{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ria2J2Ciiu8f"
      },
      "source": [
        "# Abstractive Summarization with Hugging Face Transformers\n",
        "\n",
        "\n",
        "**Description:** Training T5 using Hugging Face Transformers for Abstractive Summarization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81r_9WQIiu8k"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "Automatic summarization is one of the central problems in\n",
        "Natural Language Processing (NLP). It poses several challenges relating to language\n",
        "understanding (e.g. identifying important content)\n",
        "and generation (e.g. aggregating and rewording the identified content into a summary).\n",
        "\n",
        "In this tutorial, we tackle the single-document summarization task\n",
        "with an abstractive modeling approach. The primary idea here is to generate a short,\n",
        "single-sentence news summary answering the question “What is the news article about?”.\n",
        "This approach to summarization is also known as *Abstractive Summarization* and has\n",
        "seen growing interest among researchers in various disciplines.\n",
        "\n",
        "Following prior work, we aim to tackle this problem using a\n",
        "sequence-to-sequence model. [Text-to-Text Transfer Transformer (`T5`)](https://arxiv.org/abs/1910.10683)\n",
        "is a [Transformer-based](https://arxiv.org/abs/1706.03762) model built on the encoder-decoder\n",
        "architecture, pretrained on a multi-task mixture of unsupervised and supervised tasks where each task\n",
        "is converted into a text-to-text format. T5 shows impressive results in a variety of sequence-to-sequence\n",
        "(sequence in this notebook refers to text) like summarization, translation, etc.\n",
        "\n",
        "In this notebook, we will fine-tune the pretrained T5 on the Abstractive Summarization\n",
        "task using Hugging Face Transformers on the `XSum` dataset loaded from Hugging Face Datasets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QY0Tz1x7iu8l"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t01yftQNiu8l"
      },
      "source": [
        "### Installing the requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "-eNK3YNRiu8m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e964159b-4a2d-4cb0-ad3d-0e408bf04a1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: rouge-score\n",
            "Successfully installed rouge-score-0.1.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.20.0\n",
        "!pip install keras_nlp==0.3.0\n",
        "!pip install datasets\n",
        "!pip install huggingface-hub\n",
        "!pip install nltk\n",
        "!pip install rouge-score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMnWszsDiu8o"
      },
      "source": [
        "### Importing the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "kSrcIKI2iu8p"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import logging\n",
        "\n",
        "import nltk\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Only log error messages\n",
        "tf.get_logger().setLevel(logging.ERROR)\n",
        "\n",
        "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ny5rG_2eiu8q"
      },
      "source": [
        "### Define certain variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Eu0Pph4Diu8q"
      },
      "outputs": [],
      "source": [
        "# The percentage of the dataset you want to split as train and test\n",
        "TRAIN_TEST_SPLIT = 0.1\n",
        "\n",
        "MAX_INPUT_LENGTH = 1024  # Maximum length of the input to the model\n",
        "MIN_TARGET_LENGTH = 5  # Minimum length of the output by the model\n",
        "MAX_TARGET_LENGTH = 128  # Maximum length of the output by the model\n",
        "BATCH_SIZE = 8  # Batch-size for training our model\n",
        "LEARNING_RATE = 2e-5  # Learning-rate for training our model\n",
        "MAX_EPOCHS = 1  # Maximum number of epochs we will train the model for\n",
        "\n",
        "# This notebook is built on the t5-small checkpoint from the Hugging Face Model Hub\n",
        "MODEL_CHECKPOINT = \"t5-small\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpzhYANyiu8q"
      },
      "source": [
        "## Load the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        },
        "outputId": "bb22dfb5-0e94-449a-91e3-51ec40456f15"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.76k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a528f5cdffc47e6acebf1372bc13c3e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a6984233ee546a09632a6943276ec61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3acdd4c085634d8a938734727cacc03f"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/204045 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53db9fcd6f87428483c5d53af20c7f46"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/11332 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1b67165893d4780bfe7d3587136af49"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/11334 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9c685f1138042c282acefe0532ce085"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"xsum\", split=\"train\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkKPBMH2iu8r"
      },
      "source": [
        "The dataset has the following fields:\n",
        "\n",
        "- **document**: the original BBC article to be summarized\n",
        "- **summary**: the single sentence summary of the BBC article\n",
        "- **id**: ID of the document-summary pair"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "W_940-q2iu8r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67389db3-ee02-45f6-a5bb-1d0a6bcdce0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['document', 'summary', 'id'],\n",
            "    num_rows: 204045\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "print(raw_datasets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWZA4Oyciu8r"
      },
      "source": [
        "We will now see how the data looks like:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "B61vxNQ3iu8r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22add46a-ed79-4494-963c-aef09cde1500"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'document': 'The full cost of damage in Newton Stewart, one of the areas worst affected, is still being assessed.\\nRepair work is ongoing in Hawick and many roads in Peeblesshire remain badly affected by standing water.\\nTrains on the west coast mainline face disruption due to damage at the Lamington Viaduct.\\nMany businesses and householders were affected by flooding in Newton Stewart after the River Cree overflowed into the town.\\nFirst Minister Nicola Sturgeon visited the area to inspect the damage.\\nThe waters breached a retaining wall, flooding many commercial properties on Victoria Street - the main shopping thoroughfare.\\nJeanette Tate, who owns the Cinnamon Cafe which was badly affected, said she could not fault the multi-agency response once the flood hit.\\nHowever, she said more preventative work could have been carried out to ensure the retaining wall did not fail.\\n\"It is difficult but I do think there is so much publicity for Dumfries and the Nith - and I totally appreciate that - but it is almost like we\\'re neglected or forgotten,\" she said.\\n\"That may not be true but it is perhaps my perspective over the last few days.\\n\"Why were you not ready to help us a bit more when the warning and the alarm alerts had gone out?\"\\nMeanwhile, a flood alert remains in place across the Borders because of the constant rain.\\nPeebles was badly hit by problems, sparking calls to introduce more defences in the area.\\nScottish Borders Council has put a list on its website of the roads worst affected and drivers have been urged not to ignore closure signs.\\nThe Labour Party\\'s deputy Scottish leader Alex Rowley was in Hawick on Monday to see the situation first hand.\\nHe said it was important to get the flood protection plan right but backed calls to speed up the process.\\n\"I was quite taken aback by the amount of damage that has been done,\" he said.\\n\"Obviously it is heart-breaking for people who have been forced out of their homes and the impact on businesses.\"\\nHe said it was important that \"immediate steps\" were taken to protect the areas most vulnerable and a clear timetable put in place for flood prevention plans.\\nHave you been affected by flooding in Dumfries and Galloway or the Borders? Tell us about your experience of the situation and how it was handled. Email us on selkirk.news@bbc.co.uk or dumfries@bbc.co.uk.', 'summary': 'Clean-up operations are continuing across the Scottish Borders and Dumfries and Galloway after flooding caused by Storm Frank.', 'id': '35232142'}\n"
          ]
        }
      ],
      "source": [
        "print(raw_datasets[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "td_UWyc3iu8r"
      },
      "source": [
        "I only took a small stratified balanced splits (10%) of the train as our training and test sets.\n",
        "We can easily split the dataset using the `train_test_split` method which expects\n",
        "the split size and the name of the column relative to which I want to stratify."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "QaLGXmmSiu8r"
      },
      "outputs": [],
      "source": [
        "raw_datasets = raw_datasets.train_test_split(\n",
        "    train_size=TRAIN_TEST_SPLIT, test_size=TRAIN_TEST_SPLIT\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdoqvAykiu8s"
      },
      "source": [
        "## Data Pre-processing\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "vzMKa9sIiu8s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "e616f47b2a1448c19594a50f0301ddb0",
            "d46f320152c546b4adcb66bca33f1f48",
            "7c80bbf8f9b14935b67ad693cd1c20e5"
          ]
        },
        "outputId": "f1965adc-3f70-4cae-e1a8-6e66e4b0b2c3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e72c19d5ee44ec1a7dcf53cf1e389d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/773k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20146b39c3c24a809e910e36b1cd30ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.32M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a392c48e1434b7db4dae48f9ff39a1b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CHECKPOINT)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Zi_T-kiNiu8s"
      },
      "outputs": [],
      "source": [
        "if MODEL_CHECKPOINT in [\"t5-small\", \"t5-base\", \"t5-large\", \"t5-3b\", \"t5-11b\"]:\n",
        "    prefix = \"summarize: \"\n",
        "else:\n",
        "    prefix = \"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EgXF2Lkuiu8s"
      },
      "source": [
        "We will write a simple function that helps us in the pre-processing that is compatible\n",
        "with Hugging Face Datasets. To summarize, our pre-processing function should:\n",
        "\n",
        "- Tokenize the text dataset (input and targets) into it's corresponding token ids that\n",
        "will be used for embedding look-up in BERT\n",
        "- Add the prefix to the tokens\n",
        "- Create additional inputs for the model like `token_type_ids`, `attention_mask`, etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "4BaYo0Q_iu8s"
      },
      "outputs": [],
      "source": [
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [prefix + doc for doc in examples[\"document\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=MAX_INPUT_LENGTH, truncation=True)\n",
        "\n",
        "    # Setup the tokenizer for targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(\n",
        "            examples[\"summary\"], max_length=MAX_TARGET_LENGTH, truncation=True\n",
        "        )\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "\n",
        "    return model_inputs\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ugn14Z9yiu8t"
      },
      "source": [
        "To apply this function on all the pairs of sentences in our dataset, we just use the\n",
        "`map` method of our `dataset` object we created earlier. This will apply the function on\n",
        "all the elements of all the splits in `dataset`, so our training and testing\n",
        "data will be preprocessed in one single command."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "qA_S9uu6iu8t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "887748e63925422e877e9f7d01d3ec86",
            "8365f52fe0c043cf9cd0e21016b8c7ec",
            "6de9f6ef3791496b8fd7a6460a31bd76",
            "d47fa142b3fc4d29914a2ad9da018467",
            "bf8d6183dda142a4abe40070de0aba96",
            "79737268312a44cb97749dbe0f33c2fa",
            "afc6d9823717467f900012bf81f89013",
            "e021a69ff2764d3e8bc516af40ef401e",
            "a4aa8563c0a84ac2978925220d5ac1db",
            "ab5c85ece8244fd2957b258a2bcf71db",
            "87f9dff5462948debe0e2979587fb6c1",
            "cd13ef24bf484988b85b6f3fa5c022fb",
            "f1f889276f6c423ea06e5e88a6421003",
            "c0695261b5ef43f08eba01e28feae901",
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            "6e16089ea20246f8990957841414a322",
            "7c30602f2ace4a6093f71f58bc20fbba",
            "678cd95693e94baa9e261752311f2ac7",
            "3069ca8244e34fc49c1e587ec00baada",
            "97ce6a2b4d9841608db1064d4cff2da6",
            "754a2e94686449a59a61c64965c05c19",
            "cf53f9c04ebc4e668a802c20b440c9ff"
          ]
        },
        "outputId": "5329b5c9-8fa4-4319-8598-a76d74642cc3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/20404 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "887748e63925422e877e9f7d01d3ec86"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/20405 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd13ef24bf484988b85b6f3fa5c022fb"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jXFhBOaiiu8t"
      },
      "source": [
        "## Defining the model\n",
        "\n",
        "Now we can download the pretrained model and fine-tune it. Since our task is\n",
        "sequence-to-sequence (both the input and output are text sequences), we use the\n",
        "`TFAutoModelForSeq2SeqLM` class from the Hugging Face Transformers library. Like with the\n",
        "tokenizer, the `from_pretrained` method will download and cache the model for us.\n",
        "\n",
        "The `from_pretrained()` method expects the name of a model from the Hugging Face Model Hub. As\n",
        "mentioned earlier, we will use the `t5-small` model checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "aQKRJbqoiu8t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "e31db4249b0f439ba993519dbcc097d3",
            "aa1dd2098d53400987fcb48ae58f2daa",
            "6748174db98b4654a94315caae7e4827",
            "62ad6b1520b949a688438ee59860ccdf",
            "5a724f82d9fa4b53a859871e14c16ebf",
            "0ce9d3ded3f44cefa3a666acd515d963",
            "3581da40a037427eab8c9d572d2484f7",
            "e666007a8b9a46a7931b0adca6bffa09",
            "69263ffb31dd4390bd3d6c7dee150a8d",
            "3ee6bf7ea62b4390be14085bda1cb3c7",
            "c802a4d18f6040208a7faf75353e76a3",
            "749df752672c435bb303fbd2f6b87fb9",
            "9ecc20c886854797bb575fbb39a23aaf",
            "1269675ee0984a60b79e279066a8040b",
            "d817ce64d59147cf9d7945377535d028",
            "12ba7f1f96464cfeb4bb7512bcfad95c",
            "91e12338595446d28ae2b8683895eae6",
            "e07be565d0894db1aaadb78dc1587f45",
            "f0da127ed2954db49df1e4b590fe5774",
            "6ca34939777449f79e12d25c6f14036a",
            "fa9a38f395164f0f8ab491a4585c4c19",
            "b26854b4fa0d46fbac3ca4b25fc945bd"
          ]
        },
        "outputId": "715ff73e-1371-4643-914f-eb25b0ca491e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.18k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e31db4249b0f439ba993519dbcc097d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/231M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "749df752672c435bb303fbd2f6b87fb9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n",
            "\n",
            "All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-small.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n",
        "\n",
        "model = TFAutoModelForSeq2SeqLM.from_pretrained(MODEL_CHECKPOINT)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "XeRYXcFZiu8t"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5iZK_FIriu8u"
      },
      "source": [
        "Next we define our training and testing sets with which we will train our model. Again, Hugging Face\n",
        "Datasets provides us with the `to_tf_dataset` method which will help us integrate our\n",
        "dataset with the `collator` defined above. The method expects certain parameters:\n",
        "\n",
        "- **columns**: the columns which will serve as our independant variables\n",
        "- **batch_size**: our batch size for training\n",
        "- **shuffle**: whether we want to shuffle our dataset\n",
        "- **collate_fn**: our collator function\n",
        "\n",
        "Additionally, we also define a relatively smaller `generation_dataset` to calculate\n",
        "`ROUGE` scores on the fly while training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "XKUfJTvxiu8v"
      },
      "outputs": [],
      "source": [
        "train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
        "    batch_size=BATCH_SIZE,\n",
        "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "test_dataset = tokenized_datasets[\"test\"].to_tf_dataset(\n",
        "    batch_size=BATCH_SIZE,\n",
        "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "generation_dataset = (\n",
        "    tokenized_datasets[\"test\"]\n",
        "    .shuffle()\n",
        "    .select(list(range(200)))\n",
        "    .to_tf_dataset(\n",
        "        batch_size=BATCH_SIZE,\n",
        "        columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
        "        shuffle=False,\n",
        "        collate_fn=data_collator,\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R7e_yzr3iu8v"
      },
      "source": [
        "## Building and Compiling the the model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "114y1e-9iu8v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68ed8320-3e81-40ff-8991-b9c2097beaa4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"
          ]
        }
      ],
      "source": [
        "optimizer = keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n",
        "model.compile(optimizer=optimizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VFY660Iiu8v"
      },
      "source": [
        "## Training and Evaluating the model\n",
        "\n",
        "To evaluate our model on-the-fly while training, we will define `metric_fn` which will\n",
        "calculate the `ROUGE` score between the groud-truth and predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "5EmMGmbYiu8w"
      },
      "outputs": [],
      "source": [
        "import keras_nlp\n",
        "\n",
        "rouge_l = keras_nlp.metrics.RougeL()\n",
        "\n",
        "\n",
        "def metric_fn(eval_predictions):\n",
        "    predictions, labels = eval_predictions\n",
        "    decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "    for label in labels:\n",
        "        label[label < 0] = tokenizer.pad_token_id  # Replace masked label tokens\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "    result = rouge_l(decoded_labels, decoded_predictions)\n",
        "    # We will print only the F1 score, you can use other aggregation metrics as well\n",
        "    result = {\"RougeL\": result[\"f1_score\"]}\n",
        "\n",
        "    return result\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xay9knltiu8w"
      },
      "source": [
        "## Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "KkIYXIg3iu8w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eab5759-259f-4915-e704-aa1f224a1da3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:No label_cols specified for KerasMetricCallback, assuming you want the 'labels' key.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2551/2551 [==============================] - 2378s 915ms/step - loss: 2.9278 - val_loss: 2.5840 - RougeL: 0.1995\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7899b613b040>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "from transformers.keras_callbacks import KerasMetricCallback\n",
        "\n",
        "metric_callback = KerasMetricCallback(\n",
        "    metric_fn, eval_dataset=generation_dataset, predict_with_generate=True\n",
        ")\n",
        "\n",
        "callbacks = [metric_callback]\n",
        "\n",
        "# For now we will use our test set as our validation_data\n",
        "model.fit(\n",
        "    train_dataset, validation_data=test_dataset, epochs=MAX_EPOCHS, callbacks=callbacks\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvTctf4oiu8w"
      },
      "source": [
        "For best results, we recommend training the model for atleast 5 epochs on the entire\n",
        "training dataset!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AU1R5y7Ciu8w"
      },
      "source": [
        "## Inference\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "lyJVQR6Ciu81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23093e43-4fd1-48e0-c83b-13735f80ee5a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': 'Scotland have lost their last nine games in the Six Nations competition in Rome, according to the Scottish forward.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "summarizer = pipeline(\"summarization\", model=model, tokenizer=tokenizer, framework=\"tf\")\n",
        "\n",
        "summarizer(\n",
        "    raw_datasets[\"test\"][0][\"document\"],\n",
        "    min_length=MIN_TARGET_LENGTH,\n",
        "    max_length=MAX_TARGET_LENGTH,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7-J2d6iiu81"
      },
      "source": [
        "Now you can push this model to Hugging Face Model Hub and also share it with with all your friends,\n",
        "family, favorite pets: they can all load it with the identifier\n",
        "`\"your-username/the-name-you-picked\"` so for instance:\n",
        "\n",
        "```python\n",
        "model.push_to_hub(\"transformers-qa\", organization=\"keras-io\")\n",
        "tokenizer.push_to_hub(\"transformers-qa\", organization=\"keras-io\")\n",
        "```\n",
        "And after you push your model this is how you can load it in the future!\n",
        "\n",
        "```python\n",
        "from transformers import TFAutoModelForSeq2SeqLM\n",
        "\n",
        "model = TFAutoModelForSeq2SeqLM.from_pretrained(\"your-username/my-awesome-model\")\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"transformers-qa\", organization=\"keras-io\")\n",
        "tokenizer.push_to_hub(\"transformers-qa\", organization=\"keras-io\")\n",
        "from transformers import TFAutoModelForSeq2SeqLM\n",
        "\n",
        "model = TFAutoModelForSeq2SeqLM.from_pretrained(\"https://huggingface.co/kunaltilaganji/Abstractive-Summarization-with-Transformers/tree/main\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "NMEYpVNCyQKv",
        "outputId": "ea346be1-cb60-4d46-f6e9-ee0a5dc34f05"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-d4d448401cc2>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"transformers-qa\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morganization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"keras-io\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"transformers-qa\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morganization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"keras-io\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTFAutoModelForSeq2SeqLM\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTFAutoModelForSeq2SeqLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"https://huggingface.co/kunaltilaganji/Abstractive-Summarization-with-Transformers/tree/main\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, repo_path_or_name, repo_url, use_temp_dir, commit_message, organization, private, use_auth_token, **model_card_kwargs)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m         \u001b[0;31m# Create or clone the repo. If the repo is already cloned, this just retrieves the path to the repo.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m         repo = self._create_or_get_repo(\n\u001b[0m\u001b[1;32m    932\u001b[0m             \u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0mrepo_url\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_url\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36m_create_or_get_repo\u001b[0;34m(cls, repo_path_or_name, repo_url, organization, private, use_auth_token)\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrepo_url\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0mrepo_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_path_or_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m             repo_url = cls._get_repo_url_from_name(\n\u001b[0m\u001b[1;32m   1008\u001b[0m                 \u001b[0mrepo_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morganization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morganization\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprivate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36m_get_repo_url_from_name\u001b[0;34m(repo_name, organization, private, use_auth_token)\u001b[0m\n\u001b[1;32m    967\u001b[0m             \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHfFolder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 969\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    970\u001b[0m                     \u001b[0;34m\"You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m                     \u001b[0;34m\"entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own token as the `use_auth_token` argument."
          ]
        }
      ]
    },
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        "outputId": "14ab0fba-1d69-47df-f433-fb3958e9df16"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-23-9fd4b7151dd5>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    transformers-cli login\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
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