{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/bloom_ft_cnn_dailymail_summarization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune BLOOM 💮 (560M) on CNN/Dailymal 📰 dataset for `Summarization`\n",
        "\n",
        "It is still WIP\n",
        "> Made by [Manu Romero/mrm8488](https://twittercom/mrm8488)"
      ],
      "metadata": {
        "id": "ddg-W3Acrc2N"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqPkvOg2qhrq",
        "outputId": "d2485ede-0bdb-43a4-d55c-3383072669ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Oct  7 22:27:29 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  A100-SXM4-40GB      Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   31C    P0    41W / 400W |      0MiB / 40536MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install required libraries"
      ],
      "metadata": {
        "id": "jIvqGuqyGENt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PXB60Jj4qtFG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34cfe376-867b-4495-d0c0-9385cf2e2d58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.9 MB 5.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 432 kB 82.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 148 kB 89.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 73.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 163 kB 86.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 115 kB 6.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 90.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 92.8 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install -q transformers datasets accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### HF login"
      ],
      "metadata": {
        "id": "5MQOr7_cGQQe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-vkeGp63-TN",
        "outputId": "3334ae8e-924c-419c-9c2e-e9e23c292cfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "! huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git config --global credential.helper store"
      ],
      "metadata": {
        "id": "MlesoFnuGWc8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the model and tokenizer"
      ],
      "metadata": {
        "id": "vG1UWV9zI6W-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BloomTokenizerFast, BloomForCausalLM"
      ],
      "metadata": {
        "id": "BhZUY20_I-Av"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xWMRACCuI-NJ"
      },
      "outputs": [],
      "source": [
        "ckpt = 'bigscience/bloom-560m'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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            "d7768cefd77541f29058d4ec5bd0afcd"
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        },
        "outputId": "7450ceca-274d-4954-e1a1-8b01860fe0d4",
        "id": "EzTgh6yfI-NJ"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/222 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a2454673f7f4d37b31407e8d4296ec5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0995502acc6e446f83bb40f945fe07cc"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/85.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc7defd20d654b46aebab667ea403eff"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/688 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "276f42c2645c406a8ba4753280abb078"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d33eabb1bf1b4498821122cfec9dacfe"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = BloomTokenizerFast.from_pretrained(ckpt)\n",
        "model = BloomForCausalLM.from_pretrained(ckpt)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the dataset"
      ],
      "metadata": {
        "id": "lWNfLKgqkMZE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          ]
        },
        "id": "igW_vNGbq9Jw",
        "outputId": "8c5d4462-9b89-4889-f02f-60dc264e55db"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.33k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8095d45fb7124bc5a8ad46f3de4ad6a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/9.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a808939988ab4937a95ef8adef519ae9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/1b3c71476f6d152c31c1730e83ccb08bcf23e348233f4fcc11e182248e6bf7de...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "937777786dfc472cb5e1a65bea4914d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/159M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19db409ca20a410db3ed9fcec2907613"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/376M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbbbd1abcc6c4e2a9e1e73aff459cced"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/12.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6487029ff8f43f69bd3e1741d408628"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/661k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df00174259e144cb9ec20154be75b83f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/572k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7487355cc78c48738fb3a3c4905e37b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/287113 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "272f915efc764c2bac1fabe8b88471e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/13368 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "782a4151ed264598a7df23aa5c9a567b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/11490 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8dc88890b81431a8487a68d40c0269a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset cnn_dailymail downloaded and prepared to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/1b3c71476f6d152c31c1730e83ccb08bcf23e348233f4fcc11e182248e6bf7de. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e9124c67aa54ee988341e8a9b9350a9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Rename columns and visualize the dataset"
      ],
      "metadata": {
        "id": "8pfHuBgnHfEN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q_rAKfc0ub8z"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.rename_columns({\"article\": \"document\", \"highlights\": \"summary\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H1g51DdUuyfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0be1378e-5ad9-404d-de71-d97ade29474f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['document', 'summary', 'id'],\n",
              "        num_rows: 287113\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['document', 'summary', 'id'],\n",
              "        num_rows: 13368\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['document', 'summary', 'id'],\n",
              "        num_rows: 11490\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gATpjapLm_op",
        "outputId": "e464a051-d963-4f6b-95d6-579efb8cd205"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'document': 'LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won\\'t cast a spell on him. Daniel Radcliffe as Harry Potter in \"Harry Potter and the Order of the Phoenix\" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"I don\\'t plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an Australian interviewer earlier this month. \"I don\\'t think I\\'ll be particularly extravagant. \"The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs.\" At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"Hostel: Part II,\" currently six places below his number one movie on the UK box office chart. Details of how he\\'ll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. \"I\\'ll definitely have some sort of party,\" he said in an interview. \"Hopefully none of you will be reading about it.\" Radcliffe\\'s earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"People are always looking to say \\'kid star goes off the rails,\\'\" he told reporters last month. \"But I try very hard not to go that way because it would be too easy for them.\" His latest outing as the boy wizard in \"Harry Potter and the Order of the Phoenix\" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films.  Watch I-Reporter give her review of Potter\\'s latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called \"My Boy Jack,\" about author Rudyard Kipling and his son, due for release later this year. He will also appear in \"December Boys,\" an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer\\'s \"Equus.\" Meanwhile, he is braced for even closer media scrutiny now that he\\'s legally an adult: \"I just think I\\'m going to be more sort of fair game,\" he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.',\n",
              " 'summary': \"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\\nYoung actor says he has no plans to fritter his cash away .\\nRadcliffe's earnings from first five Potter films have been held in trust fund .\",\n",
              " 'id': '42c027e4ff9730fbb3de84c1af0d2c506e41c3e4'}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "dataset['train'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Format the dataset"
      ],
      "metadata": {
        "id": "vx6TW_KMHmYx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "instruction = \"Summarize the following news article:\""
      ],
      "metadata": {
        "id": "YjM3Y5UMHZkX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hhM-dCbhvBTn"
      },
      "outputs": [],
      "source": [
        "def format_ds(example):\n",
        "  return{\n",
        "      'text': tokenizer.bos_token + instruction + '\\n' + example['document'] + '\\n' + 'Summary:' + '\\n' + example['summary'] + tokenizer.eos_token\n",
        "  }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sas2SlT0xmFj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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          ]
        },
        "outputId": "f7f55623-bf46-4efb-81d4-73118a90a277"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/287113 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ec76555f2ff4449a6b5e05d6988f3aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/13368 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "823c538dddc745fc8e4a2ab30710be37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/11490 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "6d06ebb8b1654830bbf4142aeed038c6"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = dataset.map(format_ds, remove_columns=['document', 'summary', 'id'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's visualize the new format"
      ],
      "metadata": {
        "id": "g2okC3wRlGC-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ApOX-T_43Ncc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6eb6ce8e-ccdb-4479-eb78-346e184b1c42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>Summarize the following news article:\n",
            "LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won't cast a spell on him. Daniel Radcliffe as Harry Potter in \"Harry Potter and the Order of the Phoenix\" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"I don't plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an Australian interviewer earlier this month. \"I don't think I'll be particularly extravagant. \"The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs.\" At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"Hostel: Part II,\" currently six places below his number one movie on the UK box office chart. Details of how he'll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. \"I'll definitely have some sort of party,\" he said in an interview. \"Hopefully none of you will be reading about it.\" Radcliffe's earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"People are always looking to say 'kid star goes off the rails,'\" he told reporters last month. \"But I try very hard not to go that way because it would be too easy for them.\" His latest outing as the boy wizard in \"Harry Potter and the Order of the Phoenix\" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films.  Watch I-Reporter give her review of Potter's latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called \"My Boy Jack,\" about author Rudyard Kipling and his son, due for release later this year. He will also appear in \"December Boys,\" an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer's \"Equus.\" Meanwhile, he is braced for even closer media scrutiny now that he's legally an adult: \"I just think I'm going to be more sort of fair game,\" he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.\n",
            "Summary:\n",
            "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\n",
            "Young actor says he has no plans to fritter his cash away .\n",
            "Radcliffe's earnings from first five Potter films have been held in trust fund .</s>\n"
          ]
        }
      ],
      "source": [
        "print(dataset['train'][0]['text'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenize the dataset"
      ],
      "metadata": {
        "id": "q-QXIQUYlXei"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bAAu7zLEzLg0"
      },
      "outputs": [],
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470,
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      "source": [
        "tokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Group examples in bolcks of model max length"
      ],
      "metadata": {
        "id": "x6DfASE-lamB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ppIXTW0eztN_"
      },
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      "source": [
        "block_size = 2048"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_hIRjOPzzxw9"
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      "source": [
        "def group_texts(examples):\n",
        "    # Concatenate all texts.\n",
        "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
        "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
        "    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n",
        "        # customize this part to your needs.\n",
        "    total_length = (total_length // block_size) * block_size\n",
        "    # Split by chunks of max_len.\n",
        "    result = {\n",
        "        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
        "        for k, t in concatenated_examples.items()\n",
        "    }\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result"
      ]
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        },
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            "  "
          ]
        },
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            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "lm_datasets = tokenized_datasets.map(\n",
        "    group_texts,\n",
        "    batched=True,\n",
        "    batch_size=1000,\n",
        "    num_proc=4,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mR8vPTbo1abC"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pn5Q-vSm1rrD"
      },
      "outputs": [],
      "source": [
        "model_name = ckpt.split(\"/\")[-1]\n",
        "training_args = TrainingArguments(\n",
        "    \"/content/bloom-560m-ft-summarization-cnn\",\n",
        "    num_train_epochs=2,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_train_batch_size=1,\n",
        "    per_device_eval_batch_size=2,\n",
        "    prediction_loss_only=True,\n",
        "    eval_steps=5000,\n",
        "    save_steps=5000,\n",
        "    logging_steps=100,\n",
        "    save_total_limit=2,\n",
        "    load_best_model_at_end=True,\n",
        "    fp16=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0kEsu-p2F3O",
        "outputId": "af1cf04d-c651-4e3e-8d53-e33004350520"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/repository.py:708: FutureWarning: Creating a repository through 'clone_from' is deprecated and will be removed in v0.11.\n",
            "  FutureWarning,\n",
            "Cloning https://huggingface.co/mrm8488/bloom-560m-ft-summarization-cnn into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/mrm8488/bloom-560m-ft-summarization-cnn into local empty directory.\n",
            "Using cuda_amp half precision backend\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_args,\n",
        "    train_dataset=lm_datasets['train'],\n",
        "    eval_dataset=lm_datasets['validation']\n",
        ")"
      ]
    },
    {
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        "outputId": "6e7c104d-429f-45da-a56d-b2cbb13fe842"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
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              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ]
          },
          "metadata": {}
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      ],
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir /content/bloom-560m-ft-summarization-cnn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 133377\n",
            "  Num Epochs = 2\n",
            "  Instantaneous batch size per device = 1\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 266754\n",
            "You're using a BloomTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
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              "      [  1101/266754 05:33 < 22:22:15, 3.30 it/s, Epoch 0.01/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
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              "      [  2398/266754 12:05 < 22:14:21, 3.30 it/s, Epoch 0.02/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
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              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.create_model_card()"
      ],
      "metadata": {
        "id": "LwqNGaILnhhe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "id": "K1c5EYrBnhpy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validation (TO-DO)"
      ],
      "metadata": {
        "id": "p5a0opmGneqA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "# load rouge\n",
        "rouge = load_metric(\"rouge\")\n",
        "\n",
        "test_data =load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"test\")\n",
        "\n",
        "# only use 16 training examples for notebook - DELETE LINE FOR FULL TRAINING\n",
        "test_data = test_data.select(range(2))\n",
        "test_data = test_data.rename_columns({\"article\": \"document\", \"highlights\": \"summary\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrvq-TV8S6Bg",
        "outputId": "3f6fffd0-28bc-44c5-e2c5-aa2ed761b086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset cnn_dailymail (/root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/1b3c71476f6d152c31c1730e83ccb08bcf23e348233f4fcc11e182248e6bf7de)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model = trainer.model\n",
        "model.to(\"cuda\")"
      ],
      "metadata": {
        "id": "uswgOyszRK51"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_summary(example):\n",
        "    prompt = tokenizer.bos_token + 'Summarize the following news article:' + '\\n' + example['document'] + '\\n' + 'Summary:' + '\\n'\n",
        "    inputs = tokenizer(prompt, return_tensors='pt')\n",
        "    input_ids = inputs.input_ids.to(\"cuda\")\n",
        "    attention_mask = inputs.attention_mask.to(\"cuda\")\n",
        "    outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=2048, eos_token_id=tokenizer.eos_token_id)\n",
        "    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=False)\n",
        "\n",
        "    example[\"pred\"] = output_str\n",
        "\n",
        "    return example"
      ],
      "metadata": {
        "id": "unKLuTGcQ-eo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = test_data.map(generate_summary, batched=False, remove_columns=[\"document\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?ex/s]"
            ],
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      "cell_type": "code",
      "source": [
        "pred_str = results[\"pred\"]\n",
        "label_str = results[\"summary\"]"
      ],
      "metadata": {
        "id": "DcaRQ7iSWzOb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "pred_sum_only = list()\n",
        "for preds in pred_str:\n",
        "  for p in preds:\n",
        "    p = p.split(\"Summary:\")[1].split(\"</s>\")[0].replace(\"\\n\",\"\",1)\n",
        "    pred_sum_only.append([p])"
      ],
      "metadata": {
        "id": "-8maAONzXIxk"
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      "execution_count": null,
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    },
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        "rouge_output = rouge.compute(predictions=pred_sum_only, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n",
        "\n",
        "print(rouge_output)"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
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