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      "source": [
        "<a href=\"https://colab.research.google.com/github/pavaris-pm/machine-translation-from-th/blob/main/translation_th_en.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Install necessary packages"
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 13.4 MB/s \n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 50.7 MB/s \n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.11.1 tokenizers-0.13.2 transformers-4.25.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.8.0-py3-none-any.whl (452 kB)\n",
            "\u001b[K     |████████████████████████████████| 452 kB 15.1 MB/s \n",
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            "Collecting xxhash\n",
            "  Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
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            "Collecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
            "Collecting multiprocess\n",
            "  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
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            "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=6bd82d783483d0e33b2a086254df131ddf9d1d0cf3945664a5f90cf24a442f06\n",
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            "Installing collected packages: sacremoses\n",
            "Successfully installed sacremoses-0.0.53\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install evaluate\n",
        "!pip install sacrebleu\n",
        "!pip install sentencepiece\n",
        "!pip install sacremoses"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Translation model from Thai to English using KDE4 datasets\n",
        "- to get a valid pair of code, you need to take a look at https://opus.nlpl.eu/KDE4.php"
      ],
      "metadata": {
        "id": "5KpleW_2bVcL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/en-th.txt.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SihA05kfdXiN",
        "outputId": "0a07a559-d78f-4ed6-a3e4-9f2169e86401"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unzip:  cannot find or open /content/en-th.txt.zip, /content/en-th.txt.zip.zip or /content/en-th.txt.zip.ZIP.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# we will choose a target language later, in this part, we need to import the data in the format that it has first\n",
        "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"th\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
          "referenced_widgets": [
            "390a5689c6ad445dbaf1addc7ffb4cf6",
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            "9d95a34fa66c4533acff8b5bd3a5c88a",
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            "aa77843184094808b49677ed452c8bef",
            "e18d7c761f924e91a16b35db1f1851c5",
            "609545c39f9344e4855730a29aafe9bd",
            "8c8fdb9469c54ffbb2ee2ec8f4841607",
            "d3777cf19f6a4e99b988e532f68c691d",
            "3aa2a07ac0684454bace8e331cd540c7",
            "6230c5cc0b4e41039269376c8800bd1b",
            "be315305cc6f4961a8573eb3d865caa7",
            "0ff4b6fce29242f29c9125e3425ac6a4",
            "d7b78508f49549db89f4b7e51ca5dfe1",
            "6c022b3a711740fdb38356b043f5d415",
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            "26ce90b33fee44049dfd25ecb474f824",
            "4ee4bdffcffb45929dd80f39512c85b4",
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            "f01d4aa2040d40b8a293e13aa4e36292",
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            "b922134ad10f4faf8ad042807322202e",
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            "59ef28ba67f048918b2f55d52a44746e",
            "7d8bfa2d11cb49fc8b8effce15457cd5",
            "840373f4003d42ae826241d9cbbf1e15",
            "c65477aa2a0f40b3bbfe1dd2516adfad",
            "cc6c57104e8a4866bda68fc38aa56ee8",
            "535fc3369b3c4872832616869fd14d75"
          ]
        },
        "id": "kEKKTtsfbZ3d",
        "outputId": "3ae3dee8-dcf5-4820-c9f4-e58fefc6c286"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.25k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "390a5689c6ad445dbaf1addc7ffb4cf6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/8.45k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa77843184094808b49677ed452c8bef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.10k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "929d37e599ad4f0eab33cbe63ef5481a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration en-th-lang1=en,lang2=th\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset kde4/en-th to /root/.cache/huggingface/datasets/kde4/en-th-lang1=en,lang2=th/0.0.0/243129fb2398d5b0b4f7f6831ab27ad84774b7ce374cf10f60f6e1ff331648ac...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.64M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ee4bdffcffb45929dd80f39512c85b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53f0e406d56b497b92d5e383d8d763f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset kde4 downloaded and prepared to /root/.cache/huggingface/datasets/kde4/en-th-lang1=en,lang2=th/0.0.0/243129fb2398d5b0b4f7f6831ab27ad84774b7ce374cf10f60f6e1ff331648ac. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
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              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80dde310eddc4d5c89d4d1fde3cfc2b0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "irIHXdxMboPF",
        "outputId": "6bc11c2f-ff7a-4cef-fee7-da46d5e77248"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 70634\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split the dataset to get a train and validation dataset\n",
        "split_datasets = raw_datasets['train'].train_test_split(train_size=0.9, seed = 100)\n",
        "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
      ],
      "metadata": {
        "id": "VvIes1dVeeIT"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "split_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6Sr9OKmhUYS",
        "outputId": "c837d3d5-0cd1-400b-f469-06afb081ef36"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 63570\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'translation'],\n",
              "        num_rows: 7064\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# observe our datasets\n",
        "split_datasets['train'][1]['translation']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZl3ibMGhVm9",
        "outputId": "63636b19-a9f5-48f9-fe22-3e164e3dbbf1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'en': \"An error occurred while trying to share folder '%1 '. Make sure that the Perl script'fileshareset' is set suid root.\",\n",
              " 'th': \"เกิดข้อผิดพลาดระหว่างปรับให้ใช้โฟลเดอร์ '% 1' ร่วมกัน โปรดตรวจสอบว่า สคริปต์คำสั่งเพิร์ล 'fileshareset' ได้ถูกตั้งให้ประมวลผลโดยใช้สิทธิ์ของ root แล้ว (suid root)\"}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing\n",
        "- we need to process our data before pass it into our model, in order to do that, we should process our dataset first.\n",
        "which can be done by using `AutoTokenizer` from transformers library"
      ],
      "metadata": {
        "id": "CTT0QHdPhoyW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"Helsinki-NLP/opus-mt-th-en\""
      ],
      "metadata": {
        "id": "s2XiuFaRiOB9"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors='pt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to see how our tokenizer works\n",
        "en_sentence = split_datasets['train'][1]['translation']['en']\n",
        "th_sentence = split_datasets['train'][1]['translation']['th']\n",
        "\n",
        "print(f\"en sentence : {en_sentence}\")\n",
        "print(f\"th sentence : {th_sentence}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4SIZTmmjM63",
        "outputId": "b086532d-87fe-481c-a1da-1c79fac630b8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "en sentence : An error occurred while trying to share folder '%1 '. Make sure that the Perl script'fileshareset' is set suid root.\n",
            "th sentence : เกิดข้อผิดพลาดระหว่างปรับให้ใช้โฟลเดอร์ '% 1' ร่วมกัน โปรดตรวจสอบว่า สคริปต์คำสั่งเพิร์ล 'fileshareset' ได้ถูกตั้งให้ประมวลผลโดยใช้สิทธิ์ของ root แล้ว (suid root)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use our tokenizer, note that we need to specify the target lang as well\n",
        "tokenizer(th_sentence, text_target = en_sentence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Y4_N02bqWoy",
        "outputId": "2868dff0-4b0d-486e-98bc-78525bf1dc57"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [19987, 1795, 6230, 186, 537, 3405, 4348, 10731, 12, 5308, 46840, 12, 15589, 3722, 34427, 320, 25257, 5325, 7237, 9, 3280, 5, 43, 540, 1377, 186, 10596, 12192, 7085, 119, 2908, 35797, 101, 77, 8885, 5106, 2908, 35797, 80, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [1607, 3829, 7037, 668, 770, 7, 1709, 1502, 10483, 320, 2, 2635, 482, 16, 4, 9506, 1075, 13558, 5, 25257, 51982, 12069, 5, 19, 669, 19065, 5106, 3711, 2, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# observe its performance, to see it works perfectly on the language that we have\n",
        "print(tokenizer.convert_ids_to_tokens(tokenizer(th_sentence, text_target = en_sentence)['input_ids']))\n",
        "print(tokenizer.convert_ids_to_tokens(tokenizer(th_sentence, text_target = en_sentence)['labels']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5rrO6wgqksd",
        "outputId": "f91bae91-3e45-4db9-8f39-f87e23a62299"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['▁เกิดข้อผิดพลาด', 'ระหว่าง', 'ปรับ', 'ให้', 'ใช้', 'โฟลเดอร์', \"▁'%\", \"▁1'\", '▁', 'ร่วมกัน', '▁โปรดตรวจสอบว่า', '▁', 'สคริปต์', 'คําสั่ง', 'เพิร์ล', \"▁'\", 'file', 'sh', 'are', 's', 'et', \"'\", '▁ได้', 'ถูก', 'ตั้ง', 'ให้', 'ประมวลผล', 'โดยใช้', 'สิทธิ์', 'ของ', '▁r', 'oot', '▁แล้ว', '▁(', 'su', 'id', '▁r', 'oot', ')', '</s>']\n",
            "['▁An', '▁error', '▁occurred', '▁while', '▁trying', '▁to', '▁share', '▁folder', \"▁'%1\", \"▁'\", '.', '▁Make', '▁sure', '▁that', '▁the', '▁Per', 'l', '▁script', \"'\", 'file', 'share', 'set', \"'\", '▁is', '▁set', '▁su', 'id', '▁root', '.', '</s>']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to see all keys produced by the tokenizer\n",
        "sample = tokenizer(th_sentence, text_target=en_sentence)\n",
        "sample.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xEy4coWqq3CC",
        "outputId": "1e2d454b-085c-407d-d51f-a3b092c16b12"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask', 'labels'])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define function to preprocess our datasets, where maxlength we consier it to be 256\n",
        "# since thai language seems to have longer sequence compared to another language\n",
        "max_length = 128\n",
        "\n",
        "def preprocess_function(examples):\n",
        "  inputs = [txt['th'] for txt in examples['translation']]\n",
        "  targets = [txt['en'] for txt in examples['translation']]\n",
        "\n",
        "  model_inputs = tokenizer(\n",
        "      inputs, text_target=targets, truncation=True, max_length=max_length\n",
        "  )\n",
        "\n",
        "  return model_inputs"
      ],
      "metadata": {
        "id": "XBJRlXhlrwub"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply the preprocess dataset into our entire dataset that was splitted\n",
        "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,
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        "outputId": "55713e6c-7e5d-4085-efaf-15ead51117d5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/64 [00:00<?, ?ba/s]"
            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBmz9_rhtSc_",
        "outputId": "ac46822f-6430-4b68-df22-b6b7307718f8"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 63570\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 7064\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# observe the value in tokenized datasets\n",
        "print(tokenized_datasets['train'][1]['input_ids'])\n",
        "print(tokenized_datasets['train'][1]['labels'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rbsvIAXatdZs",
        "outputId": "e0d322b5-3a3f-4908-d92b-d1ae6919d02a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[19987, 1795, 6230, 186, 537, 3405, 4348, 10731, 12, 5308, 46840, 12, 15589, 3722, 34427, 320, 25257, 5325, 7237, 9, 3280, 5, 43, 540, 1377, 186, 10596, 12192, 7085, 119, 2908, 35797, 101, 77, 8885, 5106, 2908, 35797, 80, 0]\n",
            "[1607, 3829, 7037, 668, 770, 7, 1709, 1502, 10483, 320, 2, 2635, 482, 16, 4, 9506, 1075, 13558, 5, 25257, 51982, 12069, 5, 19, 669, 19065, 5106, 3711, 2, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train the model with Trainer API"
      ],
      "metadata": {
        "id": "KBGRolk_wa7T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "b12b3e62b0d94094a21b7c8983a1378d"
          ]
        },
        "id": "D9N7n_DnwmN7",
        "outputId": "178c7692-ff2d-4e5b-b49d-681a75a6d64d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/307M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# once we already have our model, the next one is to add a data collator for make a padding in every batch\n",
        "# note that in this case we use seq2seq model, so that the data collator that we will use maybe different from other tasks\n",
        "\n",
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "# we need to specify the model as well since we deal with different architecture in translation task\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)"
      ],
      "metadata": {
        "id": "cO5KUMNKwthG"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# to observe how our data collator work\n",
        "batch = data_collator([tokenized_datasets['train'][i] for i in range(1,3)])"
      ],
      "metadata": {
        "id": "dSQe6RB_xYta"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttItPiXUxp7W",
        "outputId": "d54c09e0-dfd8-408e-f8c0-ed666f63e793"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask', 'labels', 'decoder_input_ids'])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to see what data collator work in backend and also the reason that we didnt specify padding at the tokenizer\n",
        "print(batch['labels'])\n",
        "print(batch['decoder_input_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YHz4K2YxrAQ",
        "outputId": "bc785d52-b603-481e-a746-a0ecd462e21d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 1607,  3829,  7037,   668,   770,     7,  1709,  1502, 10483,   320,\n",
            "             2,  2635,   482,    16,     4,  9506,  1075, 13558,     5, 25257,\n",
            "         51982, 12069,     5,    19,   669, 19065,  5106,  3711,     2,     0],\n",
            "        [  503, 37323, 43856,   365,    34,  1822,     7,  2036, 46128,     2,\n",
            "             0,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
            "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100]])\n",
            "tensor([[62306,  1607,  3829,  7037,   668,   770,     7,  1709,  1502, 10483,\n",
            "           320,     2,  2635,   482,    16,     4,  9506,  1075, 13558,     5,\n",
            "         25257, 51982, 12069,     5,    19,   669, 19065,  5106,  3711,     2],\n",
            "        [62306,   503, 37323, 43856,   365,    34,  1822,     7,  2036, 46128,\n",
            "             2,     0, 62306, 62306, 62306, 62306, 62306, 62306, 62306, 62306,\n",
            "         62306, 62306, 62306, 62306, 62306, 62306, 62306, 62306, 62306, 62306]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define the metrics to evaluate performance of our translation model\n",
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"sacrebleu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7cb4f62da5d44fd6a0e44834faf00acc",
            "c379f12284534890927f05f502cff8d5",
            "0b1dea179f6645089044ecb2f22c2c16",
            "f4be991f3b3e41d3a77b66cc2f98d27d",
            "e4b603f917ba471cb163a73caa9aafc3",
            "9593193c0df3452bad3d95e1a0be25f2",
            "c941588825e4409ab260b0106d9de104",
            "d31a76675ba9441b9cc4fd7c92a26cbd",
            "fb77310eeca04cb79e24db958223251f",
            "8d54818e2f694205b25b34c8b358d197",
            "755c7dbf97b04c2f8ce90fe7881beeab"
          ]
        },
        "id": "Sx1a2vzyxxzR",
        "outputId": "baeba94d-db05-48f0-b047-0f5ebd1080ea"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cb4f62da5d44fd6a0e44834faf00acc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define function before put it into trainer API in order to make it compute the performance of our model\n",
        "import numpy as np # in order to implement 'where' command\n",
        "\n",
        "def compute_metric(eval_preds):\n",
        "  preds, labels = eval_preds\n",
        "\n",
        "  # to take just only one logit since the predictions has more than one logit\n",
        "  if isinstance(preds, tuple):\n",
        "    preds = preds[0]\n",
        "\n",
        "  # to process data before put it into the bleu\n",
        "  # since the translation task, we need to text as an output and also to evaluate, so we need to decode our ID back\n",
        "  preds_decode = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "\n",
        "  # then, replace -100 in the label that we cannot encode it (can be done by our tokenizer)\n",
        "  labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "  labels_decode = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "  # some simple post processing (using strip() to remove whitespace)\n",
        "  preds_decode = [pred.strip() for pred in preds_decode]\n",
        "  labels_decode = [[label.strip()] for label in labels_decode]\n",
        "\n",
        "  result = metric.compute(predictions=preds_decode, references=labels_decode)\n",
        "\n",
        "  return {\"bleu score\" : result[\"score\"]}\n"
      ],
      "metadata": {
        "id": "oLgn0YvUyJoP"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.mkdir(\"/content/th-en_translation_model\")"
      ],
      "metadata": {
        "id": "TXD7dN7O0o-6"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "# since the translation model will be evaluated just only before and after train only!\n",
        "# that is why we specify evaluation strategy to be \"no\" since we do not need it during training\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    \"/content/th-en_translation_model\",\n",
        "    evaluation_strategy=\"no\",\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=10,\n",
        "    predict_with_generate=True,\n",
        ")"
      ],
      "metadata": {
        "id": "jseM_Dy30Yuw"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define our trainer\n",
        "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_metric\n",
        ")"
      ],
      "metadata": {
        "id": "fYMkFYl90lmH"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now, the trainer still not trained, let's see how its performance our data before trained\n",
        "trainer.evaluate(max_length = max_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "8PF23oYd13Qk",
        "outputId": "05d8776c-5650-46a8-dcc5-e4e0aabb1709"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 7064\n",
            "  Batch size = 64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='111' max='111' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [111/111 04:30]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 2.5177578926086426,\n",
              " 'eval_bleu score': 48.867679986792915,\n",
              " 'eval_runtime': 291.732,\n",
              " 'eval_samples_per_second': 24.214,\n",
              " 'eval_steps_per_second': 0.38}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache() # to allocate CUDA memory before trained"
      ],
      "metadata": {
        "id": "tSd7-x9H4HQ4"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# once we saw the performance, now, try to train it to see its performance after train (fine-tuned) with the dataset\n",
        "%%time\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6JgiEsRU2GVX",
        "outputId": "961340c5-cee7-4814-956b-d21a8756e501"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 63570\n",
            "  Num Epochs = 10\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 = 19870\n",
            "  Number of trainable parameters = 76039680\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='19870' max='19870' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [19870/19870 1:22:41, Epoch 10/10]\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>1.996500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.922100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.888000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>1.835200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>1.629500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.631500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>1.592800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>1.590500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>1.428000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>1.410000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>1.461600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>1.465300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>1.318000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>1.319400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>1.344000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>1.326300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>1.231600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>1.233300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>1.222100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>1.224400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>1.149000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11000</td>\n",
              "      <td>1.169000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11500</td>\n",
              "      <td>1.147500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>1.163200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12500</td>\n",
              "      <td>1.078000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13000</td>\n",
              "      <td>1.115900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13500</td>\n",
              "      <td>1.099500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14000</td>\n",
              "      <td>1.105400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14500</td>\n",
              "      <td>1.036100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>1.056200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15500</td>\n",
              "      <td>1.061300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16000</td>\n",
              "      <td>1.042100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16500</td>\n",
              "      <td>1.018700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17000</td>\n",
              "      <td>1.035500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17500</td>\n",
              "      <td>1.009700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18000</td>\n",
              "      <td>1.019400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18500</td>\n",
              "      <td>1.005300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19000</td>\n",
              "      <td>1.009600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19500</td>\n",
              "      <td>0.976600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-500/special_tokens_map.json\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-1000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-1000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-1000/special_tokens_map.json\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-1500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-1500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-1500/special_tokens_map.json\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-2000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-2000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-2000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-2500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-2500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-2500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-2500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-2500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-1000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-3000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-3000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-3000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-3000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-3000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-1500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-3500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-3500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-3500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-3500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-3500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-2000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-4000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-4000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-4000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-4000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-4000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-2500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-4500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-4500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-4500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-4500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-4500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-3000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-5000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-5000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-5000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-5000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-5000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-3500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-5500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-5500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-5500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-5500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-5500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-4000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-6000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-6000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-6000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-6000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-6000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-4500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-6500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-6500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-6500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-6500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-6500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-5000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-7000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-7000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-7000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-7000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-7000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-5500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-7500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-7500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-7500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-7500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-7500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-6000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-8000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-8000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-8000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-8000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-8000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-6500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-8500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-8500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-8500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-8500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-8500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-7000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-9000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-9000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-9000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-9000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-9000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-7500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-9500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-9500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-9500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-9500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-9500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-8000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-10000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-10000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-10000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-10000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-10000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-8500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-10500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-10500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-10500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-10500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-10500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-9000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-11000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-11000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-11000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-11000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-11000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-9500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-11500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-11500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-11500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-11500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-11500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-10000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-12000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-12000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-12000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-12000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-12000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-10500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-12500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-12500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-12500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-12500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-12500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-11000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-13000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-13000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-13000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-13000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-13000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-11500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-13500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-13500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-13500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-13500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-13500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-12000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-14000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-14000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-14000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-14000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-14000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-12500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-14500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-14500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-14500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-14500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-14500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-13000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-15000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-15000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-15000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-15000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-15000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-13500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-15500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-15500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-15500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-15500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-15500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-14000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-16000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-16000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-16000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-16000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-16000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-14500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-16500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-16500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-16500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-16500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-16500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-15000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-17000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-17000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-17000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-17000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-17000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-15500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-17500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-17500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-17500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-17500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-17500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-16000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-18000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-18000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-18000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-18000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-18000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-16500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-18500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-18500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-18500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-18500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-18500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-17000] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-19000\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-19000/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-19000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-19000/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-19000/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-17500] due to args.save_total_limit\n",
            "Saving model checkpoint to /content/th-en_translation_model/checkpoint-19500\n",
            "Configuration saved in /content/th-en_translation_model/checkpoint-19500/config.json\n",
            "Model weights saved in /content/th-en_translation_model/checkpoint-19500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/th-en_translation_model/checkpoint-19500/tokenizer_config.json\n",
            "Special tokens file saved in /content/th-en_translation_model/checkpoint-19500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/th-en_translation_model/checkpoint-18000] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1h 8min 50s, sys: 12min 47s, total: 1h 21min 37s\n",
            "Wall time: 1h 22min 41s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=19870, training_loss=1.2861123210290717, metrics={'train_runtime': 4961.5345, 'train_samples_per_second': 128.126, 'train_steps_per_second': 4.005, 'total_flos': 7384951596515328.0, 'train_loss': 1.2861123210290717, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# finally, observe performance of the model after training\n",
        "trainer.evaluate(max_length = max_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "811pCaq026Iy",
        "outputId": "ebd6ce00-074d-41d2-f805-eecde3d750d6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 7064\n",
            "  Batch size = 64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='222' max='111' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [111/111 1:34:12]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 1.5220731496810913,\n",
              " 'eval_bleu score': 49.54953075509462,\n",
              " 'eval_runtime': 420.2678,\n",
              " 'eval_samples_per_second': 16.808,\n",
              " 'eval_steps_per_second': 0.264,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# since we observe loss during training, the loss at checkpoint 19500 performs the best, so we will use it as our pipeline\n",
        "from transformers import pipeline\n",
        "\n",
        "checkpoint = \"/content/th-en_translation_model/checkpoint-19500\"\n",
        "tuned_model = pipeline(\"translation\", model=checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5D7TxxAvA3U8",
        "outputId": "c7883732-5460-408e-d61a-b562b9ec530f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file /content/th-en_translation_model/checkpoint-19500/config.json\n",
            "Model config MarianConfig {\n",
            "  \"_name_or_path\": \"/content/th-en_translation_model/checkpoint-19500\",\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",
            "      62306\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\": 62306,\n",
            "  \"decoder_vocab_size\": 62307,\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",
            "  \"extra_pos_embeddings\": 62307,\n",
            "  \"forced_eos_token_id\": 0,\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\": 6,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 62306,\n",
            "  \"scale_embedding\": true,\n",
            "  \"share_encoder_decoder_embeddings\": true,\n",
            "  \"static_position_embeddings\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 62307\n",
            "}\n",
            "\n",
            "loading configuration file /content/th-en_translation_model/checkpoint-19500/config.json\n",
            "Model config MarianConfig {\n",
            "  \"_name_or_path\": \"/content/th-en_translation_model/checkpoint-19500\",\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",
            "      62306\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\": 62306,\n",
            "  \"decoder_vocab_size\": 62307,\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",
            "  \"extra_pos_embeddings\": 62307,\n",
            "  \"forced_eos_token_id\": 0,\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\": 6,\n",
            "  \"num_hidden_layers\": 6,\n",
            "  \"pad_token_id\": 62306,\n",
            "  \"scale_embedding\": true,\n",
            "  \"share_encoder_decoder_embeddings\": true,\n",
            "  \"static_position_embeddings\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 62307\n",
            "}\n",
            "\n",
            "loading weights file /content/th-en_translation_model/checkpoint-19500/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing MarianMTModel.\n",
            "\n",
            "All the weights of MarianMTModel were initialized from the model checkpoint at /content/th-en_translation_model/checkpoint-19500.\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",
            "loading file source.spm\n",
            "loading file target.spm\n",
            "loading file vocab.json\n",
            "loading file target_vocab.json\n",
            "loading file tokenizer_config.json\n",
            "loading file added_tokens.json\n",
            "loading file special_tokens_map.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test the fine-tuned model"
      ],
      "metadata": {
        "id": "ajIj0Sg0krlb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = [\n",
        "    \"สวัสดีปีใหม่นะครับ ขอให้ปีนี้เป็นปีที่ดีสำหรับทุกคนนะ\",\n",
        "    \"ในทุก ๆ วัน หากเราอยู่กับใครมาก ๆ จะเป็นคนในครอบครัว หรือทำงานร่วมกับใครบ่อย ๆ แล้วต้อง “เป็นฝ่ายขอบคุณเสมอ” นั่นหมายความว่าเราเป็นผู้รับจากเขามากกว่า เขาต้องช่วยเหลือเรามากกว่า เลยเถิดไปถึงว่าเราอาจทำอะไรได้ไม่ค่อยดีประจำเขาต้องช่วยประจำ มีศักยภาพน้อยเกินไป ใช่ว่าจะเป็นความผิดเสียทีเดียว\",\n",
        "    \"ประเทศไทยมีประชากรประมาณ 60 ล้านคน\"\n",
        "]"
      ],
      "metadata": {
        "id": "6QQVv8ZEFJci"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test our translation with test data (better if we split the data to have a test dataset as well)\n",
        "tuned_model(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xXQgwKTF8KP",
        "outputId": "55d5e7b8-000b-400d-b448-00b23418b6b0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'Have a new year. Have a nice year for everybody.'},\n",
              " {'translation_text': 'On every day, if we are with one another, family members, or often working with one another, be sure to be \"genuine\". This means that we are more of them than they are for us. Even if there is no good in us, they are less likely to do so, and are less likely to be able to do so. This is not a sin.'},\n",
              " {'translation_text': 'Thailand has a population of 60 billion.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# so that if we want the better model, we need to train with a lot more epochs\n",
        "# there also has an extreme case, which need some specific dataset to handle with that\n",
        "extreme_data = \"วันมาฆบูชา เป็นวันขึ้น ๑๕ ค่ำ เดือน ๓ มีเหตุการณ์อัศจรรย์ที่ พระสงฆ์สาวกของพระพุทธเจ้าจำนวน ๑,๒๕๐ รูป มาเฝ้าพระพุทธเจ้า ณ วัดเวฬุวัน เมืองราชคฤห์ แคว้นมคธ โดยมิได้นัดหมายกันพระสงฆ์ ทั้งหมดเป็นพระอรหันต์\""
      ],
      "metadata": {
        "id": "RY6dXdoMmneH"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# to visualize how it perform with technical term data (better with a larger and related dataset)\n",
        "tuned_model(extreme_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwaMl6mWogmH",
        "outputId": "a122d13e-a5e7-4ceb-9342-585e15a4c147"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'It is a new moon of the year: a new moon, a new moon, a new moon, a new moon, a day of the year, a day of the year, a day of the year, a day of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year, a year of the year of the year, when all the sun and the moon'}]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save the model\n",
        "- save the fine-tuned into Google Drive"
      ],
      "metadata": {
        "id": "Juo6kmsjo07S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_1bXQNWo2yd",
        "outputId": "a9365883-4660-4061-81f5-9aac21c0e7e0"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to zip file as a tar format\n",
        "!tar chvfz th-en_model_10epch.tar.gz \"/content/th-en_translation_model\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXCwd7lwr0NZ",
        "outputId": "95206484-4230-4ec8-bfc8-c96245b3e97c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tar: Removing leading `/' from member names\n",
            "/content/th-en_translation_model/\n",
            "/content/th-en_translation_model/runs/\n",
            "/content/th-en_translation_model/runs/Jan01_11-45-32_1ba8c0635139/\n",
            "/content/th-en_translation_model/runs/Jan01_11-45-32_1ba8c0635139/events.out.tfevents.1672573828.1ba8c0635139.99.0\n",
            "/content/th-en_translation_model/runs/Jan01_11-45-32_1ba8c0635139/events.out.tfevents.1672579210.1ba8c0635139.99.2\n",
            "/content/th-en_translation_model/runs/Jan01_11-45-32_1ba8c0635139/1672573828.8501909/\n",
            "/content/th-en_translation_model/runs/Jan01_11-45-32_1ba8c0635139/1672573828.8501909/events.out.tfevents.1672573828.1ba8c0635139.99.1\n",
            "/content/th-en_translation_model/checkpoint-19500/\n",
            "/content/th-en_translation_model/checkpoint-19500/config.json\n",
            "/content/th-en_translation_model/checkpoint-19500/rng_state.pth\n",
            "/content/th-en_translation_model/checkpoint-19500/source.spm\n",
            "/content/th-en_translation_model/checkpoint-19500/scheduler.pt\n",
            "/content/th-en_translation_model/checkpoint-19500/special_tokens_map.json\n",
            "/content/th-en_translation_model/checkpoint-19500/training_args.bin\n",
            "/content/th-en_translation_model/checkpoint-19500/optimizer.pt\n",
            "/content/th-en_translation_model/checkpoint-19500/tokenizer_config.json\n",
            "/content/th-en_translation_model/checkpoint-19500/trainer_state.json\n",
            "/content/th-en_translation_model/checkpoint-19500/pytorch_model.bin\n",
            "/content/th-en_translation_model/checkpoint-19500/target.spm\n",
            "/content/th-en_translation_model/checkpoint-19500/vocab.json\n",
            "/content/th-en_translation_model/checkpoint-18500/\n",
            "/content/th-en_translation_model/checkpoint-18500/config.json\n",
            "/content/th-en_translation_model/checkpoint-18500/rng_state.pth\n",
            "/content/th-en_translation_model/checkpoint-18500/source.spm\n",
            "/content/th-en_translation_model/checkpoint-18500/scheduler.pt\n",
            "/content/th-en_translation_model/checkpoint-18500/special_tokens_map.json\n",
            "/content/th-en_translation_model/checkpoint-18500/training_args.bin\n",
            "/content/th-en_translation_model/checkpoint-18500/optimizer.pt\n",
            "/content/th-en_translation_model/checkpoint-18500/tokenizer_config.json\n",
            "/content/th-en_translation_model/checkpoint-18500/trainer_state.json\n",
            "/content/th-en_translation_model/checkpoint-18500/pytorch_model.bin\n",
            "/content/th-en_translation_model/checkpoint-18500/target.spm\n",
            "/content/th-en_translation_model/checkpoint-18500/vocab.json\n",
            "/content/th-en_translation_model/checkpoint-19000/\n",
            "/content/th-en_translation_model/checkpoint-19000/config.json\n",
            "/content/th-en_translation_model/checkpoint-19000/rng_state.pth\n",
            "/content/th-en_translation_model/checkpoint-19000/source.spm\n",
            "/content/th-en_translation_model/checkpoint-19000/scheduler.pt\n",
            "/content/th-en_translation_model/checkpoint-19000/special_tokens_map.json\n",
            "/content/th-en_translation_model/checkpoint-19000/training_args.bin\n",
            "/content/th-en_translation_model/checkpoint-19000/optimizer.pt\n",
            "/content/th-en_translation_model/checkpoint-19000/tokenizer_config.json\n",
            "/content/th-en_translation_model/checkpoint-19000/trainer_state.json\n",
            "/content/th-en_translation_model/checkpoint-19000/pytorch_model.bin\n",
            "/content/th-en_translation_model/checkpoint-19000/target.spm\n",
            "/content/th-en_translation_model/checkpoint-19000/vocab.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save model into Google Drive (need to wait for a while until the tar file appeared in Google Drive, not more than 1 min)\n",
        "!cp -r \"/content/th-en_model_10epch.tar.gz\" \"/content/gdrive/MyDrive/AIML/trained_model\""
      ],
      "metadata": {
        "id": "H5J6tsWspePq"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@InProceedings{TIEDEMANN12.463,\n",
        "  author = {Jörg Tiedemann},\n",
        "  title = {Parallel Data, Tools and Interfaces in OPUS},\n",
        "  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},\n",
        "  year = {2012},\n",
        "  month = {may},\n",
        "  date = {23-25},\n",
        "  address = {Istanbul, Turkey},\n",
        "  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},\n",
        "  publisher = {European Language Resources Association (ELRA)},\n",
        "  isbn = {978-2-9517408-7-7},\n",
        "  language = {english}\n",
        " }"
      ],
      "metadata": {
        "id": "3sJjbd5oByuG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}