{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Fine_tune_SwitchTransformer_summarization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-Tuning a Switch Transformer for Conversation Summarization using the Hugging Face 🤗 Trainer\n",
        "\n",
        "> Colab by: [Manu Romero / mrm8488](https://twitter.com/mrm8488)\n",
        "\n",
        "> This is WIP and Feedback is welcome!"
      ],
      "metadata": {
        "id": "ay0fVJ2X_eFO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The model 🧠\n",
        "![Switch Transformer Architecture](https://s3.amazonaws.com/moonup/production/uploads/1666966931908-62441d1d9fdefb55a0b7d12c.png)\n",
        "\n",
        "\n",
        "Switch Transformers is a Mixture of Experts (MoE) model trained on Masked Language Modeling (MLM) task. The model architecture is similar to the classic T5, but with the Feed Forward layers replaced by the Sparse MLP layers containing \"experts\" MLP.\n",
        "\n",
        "According to the [original paper](https://arxiv.org/pdf/2101.03961.pdf) the model enables **faster training (scaling properties) while being better than T5 on fine-tuned tasks**."
      ],
      "metadata": {
        "id": "hGpf3hyT_5Qw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The dataset 📚\n",
        "\n",
        "The **SAMSum** dataset is a collection of around 16,000 conversations in a messenger-like format, along with summaries of those conversations. These conversations were created by linguists who are fluent in English, and were designed to be similar to the types of conversations that the linguists have in their daily lives, including a variety of different topics. The conversations are diverse in terms of style and register, and can range from informal to formal, and may contain slang, emoticons, and typos. The conversations have been annotated with summaries, which are intended to be a brief summary of what was discussed in the conversation, presented in the third person. The SAMSum dataset was created by Samsung R&D Institute Poland and is intended for research use under a non-commercial license (CC BY-NC-ND 4.0)."
      ],
      "metadata": {
        "id": "hN76opKtAydX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The fine-tuning script ⚙"
      ],
      "metadata": {
        "id": "JQtr_1dBCEgw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the GPU"
      ],
      "metadata": {
        "id": "VbyMwfCwCUJg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a71dGjf6ZlsW",
        "outputId": "aa10c9bc-3011-43fe-b3af-3255d41e0bda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Jan 17 18:41:52 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install the required libraries"
      ],
      "metadata": {
        "id": "jG7SBOzNCZm7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrIguCPWY0Ty",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ac8742e-2895-484c-a324-5e4dc787e30e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m72.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.4/182.4 KB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m379.0/379.0 KB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "! pip install -q transformers datasets accelerate sentencepiece evaluate py7zr rouge_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Log in to the HuggingFace hub to automatically push our model once the training has finished"
      ],
      "metadata": {
        "id": "z-Qv_ISyChp8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d9ZyGfA5gf7J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2242e376-bf56-45e6-f9f8-716aff300d55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "    \n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid.\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "! huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import the required libraries"
      ],
      "metadata": {
        "id": "XVJiGmuiDDm8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wqjhax34ZnhC"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "import nltk\n",
        "import numpy as np\n",
        "from nltk.tokenize import sent_tokenize\n",
        "from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration, DataCollatorForSeq2Seq, Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "from datasets import load_dataset, concatenate_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nuA7x3Yweq6G",
        "outputId": "55340382-f4d0-4b08-d502-6cffa171d77c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "nltk.download(\"punkt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set the model and dataset ids (names in the HF hub)"
      ],
      "metadata": {
        "id": "nVTx9lruDLEn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ZPtgSfHikLU"
      },
      "outputs": [],
      "source": [
        "model_id=\"google/switch-base-8\"\n",
        "dataset_id=\"samsum\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's load and visualize the dataset"
      ],
      "metadata": {
        "id": "er8IRA3aDVjL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
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            "ceb3460290b04624bf0012f8aaeae5a8",
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            "794ff9d032f443da9c6b7807577fb8e5",
            "01ee61f52dc74a8fbd11da10f417f0da",
            "b1541eedae20433d8fa1b79585bf08a5",
            "3662bd48e0564b45bc1ac4a5789b8c6e",
            "34c9debc20164a89a0bde53574e2fca5",
            "459eee32ae4047f48ca0b6efccff8fd2",
            "9ba9aef989524f04a2646857a8dc4c65"
          ]
        },
        "id": "dy-6LjEDaDfV",
        "outputId": "ccf9ca34-ec0a-46bd-d593-0291fdfe2e00"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.36k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0756bca5f6514134be4240db8ed3ca7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbe2dba626354eb1adeeb1a15ade7b6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.04k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce36f87d87dd49788950a07982e28654"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset samsum/samsum to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f39dd87ce4a642dd9783febd1e199943"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/14732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4936424df2a4b3facb73ff1c0e7f8dd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1385c12d4734403d8dd30ea787694db4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cbac6071fbe5447eb90ca7a2a7815c37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset samsum downloaded and prepared to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8bfe66aec6748a8917a178821214bf3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = load_dataset(dataset_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTM-9Kv8ck_G",
        "outputId": "179b1d70-a2ea-4ddd-d328-53af067ce76a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 14732\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 819\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 818\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPl_MVLOcn-j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65bc0b53-3150-4686-c16c-152bb2c359ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '13818513',\n",
              " 'dialogue': \"Amanda: I baked  cookies. Do you want some?\\r\\nJerry: Sure!\\r\\nAmanda: I'll bring you tomorrow :-)\",\n",
              " 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dataset['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def show_sample(idx):\n",
        "  for key in dataset['train'][idx].keys():\n",
        "    print(f\"{key}:\")\n",
        "    print(dataset['train'][idx][key])\n",
        "    print()"
      ],
      "metadata": {
        "id": "XCHav-mMEJsX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_sample(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_RqA63HjDraW",
        "outputId": "4c761987-6da2-4d9f-a9d8-a72ddd7871bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id:\n",
            "13818513\n",
            "\n",
            "dialogue:\n",
            "Amanda: I baked  cookies. Do you want some?\r\n",
            "Jerry: Sure!\r\n",
            "Amanda: I'll bring you tomorrow :-)\n",
            "\n",
            "summary:\n",
            "Amanda baked cookies and will bring Jerry some tomorrow.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PzeOtyU8EsQx",
        "outputId": "311a9751-3a5a-47e2-b42f-f06916d19fee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id:\n",
            "13727633\n",
            "\n",
            "dialogue:\n",
            "Lucas: Hey! How was your day?\r\n",
            "Demi: Hey there! \r\n",
            "Demi: It was pretty fine, actually, thank you!\r\n",
            "Demi: I just got promoted! :D\r\n",
            "Lucas: Whoa! Great news!\r\n",
            "Lucas: Congratulations!\r\n",
            "Lucas: Such a success has to be celebrated.\r\n",
            "Demi: I agree! :D\r\n",
            "Demi: Tonight at Death & Co.?\r\n",
            "Lucas: Sure!\r\n",
            "Lucas: See you there at 10pm?\r\n",
            "Demi: Yeah! See you there! :D\n",
            "\n",
            "summary:\n",
            "Demi got promoted. She will celebrate that with Lucas at Death & Co at 10 pm.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_sample(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTzHUBB-Eu9D",
        "outputId": "3416f5a3-3bd0-4d63-fbd7-2c4572a4083e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id:\n",
            "13682381\n",
            "\n",
            "dialogue:\n",
            "Gabby: How is you? Settling into the new house OK?\r\n",
            "Sandra: Good. The kids and the rest of the menagerie are doing fine. The dogs absolutely love the new garden. Plenty of room to dig and run around.\r\n",
            "Gabby: What about the hubby?\r\n",
            "Sandra: Well, apart from being his usual grumpy self I guess he's doing OK.\r\n",
            "Gabby: :-D yeah sounds about right for Jim.\r\n",
            "Sandra: He's a man of few words. No surprises there. Give him a backyard shed and that's the last you'll see of him for months.\r\n",
            "Gabby: LOL that describes most men I know.\r\n",
            "Sandra: Ain't that the truth! \r\n",
            "Gabby: Sure is. :-) My one might as well move into the garage. Always tinkering and building something in there.\r\n",
            "Sandra: Ever wondered what he's doing in there?\r\n",
            "Gabby: All the time. But he keeps the place locked.\r\n",
            "Sandra: Prolly building a portable teleporter or something. ;-)\r\n",
            "Gabby: Or a time machine... LOL\r\n",
            "Sandra: Or a new greatly improved Rabbit :-P\r\n",
            "Gabby: I wish... Lmfao!\n",
            "\n",
            "summary:\n",
            "Sandra is setting into the new house; her family is happy with it. Then Sandra and Gabby discuss the nature of their men and laugh about their habit of spending time in the garage or a shed.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As mentioned in the model section, the architecture is similar to T5 one, so our model is an encoder-decoder model. Thus, we are going to load the tokenizer and calculate the max number of tokens for the encoder (we will input it the `dialogue`) and the max number of tokens for the decoder (we expect it to produce the `summary`) to efficiently batch our data."
      ],
      "metadata": {
        "id": "i3yi29bQE7Zm"
      }
    },
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      "cell_type": "code",
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        "id": "_g-uuCVpcxTX",
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        "outputId": "c255b7d2-c19e-445b-81a3-c6320fb5d305"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.35k [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
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              "model_id": "401f01e7a0774a6394d01387f8f51e3b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
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        },
        "id": "StGWzpzKc9XU",
        "outputId": "fdc732aa-8f38-4c16-fc39-e4a9d7517a2d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?ba/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max source length: 512\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?ba/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max target length: 95\n"
          ]
        }
      ],
      "source": [
        "# This way of doing comes from https://www.philschmid.de/fine-tune-flan-t5, so all credits to him\n",
        "\n",
        "# The maximum total input sequence length after tokenization. \n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"dialogue\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n",
        "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization. \n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"summary\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n",
        "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's tokenize the dataset!"
      ],
      "metadata": {
        "id": "0E0v4tbeGtwB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LvxNsb-KdMFo"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "  \n",
        "    inputs = [\"summarize: \" + item for item in sample[\"dialogue\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "    labels = tokenizer(text_target=sample[\"summary\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        },
        "id": "58xZ_dPWdPCB",
        "outputId": "c61b379c-c5b3-4173-db03-3ee903eb68dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3f40f4a08df146269ec8c6188eb02ede"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5fa03b6dc98c469a8617eca6381296f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "d24526644b8648ada118ed5a06f3d0ad"
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          "metadata": {}
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      ],
      "source": [
        "tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=[\"dialogue\", \"summary\", \"id\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the model"
      ],
      "metadata": {
        "id": "UVDNODeVGzFF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iYzbPlMMdXV_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "outputId": "2f1d9de7-ce02-494f-dbd1-9405cef3e7b4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.86k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1dc54f381b9d4edc90aa0e9b43bce653"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.24G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "175b217aa98b45b4899c7ce213a38429"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = SwitchTransformersForConditionalGeneration.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use `rouge` as the metric to evaluate the quality of the summaries the model generates during the fine-tuning process"
      ],
      "metadata": {
        "id": "7aT5EIWcG3gM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WwRjTC4LdmKZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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          ]
        },
        "outputId": "e6a9fceb-fcc0-4b94-bb83-77bde974828f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bdc572316c9a41ed80d8bc2418b77783"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "metric = evaluate.load(\"rouge\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create our function for the model evaluation"
      ],
      "metadata": {
        "id": "nDzj7iWlHgZC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HvWTcIDIdyxC"
      },
      "outputs": [],
      "source": [
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [label.strip() for label in labels]\n",
        "\n",
        "    # rougeLSum expects newline after each sentence\n",
        "    preds = [\"\\n\".join(sent_tokenize(pred)) for pred in preds]\n",
        "    labels = [\"\\n\".join(sent_tokenize(label)) for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, 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",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "    result = {k: round(v * 100, 4) for k, v in result.items()}\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set the data collator"
      ],
      "metadata": {
        "id": "Bnsf3dDVHyg9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UA5Tt_mNeHMj"
      },
      "outputs": [],
      "source": [
        "label_pad_token_id = -100\n",
        "# Data collator\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    tokenizer,\n",
        "    model=model,\n",
        "    label_pad_token_id=label_pad_token_id,\n",
        "    pad_to_multiple_of=4\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set the model name"
      ],
      "metadata": {
        "id": "Cccg7FG6H8Ng"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FjVprbknfuP6"
      },
      "outputs": [],
      "source": [
        "model_name = model_id.split(\"/\")[-1] + \"-finetuned-\" + dataset_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "F3qGxXl4i87h",
        "outputId": "a1ee5c05-22a3-4568-dbb2-8b824943b3e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'switch-base-8-finetuned-samsum'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "model_name"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the training arguments"
      ],
      "metadata": {
        "id": "pghwLJY_H_40"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tnBLtAx8ffnW"
      },
      "outputs": [],
      "source": [
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=model_name,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    predict_with_generate=True,\n",
        "    fp16=False,\n",
        "    learning_rate=5e-5,\n",
        "    num_train_epochs=5,\n",
        "    logging_strategy=\"steps\",\n",
        "    logging_steps=500,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    save_total_limit=2,\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=True,\n",
        "\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a `Trainer` object"
      ],
      "metadata": {
        "id": "wopY566LIEpw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZOqzZHSgJ8M",
        "outputId": "76d457d7-8e81-4da9-df5d-9dde062520fa"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/mrm8488/switch-base-8-finetuned-samsum into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/mrm8488/switch-base-8-finetuned-samsum into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    tokenizer=tokenizer,\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"validation\"],\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For tracking the fine-tuning process we are going to use `tensorboard`"
      ],
      "metadata": {
        "id": "t1veautkIKKv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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              "ok": true,
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            "https://localhost:6006/data/plugin/scalars/scalars?run=Jan11_15-56-31_79805fb8b386&tag=eval%2FrougeLsum": {
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            "https://localhost:6006/data/plugin/scalars/scalars?run=Jan11_15-56-31_79805fb8b386&tag=eval%2Fruntime": {
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            "https://localhost:6006/data/plugin/scalars/scalars?run=Jan11_15-56-31_79805fb8b386&tag=eval%2Fsamples_per_second": {
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            "https://localhost:6006/data/plugin/scalars/scalars?run=Jan11_15-56-31_79805fb8b386&tag=eval%2Fsteps_per_second": {
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              "ok": true,
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            "https://localhost:6006/data/plugin/scalars/scalars?run=Jan11_15-56-31_79805fb8b386&tag=train%2Fepoch": {
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              ],
              "ok": true,
              "status": 200,
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            "https://localhost:6006/data/plugin/scalars/scalars?run=Jan11_15-56-31_79805fb8b386&tag=train%2Flearning_rate": {
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              ],
              "ok": true,
              "status": 200,
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            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Jan11_15-56-31_79805fb8b386&tag=train%2Floss": {
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              ],
              "ok": true,
              "status": 200,
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            "https://localhost:6006/data/plugin/scalars/tags": {
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              "ok": true,
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        "outputId": "1d9fb743-fb74-435e-a021-72c957f375e9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir $model_name/runs"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's run a sanity check"
      ],
      "metadata": {
        "id": "kPQUcQdUITpz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "mAtJjjNcgPt3",
        "outputId": "8243972c-7b34-4f0b-ac9d-203d9fafddeb"
      },
      "outputs": [
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 50\n",
            "  Batch size = 4\n",
            "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
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              "      [13/13 00:17]\n",
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              "    "
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        {
          "data": {
            "text/plain": [
              "{'eval_loss': 24.1905517578125,\n",
              " 'eval_rouge1': 11.3863,\n",
              " 'eval_rouge2': 0.7705,\n",
              " 'eval_rougeL': 9.83,\n",
              " 'eval_rougeLsum': 10.6076,\n",
              " 'eval_gen_len': 18.62,\n",
              " 'eval_runtime': 22.5578,\n",
              " 'eval_samples_per_second': 2.217,\n",
              " 'eval_steps_per_second': 0.576}"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate(eval_dataset=tokenized_dataset[\"validation\"].select(range(50)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time to train the model!"
      ],
      "metadata": {
        "id": "0PKOPaWqIW4M"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "nDqj-Vh3gR9n",
        "outputId": "c4cdff63-eea2-4890-d174-9eea15d080f2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 14732\n",
            "  Num Epochs = 6\n",
            "  Instantaneous batch size per device = 4\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 4\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 22098\n",
            "  Number of trainable parameters = 619339008\n"
          ]
        },
        {
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              "      [22098/22098 5:41:13, Epoch 6/6]\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",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.887400</td>\n",
              "      <td>1.520985</td>\n",
              "      <td>45.765100</td>\n",
              "      <td>22.937900</td>\n",
              "      <td>38.855400</td>\n",
              "      <td>42.626900</td>\n",
              "      <td>17.248166</td>\n",
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              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.630100</td>\n",
              "      <td>1.462831</td>\n",
              "      <td>47.271900</td>\n",
              "      <td>24.897600</td>\n",
              "      <td>40.391300</td>\n",
              "      <td>43.928500</td>\n",
              "      <td>16.836186</td>\n",
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              "      <td>1.432600</td>\n",
              "      <td>1.440192</td>\n",
              "      <td>47.827500</td>\n",
              "      <td>25.226200</td>\n",
              "      <td>40.617000</td>\n",
              "      <td>44.294800</td>\n",
              "      <td>16.952323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.299200</td>\n",
              "      <td>1.448911</td>\n",
              "      <td>48.393000</td>\n",
              "      <td>25.388800</td>\n",
              "      <td>40.953400</td>\n",
              "      <td>44.797000</td>\n",
              "      <td>17.150367</td>\n",
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              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.225900</td>\n",
              "      <td>1.449474</td>\n",
              "      <td>49.218600</td>\n",
              "      <td>26.312000</td>\n",
              "      <td>41.721000</td>\n",
              "      <td>45.508700</td>\n",
              "      <td>17.195599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.147700</td>\n",
              "      <td>1.460994</td>\n",
              "      <td>49.001800</td>\n",
              "      <td>26.347400</td>\n",
              "      <td>41.521700</td>\n",
              "      <td>45.408100</td>\n",
              "      <td>17.078240</td>\n",
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        {
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          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 818\n",
            "  Batch size = 4\n"
          ]
        },
        {
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              "      [13/13 56:29]\n",
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              "    "
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        {
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          "text": [
            "Saving model checkpoint to switch-base-8-finetuned-samsum/checkpoint-3683\n",
            "Configuration saved in switch-base-8-finetuned-samsum/checkpoint-3683/config.json\n",
            "Model weights saved in switch-base-8-finetuned-samsum/checkpoint-3683/pytorch_model.bin\n",
            "tokenizer config file saved in switch-base-8-finetuned-samsum/checkpoint-3683/tokenizer_config.json\n",
            "Special tokens file saved in switch-base-8-finetuned-samsum/checkpoint-3683/special_tokens_map.json\n",
            "Copy vocab file to switch-base-8-finetuned-samsum/checkpoint-3683/spiece.model\n",
            "tokenizer config file saved in switch-base-8-finetuned-samsum/tokenizer_config.json\n",
            "Special tokens file saved in switch-base-8-finetuned-samsum/special_tokens_map.json\n",
            "Copy vocab file to switch-base-8-finetuned-samsum/spiece.model\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 818\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to switch-base-8-finetuned-samsum/checkpoint-7366\n",
            "Configuration saved in switch-base-8-finetuned-samsum/checkpoint-7366/config.json\n",
            "Model weights saved in switch-base-8-finetuned-samsum/checkpoint-7366/pytorch_model.bin\n",
            "tokenizer config file saved in switch-base-8-finetuned-samsum/checkpoint-7366/tokenizer_config.json\n",
            "Special tokens file saved in switch-base-8-finetuned-samsum/checkpoint-7366/special_tokens_map.json\n",
            "Copy vocab file to switch-base-8-finetuned-samsum/checkpoint-7366/spiece.model\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 818\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to switch-base-8-finetuned-samsum/checkpoint-11049\n",
            "Configuration saved in switch-base-8-finetuned-samsum/checkpoint-11049/config.json\n",
            "Model weights saved in switch-base-8-finetuned-samsum/checkpoint-11049/pytorch_model.bin\n",
            "tokenizer config file saved in switch-base-8-finetuned-samsum/checkpoint-11049/tokenizer_config.json\n",
            "Special tokens file saved in switch-base-8-finetuned-samsum/checkpoint-11049/special_tokens_map.json\n",
            "Copy vocab file to switch-base-8-finetuned-samsum/checkpoint-11049/spiece.model\n",
            "Deleting older checkpoint [switch-base-8-finetuned-samsum/checkpoint-3683] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 818\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to switch-base-8-finetuned-samsum/checkpoint-14732\n",
            "Configuration saved in switch-base-8-finetuned-samsum/checkpoint-14732/config.json\n",
            "Model weights saved in switch-base-8-finetuned-samsum/checkpoint-14732/pytorch_model.bin\n",
            "tokenizer config file saved in switch-base-8-finetuned-samsum/checkpoint-14732/tokenizer_config.json\n",
            "Special tokens file saved in switch-base-8-finetuned-samsum/checkpoint-14732/special_tokens_map.json\n",
            "Copy vocab file to switch-base-8-finetuned-samsum/checkpoint-14732/spiece.model\n",
            "Deleting older checkpoint [switch-base-8-finetuned-samsum/checkpoint-7366] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 818\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to switch-base-8-finetuned-samsum/checkpoint-18415\n",
            "Configuration saved in switch-base-8-finetuned-samsum/checkpoint-18415/config.json\n",
            "Model weights saved in switch-base-8-finetuned-samsum/checkpoint-18415/pytorch_model.bin\n",
            "tokenizer config file saved in switch-base-8-finetuned-samsum/checkpoint-18415/tokenizer_config.json\n",
            "Special tokens file saved in switch-base-8-finetuned-samsum/checkpoint-18415/special_tokens_map.json\n",
            "Copy vocab file to switch-base-8-finetuned-samsum/checkpoint-18415/spiece.model\n",
            "Deleting older checkpoint [switch-base-8-finetuned-samsum/checkpoint-14732] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 818\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to switch-base-8-finetuned-samsum/checkpoint-22098\n",
            "Configuration saved in switch-base-8-finetuned-samsum/checkpoint-22098/config.json\n",
            "Model weights saved in switch-base-8-finetuned-samsum/checkpoint-22098/pytorch_model.bin\n",
            "tokenizer config file saved in switch-base-8-finetuned-samsum/checkpoint-22098/tokenizer_config.json\n",
            "Special tokens file saved in switch-base-8-finetuned-samsum/checkpoint-22098/special_tokens_map.json\n",
            "Copy vocab file to switch-base-8-finetuned-samsum/checkpoint-22098/spiece.model\n",
            "Deleting older checkpoint [switch-base-8-finetuned-samsum/checkpoint-18415] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from switch-base-8-finetuned-samsum/checkpoint-11049 (score: 1.4401919841766357).\n"
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