13: FineTune_T5_Summarization.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_transformers/blob/main/13%3A%20FineTune_T5_Summarization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MOsHUjgdIrIW"
      },
      "outputs": [],
      "source": [
        "! pip install datasets transformers rouge-score nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GoB5bF4RtGmM"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzuiOZsatGmO"
      },
      "source": [
        "Then you need to install Git-LFS. Uncomment the following instructions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4cGaDzSetGmQ"
      },
      "outputs": [],
      "source": [
        "# !apt install git-lfs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tst1DbtttGmR"
      },
      "source": [
        "Make sure your version of Transformers is at least 4.11.0 since the functionality was introduced in that version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZQT4BTk4tGmT"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "print(transformers.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HFASsisvIrIb"
      },
      "source": [
        "You can find a script version of this notebook to fine-tune your model in a distributed fashion using multiple GPUs or TPUs [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvwf1PlVtGmW"
      },
      "source": [
        "We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8JxsacaetGmY"
      },
      "outputs": [],
      "source": [
        "from transformers.utils import send_example_telemetry\n",
        "send_example_telemetry(\"summarization_notebook\", framework=\"pytorch\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEJBSTyZIrIb"
      },
      "source": [
        "# Fine-tuning a model on a summarization task"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTCFado4IrIc"
      },
      "source": [
        "In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) model for a summarization task. We will use the [XSum dataset](https://arxiv.org/pdf/1808.08745.pdf) (for extreme summarization) which contains BBC articles accompanied with single-sentence summaries.\n",
        "\n",
        "Load the dataset for this task using 🤗 Datasets and how to fine-tune a model on it using the `Trainer` API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mslJaluVtGme"
      },
      "outputs": [],
      "source": [
        "model_checkpoint = \"t5-small\" #google/flan-t5-small google/t5-v1_small goole/t5-small-lm-adapt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RRkXuteIrIh"
      },
      "source": [
        "This notebook is built to run  with any model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a sequence-to-sequence version in the Transformers library. Here we picked the [`t5-small`](https://huggingface.co/t5-small) checkpoint. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whPRbBNbIrIl"
      },
      "source": [
        "## Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "IreSlFmlIrIm",
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      },
      "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": "0e736401d0274c22964f648f307a8003"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/6.24k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb1f7d2a13ed445798fc733a260a58f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset xsum/default to /root/.cache/huggingface/datasets/xsum/default/1.2.0/082863bf4754ee058a5b6f6525d0cb2b18eadb62c7b370b095d1364050a52b71...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3243c2ff7d6346c3be300006fe247646"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cca9d23f50544893a2f1ea3e8dc2b1bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b132d6063f074a83b124e39bd2275caa"
            }
          },
          "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": "90bf4441c3544819a5c747bd1f77500c"
            }
          },
          "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": "998035c9e96e48249ef18a72196900af"
            }
          },
          "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": "1ee40bc971624831b210fad13ac8c739"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset xsum downloaded and prepared to /root/.cache/huggingface/datasets/xsum/default/1.2.0/082863bf4754ee058a5b6f6525d0cb2b18eadb62c7b370b095d1364050a52b71. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61e3cac66e5247efaea3cbb7e4e26364"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-41d8112dae1f>:4: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"rouge\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db27a501d17e4e70909dacb79a89fe9f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "raw_datasets = load_dataset(\"xsum\")\n",
        "metric = load_metric(\"rouge\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzfPtOMoIrIu"
      },
      "source": [
        "The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GWiVUF0jIrIv",
        "outputId": "f6797866-01e2-42c1-d960-9a957ebe0353",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['document', 'summary', 'id'],\n",
              "        num_rows: 204045\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['document', 'summary', 'id'],\n",
              "        num_rows: 11332\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['document', 'summary', 'id'],\n",
              "        num_rows: 11334\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "raw_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3EtYfeHIrIz"
      },
      "source": [
        "To access an actual element, you need to select a split first, then give an index:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X6HrpprwIrIz",
        "outputId": "501cef50-1a83-4d9c-bb0c-d2c05910fee8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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.',\n",
              " 'summary': 'Clean-up operations are continuing across the Scottish Borders and Dumfries and Galloway after flooding caused by Storm Frank.',\n",
              " 'id': '35232142'}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "raw_datasets[\"train\"][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHUmphG3IrI3"
      },
      "source": [
        "To get a sense of what the data looks like, the following function will show some examples picked randomly in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i3j8APAoIrI3"
      },
      "outputs": [],
      "source": [
        "import datasets\n",
        "import random\n",
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "def show_random_elements(dataset, num_examples=5):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "    \n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, datasets.ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "    display(HTML(df.to_html()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SZy5tRB_IrI7",
        "outputId": "25474aa5-9588-4f90-b7d7-77496a75c526",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>document</th>\n",
              "      <th>summary</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>From 5th January 2017, the dinosaur will be taken apart piece by piece and cleaned, before visiting museums around the UK.\\nA huge blue whale skeleton will take his place in the Museum's main Hintze Hall.\\nWatch our video to find out if Dippy will be visiting a museum near you!\\nSo in case you miss him while he gets dismantled and packed away, here's five Dippy Dino Facts to keep you going</td>\n",
              "      <td>Dippy the Diplodocus at London's Natural History Museum is a legend, and now he's going on tour.</td>\n",
              "      <td>38505748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>In the searing Florida heat I counted at least 20 television satellite trucks (yes, including one used by the BBC); plus assorted technicians, camera crews and glistening correspondents (including me).\\nOn the main road, a small group of banner-waving protesters denounced the pastor's views on everything from Islam to abortion to homosexuality. Inside, hardier members of Mr Jones' tiny flock strode around, with pistols hanging from high-waisted trousers.\\nThe man himself was big of voice and moustache, but strikingly short on clarity of message - not to mention understanding of the Muslim religion which he considers the \"devil's work.\"\\nWe did, however, get a fuller understanding of Terry Jones' wardrobe. His increasingly frequent news conferences took place against various hues of shirt and tie. On Thursday, Pastor Jones appeared to have completely changed clothes between two press conferences, barely an hour apart.\\nAnd then there were the self-styled peacemakers.\\nFirst, the local imam who brought the offer of a quid pro quo, in which the Korans would be reprieved in return for the relocation of a proposed Islamic centre near Ground Zero. A \"deal\" which rapidly unravelled, when it became clear that Imam Muhammad Musri had no authority to offer it. Hopelessly befuddled, Pastor Jones appeared not to know his imams from his Iman - at one stage suggesting he had struck a deal with David Bowie's wife.\\nNext up to the plate was Dr KA Paul, a Christian evangelist who previously defended the honour of Charles Taylor, the former Liberian leader currently on trial for crimes against humanity.\\nWith no deficiency in self-assurance, Dr Paul issued an ultimatum to the Manhattan site developers - giving them two hours to formalise a meeting with Terry Jones. The \"or else\" was never made clear; and predictably, there was no outreach from New York.\\nAs the sun finally eased, I took a walk around the site of the corrugated-iron church, which bears a striking resemblance to an exhaust factory just up the road. To one side was a children's climbing frame and paddling pool, and a basketball hoop under trees draped with Spanish moss. At the foot of the Church's locked, mirrored doors lay three dozen red roses, under a notice stating \"expect a miracle.\"\\nWhen I wandered towards an out-building, a wispy-moustached youth barred my way, explaining that this was a \"work area.\" Stacks of carefully-wrapped chairs and tables behind him seemed consistent with reports of Pastor Jones' sideline furniture business. Carpentry may be the only thing he has in common with Jesus.\\nWith the threat of lighted match meeting holy book apparently receding, the Gainesville constabulary seemed to relax, and by day's-end there were no uniformed police officers on view. On Northwest 37th Street a lone protestor paraded a simple banner proclaiming \"Shame on you\" - drawing supportive toots from passing motorists.\\n\"Pastor Jones just wanted 15 minutes of celebrity whoredom,\" explained the protestor, aptly named Paula Pope. \"He's a short-sighted, idiotic, fanatical narcissist.\"\\nHer remarks left me uneasy. Could this have been avoided, had we in the media denied the narcissistic Jones the mirror of publicity? Possibly - although I would argue it was the warnings from generals and politicians which transformed the Dove Outreach Center from obscurity into headline news. Warnings which the Obama administration clearly felt had to be made.\\nAs I write, the battalion of satellite trucks is thinning, and Gainesville is turning its thoughts to this campus town's true religion - college football - ahead of a fiercely-anticipated local derby. Few here will mourn the fading of Terry Jones from the headlines.</td>\n",
              "      <td>Pastor Terry Jones shares a name with one of the Monty Python troupe, and the scene outside his Gainesville church is undoubtedly a circus.</td>\n",
              "      <td>11269626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>There was controversy at last year's event, when some competitors said they thought the course was shorter than the stipulated 13.1 miles.\\nFour cyclists set off from the city's George Square at 04:30, and finished in two stages at Glasgow Green at about 07:00.\\nThey were accompanied by a police escort whose job was to stop traffic.\\nThe riders' bikes were equipped with specialist measuring equipment.\\nThe course should be 13.1 miles, but after last year's event, some runners said they thought it was up to 200 metres shorter.\\nThat difference could be crucial for professional runners - including last year's winner Callum Hawkins, who also set a new course record.\\nIf the course is found to be a different length, it mean's the winner's record, and that of all the other runners, could be in doubt.\\nThe riders cycled the full half-marathon course, including going through a darkened Pollock Country Park.\\nThe time and date were kept secret in advance, chosen in order to allow the officials an unimpeded ride.\\nThe results of the measuring exercise will be announced on Monday by the run organisers, The Great Run Company.</td>\n",
              "      <td>The organisers of the Great Scottish Run half-marathon have been out measuring the distance of the course.</td>\n",
              "      <td>38787802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Asylum seekers in Middlesbrough told The Times eggs and stones had been thrown at their houses because the doors made them easy to identify.\\nThe immigration minister said he was \"deeply concerned\" about the issue.\\nG4S said there was no policy to house asylum seekers behind red doors but its subcontractor would be repainting them.\\nThe Times visited 168 houses in Middlesbrough owned by Jomast, a sub contractor for the global security firm G4S, and found 155 had red doors.\\nIs Middlesbrough the UK's Germany?\\nBBC correspondent Andy Moore said it was thought the door colour may have been a convenient way for the company to identify its properties.\\nFormer local councillor Suzanne Fletcher told the BBC's Today programme she had raised the issue with G4S as far back as 2012 but was told the company would not ask Jomast to repaint the doors.\\nShe went on to submit evidence to the Home Affairs Select Committee, Public Accounts Committee and National Audit Office.\\nShe said: \"Over four years ago when I was working with groups of asylum seekers, they were worried it marked them out and were worried about attacks.\"\\nThe charity Refugee Council said it \"has long held concerns about the quality and security of asylum accommodation\".\\nIt added: \"The government must not tolerate its contractors taking a lax attitude towards housing these vulnerable people. Such an approach is clearly jeopardising their safety.\"\\nG4S said Jomast had no policy to house asylum seekers behind red doors but accepted the majority of doors, both for private and asylum accommodation, were painted red.\\nImmigration minister James Brokenshire said he had commissioned an urgent Home Office audit of asylum seeker housing in the North East.\\nHe said: \"I expect the highest standards from our contractors. If we find any evidence of discrimination against asylum seekers it will be dealt with immediately as any such behaviour will not be tolerated.\"\\nG4S said in a statement: \"Although we have received no complaints or requests on this issue from asylum seekers we house, in light of the concerns raised Jomast has agreed to address the issue by repainting front doors in the area so that there is no predominant colour.\\n\"Our asylum accommodation is inspected frequently by the Home Office and has been found to meet the required standards.\"</td>\n",
              "      <td>The front doors of houses used by asylum seekers are to be repainted, after claims they were targeted because nearly all of the doors were red.</td>\n",
              "      <td>35358925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nearby, other farm workers are using pitchforks to do the same job, throwing the grass into the air in an ancient process known as winnowing.\\nThis is a harvest scene in rural Ethiopia, which at this time of the year is replicated across the length and breadth of the country.\\nThe seed, or grain, in question is called teff.\\nEthiopians have been growing and obsessing about teff for millennia, and it may become the new \"super grain\" of choice in Europe and North America, overtaking the likes of quinoa and spelt.\\nHigh in protein and calcium, and gluten-free, teff is already growing in popularity on the international stage.\\nYet as teff is a staple foodstuff in Ethiopia, particularly when turned into a grey flatbread called injera, the country currently has a long-standing ban on exporting the grain, either in its raw form, or after it has been ground into flour.\\nInstead, entrepreneurial Ethiopian companies can at present only export injera and other cooked teff products, such as cakes and biscuits.\\nHowever, the hope is that if Ethiopia can sufficiently increase its teff harvest, then exports of the grain itself may be able to start in the not too distant future.\\n\"We started from scratch, and are now introducing our traditional food all over the world,\" says Hailu Tessema, founder of Mama Fresh, Ethiopia's first large-scale producer of injera.\\nSix days every week Mama Fresh uses Ethiopian Airlines to fly 3,000 injera flatbreads from Addis Ababa, the Ethiopian capital, to Washington DC in the US.\\nInjera is also flown to Sweden three times a week, Norway twice a week, and Germany three times a month.\\n\"Demand is increasing by about 10% every month,\" says Mr Tessema, 60, who does not see the ban on exporting teff seeds as a problem.\\n\"It's better to export a value-added product as that creates more jobs.\"\\nMama Fresh employs more than 100 people, and plans to take on another 50 this year. It also works with 300 farmers supplying teff.\\nMr Tessema started the business in 2003 with 100,000 Ethiopian birr ($5,000; Â£3,225), operating out of a rickety shack.\\nThe firm's annual revenue now stands at around 17m birr ($836,000; Â£566,000), and last year the business moved into a new factory.\\nInside the facility, blue barrels contain teff flour mixed with water, which is left to ferment for four days.\\nAfterwards, women pour small jug-sized amounts onto heated-clay cookers to sizzle and become injera, ready for packaging and speedy onward flights to eager overseas customers, mainly diaspora Ethiopians.\\nA tiny grain the size of a poppy seed, teff is ground into a flour which can also be made into loaves of European-style risen bread or pasta.\\nAt London-based business, Tobia Teff, they use US-grown teff to make various breads and a porridge.\\nThe company was founded by British-Ethiopian co-owner Sophie Sirak-Kebede, who originally opened an Ethiopian restaurant in the UK capital in 2003.\\nIn 2008, she closed the restaurant to concentrate solely on selling teff.\\n\"People are dreaming of teff nowadays, after thousands of years it has become the trendy thing over here,\" says the 58-year-old.\\nTobia Teff's sales have increased by up to 40% during the last 14 months.\\nEven the UK's National Health Service has become a customer to cater for gluten-intolerant patients.\\nYet despite praise for teff's nutritional properties, its previously sheltered existence in Ethiopia comes with a drawback.\\n\"Teff does not give much yield,\" says Zerihun Tadele, an Ethiopian researcher at the Institute of Plant Sciences at the University of Bern, Switzerland. \"Very little research and investment has been done on the crop.\"\\nThe average yield per hectare of teff in Ethiopia is 1.4 tonnes, which is less than half as much as the global average of 3.2 tonnes for modern varieties of wheat.\\nMr Tadele hopes that through research and improved farming methods teff yields in Ethiopia can be raised to 5 tonnes a hectare.\\nThis improvement will not come soon enough because recent teff harvests have failed to kept pace with Ethiopia's increasing population, driving prices beyond many Ethiopians' pockets, especially outside Addis Ababa.\\nMs Sirak-Kebede says this situation creates a dilemma because \"teff is Ethiopia's backbone\".  She adds: \"A shortage of teff would be like asking an Ethiopian not to breathe\".\\nBut at the same time, Ms Sirak-Kebede notes that the Ethiopian government should not squander a global opportunity that could benefit the more than six million farmers in the country that grow the seed, while also generating valuable foreign currencies.\\nThe government's Agricultural Transformation Agency is now focused on increasing teff production to at least match domestic demand, after which exporting seeds and flour may become possible.\\n\"The opportunity this presents to the country is significant and the benefit over the long term will far outweigh the risks,\" says Matthew Davis, a partner at US-based Renew Strategies, an early stage venture capital company investing in Mama Fresh.\\n\"The government would likely proceed cautiously, only giving licences to select exporting companies.\"\\nNo doubt the Ethiopian government has already looked nervously at the example of quinoa, which has become so popular on the global stage that many people in its native countries, such as Peru and Bolivia, can no longer afford to buy it.\\nIf Ethiopia's teff export ban is ever lifted, Ms Sirak-Kebede says she wants to buy land in Ethiopia to farm the crop for her UK business.\\n\"Being of Ethiopian origin I would prefer to get teff from Ethiopia,\" Ms Sirak-Kebede says.\\n\"Who better than an Ethiopian farmer when it comes to teff? The quality is incomparable.\"</td>\n",
              "      <td>Under a bright blue sky, a farmer in a sleeveless red jumper is encouraging his five oxen to stamp on piles of dried grass, to help dislodge the seeds.</td>\n",
              "      <td>32128441</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "show_random_elements(raw_datasets[\"train\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnjDIuQ3IrI-"
      },
      "source": [
        "The metric is an instance of [`datasets.Metric`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Metric):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5o4rUteaIrI_",
        "outputId": "2ccb7c70-850b-412b-e2ba-17061ad53a94",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metric(name: \"rouge\", features: {'predictions': Value(dtype='string', id='sequence'), 'references': Value(dtype='string', id='sequence')}, usage: \"\"\"\n",
              "Calculates average rouge scores for a list of hypotheses and references\n",
              "Args:\n",
              "    predictions: list of predictions to score. Each prediction\n",
              "        should be a string with tokens separated by spaces.\n",
              "    references: list of reference for each prediction. Each\n",
              "        reference should be a string with tokens separated by spaces.\n",
              "    rouge_types: A list of rouge types to calculate.\n",
              "        Valid names:\n",
              "        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n",
              "        `\"rougeL\"`: Longest common subsequence based scoring.\n",
              "        `\"rougeLSum\"`: rougeLsum splits text using `\"\n",
              "\"`.\n",
              "        See details in https://github.com/huggingface/datasets/issues/617\n",
              "    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n",
              "    use_aggregator: Return aggregates if this is set to True\n",
              "Returns:\n",
              "    rouge1: rouge_1 (precision, recall, f1),\n",
              "    rouge2: rouge_2 (precision, recall, f1),\n",
              "    rougeL: rouge_l (precision, recall, f1),\n",
              "    rougeLsum: rouge_lsum (precision, recall, f1)\n",
              "Examples:\n",
              "\n",
              "    >>> rouge = datasets.load_metric('rouge')\n",
              "    >>> predictions = [\"hello there\", \"general kenobi\"]\n",
              "    >>> references = [\"hello there\", \"general kenobi\"]\n",
              "    >>> results = rouge.compute(predictions=predictions, references=references)\n",
              "    >>> print(list(results.keys()))\n",
              "    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n",
              "    >>> print(results[\"rouge1\"])\n",
              "    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n",
              "    >>> print(results[\"rouge1\"].mid.fmeasure)\n",
              "    1.0\n",
              "\"\"\", stored examples: 0)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "metric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAWdqcUBIrJC"
      },
      "source": [
        "You can call its `compute` method with your predictions and labels, which need to be list of decoded strings:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6XN1Rq0aIrJC",
        "outputId": "04ce57da-6893-49e8-98ec-e373f2195bd6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0)),\n",
              " 'rouge2': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0)),\n",
              " 'rougeL': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0)),\n",
              " 'rougeLsum': AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "fake_preds = [\"hello there\", \"general kenobi\"]\n",
        "fake_labels = [\"hello there\", \"general kenobi\"]\n",
        "metric.compute(predictions=fake_preds, references=fake_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9qywopnIrJH"
      },
      "source": [
        "## Preprocessing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVx71GdAIrJH"
      },
      "source": [
        "Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers `Tokenizer` which will (as the name indicates) tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary) and put it in a format the model expects, as well as generate the other inputs that the model requires.\n",
        "\n",
        "To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure:\n",
        "\n",
        "- we get a tokenizer that corresponds to the model architecture we want to use,\n",
        "- we download the vocabulary used when pretraining this specific checkpoint.\n",
        "\n",
        "That vocabulary will be cached, so it's not downloaded again the next time we run the cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXNLu_-nIrJI",
        "outputId": "48265185-78e2-4927-be06-a0b704f74d87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293,
          "referenced_widgets": [
            "e743d453ed2d4aaba4527ca725c8439a",
            "1f635169cd754598aeb95b383544f282",
            "be0b689e094e47cca4b26c35f2363fb2",
            "0822755bb88f4ebfaded1c11dfbd3dca",
            "eddd88e7e6f7452693605796ed7d12d4",
            "22e50897b27b41ecaa6379f19b63afe1",
            "532bb77e2e95442cb7dbf4bfe34aea89",
            "6c7e8316c4bb42f8866c26c04dbe55aa",
            "6934fd8bba884eb18bd1ba7d64037ba2",
            "0e6d56f15c2049d4b2e161c77470f1ac",
            "9de3704caf974363a5eb816e7f6dd566",
            "eb764182825f405e8fb464e507eb4f11",
            "feb6b006c076432eb2ee92e1efa776a1",
            "f974e3cb4476453b93e8805172647eb8",
            "9be2a8061c7f4c49beaec73aab75a495",
            "06d34fa57ac5413487d1f64dfb7362b9",
            "a425b94784724c7691974c6dd1f41498",
            "c2ae044e46b34b08ab49050963ccc5b1",
            "84b2f7f17eb444e2853c847b1f8932d3",
            "4bdc046af0444608972d35b948f927c9",
            "ce24be46ae324195b65217ae10481487",
            "002faebcc8524b40967de64fa513ec62",
            "2ba9ca5578fd4bc1b267bee3ec6c36a9",
            "2b50f7633f1945159a25ebe9a4cef71e",
            "26619f97901543d49870bc1584807a37",
            "869520e0330742a0973be2abd8aa3aef",
            "d496769c170741b9ac4248789eb364fb",
            "a003e0e7102146a791fcf154b934c831",
            "41cf477585a442f5a6af32278a611360",
            "64e846df6128454693806c8da1c9096e",
            "2ce49b7f276f47e5ae10a7c89c1e5cef",
            "cde01e66966140a6860146c55923b848",
            "dc0a1a794fbc468eab397345f217af05"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e743d453ed2d4aaba4527ca725c8439a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb764182825f405e8fb464e507eb4f11"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ba9ca5578fd4bc1b267bee3ec6c36a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vl6IidfdIrJK"
      },
      "source": [
        "By default, the call above will use one of the fast tokenizers (backed by Rust) from the 🤗 Tokenizers library."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdgUiapktGnA"
      },
      "source": [
        "To prepare the targets for our model, we need to tokenize them inside the `as_target_tokenizer` context manager. This will make sure the tokenizer uses the special tokens corresponding to the targets:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qywpmzj6tGnB",
        "outputId": "478b20b9-8335-47f8-e42a-c5b9829c415a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'input_ids': [[8774, 6, 48, 80, 7142, 55, 1], [100, 19, 430, 7142, 5, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]}\n"
          ]
        }
      ],
      "source": [
        "with tokenizer.as_target_tokenizer():\n",
        "    print(tokenizer([\"Hello, this one sentence!\", \"This is another sentence.\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2C0hcmp9IrJQ"
      },
      "source": [
        "If you are using one of the five T5 checkpoints we have to prefix the inputs with \"summarize:\" (the model can also translate and it needs the prefix to know which task it has to perform)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-lNweDUPtGnC"
      },
      "outputs": [],
      "source": [
        "if model_checkpoint in [\"t5-small\", \"t5-base\", \"t5-larg\", \"t5-3b\", \"t5-11b\"]:\n",
        "    prefix = \"summarize: \"\n",
        "else:\n",
        "    prefix = \"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KU-PtinutGnD"
      },
      "source": [
        "We can then write the function that will preprocess our samples. We just feed them to the `tokenizer` with the argument `truncation=True`. This will ensure that an input longer that what the model selected can handle will be truncated to the maximum length accepted by the model. The padding will be dealt with later on (in a data collator) so we pad examples to the longest length in the batch and not the whole dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vc0BSBLIIrJQ"
      },
      "outputs": [],
      "source": [
        "max_input_length = 1024\n",
        "max_target_length = 128\n",
        "\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(examples[\"summary\"], max_length=max_target_length, truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-b70jh26IrJS",
        "outputId": "6d065f80-d2f1-4b1a-f15d-ba503fcd1bd9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:3586: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[21603, 10, 37, 423, 583, 13, 1783, 16, 20126, 16496, 6, 80, 13, 8, 844, 6025, 4161, 6, 19, 341, 271, 14841, 5, 7057, 161, 19, 4912, 16, 1626, 5981, 11, 186, 7540, 16, 1276, 15, 2296, 7, 5718, 2367, 14621, 4161, 57, 4125, 387, 5, 15059, 7, 30, 8, 4653, 4939, 711, 747, 522, 17879, 788, 12, 1783, 44, 8, 15763, 6029, 1813, 9, 7472, 5, 1404, 1623, 11, 5699, 277, 130, 4161, 57, 18368, 16, 20126, 16496, 227, 8, 2473, 5895, 15, 147, 89, 22411, 139, 8, 1511, 5, 1485, 3271, 3, 21926, 9, 472, 19623, 5251, 8, 616, 12, 15614, 8, 1783, 5, 37, 13818, 10564, 15, 26, 3, 9, 3, 19513, 1481, 6, 18368, 186, 1328, 2605, 30, 7488, 1887, 3, 18, 8, 711, 2309, 9517, 89, 355, 5, 3966, 1954, 9233, 15, 6, 113, 293, 7, 8, 16548, 13363, 106, 14022, 84, 47, 14621, 4161, 6, 243, 255, 228, 59, 7828, 8, 1249, 18, 545, 11298, 1773, 728, 8, 8347, 1560, 5, 611, 6, 255, 243, 72, 1709, 1528, 161, 228, 43, 118, 4006, 91, 12, 766, 8, 3, 19513, 1481, 410, 59, 5124, 5, 96, 196, 17, 19, 1256, 68, 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14441, 5, 94, 47, 70, 166, 706, 16, 5961, 5316, 5, 37, 2535, 13, 80, 13, 8, 14264, 243, 186, 13, 8, 9234, 141, 646, 525, 12770, 7, 30, 1476, 11, 175, 141, 118, 10932, 5, 2867, 1637, 43, 13666, 3709, 11210, 11, 56, 1731, 70, 1552, 13, 8, 3457, 4939, 865, 145, 79, 141, 4355, 5, 5076, 43, 3958, 15, 26, 21, 251, 81, 8, 3211, 5, 86, 7, 102, 1955, 24723, 243, 10, 96, 196, 17, 3475, 38, 713, 8, 1472, 708, 365, 80, 13, 8, 14264, 274, 16436, 12, 8, 511, 5, 96, 27674, 8, 2883, 1137, 19, 341, 365, 4962, 6, 34, 19, 816, 24, 8, 1472, 47, 708, 24067, 535, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1584, 739, 3211, 16, 27874, 690, 2050, 5, 1]]}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "preprocess_function(raw_datasets['train'][:2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zS-6iXTkIrJT"
      },
      "source": [
        "To apply this function on all the pairs of sentences in our dataset, we just use the `map` method of our `dataset` object we created earlier. This will apply the function on all the elements of all the splits in `dataset`, so our training, validation and testing data will be preprocessed in one single command."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DDtsaJeVIrJT",
        "outputId": "08851df5-f832-4e84-f948-80545ef554bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "3d8d54a0015245f09680fa4a78905599",
            "d32427b2667f449599018525b8ef9e3a",
            "9c9d44fad3d24b1896e35ecbb0b700b7",
            "8d79017c0a354c6d92fb8459b8a18a1b",
            "d50689ccf4e44c3ba52026c24346d47f",
            "98f87f584c744aad9b6321461461a1bb",
            "10e18cf145c64079855eeb8f5d315128",
            "b12c3260d8c644a299905a5bfd8ff250",
            "ad8eb7efee054694a3d667b698ceb48e",
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            "ea4ece9313e844b7a4360021cfdf5980",
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            "32b9af976b94460788ecb9d6024da11e",
            "74773cbf32864838a02b13267a06777b",
            "6f35de7304394987bcfdfeb6d7a2fde0",
            "493f2b01185d4dadbc2814090cbb4ddc",
            "109adab721234738b49f0bce59b9d233",
            "08e07ea6b310451b96f94059f1b5d192",
            "f55e252de8ee4e089b8c8c068a1fc1df",
            "1d6046ed3bd04a7abe9972d115fd77f9",
            "f9651752291f44209d69a402c4712b96",
            "a58cf1849cd749159b4fc4141dc8ec02",
            "78cc2efa5b36437e855786d13c7c8c85",
            "945012f7820f43c4970de96db69b2226",
            "13d5880e787a4eebb2c0e5fd8bb4867c",
            "5f9bed49d89b473982752cb09fd114bd",
            "2e0c978b03aa4a4d991c7b5b6cc8c4d6",
            "4b7d8cb927794faab61429c91d084ec6"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/204045 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "99a5a4f7df3e48f7bcf282c3dba03782"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/11332 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10e18cf145c64079855eeb8f5d315128"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/11334 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08e07ea6b310451b96f94059f1b5d192"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "voWiw8C7IrJV"
      },
      "source": [
        "Even better, the results are automatically cached by the 🤗 Datasets library to avoid spending time on this step the next time you run your notebook. The 🤗 Datasets library is normally smart enough to detect when the function you pass to map has changed (and thus requires to not use the cache data). For instance, it will properly detect if you change the task in the first cell and rerun the notebook. 🤗 Datasets warns you when it uses cached files, you can pass `load_from_cache_file=False` in the call to `map` to not use the cached files and force the preprocessing to be applied again.\n",
        "\n",
        "Note that we passed `batched=True` to encode the texts by batches together. This is to leverage the full benefit of the fast tokenizer we loaded earlier, which will use multi-threading to treat the texts in a batch concurrently."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "545PP3o8IrJV"
      },
      "source": [
        "## Fine-tuning the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBiW8UpKIrJW"
      },
      "source": [
        "Now that our data is ready, we can download the pretrained model and fine-tune it. Since our task is of the sequence-to-sequence kind, we use the `AutoModelForSeq2SeqLM` class. Like with the tokenizer, the `from_pretrained` method will download and cache the model for us."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TlqNaB8jIrJW"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_N8urzhyIrJY"
      },
      "source": [
        "To instantiate a `Seq2SeqTrainer`, we will need to define three more things. The most important is the [`Seq2SeqTrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.Seq2SeqTrainingArguments), which is a class that contains all the attributes to customize the training. It requires one folder name, which will be used to save the checkpoints of the model, and all other arguments are optional:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_8gwrH5PtGnO"
      },
      "outputs": [],
      "source": [
        "batch_size = 16\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    f\"{model_name}-finetuned-xsum\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=1,\n",
        "    predict_with_generate=True,\n",
        "    fp16=True,\n",
        "    push_to_hub=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "km3pGVdTIrJc"
      },
      "source": [
        "Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, use the `batch_size` defined at the top of the cell and customize the weight decay. Since the `Seq2SeqTrainer` will save the model regularly and our dataset is quite large, we tell it to make three saves maximum. Lastly, we use the `predict_with_generate` option (to properly generate summaries) and activate mixed precision training (to go a bit faster).\n",
        "\n",
        "The last argument to setup everything so we can push the model to the [Hub](https://huggingface.co/models) regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally in a name that is different than the name of the repository it will be pushed, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"sgugger/t5-finetuned-xsum\"` or `\"huggingface/t5-finetuned-xsum\"`).\n",
        "\n",
        "Then, we need a special kind of data collator, which will not only pad the inputs to the maximum length in the batch, but also the labels:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DGCF3UpltGnQ"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7sZOdRlRIrJd"
      },
      "source": [
        "The last thing to define for our `Seq2SeqTrainer` is how to compute the metrics from the predictions. We need to define a function for this, which will just use the `metric` we loaded earlier, and we have to do a bit of pre-processing to decode the predictions into texts:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UmvbnJ9JIrJd"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "    \n",
        "    # Rouge expects a newline after each sentence\n",
        "    decoded_preds = [\"\\n\".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
        "    decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]\n",
        "    \n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "    # Extract a few results\n",
        "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
        "    \n",
        "    # Add mean generated length\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    \n",
        "    return {k: round(v, 4) for k, v in result.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXuFTAzDIrJe"
      },
      "source": [
        "Then we just need to pass all of this along with our datasets to the `Seq2SeqTrainer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "imY1oC3SIrJf"
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      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdzABDVcIrJg"
      },
      "source": [
        "We can now finetune our model by just calling the `train` method:"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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        "id": "uNx5pyRlIrJh",
        "outputId": "077e661e-d36c-469b-89b8-7ff7f73541ec",
        "scrolled": false
      },
      "outputs": [
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              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "        </style>\n",
              "      \n",
              "      <progress value='12753' max='12753' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [12753/12753 1:21:48, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "      <th>Gen Len</th>\n",
              "      <th>Runtime</th>\n",
              "      <th>Samples Per Second</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.721100</td>\n",
              "      <td>2.479327</td>\n",
              "      <td>28.300900</td>\n",
              "      <td>7.721100</td>\n",
              "      <td>22.243000</td>\n",
              "      <td>22.249600</td>\n",
              "      <td>18.822500</td>\n",
              "      <td>326.333800</td>\n",
              "      <td>34.725000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "data": {
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              "TrainOutput(global_step=12753, training_loss=2.7692033505520146, metrics={'train_runtime': 4909.3835, 'train_samples_per_second': 2.598, 'total_flos': 7.774481450954342e+16, 'epoch': 1.0, 'init_mem_cpu_alloc_delta': 335248, 'init_mem_gpu_alloc_delta': 242026496, 'init_mem_cpu_peaked_delta': 18306, 'init_mem_gpu_peaked_delta': 0, 'train_mem_cpu_alloc_delta': 2637782, 'train_mem_gpu_alloc_delta': 728138240, 'train_mem_cpu_peaked_delta': 138226182, 'train_mem_gpu_peaked_delta': 14677017088})"
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      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTpbkEnItGnU"
      },
      "source": [
        "You can now upload the result of the training to the Hub, just execute this instruction:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FLDymnnCtGnV"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNBZjvc2tGnW"
      },
      "source": [
        "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n",
        "\n",
        "```python\n",
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"sgugger/my-awesome-model\")\n",
        "```"
      ]
    },
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