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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oK7PPVm2XBgr"
      },
      "source": [
        "## 1. Find a dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Up82H83l5Bv9",
        "outputId": "f8ae6a7c-79c3-42f2-c341-10b9b786a0e2"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "['Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/',\n",
              " 'Collecting datasets',\n",
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              " '\\x1b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (4.1.1)',\n",
              " 'Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (3.0.9)',\n",
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              " 'Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)',\n",
              " 'Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2022.6.15)',\n",
              " 'Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1',\n",
              " '  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)',\n",
              " '\\x1b[?25l',\n",
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              " '\\x1b[K     |████████████████████████████████| 127 kB 75.8 MB/s ',\n",
              " '\\x1b[?25hRequirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (21.4.0)',\n",
              " 'Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (2.1.0)',\n",
              " 'Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (6.0.2)',\n",
              " 'Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (4.0.2)',\n",
              " 'Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.2.0)',\n",
              " 'Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (0.13.0)',\n",
              " 'Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.3.0)',\n",
              " 'Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.7.2)',\n",
              " 'Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.8.1)',\n",
              " 'Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)',\n",
              " 'Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.1)',\n",
              " 'Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)',\n",
              " 'Installing collected packages: urllib3, pyyaml, fsspec, xxhash, responses, huggingface-hub, datasets',\n",
              " '  Attempting uninstall: urllib3',\n",
              " '    Found existing installation: urllib3 1.24.3',\n",
              " '    Uninstalling urllib3-1.24.3:',\n",
              " '      Successfully uninstalled urllib3-1.24.3',\n",
              " '  Attempting uninstall: pyyaml',\n",
              " '    Found existing installation: PyYAML 3.13',\n",
              " '    Uninstalling PyYAML-3.13:',\n",
              " '      Successfully uninstalled PyYAML-3.13',\n",
              " \"\\x1b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\",\n",
              " 'datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\\x1b[0m',\n",
              " 'Successfully installed datasets-2.4.0 fsspec-2022.7.1 huggingface-hub-0.8.1 pyyaml-6.0 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0']"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets"
      ],
      "metadata": {
        "id": "YMHttZKX5Nqh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_ds = datasets.list_datasets()\n",
        "print(len(all_ds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXJDJow25Q-K",
        "outputId": "745f7ebc-8c1b-415e-9a73-a8563955de0e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7918\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_ds[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW0BbRP95UF_",
        "outputId": "fc36072b-980c-42d1-d92f-d75074e238be"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['acronym_identification',\n",
              " 'ade_corpus_v2',\n",
              " 'adversarial_qa',\n",
              " 'aeslc',\n",
              " 'afrikaans_ner_corpus',\n",
              " 'ag_news',\n",
              " 'ai2_arc',\n",
              " 'air_dialogue',\n",
              " 'ajgt_twitter_ar',\n",
              " 'allegro_reviews',\n",
              " 'allocine',\n",
              " 'alt',\n",
              " 'amazon_polarity',\n",
              " 'amazon_reviews_multi',\n",
              " 'amazon_us_reviews',\n",
              " 'ambig_qa',\n",
              " 'americas_nli',\n",
              " 'ami',\n",
              " 'amttl',\n",
              " 'anli']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = datasets.load_dataset('oscar', 'unshuffled_deduplicated_la')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264,
          "referenced_widgets": [
            "395d3a929454489c876a67e4818352f4",
            "ac156be9a88342f586240ee6f59f096f",
            "24ef7e0774224814bf67cfcc2ff873c5",
            "caee997fc4d042a68de7970eb5d042a5",
            "0ef49ee1317c480a84e3457355a5c19b",
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            "b6ee863290994a79ab0ffdfe55e8cfb1",
            "40938fef24c04975a28d13fe86a38fbe",
            "8bbee8b04a8e48e794eac85dc3a8bf5c",
            "27a3ccf8be19486e9196b026f8dcf97b",
            "e726165e43d14e9195fd2b1523410eec",
            "23951ec9ae4f44c1bde75b5f8cedafbb",
            "bec1e57484ad4ab28664e738d6b7989c",
            "7b0fb4c9d06846328f3191606e89a8dd",
            "f893f088ccca450fbd88eecf5639bbfb",
            "fdd5f25f2a474c1a90b5ced84c7c30e4",
            "304f103d76d44e488566d209793c66cf",
            "1725e7184da946e7b7bb4cddcc42b970",
            "b1b54a4b43c34a1abdbca97c694cf855",
            "41011d8a7ffe4d4a9ead93685a045f65",
            "38039c2b5ea342bcab62dc2c02f8d1d1",
            "2022fa29fda04edca84c80050607883d",
            "849bbc8fe78a40aeac356ed7bbd54f06",
            "caaf1489eea04ef6b61260ccbca0ebd1",
            "a45f7035dcd04aceb58f513ce305f6db",
            "ca365caec3034df5bbd6024760a3d41e",
            "a8d07d08aa8247de8e8c8c6918514ce9",
            "8d95a111c85a4fbb9708d8560382247a",
            "c578955a57874539a2511f5d5c934c8f",
            "0ff8766cf3704eca89b9369dcdb962bc",
            "0764062bb3184458bcd217139a291d90",
            "5d95f7f1d70b4dcca4377b38f4323981",
            "a99e4213c66c4785b84a9ac0d899417b",
            "7bb05be308254e3f88b81732d8c08abb",
            "1204a5ba22aa41609c3107e77a1c1281",
            "f3da3ba0f1c140889e166b541f5f161f",
            "9deaf91dad72475e8857ecc20f9adeac",
            "c956606d6e454a4c9b1c9e3f3b4dcec9",
            "a0b9a32214e54adea44cee43e1966a43",
            "d2a5844a7a2d4250b3b276185bcc7489",
            "1bdb4b22ab8b498b859c5e3c106b029e",
            "6052790c180c4e5596b3ce6a9b132f1f",
            "bb019993650140ee87eac294b9eb9121",
            "f9ac6727e59b433bbde128cb93e62d1f",
            "8f76136694ba48398f3a26b339a3361e",
            "c8ac53d335f44bf58da966db6f0518cf",
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            "5f95a417522045b4b6e27e800014f744",
            "7bc4d2481fae47a2b72dc81cc7932158",
            "e79534a578e243068975a784cb231aa4",
            "af4fc9b55b8a427584d5f958757dd04e",
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            "635ee58b8f764cf385a5099ffd028f65",
            "665cb367ce2c4f9f9e3ffead09468693",
            "30e0c858fae14c6d8d36eb85a95ca1d3",
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            "5e06ad737c034a8794945b57c288ebe2",
            "b02c2f1bddeb4060b2d9609c56dec342",
            "06e72050aa7241488c793dd72880ef7c",
            "2e6cc7af88e447a1938946cde4f1bb1e",
            "e210a0dc7c3a41fab67dd0fe583c40cb",
            "e6105d124a094c10a7dd72135760f223",
            "5743b69201494a3287401bbf582ba57f",
            "4daaf90acd0d45a69b28d4aad7d7b0dc",
            "440ba6bd5ab24ba2bfdee70498352f07",
            "47f9dd64a0924703be7afa260dbba4bd",
            "642046cbc6d74b5f81143ec7f556bb80",
            "072ead434e81459d986b305c389afa8d",
            "e2f79a9ac2414560baff79ba2f833277",
            "8ca4cfbfb57249d49dda0c49b4e761bb",
            "9d5e017e6d8145b9868c0f78ee0b7193",
            "2147ff4985324fca9691e0b5c612c4bf",
            "8d2443365868400e975b94ecd2a34385",
            "23844425229b43118ee00af7dafa9853"
          ]
        },
        "id": "GJM7gUd36ISd",
        "outputId": "b84972c5-0a4d-4282-babb-56d68bfbbd50"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.58k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "395d3a929454489c876a67e4818352f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/359k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27a3ccf8be19486e9196b026f8dcf97b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset oscar/unshuffled_deduplicated_la (download: 3.26 MiB, generated: 8.46 MiB, post-processed: Unknown size, total: 11.72 MiB) to /root/.cache/huggingface/datasets/oscar/unshuffled_deduplicated_la/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/81.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38039c2b5ea342bcab62dc2c02f8d1d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d95f7f1d70b4dcca4377b38f4323981"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/3.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb019993650140ee87eac294b9eb9121"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/18808 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "665cb367ce2c4f9f9e3ffead09468693"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset oscar downloaded and prepared to /root/.cache/huggingface/datasets/oscar/unshuffled_deduplicated_la/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4daaf90acd0d45a69b28d4aad7d7b0dc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_Vgtl6N6Nlu",
        "outputId": "3738789d-4fad-4dce-e90a-cecf1f317ff5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'text'],\n",
              "        num_rows: 18808\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['train'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GxRqt84H6SMs",
        "outputId": "c274265d-5142-4722-8033-4c8952847e31"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': 1,\n",
              " 'text': 'Sicut enim maius est illuminare quam lucere solum, ita maius est contemplata aliis tradere quam solum contemplari.'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm  # for our loading bar\n",
        "\n",
        "text_data = []\n",
        "file_count = 0\n",
        "\n",
        "for sample in tqdm(dataset['train']):\n",
        "    # remove newline characters from each sample as we need to use exclusively as seperators\n",
        "    sample = sample['text'].replace('\\n', '')\n",
        "    text_data.append(sample)\n",
        "    if len(text_data) == 5_000:\n",
        "        # once we hit the 5K mark, save to file\n",
        "        with open(f'./data/text_{file_count}.txt', 'w', encoding='utf-8') as fp:\n",
        "            fp.write('\\n'.join(text_data))\n",
        "        text_data = []\n",
        "        file_count += 1\n",
        "# after saving in 5K chunks, we will have ~3808 leftover samples, we save those now too\n",
        "with open(f'./data/text_{file_count}.txt', 'w', encoding='utf-8') as fp:\n",
        "    fp.write('\\n'.join(text_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "88a2ba02d0b54eb9bff540dee8b59e97",
            "07e6e8d9a2b14e4c86b8e332dda2a004",
            "5ba232df967746b299509bf6b654680b",
            "b9431241296543c7aff4ec115357e1ee",
            "461aec6804064112a6162f2b8db02db7",
            "aec335215e19416a8cc4c66b2ccbd483",
            "b619109ba3ea427f972ec6c7a567674d",
            "ca35449fcbe240698b980d89f6d71c6e",
            "80c4fc67feb74e3e81df12087d178dca",
            "beaba475334c4313bbba4d575a86b81b",
            "2331cabfbc824a72b17ddb5bc1413651"
          ]
        },
        "id": "kqCzM05D6cZ6",
        "outputId": "2a404ddc-aae6-4fe3-c346-d44a98c721e0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/18808 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88a2ba02d0b54eb9bff540dee8b59e97"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "paths = [str(x) for x in Path('/content/data/').glob('**/*.txt')]\n",
        "paths"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnWARsPY6sND",
        "outputId": "5d6db52c-824c-45b4-ba19-441e78d3a1ec"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/data/text_0.txt',\n",
              " '/content/data/text_1.txt',\n",
              " '/content/data/text_3.txt',\n",
              " '/content/data/text_2.txt']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-kkz81OY6xH"
      },
      "source": [
        "## 2. Train a tokenizer\n",
        "\n",
        "We choose to train a byte-level Byte-pair encoding tokenizer (the same as GPT-2), with the same special tokens as RoBERTa. Let’s arbitrarily pick its size to be 52\n",
        ",000.\n",
        "\n",
        "We recommend training a byte-level BPE (rather than let’s say, a WordPiece tokenizer like BERT) because it will start building its vocabulary from an alphabet of single bytes, so all words will be decomposable into tokens (no more `<unk>` tokens!).\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaS5N5CiC4q4",
        "outputId": "56e25063-0bf0-4a39-c09e-50f9a83265fc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.21.0-py3-none-any.whl (4.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 32.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.8.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 63.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Installing collected packages: tokenizers, transformers\n",
            "Successfully installed tokenizers-0.12.1 transformers-4.21.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "paths = [str(x) for x in Path(\".\").glob(\"**/*.txt\")]\n",
        "print(len(paths))"
      ],
      "metadata": {
        "id": "0mmw6htwFK4a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a6f36bc-36ea-4783-9837-3a26ba8c6c98"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMnymRDLe0hi"
      },
      "source": [
        "from tokenizers import ByteLevelBPETokenizer\n",
        "# initialize\n",
        "tokenizer = ByteLevelBPETokenizer()\n",
        "# and train\n",
        "tokenizer.train(files=paths, vocab_size=30000, min_frequency=2,\n",
        "                special_tokens=['<s>', '<pad>', '</s>', '<unk>', '<mask>'])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Ei7bqpRf1LH"
      },
      "source": [
        "Now let's save files to disk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIS-irI0f32P",
        "outputId": "bff22939-ad9a-426d-fcfe-e97bff904a1d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#@title\n",
        "!mkdir LaRoBERTo\n",
        "tokenizer.save_model(\"LaRoBERTo\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['LaRoBERTo/vocab.json', 'LaRoBERTo/merges.txt']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKVWB8WShT-z"
      },
      "source": [
        "from transformers import RobertaTokenizerFast\n",
        "\n",
        "tokenizer = RobertaTokenizerFast.from_pretrained('LaRoBERTo')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3Ye27nchfzq",
        "outputId": "02fd3d65-fde4-43a1-996f-0a6e7cae5f03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "tokenizer('Sicut enim maius est illuminare quam lucere solum, ita maius est contemplata aliis tradere quam solum contemplari.', max_length=512, padding='max_length', truncation=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 10046, 411, 2118, 297, 21702, 350, 24799, 980, 16, 585, 2118, 297, 27869, 1360, 10251, 350, 980, 23314, 18, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "mRGad3uy8Kc5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "cqYgVLvO8K9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "tHJoLbq78LDj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xnEAMaCH8LJi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " "
      ],
      "metadata": {
        "id": "vj7CywiY8LNb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VRS9aojc8LQP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQpUC_CDhnWW"
      },
      "source": [
        "## 3. Train a language model from scratch\n",
        "\n",
        "**Update:** This section follows along the [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/legacy/run_language_modeling.py) script, using our new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) directly. Feel free to pick the approach you like best.\n",
        "\n",
        "> We’ll train a RoBERTa-like model, which is a BERT-like with a couple of changes (check the [documentation](https://huggingface.co/transformers/model_doc/roberta.html) for more details).\n",
        "\n",
        "As the model is BERT-like, we’ll train it on a task of *Masked language modeling*, i.e. the predict how to fill arbitrary tokens that we randomly mask in the dataset. This is taken care of by the example script.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kD140sFjh0LQ"
      },
      "source": [
        "# Check that we have a GPU\n",
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNZZs-r6iKAV"
      },
      "source": [
        "# Check that PyTorch sees it\n",
        "import torch\n",
        "torch.cuda.is_available()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0qQzgrBi1OX"
      },
      "source": [
        "### We'll define the following config for the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LTXXutqeDzPi"
      },
      "source": [
        "from transformers import RobertaConfig\n",
        "\n",
        "config = RobertaConfig(\n",
        "    vocab_size=52_000,\n",
        "    max_position_embeddings=514,\n",
        "    num_attention_heads=12,\n",
        "    num_hidden_layers=6,\n",
        "    type_vocab_size=1,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAwQ82JiE5pi"
      },
      "source": [
        "Now let's re-create our tokenizer in transformers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4keFBUjQFOD1"
      },
      "source": [
        "from transformers import RobertaTokenizerFast\n",
        "\n",
        "tokenizer = RobertaTokenizerFast.from_pretrained(\"./EsperBERTo\", max_len=512)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yNCw-3hFv9h"
      },
      "source": [
        "Finally let's initialize our model.\n",
        "\n",
        "**Important:**\n",
        "\n",
        "As we are training from scratch, we only initialize from a config, not from an existing pretrained model or checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzMqR-dzF4Ro"
      },
      "source": [
        "from transformers import RobertaForMaskedLM\n",
        "\n",
        "model = RobertaForMaskedLM(config=config)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jU6JhBSTKiaM"
      },
      "source": [
        "model.num_parameters()\n",
        "# => 84 million parameters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBtUHRMliOLM"
      },
      "source": [
        "### Now let's build our training Dataset\n",
        "\n",
        "We'll build our dataset by applying our tokenizer to our text file.\n",
        "\n",
        "Here, as we only have one text file, we don't even need to customize our `Dataset`. We'll just use the `LineByLineDataset` out-of-the-box."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlvP_A-THEEl"
      },
      "source": [
        "%%time\n",
        "from transformers import LineByLineTextDataset\n",
        "\n",
        "dataset = LineByLineTextDataset(\n",
        "    tokenizer=tokenizer,\n",
        "    file_path=\"./oscar.eo.txt\",\n",
        "    block_size=128,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDLs73HcIHk5"
      },
      "source": [
        "Like in the [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script, we need to define a data_collator.\n",
        "\n",
        "This is just a small helper that will help us batch different samples of the dataset together into an object that PyTorch knows how to perform backprop on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTgWPa9Dipk2"
      },
      "source": [
        "from transformers import DataCollatorForLanguageModeling\n",
        "\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri2BIQKqjfHm"
      },
      "source": [
        "### Finally, we are all set to initialize our Trainer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpvnFFmZJD-N"
      },
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./EsperBERTo\",\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=1,\n",
        "    per_gpu_train_batch_size=64,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        "    prediction_loss_only=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=dataset,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6sASa36Nf-N"
      },
      "source": [
        "### Start training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmaHZXzmkNtJ"
      },
      "source": [
        "%%time\n",
        "trainer.train()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZkooHz1-_2h"
      },
      "source": [
        "#### 🎉 Save final model (+ tokenizer + config) to disk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDNgPls7_l13"
      },
      "source": [
        "trainer.save_model(\"./EsperBERTo\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0caceCy_p1-"
      },
      "source": [
        "## 4. Check that the LM actually trained"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iIQJ8ND_AEhl"
      },
      "source": [
        "Aside from looking at the training and eval losses going down, the easiest way to check whether our language model is learning anything interesting is via the `FillMaskPipeline`.\n",
        "\n",
        "Pipelines are simple wrappers around tokenizers and models, and the 'fill-mask' one will let you input a sequence containing a masked token (here, `<mask>`) and return a list of the most probable filled sequences, with their probabilities.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltXgXyCbAJLY"
      },
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "fill_mask = pipeline(\n",
        "    \"fill-mask\",\n",
        "    model=\"./EsperBERTo\",\n",
        "    tokenizer=\"./EsperBERTo\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIvgZ3S6AO0z"
      },
      "source": [
        "# The sun <mask>.\n",
        "# =>\n",
        "\n",
        "fill_mask(\"La suno <mask>.\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0qCyyhNAWZi"
      },
      "source": [
        "Ok, simple syntax/grammar works. Let’s try a slightly more interesting prompt:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ9HSQxAAbme"
      },
      "source": [
        "fill_mask(\"Jen la komenco de bela <mask>.\")\n",
        "\n",
        "# This is the beginning of a beautiful <mask>.\n",
        "# =>"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RsGaD1qAfLP"
      },
      "source": [
        "## 5. Share your model 🎉"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5oESe8djApQw"
      },
      "source": [
        "Finally, when you have a nice model, please think about sharing it with the community:\n",
        "\n",
        "- upload your model using the CLI: `transformers-cli upload`\n",
        "- write a README.md model card and add it to the repository under `model_cards/`. Your model card should ideally include:\n",
        "    - a model description,\n",
        "    - training params (dataset, preprocessing, hyperparameters), \n",
        "    - evaluation results,\n",
        "    - intended uses & limitations\n",
        "    - whatever else is helpful! 🤓\n",
        "\n",
        "### **TADA!**\n",
        "\n",
        "➡️ Your model has a page on http://huggingface.co/models and everyone can load it using `AutoModel.from_pretrained(\"username/model_name\")`.\n",
        "\n",
        "[![tb](https://huggingface.co/blog/assets/01_how-to-train/model_page.png)](https://huggingface.co/julien-c/EsperBERTo-small)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aw9ifsgqBI2o"
      },
      "source": [
        "If you want to take a look at models in different languages, check https://huggingface.co/models\n",
        "\n",
        "[![all models](https://huggingface.co/front/thumbnails/models.png)](https://huggingface.co/models)\n"
      ]
    }
  ]
}