e1bb66d8668bfb36021",
+            "0137a627c55548a686347238703f4b20",
+            "84bd9cc592fe45cfaa5caa123d690c23",
+            "2f475e4e9be240919b72b62abdd3b9a1",
+            "3cad88de9d4d49c1bcd0638de8b4f57f",
+            "464571d71b994bf88abd1a647fac66e9",
+            "369f6402acca4878815392bec0189da2",
+            "c866badd116a470e905aa9aa69a2500d",
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+            "11beee80d98f4e5583c5cb8d6b0a9aad",
+            "a7a90cd4f1fc4822ad55153372bb1f49",
+            "1fd7d983199740298d338d8307f85d05",
+            "2b65efb2e63c44279d04ddd165cba9e5",
+            "8c50955ecbe84ca281842c05fb7c58a3",
+            "1431875a121443cc8946aa7d29d2c082",
+            "67126f36efd64ca588d7a993ad950f82",
+            "1210999efbc94612879937ccddde726f",
+            "ba4f1fbeaf5d4fc7a4caed3c09a442bd",
+            "34189bcc72ac4cc5b8bc333af4eb102f",
+            "46ca865077894914ad82bd6cc46f94b6",
+            "23ad31d71e1e44b28cf58a92f38ac7d0",
+            "512d9a51a12f40a3b70c8351047aca6e",
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+            "b97b79ca1c2d42ed86f30234d9e6437e",
+            "4c47ee3528a34ac4b9e75bb6d81612b4",
+            "09d7fee497ce4e0a86159857a683ef91"
+          ]
+        }
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map (num_proc=8):   0%|          | 0/287113 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2495b11431534fef9fe179d574a12647"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map (num_proc=8):   0%|          | 0/13368 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "464571d71b994bf88abd1a647fac66e9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map (num_proc=8):   0%|          | 0/11490 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "67126f36efd64ca588d7a993ad950f82"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 287113\n",
+              "    })\n",
+              "    validation: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 13368\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 11490\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import re\n",
+        "\n",
+        "# Initial text\n",
+        "text = \"\"\"a stapler , particularly for suturing skin wounds or incisions , is disclosed which comprises a channel in which a driver is advanced by a slide in the direction of an anvil surface . a staple magazine which extends substantially parallel with the driver includes a curved section which opens into the channel to deliver staples into the channel for engagement by the driver . during forward displacement of the driver , a projection on the driver presses a leaf spring to which the anvil surface is connected . the anvil surface at the forward end of the leaf spring is thereby brought into its operating position and is automatically moved back into its retracted position upon release of the spring after the driver is retracted . the curved section in the staple magazine enables the stapler to have a slim profile which does not obscure the working area during a stapling operation . after completion of a stapling operation , the anvil surface is automatically retracted from a closed , implanted staple .\n",
+        "\"\"\"\n",
+        "\n",
+        "# Function to clean the text with regex\n",
+        "def clean_text_with_regex(text):\n",
+        "    # Step 1: Replace spaces before punctuation\n",
+        "    text = re.sub(r'\\s+([,.\\)])', r'\\1', text)\n",
+        "    # Step 2: Capitalize the start of sentences\n",
+        "    text = re.sub(r'(^|\\.\\s+)([a-z])', lambda m: m.group(1) + m.group(2).upper(), text)\n",
+        "    # Step 3: Fix redundant spaces\n",
+        "    text = re.sub(r'\\s+', ' ', text).strip()\n",
+        "    return text\n",
+        "\n",
+        "# Cleaned text\n",
+        "cleaned_text = clean_text_with_regex(text)\n",
+        "cleaned_text"
+      ],
+      "metadata": {
+        "id": "77QHnIE_mTQa",
+        "outputId": "1e52061e-c8eb-4f3e-d686-a83609b6463d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 107
+        }
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'A stapler, particularly for suturing skin wounds or incisions, is disclosed which comprises a channel in which a driver is advanced by a slide in the direction of an anvil surface. A staple magazine which extends substantially parallel with the driver includes a curved section which opens into the channel to deliver staples into the channel for engagement by the driver. During forward displacement of the driver, a projection on the driver presses a leaf spring to which the anvil surface is connected. The anvil surface at the forward end of the leaf spring is thereby brought into its operating position and is automatically moved back into its retracted position upon release of the spring after the driver is retracted. The curved section in the staple magazine enables the stapler to have a slim profile which does not obscure the working area during a stapling operation. After completion of a stapling operation, the anvil surface is automatically retracted from a closed, implanted staple.'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# apply clean_text_with_regex to 'summary' col\n",
+        "ds = ds.map(lambda x: {\"summary\": clean_text_with_regex(x[\"summary\"])}, num_proc=8)\n",
+        "ds"
+      ],
+      "metadata": {
+        "id": "OF7oSDhGmmX-",
+        "outputId": "a92df4df-5f02-4afc-b049-e5ff1b8810e9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356,
+          "referenced_widgets": [
+            "fbc9c5b9c77641cc8c32b8d3961664a5",
+            "82becf9c152b49ac9840f46292ca9112",
+            "7bb7d2a1fbae49b386a2df2fb55fce88",
+            "5ca34ce4bee4471ca2fd32501340cb0b",
+            "06b806f03e5a47beb20a5e1143414c56",
+            "f9327dddf3cd40009f895801a53b204b",
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+          ]
+        }
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map (num_proc=8):   0%|          | 0/287113 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fbc9c5b9c77641cc8c32b8d3961664a5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map (num_proc=8):   0%|          | 0/13368 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "da4d46c7594e4fffa90fb7eb14cea5de"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map (num_proc=8):   0%|          | 0/11490 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f3acd98418b343bd919157c2c6212ace"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 287113\n",
+              "    })\n",
+              "    validation: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 13368\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['text', 'summary', 'id'],\n",
+              "        num_rows: 11490\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# print a random row from train\n",
+        "import random\n",
+        "row = random.choice(ds['train'])\n",
+        "# print(row['text'])\n",
+        "print(row['summary'])"
+      ],
+      "metadata": {
+        "id": "6V529H0Dmy8z",
+        "outputId": "15169b49-f524-4a76-f8f8-5df3b5ace329",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The WHO recommends sugar makes up less than 10% of energy intake. This is the equivalent of about 12 level teaspoons a day. Now, experts say halving this could have additional health benefits. Guideline amount slashed amid fears sugar poses same threat as tobacco. They say 5%, or six teaspoons, is the ideal figure that people should aim for. Labour suggest it would impose a maximum limit on sugar, fat and salt in products marketed at children. Crisis is fuelled by hidden sugar in juice and processed foods, says WHO.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ds.push_to_hub(\"pszemraj/cnn_dailymail-cleaned\", private=True)"
+      ],
+      "metadata": {
+        "id": "8nahUS6dm6Qo",
+        "outputId": "b522c9f9-59a4-47d8-8ace-5857f7812b1d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 325,
+          "referenced_widgets": [
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