diff --git "a/Squad_V1_Question_Generation.ipynb" "b/Squad_V1_Question_Generation.ipynb"
new file mode 100644--- /dev/null
+++ "b/Squad_V1_Question_Generation.ipynb"
@@ -0,0 +1,8646 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "Model_finetuning_scores.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Question Generation\n",
+    "The goal is to create a question generation dataset from Squad V1.1.\n",
+    "\n",
+    "Based on [arxiv](https://arxiv.org/pdf/2005.01107v1.pdf) paper of *Transformer-based End-to-End Question Generation* \n",
+    "\n",
+    "I think there is a convenience around All Questions Per Line (AQPL), though it didnt perform as well as One Question Per Line (OQPL). The performances are quite similar as shown here:\n",
+    "\n",
+    "![Model_finetuning_scores.png](attachment:Model_finetuning_scores.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print multiple outputs per cell\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = \"all\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Ud-QcYd2cmhX",
+    "outputId": "20e1811d-c54f-41a2-b496-d61829d73ed4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: datasets in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (2.9.0)\n",
+      "Requirement already satisfied: huggingface_hub in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (0.12.0)\n",
+      "Requirement already satisfied: dill<0.3.7 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (0.3.6)\n",
+      "Requirement already satisfied: multiprocess in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (0.70.14)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (6.0)\n",
+      "Requirement already satisfied: numpy>=1.17 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (1.24.1)\n",
+      "Requirement already satisfied: responses<0.19 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (0.18.0)\n",
+      "Requirement already satisfied: packaging in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (23.0)\n",
+      "Requirement already satisfied: tqdm>=4.62.1 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (4.64.1)\n",
+      "Requirement already satisfied: pyarrow>=6.0.0 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (11.0.0)\n",
+      "Requirement already satisfied: requests>=2.19.0 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (2.28.2)\n",
+      "Requirement already satisfied: aiohttp in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (3.8.3)\n",
+      "Requirement already satisfied: fsspec[http]>=2021.11.1 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (2023.1.0)\n",
+      "Requirement already satisfied: xxhash in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (3.2.0)\n",
+      "Requirement already satisfied: pandas in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from datasets) (1.5.3)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from huggingface_hub) (4.4.0)\n",
+      "Requirement already satisfied: filelock in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from huggingface_hub) (3.9.0)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from aiohttp->datasets) (1.8.2)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from aiohttp->datasets) (1.3.3)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from aiohttp->datasets) (6.0.4)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from aiohttp->datasets) (22.2.0)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from aiohttp->datasets) (1.3.1)\n",
+      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from aiohttp->datasets) (2.1.1)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from aiohttp->datasets) (4.0.2)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from requests>=2.19.0->datasets) (1.26.14)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from requests>=2.19.0->datasets) (3.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from pandas->datasets) (2022.7.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from pandas->datasets) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in /opt/homebrew/Caskroom/miniconda/base/envs/flan-t5-e2e-qg/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install datasets huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "otv5EWHoSJfB"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from huggingface_hub import notebook_login"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2dxbK04V_2Kx"
+   },
+   "source": [
+    "## Connect to Hugging Face 🤗\n",
+    "- To be able to share the datset in the Hub, we need to login with a token\n",
+    "- Learn to create tokens [here](https://huggingface.co/docs/hub/security-tokens) if you dont already have one\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 284,
+     "referenced_widgets": [
+      "e9e1dd47406b4f88bc587c8160c3a920",
+      "689892b641c54ab69e76338df7014f91",
+      "1c7c46024916488d9e5f9590f1156868",
+      "8faa46b06b2b49b586b8ceb3659022d8",
+      "6f5b717f09d3485eac83167766db7dbe",
+      "b9d338b54c204afa92318cdef36902e8",
+      "67ec8a643f014737a503c16fb0eb667d",
+      "8e41b64d520447ccb0ca31b36607434c",
+      "6201631591a848ffadfb1fa4f84ac5f5",
+      "0358ea9fd5674bd4bfd62120b1979560",
+      "c1caeeea2e204014b1a1163881f83147",
+      "0c3a9969d3e94eed806edbd1e6be97e5",
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+      "324ae706801b487babe83946c5c5ba47",
+      "ccbc726a87164469967bf8104b459938",
+      "1c9806a847a74c53af6ec286f647e1f2"
+     ]
+    },
+    "id": "ePD0q4Uv_znZ",
+    "outputId": "3c86d961-3833-432b-e3bf-87a73ee567ae"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token is valid.\n",
+      "Your token has been saved in your configured git credential helpers (osxkeychain).\n",
+      "Your token has been saved to /Users/derekthomas/.cache/huggingface/token\n",
+      "Login successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "notebook_login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rXDcNMxMchNt"
+   },
+   "source": [
+    "## Dataset origin\n",
+    "I took the code from [Thomas Simonini notebook](https://colab.research.google.com/drive/1z-Zl2hftMrFXabYfmz8o9YZpgYx6sGeW?usp=sharing#scrollTo=YF1u2pxsFvQl) and Ill add it to the hub since he never got around to it!\n",
+    "\n",
+    "\n",
+    "> - We use [SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/), but a **modified version** where questions for a context are **concatenated**.\n",
+    "> - You need to [download the file here](https://www.simoninithomas.com/hfdataset/squad_modified_for_t5_qg.zip), unzip it and upload it in the next cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 234,
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+      "5ba5831683124e47ba22709ba1c8ec8b",
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+     ]
+    },
+    "id": "r8sspv14owy9",
+    "outputId": "de3d27d0-b40a-4e7d-910a-0b743822ef10"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset squad_modified_for_t5_qg/plain_text to /Users/derekthomas/.cache/huggingface/datasets/squad_modified_for_t5_qg/plain_text/2.9.0/51e36f85e69301b9554545b12c18534864e91ffede9852cbecbb24a8bb53c6ca...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12e9e620b43f4477be1d3d1c5131f0cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab82d65441a9442b920db525a1533015",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8b6e06e6bc248cabdaafc68bd6e5151",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fee8c0eff2234062a3a33ec37da48dc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating validation split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset squad_modified_for_t5_qg downloaded and prepared to /Users/derekthomas/.cache/huggingface/datasets/squad_modified_for_t5_qg/plain_text/2.9.0/51e36f85e69301b9554545b12c18534864e91ffede9852cbecbb24a8bb53c6ca. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd46a0d7d7ac4b4f91a698f2460a694a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "raw_dataset = load_dataset(\"squad_modified_for_t5_qg.py\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Verification\n",
+    "Let's make sure it looks good"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['context', 'questions']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(raw_dataset[\"train\"][0].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hanZc1Qyq2xY",
+    "outputId": "70252538-ccb2-4ba6-b883-2b498ab92004"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'generate questions: Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? {sep_token} What is in front of the Notre Dame Main Building? {sep_token} The Basilica of the Sacred heart at Notre Dame is beside to which structure? {sep_token} What is the Grotto at Notre Dame? {sep_token} What sits on top of the Main Building at Notre Dame? {sep_token}'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_dataset[\"train\"][0]['context']\n",
+    "raw_dataset[\"train\"][0]['questions']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the context is prepended with `generate questions: `. This is so that T5/Flan T5 can identify the task. Its not important if we only have 1 intention, but its handy to maintain the rest of what it has been trained to do. Check out all the prefixes in [Appendix D](https://arxiv.org/pdf/1910.10683.pdf#page=47).\n",
+    "\n",
+    "It looks good, lets add it to the hub."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pushing split train to the Hub.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33bf1a6f51a84aa9aca175de30b2b2b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1b0379055124412877f27b6c4055d78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/19 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d287cad9c434b6fb9ff0654d6cdea2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d56448807364417eb278b4d5674e1a50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Deleting unused files from dataset repository:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Pushing split validation to the Hub.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35b2e1b96aeb4e2394c755960c861b74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a285f6425c9d49a4b683955698659e21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/3 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fad86893cb124b14822e1e910e219e10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d956888d6474448a0033fab3196564f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Deleting unused files from dataset repository:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d25a0f67e254436c98e67e1c1bc62a73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading metadata:   0%|          | 0.00/521 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "raw_dataset.push_to_hub(\"squad-v1.1-t5-question-generation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20963"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(raw_dataset['train']) + len(raw_dataset['validation'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset Card\n",
+    "Using this [tagging tool](https://huggingface.co/spaces/huggingface/datasets-tagging), you can properly tag your work! Ill put the data below, but will manually add it to the [generated dataset](https://huggingface.co/datasets/derek-thomas/squad-v1.1-t5-question-generation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "annotations_creators:\n",
+      "- crowdsourced\n",
+      "language:\n",
+      "- en\n",
+      "language_creators:\n",
+      "- crowdsourced\n",
+      "license:\n",
+      "- cc-by-4.0\n",
+      "multilinguality:\n",
+      "- monolingual\n",
+      "pretty_name: Question Generation dataset based on Squad V1.1\n",
+      "size_categories:\n",
+      "- 10K<n<100K\n",
+      "source_datasets:\n",
+      "- extended|squad\n",
+      "tags:\n",
+      "- questiongeneration\n",
+      "- question-generation\n",
+      "- text2text-generation\n",
+      "task_categories:\n",
+      "- text2text-generation\n",
+      "task_ids: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\"\"\n",
+    "annotations_creators:\n",
+    "- crowdsourced\n",
+    "language:\n",
+    "- en\n",
+    "language_creators:\n",
+    "- crowdsourced\n",
+    "license:\n",
+    "- cc-by-4.0\n",
+    "multilinguality:\n",
+    "- monolingual\n",
+    "pretty_name: Question Generation dataset based on Squad V1.1\n",
+    "size_categories:\n",
+    "- 10K<n<100K\n",
+    "source_datasets:\n",
+    "- extended|squad\n",
+    "tags:\n",
+    "- questiongeneration\n",
+    "- question-generation\n",
+    "- text2text-generation\n",
+    "task_categories:\n",
+    "- text2text-generation\n",
+    "task_ids: []\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "0072acf3e22949f786a737e4deb00e55": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
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