notebooks/06n_Machine_Translation.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# บทที่ 6n - การเทรนโมเดลบน Hugging Face พาร์ท 2.1"
      ],
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        "id": "Hq-7c4brzfch"
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      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/ai-builders/curriculum/blob/main/notebooks/06n_Machine_Translation.ipynb)\n"
      ],
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            "  Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
            "\u001b[K     |████████████████████████████████| 271 kB 50.1 MB/s \n",
            "\u001b[?25hCollecting multidict<7.0,>=4.5\n",
            "  Downloading multidict-6.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (94 kB)\n",
            "\u001b[K     |████████████████████████████████| 94 kB 948 kB/s \n",
            "\u001b[?25hCollecting frozenlist>=1.1.1\n",
            "  Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
            "\u001b[K     |████████████████████████████████| 144 kB 10.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets==1.17.0) (3.8.0)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets==1.17.0) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers[sentencepiece]==4.15.0) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers[sentencepiece]==4.15.0) (1.1.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=f344aa20b6da780e01a68ce7b8b46d676068a048951e87d04562a7dd4fb3ba19\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: multidict, frozenlist, yarl, pyyaml, asynctest, async-timeout, aiosignal, tokenizers, sacremoses, huggingface-hub, fsspec, aiohttp, xxhash, transformers, sentencepiece, datasets\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-1.17.0 frozenlist-1.3.0 fsspec-2022.3.0 huggingface-hub-0.5.1 multidict-6.0.2 pyyaml-6.0 sacremoses-0.0.53 sentencepiece-0.1.96 tokenizers-0.10.3 transformers-4.15.0 xxhash-3.0.0 yarl-1.7.2\n",
            "Collecting accelerate==0.5.1\n",
            "  Downloading accelerate-0.5.1-py3-none-any.whl (58 kB)\n",
            "\u001b[K     |████████████████████████████████| 58 kB 2.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from accelerate==0.5.1) (1.11.0+cu113)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from accelerate==0.5.1) (1.21.6)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from accelerate==0.5.1) (6.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4.0->accelerate==0.5.1) (4.2.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.5.1\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (2.3.4-1).\n",
            "The following packages were automatically installed and are no longer required:\n",
            "  libnvidia-common-460 nsight-compute-2020.2.0\n",
            "Use 'apt autoremove' to remove them.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 42 not upgraded.\n",
            "Collecting sacrebleu==2.0.0\n",
            "  Downloading sacrebleu-2.0.0-py3-none-any.whl (90 kB)\n",
            "\u001b[K     |████████████████████████████████| 90 kB 3.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacrebleu==2.0.0) (2019.12.20)\n",
            "Collecting portalocker\n",
            "  Downloading portalocker-2.4.0-py2.py3-none-any.whl (16 kB)\n",
            "Collecting colorama\n",
            "  Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from sacrebleu==2.0.0) (1.21.6)\n",
            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu==2.0.0) (0.8.9)\n",
            "Installing collected packages: portalocker, colorama, sacrebleu\n",
            "Successfully installed colorama-0.4.4 portalocker-2.4.0 sacrebleu-2.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets==1.17.0 transformers[sentencepiece]==4.15.0\n",
        "!pip install accelerate==0.5.1\n",
        "# To run the training on TPU, you will need to uncomment the followin line:\n",
        "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
        "!apt install git-lfs\n",
        "!pip install sacrebleu==2.0.0"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global user.email \"hugging_face_email\"\n",
        "!git config --global user.name \"hugging_face_user_name\""
      ],
      "metadata": {
        "id": "VFVxxpLPsI-O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386,
          "referenced_widgets": [
            "0553018238124ed482d870780e4a13c4",
            "de2e25e85481445191e99412607e65ea",
            "a2de0ab702994fccb010b90de6184b1a",
            "431462170b9542c8b493641d8102866f",
            "564fc47364bd441a8bae9338ed4d9da7",
            "ee086f67be544e7b931ce9cce0fcd349",
            "b158cbdc7c244fec8dcdc0ea598244cf",
            "950a45fbd37c42ac8431dbf8d74c8972",
            "49f47ee9659644fd955b7c9b6e7014b3",
            "be8958f699f04eae9c0904676919bc5e",
            "6b1afa9c47a54fa281748bcdf0fd84b6",
            "86d7a8bb3b5f4c4aaeef99fd916cc234",
            "f11ba0b3882242ec9aa67ba6d1a76090",
            "5e9860cbd2754281a0f47dafc5ae0a0b",
            "17b4e25a9d314c1a88ff5cfc8d2abfe5",
            "15f59a29390644298c01776fb212203d",
            "dd95151b749a46dd95f0410dab493f49"
          ]
        },
        "id": "gw9Ms_Yqrstj",
        "outputId": "3781550c-fdf5-4805-b1e8-11e22d409f3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# โจทย์วันนี้: สร้างเครื่องแปลภาษาจาก EN-FR "
      ],
      "metadata": {
        "id": "jFNhmLoO-chg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\") # ดาต้าเซ็ต en-fr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
            "a7fa0a8bf86442059944376bba6fd9cb",
            "ae6615054c974e2cacf8bdff52dd5e2b",
            "e9e45f7921ca48feb3b121b884e7c539",
            "64db05c429aa4801be8dd1bf50edd0d9",
            "5079b886fdbf488ca875fa34f719ef1d",
            "20b939e78c6b43208a84b869bd59632d",
            "dbc56b2e64f24cf3af32a3a8e167ed68",
            "eafc032dc8ba45fd8ab398ac1f01253f",
            "392a9f6ef3404d1c97a706359a46874f",
            "a15b08e0825047fb92a48e39110be065",
            "aec90266bba141a0a2e67866eaa6a4cd",
            "29ffa3f44e73460b9e53d7ff3eca1293",
            "529f609c84234b549d1554aa067355da",
            "9ad68000351d4e428ef3692a78ebea35",
            "3b0fa5372fa645d7ac67ed7d5a27178c",
            "67e62ac7708f452abb2820aa0fcf6d03",
            "121756e7d6354e75b9890d7f5c53c4e3",
            "3714a214c9d942efa2c81a6c5f403b68",
            "8528fefdfe9048818837afb8025a3fa7",
            "f7df6417b6c64e3e83a0c0b2f62c8279",
            "aca375dddc18453888f8ff2ef8a7e169",
            "f9d3f2bdfbb64145bc44c984ac33981b",
            "2469c28959b7495784e4cd205518f6b0",
            "600881d41b034811aa8a99e83ca326a6",
            "24e65db79fe3429d98a196505162f76f",
            "45bacfddc22d47c7bcb49e1cdb4ec9ed",
            "098c011383824c2d97dc582e9af5bbd2",
            "ad70097aed9e43b48cfb1b8a4c47c711",
            "c8c509a1d01442d59fe07738c4ab48d2",
            "14ff6d3c7edf4b89a3ab138233f7749a",
            "ab3b471ce661446094b2dba41eaf5abc",
            "6ba13adf9b9a4f019a749ba0729e3ebd",
            "8cb0838670d3478eb611686cb35b7850",
            "ebff6bf2ece3454193f14ef854b16dec",
            "a057c03bfda443c6b99e2fd7f459aaa0",
            "98406125b22646d2a2e58fd4be6b4f53",
            "78df6c5d4adf4492aebc05d76e50f7d9",
            "d6cb44770f1d4a649537f7f85aa61af8",
            "ae3ef5e2e1474ea39c288fefbd220fae",
            "276cefeb5b064a2f8b0bf91e9c790b32",
            "e81b8c1e87f4457886504c4b05021926",
            "60a63dbaee64416289b29f055e6720cb",
            "d27c03eeff3f4c3a9e819adbfea7a7c7",
            "247e26e3c89c4ec58587c3277e511513",
            "d45ed8eb5cbd49c48d2583cbfa7276c2",
            "7ec5f73d20ac4e64aabd402a54b182cc",
            "62cd213a678f47b3a9ced07704e7fb57",
            "08f5d07948da43b69da0c4d939854715",
            "5991d10bcb39451d8c63f1e9bdf44866",
            "2c767681b01f483ba486e5b5fe8c72b0",
            "21d3a333c211429fb68751e1e7a68858",
            "cd1b4c6814c242f2a116a2e595d9f1c0",
            "0bfdd42b8913460b9fc877798485eb4a",
            "d43d6adc9d0946d4a16f73f141a43f5f",
            "3e4373cdf5fe4c21a0e8327aabf252eb"
          ]
        },
        "id": "iH9ynfRssCdp",
        "outputId": "c0571b69-97a6-4b48-b77d-af90859e4fb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.89k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7fa0a8bf86442059944376bba6fd9cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29ffa3f44e73460b9e53d7ff3eca1293"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration en-fr-lang1=en,lang2=fr\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset kde4/en-fr (download: 6.72 MiB, generated: 24.46 MiB, post-processed: Unknown size, total: 31.18 MiB) to /root/.cache/huggingface/datasets/kde4/en-fr-lang1=en,lang2=fr/0.0.0/243129fb2398d5b0b4f7f6831ab27ad84774b7ce374cf10f60f6e1ff331648ac...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/7.05M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2469c28959b7495784e4cd205518f6b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebff6bf2ece3454193f14ef854b16dec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset kde4 downloaded and prepared to /root/.cache/huggingface/datasets/kde4/en-fr-lang1=en,lang2=fr/0.0.0/243129fb2398d5b0b4f7f6831ab27ad84774b7ce374cf10f60f6e1ff331648ac. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d45ed8eb5cbd49c48d2583cbfa7276c2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3-Pm6itsS4T",
        "outputId": "8981f93d-cb85-437f-f0b4-c353eebfb178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 210173\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train']['translation'][:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJl2rxpdLf83",
        "outputId": "b740232d-2873-48ff-993f-9d82a9fd2be4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'en': 'Lauri Watts', 'fr': 'Lauri Watts'},\n",
              " {'en': '& Lauri. Watts. mail;', 'fr': '& Lauri. Watts. mail;'},\n",
              " {'en': 'ROLES_OF_TRANSLATORS', 'fr': '& traducteurJeromeBlanc;'}]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
        "split_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QeRrSuohsWlT",
        "outputId": "22aae081-d6b3-499d-ffc3-74719c81a2a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 189155\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 21018\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
      ],
      "metadata": {
        "id": "OYr7pIdqsaz8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "split_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTrzj7VlLrsw",
        "outputId": "7719d3bb-378f-4784-a9eb-51f91e265108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 189155\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 21018\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\" # โมเดลสำเร็จรูป\n",
        "translator = pipeline(\"translation\", model=model_checkpoint)"
      ],
      "metadata": {
        "id": "uuz3wKiLsefx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "translator(\"Default to expanded threads\") # ผลการแปล"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9h4GcCnsgCz",
        "outputId": "d473ecaa-e880-4c12-86d3-50b0f9b216c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'Par défaut pour les threads élargis'}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split_datasets[\"train\"][1][\"translation\"] # ผลเฉลย"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdT4t_RUsowm",
        "outputId": "ddb620d9-e8b5-4b7f-c184-6b94f4632080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'en': 'Default to expanded threads',\n",
              " 'fr': 'Par défaut, développer les fils de discussion'}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "translator([x['en'] for x in raw_datasets['train']['translation'][3:7]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uuz6JqdxLytN",
        "outputId": "62938e60-87d4-47ac-8b11-38d64ab1824f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': '2006-02-26 3.5.1'},\n",
              " {'translation_text': 'Le plugin Babel & konqueror; vous donne un accès rapide au service de traduction Babelfish.'},\n",
              " {'translation_text': 'KDE'},\n",
              " {'translation_text': 'kdeaddons'}]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train']['translation'][3:7]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6G_yRT4xMGnO",
        "outputId": "7992df13-b138-48e8-8950-761d07c214ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'en': '2006-02-26 3.5.1', 'fr': '2006-02-26 3.5.1'},\n",
              " {'en': 'The Babel & konqueror; plugin gives you quick access to the Babelfish translation service.',\n",
              "  'fr': 'Le module externe Babel pour & konqueror; vous donne un accès rapide au service de traduction Babelfish.'},\n",
              " {'en': 'KDE', 'fr': 'KDE'},\n",
              " {'en': 'kdeaddons', 'fr': 'kdeaddons'}]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# การจัดการดาต้า (Processing)"
      ],
      "metadata": {
        "id": "A0W8z4K9s2C9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "voKT6_RAsrjE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
        "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
        "\n",
        "inputs = tokenizer(en_sentence) \n",
        "with tokenizer.as_target_tokenizer():\n",
        "    targets = tokenizer(fr_sentence)"
      ],
      "metadata": {
        "id": "GTwI5Ancs5He"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# สิ่งที่ต้องระวัง\n",
        "ในบางครั้งตัวตัดคำ (tokenizer) ไม่ได้รองรับภาษานั้นๆ จะต้องเลือกใช้ให้ถูกต้องด้วย ซึ่งถ้าไม่ได้ใช้ \n",
        "```\n",
        "with tokenizer.as_target_tokenizer():\n",
        "```\n",
        "จะทำให้การตัดคำของเราผิดได้ ยกตัวอย่างเช่น:\n",
        "\n"
      ],
      "metadata": {
        "id": "1n78eGlqtZjr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wrong_targets = tokenizer(fr_sentence)\n",
        "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
        "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxkywTMjtA-l",
        "outputId": "4043323f-ab85-4c7a-c142-e9487afd8c7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['▁Par', '▁dé', 'f', 'aut', ',', '▁dé', 've', 'lop', 'per', '▁les', '▁fil', 's', '▁de', '▁discussion', '</s>']\n",
            "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with tokenizer.as_target_tokenizer():\n",
        "    correct_targets = tokenizer(fr_sentence)\n",
        "print(tokenizer.convert_ids_to_tokens(correct_targets[\"input_ids\"]))\n",
        "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfiETpAwMfMC",
        "outputId": "02817e42-c441-43fb-f0c2-960fcb497032"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']\n",
            "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with tokenizer.as_target_tokenizer():\n",
        "    wrong_source = tokenizer(en_sentence)\n",
        "print(tokenizer.convert_ids_to_tokens(wrong_source[\"input_ids\"]))\n",
        "print(tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nX09Vgf5M4Nu",
        "outputId": "37d343f3-4899-4135-9dc8-c6e8f68980de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['▁De', 'f', 'ault', '▁to', '▁exp', 'ande', 'd', '▁th', 'read', 's', '</s>']\n",
            "['▁Default', '▁to', '▁expanded', '▁thread', 's', '</s>']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_input_length = 128 # ยาวเกินไม่เอา\n",
        "max_target_length = 128\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
        "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
        "\n",
        "    # ใช้ tokenizer ให้ตรงกับภาษานั้นๆ \n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "gnjt4oHVtGxK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = split_datasets.map(\n",
        "    preprocess_function,\n",
        "    batched=True,\n",
        "    remove_columns=split_datasets[\"train\"].column_names,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "id": "vnEklocetYlR",
        "outputId": "20796212-fe12-4225-d6e9-c7e1b0a2a9d6"
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              "  0%|          | 0/190 [00:00<?, ?ba/s]"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhFxwbF8NLma",
        "outputId": "7d39a120-398e-467f-ab3c-377e40fcbd8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'labels'],\n",
              "        num_rows: 189155\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'labels'],\n",
              "        num_rows: 21018\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.convert_ids_to_tokens(tokenized_datasets['train']['input_ids'][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnkUuAuEN8sh",
        "outputId": "89aee780-b230-4fa6-9142-69e99ff4f2da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['▁Default', '▁to', '▁expanded', '▁thread', 's', '</s>']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.convert_ids_to_tokens(tokenized_datasets['train']['labels'][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNi5_9JxOLFs",
        "outputId": "bdd0f4b4-951d-4065-9c01-b6895e90ef30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['▁Par',\n",
              " '▁défaut',\n",
              " ',',\n",
              " '▁développer',\n",
              " '▁les',\n",
              " '▁fils',\n",
              " '▁de',\n",
              " '▁discussion',\n",
              " '</s>']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# คำถามน่าคิด:\n",
        "คิดว่า token\n",
        "\n",
        "```\n",
        "</s>\n",
        "```\n",
        "\n",
        "คือ token ไว้บ่งบอกอะไร?"
      ],
      "metadata": {
        "id": "j82BrWCbOXvZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# การ Finetuning โมเดลโดยใช้ฟังก์ชั่นสำเร็จรูป"
      ],
      "metadata": {
        "id": "g9wi03jWuRvR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# สิ่งที่เปลี่ยนไปจากโจทย์ก่อนหน้า?\n",
        "= จากงานเดิมที่เราต้องใช้ AutoModelForSequenceClassification สำหรับการทำนายประโยค เราจะต้องใช้ Seq2Seq ในการแปลภาษาแทน"
      ],
      "metadata": {
        "id": "Az_k_MZI_ets"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "id": "y2xRjO7wtfmi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# อะไรคือ Seq2Seq?\n",
        "![111318_0848_seq2seqSequ1.webp](data:image/webp;base64,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)\n",
        "\n",
        "from: https://www.guru99.com/seq2seq-model.html"
      ],
      "metadata": {
        "id": "Q0ncfZng_uHK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LZiUMFARXJC",
        "outputId": "4747edcf-b006-402a-85f0-b88d5efc4715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MarianMTModel(\n",
              "  (model): MarianModel(\n",
              "    (shared): Embedding(59514, 512, padding_idx=59513)\n",
              "    (encoder): MarianEncoder(\n",
              "      (embed_tokens): Embedding(59514, 512, padding_idx=59513)\n",
              "      (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
              "      (layers): ModuleList(\n",
              "        (0): MarianEncoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (1): MarianEncoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (2): MarianEncoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (3): MarianEncoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (4): MarianEncoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (5): MarianEncoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (decoder): MarianDecoder(\n",
              "      (embed_tokens): Embedding(59514, 512, padding_idx=59513)\n",
              "      (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
              "      (layers): ModuleList(\n",
              "        (0): MarianDecoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (1): MarianDecoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (2): MarianDecoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (3): MarianDecoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (4): MarianDecoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (5): MarianDecoderLayer(\n",
              "          (self_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder_attn): MarianAttention(\n",
              "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "          )\n",
              "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (lm_head): Linear(in_features=512, out_features=59514, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Goal ของ Seq2Seq\n",
        "* รับ input ส่งเข้า Encoder แล้วอัดข้อมูลทุกอย่างให้เป็น S \n",
        "* ความคาดหวังคือ \"S\" จะมี information สำหรับการทำนาย\n",
        "* จากนั้น Decoder มีหน้าที่รับ S มาทำนาย\n",
        "* ทำงานแบบ \"Order มีผลต่อผลลัพธ์\" (บทเรียนที่ 1 RNN) ทั้ง Encoder และ Decoder ทำงานเหมือนกัน (Sequence ทั้งต้นและปลาย)\n",
        "* ตัว Decoder จะทำนายได้เรื่อยๆ จนกว่าทำนายจนเจอ token บอกให้หยุด (บางครั้งก็กำหนดได้ว่าจะหยุดเมื่อไหร่เช่น ทำนายแค่ 1 ครั้งพอ อารมณ์เดียวกับ next word prediction)\n",
        "\n"
      ],
      "metadata": {
        "id": "s_5adwiwNWWd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ฟังก์ชั่นพิเศษ DataCollatorForSeq2Seq"
      ],
      "metadata": {
        "id": "HbR0_xzuASCi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ],
      "metadata": {
        "id": "8LpdvzmtuVNa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
        "batch.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBqtSd5VuXF8",
        "outputId": "6b5e3bbb-c1eb-4985-c51b-ab338ff52ce8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['attention_mask', 'input_ids', 'labels', 'decoder_input_ids'])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch['input_ids'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdsaW6dSPVPy",
        "outputId": "798a2b7a-269e-475f-b48b-2bf993c42b97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([47591,    12,  9842, 19634,     9,     0, 59513, 59513, 59513, 59513,\n",
              "        59513, 59513, 59513, 59513, 59513])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.convert_ids_to_tokens(batch['input_ids'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDTDQYumPB7R",
        "outputId": "a95b350d-ba94-4dc7-8fd7-448c7ee3f48a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['▁Default',\n",
              " '▁to',\n",
              " '▁expanded',\n",
              " '▁thread',\n",
              " 's',\n",
              " '</s>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>']"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch['decoder_input_ids'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MI8R4KSePceV",
        "outputId": "7f903a7f-c191-43a0-a895-2e721ae47ab8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([59513,   577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,\n",
              "        59513, 59513, 59513, 59513, 59513, 59513])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.convert_ids_to_tokens(batch['decoder_input_ids'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hi38MZw5O59o",
        "outputId": "41539dbe-ea4f-4a4f-8ef3-4a26388fdee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<pad>',\n",
              " '▁Par',\n",
              " '▁défaut',\n",
              " ',',\n",
              " '▁développer',\n",
              " '▁les',\n",
              " '▁fils',\n",
              " '▁de',\n",
              " '▁discussion',\n",
              " '</s>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>',\n",
              " '<pad>']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch[\"labels\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oug8l7qzubFz",
        "outputId": "43456eaf-9f37-4d79-a8b5-dddb1ebaecd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[  577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,  -100,\n",
              "          -100,  -100,  -100,  -100,  -100,  -100],\n",
              "        [ 1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,   817,\n",
              "           550,  7032,  5821,  7907, 12649,     0]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.convert_ids_to_tokens(batch[\"labels\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R8OoSDnCPhSr",
        "outputId": "de16fc9a-d5b1-449b-c3fa-90e13f9f4be6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['▁Par',\n",
              " '▁défaut',\n",
              " ',',\n",
              " '▁développer',\n",
              " '▁les',\n",
              " '▁fils',\n",
              " '▁de',\n",
              " '▁discussion',\n",
              " '</s>',\n",
              " '<unk>',\n",
              " '<unk>',\n",
              " '<unk>',\n",
              " '<unk>',\n",
              " '<unk>',\n",
              " '<unk>',\n",
              " '<unk>']"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Input ของ Decoder\n",
        "= เอา input ของ encoder มา right-shift หรือขยับไปด้านขวา 1 ช่อง เช่น \n",
        "\n",
        "\n",
        "```\n",
        "Encoder = \"ฉัน กิน ข้าว\"\n",
        "Decoder = \"<start> ฉัน กิน ข้าว <end>\"\n",
        "```\n",
        "(จากรูปข้างบนก็จะเห็นว่ามีการใส่ NULL เข้ามา)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "uN9iQGX2us0A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# วิธีประเมินผลของโมเดล?\n",
        "= ในงานการแปลภาษา เราจะใช้ \"BLEU score\" เป็นสกอร์ที่บอกว่าการแปลของเราใกล้เคียงกับเฉลยแค่ไหน โดยการเทียบ \"ชุดของคำ โดยนำมาจาก เฉลย & จากการทำนาย\" \n",
        "\n",
        "อ่านเพิ่มเติม: https://en.wikipedia.org/wiki/BLEU"
      ],
      "metadata": {
        "id": "3DFkLzEwvD8T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"sacrebleu\")"
      ],
      "metadata": {
        "id": "9Yh8bgLUusPh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "18fb8662075548108c580734aaccaa91",
            "4506bc967a3046d385741d2b10fdb481",
            "42e155db84594edc879aad0bda23d4f8",
            "48c0e31cb5bb44cd997aab5200f507f7",
            "58e9966df30f4fdc889d1002fe4e0942",
            "d1b74e8381464dc0b4e3f69968ef7bb5",
            "23d7569bf0f84c3f80552d288a85cd5d",
            "64faed36ef6f482db8ee89422894dcc8",
            "8aefe058641448acbce3308a57d3559f",
            "8ec54518ef8548e892c942264c57bea1",
            "f2540c72bfd146d68f4e0b2fbad673ff"
          ]
        },
        "outputId": "1c6eec20-ecea-413b-ab6e-d9541cfc0d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.37k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18fb8662075548108c580734aaccaa91"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ตย.1"
      ],
      "metadata": {
        "id": "RAKvJz40BlZF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = [\n",
        "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
        "]\n",
        "references = [\n",
        "    [\n",
        "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
        "    ]\n",
        "]\n",
        "metric.compute(predictions=predictions, references=references)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tGqCzwl-vOFA",
        "outputId": "c1de3ea4-25a7-4be4-be14-32fe33ea29a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bp': 0.9200444146293233,\n",
              " 'counts': [11, 6, 4, 3],\n",
              " 'precisions': [91.66666666666667,\n",
              "  54.54545454545455,\n",
              "  40.0,\n",
              "  33.333333333333336],\n",
              " 'ref_len': 13,\n",
              " 'score': 46.750469682990165,\n",
              " 'sys_len': 12,\n",
              " 'totals': [12, 11, 10, 9]}"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ตย.2"
      ],
      "metadata": {
        "id": "pHsm3O01Bn38"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = [\"This This This This\"]\n",
        "references = [\n",
        "    [\n",
        "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
        "    ]\n",
        "]\n",
        "metric.compute(predictions=predictions, references=references)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxMPfpBCvOcz",
        "outputId": "92c2bab5-799e-44b0-c588-677ebf8b5cb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bp': 0.10539922456186433,\n",
              " 'counts': [1, 0, 0, 0],\n",
              " 'precisions': [25.0, 16.666666666666668, 12.5, 12.5],\n",
              " 'ref_len': 13,\n",
              " 'score': 1.683602693167689,\n",
              " 'sys_len': 4,\n",
              " 'totals': [4, 3, 2, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ตย. 3"
      ],
      "metadata": {
        "id": "Hk6cThjyQPof"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = [\"plugin you web between languages\"]\n",
        "references = [\n",
        "    [\n",
        "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
        "    ]\n",
        "]\n",
        "metric.compute(predictions=predictions, references=references)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_azmJ-YtQQLo",
        "outputId": "760f0b34-73f1-4a52-ebcf-1045f49b1df5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bp': 0.20189651799465538,\n",
              " 'counts': [5, 0, 0, 0],\n",
              " 'precisions': [100.0, 12.5, 8.333333333333334, 6.25],\n",
              " 'ref_len': 13,\n",
              " 'score': 3.225009698600463,\n",
              " 'sys_len': 5,\n",
              " 'totals': [5, 4, 3, 2]}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# จุดอ่อนของ BP"
      ],
      "metadata": {
        "id": "6u7sJDKKQhet"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = [\"to plugin translate several you web This between languages allows pages\"]\n",
        "references = [\n",
        "    [\n",
        "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
        "    ]\n",
        "]\n",
        "metric.compute(predictions=predictions, references=references)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjDEAibbQg12",
        "outputId": "c7440855-d7d7-45fa-a9df-4f5f9b4a0230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bp': 0.8337529180751805,\n",
              " 'counts': [11, 0, 0, 0],\n",
              " 'precisions': [100.0, 5.0, 2.7777777777777777, 1.5625],\n",
              " 'ref_len': 13,\n",
              " 'score': 5.690615589954034,\n",
              " 'sys_len': 11,\n",
              " 'totals': [11, 10, 9, 8]}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    # In case the model returns more than the prediction logits\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    \n",
        "    # Replace -100s 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 = [pred.strip() for pred in decoded_preds]\n",
        "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "    return {\"bleu\": result[\"score\"]}"
      ],
      "metadata": {
        "id": "VxMcCmxavWJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    f\"marian-finetuned-kde4-en-to-fr\",\n",
        "    evaluation_strategy=\"no\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=64,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=1,\n",
        "    predict_with_generate=True,\n",
        "    fp16=True, # ถ้าใช้ GPU\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "1FVVSj6ovZBs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "\n",
        "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",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371,
          "referenced_widgets": [
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        "id": "Gbjz7vQ2vcHb",
        "outputId": "e1c7db85-850b-4d6e-e015-46ac6981d526"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr into local empty directory.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Download file pytorch_model.bin:   0%|          | 3.47k/285M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "796e01c02a3b4212ad79baa36a3cb2fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Download file training_args.bin: 100%|##########| 3.05k/3.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Download file runs/Jan20_02-56-28_813f8d783f9b/events.out.tfevents.1642648752.813f8d783f9b.72.0:  55%|#####5  …"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "c9463b7084ae4a42af7f34db75751b53"
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        },
        {
          "output_type": "display_data",
          "data": {
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              "Download file runs/Jan20_02-56-28_813f8d783f9b/events.out.tfevents.1642651521.813f8d783f9b.72.2: 100%|########…"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Clean file runs/Jan20_02-56-28_813f8d783f9b/events.out.tfevents.1642651521.813f8d783f9b.72.2: 100%|##########|…"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Download file runs/Jan20_02-56-28_813f8d783f9b/1642648752.5330608/events.out.tfevents.1642648752.813f8d783f9b.…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Clean file runs/Jan20_02-56-28_813f8d783f9b/1642648752.5330608/events.out.tfevents.1642648752.813f8d783f9b.72.…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9948817dd3fa4a52a90539c3b7630dce"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Clean file pytorch_model.bin:   0%|          | 1.00k/285M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2451f76eea6412c8de8d98891f19cdb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using amp half precision backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate(max_length=max_target_length) # ก่อนเทรน สกอร์ก็ดีพอตัว"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "kQEzIWDsvgKS",
        "outputId": "e18cd28f-fef0-4192-f58a-2a68df5d8a3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 21018\n",
            "  Batch size = 64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='329' max='329' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [329/329 21:25]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_bleu': 39.27124165416069,\n",
              " 'eval_loss': 1.6964441537857056,\n",
              " 'eval_runtime': 1347.2741,\n",
              " 'eval_samples_per_second': 15.6,\n",
              " 'eval_steps_per_second': 0.244}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "Xujd8h-rvjuf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "outputId": "e356d997-8008-4603-822d-90b705805f5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 189155\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 5912\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5912' max='5912' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5912/5912 20:45, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.416600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.224000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.174500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>1.136800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>1.130400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.082400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>1.084300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>1.049700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>1.049200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>1.055300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>1.032300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to marian-finetuned-kde4-en-to-fr/checkpoint-5912\n",
            "Configuration saved in marian-finetuned-kde4-en-to-fr/checkpoint-5912/config.json\n",
            "Model weights saved in marian-finetuned-kde4-en-to-fr/checkpoint-5912/pytorch_model.bin\n",
            "tokenizer config file saved in marian-finetuned-kde4-en-to-fr/checkpoint-5912/tokenizer_config.json\n",
            "Special tokens file saved in marian-finetuned-kde4-en-to-fr/checkpoint-5912/special_tokens_map.json\n",
            "tokenizer config file saved in marian-finetuned-kde4-en-to-fr/tokenizer_config.json\n",
            "Special tokens file saved in marian-finetuned-kde4-en-to-fr/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5912, training_loss=1.123947184850463, metrics={'train_runtime': 1246.3041, 'train_samples_per_second': 151.773, 'train_steps_per_second': 4.744, 'total_flos': 3773950253531136.0, 'train_loss': 1.123947184850463, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate(max_length=max_target_length) #จาก 39 => 50 !!"
      ],
      "metadata": {
        "id": "7vkPzQ7wvnjk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "264dd75a-5811-4b5c-a18f-604248b86039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 21018\n",
            "  Batch size = 64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='658' max='329' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [329/329 1:07:34]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epoch': 1.0,\n",
              " 'eval_bleu': 50.20410659441166,\n",
              " 'eval_loss': 0.9643106460571289,\n",
              " 'eval_runtime': 1523.0974,\n",
              " 'eval_samples_per_second': 13.8,\n",
              " 'eval_steps_per_second': 0.216}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(tags=\"translation\", commit_message=\"Training complete\") # อัพขึ้น hugging face"
      ],
      "metadata": {
        "id": "Op6cHhm5vpYI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "c11fa9d4-94ec-47e2-d5f0-2d1883e55428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to marian-finetuned-kde4-en-to-fr\n",
            "Configuration saved in marian-finetuned-kde4-en-to-fr/config.json\n",
            "Model weights saved in marian-finetuned-kde4-en-to-fr/pytorch_model.bin\n",
            "tokenizer config file saved in marian-finetuned-kde4-en-to-fr/tokenizer_config.json\n",
            "Special tokens file saved in marian-finetuned-kde4-en-to-fr/special_tokens_map.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "To https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr\n",
            "   6722933..a9aec84  main -> main\n",
            "\n",
            "To https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr\n",
            "   a9aec84..ba8bf15  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/commit/a9aec8499fa2dc158869957189822bb6daed6b2d'"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Replace this with your own checkpoint\n",
        "model_checkpoint = \"XXXX\" # ลองโหลดโมเดลมาใช้\n",
        "translator = pipeline(\"translation\", model=model_checkpoint)"
      ],
      "metadata": {
        "id": "rg4FBvwSvreW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "outputId": "cc1315c8-d1c1-481d-e7a0-60056ed0ba72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp75iu2h1l\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7651a16fc2cd4cb18bca1d22a56efce1",
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            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.33k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/1c92fe3bf0b9fc397b3b68fed2eb636f7f5c3f006a9f0b044e449b477b9d4f7c.f39bd3c2362a84b75eb6d388a96515ed46798865b8f6377283b55bbb1996776d\n",
            "creating metadata file for /root/.cache/huggingface/transformers/1c92fe3bf0b9fc397b3b68fed2eb636f7f5c3f006a9f0b044e449b477b9d4f7c.f39bd3c2362a84b75eb6d388a96515ed46798865b8f6377283b55bbb1996776d\n",
            "loading configuration file https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/1c92fe3bf0b9fc397b3b68fed2eb636f7f5c3f006a9f0b044e449b477b9d4f7c.f39bd3c2362a84b75eb6d388a96515ed46798865b8f6377283b55bbb1996776d\n",
            "Model config MarianConfig {\n",
            "  \"_name_or_path\": \"mrp/marian-finetuned-kde4-en-to-fr\",\n",
            "  \"_num_labels\": 3,\n",
            "  \"activation_dropout\": 0.0,\n",
            "  \"activation_function\": \"swish\",\n",
            "  \"add_bias_logits\": false,\n",
            "  \"add_final_layer_norm\": false,\n",
            "  \"architectures\": [\n",
            "    \"MarianMTModel\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"bad_words_ids\": [\n",
            "    [\n",
            "      59513\n",
            "    ]\n",
            "  ],\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classif_dropout\": 0.0,\n",
            "  \"classifier_dropout\": 0.0,\n",
            "  \"d_model\": 512,\n",
            "  \"decoder_attention_heads\": 8,\n",
            "  \"decoder_ffn_dim\": 2048,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_layers\": 6,\n",
            "  \"decoder_start_token_id\": 59513,\n",
            "  \"dropout\": 0.1,\n",
            "  \"encoder_attention_heads\": 8,\n",
            "  \"encoder_ffn_dim\": 2048,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_layers\": 6,\n",
            "  \"eos_token_id\": 0,\n",
            "  \"forced_eos_token_id\": 0,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"max_length\": 512,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"marian\",\n",
            "  \"normalize_before\": false,\n",
            "  \"normalize_embedding\": false,\n",
            "  \"num_beams\": 4,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 59513,\n",
            "  \"scale_embedding\": true,\n",
            "  \"static_position_embeddings\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 59514\n",
            "}\n",
            "\n",
            "loading configuration file https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/1c92fe3bf0b9fc397b3b68fed2eb636f7f5c3f006a9f0b044e449b477b9d4f7c.f39bd3c2362a84b75eb6d388a96515ed46798865b8f6377283b55bbb1996776d\n",
            "Model config MarianConfig {\n",
            "  \"_name_or_path\": \"mrp/marian-finetuned-kde4-en-to-fr\",\n",
            "  \"_num_labels\": 3,\n",
            "  \"activation_dropout\": 0.0,\n",
            "  \"activation_function\": \"swish\",\n",
            "  \"add_bias_logits\": false,\n",
            "  \"add_final_layer_norm\": false,\n",
            "  \"architectures\": [\n",
            "    \"MarianMTModel\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"bad_words_ids\": [\n",
            "    [\n",
            "      59513\n",
            "    ]\n",
            "  ],\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classif_dropout\": 0.0,\n",
            "  \"classifier_dropout\": 0.0,\n",
            "  \"d_model\": 512,\n",
            "  \"decoder_attention_heads\": 8,\n",
            "  \"decoder_ffn_dim\": 2048,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_layers\": 6,\n",
            "  \"decoder_start_token_id\": 59513,\n",
            "  \"dropout\": 0.1,\n",
            "  \"encoder_attention_heads\": 8,\n",
            "  \"encoder_ffn_dim\": 2048,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_layers\": 6,\n",
            "  \"eos_token_id\": 0,\n",
            "  \"forced_eos_token_id\": 0,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"max_length\": 512,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"marian\",\n",
            "  \"normalize_before\": false,\n",
            "  \"normalize_embedding\": false,\n",
            "  \"num_beams\": 4,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 59513,\n",
            "  \"scale_embedding\": true,\n",
            "  \"static_position_embeddings\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 59514\n",
            "}\n",
            "\n",
            "https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpihizgbxp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/858f96e2318a8ab29a81191ea997225a0d8ceaa75ba7c294f39d632a1bc35d9c.b529404a954b1d8a67c658370448d9b176e190317e223f476e8141087bfa2d65\n",
            "creating metadata file for /root/.cache/huggingface/transformers/858f96e2318a8ab29a81191ea997225a0d8ceaa75ba7c294f39d632a1bc35d9c.b529404a954b1d8a67c658370448d9b176e190317e223f476e8141087bfa2d65\n",
            "loading weights file https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/858f96e2318a8ab29a81191ea997225a0d8ceaa75ba7c294f39d632a1bc35d9c.b529404a954b1d8a67c658370448d9b176e190317e223f476e8141087bfa2d65\n",
            "All model checkpoint weights were used when initializing MarianMTModel.\n",
            "\n",
            "All the weights of MarianMTModel were initialized from the model checkpoint at mrp/marian-finetuned-kde4-en-to-fr.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use MarianMTModel for predictions without further training.\n",
            "https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/tokenizer_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpbgp7kz96\n"
          ]
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/tokenizer_config.json in cache at /root/.cache/huggingface/transformers/ed3231b95a780739cc842aa29a93409c9dfbf2e3ba80885576a4e282c576d06b.3e2cb67f13186337b50025aa21e23e3239082196cba3fc46ed38bc79db27e477\n",
            "creating metadata file for /root/.cache/huggingface/transformers/ed3231b95a780739cc842aa29a93409c9dfbf2e3ba80885576a4e282c576d06b.3e2cb67f13186337b50025aa21e23e3239082196cba3fc46ed38bc79db27e477\n",
            "https://huggingface.co/mrp/marian-finetuned-kde4-en-to-fr/resolve/main/source.spm not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp_25s3v0v\n"
          ]
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'Par défaut pour les fils de discussion étendus'}]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "translator(\"Default to expanded threads\")"
      ],
      "metadata": {
        "id": "a2K9N44LPfcL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1af3fecb-857a-4cbb-8b8c-e711bb8588a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'Par défaut pour les fils de discussion étendus'}]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "cDDwRpA-s4S_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}